Method and system for evaluating land degradation condition by fusing multi-source remote sensing indexes

Abstract

The invention relates to a method and system for evaluating a land degradation condition by fusing multi-source remote sensing indexes, and relates to the technical field of data processing, and the method comprises the steps: obtaining and preprocessing multi-source remote sensing data of a to-be-evaluated land, and obtaining preprocessed data; according to the preprocessed data, constructing a multi-dimensional index system comprising an improved vegetation degradation index, a water stress index and a human activity intensity index; according to the multi-dimensional index system, constructing a comprehensive land degradation evaluation model; calculating a land degradation evaluation value of the land according to the comprehensive land degradation evaluation model, and calculating a land degradation dynamic evolution value of the land based on the land degradation evaluation value; and according to the degradation dynamic evolution value of the land and the current land degradation evaluation value, predicting a future land degradation evaluation value of the land, and generating a degradation level spatial distribution map of the land. The accuracy of the land degradation condition can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, system, electronic device and non-transient computer-readable storage medium for evaluating land degradation status by integrating multi-source remote sensing indicators. Background Art

[0002] Today, land degradation assessment mainly relies on single or limited remote sensing indicators, such as the vegetation index (NDVI), the soil adjusted vegetation index (SAVI) or the land surface temperature (LST). These methods are usually based on optical or thermal infrared remote sensing data and use specific thresholds or empirical models to identify degraded areas. In addition, some studies combine statistical analysis or machine learning methods to monitor the dynamic changes of land degradation through multi-temporal data.

[0003] However, the evaluation method of a single indicator or limited data source has certain limitations. Different remote sensing indicators have different sensitivities to land degradation, and a single indicator may not be able to fully reflect the degradation process. Secondly, optical remote sensing is easily affected by factors such as cloud cover and climate conditions, resulting in discontinuous data acquisition. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a method, system, electronic device and non-transitory computer-readable storage medium for evaluating land degradation status by fusing multi-source remote sensing indicators, which can improve the accuracy of land degradation status.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: The present invention provides a method for evaluating land degradation status by fusing multi-source remote sensing indicators, the method comprising: Acquire and preprocess multi-source remote sensing data of the land to be assessed to obtain preprocessed data; Based on the preprocessed data, a multidimensional index system including an improved vegetation degradation index, a water stress index and a human activity intensity index is constructed; Based on the multi-dimensional indicator system, a comprehensive land degradation assessment model is constructed; Calculating a land degradation assessment value of the land according to the comprehensive land degradation assessment model, and calculating a land degradation dynamic evolution value of the land based on the land degradation assessment value; According to the land degradation dynamic evolution value and the current land degradation assessment value, the future land degradation assessment value of the land is predicted, and a spatial distribution map of the land degradation level is generated.

[0006] Optionally, the improved vegetation degradation index is constructed in the following manner: Calculating a normalized vegetation index based on the spectral reflectance characteristics of vegetation on the land; Obtaining soil-adjusted vegetation index to reduce the impact of soil background on vegetation index; Obtaining the surface temperature of the land and the upper limit value of the surface temperature; Calculating a bare soil index for quantifying the degree of soil exposure based on the spectral reflectance characteristics of the vegetation and soil of the land; The improved vegetation degradation index is obtained according to the normalized vegetation index, soil adjusted vegetation index, surface temperature, upper limit of surface temperature and bare soil index of the land.

[0007] Optionally, the improved vegetation degradation index is expressed as: in, is the improved vegetation degradation index. is the Normalized Difference Vegetation Index, is the soil adjusted vegetation index, is the surface temperature, is the upper limit of the surface temperature, is the bare soil index, They are the first weight, the second weight and the third weight respectively.

[0008] Optionally, the water stress index is constructed in the following manner: Obtain precipitation, actual evapotranspiration and potential evapotranspiration for the area where the land is located; Obtaining the soil water stress coefficient for adjusting the effect of soil water status on water stress; obtaining a normalized water index for use in assessing the condition of surface water over said land; The water stress index is determined according to the precipitation, actual evapotranspiration, potential evapotranspiration, soil water stress coefficient and normalized water index of the land.

[0009] Optionally, the water stress index is expressed as: in, is the water stress index, P is the precipitation, is the actual evaporation, is the soil water stress coefficient, is the potential evapotranspiration, is the normalized water index.

[0010] Optionally, the human activity intensity index is constructed in the following manner: Obtaining standardized nighttime light values ​​for assessing the intensity and distribution of human activities in the area where the land is located; Obtaining a built-up area index for assessing the extent and density of built-up areas on said land; Obtaining land use change rates for assessing land use dynamics; The human activity intensity index is obtained according to the standardized night light value, built-up area index and land use change rate of the land.

[0011] Optionally, the calculating the land degradation assessment value of the land according to the comprehensive land degradation assessment model includes: Obtain the time decay coefficient to reflect the impact of time on land degradation; Obtain the length of the time series for assessing the time span of land degradation; Obtaining a terrain-slope correction function for taking into account the influence of terrain factors of the land on land degradation; The land degradation assessment value of the land is determined based on the time attenuation coefficient, time series length and terrain-slope correction function of the land.

[0012] Optionally, the calculating the land degradation dynamic evolution value of the land based on the land degradation assessment value includes: Obtain the assessment year and base year used to conduct the degradation assessment of the land in question; Obtaining a land degradation assessment value of the land at a first moment and a land degradation assessment value of the land at a second moment; The land degradation dynamic evolution value of the land is determined according to the assessment year, the base year, the land degradation assessment value at the first moment and the land degradation assessment value at the second moment of the land.

[0013] Optionally, predicting the future land degradation assessment value of the land according to the land degradation dynamic evolution value and the current land degradation assessment value includes: Obtain the human activity impact coefficient to quantify the impact of human activities on land degradation; Obtain climate change sensitivity coefficients that represent the impact of climate change on land degradation; Obtain a climate-policy response function that comprehensively considers the impacts of future climate conditions and policy interventions on land degradation; The future land degradation assessment value of the land is determined based on the dynamic evolution value of land degradation, the current land degradation assessment value, the water stress index, the human activity intensity index, the human activity impact coefficient, the climate change sensitivity coefficient and the climate-policy response function.

[0014] The present invention also provides a system for evaluating land degradation status by fusing multi-source remote sensing indicators, the system comprising: A data acquisition module is used to acquire and pre-process multi-source remote sensing data of the land to be evaluated to obtain pre-processed data; An indicator construction module, used to construct a multidimensional indicator system including an improved vegetation degradation index, a water stress index and a human activity intensity index according to the preprocessed data; A model building module, used to build a comprehensive land degradation assessment model based on the multi-dimensional indicator system; A degradation evolution module, used to calculate the land degradation assessment value of the land according to the comprehensive land degradation assessment model, and calculate the land degradation dynamic evolution value of the land based on the land degradation assessment value; The degradation assessment module is used to predict the future land degradation assessment value of the land according to the land degradation dynamic evolution value and the current land degradation assessment value, and generate a spatial distribution map of the land degradation level.

[0015] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing a method for evaluating land degradation status by fusing multi-source remote sensing indicators as described above.

[0016] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, a method for evaluating land degradation conditions by fusing multi-source remote sensing indicators as described above is implemented.

[0017] The beneficial effects of the present invention are: (1) The present invention integrates optical remote sensing (Landsat, Sentinel-2), radar remote sensing (Sentinel-1), night light data (VIIRS / NPP), meteorological data and DEM data. This fusion of multi-source data can make up for the shortcomings of a single data source. For example, optical data is easily affected by clouds and fog, while radar data can penetrate clouds; night light data can reflect the impact of human activities. The synergy of multi-source data significantly improves the comprehensiveness and accuracy of land degradation assessment.

[0018] (2) The present invention constructs a multi-dimensional indicator system including the minimum vegetation degradation index (MVDI), water stress index (WSI), human activity intensity index (HAI), etc., which can comprehensively evaluate the land degradation status from multiple angles such as natural factors, water conditions and human activities, avoiding the one-sidedness of single indicator evaluation.

[0019] (3) The present invention not only provides a comprehensive assessment of current land degradation, but also reflects the changing trend of land degradation over time through the time decay coefficient and dynamic evolution assessment model. In addition, the future land degradation prediction model combines factors such as human activities, climate change and policy response, providing a powerful tool for long-term prediction of land degradation. This dynamism and foresight enable the scheme to provide a scientific basis for ecological protection and land management, and support early measures to address land degradation issues.

[0020] In summary, the present invention significantly improves the comprehensiveness, accuracy and dynamism of land degradation assessment by integrating multi-source remote sensing data, constructing a multi-dimensional indicator system, introducing time decay and terrain correction factors, and constructing a dynamic assessment and prediction model. Its scientificity and adaptability enable the scheme to provide strong support for land management and ecological protection, and provide a scientific basis for regional sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a method for evaluating land degradation status by fusing multi-source remote sensing indicators provided by the present invention; Figure 2 A schematic diagram of the structure of a system for evaluating land degradation status by integrating multi-source remote sensing indicators provided by the present invention; Figure 3 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention; Figure 4 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0024] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0025] See also Figure 1 , provides a flow chart of a method for evaluating land degradation status by fusing multi-source remote sensing indicators of the present invention, comprising the following steps: Step 201: Acquire and preprocess multi-source remote sensing data of the land to be evaluated to obtain preprocessed data.

[0026] Specifically, a variety of remote sensing data related to the land to be evaluated can be collected. These data sources include: Optical remote sensing data: such as Landsat and Sentinel-2 satellite images, used to extract information such as vegetation cover and land use type. Radar remote sensing data: such as Sentinel-1 SAR data, used to obtain terrain and surface information that penetrates the clouds, especially suitable for monitoring vegetation cover and soil moisture. Night light data: such as VIIRS / NPP data, used to reflect the distribution and intensity of human activities. Meteorological data: including precipitation, temperature, wind speed, etc., used to assess the impact of climate conditions on land degradation. DEM (digital elevation model) data: used to analyze terrain features, such as slope and aspect, which affect the degree of land degradation.

[0027] In some embodiments, the remote sensing images can be radiometrically and geometrically corrected to eliminate sensor errors and atmospheric effects. Remote sensing data of different resolutions and types can be fused to improve the richness and accuracy of information. According to the boundaries of the assessment area, the required range of data is cropped, and multiple images are spliced ​​to form a complete coverage area. Data from different sources are converted to a unified scale and format to facilitate subsequent calculations and analysis. For example, night light data is normalized to a value between 0 and 1. Outliers and noise in the data are removed to improve the reliability of the data.

[0028] After the above preprocessing steps, the data obtained are called "preprocessed data". Preprocessed data improves data accuracy through correction and noise removal. Standardization and fusion make data from different sources have a unified format and scale. Cropping and splicing ensure that the data covers the assessment area completely. Preprocessed data is the basis for subsequent land degradation assessment, and its quality directly affects the accuracy and reliability of the assessment results.

[0029] Step 202: construct a multidimensional index system including an improved vegetation degradation index, a water stress index and a human activity intensity index based on the preprocessed data.

[0030] In some embodiments, step 202 may include: Calculating a normalized vegetation index based on the spectral reflectance characteristics of vegetation on the land; Obtaining soil-adjusted vegetation index to reduce the impact of soil background on vegetation index; Obtaining the surface temperature of the land and the upper limit value of the surface temperature; Calculating a bare soil index for quantifying the degree of soil exposure based on the spectral reflectance characteristics of the vegetation and soil of the land; The improved vegetation degradation index is obtained according to the normalized vegetation index, soil adjusted vegetation index, surface temperature, upper limit of surface temperature and bare soil index of the land.

[0031] In some embodiments, the improved vegetation degradation index can be expressed as: in, is the improved vegetation degradation index. is the Normalized Difference Vegetation Index, is the soil adjusted vegetation index, is the surface temperature, is the upper limit of the surface temperature, is the bare soil index, They are the first weight, the second weight and the third weight respectively.

[0032] In the specific implementation, It is an index calculated based on the spectral reflectance characteristics of vegetation and is used to assess vegetation coverage and growth conditions.

[0033] Among them, NIR is the reflectivity of the near-infrared band, and Red is the reflectivity of the red light band.

[0034] The value range is -1 to 1. Positive values ​​indicate vegetation cover, and higher values ​​indicate denser vegetation; negative values ​​usually indicate non-vegetated areas (such as water bodies or bare soil). It is a core indicator that reflects the status of vegetation coverage. By combining with other indicators, the health status of vegetation can be more accurately assessed.

[0035] SAVI is an improved vegetation index used to reduce the impact of soil background on the vegetation index. It is particularly suitable for areas with low vegetation coverage.

[0036] Where L is the soil brightness coefficient (usually 0.5).

[0037] SAVI has a similar range to NDVI, but is more sensitive to areas of low vegetation cover. SAVI makes the assessment of vegetation cover more accurate by adjusting for the effects of soil background. The ratio of NDVI to SAVI can further enhance the ability to assess vegetation health, especially in areas with complex soil backgrounds.

[0038] LST is the temperature of the land surface, which is usually obtained by inverting remote sensing data in the thermal infrared band. Based on the radiation brightness of the thermal infrared band, the land surface temperature is inverted through a physical model. Depending on the region and season, it is usually between 10℃ and 50℃. Land surface temperature is an important indicator for assessing vegetation heat stress. The ratio reflects the current level of surface temperature relative to its upper limit. The higher the value, the greater the heat stress that vegetation may face.

[0039] It is the upper limit of the surface temperature in a certain area under specific conditions, usually estimated from historical data or models. It is used as a reference value to standardize LST so that it is comparable in different regions and seasons.

[0040] BSI is an index used to quantify the degree of soil bareness and is calculated based on the spectral reflectance properties of vegetation and soil.

[0041] The BSI value range is from -1 to 1, with positive values ​​indicating bare soil areas and negative values ​​indicating vegetation-covered areas. BSI directly reflects the degree of soil exposure and is an important indicator for assessing land degradation. A higher BSI value usually means a decrease in vegetation cover and an increased risk of land degradation.

[0042] It is the weight coefficient, which is used to adjust the contribution ratio of each indicator in MVDI. Importance of adjusting vegetation cover (NDVI / SAVI) in MVDI. Regulating heat stress impact. Contribution of conditioning bare soil exposure (BSI).

[0043] The weight coefficient can be determined through expert experience, principal component analysis (PCA), analytic hierarchy process (AHP) or optimization algorithm (such as genetic algorithm) to ensure that the contribution of each indicator is consistent with the actual situation.

[0044] In some embodiments, the calculation process of MVDI may include: Data acquisition: Optical remote sensing data (such as Landsat, Sentinel-2) is used to calculate NDVI, SAVI and BSI, and thermal infrared data is used to calculate LST. It can be estimated through historical data or models.

[0045] Calculate various indicators: Calculate NDVI, SAVI, LST, and BSI.

[0046] Determine weight coefficients: Determine through expert experience or optimization algorithm .

[0047] Calculate MVDI: The higher the MVDI value, the more serious the vegetation degradation; the lower the value, the better the vegetation condition.

[0048] The present invention not only considers vegetation coverage (NDVI / SAVI) by calculating MVDI, but also introduces heat stress (LST / ) and bare soil exposure (BSI), which can more comprehensively assess the status of vegetation degradation. By adjusting the weight coefficient, MVDI can adapt to the vegetation types and environmental conditions in different regions. Calculated based on multi-source remote sensing data (optical, thermal infrared), the accuracy and reliability of the assessment are improved.

[0049] MVDI can be used to assess the degree of regional vegetation degradation and identify degradation hotspots. It can provide a scientific basis for ecological restoration projects and evaluate restoration effects. , study the impact of climate change on thermal stress of vegetation. Provide reference for land use planning, optimize land use and reduce the risk of land degradation.

[0050] In summary, the MVDI of the present invention is an improved vegetation degradation index that can more comprehensively and accurately assess vegetation degradation by comprehensively considering multiple factors such as vegetation cover, heat stress and soil exposure. Its calculation process is clear, it has high adaptability and application value, and is an important tool in the field of land degradation assessment.

[0051] In some embodiments, the water stress index is constructed by: Obtain precipitation, actual evapotranspiration and potential evapotranspiration for the area where the land is located; Obtaining the soil water stress coefficient for adjusting the effect of soil water status on water stress; obtaining a normalized water index for use in assessing the condition of surface water over said land; The water stress index is determined according to the precipitation, actual evapotranspiration, potential evapotranspiration, soil water stress coefficient and normalized water index of the land.

[0052] In some embodiments, the water stress index can be expressed as: in, is the water stress index, P is the precipitation, is the actual evaporation, is the soil water stress coefficient, is the potential evapotranspiration, is the normalized water index.

[0053] In specific implementation, the Water Stress Index (WSI) is an indicator used to assess the water stress status of vegetation or ecosystems. It quantifies the degree of water stress by comprehensively considering multiple factors such as precipitation, actual evapotranspiration, potential evapotranspiration and normalized water index.

[0054] P is precipitation, which represents the total precipitation in a certain period. is actual evapotranspiration, which represents the amount of water actually released into the atmosphere by vegetation and soil. is the soil water stress coefficient, which is used to adjust the effect of soil moisture status on water stress. is potential evapotranspiration, which represents the maximum amount of water that vegetation and soil can release into the atmosphere under sufficient water supply conditions. NDWI is the Normalized Difference Water Index, which is used to assess the status of surface water bodies. ln is the natural logarithm function, which is used to adjust the impact of NDWI.

[0055] Specifically, P represents the total precipitation in a certain period and is an important indicator for measuring water input. It is usually obtained through meteorological station observations or remote sensing inversion. P is a key input variable in water stress assessment, reflecting the water replenishment capacity of natural precipitation for vegetation and soil.

[0056] It indicates the amount of water released into the atmosphere by vegetation and soil under actual moisture conditions. It is an important indicator for measuring water output. It can be obtained through remote sensing inversion (such as the SEBAL model) or ground observations. It reflects the actual water consumption of vegetation and soil and is an important basis for assessing water stress.

[0057] It is an empirical coefficient used to adjust the effect of soil moisture status on water stress. The value range is usually between 0 and 1, and the smaller the value, the more severe the soil moisture stress. It is used to correct the nonlinear effect of soil moisture conditions on water stress, making WSI closer to the actual situation.

[0058] PET represents the maximum amount of water that vegetation and soil can release into the atmosphere under sufficient water supply conditions, reflecting the atmospheric demand for water. It can be calculated using the Penman-Monteith formula or remote sensing inversion model. PET is an important indicator for measuring water demand and is related to actual evapotranspiration. By comparing the soil and water content of the samples, the degree of water stress can be assessed.

[0059] NDWI is an index calculated based on remote sensing data and is used to assess the condition of surface water bodies.

[0060] Among them, Green is the reflectance of the green light band, and NIR is the reflectance of the near infrared band. The value range is -1 to 1, with positive values ​​representing water bodies and negative values ​​representing non-water areas. NDWI is used to reflect the existence and distribution of surface water bodies. Adjusting its influence through the natural logarithm function can enhance the sensitivity to water stress.

[0061] The natural logarithm function is used to adjust the effect of NDWI to make its contribution to water stress more nonlinear. When NDWI increases, the value of ln(1+NDWI) will also increase, but the growth rate will gradually slow down, which is consistent with the actual situation (i.e., the effect of water increase on water stress is gradually saturated).

[0062] It can be understood that the higher the WSI value, the lower the degree of water stress (the better the water conditions); the lower the WSI value, the higher the degree of water stress (the worse the water conditions).

[0063] WSI takes into account precipitation, actual evapotranspiration, potential evapotranspiration and surface water conditions, and can comprehensively assess the degree of water stress. By adjusting the impact of NDWI through the natural logarithm function, WSI can better reflect the dynamic changes of water stress. WSI is applicable to a variety of ecosystems and land types, and can provide a scientific basis for regional water stress assessment. The calculation of WSI is based on common meteorological and remote sensing data, and data acquisition is relatively easy.

[0064] WSI can be used to assess water stress during vegetation growth and identify areas with high water stress risk. It can provide a basis for irrigation decision-making and optimize water resource utilization. Combined with long-term meteorological data, it can study the impact of climate change on water stress. It can provide a reference for land use planning, optimize land use methods, and reduce water stress risks.

[0065] In summary, the water stress index WSI of the present invention is an indicator for comprehensively evaluating water stress conditions. By comprehensively considering precipitation, actual evapotranspiration, potential evapotranspiration and surface water conditions, it can more comprehensively and accurately reflect the degree of water stress. Its calculation process is clear, it has high adaptability and application value, and is an important tool in the field of ecological monitoring and water resources management.

[0066] In some embodiments, the human activity intensity index may be constructed in the following manner: Obtaining standardized nighttime light values ​​for assessing the intensity and distribution of human activities in the area where the land is located; Obtaining a built-up area index for assessing the extent and density of built-up areas on said land; Obtaining land use change rates for assessing land use dynamics; The human activity intensity index is obtained according to the standardized night light value, built-up area index and land use change rate of the land.

[0067] In some embodiments, the human activity intensity index may be expressed as: in, is an indicator of the intensity of human activities. is the normalized night light value, is the built-up area index, is the land use change rate, They are the fourth weight, the fifth weight and the sixth weight respectively.

[0068] In specific implementation, the Human Activity Intensity Index (HAI) is a comprehensive indicator used to quantify the impact of human activities on land use and ecosystems. It evaluates the intensity and impact of human activities by comprehensively considering multiple factors such as nighttime light intensity, built-up area, and land use change rate.

[0069] NTL is the Normalized Night Light Value, which reflects the intensity and distribution of human activities. NDBI is the Built-up Area Index, which is used to assess the extent and density of built-up areas. ΔLULC is the rate of land use change, which reflects the degree of change of land use types over time. They are the fourth weight, the fifth weight and the sixth weight, which are used to adjust the contribution ratio of each indicator in HAI.

[0070] Specifically, NTL is an index calculated based on night light remote sensing data, which is used to reflect the intensity and distribution of human activities. Night light data usually comes from VIIRS / NPP satellites.

[0071] Raw night light data (such as DN values ​​from VIIRS / NPP) need to be standardized to eliminate differences between different times and sensors. Standardization methods usually include linear normalization (scaling values ​​between 0 and 1) or logarithmic transformation. The value range is 0 to 1, and higher values ​​indicate greater intensity of human activity. NTL is a direct indicator for assessing the intensity of human activities and can reflect the distribution of urbanization, industrialization, and human residential activities.

[0072] NDBI is an index calculated based on remote sensing data to assess the extent and density of built-up areas. It distinguishes between built-up areas and non-built-up areas by analyzing the spectral reflectance characteristics of vegetation and soil. The calculation formula is: Among them, SWIR is the reflectance of the short-wave infrared band, and NIR is the reflectance of the near-infrared band. The value range of NDBI is -1 to 1, with positive values ​​representing built-up areas and negative values ​​representing non-built-up areas (such as vegetation-covered areas). NDBI can effectively identify the scope of built-up areas and reflect the degree of occupation and transformation of land by human activities. ΔLULC represents the degree of change in land use types within a certain period of time, which can be expressed as: in, and Respectively represent land use type data in two different periods.

[0073] ΔLULC is usually expressed as a percentage, with positive values ​​indicating an increase in land use type and negative values ​​indicating a decrease. ΔLULC can reflect the dynamic changes in land use types, such as deforestation and urban expansion, and is an important indicator for assessing the impact of human activities on land use.

[0074] is the weight coefficient, which is used to adjust the contribution ratio of NTL, NDBI and ΔLULC in HAI. Regulates the contribution of night light intensity (NTL). Contribution of Adjusted Built-up Area Extent (NDBI). Adjust the contribution of land use change rate (ΔLULC). The weight coefficient can be determined by expert experience, principal component analysis (PCA), analytic hierarchy process (AHP) or optimization algorithm (such as genetic algorithm) to ensure that the contribution of each indicator is consistent with the actual situation.

[0075] It can be understood that the higher the HAI value, the greater the intensity of human activities and the more significant the impact on land use and ecosystems; the lower the HAI value, the smaller the intensity of human activities and the smaller the land use changes.

[0076] HAI takes into account the intensity of nighttime lights, the extent of built-up areas and the rate of change in land use, and can comprehensively assess the intensity and impact of human activities. Based on nighttime light data, optical remote sensing data and land use data, data acquisition is relatively easy and has high temporal and spatial resolution.

[0077] Through the land use change rate (ΔLULC), HAI can reflect the dynamic changes of human activities, rather than just a static assessment. It is suitable for land use assessment in different regions and types, and can provide a scientific basis for land management and ecological protection.

[0078] HAI can be used to assess the impact of human activities on land use and provide a reference for land use planning and ecological protection. It can identify the extent of human activities on the ecosystem and provide a basis for ecological protection and restoration projects. It can analyze urban expansion and changes in built-up areas to provide support for urban planning and sustainable development. It can provide a scientific basis for the formulation of land management policies and ecological protection policies and optimize the impact of human activities on land.

[0079] In summary, the human activity intensity index HAI of the present invention is a tool for comprehensively evaluating the impact of human activities on land use and ecosystems. By combining the night light intensity NTL, the built-up area index NDBI and the land use change rate ΔLULC, HAI can comprehensively and dynamically reflect the intensity and impact of human activities. Its calculation process is clear, with high adaptability and application value, and is an important tool in the field of land management and ecological protection.

[0080] Step 203: construct a comprehensive land degradation assessment model based on the multi-dimensional indicator system.

[0081] Among them, the multidimensional indicator system is the basis for building a comprehensive land degradation assessment model, including the following key indicators: Vegetation Degradation Index (MVDI): reflects vegetation coverage and health status. The lower the value, the more serious the vegetation degradation. Water Stress Index (WSI): reflects the impact of water conditions on land degradation. The lower the value, the more serious the water stress. Human Activity Intensity Index (HAI): reflects the contribution of human activities to land degradation. The higher the value, the greater the impact of human activities. These indicators comprehensively depict the current status of land degradation from multiple dimensions such as natural factors, water conditions and human activities.

[0082] The comprehensive land degradation assessment model integrates the above multi-dimensional indicators into one model and calculates a comprehensive assessment value through mathematical formulas to quantify the degree of land degradation. The specific steps are as follows: Each indicator may contribute to different degrees to land degradation, so it is necessary to assign weights to each indicator (such as α, β, γ, etc.). The weights can be determined by the following methods: Expert experience: The weights are given by experts in the field based on their experience. Principal component analysis (PCA): The relative importance of each indicator is determined by statistical methods. Analytic hierarchy process (AHP): The weights of each indicator are determined by constructing a hierarchical model.

[0083] Each indicator and its weight can be integrated into a formula to calculate the comprehensive land degradation assessment value (CLDI). The model combines the contributions of multiple indicators into one assessment value, which can fully reflect the overall status of land degradation. By introducing the time decay factor (θ) and the terrain correction function (F(s,a)), the model can consider the impact of time and terrain factors on land degradation, making the assessment results more scientific and closer to the actual situation.

[0084] The CLDI value calculated by the above model can be used to: quantify the degree of land degradation: the higher the CLDI value, the lighter the land degradation; the lower the CLDI value, the more serious the land degradation. Generate a degradation level distribution map: divide the degradation level according to the CLDI value and visualize it in geographic space, providing an intuitive basis for land management and ecological protection.

[0085] In summary, the construction of a comprehensive land degradation assessment model in this invention is a process of integrating a multidimensional indicator system into a unified model. By determining the indicator weights, constructing the model formula and calculating the CLDI value, the degree of land degradation can be comprehensively and dynamically assessed, providing a scientific basis for land management and ecological protection.

[0086] Step 204: Calculate the land degradation assessment value of the land according to the comprehensive land degradation assessment model, and calculate the land degradation dynamic evolution value of the land based on the land degradation assessment value.

[0087] In some embodiments, step 204 may include: Obtain the time decay coefficient to reflect the impact of time on land degradation; Obtain the length of the time series for assessing the time span of land degradation; Obtaining a terrain-slope correction function for taking into account the influence of terrain factors of the land on land degradation; The land degradation assessment value of the land is determined based on the time attenuation coefficient, time series length and terrain-slope correction function of the land.

[0088] In some embodiments, the land degradation assessment value of the land may be expressed as: in, is the land degradation assessment value, is the time decay coefficient, t is the length of the time series, is the terrain-slope correction function, are the first scale parameter and the second scale parameter respectively.

[0089] In specific implementation, CLDI is a comprehensive indicator for quantifying the degree of land degradation. It comprehensively evaluates the status and dynamic changes of land degradation by integrating the vegetation degradation index (MVDI), water stress index (WSI) and human activity intensity index (HAI), and introducing time decay and terrain correction factors.

[0090] MVDI is the modified vegetation degradation index, which reflects the vegetation coverage and health status. WSI is the water stress index, which reflects the impact of water conditions on land degradation. HAI is the human activity intensity index, which reflects the contribution of human activities to land degradation. are the first scale parameter and the second scale parameter, respectively, which are used to adjust the contribution ratio of MVDI⋅WSI and (1-HAI). θ is the time decay coefficient, which is used to reflect the impact of time on land degradation. t is the length of the time series, indicating the time span of the assessment. It is a terrain-slope correction function, which is used to consider the impact of terrain factors on land degradation.

[0091] for , It is the product of the vegetation degradation index (MVDI) and the water stress index (WSI), reflecting the impact of natural factors (vegetation and water conditions) on land degradation. MVDI measures vegetation cover and health. The lower the value, the more severe the vegetation degradation. WSI measures the degree of water stress. The lower the value, the worse the water conditions. MVDI⋅WSI comprehensively reflects the contribution of natural factors to land degradation. The lower the value, the more severe the degradation caused by natural factors. (1-HAI) is the complement of the human activity intensity index (HAI), which is used to reflect the negative impact of human activities on land degradation. The higher the HAI value, the greater the intensity of human activities and the greater the contribution to land degradation. The lower the (1-HAI) value, the greater the negative impact of human activities on land degradation.

[0092] is a scale parameter used to adjust the contribution ratio of MVDI⋅WSI and (1-HAI). Adjust the contribution of natural factors (MVDI⋅WSI). Moderating contribution of human activity factor (1-HAI). It is usually adjusted according to actual conditions and is generally a positive number.

[0093] Time decay module , It is the time attenuation coefficient, which is used to reflect the impact of time on land degradation. It is usually a positive number, and the larger the value, the more significant the impact of time on land degradation.

[0094] t is the length of the time series, indicating the time span of the assessment. Through the time decay module, CLDI can reflect the changing trend of land degradation over time and avoid the bias of assessment results caused by different time spans.

[0095] The time decay function is used to adjust the effect of time on land degradation. When θ and t are large, When it approaches 1, it means that time has a greater impact on land degradation; when θ and t are small, It is close to 0, indicating that time has little impact on land degradation.

[0096] The terrain correction module F(s, a), terrain-slope correction function, is used to consider the impact of terrain factors on land degradation. s is the slope, which reflects the steepness of the terrain. a is the slope aspect, which reflects the direction of the terrain. Topographic factors (such as slope and slope aspect) have a significant impact on land degradation. For example, steep slopes are more prone to soil erosion, and slope aspect affects light and water conditions.

[0097] F(s,a) can be designed based on the specific effects of slope and aspect. For example, the degradation weight can be increased for areas with steeper slopes, and the differences in light and water conditions can be considered for south-facing aspects.

[0098] It can be understood that the higher the CLDI value, the lighter the degree of land degradation; the lower the CLDI value, the more serious the degree of land degradation. CLDI takes into account natural factors (vegetation and water conditions), human activity factors and temporal dynamic changes, and can comprehensively evaluate the degree of land degradation. Through the time decay module, CLDI can reflect the changing trend of land degradation over time and avoid the limitations of static evaluation. The introduction of the terrain correction function F(s, a) takes into account the impact of terrain factors on land degradation, and improves the accuracy and adaptability of the evaluation results. Based on multi-source remote sensing data and terrain data, the data sources are extensive and the calculation process is clear.

[0099] CLDI can be used to assess the status and dynamic changes of regional land degradation and identify degradation hotspots. It can provide a scientific basis for ecological protection and restoration projects and optimize restoration measures. It can provide a reference for land use planning, optimize land use methods, and reduce land degradation risks. It can also provide a scientific basis for the formulation of land management and ecological protection policies and support sustainable development.

[0100] In summary, the CLDI in this invention is a comprehensive and dynamic land degradation assessment tool. By integrating natural factors, human activity factors, temporal dynamic changes and terrain correction, CLDI can more accurately assess the extent and trend of land degradation. Its calculation process is clear, with high adaptability and application value, and is an important tool in the field of land management and ecological protection.

[0101] In some embodiments, step 204 may further include: Obtain the assessment year and base year used to conduct the degradation assessment of the land in question; Obtaining a land degradation assessment value of the land at a first moment and a land degradation assessment value of the land at a second moment; The land degradation dynamic evolution value of the land is determined according to the assessment year, the base year, the land degradation assessment value at the first moment and the land degradation assessment value at the second moment of the land.

[0102] In some embodiments, the land degradation dynamics evolution value of land can be expressed as: in, is the dynamic evolution value of land degradation, is the land degradation assessment value at time t1, is the land degradation assessment value at time t2, is the year of assessment, is the base year.

[0103] In specific implementation, the land degradation dynamic evolution assessment index, namely the land degradation dynamic evolution value ΔCLDI, is used to quantify the degree of change of land degradation between two different periods (for example, the first moment t1 and the second moment t2). It evaluates the dynamic evolution trend of land degradation by comparing the land degradation assessment values ​​(CLDI) of different periods and combining the time factor (the ratio of the assessment year to the base year).

[0104] Relative rate of change This part calculates the relative rate of change of land degradation assessment values ​​between two different periods. When , ΔCLDI is positive, indicating that the degree of land degradation has been reduced from t1 to t2. When , ΔCLDI is negative, indicating that the degree of land degradation has increased from t1 to t2. When , ΔCLDI is 0, indicating that the degree of land degradation remains unchanged from t1 to t2. The value range is theoretically -∞ to +∞, but in practical applications it is usually between -1 and +1.

[0105] Time adjustment factor This part adjusts the influence of time factors through the natural logarithm function, so that ΔCLDI can reflect the impact of time span on the dynamic evolution of land degradation.

[0106] The ratio of the assessment year to the base year reflects the size of the time span. The natural logarithm function (ln) adjusts the time ratio so that the impact of the time factor changes nonlinearly.

[0107] when( ), the time adjustment factor is 1, indicating that the assessment year is the same as the base year and the time factor has no effect. ), the time adjustment factor is greater than 1, indicating that the assessment year is later than the benchmark year. The larger the time span, the larger the adjustment factor, and the more significant the impact of the time factor. ), the time adjustment factor is less than 1, indicating that the assessment year is earlier than the base year (this situation is rare but may occur in some retrospective studies). The value range is usually greater than 0, and the specific value depends on size.

[0108] It can be understood that when ΔCLDI>0, it means that the degree of land degradation has been reduced from t1 to t2, which may be due to ecological protection measures or natural recovery. ΔCLDI<0 means that the degree of land degradation has increased from t1 to t2, which may be due to intensified human activities or climate change. ΔCLDI=0 means that the degree of land degradation remains unchanged from t1 to t2.

[0109] ΔCLDI not only considers the relative change of land degradation assessment value, but also considers the influence of time span through time adjustment factor, which can more accurately reflect the dynamic evolution trend of land degradation. The natural logarithm function is introduced to adjust the time factor, so that the influence of time span on land degradation changes nonlinearly, which is more in line with the actual situation. It is suitable for dynamic assessment of land degradation with different time spans, and can provide a unified quantitative indicator for long-term monitoring and short-term assessment. Based on the combination of relative change rate and time adjustment factor, ΔCLDI can provide a scientific basis for land management and ecological protection.

[0110] ΔCLDI can be used to assess the changing trend of land degradation in different time periods and identify areas where degradation has intensified or reduced. By comparing the ΔCLDI values ​​before and after the implementation of ecological protection measures, the effectiveness of ecological protection measures can be evaluated. It provides a dynamic evaluation basis for the formulation of land management and ecological protection policies and supports the scientific adjustment of policies. Combined with socio-economic data, it analyzes the relationship between the dynamic evolution of land degradation and regional development, providing a reference for sustainable development.

[0111] The ΔCLDI is a tool for quantifying the dynamic changes of land degradation. By combining the relative change rate and the time adjustment factor, ΔCLDI can more accurately reflect the changing trend of land degradation in different periods. Its calculation process is clear, with high scientificity and adaptability, and it is an important evaluation indicator in the field of land management and ecological protection.

[0112] Step 205: predict the future land degradation assessment value of the land according to the land degradation dynamic evolution value and the current land degradation assessment value, and generate a spatial distribution map of the land degradation level.

[0113] In some embodiments, step 205 may include: Obtain the human activity impact coefficient to quantify the impact of human activities on land degradation; Obtain climate change sensitivity coefficients that represent the impact of climate change on land degradation; Obtain a climate-policy response function that comprehensively considers the impacts of future climate conditions and policy interventions on land degradation; The future land degradation assessment value of the land is determined based on the dynamic evolution value of land degradation, the current land degradation assessment value, the water stress index, the human activity intensity index, the human activity impact coefficient, the climate change sensitivity coefficient and the climate-policy response function.

[0114] In some embodiments, the future land degradation assessment value of the land can be expressed as: in, is the future land degradation assessment value, is the current land degradation assessment value, is the human activity impact coefficient, is the climate change sensitivity coefficient, is the climate-policy response function.

[0115] In specific implementation, the current land degradation assessment value can be used , and combined with the impact of factors such as human activities, climate change and policy responses, dynamic predictions of future land degradation are made.

[0116] It is the land degradation assessment value at the current moment, calculated by the previous comprehensive land degradation assessment model (CLDI). As a benchmark value for future predictions, it reflects the current status of land degradation.

[0117] Human Activity Impact Module , It is the human activity impact coefficient, which is used to quantify the impact of human activities on land degradation. The value range is usually positive, and the larger the value, the more significant the impact of human activities on land degradation.

[0118] HAI is a human activity intensity index that reflects the intensity of current human activities. (1-HAI) represents the complement of the negative impact of human activities on land degradation. The lower the value, the greater the negative impact of human activities on land degradation.

[0119] This exponential function is used to adjust for the impact of human activities. When HAI is high, (1-HAI) is small. When HAI is close to 0, it means that human activities have a greater negative impact on land degradation, and the degree of degradation may worsen in the future. When HAI is low, (1-HAI) is large. A value close to 1 indicates that human activities have a smaller negative impact on land degradation and the degree of degradation may be reduced in the future.

[0120] Climate Change Impacts Module , The climate change sensitivity coefficient is used to quantify the impact of climate change on land degradation. The value range is usually positive, and the larger the value, the more significant the impact of climate change on land degradation.

[0121] WSI is the Water Stress Index, which reflects the impact of current water conditions on land degradation. The lower the WSI value, the worse the water conditions and the higher the risk of land degradation. ]: Linear adjustment function is used to take into account the impact of climate change. When WSI is low, is a negative value, [ ] is less than 1, indicating that climate change (such as drought) may exacerbate land degradation. When WSI is high, is a positive value, [ ] is greater than 1, indicating that good water conditions may alleviate land degradation.

[0122] Climate-policy response function (R(c,p)) is a function that comprehensively considers the impact of future climate conditions (c) and policy interventions (p) on land degradation. Climate conditions (c) consider the impact of future climate change (such as changes in temperature and precipitation patterns) on land degradation. Policy interventions (p) consider the mitigation or aggravation of land degradation by land management policies and ecological protection measures that may be implemented in the future.

[0123] R(c, p) can be designed according to specific climate prediction models and policy scenarios. For example, if strict ecological protection policies are implemented in the future, R(c, p) may be greater than 1, indicating that the policy has a mitigating effect on land degradation; if the climate deteriorates in the future and there is a lack of effective policies, R(c, p) may be less than 1, indicating that land degradation is intensifying.

[0124] Understandably, when Indicates that land degradation levels may decrease in the future, possibly due to effective policy interventions or favorable climate conditions. It indicates that the extent of land degradation is likely to increase in the future, possibly due to increased human activities or the negative impacts of climate change. It indicates that the degree of land degradation may remain the same in the future.

[0125] Not only is it based on the current land degradation situation, but it also takes into account the combined impact of human activities, climate change and policy interventions, and can dynamically predict future land degradation. By adjusting μ, σ and R(c,p), it is possible to simulate land degradation trends under different scenarios, providing a scientific basis for policy making and land management. Combining factors such as human activities, climate change and policy responses makes the prediction results more scientific and practical. R(c,p) can be flexibly designed according to specific climate forecasts and policy scenarios to meet the prediction needs of different regions.

[0126] It can be used to predict the degree of land degradation at a certain point in the future, providing a forward-looking reference for ecological protection and land management. By simulating different policy scenarios, the potential impact of policies on land degradation can be evaluated and land management strategies can be optimized. Combined with climate change models, the impact of future climate change on land degradation can be analyzed to support regional adaptive planning. It provides a scientific basis for regional sustainable development and supports the balance between ecological protection and economic development.

[0127] In summary, this invention makes a scientific prediction of future land degradation by combining current land degradation conditions, human activities, climate change, and policy responses. Its calculation process is clear, with high scientificity and practicality, and it is an important prediction tool in the fields of land management and ecological protection.

[0128] In some embodiments, the calculated CLDI values ​​may be divided into different degradation levels. The degradation levels may be divided using one of the following methods: Natural breakpoint method: Automatically divide the levels according to the distribution characteristics of the CLDI values, so that the CLDI values ​​within each level are distributed relatively evenly, highlighting the spatial differences. Equal spacing method: Divide the range of the CLDI values ​​into several intervals, each of which corresponds to a degradation level. Expert experience method: Manually set the threshold of the CLDI value according to expert experience and actual needs to divide the degradation levels.

[0129] Generally, the degradation level can be divided into the following categories: No degradation: A high CLDI value indicates that the land is in good condition. Slight degradation: A medium CLDI value indicates that the land is slightly degraded. Moderate degradation: A low CLDI value indicates that the land is significantly degraded. Severe degradation: An extremely low CLDI value indicates that the land is severely degraded.

[0130] According to the classified degradation levels, the degradation level of each assessment unit can be visualized in geographic space to generate a spatial distribution map of land degradation levels. The specific steps are as follows: Geographic Information System (GIS) Software: Use GIS software (e.g. ArcGIS, QGIS) to associate CLDI values ​​with geographic coordinates and generate raster or vector maps.

[0131] Color coding: Assign a different color to each degradation level (e.g. green for no degradation, yellow for mild degradation, orange for moderate degradation, and red for severe degradation).

[0132] Mapping: Display the degradation level on the map in the form of color to intuitively reflect the spatial distribution of land degradation.

[0133] It can be understood that through color coding, the spatial distribution of land degradation is intuitively displayed, which facilitates the rapid identification of degradation hotspots. It provides a scientific basis for land management departments to support decisions on ecological protection, land restoration and sustainable use. Combined with the degradation level distribution maps of different periods, the temporal and spatial evolution trend of land degradation is analyzed.

[0134] In summary, the spatial distribution map of land degradation levels generated by the present invention is one of the important outputs of this solution. It is based on the calculation and classification of CLDI values, and uses GIS technology to visualize the degradation levels in geographic space, providing an intuitive decision support tool for land management and ecological protection.

[0135] See also Figure 2 , Figure 2 A schematic diagram of the structure of a system for evaluating land degradation status by integrating multi-source remote sensing indicators provided by the present invention.

[0136] like Figure 2As shown, a system for evaluating land degradation status by integrating multi-source remote sensing indicators proposed in an embodiment of the present invention includes: The data acquisition module 301 is used to acquire and pre-process the multi-source remote sensing data of the land to be evaluated to obtain pre-processed data; An index construction module 302 is used to construct a multidimensional index system including an improved vegetation degradation index, a water stress index and a human activity intensity index according to the preprocessed data; A model building module 303 is used to build a comprehensive land degradation assessment model based on the multi-dimensional indicator system; A degradation evolution module 304 is used to calculate a land degradation assessment value of the land according to the comprehensive land degradation assessment model, and calculate a land degradation dynamic evolution value of the land based on the land degradation assessment value; The degradation assessment module 305 is used to predict the future land degradation assessment value of the land according to the land degradation dynamic evolution value and the current land degradation assessment value, and generate a spatial distribution map of the land degradation level.

[0137] See also Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented: Acquire and preprocess multi-source remote sensing data of the land to be assessed to obtain preprocessed data; Based on the preprocessed data, a multidimensional index system including an improved vegetation degradation index, a water stress index and a human activity intensity index is constructed; Based on the multi-dimensional indicator system, a comprehensive land degradation assessment model is constructed; Calculating a land degradation assessment value of the land according to the comprehensive land degradation assessment model, and calculating a land degradation dynamic evolution value of the land based on the land degradation assessment value; According to the land degradation dynamic evolution value and the current land degradation assessment value, the future land degradation assessment value of the land is predicted, and a spatial distribution map of the land degradation level is generated.

[0138] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented: Acquire and preprocess multi-source remote sensing data of the land to be assessed to obtain preprocessed data; Based on the preprocessed data, a multidimensional index system including an improved vegetation degradation index, a water stress index and a human activity intensity index is constructed; Based on the multi-dimensional indicator system, a comprehensive land degradation assessment model is constructed; Calculating a land degradation assessment value of the land according to the comprehensive land degradation assessment model, and calculating a land degradation dynamic evolution value of the land based on the land degradation assessment value; According to the land degradation dynamic evolution value and the current land degradation assessment value, the future land degradation assessment value of the land is predicted, and a spatial distribution map of the land degradation level is generated.

[0139] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0141] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0142] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0144] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0145] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for evaluating land degradation by integrating multi-source remote sensing indicators, characterized in that: The method comprises: Acquire and preprocess multi-source remote sensing data of the land to be assessed to obtain preprocessed data; Based on the preprocessed data, a multidimensional index system including an improved vegetation degradation index, a water stress index and a human activity intensity index is constructed; Based on the multi-dimensional indicator system, a comprehensive land degradation assessment model is constructed; Calculating a land degradation assessment value of the land according to the comprehensive land degradation assessment model, and calculating a land degradation dynamic evolution value of the land based on the land degradation assessment value; According to the land degradation dynamic evolution value and the current land degradation assessment value, the future land degradation assessment value of the land is predicted, and a spatial distribution map of the land degradation level is generated.

2. The method for evaluating land degradation status by integrating multi-source remote sensing indicators according to claim 1 is characterized in that: The improved vegetation degradation index is constructed in the following way: Calculating a normalized vegetation index based on the spectral reflectance characteristics of vegetation on the land; Obtaining soil-adjusted vegetation index to reduce the impact of soil background on vegetation index; Obtaining the surface temperature of the land and the upper limit value of the surface temperature; Calculating a bare soil index for quantifying the degree of soil exposure based on the spectral reflectance characteristics of the vegetation and soil of the land; The improved vegetation degradation index is obtained according to the normalized vegetation index, soil adjusted vegetation index, surface temperature, upper limit of surface temperature and bare soil index of the land.

3. The method for evaluating land degradation status by integrating multi-source remote sensing indicators according to claim 2 is characterized in that: The improved vegetation degradation index is expressed as: in, is the improved vegetation degradation index. is the Normalized Difference Vegetation Index, is the soil adjusted vegetation index, is the surface temperature, is the upper limit of the surface temperature, is the bare soil index, They are the first weight, the second weight and the third weight respectively.

4. The method for evaluating land degradation status by integrating multi-source remote sensing indicators according to claim 3 is characterized in that: The water stress index is constructed in the following way: Obtain precipitation, actual evapotranspiration and potential evapotranspiration for the area where the land is located; Obtaining the soil water stress coefficient for adjusting the effect of soil water status on water stress; obtaining a normalized water index for use in assessing the condition of surface water over said land; The water stress index is determined according to the precipitation, actual evapotranspiration, potential evapotranspiration, soil water stress coefficient and normalized water index of the land.

5. The method for evaluating land degradation status by integrating multi-source remote sensing indicators according to claim 4 is characterized in that: The water stress index is expressed as: in, is the water stress index, P is the precipitation, is the actual evaporation, is the soil water stress coefficient, is the potential evapotranspiration, is the normalized water index.

6. The method for evaluating land degradation status by integrating multi-source remote sensing indicators according to claim 5 is characterized in that: The human activity intensity index is constructed in the following way: Obtaining standardized nighttime light values ​​for assessing the intensity and distribution of human activities in the area where the land is located; Obtaining a built-up area index for assessing the extent and density of built-up areas on said land; Obtaining land use change rates for assessing land use dynamics; The human activity intensity index is obtained according to the standardized night light value, built-up area index and land use change rate of the land.

7. The method for evaluating land degradation status by integrating multi-source remote sensing indicators according to claim 6 is characterized in that: Calculating the land degradation assessment value of the land according to the comprehensive land degradation assessment model includes: Obtain the time decay coefficient to reflect the impact of time on land degradation; Obtain the length of the time series for assessing the time span of land degradation; Obtaining a terrain-slope correction function for taking into account the influence of terrain factors of the land on land degradation; The land degradation assessment value of the land is determined based on the time attenuation coefficient, time series length and terrain-slope correction function of the land.

8. The method for evaluating land degradation status by integrating multi-source remote sensing indicators according to claim 7 is characterized in that: The calculating the land degradation dynamic evolution value of the land based on the land degradation assessment value comprises: Obtain the assessment year and base year used to conduct the degradation assessment of the land in question; Obtaining a land degradation assessment value of the land at a first moment and a land degradation assessment value of the land at a second moment; The land degradation dynamic evolution value of the land is determined according to the assessment year, the base year, the land degradation assessment value at the first moment and the land degradation assessment value at the second moment of the land.

9. The method for evaluating land degradation status by integrating multi-source remote sensing indicators according to claim 8, characterized in that: The method of predicting the future land degradation assessment value of the land according to the land degradation dynamic evolution value and the current land degradation assessment value comprises: Obtain the human activity impact coefficient to quantify the impact of human activities on land degradation; Obtain climate change sensitivity coefficients that represent the impact of climate change on land degradation; Obtain a climate-policy response function that comprehensively considers the impacts of future climate conditions and policy interventions on land degradation; The future land degradation assessment value of the land is determined based on the dynamic evolution value of land degradation, the current land degradation assessment value, the water stress index, the human activity intensity index, the human activity impact coefficient, the climate change sensitivity coefficient and the climate-policy response function.

10. A system for evaluating land degradation by integrating multi-source remote sensing indicators, characterized in that: The system comprises: A data acquisition module is used to acquire and pre-process multi-source remote sensing data of the land to be evaluated to obtain pre-processed data; An indicator construction module, used to construct a multidimensional indicator system including an improved vegetation degradation index, a water stress index and a human activity intensity index according to the preprocessed data; A model building module, used to build a comprehensive land degradation assessment model based on the multi-dimensional indicator system; A degradation evolution module, used to calculate the land degradation assessment value of the land according to the comprehensive land degradation assessment model, and calculate the land degradation dynamic evolution value of the land based on the land degradation assessment value; The degradation assessment module is used to predict the future land degradation assessment value of the land according to the land degradation dynamic evolution value and the current land degradation assessment value, and generate a spatial distribution map of the land degradation level.

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