Ecological tool for monitoring changes in biodiversity

Abstract

There is provided a computer-implemented method for generating an interactive map, via a graphical user interface, where the interactive map comprises a plurality of layers A geographic regions layer is provided representing a set of geographic regions. A temporal value is defined representing a date or period in time selectable by a user. At least one habitat type of a selected geographic region is determined, where the habitat type is associated with the temporal value, and a condition score is determined for the selected geographic region. The condition score indicates a biodiversity condition and is determined using environmental indices for the selected geographic region using the temporal value. An area is defined against which the selected geographic region can be benchmarked to indicate the biodiversity performance of the selected geographic region compared to other regions having a same or similar habitat. A biodiversity condition layer is provided to indicate the condition score for the selected geographic region.

Description

[0001] ECOLOGICAL TOOL FOR MONITORING CHANGES IN BIODIVERSITY

[0002] INCORPORATION BY REFERENCE

[0003] This patent application claims priority to G.B. Patent Application No. 2316130.0, titled “ENVIRONMENTAL CHARACTERISTICS OF REGIONS”, filed on 22 October 2023, which is herein incorporated by reference in its entirety.

[0004] TECHNICAL FIELD

[0005] The present disclosure relates, in general, to the monitoring of changes in biodiversity based on determining the environmental characteristics of geographic regions using data from various data streams.

[0006] BACKGROUND

[0007] In any given region of Earth, there exists a large variety of life comprising, e.g., animals, plants, fungi, and microorganisms (e.g., bacteria). Such biological variety is referred to as biodiversity, and comprises a measure of variation at the genetic, species, and ecosystem levels. Biodiversity is known to be affected by many factors, including climate change, human activity, and pollution to name a few. For example, the development of land for the purposes of, e.g., agriculture and / or building (such as housing) can have a significant impact on the biodiversity of a region.

[0008] The loss of biodiversity can have significant impacts on human health and the spread of disease or of invasive species, and can upset the natural balance of an ecosystem, leading to flooding and loss of life or food resources. It is in our interest as humans to protect biodiversity to ensure survival of life on Earth.

[0009] Whilst there is a wealth of raw environmental data available to us from numerous different data sources, the data is so varied that it is difficult to provide any meaningful metrics that can be used to measure the changes in biodiversity of a region and the extent to which it may have been affected by influencing factors. For example, sustainable development strategies aim to minimize the impact of human development on wild ecosystems in order to preserve biodiversity, or at least minimise any adverse effects, however, it can be difficult to provide any meaningful metrics that can be used to measure the effects and the effectiveness of sustainable development strategies. That is, determining, for a given region, the degree of biodiversity and the extent to which it may be or has been affected is problematic.

[0010] SUMMARY OF THE DISCLOSURE

[0011] An objective of the present disclosure is to provide a computer-implemented method and apparatus for generating an interactive map, via a graphical user interface, according to claims 1 and 15 respectively. A further objective is to provide a machine-readable storage medium encoded with instructions for generating an interactive map according to claim 13.

[0012] The foregoing and other objectives are achieved by the features of the independent claims. Further implementation forms are apparent from the dependent claims, the description and the Figures.

[0013] A first aspect of the present disclosure provides a computer-implemented method for generating an interactive map, via a graphical user interface, the interactive map comprising a plurality of layers, the method comprising the steps of providing a geographic regions layer representing a set of geographic regions, the geographic regions layer comprising a tessellation of polygons, each polygon corresponding to a geographic region that is selectable by a user, defining a temporal value representing a date or period in time, the temporal value being selectable by a user, determining at least one habitat type of a selected geographic region, the habitat type being associated with the temporal value, and determining a condition score associated with the temporal value for the selected geographic region, the condition score indicating a biodiversity condition and being determined based on generating an environmental metric for the selected geographic region on the basis of data defining the at least one habitat type of the selected geographical region, the environmental metric being generated through a weighted combination of multiple environmental indices, wherein the multiple environmental indices for the selected geographic region are generated through processing of selected data, the selected data comprising environmental measurements for the selected geographic region that is associated with the temporal value, defining an area on the geographic regions layer, the area having a tessellation of multiple geographic regions at least one of which is adjacent the selected geographic region, determining at least one habitat type for each geographical region within the area, wherein at least some of the multiple geographic regions within the area comprise a habitat type associated with the temporal value and that is the same as, or similar to, the at least one habitat type of the selected geographical region, defining set of other environmental metrics for the multiple geographic regions within the area, the set of other environmental metrics being associated with the same, or a similar, habitat type of the selected geographic region, benchmarking the environmental metric for the selected geographic region against the set of other environmental metrics for the multiple geographic regions within the area, thereby to determine the condition score for the selected geographic region, and providing a biodiversity condition layer based on populating the geographic regions layer with the condition score associated with the temporal value for the selected geographic region.

[0014] In an implementation of the first aspect, the method can further comprise defining a further temporal value representing a further date or further period in time, determining at least one habitat type of the selected geographic region, the habitat type being associated with the further temporal value, determining a presence or absence of a change in habitat type based on comparing the respective habitat types associated with the temporal value and further temporal value, the change in habitat type corresponding to a further habitat type that is different to the habitat type associated with the temporal value, and outputting a notification to indicate to the user the determined presence or absence of the change in habitat type.

[0015] The selected data comprising environmental measurements for the selected geographic region may be associated with user-selectable environmental attributes.

[0016] Determining the at least one habitat type of the selected geographic region may comprise deriving the at least one habitat type using the multiple environmental indices, or querying a database to obtain the predetermined at least one habitat type associated with the selected geographic region, optionally, wherein the selected geographic region comprises a plurality of habitat types and the at least one habitat type is determined as a dominant habitat type for the selected geographic region.

[0017] The step of determining the condition score for benchmarking the environmental metrics may comprise representing the highest and lowest environmental metric values within the area as upper and lower bounds, ordering the environmental metric values according to magnitude, thereby to generate an ordered list of benchmarked environmental metrics, and assigning respective portions of adjacent environmental metrics in the list at the upper and lower bounds to a respective condition score.

[0018] The step of defining the area on the geographic regions layer may comprise the area having multiple geographic regions within a selected radius of the selected geographic region.

[0019] The selected data may comprise image data having information from multiple spectral bands, including the infrared spectrum. The method can further comprise, on the basis of determining the presence of the change in habitat type, determining a further condition score associated with the further temporal value for the selected geographic region, the further condition score being determined based on generating a further environmental metric for the selected geographic region on the basis of data defining the further habitat type of the selected geographical region, the further environmental metric being generated through a weighted combination of multiple environmental indices, wherein the multiple environmental indices for the selected geographic region are generated through processing of selected data, the selected data comprising environmental measurements for the selected geographic region that is associated with the further temporal value, defining an area on the geographic regions layer, the area having a tessellation of multiple geographic regions at least one of which is adjacent the selected geographic region, wherein at least some of the multiple geographic regions within the area comprise a habitat type associated with the further temporal value and that is the same as, or similar to, the further habitat type of the selected geographical region, and benchmarking the further environmental metric for the selected geographic region against a set of other environmental metrics for the multiple geographic regions within the area, thereby to determine the further condition score for the selected geographic region.

[0020] The method can further comprise determining a biodiversity unit representing a weighted combination of biodiversity attributes including the condition score for the selected geographic area and the temporal value, determining a further biodiversity unit for the selected geographic area for the further temporal value and further condition score, comparing the biodiversity unit and further biodiversity unit to determine a magnitude of change in biodiversity, and on the basis of the magnitude of change falling above or below a pre-determined threshold, outputting an alert.

[0021] The method can further comprise determining the presence or absence of a habitat type that corresponds to an irreplaceable habitat, and on the basis of determining the presence of the change in habitat type, outputting an alert.

[0022] The method can further comprise providing at least one habitat layer based on populating the geographic regions layer with the habitat type for the selected geographic region, optionally, populating the geographic regions layer with the habitat type for each respective geographic region within the area, wherein a further habitat layer is provided per further habitat type.

[0023] The method can further comprise determining the condition score associated with the temporal value for each geographic region within the area per habitat type, and updating the biodiversity condition layer based on populating the geographic regions layer with the condition score associated with the temporal value for each geographic region.

[0024] A second aspect of the present disclosure provides a machine-readable storage medium encoded with instructions for generating an interactive map, via a graphical user interface, the interactive map comprising a plurality of layers, the instructions executable by a processor, whereby to cause the processor to carry out the computer-implemented method according to the first aspect of the invention.

[0025] A third aspect of the present disclosure provides an apparatus for generating an interactive map, via a graphical user interface, the interactive map comprising a plurality of layers, the apparatus comprising a processor configured to carry out the computer-implemented method according to the first aspect of the invention.

[0026] These and other aspects of the invention will be apparent from the embodiment s) described below.

[0027] BRIEF DESCRIPTION OF DRAWINGS

[0028] In order that the present disclosure may be more readily understood, embodiments will now be described, by way of example, with reference to the accompanying drawings, in which:

[0029] Figure 1 A is a schematic representation of a geographic regions layer, according to an example;

[0030] Figure IB is a schematic representation of habitat layers, according to an example;

[0031] Figure 1C is a schematic representation of a selected geographic region in a set of geographic regions, according to an example;

[0032] Figure ID is a schematic representation of a machine, according to an example;

[0033] Figure 2A is a flow chart of a method for determining a condition score for the selected geographic region, according to an example;

[0034] Figures 2B and 2C are schematic representations of an area defining a set of geographic regions, according to examples;

[0035] Figures 2D and 2E are schematic representations showing a user comparison of the biodiversity health for a particular habitat across two areas, according to an example; Figure 3 is a schematic representation of a biodiversity condition layer, according to an example;

[0036] Figures 4A and 4B are schematic representations of an interactive map on a graphical user interface, respectively showing habitat layers and a biodiversity condition layer, according to examples;

[0037] Figure 5 is a flow chart of a method for monitoring changes in biodiversity, at a first and second period of time, for the selected geographic region as described herein, according to an example; and

[0038] Figure 6 is a flow chart of a method for generating an interactive map according to an example.

[0039] DETAILED DESCRIPTION

[0040] Example embodiments are described below in sufficient detail to enable those of ordinary skill in the art to embody and implement the systems and processes herein described. It is important to understand that embodiments can be provided in many alternate forms and should not be construed as limited to the examples set forth herein.

[0041] Accordingly, while embodiments can be modified in various ways and take on various alternative forms, specific embodiments thereof are shown in the drawings and described in detail below as examples. There is no intent to limit to the particular forms disclosed. On the contrary, all modifications, equivalents, and alternatives falling within the scope of the appended claims should be included. Elements of the example embodiments are consistently denoted by the same reference numerals throughout the drawings and detailed description where appropriate.

[0042] The terminology used herein to describe embodiments is not intended to limit the scope. The articles “a,” “an,” and “the” are singular in that they have a single referent, however the use of the singular form in the present document should not preclude the presence of more than one referent. In other words, elements referred to in the singular can number one or more, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes,” and / or “including,” when used herein, specify the presence of stated features, items, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, items, steps, operations, elements, components, and / or groups thereof. The term “and / or” is only an association relationship for describing associated objects and represents that three relationships may exist such that A and / or B may indicate that A exists alone, A and B exist at the same time, or B exists alone. The character “ / ” generally represents that the associated objects are in an “or” relationship.

[0043] Unless otherwise defined, all terms (including technical and scientific terms) used herein are to be interpreted as is customary in the art. It will be further understood that terms in common usage should also be interpreted as is customary in the relevant art and not in an idealized or overly formal sense unless expressly so defined herein.

[0044] The following contains specific information related to implementations of the present disclosure. The drawings and their accompanying detailed disclosure are merely directed to implementations. However, the present disclosure is not limited to these implementations. Other variations and implementations of the present disclosure will be obvious to those skilled in the art.

[0045] The phrases “in one implementation,” or “in some implementations,” may each refer to one or more of the same or different implementations. The term “coupled” is defined as connected whether directly or indirectly through intervening components and is not necessarily limited to physical connections. The expression “at least one of A, B and C” or “at least one of the following: A, B and C” means “only A, or only B, or only C, or any combination of A, B and C.”

[0046] The terms “system” and “network” may be used interchangeably.

[0047] For the purposes of explanation and non-limitation, specific details such as functional entities, techniques, protocols, and standards are set forth for providing an understanding of the present disclosure. In other examples, detailed disclosure of well-known methods, technologies, systems, and architectures are omitted so as not to obscure the present disclosure with unnecessary details.

[0048] Persons skilled in the art will immediately recognize that any network function(s) or algorithm(s) disclosed may be implemented by hardware, software or a combination of software and hardware. Disclosed functions may correspond to modules which may be software, hardware, firmware, or any combination thereof.

[0049] A software implementation may include machine- and / or computer- readable and / or executable instructions stored on a machine- and / or computer-readable medium such as memory or other types of storage devices. One or more microprocessors or general-purpose computers with communication processing capability may be programmed with corresponding executable instructions and perform the disclosed network function(s) or algorithm(s).

[0050] The microprocessors or general-purpose computers may include Applications Specific Integrated Circuitry (ASIC), programmable logic arrays, and / or using one or more Digital Signal Processor (DSPs). Although some of the disclosed implementations are oriented to software installed and executing on computer hardware, alternative implementations implemented as firmware or as hardware or as a combination of hardware and software are well within the scope of the present disclosure. The computer readable medium includes but is not limited to Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory, Compact Disc Read-Only Memory (CD-ROM), magnetic cassettes, magnetic tape, magnetic disk storage, or any other equivalent medium capable of storing computer-readable instructions.

[0051] Interactive Map

[0052] An ecological tool by way of an interactive map is provided to a user via a graphical user interface. Example interactive maps are shown in figures 4A and 4B. The interactive map comprises a number of different layers that are populated with data that is derived from various data sources according to user-selectable attributes.

[0053] The various data sources comprise temporal data having a number of associated environmental attributes. For example, the data may have a temporal component, such that it can be associated with data measurements taken for a particular geographic region during a specific period of time. For example, the period of time may be a day, month, year, season, etc.. The map layers provided for interaction by the user can include a geographic regions layer, one or more habitat layers, a biodiversity condition layer, and a temporal layer. The map layers can be populated with associated metadata.

[0054] The interactive map is provided with a geographic regions layer representing a set of geographic regions. An example geographic regions layer is shown in figure 1A. The geographic regions layer can comprise a tessellation of polygons, where each polygon corresponds to a geographic region that is selectable by a user. Alternatively, the user may draw a polygon on the map to define a geographic region of interest. The geographic regions layer may reference a geo-spatial indexing system. The interactive map is generated using the temporal data sets and user-selected attributes. The geographies regions layer can be populated with data. For example, a biodiversity condition layer is provided based on populating the geographic regions layer with a condition score associated with the period of time of interest selected by the user. The condition score indicates a biodiversity condition for the selected geographic region that is derived using environmental measurements for the selected geographic region. One or more habitat layers can be provided representing a habitat type for the selected geographic region or regions. Similarly, the habitat layers can be provided based on populating the geographic regions layer with a habitat type associated with the period of time of interest selected by the user.

[0055] The data that is used to populate the layers of the interactive map can be derived in real-time or derived previously. For data derived in advance, the data can be stored in a database, such that when the user interacts with the map the database can be queried for the data and the layers populated.

[0056] Selection of Data from Multiple Data Streams

[0057] A subset of data from the various data sources is selected according to selections made by the user on the interactive map, or according to specified attributes of interest. The user is able to select various parameters on the interactive map relating to, for example, a period of time, geographic location, environmental attributes, amongst many other parameters. The parameters selected by the user are used to obtain and process relevant data from the various data sources to provide the user with meaningful information that is outputted to the user on the map. This allows the user to interact with the map to gain an understanding of any changes in biodiversity over time. As such, the selected data can comprise environmental measurements for the selected geographic region that is associated with the user-selected environmental attributes.

[0058] For example, a user may be interested in comparing a degree of biodiversity for the present day in a selected geographic region against a degree of biodiversity for the same geographic region at a previous period of time. In this example, the user may select a geographic region (e.g., Somerset) and a relevant time period of interest (e.g., the year 2021) on the interactive map, where the subset of data is selected from the various data sources having data measurements associated with Somerset and having a temporal component relating to “the year 2021”. It is this subset of data that is then processed to provide the user with a degree of biodiversity in Somerset in 2021. Other parameters may be selected by the user to limit the selection of data and processing from the various data sources. For example, the user may only be interested in assessing or monitoring a change in biodiversity for irreplaceable habitats, such as ancient woodland.

[0059] In an example, the set of data can comprise data from one or more of multiple data streams for the selected geographic region. For example, one of the multiple data streams can comprise multi- or hyperspectral data for the selected geographic region. The set of data can comprise data generated at a single point in time, or data that has been generated over, or is selected from, a preselected period of time. Example data streams are shown in figure ID.

[0060] In an example, other data streams of the multiple data streams can comprise data for the selected region representing any one or more of weather and climate data (e.g., historical weather and climate records), biodiversity data comprising a measure representing one or more animal populations (e.g., number of animals per unit area), size and / or flow rate of any watercourses. Accordingly, the set of data for the selected region can comprise information from multiple sources including, but not limited to, multi- or hyperspectral data.

[0061] Data about a given region can be obtained in a number of ways. For example, in the context of agriculture or land development, ground sensing technologies (proximal sensing) can be used to collect data on crop and / or process conditions through fixed or mobile ground sensors, while remote-sensing technologies can utilise satellite, or drone-mounted sensors, which may be used to generate multispectral or hyperspectral images.

[0062] Multi- or hyperspectral data for the selected geographic region, as well as (if desired) other data sources such as any one or more of the multiple data streams described above can be selected for the geographic region. That is, in an example, a subset of data from the multiple data streams is selected for the selected geographic region. The multiple data streams for the selected geographic region can comprise raw data representing environmental measurements for the selected geographic region. For example, raw data can comprise multi- or hyperspectral image data for the selected region, data representing precipitation values for the selected region, data representing a number of a particular species of animal per unit area for the selected region, and so on.

[0063] For example, satellite imagery or imagery generated from an unmanned aerial vehicle (UAV) can be used to provide image data for a region that can enable, e.g., changes in environmental characteristics of the region to be determined. Such image data, which can be generated over a predefined period of time, can be used to monitor multiple characteristics of a region such as development, erosion and degradation, and monitoring of crop yields and agriculture for example. Aside from image data that comprises information in the visible parts of the electromagnetic (EM) spectrum, regional scale multi- or hyperspectral data can be generated that comprises information from, e.g., the visible (VIS) to shortwave infrared (SWIR) regions of the EM spectrum. Some imaging devices can generate data with broad spectral ranges from, e.g., 420 nm to 1000 nm (visible to near infrared - VNIR) and from 900 nm to 2450 nm (SWIR) with high radiometric resolution and stability in both spectral ranges. In some examples, hyperspectral data can comprise in excess of 100 spectral bands of relatively narrow bandwidths (e.g., 5-10 nm), while multispectral data can comprise of around 5-10 bands of relatively large bandwidths.

[0064] Sensors used to generate multi- or hyperspectral images collect information as a set of images representing different bands of the EM spectrum, which are combined to form a cube of multi- or hyperspectral data. Such a cube thus comprises a layered set of images with each image representing a particular wavelength band. In a multi- or hyperspectral image, each pixel consists of a spectrum over an appropriate spectral region and each image region and / or object has a unique characteristic between the different wavelength bands known is its “spectral signature”. For example, multi- or hyperspectral data can be used to detect chlorophyll or very small colour changes in foliage, thereby enabling specific plant varieties to be determined.

[0065] In practice, constellations of low-cost miniaturized satellites (e.g., CubeSats) can be used for multi - or hyperspectral remote sensing and may scan the same location almost daily, thereby increasing the temporal resolution of generated data, which is advantageous for applications such as agriculture where significant changes can occur rapidly.

[0066] Environmental Indices

[0067] According to an example, there is provided a computer-implemented method for generating a graphical user interface comprising an interactive map with a plurality of layers, comprising processing a set of data associated with a selected geographic region and particular period of time.

[0068] The subset of data can be processed in order to generate multiple environmental indices for the selected geographic region. In an example, environmental indices can represent respective selected environmental attributes for the selected geographic region. For example, the subset of data can comprise data representing an amount of reflected infrared (IR) radiation for the selected region, which can provide information representing the composition of biological plant material for the selected region. For example, an environmental index that represents a normalized difference moisture index (NDMI), representing moisture levels in vegetation and having a value from -1 to 1, can be determined from the subset of data by calculating the ratio of NIR to SWIR for the selected region.

[0069] Healthy vegetation is a good absorber of visible light. For example, chlorophyll highly absorbs blue (0.4 - 0.5 pm) and red (0.6 - 0.7 pm) and reflects green (0.5 - 0.6 pm). Healthy vegetation is therefore viewed as green. Healthy plants also exhibit a high reflectance in the NIR (between, e.g., 0.7 to 1.3 pm). This is primarily due to internal structure of plant leaves. Accordingly, high reflectance in the NIR and high absorption in the red part of the spectrum are used to calculate a normalized difference vegetation index (ND VI).

[0070] Other indices can include, but are not limited to a normalized difference vegetation index (ND VI), a leaf area index (LAI), Enhanced Vegetation Index (EVI), Normalised Difference Water Index (NDWI), Soil Adjusted Vegetation Index (SAVI), Perpendicular Vegetation Index (PVI), Ration Vegetation Index (RVI), Normalised Difference Built-up Index (NDBI). For example, NDVI quantifies vegetation by measuring the difference between NIR (which vegetation strongly reflects) and red light (which vegetation absorbs). Other indices can be similarly calculated using information from multi- or hyperspectral data.

[0071] According to an example, a selected geographic region can comprise multiple sub-areas. An example of multiple sub-areas for a region is shown in figure 1C. The shape of a sub-area and / or a selected geographic region can be chosen as desired. In an example, a sub-area can comprise an approximately 10 square metre region. A selected geographic region can comprise, e.g., 25-35 sub-areas that are adjacent to one another. A sub-area can be square or rectangular, or approximately square or rectangular. A selected geographic region can be hexagonal, or approximately hexagonal.

[0072] For each sub-area, a value can be calculated for an index. For example, for each sub-area a value can be calculated from raw data for, e.g., NDVI. Values for other indices can be calculated for each sub-area. Each calculated value for an index can be used to calculate an average for that index for a selected geographic region. That is, calculated index values for multiple sub-areas are used to calculate an average for the index value for a selected geographic region that comprises the sub-areas. The value for a selected geographic region can comprise a value at a given point in time or can comprise a value that is determined over a period of time. For example, an average for the index value for a selected geographic region can be calculated at periodic intervals over a selected period of time. These average values can themselves be averaged to form a value for an index over the selected period of time.

[0073] In an example, respective metrics can be generated for individual sub-areas. That is, different metrics can be generated at a level of granularity of the sub-areas that go to define that selected geographic region.

[0074] For each selected geographic region, there can therefore be provided multiple index values, which may comprise average index values over a period of time for example. As described below, multiple selected ones of these index values according to habitat type for a selected geographic region can be weighted and combined in a predefined manner in order to generate a metric for the selected geographic region per habitat type.

[0075] Defining the Nature or Habitat Type of a Geographic Region

[0076] In an example, using data from at least one of the multiple data streams, the nature of the selected geographic region can be determined. For example, the nature of the selected geographic region can define or represent an ecological typology of the selected geographical region. As described herein, this can represent whether the selected geographic region is, e.g., woodland, grassland, wetland and so on. Accordingly, a metric that is geared to provide useful contextual information for a specific ecological typology can be appropriately selected. For example, a metric that is based on LAI indices can provide a useful measure for woodland regions, but may be less appropriate for wetland regions, and so on.

[0077] The habitat type may be derived using the selected data or multiple environmental indices for the geographic region. In an example, multi- or hyperspectral data for a selected geographic region can be provided as part of an image for the region. That is, an image can comprise information from multiple spectral bands (including the visible part of the spectrum) for example. Accordingly, a spectral signature for a selected geographical region can be determined that represents or identifies the region from data provided in one or more of the spectral bands for the region. In an example, a spectral signature can be used to map a region to an indication of the nature or habitat of the region. Alternatively, the habitat type may be defined by querying a database to obtain the predetermined habitat type associated with the selected geographic region.

[0078] In an example, the selected geographic region can comprise a plurality of habitat types, and at least one habitat type may be determined based on the dominant habitat type occupying the greatest land cover for the selected geographic region. The habitat type of a geographic region at a particular period of time that is determined can be used to provide the habitat layers of the interactive map. An example of habitat layers is shown in figure IB. Each habitat layer within the habitat layers may comprise a habitat type that is different to the other habitat layers, i.e., there is defined a habitat type per habitat layer. The interactive map may be provided with one or more habitat layers based on populating the geographic regions layer with the habitat type that is determined for the selected geographic region or regions, i.e., the habitat type for each respective geographic region within the area can be indicated on the interactive map.

[0079] In an example, a predefined colour scheme may be used to assign a colour to each habitat type on the interactive map. For example, the colour scheme may be aligned with the UK Habitat Classification Habitat Definitions and DEFRA Biodiversity Metric Calculator. This provides information on habitat names (habitat types and habitat groups), definitions, and distinctiveness values. In an example, the habitat may relate to “Deciduous Woodland” having the UK Habitat Classification Habitat Definition of “A mixture of broadleaved and coniferous trees in which neither make up more than 80% of the tree cover. Mixed woodland that does not meet the definition of any other woodland types.”. The “Deciduous Woodland” may be coloured green. The colour scheme information per habitat type may be provided as a look-up-table, defining the red, green, blue (RGB) or cyan, magenta, yellow, black (CMYK) combinations for colouring each geographic region on the interactive map according to the habitat type(s) that are present in the region.

[0080] Environmental Metric

[0081] The set of data can be used to generate a metric representing an environmental characteristic per habitat type for the selected geographic region. According to an example, on the basis of data defining the nature or habitat type of the selected geographic region, a metric can be generated for the selected geographic region. The metric for the selected geographic region can comprise a weighted combination of multiple environmental indices. For example, a selected geographic region can comprise the habitat type of pasture or grassland, which may be used for grazing for example. On the basis of this information, a predefined metric that is configured to provide a biodiversity measure that is specific to pasture / grassland can be calculated.

[0082] In an example, such a metric can be calculated using a weighted combination of indices (such as those described above), which indices are selected on the basis of the nature or habitat type of the selected geographic region. Furthermore, the weighting applied to individual ones of the selected indices can be predefined. For example, a metric, M, can be calculated according to equation (1): Equation (1)

[0083] That is, the metric, M, is the weighted (weight, w) sum of p indices, z, selected for the selected geographic region. For example, following on from the example above, a selected region may comprise grassland. A predefined set of p indices can be associated with such regions (i.e., grassland) in order to provide a metric that provides a measure of the biodiversity (or some other desired indicator) of the selected region. That is, the combination of indices and the weightings applied to them in calculating the metric can be preselected in order to provide a metric that is meaningful for the selected region. For example, a region rich in vegetation, such as woodland, grassland and so on, can have a predefined set of indices and corresponding weightings associated with it in order to enable a metric to reflect a measure of interest for the region. Although some conclusions may be drawn from use of the LAI index about a region that is devoid of vegetation for example, it may generally be the case that an index of this kind for such a region may not be considered useful. That is, the nature of such a region would be known beforehand, from comparison with neighbouring regions and / or from a simple indication based on, e.g., visual data (local and / or remote) of the region. The use of such an index would therefore be pointless, unless its use is required for verifying the nature of an area in the course of subsequent selection of indices for a metric.

[0084] According to an example, data, such as a list for example, can be provided that maps regions to indices and weighting selections. For example, multiple regions, each defining respective biotopes or habitats (i.e., a generally uniform biological environment with environmental conditions providing a living place for a specific assemblage of plants and animals) can each map to one (or more) selected tuples, each tuple comprising a set of weighting / index pairs. Generally, in the context of the present disclosure, a region, such as a selected geographic region will comprise a terrestrial biotope (habitat or environment), such as grasslands, mountain, forest and so on. However, the present disclosure is also applicable to aquatic and mixed biotopes.

[0085] An example of a mapping from biotope to a weight / index tuple is provided below.

[0086] Table I.

[0087] Accordingly, the tuple for grassland for example comprises indices il and i3 and their associated weights wl and w3. In an example, the weights may be specific to the environment in question. That is, weight w3 for grassland, which is the weighting used for index i3, may be different to the weight w3 for forest and so on. For grassland, a metric, Mg, may therefore be calculated according to equation (2): Equation (2)

[0088] It may be considered that, for this particular metric, the weights relating to other indices (such as i2, i4 etc.) may be zero. This can similarly be the case for the other examples provided in the table above (an index can be removed from consideration from a metric by setting its weight to zero for example, or simply not including it).

[0089] Weights for indices can be configured according to the region in question. For example, more weight may be given to a moisture index (e.g. Normalized Difference Moisture Index (NDMI)) for a habitat that is considered within a wetland.

[0090] Additional Data

[0091] According to an example, additional data (such as data that is not generated by or derived from that obtained via a satellite for example) can be used to augment, refine or modify a metric value and / or individual index values for a selected geographic region and / or sub-area and / or sub-areas. For example, additional data can include data collected locally, such as ancillary habitat data, can be combined with an index and / or a metric for a selected geographic region / sub-area etc., according to a predefined combinatory relationship. Such ancillary habitat data can comprise, for example, migration data for one or more species within or passing through an area, water table information, and so on.

[0092] For example, a value representing a metric or index can be augmented with additional data representing, e.g., migratory pattern data for a species for which the selected geographic region to which the metric corresponds has been identified as a habitat. Such additional data can therefore enrich a metric for a region with information that can further enhance the understanding of the biodiversity of that region, and which may ultimately inform decisions, such as development decisions, to be taken in respect of the region.

[0093] In an example, additional data can be used to modify the value calculated for an index or metric, thereby ultimately modifying the representation of environmental characteristics of the selected geographic region. In another example, additional data can be used to colour an index or metric. That is, additional data can effectively form metadata for an index or metric that provides additional contextual information about an area. The underlying value of an index or metric may not be altered by such additional data, but the interpretation of the index or metric, and therefore of the nature of the area (for example, in the context of development potential) may be altered.

[0094] Ground Truthing

[0095] According to an example, an index value and / or a metric for a geographic region and / or a sub-area can be verified using data collected in the field, i.e., local data. Such local data can also be used in order to calibrate the calculation of an index value and / or a metric for a geographic region and / or a sub-area. For example, an index calculated using remote (e.g., satellite) data for a geographic region and / or a sub-area can be compared against an index value that has been calculated using local data. Any discrepancy can be used to calibrate future index values calculated from remote data, e.g., by shifting a calculated value by a set amount or percentage, and so on. Multiple local calculations can be used to generate a calibration correction. For example, multiple local calculations of an index value can be averaged to provide a final value that can be used to generate a calibration factor.

[0096] In terms of verification, again, an index calculated using remote (e.g., satellite) data for a geographic region and / or a sub-area can be compared against an index value that has been calculated using local data in order to verify the accuracy of the index calculated using remote data.

[0097] In an example, local data can include data collected on the ground for a region representing, e.g., local measurements for one of more of the indices described above, measurements representing the presence, absence and / or density of a particular species of flora and / or fauna, migration data, and so on. Determining a Condition Score

[0098] Benchmarking of the derived metric per habitat type allows comparison of the selected geographic region against other geographic regions having the same or a similar habitat type. This provides the user with a direct comparison or indication of how the biodiversity of the selected geographic region of interest is performing in comparison to other regions in a wider area. The nature or habitat type of a geographic region at a particular period of time is determined and the habitat type is used for a direct comparison between geographic regions.

[0099] The environmental metrics for the selected geographic region and other regions within an area are compared by benchmarking. This benchmarking provides a “standardised” condition score for the selected geographic region, per habitat type, making it easier to assess the biodiversity performance of the selected geographic region within a wider area of similar habitats.

[0100] The condition score is determined per habitat type and where the condition score is associated with the user-selected temporal value for the selected geographic region within the area. The condition score can be used to understand the biodiversity health and performance of the selected geographic region compared to other geographic regions within the area, where the other geographic regions have the same or a similar habitat type to the selected geographic region. In an example, the other geographic regions may fall within the same geographic location as the selected geographic region (e.g., adjacent geographic regions).

[0101] The user can interact with the map to define an area for benchmarking the selected geographic region. In an example, the user can define an area on the geographic regions layer within which the selected geographic region is to be benchmarked against the other geographic regions falling within the chosen area. Examples of areas to be defined are shown in figures 2B and 2C.

[0102] In the example of figure 2B, the area comprises multiple geographic regions within a selected radius of the selected geographic region of interest. At least one habitat type for each geographical region within the area is determined and at least some of the multiple geographic regions within the area will comprise a habitat type that is the same as, or similar to, at least one habitat type of the selected geographical region for the time period of interest, i.e., that is associated with the temporal value. In an example, if the user defines an area that does not have a geographic region having the same or similar habitat type as the selected geographic region the user may be notified, or the area expanded to capture more regions and habitats to allow for the benchmarking. In an example, the user defines an area comprising multiple geographic regions, each of which having a metric calculated according to the processes described herein. An example is shown in figure 2C. A set of other environmental metrics for the multiple geographic regions within the area is defined, where the set of other environmental metrics are associated with the same, or a similar, habitat type of the selected geographic region.

[0103] In the given area, the highest and lowest metric values can represent upper and lower bounds for the environmental metric respectively. Metric values for other geographic regions within the area can then be ordered within these bounds to form an ordered list for metric values for the area. In an example, a position within the ordered list can be used to indicate the biodiversity condition and performance for geographic regions within the area.

[0104] For example, the geographic regions within the area may all comprise wetlands. Accordingly, a metric for these geographic regions within the area can be used to provide an indication of the health of these wetland regions. Those regions within, e.g., the top third of the ordered list can be mapped to a “healthy” benchmark identifier, regions within, e.g., the bottom third of the ordered list can be mapped to an “unhealthy” benchmark identifier, and remaining regions can be mapped to an “average” benchmark identifier. As such, regions within a given area can be associated with an easy to interpret benchmarking representation that can be used to indicate the general condition and / or biodiversity of an area. In an example, a “healthy” benchmark identifier is assigned a condition score of “3”, an “average” benchmark identifier is assigned a condition score of “2”, and an “unhealthy” benchmark identifier is assigned a condition score of “1”. The number of benchmark identifiers that are used to determine the condition score may be determined by the user.

[0105] Hence, the condition score of a particular geographic region at a particular period of time can be determined and can be used to provide the biodiversity condition layer of the interactive map. An example method for determining a condition score for a selected region is shown in figure 2A.

[0106] The biodiversity condition layer may be provided based on populating the geographic regions layer with the condition score that is determined for the selected geographic region or regions, i.e., the condition score for each respective geographic region within the area can be indicated on the interactive map.

[0107] In an example, a predefined colour scheme may be used to colour each condition score on the interactive map. For example, the colour scheme may be aligned with the UK Habitat Classification Habitat Definitions and DEFRA Biodiversity Metric Calculator. In an example, the condition score for “Deciduous Woodland” in the selected geographic region for the chosen period of time may have a condition score of “3” indicating a “healthy” geographic region, and be coloured dark green, whilst other geographic regions having a condition score of “2” indicating an “average” geographic region may be coloured a medium green, and other geographic regions having a condition score of “1” indicating an “unhealthy” geographic region may be coloured light green. The colour scheme information for the condition scores and regions may be provided as a look-up-table, for example defining the RGB or CMYK colour combinations, for colouring each geographic region on the interactive map according to each condition score per habitat type. An example biodiversity condition layer is shown in figure 3.

[0108] As such, the selected data is processed in a manner that allows the user to more easily interpret the data selected from the various data sources. The data is processed according to user-selectable attributes per habitat type to derive a biodiversity condition score for a selected period of time for a geographic region of interest. The biodiversity score is based on a benchmarked set of environmental, metrics in which a metric for the selected geographic region is compared against a set of metrics for other geographic regions that share the same, or a similar, habitat type as the selected geographic region. The processing of data according to an environmental metric that is linked to a common habitat type for each geographic region makes it easier for the user to compare the relevant data at various selected periods of time. Hence, the generation of the interactive map in this manner makes it easier for a user to identify and understand any changes in biodiversity over time for the geographic region of interest.

[0109] The interactive map provides the user with a powerful and versatile tool for accessing and processing relevant data from the various data sources, in order to provide a meaningful output to the user that is more easily interpreted and translatable across different regions and habitats. For example, the user may compare the biodiversity health for a particular habitat across two areas, such as for a habitat type of lowland calcareous grassland in the South West region compared to the South East region, as shown in figures 2D and 2E.

[0110] Biodiversity Units and Other Metadata

[0111] In an example, a biodiversity unit for the selected geographic area, and associated with the relevant temporal value and condition score, can be determined. The biodiversity condition layer may be updated to comprise metadata associated with the biodiversity unit(s). Examples of metadata used to provide information on the interactive map are shown in figures 4 A and 4B.

[0112] For example, the biodiversity unit may be determined in alignment with a biodiversity metric calculator, such as the DEFRA Biodiversity Metric Calculator. In other examples, the data may be aligned to a different biodiversity metric calculator associated with a different country, or where the calculation of the biodiversity unit may differ by country.

[0113] The biodiversity unit quantifies the biodiversity value of a particular habitat. In an example, there are three types of biodiversity units: area habitat units, hedgerow units, and watercourse units. Each of these can be calculated in separate ‘modules’ of the biodiversity metric calculator. The biodiversity unit is calculated using a standardised biodiversity metric calculator, which assesses various attributes of the habitat such as its size, condition and distinctiveness.

[0114] An example method for generating an interactive map as described herein is shown in figure 6.

[0115] Monitoring Changes in Biodiversity

[0116] To determine a change in the environmental character! stic(s) of the selected geographic region over time, a metric for a selected geographic region can be calculated at a first period of time, tl, and can be compared with a metric or metrics for the same region at a second period of time, t2. The first period of time, tl, may correspond to a present time, and the second period of time, t2, may correspond to a previous time, and vice versa.

[0117] According to an example, a metric for a region calculated at a given time, tl, can be compared with a historic metric or metrics for the region at a previous time, t2, in order to determine a change in the environmental character! stic(s) of the selected geographic region over time. This can be useful to determine a change in the biodiversity of a region over time, such as to determine the effects on biodiversity of a region as a result of land development (of the region in question and / or of any one or more surrounding regions).

[0118] Using the interactive map as described above, the user is able to obtain a meaningful comparison of a particular region and habitat at various different times. An example method for monitoring changes in biodiversity is shown in figure 5.

[0119] The user is able to select an initial period of time of interest upon which to base the condition scoring. For example, this may correspond to a biodiversity condition at the present time. The user may select a different period of time at which to compare the same geographic region and habitat types against the initial period of time. This allows the user to identify changes in biodiversity over time. In an example, the biodiversity condition per habitat and region for different periods of time may be provided in a separate temporal layer.

[0120] The user may define a temporal value representing a first date or period in time at which at least one habitat type of the selected geographic region can be determined, i.e., the habitat type is associated with the first temporal value defined by the user, which may correspond to a previous date or period in time. The user may also define a temporal value representing a second date or period in time at which at least one habitat type of the selected geographic region can be determined, i.e., the habitat type is associated with the second temporal value defined by the user, which may correspond to a present date or period in time. A comparison can then be made for the respective habitat types associated with the temporal values for the first and second dates or periods in time. This comparison allows a determination of a presence or absence of a change in habitat type, where the change in habitat type corresponds to a habitat type for the first temporal value that is different to the habitat type associated with the second temporal value, or vice versa. A notification may be outputted to the user, via the graphical user interface, to indicate to the user the determined presence or absence of the change in habitat type.

[0121] The condition scores associated with the temporal values for the selected geographic region or regions within the area per habitat type can be determined, and the biodiversity condition layer can be updated based on populating the geographic regions layer with the condition score associated with the temporal values for each geographic region.

[0122] On the basis of determining a presence of a change in habitat type, an alert may be outputted to the user. Similarly, more importance may be placed on certain habitat types, such as irreplaceable habitat. For example, on the basis of determining a change in irreplaceable habitat, an alert may be outputted to the user.

[0123] In an example when a change in habitat types is detected, a condition score for the first and / or second temporal values for the selected geographic region can determined as described above. The conditions scores associated with each temporal value can be compared to determine a magnitude of change in biodiversity. For example, a change in habitat units may be determined based on the difference between the habitat unit at a first and second temporal value. The habitat units may be calculated as the product of the multiplication of “distinctiveness value x condition score x area”, for each period of time tl, and t2. The magnitude of change in biodiversity can be compared against a pre-determined threshold. If the magnitude of change falls above or below the pre-determined threshold, an alert may be outputted to the user. In other words, the biodiversity unit represents a weighted combination of biodiversity attributes, whilst taking into account the condition scoring for a region. The biodiversity units determined for the selected region at the relevant temporal values can be compared to determine a magnitude of change in biodiversity. The magnitude of change in biodiversity can be compared against a pre-determined threshold. If the magnitude of change falls above or below the pre-determined threshold, an alert may be outputted to the user.

[0124] Example(s)

[0125] Figure 1A is a schematic representation of a geographic regions layer 100, according to an example. The geographic regions layer comprises a tessellation of polygons 104. In this example, the polygons are represented as hexagons. Each individual polygon 106 corresponds to a geographic region. The user is able to select a polygon to indicate a selected geographic region of interest 101. To provide the user with a set of selectable geographic regions, an existing map for an area of interest (e.g., OS map) may be divided into geographic regions using the tessellation of polygons. The geo-spatial co-ordinates for each geographic region can be mapped to a respective polygon. The geo-spatial co-ordinates can be provided as metadata for each polygon.

[0126] Figure IB is a schematic representation of habitat layers 150, according to an example. An ancillary data set, such as satellite imagery, may be divided into each geographic region or polygon based on geo-spatial co-ordinates, and divided into separate habitat types. A subset of data from the ancillary data sets is processed to generate multiple environmental indices for the selected geographic region. The environmental indices are used to map the habitat types to respective geospatial co-ordinates and provide the habitat layers for the area of interest. As such, a habitat type is aligned with each geographic region. A colour scheme is selected and mapped to the habitat types for the geographic regions within the area and the habitat layers 150a, 150b, 150c, 150d, 150e, etc. are provided to the user in alignment with the tessellation of polygons on the geographic regions layer.

[0127] Figure 1C is a schematic representation of a selected geographic region, according to an example. The selected geographic region 101 is hexagonal in the example of figure 1A, and comprises multiple sub-areas 103, which are square in this instance. As noted above, the shape of a sub-area and / or a selected geographic region can be chosen as desired. A selected geographic region can comprise multiple sub-areas for which greater than, e.g., 50% of the sub-area is within the bounds of the selected geographic region. Accordingly, some sub-areas 105 (depicted with hatched markings) may not be part of the selected geographic region 101 (but may form part of another geographic region adjacent to the selected geographic region 101 for example). As such, an index value for the selected geographic region 101 can comprise an average of the index value for subareas (or some other desired combination of sub-area indices for example) that takes into account only sub-areas 101 with more than, e.g., 50% of their area within the bounds of a selected geographic region. In an example, the degree of area within the bounds of a selected geographic region can be more or less than 50%. So, for example, an adjacent geographic region 107 can comprise (at least) sub-areas 109 and 111 (but not sub-area 113).

[0128] Referring to the example of figure 1C, multiple indices can be generated for each sub-area 103. As described herein, each index can provide a representation of a characteristic for the sub-area for which it has been generated (such as ND VI etc.). Respective index values for the sub-areas can be combined in a predetermined way (such as by averaging for example) to provide an index value for a selected geographic region. Multiple indices for a selected geographic region can then be combined according to a predefined profile in order to generate a metric for the selected geographic region.

[0129] Figure ID is a schematic representation of a machine according to an example. The machine 120 comprises a processor 123, a data storage 125, a memory 127 to store instructions 129, executable by the processor 123, and a data set module 131. The machine comprises the data storage 125 that can be used to store data obtained by the data set module 131 from one or more data streams 133, comprising, e.g., local data 135 (i.e., data generated locally to a region, regions, sub-area(s) etc.), multi- and / or hyper-spectral data 137, which can comprise image data (comprising information from multiple spectral bands for example) and additional data 139, such as that described herein for example.

[0130] The instructions 129, executable by the processor 123, can cause the machine 120 to select a subset of data from multiple data streams for the selected geographic region, wherein the multiple data streams for the selected geographic region comprise raw data representing environmental measurements for the selected geographic region, process the subset of data to generate multiple environmental indices for the selected geographic region, wherein the environmental indices represent respective selected environmental attributes for the selected geographic region, and on the basis of data defining the nature of the selected geographic region, generate a metric for the selected geographic region, wherein the metric for the selected geographic region comprises a weighted combination of the multiple environmental indices.

[0131] Accordingly, the machine 120 can implement a method for processing a set of data associated with a selected geographic region, whereby to generate a metric representing an environmental characteristic of the selected geographic region.

[0132] Figure 2A is a flow chart of a method for determining a condition score for the selected geographic region according to an example. In the example of figure 2A, the method enables generation of a condition score based on a generated metric representing an environmental characteristic of a selected geographic region by processing a set of data associated with that selected geographic region.

[0133] In block 230, a subset of data is selected from multiple data streams for the selected geographic region and time period of interest. The multiple data streams for the selected geographic region comprise raw data representing environmental measurements for the selected geographic region. In an example, the raw data can comprise data that is collected locally and / or remotely, as described above for example. In the present context, raw data comprises data that has not been processed or altered, but which may comprise data that has been pre-processed in order to, e.g., remove spurious data values, format data, filter data and so on.

[0134] In block 235, the subset of data is processed to generate multiple environmental indices for the selected geographic region. The environmental indices represent respective selected environmental attributes for the selected geographic region, such as those described above for example, which may be selected by the user.

[0135] In block 240, the habitat types for the selected geographic region are defined using selected data or multiple environmental indices for the selected geographic region, and the dominant habitat type is defined.

[0136] In block 245, on the basis of data defining the nature of the selected geographic region, a metric is generated for the selected geographic region. The metric for the selected geographic region comprises a weighted combination of the multiple environmental indices. The weighting and specific combination of indices can be implemented as described above in order to enable a measure representing an environmental characteristic of the selected geographic region to be determined, which may be further augmented, modified or refined using additional data, as described above.

[0137] In block 250, an area of multiple geographic regions is defined, where the user can select an area of polygons, or draw an area having multiple geographic regions, on the geographic regions layer. Reference is made to figure 2B which is described below.

[0138] Figures 2B and 2C are schematic representations of an area defining a set of geographic regions, according to examples. Each geographic region within the area has an associated environmental metric per habitat type. The environmental metric for a selected geographic region 201 can be benchmarked against the set of surrounding geographic regions. The number of surrounding geographic regions can be preselected.

[0139] In the example shown in figure 2B, the selected geographic region 201 can be benchmarked against a first set, SI, of surrounding geographic regions 203, 205, 207, 209, 211, 213, associated with a first area 215 having a first radius Rl, comprising regions that are adjacent to the region 201. The selected geographic region 201 can be benchmarked against a second set, S2, of surrounding geographic regions associated with a second area 217 having a second radius R2. The second set, S2, can comprise regions of the first set, SI, along with additional regions, such as those regions adjacent to respective ones of the regions of SI. Further such sets and / or subsets of regions can be utilised for benchmarking as will be apparent, such as for the example shown in figure 2C. The multiple geographic regions have the same, or a similar, habitat type as the selected geographic region.

[0140] In the example of figure 2C, the selected geographic region 201 can be benchmarked against a set of geographic regions associated with an area 219 having an irregular shape. The area 219 may comprise a tessellation of multiple geographic regions at least one of which is adjacent the selected geographic region 201.

[0141] In other examples, the selected geographic region 201 can be benchmarked against a set of geographic regions that is not limited to the shapes described in the examples of figures 2B and 2C but may be associated with an area having any shape. In other examples, the set of geographic regions for benchmarking purposes may not have a geographic region that is adjacent to the selected geographic region 201. Looking back at figure 2A, at block 255, the metric for the selected geographic region is benchmarked against the other geographic regions within the defined area, and having the same or a similar habitat type as the selected geographic region.

[0142] According to an example, a benchmark per habitat for region 201 can be determined by, e.g., comparing the metric for region 201 with those of the other selected benchmarking regions. For example, considering the regions 201 - 213, the following metrics may be calculated:

[0143] Table II.

[0144] Accordingly, when reordered (largest metric value to smallest) this results in:

[0145] Table III. from which a relatively simple deduction as to the relative position for the metric of region 201 within the set of regions can be observed.

[0146] The following statistical data can be obtained:

[0147] Table IV.

[0148] The metric value can be benchmarked by representing the highest and lowest environmental metric values within the area as upper and lower bounds, then ordering the environmental metric values according to magnitude to generate an ordered list of environmental metrics.

[0149] In figure 2A, at block 260, a condition score is determined for the selected geographic region based on benchmarking the metric for the selected geographic region against the other geographic regions within the area, where the benchmarking is performed for the metric per habitat type. Respective portions of adjacent environmental metrics in the list at the upper and lower bounds are respectively assigned to a condition score. The condition score can be assigned to a benchmark identifier and a colour scheme is selected. The condition scores are respectively mapped to the geographic regions within the area and a biodiversity condition layer is provided to the user in alignment with the tessellation of polygons on the geographic regions layer for the appropriate colour scheme.

[0150] Figures 2D and 2E are schematic representations showing a user comparison of the biodiversity health for a particular habitat across two areas, according to an example. In this example, the user has compared a habitat type of lowland calcareous grassland in the South West region against the same habitat in the South East region. The number of polygons in the area of interest having a condition score of “poor”, “moderate” and “good” are tallied and represented as a histogram. The comparison of regions in this example shows that lowland calcareous grassland habitats in the South West region appear to be healthier than the same habitats in the South East region, based on the South West region having a greater number of moderate-good regions compared to the South East having a greater number of poor-moderate regions. Figure 3 is a schematic representation of a biodiversity condition layer 310, according to an example. The benchmarked environmental metrics for the geographic regions within the area are mapped to respective condition scores for each region or polygon based on geo-spatial coordinates. The condition score for the geographic regions within the area are provided per habitat type. In the example, the condition scores are represented according to a colour scheme indicating “healthy” regions 313 as dark green (condition score “3”), “average” regions 312 as light green (condition score “2”), and “unhealthy” regions 311 in white (condition score “1”).

[0151] Figures 4A and 4B are schematic representations of an interactive map 400 on a graphical user interface respectively showing the habitat layers 450 or biodiversity condition layer 410, according to examples. Metadata such as biodiversity unit that are calculated are shown on the interactive map. The user can select a layer 419 for display, such as the habitat layers 450 as shown in figure 4 A or the biodiversity condition layer 410 as shown in figure 4B. Other layers as described herein may be selected by the user for display on the interactive map and showing associated metadata. For example, metadata shown in these examples for the selected geographic region 401 can correspond to geo-spatial co-ordinates 411 for the selected region, habitat type 412, habitat distinctiveness 413, habitat condition score 414, habitat quality 415, habitat units 416, optimal habitat units 417 and an indication of an irreplaceable habitat 418. In another example, the time period, tl or t2, may be shown, amongst other information.

[0152] In an example, the geographic regions layer, habitat layers 450, and biodiversity condition layer 420 may be overlaid onto an OS map or satellite map image. This has the benefit of making reference to the selected geographic region easier for the user, for example by showing field boundaries in an area of agriculture.

[0153] Figure 5 is a flow chart of a method for monitoring changes in biodiversity, at a first and second period of time, for the selected geographic region as described above, according to an example. The user can select or enter the temporal values, tl and t2, for the periods of time on which to base the data comparison. At least one habitat type for the selected geographic region can be determined for each temporal value and a comparison made to determine a presence or absence of a change in habitat type. A change in habitat type is present for a habitat type for the first temporal value that is different to the habitat type associated with the second temporal value, or vice versa. On determining the presence or absence of a change in habitat type, a notification may be outputted to the user, via the graphical user interface, to indicate to the user the determined presence or absence of the change in habitat type. The new habitat type is identified and can be checked to determine if it relates to an irreplaceable habitat having more importance for the monitoring process. If the new habitat relates to an irreplaceable habitat, the user can be notified. The environmental indices associated with the new habitat type are used to determine biodiversity attributes such as condition score and habitat units for the new habitat type. The selected geographic area is divided into geographic regions according to the polygons on the geographic regions layer and the data set is divided into separate habitat types. The subset of data is processed to derive environmental indices and metrics for each habitat. The metrics for the selected geographic region are benchmarked against the metrics for the same or similar habitat types for the other geographic regions within a defined area. A condition score is determined for each geographic region per habitat within the area. A habitat unit for each habitat is calculated for each period of time, tl and t2, based on the distinctiveness values, condition scores and area. A change in habitat units between the period of time, tl and t2, is determined and compared to a threshold value. If the change is outside of the threshold value, an alert may be outputted to the user. The biodiversity condition layer may be updated with the new habitat types and metadata.

[0154] Figure 6 is a flow chart of a method for generating an interactive map according to an example. As described herein, a geographic region layer is provided, which may be based on a Spatial Framework 601 representing a polygon tessellation grid with a spatial data structure that uses a hierarchical geospatial indexing system. It can serve as a foundational layer for defining a condition score by combining both the habitat type and condition of the habitat. In an example, the polygon tessellation grid can be implemented using a national polygon tessellation of layers 602, where each polygon edge measures approximately 29 meters in length (for example). Spatial- temporal aggregation 603 of information on the polygon tessellation grid and area comprises the process of grouping or summarising spatial and temporal data. Spatial data can be grouped or summarised into larger units or areas to analyse and visualise patterns, trends, and relationships in geographic information. For example, with the help of spatial aggregation, the mean Normalised Difference Vegetation Index (ND VI) value of a specific region can be calculated to monitor the change in condition over time. Temporal data can be summarised by processing time-series data into larger time units, such as aggregating hourly data into daily, weekly, monthly, or yearly summaries. An example of temporal aggregation is converting daily Normalised Difference Vegetation Index (ND VI) into monthly averages to monitor vegetation health. This process helps in identifying seasonal trends. A data set (604) of biodiversity data, for example on a national level is created as a result of the data processing. The data set 604 is used to create the interactive map 605 for the user based on age-of-interest (AOI) information. INDUSTRIAL APPLICABILITY

[0155] The ecological tool described herein is versatile and can be used in many different areas of industry. Some example applications and scenarios include environmental monitoring, flood risk monitoring, habitat development planning, sustainable property development, pollution monitoring, prediction modelling such as predicting the impact of changes to land use, to name a few. For example, in the context of flood risk monitoring, environmental attributes relating to environmental measurements taken at different time periods can be used to indicate a change in habitat type, such as a change from woodland to grassland based on deforestation, and this can be linked to a geographic region having an increased risk of flooding. As such, the ecological tool may be used by local planning authorities, property developers, and landowners, to name a few example users.

[0156] According to an example, a metric representing an environmental characteristic of a selected geographic region can be used to track changes in biodiversity of the selected region over time. For example, in the context of development (such as building and so on), the biodiversity of a selected region and / or another region(s) nearby can change as the selected region / region(s) is / are developed. For example, development of a selected region / region(s) can comprise alteration of the selected region / region(s) by, e.g., changing landforms from a natural or semi-natural state for a purpose such as agriculture, housing, road building, drainage, restoration and so on. However, changes to a selected region / region(s) can directly affect wildlife habitats of the selected region / region(s) and surrounding region(s), thereby impacting local and global biodiversity. For example, development can result in habitat loss, degradation, fragmentation, all of which affect biodiversity in generally negative ways. If a development is found to be negatively impacting biodiversity the user can be notified or an alert outputted to bring the user’s attention to the issue so that remedial action can be taken. The ecological tool can help in determining the ecological impact of land use changes and can be used to ensure that development projects achieve a net gain in biodiversity. The ecological tool can be used to alert a user to non-compliance of legislation, such as if a development project is not meeting a required net gain in biodiversity.

[0157] In order to compensate for adverse effects on biodiversity from development (of a selected region and / or other regions that may be affected), some countries can impose requirements for developers to offset negative changes associated with development of a region(s) in a positive way. For example, it can be a requirement for a developer to offset carbon emissions due to development (in terms of the actual development itself and / or in terms of the subsequent change in nature of the region(s), which may itself result in increased emissions etc.).

[0158] In an example, a metric representing an environmental characteristic of a selected geographic region can be used to provide a measure representing how a region(s) has changed (e.g. in terms of the biodiversity of the region(s)) for the purpose of determining, e.g., a degree to which carbon emissions should be offset, and / or how another region (that or may not be local to the selected region(s)) should (or could) be altered in order to offset the changes (e.g., development) to the selected region(s). The alterations or changes can comprise changes in biodiversity. For example, development of a selected region may result in reduction in the number of a species as a result of habitat degradation. Accordingly, another region can be modified in order to increase the number of that species, thereby offsetting negative changes that have occurred as a result of the development of the selected region. As such, carbon emissions may not be the one or only thing being monitored and offset. Any change in biodiversity of a selected region can be monitored and changes may be made to any other region in order to compensate for the adverse effects that development may have had on the selected region. As such, a metric can be generated for a selected region, in order to determine a measure that can be used to track changes in biodiversity of the selected region over time, as well as for another region in order to determine a measure that can be used to track changes in biodiversity of the another region over time. That way, it is possible to compensate for negative changes in biodiversity of a selected region with positive changes in biodiversity of the other region in a meaningful and measurable way. This can enable a developer to be able to discharge any duty (e.g., a regulatory duty) they may have in respect of a development of a selected region.

[0159] For example, a change in the metric over time can provide a quantitative measure of the effect of development on a region that can be used to inform how another region may need to be (or should be) altered to compensate for any adverse impact of the development. That could include, e.g., altering the other region to increase biodiversity of the another region. As such, a metric representing the biodiversity of the another region can be used to measure and / or monitor changes in biodiversity in order to enable measurement and / or validation of the biodiversity of the another region in order to, e.g., track changes, thereby enabling the effects of any compensating changes to be monitored and / or verified. It is possible to identify potential locations for habitat banks where existing habitats can be upgraded, or support local nature recovery strategies by understanding what habitats can be better managed, such as for those habitats in a “moderate” or “poor” condition. Examples in the present disclosure can be provided as methods, systems or machine-readable instructions, such as any combination of software, hardware, firmware or the like. Such machine- readable instructions may be included on a computer readable storage medium (including but not limited to disc storage, CD-ROM, optical storage, etc.) having computer readable program codes therein or thereon.

[0160] The present disclosure is described with reference to flow charts and / or block diagrams of the method, devices and systems according to examples of the present disclosure. Although the flow diagrams described above show a specific order of execution, the order of execution may differ from that which is depicted. Blocks described in relation to one flow chart may be combined with those of another flow chart. In some examples, some blocks of the flow diagrams may not be necessary and / or additional blocks may be added. It shall be understood that each flow and / or block in the flow charts and / or block diagrams, as well as combinations of the flows and / or diagrams in the flow charts and / or block diagrams can be realized by machine readable instructions.

[0161] The machine-readable instructions may, for example, be executed by a machine such as a general- purpose computer, apparatus, a platform comprising user equipment such as a smart device, e.g., a smart phone, a special purpose computer, an embedded processor or processors of other programmable data processing devices to realize the functions described in the description and diagrams.

[0162] A processor or processing apparatus may execute the machine-readable instructions. The term 'processor' is to be interpreted broadly to include a CPU, processing unit, ASIC, logic unit, programmable gate set, etc. The methods and modules may all be performed by a single processor or divided amongst several processors.

[0163] Such machine-readable instructions may also be stored in a computer readable storage that can guide the computer or other programmable data processing devices to operate in a specific mode. For example, the instructions may be provided on a non-transitory computer readable storage medium encoded with instructions, executable by a processor.

[0164] Such machine-readable instructions may also be loaded onto a computer or other programmable data processing devices, so that the computer or other programmable data processing devices perform a series of operations to produce computer-implemented processing, thus the instructions executed on the computer or other programmable devices provide an operation for realizing functions specified by flow(s) in the flow charts and / or block(s) in the block diagrams.

[0165] Further, the teachings herein may be implemented in the form of a computer or software product, such as a non-transitory machine-readable storage medium, the computer software or product being stored in a storage medium and comprising a plurality of instructions, e.g., machine readable instructions, for making a computer device implement the methods recited in the examples of the present disclosure.

[0166] In some examples, some methods can be performed in a cloud-computing or network-based environment. Cloud-computing environments may provide various services and applications via the Internet. These cloud-based services (e.g., software as a service, platform as a service, infrastructure as a service, etc.) may be accessible through a web browser or other remote interface of the user equipment for example. Various functions described herein may be provided through a remote desktop environment or any other cloud-based computing environment.

[0167] While various embodiments have been described and / or illustrated herein in the context of fully functional computing systems, one or more of these exemplary embodiments may be distributed as a program product in a variety of forms, regardless of the particular type of computer-readable- storage media used to actually carry out the distribution. The embodiments disclosed herein may also be implemented using software modules that perform certain tasks. These software modules may include script, batch, or other executable files that may be stored on a computer-readable storage medium or in a computing system. In some embodiments, these software modules may configure a computing system to perform one or more of the exemplary embodiments disclosed herein. In addition, one or more of the modules described herein may transform data, physical devices, and / or representations of physical devices from one form to another.

[0168] The preceding description has been provided to enable others skilled in the art to best utilize various aspects of the exemplary embodiments disclosed herein. This exemplary description is not intended to be exhaustive or to be limited to any precise form disclosed. Many modifications and variations are possible without departing from the spirit and scope of the instant disclosure. The embodiments disclosed herein should be considered in all respects illustrative and not restrictive. Reference should be made to the appended claims and their equivalents in determining the scope of the instant disclosure. Clauses

[0169] 1. A method for processing a set of data associated with a selected geographic region, whereby to generate a metric representing an environmental characteristic of the selected geographic region, the method comprising: selecting a subset of data from multiple data streams for the selected geographic region, wherein the multiple data streams for the selected geographic region comprise raw data representing environmental measurements for the selected geographic region; processing the subset of data to generate multiple environmental indices for the selected geographic region, wherein the environmental indices represent respective selected environmental attributes for the selected geographic region; and on the basis of data defining the nature of the selected geographic region, generating a metric for the selected geographic region, wherein the metric for the selected geographic region comprises a weighted combination of the multiple environmental indices.

[0170] 2. The method according to clause 1, further comprising: determining, using data from at least one of the multiple data streams, the nature of selected geographic region, wherein the nature of the selected geographic region defines an ecological typology of the selected geographical region.

[0171] 3. The method according to clause 1 or 2, further comprising: comparing the metric for the selected geographic region with a set of selected other metrics for multiple geographic regions within a preselected area, whereby to generate a benchmark value for the metric of the selected geographic region.

[0172] 4. The method according to clause 3, wherein the multiple geographic regions within the preselected area comprise geographic regions within a preselected radius of the selected geographic region, and wherein at least some of the multiple geographic regions within the preselected area comprise geographic regions comprising the same or a similar ecological typology to that of the selected geographical region.

[0173] 5. The method according to any preceding clause, wherein at least one of the data streams for the selected geographic region comprises image data, wherein the image data comprises information from multiple spectral bands.

[0174] 6. The method according to clause 5, further comprising: determining a spectral signature for the selected geographical region using the image data.

[0175] 7. The method according to any preceding clause, wherein the selected geographic region comprises a biotope.

[0176] 8. The method according to clause 3 or 4, wherein the multiple geographic regions and the selected geographic region are so shaped as to form a tessellation covering the preselected area.

[0177] 9. A machine-readable storage medium encoded with instructions for processing a set of data associated with a selected geographic region, whereby to generate a metric representing an environmental characteristic of the selected geographic region, the instructions executable by a processor, whereby to cause the processor to: select a subset of data from multiple data streams for the selected geographic region, wherein the multiple data streams for the selected geographic region comprise raw data representing environmental measurements for the selected geographic region; process the subset of data to generate multiple environmental indices for the selected geographic region, wherein the environmental indices represent respective selected environmental attributes for the selected geographic region; and on the basis of data defining the nature of the selected geographic region, generate a metric for the selected geographic region, wherein the metric for the selected geographic region comprises a weighted combination of the multiple environmental indices.

[0178] 10. The machine-readable storage medium according to clause 9, further encoded with instructions executable by the processor, whereby to cause the processor to: determine, using data from at least one of the multiple data streams, the nature of selected geographic region, wherein the nature of the selected geographic region defines an ecological typology of the selected geographical region.

[0179] 11. The machine-readable storage medium according to clause 9 or 10, further encoded with instructions executable by the processor, whereby to cause the processor to: compare the metric for the selected geographic region with a set of selected other metrics for multiple geographic regions within a preselected area, whereby to generate a benchmark value for the metric of the selected geographic region. 12. The machine-readable storage medium according to clause 11, further encoded with instructions executable by the processor, whereby to cause the processor to: determine a spectral signature for the selected geographical region using the image data.

[0180] 13. An apparatus for processing a set of data associated with a selected geographic region, whereby to generate a metric representing an environmental characteristic of the selected geographic region, the apparatus comprising a processor configured to: select a subset of data from multiple data streams for the selected geographic region, wherein the multiple data streams for the selected geographic region comprise raw data representing environmental measurements for the selected geographic region; process the subset of data to generate multiple environmental indices for the selected geographic region, wherein the environmental indices represent respective selected environmental attributes for the selected geographic region; and on the basis of data defining the nature of the selected geographic region, generate a metric for the selected geographic region, wherein the metric for the selected geographic region comprises a weighted combination of the multiple environmental indices.

[0181] 14. The apparatus according to clause 13, wherein the processor is configured to: determine, using data from at least one of the multiple data streams, the nature of selected geographic region, wherein the nature of the selected geographic region defines an ecological typology of the selected geographical region.

[0182] 15. The apparatus according to clause 13 or 14, wherein the processor is configured to: compare the metric for the selected geographic region with a set of selected other metrics for multiple geographic regions within a preselected area, whereby to generate a benchmark value for the metric of the selected geographic region.

Claims

CLAIMS1. A computer-implemented method for generating an interactive map (400), via a graphical user interface, the interactive map comprising a plurality of layers, the method comprising the steps of: providing a geographic regions layer (100) representing a set of geographic regions, the geographic regions layer comprising a tessellation of polygons (104), each polygon (106) corresponding to a geographic region that is selectable by a user; defining a temporal value representing a date or period in time, the temporal value being selectable by a user; determining at least one habitat type of a selected geographic region (101), the habitat type being associated with the temporal value; and determining a condition score associated with the temporal value for the selected geographic region, the condition score indicating a biodiversity condition and being determined based on: generating an environmental metric for the selected geographic region on the basis of data defining the at least one habitat type of the selected geographical region, the environmental metric being generated through a weighted combination of multiple environmental indices, wherein the multiple environmental indices for the selected geographic region are generated through processing of selected data, the selected data comprising environmental measurements for the selected geographic region that is associated with the temporal value; defining an area (215) on the geographic regions layer, the area having a tessellation of multiple geographic regions at least one of which is adjacent the selected geographic region; determining at least one habitat type for each geographical region within the area, wherein at least some of the multiple geographic regions within the area comprise a habitat type associated with the temporal value and that is the same as, or similar to, the at least one habitat type of the selected geographical region;defining set of other environmental metrics for the multiple geographic regions within the area, the set of other environmental metrics being associated with the same, or a similar, habitat type of the selected geographic region; benchmarking the environmental metric for the selected geographic region against the set of other environmental metrics for the multiple geographic regions within the area, thereby to determine the condition score for the selected geographic region; and providing a biodiversity condition layer (310) based on populating the geographic regions layer with the condition score associated with the temporal value for the selected geographic region.

2. The computer implemented method of claim 1, comprising the step of defining a further temporal value representing a further date or further period in time; determining at least one habitat type of the selected geographic region, the habitat type being associated with the further temporal value; determining a presence or absence of a change in habitat type based on comparing the respective habitat types associated with the temporal value and further temporal value, the change in habitat type corresponding to a further habitat type that is different to the habitat type associated with the temporal value; and outputting a notification to indicate to the user the determined presence or absence of the change in habitat type.

3. The computer implemented method of claim 2, comprising the step of on the basis of determining the presence of the change in habitat type, determining a further condition score associated with the further temporal value for the selected geographic region, the further condition score being determined based on: generating a further environmental metric for the selected geographic region on the basis of data defining the further habitat type of the selected geographical region, the further environmental metric being generated through a weighted combination of multiple environmental indices, wherein the multiple environmental indices for the selectedgeographic region are generated through processing of selected data, the selected data comprising environmental measurements for the selected geographic region that is associated with the further temporal value; defining an area on the geographic regions layer, the area having a tessellation of multiple geographic regions at least one of which is adjacent the selected geographic region, wherein at least some of the multiple geographic regions within the area comprise a habitat type associated with the further temporal value and that is the same as, or similar to, the further habitat type of the selected geographical region; and benchmarking the further environmental metric for the selected geographic region against a set of other environmental metrics for the multiple geographic regions within the area, thereby to determine the further condition score for the selected geographic region.

4. The computer implemented method of claim 3, comprising the step of: determining a biodiversity unit ( 16) representing a weighted combination of biodiversity attributes (413, 414) including the condition score (414) for the selected geographic area and the temporal value; determining a further biodiversity unit (416) for the selected geographic area for the further temporal value and further condition score; comparing the biodiversity unit and further biodiversity unit to determine a magnitude of change in biodiversity; and on the basis of the magnitude of change falling above or below a pre-determined threshold, outputting an alert.

5. The computer implemented method of any of claims 2-4, comprising the step of: determining the presence or absence of a habitat type that corresponds to an irreplaceable habitat; and on the basis of determining the presence of the irreplaceable habitat, and on the basis of determining the presence of the change in habitat type, outputting an alert.

6. The computer implemented method of any preceding claim, wherein the selected data comprising environmental measurements for the selected geographic region is associated with user-selectable environmental attributes.

7. The computer implemented method of any preceding claim, wherein determining the at least one habitat type of the selected geographic region comprises: deriving the at least one habitat type using the multiple environmental indices, or querying a database to obtain the predetermined at least one habitat type associated with the selected geographic region, optionally, wherein the selected geographic region comprises a plurality of habitat types and the at least one habitat type is determined as a dominant habitat type for the selected geographic region.

8. The computer implemented method of any preceding claim, comprising the step of: providing at least one habitat layer (150a) based on populating the geographic regions layer with the habitat type for the selected geographic region, optionally, populating the geographic regions layer with the habitat type for each respective geographic region within the area, wherein a further habitat layer (150b) is provided per further habitat type.

9. The computer implemented method of any preceding claim, comprising the step of: determining the condition score associated with the temporal value for each geographic region within the area per habitat type; and updating the biodiversity condition layer based on populating the geographic regions layer with the condition score associated with the temporal value for each geographic region.

10. The computer implemented method of any preceding claim, wherein the step of determining the condition score for benchmarking the environmental metrics comprises:representing the highest and lowest environmental metric values within the area as upper and lower bounds; ordering the environmental metric values according to magnitude, thereby to generate an ordered list of benchmarked environmental metrics; and assigning respective portions of adjacent environmental metrics in the list at the upper and lower bounds to a respective condition score.

11. The computer implemented method of any preceding claim, wherein the step of defining the area (215) on the geographic regions layer comprises the area having multiple geographic regions within a selected radius (Rl) of the selected geographic region.

12. The computer implemented method of any preceding claim, wherein the selected data comprises image data having information from multiple spectral bands, including the infrared spectrum.

13. A machine-readable storage medium encoded with instructions for generating an interactive map (400), via a graphical user interface, the interactive map comprising a plurality of layers, the instructions executable by a processor, whereby to cause the processor to: provide a geographic regions layer (100) representing a set of geographic regions, the geographic regions layer comprising a tessellation of polygons (104), each polygon (106) corresponding to a geographic region that is selectable by a user; define a temporal value representing a date or period in time, the temporal value being selectable by a user; determine at least one habitat type of a selected geographic region (101), the habitat type being associated with the temporal value; and determine a condition score associated with the temporal value for the selected geographic region, the condition score indicating a biodiversity condition and being determined based on: generating an environmental metric for the selected geographic region on the basis of data defining the at least one habitat type of the selected geographical region, theenvironmental metric being generated through a weighted combination of multiple environmental indices, wherein the multiple environmental indices for the selected geographic region are generated through processing of selected data, the selected data comprising environmental measurements for the selected geographic region that is associated with the temporal value; defining an area (215) on the geographic regions layer, the area having a tessellation of multiple geographic regions at least one of which is adjacent the selected geographic region; determining at least one habitat type for each geographical region within the area, wherein at least some of the multiple geographic regions within the area comprise a habitat type associated with the temporal value and that is the same as, or similar to, the at least one habitat type of the selected geographical region; defining set of other environmental metrics for the multiple geographic regions within the area, the set of other environmental metrics being associated with the same, or a similar, habitat type of the selected geographic region; benchmarking the environmental metric for the selected geographic region against a set of other environmental metrics for the multiple geographic regions within the area, thereby to determine the condition score for the selected geographic region; and provide a biodiversity condition layer (310) based on populating the geographic regions layer with the condition score associated with the temporal value for the selected geographic region.

14. The machine-readable storage medium of claim 13, wherein the processor is configured to carry out the computer-implemented method of any of claims 2-12.

15. An apparatus (120) for generating an interactive map (400), via a graphical user interface, the interactive map comprising a plurality of layers, the apparatus comprising a processor (123) configured to:provide a geographic regions layer (100) representing a set of geographic regions, the geographic regions layer comprising a tessellation of polygons (104), each polygon (106) corresponding to a geographic region that is selectable by a user; define a temporal value representing a date or period in time, the temporal value being selectable by a user; determine at least one habitat type of a selected geographic region (101), the habitat type being associated with the temporal value; and determine a condition score associated with the temporal value for the selected geographic region, the condition score indicating a biodiversity condition and being determined based on: generating an environmental metric for the selected geographic region on the basis of data defining the at least one habitat type of the selected geographical region, the environmental metric being generated through a weighted combination of multiple environmental indices, wherein the multiple environmental indices for the selected geographic region are generated through processing of selected data, the selected data comprising environmental measurements for the selected geographic region that is associated with the temporal value; defining an area (215) on the geographic regions layer, the area having a tessellation of multiple geographic regions at least one of which is adjacent the selected geographic region; determining at least one habitat type for each geographical region within the area, wherein at least some of the multiple geographic regions within the area comprise a habitat type associated with the temporal value and that is the same as, or similar to, the at least one habitat type of the selected geographical region; defining set of other environmental metrics for the multiple geographic regions within the area, the set of other environmental metrics being associated with the same, or a similar, habitat type of the selected geographic region; benchmarking the environmental metric for the selected geographic region against a set of other environmental metrics for the multiple geographic regions within the area, thereby to determine the condition score for the selected geographic region; andprovide a biodiversity condition layer (310) based on populating the geographic regions layer with the condition score associated with the temporal value for the selected geographic region.

16. The apparatus of claim 15, wherein the processor is configured to carry out the computer- implemented method of any of claims 2-12.

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