A method and system for quantifying the impact of human activity on terrestrial vegetation

Abstract

The application discloses a kind of quantification human activity to the method and system of influence of surface vegetation, belong to environmental monitoring technical field, first to the remote sensing vegetation index data obtained is handled, obtains NDVI grid data, then according to NDVI grid data obtains the grid data of KNDVI.Then is to research area according to radiation range construction buffer zone obtains influence area, on this basis continue to construct buffer zone and eliminate original area and obtain control area, to control area with the grid data of area except natural vegetation being eliminated, to influence area with original KNDVI grid data, quantification human activity to the influence of surface vegetation of research area.The application adopts a new type of remote sensing vegetation index to solve the shortcomings that traditional observation period is long, simulation model is difficult, remote sensing calculation method processing workload is big and not extensive.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and more specifically to a method and system for quantifying the impact of human activities on surface vegetation. Background Technology

[0002] Surface vegetation plays an extremely important role in ecosystems. Its coverage, type, health status, and growth dynamics are not only important indicators of ecological and environmental changes, but also directly affect key ecological functions such as biodiversity, climate regulation, and carbon cycling. Therefore, a deep understanding and quantification of the response of regional surface vegetation to environmental changes, especially to factors such as climate change, land use change, and human activities, has become a research hotspot in ecology, climatology, environmental science, and land management.

[0003] Currently, research methods for quantifying vegetation change and its ecological impacts mainly include various techniques such as remote sensing monitoring, ground observation, and model simulation. Traditional ground observation techniques, through field sampling and surveys, can provide extremely accurate vegetation data, especially when studying specific regions or plots. However, ground observations typically require long timeframes, are costly, and have limited spatial coverage, making it difficult to meet the needs for rapid monitoring across large scales and multiple regions.

[0004] Compared to ground-based observation, remote sensing technology, as a highly efficient data acquisition method, can rapidly capture the spatiotemporal distribution characteristics of vegetation changes on a large scale, and is widely used in monitoring vegetation cover, type, and growth status. For example, remote sensing vegetation indices such as NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and SAVI (Soil-Regulated Vegetation Index) can effectively reveal information such as vegetation greenness, growth status, and soil impact, showing significant advantages, especially in large-scale ecological monitoring. Researchers often analyze the spatiotemporal trends of vegetation using remote sensing images, thereby providing important quantitative data for assessing regional environmental changes.

[0005] However, current research on the quantitative impact of vegetation relies heavily on ground-based observations, requiring years of observational experiments. This approach is time-consuming, inefficient, and labor-intensive, and it is difficult to obtain long-term and large-scale data. Model simulations, on the other hand, are highly dependent on data, have poor regional applicability, and limited predictive accuracy. Furthermore, many current remote sensing methods are designed for specific regions or single-purpose scenarios, such as human-induced disturbances like reservoir restoration, mine redevelopment, and photovoltaic panel construction. Research on the spatiotemporal variability of surface vegetation with broader applicability is relatively scarce. Moreover, some remote sensing vegetation data struggle to distinguish areas with strong land background, making data processing difficult.

[0006] Given the many shortcomings of existing technologies in quantifying the impact of human activities on surface vegetation, there is an urgent need to develop a new method to overcome these deficiencies and quantify the impact of human activities on surface vegetation more accurately and efficiently.

[0007] Therefore, how to provide a more accurate and efficient method and system for quantifying the impact of human activities on surface vegetation is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0008] In view of this, the present invention provides a method and system for quantifying the impact of human activities on surface vegetation, which solves the shortcomings of traditional methods such as long observation periods, difficult simulation models, large workload of remote sensing calculation methods and lack of universality.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] On the one hand, the present invention provides a method for quantifying the impact of human activities on surface vegetation, comprising:

[0011] Acquire remote sensing vegetation data for the study area;

[0012] The remote sensing vegetation data is preprocessed to obtain the Normalized Difference Vegetation Index (NDVI).

[0013] Improved normalized vegetation index (KNDVI) raster data were calculated based on the normalized vegetation index (NDVI).

[0014] The affected area is obtained by constructing a buffer zone in the study area based on the radiation range.

[0015] Based on the affected area, a buffer zone is constructed and the affected area is removed to obtain the control area;

[0016] The improved normalized vegetation index (KNDVI) raster data of the control area is obtained by removing the land types affected by human activities from the improved KNDVI raster data.

[0017] The impact of human activities on the surface vegetation of the study area is quantified based on the assigned raster data and the improved Normalized Difference Vegetation Index (KNDVI) raster data. The quantification formula is as follows:

[0018]

[0019] in, Normalized Difference Vegetation Index (KNDVI) raster data for improved areas affected by human activities. Improved Normalized Difference Vegetation Index (KNDVI) raster data for areas affected by human activities before their impact. This provides improved normalized difference vegetation index (KNDVI) raster data for the control area after human activity. Improved Normalized Difference Vegetation Index (KNDVI) raster data for a pre-human activity control region.

[0020] Preferably, remote sensing vegetation data of the study area is acquired, specifically including:

[0021] Remote sensing vegetation data were acquired at two time points, before and after human activity disturbance.

[0022] Preferably, the improved Normalized Difference Vegetation Index (KNDVI) raster data of the control area is obtained by removing land types with human activity impacts from the improved KNDVI raster data, specifically including:

[0023] Obtain land type data before human activity disturbance.

[0024] Based on the land type data before human activity disturbance, the land types with human disturbance in the improved Normalized Difference Vegetation Index (KNDVI) raster data are converted into Nodata format.

[0025] Preferably, based on the land type data before human activity disturbance, the land types with human disturbance in the improved Normalized Difference Vegetation Index (KNDVI) raster data are converted to Nodata format, specifically including:

[0026] The land type data before the human activity disturbance was resampled;

[0027] Using the Con function in the ArcGIS raster calculator, the land types with human activity disturbance in the land type data before human activity disturbance are converted into preset values, and the remaining land types are converted into Nodata format, resulting in processed land use type raster data;

[0028] The Con function is used to convert the portion of the improved Normalized Difference Vegetation Index (KNDVI) raster data that overlaps with the processed land use type raster data into a preset value, while the portion that does not overlap is the improved KNDVI raster data.

[0029] Then, the preset value is converted into Nodata format using an empty function, and the raster data after removal is the assigned raster data of the control area.

[0030] Preferably, the expression for calculating the improved Normalized Difference Vegetation Index (NDVI) raster data based on the NDVI is as follows:

[0031] KNDVI = tanh(NDVI) 2 ).

[0032] On the other hand, the present invention provides a system for quantifying the impact of human activities on surface vegetation, comprising:

[0033] The data acquisition module is used to acquire remote sensing vegetation data of the study area;

[0034] The preprocessing module is used to preprocess the remote sensing vegetation data to obtain the Normalized Difference Vegetation Index (NDVI).

[0035] The optimization module is used to calculate improved normalized vegetation index (KNDVI) raster data based on the normalized vegetation index (NDVI).

[0036] The influence area construction module is used to construct a buffer zone for the study area based on the radiation range to obtain the influence area.

[0037] A control region construction module is used to construct a buffer zone based on the affected region and remove the affected region to obtain a control region.

[0038] The assignment module is used to remove the improved normalized vegetation index (KNDVI) raster data of land types affected by human activities from the improved normalized vegetation index (KNDVI) raster data, and obtain the assigned raster data of the control area.

[0039] The quantization module is used to quantify the impact of human activities on the surface vegetation of the study area based on the assigned raster data and the improved Normalized Difference Vegetation Index (KNDVI) raster data. The quantization formula is as follows:

[0040]

[0041] in, Normalized Difference Vegetation Index (KNDVI) raster data for improved areas affected by human activities. Improved Normalized Difference Vegetation Index (KNDVI) raster data for areas affected by human activities before their impact. This provides improved normalized difference vegetation index (KNDVI) raster data for the control area after human activity. Improved Normalized Difference Vegetation Index (KNDVI) raster data for a pre-human activity control region.

[0042] As can be seen from the above technical solution, compared with the prior art, this invention discloses a method and system for quantifying the impact of human activities on surface vegetation. It employs a novel remote sensing vegetation index. First, based on the time series and spatiotemporal resolution requirements, remote sensing data within the study area is selected, overcoming the shortcomings of traditional methods such as long observation periods, difficult simulation models, and the large workload and lack of scalability in remote sensing calculations. Furthermore, the comparative experiment using the affected area and control area, and eliminating land use types other than natural vegetation in the control area, yields ΔKNDVI by subtracting the KNDVI change in the control area from the KNDVI change in the affected area. This ΔKNDVI reflects the vegetation changes under independent regional influences. Simultaneously, this method is scalable and highly transferable, requiring no different treatments for specific areas. For studies on the impact of different regions on vegetation, only the radiation range and the size of the control area need to be changed, making the study of regional impact processes on vegetation more convenient and ensuring high accuracy. This provides decision-makers with easy-to-operate and accurate results. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of the process provided by the present invention.

[0045] Figure 2 This is a structural schematic diagram provided for the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] This invention discloses a method for quantifying the impact of human activities on surface vegetation, such as... Figure 1 As shown, it includes:

[0048] Remote sensing vegetation data for the study area was acquired; remote sensing vegetation data were acquired at two time points, before and after human activity disturbance. Satellites with suitable time series and resolution were selected using the platform, the required area was set, and the initial and final time points for the study were determined. Algorithms were then applied for data preprocessing to ensure data quality. To avoid sudden vegetation decline due to human activity disturbance in the study area, the time points of the acquired remote sensing data should be at least one year prior to the construction date; the time point before construction is T1, and the time point after construction is T2.

[0049] The remote sensing vegetation data is preprocessed to obtain the Normalized Difference Vegetation Index (NDVI). Specifically, the NDVI data is obtained by processing the infrared and near-infrared band data. The interannual maximum values ​​of NDVI for T1 and T2 are calculated respectively. The maximum value can avoid interference from other factors.

[0050] Improved Normalized Difference Vegetation Index (NNDVI) raster data was calculated based on the NNDVI. Furthermore, the maximum value at the corresponding time point was taken to obtain the final KNDVI raster data.

[0051] The influence area is obtained by constructing a buffer zone for the study area based on the radiation range. Specifically, using ArcGIS software, if human activities are densely distributed in the study area, human activities within a certain distance should be grouped together, and then a buffer zone should be constructed for this group to establish a radiation range of a certain distance as the influence area. (For different human activities, depending on the specific study, the construction area can be excluded from the influence area or not. For example, the construction area should not be excluded when studying mine restoration, but the construction area should be excluded when studying the impact of reservoirs on the surrounding vegetation ecology.)

[0052] Based on the affected area, a buffer zone is constructed and the affected area is removed to obtain the control area.

[0053] Furthermore, the selection of the buffer distance for the radiation range should be based on a specific analysis of the study area type. The study area plus the radiation range constitutes the affected area. A buffer zone with a certain distance is established on the basis of the affected area to exclude the affected area, forming the control area. This distance generally ranges from several hundred meters to several kilometers, selected according to the actual situation. For the control area, land types other than natural vegetation in the KNDVI raster data are converted to Nodata format, while the affected area uses the original KNDVI raster data. This is to reduce the influence of natural factors such as climate, thus ensuring independent regional influence. For large-scale studies covering numerous areas, the construction years of land use changes due to human activity disturbances vary significantly, making individual calculations too difficult. In such cases, the year before the earliest construction date can be selected at the beginning of the time period, and the year after the latest construction date at the end of the time period. For small-scale areas, multiple time points can be selected to observe the continuous changes in the impact on vegetation, rather than just two.

[0054] Improved Normalized Difference Vegetation Index (KNDVI) raster data with land types affected by human activities were removed from the improved normalized difference vegetation index (KNDVI) raster data to obtain the assigned raster data for the control area.

[0055] The impact of human activities on surface vegetation in the study area was quantified using assigned raster data and improved Normalized Difference Vegetation Index (KNDVI) raster data. Specifically, the zoning statistical tool in ArcGIS software was used: the affected area used raster data with KNDVI values ​​without land type removal, and the control area used KNDVI raster data with land types and water bodies significantly affected by human activity converted to Nodata format. The mean KNDVI values ​​for the affected and control areas at two time points were calculated. Then, the mean KNDVI value at the end of time period T2 was subtracted from the mean KNDVI value at the beginning of time period T1 to obtain the ΔKNDVI values ​​for each area. The final ΔKNDVI value under the influence of regional factors was obtained by subtracting the ΔKNDVI value of the control area from the ΔKNDVI value of the affected area. The quantification formula is as follows:

[0056]

[0057] in, Normalized Difference Vegetation Index (KNDVI) raster data for improved areas affected by human activities. Improved Normalized Difference Vegetation Index (KNDVI) raster data for areas affected by human activities before their impact. This provides improved normalized difference vegetation index (KNDVI) raster data for the control area after human activity. Improved Normalized Difference Vegetation Index (KNDVI) raster data for a pre-human activity control region.

[0058] When ΔKNDVI is greater than 0, the impact on vegetation in this area can be considered positive, while when ΔKNDVI is less than 0, the impact on vegetation in this area can be considered negative. The magnitude of the impact can be determined based on the value of ΔKNDVI.

[0059] Furthermore, after removing the improved Normalized Difference Vegetation Index (KNDVI) raster data containing land types affected by human activities from the improved KNDVI raster data, the assigned raster data for the control area was obtained, specifically including:

[0060] Obtain land type data before human activity disturbance.

[0061] Based on land type data before human disturbance, the land types with human disturbance in the improved Normalized Difference Vegetation Index (KNDVI) raster data are converted into Nodata format.

[0062] Preferably, based on land type data before human disturbance, the improved Normalized Difference Vegetation Index (KNDVI) raster data with human disturbance is converted to Nodata format, specifically including:

[0063] Land type data before human activity disturbance was resampled to obtain data with the same resolution as KNDVI raster data.

[0064] The ArcGIS Raster Calculator's Con function is used to convert land types with human disturbance into preset values ​​from the land type data before human activity disturbance, and the remaining land types are converted into Nodata format, resulting in processed land use type raster data. Since the value of KNDVI is between 0 and 1, the ArcGIS Raster Calculator's Con function is used to convert land types and water bodies that are significantly affected by human activity into a unified value, while other land use types are converted into Nodata format, and this value cannot be equal to a value between 0 and 1.

[0065] The Con function is used to convert the parts of the improved normalized vegetation index (KNDVI) raster data that overlap with the processed land use type raster data into preset values, while the parts that do not overlap are the improved normalized vegetation index (KNDVI) raster data.

[0066] Then, an empty function is used to convert the preset value into Nodata format, and the raster data after removal is the assigned raster data of the control area.

[0067] In another embodiment, a novel remote sensing vegetation index was employed. Among traditional remote sensing vegetation indices, the most widely used is the Normalized Difference Vegetation Index (NDVI), which is calculated using the different reflectance characteristics (band reflectance) of vegetation to visible and near-infrared light, reflecting the physiological state and coverage of the vegetation. A higher NDVI value indicates denser and healthier vegetation, providing important data for remote sensing monitoring of vegetation status, biomass, and coverage. However, NDVI has two major limitations. First, the relationship between NDVI and green organisms is non-linear and can reach saturation. The second problem is how to distinguish the near-infrared portion reflected by vegetation from the remaining near-infrared portion. A new index, NIRv, was proposed to address this, equal to NDVI multiplied by the near-infrared reflectance. However, it is linearly proportional to the near-infrared reflectance and cannot handle the saturation problem. Therefore, a remote sensing vegetation index, KNDVI, using a kernel function method was adopted. The kernel function method is used to derive a non-linear algorithm from a linear one, mapping the relevant spectral bands to a high-dimensional space using a non-linear feature map, and defining the index in this space. By defining the kernel function, the calculation results can be represented using spectral channels. Define NDVI in Hilbert space and reproduce the kernel using radial basis functions (RBF), where k(NIR,red) represents the kernel function.

[0068]

[0069] The simplified calculation is as follows:

[0070]

[0071] Where n and r refer to the near-infrared and infrared bands, respectively, and σ is a length scale parameter that needs to be specified in each specific application, representing the sensitivity of sparse / dense vegetation areas.

[0072] KNDVI has been proven to outperform NDVI and NIRv across all applications, biological communities, and climatic zones. KNDVI is used to monitor leaf area index (LAI), gross primary productivity (GPP), and solar-induced chlorophyll fluorescence (SIF), all key parameters for assessing vegetation status and ecosystem function. KNDVI is more resistant to saturation, bias, and complex phenological cycles, better captures extreme SIF values, and is more robust to noise and stable across temporal and spatial scales. However, since parameters need to be specified for specific applications, a simplified and reasonable choice to improve general applicability is to take σ as half the average of the near-infrared (NIR) and red band reflectance, i.e., σ = 0.5(n+r), which allows the index to adapt to the characteristics of each pixel. It can adapt to each pixel, meaning that σ can be adjusted according to the local environment to optimize the index's performance. Therefore, the expression for calculating the improved Normalized Difference Vegetation Index (NDVI) raster data based on NDVI is:

[0073] KNDVI = tanh(NDVI) 2 ).

[0074] The most widely used vegetation index in traditional remote sensing is the Normalized Difference Vegetation Index (NDVI). However, NDVI has significant limitations. The relationship between the NDVI and green organisms is non-linear and reaches saturation. Furthermore, in areas with low vegetation cover, it is easily affected by spectral differences between soil and vegetation. In contrast, the KNDVI reflects non-linear relationships and has been proven to perform well in many biological communities, exhibiting strong adaptability and outperforming both NDVI and NIRv. A simplified formula is also used:

[0075] KNDVI = tanh(NDVI) 2 )

[0076] By obtaining KNDVI, its advantages can be guaranteed without adjusting its parameters in specific regions.

[0077] On the other hand, the present invention provides a system for quantifying the impact of human activities on surface vegetation, such as Figure 2 As shown, it includes:

[0078] The data acquisition module is used to acquire remote sensing vegetation data of the study area;

[0079] The preprocessing module is used to preprocess remote sensing vegetation data to obtain the Normalized Difference Vegetation Index (NDVI).

[0080] The optimization module is used to calculate improved Normalized Difference Vegetation Index (NDVI) raster data based on the NDVI.

[0081] The influence area construction module is used to construct a buffer zone for the study area based on the radiation range to obtain the influence area.

[0082] The control region construction module is used to construct a buffer zone based on the affected region and remove the affected region to obtain the control region.

[0083] The assignment module is used to remove the improved normalized vegetation index (KNDVI) raster data containing land types affected by human activities from the improved normalized vegetation index (KNDVI) raster data, and obtain the assigned raster data of the control area.

[0084] The quantization module is used to quantify the impact of human activities on surface vegetation in the study area based on assigned raster data and improved Normalized Difference Vegetation Index (KNDVI) raster data. The quantification formula is as follows:

[0085]

[0086] in, Normalized Difference Vegetation Index (KNDVI) raster data for improved areas affected by human activities. Improved Normalized Difference Vegetation Index (KNDVI) raster data for areas affected by human activities before their impact. This provides improved normalized difference vegetation index (KNDVI) raster data for the control area after human activity. Improved Normalized Difference Vegetation Index (KNDVI) raster data for a pre-human activity control region.

[0087] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0088] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for quantifying the impact of human activities on surface vegetation, characterized in that, include: Acquire remote sensing vegetation data; The remote sensing vegetation data is preprocessed to obtain the Normalized Difference Vegetation Index (NDVI). Improved normalized vegetation index (KNDVI) raster data were calculated based on the normalized vegetation index (NDVI). The affected area is obtained by constructing a buffer zone in the study area based on the radiation range. Based on the affected area, a buffer zone is constructed and the affected area is removed to obtain the control area; The improved normalized vegetation index (KNDVI) raster data of the control area is obtained by removing the land types affected by human activities from the improved normalized vegetation index (KNDVI) raster data. The impact of human activities on the surface vegetation of the study area is quantified based on the assigned raster data and the improved Normalized Difference Vegetation Index (KNDVI) raster data. The quantification formula is as follows: in, Normalized Difference Vegetation Index (KNDVI) raster data for improved areas affected by human activities. Improved Normalized Difference Vegetation Index (KNDVI) raster data for areas affected by human activities before their impact. This provides improved normalized difference vegetation index (KNDVI) raster data for the control area after human activity. Improved Normalized Difference Vegetation Index (KNDVI) raster data for a pre-human activity control region.

2. The method for quantifying the impact of human activities on surface vegetation according to claim 1, characterized in that, Obtain remote sensing vegetation data for the study area, specifically including: Remote sensing vegetation data were acquired at two time points, before and after human activity disturbance.

3. The method for quantifying the impact of human activities on surface vegetation according to claim 1, characterized in that, The improved Normalized Difference Vegetation Index (KNDVI) raster data for the control area is obtained by removing land types with human activity impacts from the improved KNDVI raster data, specifically including: Obtain land type data before human activity disturbance. Based on the land type data before human activity disturbance, the land types with human disturbance in the improved Normalized Difference Vegetation Index (KNDVI) raster data are converted into Nodata format.

4. The method for quantifying the impact of human activities on surface vegetation according to claim 3, characterized in that, Based on the land type data before human activity disturbance, the land types with human disturbance in the improved Normalized Difference Vegetation Index (KNDVI) raster data are converted to Nodata format, specifically including: The land type data before the human activity disturbance was resampled; Using the Con function in the ArcGIS raster calculator, the land types with human activity disturbance in the land type data before human activity disturbance are converted into preset values, and the remaining land types are converted into Nodata format, resulting in processed land use type raster data; The Con function is used to convert the portion of the improved normalized vegetation index (KNDVI) raster data that overlaps with the processed land use type raster data into a preset value, while the portion that does not overlap is the improved normalized vegetation index (KNDVI) raster data. Then, the preset value is converted into Nodata format using an empty function, and the raster data after removal is the assigned raster data of the control area.

5. The method for quantifying the impact of human activities on surface vegetation according to claim 1, characterized in that, The expression for calculating the improved Normalized Difference Vegetation Index (NDVI) raster data based on the NDVI is as follows: 。 6. A system for quantifying the impact of human activities on surface vegetation, characterized in that, include: The data acquisition module is used to acquire remote sensing vegetation data; The preprocessing module is used to preprocess the remote sensing vegetation data to obtain the Normalized Difference Vegetation Index (NDVI). The optimization module is used to calculate improved normalized vegetation index (KNDVI) raster data based on the normalized vegetation index (NDVI). The influence area construction module is used to construct a buffer zone for the study area based on the radiation range to obtain the influence area. A control region construction module is used to construct a buffer zone based on the affected region and remove the affected region to obtain a control region. The assignment module is used to remove the improved normalized vegetation index (KNDVI) raster data of land types affected by human activities from the improved normalized vegetation index (KNDVI) raster data, and obtain the assigned raster data of the control area. The quantization module is used to quantify the impact of human activities on the surface vegetation of the study area based on the assigned raster data and the improved Normalized Difference Vegetation Index (KNDVI) raster data. The quantization formula is as follows: in, Normalized Difference Vegetation Index (KNDVI) raster data for improved areas affected by human activities. Improved Normalized Difference Vegetation Index (KNDVI) raster data for areas affected by human activities before their impact. This provides improved normalized difference vegetation index (KNDVI) raster data for the control area after human activity. Improved Normalized Difference Vegetation Index (KNDVI) raster data for a pre-human activity control region.

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