Global biodiversity hot spot area threat condition remote sensing monitoring method and system

Abstract

The invention provides a global biodiversity hot spot area threat condition remote sensing monitoring method and system, and relates to the technical field of ecological environment monitoring, spatial analysis and remote sensing and information processing, and the method comprises the steps: obtaining multi-temporal global land coverage data and auxiliary GIS data of a biodiversity hot spot area, and carrying out the preprocessing; extracting forest coverage layers of a reference period and a monitoring period from global land coverage data, and extracting pixels converted from a forest to a non-forest through a layer difference method to generate a forest loss layer; based on the forest loss map layer and the preprocessed auxiliary GIS data, calculating various ecological pressure indexes including a forest loss rate, a protection failure rate and a population pressure index, and performing normalization; and calculating a comprehensive threat index based on each ecological pressure index, and determining an ecological threat level. According to the method, automatic identification, quantitative evaluation and grading of the ecological threats of the hot spot region are realized, and technical support is provided for global ecological protection planning and management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical fields of ecological environment monitoring, spatial analysis, remote sensing and information processing, and particularly relates to a global biodiversity hotspot region threat condition remote sensing monitoring method and system. BACKGROUND

[0002] Biodiversity hotspots are regions that harbor extremely rich species on Earth, but due to human activities such as deforestation, agricultural reclamation, urban expansion, etc., the habitats of these regions are experiencing rapid loss, and biodiversity is facing serious threats. However, it is very difficult to conduct unified and continuous monitoring of the threat conditions of global hotspots.

[0003] Traditional methods often rely on local field surveys or one-time research assessments, which cannot reflect dynamic changes in a timely manner. With the development of remote sensing technology and geographic information systems (GIS), a large amount of global-scale data resources are constantly updated, providing new possibilities for comprehensive monitoring of biodiversity hotspots. Existing technologies have not fully integrated these data and intelligent analysis methods for hotspot threat monitoring, resulting in lagging monitoring results and incomplete coverage. Therefore, an innovative method and system are needed to integrate multi-source remote sensing data and spatial analysis methods to conduct real-time remote sensing monitoring and assessment of the threat conditions of global biodiversity hotspots. SUMMARY

[0004] The present application aims to at least partially solve one of the technical problems in the related art.

[0005] To this end, the first purpose of the present application is to propose a global biodiversity hotspot region threat condition remote sensing monitoring method, which realizes automatic identification, quantitative assessment and grade division of ecological threats in hotspot regions, thereby providing technical support for global ecological protection planning and management.

[0006] The second purpose of the present application is to propose a global biodiversity hotspot region threat condition remote sensing monitoring system.

[0007] To achieve the above purpose, the first aspect of the present application proposes a global biodiversity hotspot region threat condition remote sensing monitoring method, comprising:

[0008] Obtaining multi-temporal global land cover data and auxiliary GIS data of biodiversity hotspots, and preprocessing;

[0009] Extracting forest cover layers of the base period and the monitoring period from the preprocessed global land cover data, and extracting pixels that have changed from forest to non-forest through layer difference method, to generate a forest loss layer;

[0010] Based on the forest loss layer and the preprocessed auxiliary GIS data, each ecological pressure index is calculated and normalized, wherein the pressure indexes include forest loss rate, protection failure rate and population pressure index;

[0011] Based on the normalized ecological pressure indexes, a comprehensive threat index is calculated, and an ecological threat level is determined.

[0012] To achieve the above purpose, the second aspect of the present application provides a global biodiversity hotspot area threat condition remote sensing monitoring system, comprising:

[0013] The data acquisition module is configured to acquire multi-temporal global land cover data and auxiliary GIS data of the biodiversity hotspot area, and perform preprocessing;

[0014] The forest loss layer extraction module is configured to extract forest cover layers of the base period and the monitoring period from the preprocessed global land cover data, and extract pixels converted from forest to non-forest through layer difference method, to generate a forest loss layer;

[0015] The ecological threat index calculation module is configured to calculate each ecological pressure index based on the forest loss layer and the preprocessed auxiliary GIS data, and perform normalization, wherein the pressure indexes include forest loss rate, protection failure rate and population pressure index;

[0016] The comprehensive threat calculation module is configured to calculate a comprehensive threat index based on the normalized ecological pressure indexes, and determine an ecological threat level.

[0017] The global biodiversity hotspot area threat condition remote sensing monitoring method and system provided by the embodiments of the present application integrate multi-source remote sensing data and spatial analysis means, perform real-time remote sensing monitoring and evaluation on the threat condition of the global biodiversity hotspot area, automatically identify changes (such as loss of forest cover) of habitats in the hotspot area, evaluate the intensity of human disturbance, generate quantitative ecological threat indexes, and provide timely and reliable data support for biodiversity protection planning and policy making.

[0018] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0019] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the accompanying drawings, in which:

[0020] Figure 1 A flowchart of a global biodiversity hotspot area threat condition remote sensing monitoring method provided by the first embodiment of the present application;

[0021] Figure 2 The technical roadmap of the embodiments of the present application is as follows.

[0022] Figure 3 The structural schematic diagram of a global biodiversity hotspot area threat condition remote sensing monitoring system provided by the embodiments of the present application is as follows. DETAILED DESCRIPTION

[0023] The embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0024] The global biodiversity hotspot area threat condition remote sensing monitoring method and system of the embodiments of the present application are described below with reference to the accompanying drawings.

[0025] Figure 1 The flowchart of a global biodiversity hotspot area threat condition remote sensing monitoring method provided by Embodiment One of the present application is as follows.

[0026] As shown in Figure 1 The global biodiversity hotspot area threat condition remote sensing monitoring method includes the following steps:

[0027] Step 101, acquiring multi-temporal global land cover data and auxiliary GIS data of the biodiversity hotspot area, and pre-processing;

[0028] Specifically, multi-temporal global land cover data of the biodiversity hotspot area is collected and acquired, including FROM-GLC Plus data of the reference period and the monitoring period; at the same time, auxiliary GIS data is acquired, including global protected area boundary data (such as WDPA), population distribution grid data (such as WorldPop) and land classification vector data, etc.

[0029] By integrating the FROM-GLC Plus data, and fusing global forest change data (GFC), global hotspot area boundary data, protected area boundary (WDPA) and population density data (WorldPop), a comprehensive basic data set is formed.

[0030] Specifically, the data is pre-processed, including steps of spatial resampling and projection unification, etc. The classified data is re-encoded to ensure semantic consistency, and the continuous variables are pixel-registered, null-interpolated and time series filtered to improve the overall comparability and layer accuracy, and to ensure the consistency of the data in space, time and geometric accuracy.

[0031] Step 102, extract the forest coverage layers of the base period and the monitoring period from the pre-processed global land cover data, and extract the pixels that have changed from forest to non-forest by layer difference method to generate the forest loss layer;

[0032] In this embodiment, the forest mask map of the monitoring period and the base period is extracted from the global land cover data of the base period and the monitoring period respectively. Through layer difference detection, the pixels that have changed from "forest" to "non-forest" are extracted to realize the detection of the change from forest to non-forest in the hotspot area, and a forest loss raster map product is formed. At the same time, in order to improve the accuracy, the GFC annual forest change map is introduced for verification and supplement: on the one hand, the time attribute accuracy is improved by using the annual loss year mark of GFC, and on the other hand, the inconsistent areas are removed and the missing parts are modified and completed. Finally, a standardized forest loss layer with loss year attribute and good spatial continuity is generated to provide basic data support for subsequent index calculation.

[0033] Step 103, based on the forest loss layer and the pre-processed auxiliary GIS data, calculate each ecological pressure index and normalize, wherein each pressure index includes forest loss rate, protection failure rate and population pressure index;

[0034] Specifically, on the basis of forest loss identification, the spatial human activity intensity and the protection area boundary information are fused to construct an ecological threat index system for comprehensively reflecting the multi-dimensional pressure on the regional ecosystem. Considering that various indexes have different units and value ranges, in order to facilitate subsequent comparison and modeling analysis, all indexes are normalized to form a standardized and highly comparable ecological threat factor set, including:

[0035] 1) Ecological pressure index construction

[0036] The threat to the ecosystem usually comes from natural environmental changes (such as forest degradation) and human intervention (such as population density and protection gap). This embodiment selects the following three core indexes as the calculation basis from these two dimensions:

[0037] Table 1 Ecological pressure index

[0038]

[0039] Forest loss rate (FR): the ratio of forest loss area in the hotspot area to the total forest area in the base period, which is an important index to measure the degree of habitat degradation. FR is a direct reflection of habitat spatial degradation. The higher the FR, the more the forest area decreases, and the more serious the ecological integrity decreases;

[0040] Protection failure rate (PLR) is used to evaluate the proportion of forest destruction outside the protected area. If the proportion is high, it indicates that there are blind spots in the ecological protection network, and the protection plan needs to be optimized;

[0041] The population pressure index (PDI) focuses on social factors, measuring the degree of human intervention through population density, indirectly reflecting the ecological impact of activities such as agricultural expansion and construction development. The above three indicators constitute the core dimensions of ecological threats from the aspects of "natural degradation", "institutional coverage", and "human intervention", with complementarity and representativeness, and can comprehensively describe the main threats faced by the ecosystem.

[0042] 2) Index normalization processing

[0043] Considering that the forest loss rate (FR), protection loss rate (PLR), and population pressure index (PDI) come from natural ecology, protected area management, and social economic systems respectively, their data units, value ranges, and distribution patterns are different, and direct comparison or weighted integration is difficult. To solve the above problems, all original indicators are standardized to map them to the [0, 1] interval, enhancing horizontal comparability and the scientificity of subsequent calculations.

[0044] This example uses the min-max normalization method, which linearly compresses any original value to the [0, 1] range based on the maximum and minimum interval, maintaining the relative position relationship of variables while eliminating the influence of the original scale. The normalization calculation method is as follows:

[0045] Standard form (applicable to known interval upper and lower bounds):

[0046]

[0047] Simplified form (applicable to scenarios considering only maximum value control):

[0048]

[0049] Where: X is the original index value; X min , X max are the minimum and maximum values of the index in the sample; X ′ is the normalized index value.

[0050] Table 2 Index normalization processing method

[0051]

[0052] After normalization, each index not only has a unified numerical scale, but also can be used as an input factor for weight combination analysis, supporting the subsequent weighted calculation and grade division of the comprehensive threat index (I). At the same time, this method has strong scalability, and if other ecological or social indicators are introduced, the same normalization strategy can be used to achieve seamless integration of the index system.

[0053] Step 104, based on the normalized ecological stress indicators, calculate the comprehensive threat index, and determine the ecological threat level.

[0054] Specifically, after completing the normalization processing of each ecological stress indicator, a comprehensive evaluation model is further constructed to quantify the ecological risk level of the hotspot area and output multi-format result products to meet different analysis needs such as map expression, statistical comparison and text interpretation, including:

[0055] 1) Comprehensive threat index calculation

[0056] To reflect the different contribution of each indicator to the ecological system, the application introduces a weighted average model to construct a comprehensive threat index (I), and the result range is limited to [0, 1]. The comprehensive index I is defined as follows:

[0057] I = a x FR + b x PLR + c x PDI

[0058] Where: a, b, c are the weight parameters of each ecological indicator, satisfying a + b + c = 1.

[0059] Table 3 default weight setting

[0060]

[0061] This weight combination considers natural degradation, human pressure and governance coverage, while highlighting the dominant role of forest loss in overall ecological threat. The value range of I is [0, 1], and the larger the value, the higher the comprehensive threat level of the regional ecosystem.

[0062] 2) Threat level division

[0063] To further enhance the interpretability and operability of the results, the application standardizes the ecological risk level of each hotspot area based on the comprehensive threat index I, and the grade boundary is as follows:

[0064] Table 4 Threat level division

[0065]

[0066] This grade division scheme can be used for subsequent spatial priority ranking, regional classification management and differentiated policy making.

[0067] 3) Result output and multi-form display

[0068] To support subsequent ecological protection decision-making and visual display, a unified and standardized result output module is designed, covering three types of product forms of grid map, table report and text interpretation. The output format can include GeoTIFF map layer, CSV table file and PDF text report, and supports embedded calling and platform integration. Specifically:

[0069] Spatial layer (GeoTIFF format): According to the level result, a threat level distribution map is generated, and a red-orange-green three-segment color table is used to express high-medium-low level information, which is convenient for map presentation and GIS system superposition analysis.

[0070] Statistical report (CSV format): The ID, name, area, index original value, normalized value, comprehensive index and level classification of each hotspot area are output, and batch reading and horizontal comparison are supported.

[0071] Automatic report (PDF / HTML format): The system automatically generates a standardized interpretation report, which covers forest change background, index performance analysis, protection system evaluation and intervention suggestions, providing one-stop analysis materials for managers.

[0072] The global biodiversity hotspot area threat condition remote sensing monitoring method of the embodiment of the application adopts the technical roadmap as shown in the figure. Figure 2 Through the integration of multi-source remote sensing data and spatial analysis methods, the threat condition of global biodiversity hotspot areas is monitored and evaluated in real time, the changes of habitats in the hotspot area (such as forest cover loss) are automatically identified, the intensity of human disturbance is evaluated, and quantitative ecological threat indicators are generated, providing timely and reliable data support for biodiversity protection planning and policy making.

[0073] In order to realize the above-mentioned embodiment, the application further provides a global biodiversity hotspot area threat condition remote sensing monitoring system.

[0074] Figure 3 A structural schematic diagram of a global biodiversity hotspot area threat condition remote sensing monitoring system provided by the embodiment of the application.

[0075] As shown in the figure, Figure 3 The global biodiversity hotspot area threat condition remote sensing monitoring system comprises:

[0076] The data acquisition module is used for acquiring multi-temporal global land cover data and auxiliary GIS data of the biodiversity hotspot area, and performing preprocessing;

[0077] The forest loss layer extraction module is used for extracting forest cover layers of the base period and the monitoring period from the preprocessed global land cover data, and extracting pixels converted from forest to non-forest through layer difference method, and generating a forest loss layer.

[0078] The ecological threat index calculation module is configured to calculate various ecological pressure indexes based on the forest loss layer and the preprocessed auxiliary GIS data, and normalize the various ecological pressure indexes, wherein the various pressure indexes include a forest loss rate, a protection failure rate, and a population pressure index;

[0079] The comprehensive threat calculation module is configured to calculate a comprehensive threat index based on the normalized various ecological pressure indexes, and determine an ecological threat level.

[0080] Further, in the embodiments of the present application, the acquired data is preprocessed, including:

[0081] The data is uniformly subjected to spatial resampling and projection to ensure consistency of the data in space, time, and geometric accuracy.

[0082] Specifically, in the embodiments of the present application, the calculation formula of the forest loss rate is:

[0083]

[0084] The calculation formula of the protection failure rate is:

[0085]

[0086] The population pressure index PDI is an average population density in a forest loss region and a surrounding buffer zone.

[0087] Further, in the embodiments of the present application, the various ecological pressure indexes are normalized, including:

[0088] The linear normalization method is used to standardize the various ecological pressure indexes, so that the various indexes are uniformly in a dimensionless interval of [0, 1].

[0089] Specifically, in the embodiments of the present application, based on the normalized various ecological pressure indexes, the comprehensive threat index is calculated, and the ecological threat level is determined, including:

[0090] The weight parameters of the various ecological pressure indexes are set to construct a weighted calculation model to calculate the comprehensive threat index I, and the comprehensive threat index I is limited in [0, 1], wherein the weighted calculation model is represented as:

[0091] I = a x FR + b x PLR + c x PDI

[0092] wherein a, b, and c are respectively the weight parameters of the set forest loss rate FR, the protection failure rate PLR, and the population pressure index PDI;

[0093] According to the comprehensive threat index I value from small to large, the ecological threat level is divided into low threat, medium threat and high threat, and the standardized threat level is determined.

[0094] It should be noted that the foregoing explanation of the global biodiversity hotspot area threat condition remote sensing monitoring method embodiment is also applicable to the global biodiversity hotspot area threat condition remote sensing monitoring system of this embodiment, which will not be repeated here.

[0095] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0096] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "multiple" is at least two, for example, two, three, etc., unless otherwise explicitly specified.

[0097] Any process or method descriptions in the flowchart or otherwise described herein can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for implementing the specified logic functions or processes, and the scope of the preferred embodiments of the present application includes additional implementation in which the functions described are performed in a different order, including substantially simultaneously, or in reverse order, as will be understood by those skilled in the art of the embodiments to which the present application belongs.

[0098] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions stored in a computer readable medium, which can be executed by an instruction execution system, apparatus or device, such as a computer-based system, a processor-based system, or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or a combination thereof. For the purposes of this specification, a "computer readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus or device. The computer readable medium can specifically be, but is not limited to, the following: an electronic connection (electronic apparatus) having one or more wires, a portable computer diskette (magnetic apparatus), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disk read-only memory (CDROM). In addition, the computer readable medium can even be paper or other suitable medium upon which the program can be printed, because the program can be electronically obtained, for example, by optically scanning the paper or other medium, then

[0099] It should be understood that portions of the application can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or a combination thereof, can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0100] Those of ordinary skill in the art can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing relevant hardware, and the programs can be stored in a computer readable storage medium. When the programs are executed, they include one of the steps of the method embodiments or a combination thereof.

[0101] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0102] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A remote sensing monitoring method for global biodiversity hotspot threat status, characterized by: include: Obtain multi-temporal global land cover data and auxiliary GIS data of biodiversity hotspots and perform pre-processing; Extract forest cover layers for the baseline and monitoring periods from pre-processed global land cover data, and use layer difference methods to extract pixels that have transitioned from forest to non-forest, generating a forest loss layer. Based on the forest loss layer and pre-processed auxiliary GIS data, various ecological pressure indicators are calculated and normalized, where the various pressure indicators include forest loss rate, protection lapse rate, and population pressure index; Based on the normalized ecological pressure indicators, the comprehensive threat index is calculated and the ecological threat level is determined.

2. The method according to claim 1, wherein Preprocess the acquired data, including: The data is spatially resampled and projected uniformly to ensure the consistency of the data in space, time and geometric accuracy.

3. The method according to claim 1, wherein The formula for calculating the forest loss rate is: The calculation formula of the protection failure rate is: The population pressure index (PDI) is the average population density in the forest loss area and the surrounding buffer zone.

4. The method according to claim 1, wherein Normalize various ecological pressure indicators, including: The various ecological pressure indicators were standardized using the linear normalization method so that they all fell into the dimensionless interval of [0,1].

5. The method according to claim 1, wherein The above-mentioned calculation of the comprehensive threat index based on the normalized ecological pressure indicators and determination of the ecological threat level includes: The weight parameters of each ecological pressure indicator are set, and a weighted calculation model is constructed to calculate the comprehensive threat index I, and the comprehensive threat index I is limited to [0,1]. The weighted calculation model is expressed as: I=a×FR+b×PLR+c×PDI Among them, a, b, and c are the weight parameters of the forest loss rate FR, protection failure rate PLR, and population pressure index PDI respectively; According to the comprehensive threat index I value from small to large, the ecological threat level is divided into low threat, medium threat and high threat to achieve standardized threat level judgment.

6. A remote sensing monitoring system for global biodiversity hotspot threat conditions, characterized by: include: Data acquisition module, used to obtain multi-temporal global land cover data and auxiliary GIS data of biodiversity hotspots and perform pre-processing; The forest loss layer extraction module is used to extract the forest cover layers for the baseline and monitoring periods from the preprocessed global land cover data. It then uses the layer difference method to extract the pixels that have transitioned from forest to non-forest, generating a forest loss layer. An ecological threat indicator calculation module is used to calculate and normalize various ecological pressure indicators based on the forest loss layer and pre-processed auxiliary GIS data. The various pressure indicators include forest loss rate, protection lapse rate, and population pressure index; The comprehensive threat calculation module is used to calculate the comprehensive threat index based on the normalized ecological pressure indicators and determine the ecological threat level.

7. The system according to claim 6, wherein: Preprocess the acquired data, including: The data is spatially resampled and projected uniformly to ensure the consistency of the data in space, time and geometric accuracy.

8. The system according to claim 6, wherein: The formula for calculating the forest loss rate is: The calculation formula of the protection failure rate is: The population pressure index (PDI) is the average population density in the forest loss area and the surrounding buffer zone.

9. The system according to claim 6, wherein: Normalize various ecological pressure indicators, including: The various ecological pressure indicators were standardized using the linear normalization method so that they all fell into the dimensionless interval of [0,1].

10. The system according to claim 6, wherein: The above-mentioned calculation of the comprehensive threat index based on the normalized ecological pressure indicators and determination of the ecological threat level includes: The weight parameters of each ecological pressure indicator are set, and a weighted calculation model is constructed to calculate the comprehensive threat index I, and the comprehensive threat index I is limited to [0,1]. The weighted calculation model is expressed as: I=a×FR+b×PLR+c×PDI Among them, a, b, and c are the weight parameters of the forest loss rate FR, protection failure rate PLR, and population pressure index PDI respectively; According to the comprehensive threat index I value from small to large, the ecological threat level is divided into low threat, medium threat and high threat to achieve standardized threat level judgment.

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