A natural resource asset loss monitoring and early warning system based on comprehensive quality evaluation

Abstract

The present application relates to the technical field of natural resource management, and particularly relates to a natural resource asset loss monitoring and early warning system based on comprehensive quality evaluation, comprising a data calling module, a layer processing module, a resource value quality index obtaining module, a primary division module, an NPP calculation module, a secondary division module, a population pressure density calculation module, a tertiary division module and an early warning module; the present application monitors the asset change of natural resources from three dimensions of resource value quality, resource ecological quality and resource pressure quality, and early warns the regions with loss risk.

Description

Technical Field

[0001] This invention relates to the fields of natural resource management and remote sensing monitoring, and specifically to a natural resource asset loss monitoring and early warning system based on comprehensive quality assessment. Background Technology

[0002] To better protect and utilize natural resources, remote sensing-based methods for monitoring natural resource changes have emerged. These methods primarily monitor changes in land cover types, such as forest land turning into wasteland, forest land turning into grassland, arbor forests turning into shrublands, arable land turning into forests, and forests turning into arable land. As people's understanding of natural resources deepens, effectively utilizing natural resources as assets and reducing their loss has become a focus. The loss of natural resource assets involves multiple factors, and focusing solely on land cover changes often leads to misjudgments and false impressions. Building upon existing conventional land cover change monitoring, urgently needed solutions include monitoring the loss and effective utilization of natural resource assets from a quality perspective, and developing tiered early warning systems for natural resource asset loss. Summary of the Invention

[0003] To address the above problems, this invention provides a natural resource asset loss monitoring and early warning system based on comprehensive quality assessment, comprising:

[0004] The data retrieval module is used to retrieve the natural resource asset inventory results of the monitoring area in the base year and extract the value vector layer of the required natural resources from it, as well as to retrieve the natural resource asset inventory results of the monitoring area in the early warning year and extract the value vector layer of the required natural resources from it.

[0005] The layer processing module is used to calculate the total natural resource value raster layer for the base year and the warning year based on the value vector layer, and then calculate the natural resource value growth rate of each pixel in the monitoring area based on the total natural resource value raster layer.

[0006] The resource value quality index acquisition module is used to select the minimum value a and the maximum value b from the natural resource value growth rate of all pixels to form the extreme value boundary [a,b], and to calculate the decimal based on the natural resource value growth rate of all pixels.

[0007] The primary segmentation module is used to label the region type of each cell based on the extreme value boundary [a,b] and the decimal place; the region types include potential loss regions, potential value preservation regions, and potential value-added regions.

[0008] The NPP calculation module is used to calculate the NPP growth rate of each pixel in the monitoring area from the base year to the warning year.

[0009] The secondary partitioning module is used to label the unlabeled pixels in the primary partitioning module with region types based on the NPP growth rate.

[0010] The population pressure density calculation module is used to calculate the population pressure density growth rate of each pixel in the monitoring area from the base year to the warning year;

[0011] The tertiary segmentation module is used to label the unlabeled pixels in the secondary segmentation module with region types based on the population pressure density growth rate.

[0012] The early warning module is used to provide early warnings about the probability of asset loss in the monitored area.

[0013] The beneficial effects of this invention are as follows: This invention designs a comprehensive evaluation model to monitor, identify, and provide early warning of areas where natural resource assets are lost.

[0014] Compared to traditional methods that only consider changes in land parcels related to natural resources, this invention further considers the quality of natural resources, conducting a comprehensive assessment from three dimensions: resource value quality, resource ecological quality, and resource stress quality. Its monitoring and early warning results are more accurate, more reliable, and have a wider range of applications.

[0015] In the past, the value of resources was assessed using the GEP model for large-scale estimation. This method often requires the calculation of nearly 10 indicators, which is time-consuming and labor-intensive. The data sources of different indicators vary greatly, and there is a lack of horizontal comparability. However, this invention directly obtains the resource value quality indicator by separating the value information from the existing asset inventory results, and uses the indicator to determine whether there is a risk of asset loss of natural resource assets. This is not only more scientific and accurate, but also saves a lot of time and money. Attached Figure Description

[0016] Figure 1 This is a flowchart of a natural resource asset loss monitoring and early warning system based on comprehensive quality assessment, according to an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram illustrating the early warning system in an embodiment of the present invention. Detailed Implementation

[0018] 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.

[0019] To better protect and utilize natural resources, routine remote sensing monitoring is necessary. However, existing methods focus on monitoring changes in land cover types, neglecting changes in the quality of natural resources. This can easily lead to misjudgments in subsequent natural resource maintenance measures. For example, if a decrease in forest cover is detected in a certain area over a certain period, it might be concluded that the area's natural resources have been damaged, and afforestation might be chosen as the next maintenance measure. However, this could be due to changes in local climate conditions that make the area more suitable for herbaceous plants, and excessively increasing forest cover could disrupt the local water supply balance. Alternatively, it could be due to planning adjustments resulting from scenic area development, which bring in more tourists, accommodate more people, and generate more value. A single factor like changes in land cover types cannot fully explain whether changes in natural resources are positive or negative.

[0020] To this end, the present invention provides a natural resource asset loss monitoring and early warning system based on comprehensive quality assessment. It treats natural resources as an asset and monitors changes in natural resource assets from three dimensions: resource value quality, resource ecological quality, and resource pressure quality. It also provides early warnings for areas at risk of loss.

[0021] In some embodiments, such as Figure 1 As shown, a natural resource asset loss monitoring and early warning system based on comprehensive quality assessment includes:

[0022] The data retrieval module is used to retrieve the natural resource asset inventory results of the monitoring area in the base year and extract the value vector layer of the required natural resources from it, as well as to retrieve the natural resource asset inventory results of the monitoring area in the early warning year and extract the value vector layer of the required natural resources from it.

[0023] Specifically, the results of the natural resource asset inventory are a multi-dimensional data set formed by the natural resources department on an annual basis, based on systematic surveys and various special surveys of natural resources, after conducting physical quantity inventory and value accounting to comprehensively understand natural resource assets. This multidimensional dataset is updated annually using the same statistical caliber and is comparable across time scales.

[0024] The value vector layer of natural resources is the core spatial data in the results of natural resource asset inventory. It is a spatial data product that uses a vector data structure to precisely link the economic, ecological, and social value of natural resources with geographic spatial entities (such as land parcels, mining areas, and forest compartments). Through geometric elements such as points, lines, and polygons and their attribute fields, it enables the location, querying, and analysis of value information, and is a core tool supporting the refined management of natural resource assets.

[0025] The layer processing module is used to calculate the total natural resource value raster layer for the base year and the warning year based on the value vector layer, and then calculate the natural resource value growth rate of each pixel in the monitoring area based on the total natural resource value raster layer.

[0026] The resource value quality index acquisition module is used to select the minimum value 'a' and the maximum value 'b' from the natural resource value growth rate of all pixels to form the extreme value boundary [a,b], and to calculate the decimal based on the natural resource value growth rate of all pixels.

[0027] The primary segmentation module is used to label the region type of each cell based on the extreme value boundary [a,b] and the decimal place; the region types include potential loss region, potential value preservation region and potential value enhancement region.

[0028] The NPP calculation module is used to calculate the NPP growth rate of each pixel in the monitoring area from the base year to the warning year.

[0029] The secondary partitioning module is used to label the unlabeled pixels in the primary partitioning module with region types based on the NPP growth rate.

[0030] The population pressure density calculation module is used to calculate the population pressure density growth rate of each pixel in the monitoring area from the base year to the warning year.

[0031] The tertiary segmentation module is used to label the unlabeled pixels in the secondary segmentation module with region types based on the population pressure density growth rate.

[0032] The early warning module is used to provide early warnings about the probability of asset loss in the monitored area.

[0033] In some embodiments, the required natural resources include farmland, construction land, reserve land, forests, and grasslands.

[0034] In some embodiments, the layer processing module specifically includes:

[0035] A single-layer processing unit is used to rasterize each value vector layer to obtain a value raster layer.

[0036] The overlay processing unit is used to overlay the value raster layers of all natural resources corresponding to the base year to obtain the total natural resource value raster layer of the base year, and to overlay the value raster layers of all natural resources corresponding to the warning year to obtain the total natural resource value raster layer of the warning year.

[0037] The growth rate processing unit is used to calculate the growth rate of natural resource value for each pixel within the monitoring area from the base year to the warning year, based on the total natural resource value raster layer.

[0038] In some embodiments, the process by which a single-layer processing unit processes any value vector layer includes:

[0039] S11. The value vector layer is projected into two dimensions using the CGCS2000 Gauss-Kruger projection to obtain the projected image;

[0040] S12. Calculate the geometric area of ​​each patch after projection in the value vector layer;

[0041] S13. The unit area value of a patch is obtained by dividing the economic value of each patch in the value vector layer by its projected geometric area.

[0042] S14. Use Dislove to merge the patches with the same unit area value in the value vector layer to obtain the merged value vector layer;

[0043] S15. Set the cell size, and use the unit area value of each patch in the fused value vector layer multiplied by the square of the cell size to obtain the unit pixel value of the patch;

[0044] S16. The value raster layer is obtained by rasterizing the fused value vector layer according to the size of the monitoring area and the pixel size. The pixel value of each pixel in the value raster layer is the unit pixel value of its corresponding patch. If the pixel does not belong to any patch, its pixel value is 0.

[0045] Specifically, Dissolve is an attribute-driven spatial data aggregation technology in GIS. Its core mechanism is to merge adjacent features based on specified fields (such as "value per unit area") and eliminate common boundaries.

[0046] Specifically, when rasterizing the fusion value vector layer, the rasterization range is consistent with the monitoring area.

[0047] In some embodiments, the growth rate processing unit calculates the natural resource value growth rate of each pixel as follows:

[0048]

[0049] In the formula, x i This represents the growth rate of the natural resource value of the i-th pixel within the monitoring area. The pixel value of the i-th pixel in the raster layer representing the total natural resource value for the warning year. The pixel value of the i-th pixel in the raster layer represents the total natural resource value of the base year.

[0050] In some embodiments, empirical quantiles are used to divide the natural resource value growth rate of all pixels into several equal parts. This embodiment mainly uses decimals to divide the natural resource value growth rate into 10 levels.

[0051] In some embodiments, the primary segmentation module performs region type labeling on each pixel, including:

[0052] S21. Determine whether the natural resource value growth rate of this pixel is less than 0 and lies in the interval [a, Q]. 10 If so, mark the cell as a potential loss region; otherwise, proceed to step S22; where Q 10 It is the 10th percentile of the natural resource value growth rate for all pixels; the interval [a, Q] 10 The value of ] is close to the lower limit of the extreme value, indicating that the value of natural resources is showing a significant downward trend, and it can be directly regarded as a potential loss area.

[0053] S22. Determine whether the natural resource value growth rate of this pixel is greater than 0 and lies in the interval [Q]. 90 If Q is a potential value-added region, then the cell is marked as such; otherwise, it is not marked. 90 It is the 90th percentile of the growth rate of natural resource value for all pixels. The interval [Q] 90 The value of ,b] is close to the upper limit of the extreme value, which indicates that the value of natural resources is showing a significant growth trend, and it can be directly regarded as a potential value-added area.

[0054] In some embodiments, within the field of natural resources and ecology, NPP typically refers to Net Primary Productivity, a core indicator for measuring the productivity of an ecosystem. It represents the total amount of carbon fixed by green plants through photosynthesis per unit time and per unit area, minus the amount of organic matter remaining after deducting their own respiration. The formula for calculating NPP is:

[0055]

[0056] In the formula, GPP stands for Total Primary Productivity, which is the total amount of carbon fixed by plants through photosynthesis. a Carbon consumed by autotrophic respiration refers to the amount of carbon consumed by a plant during its growth and metabolism (such as respiration). Commonly used units are grams of carbon per square meter per year (g C / (m²・a)) or tons of carbon per hectare per year (t C / (hm²・a)).

[0057] The Light Use Efficiency Model (LUE model) is a core tool for estimating the total primary productivity (GPP) of an ecosystem. Its core logic is to calculate the total amount of carbon fixed by vegetation by quantifying the photosynthetically active radiation (APAR) absorbed by vegetation and the efficiency of vegetation in converting light energy into organic carbon (ε, i.e., light energy utilization rate).

[0058] The CASA model (Carnegie-Ames-Stanford Approach) is one of the most widely used light energy utilization (LUE) models, primarily used for estimating gross primary productivity (GPP) at regional / global scales. Its advantages lie in its simple principles, readily available data, and ability to rapidly monitor ecosystem productivity dynamically using remote sensing and meteorological data. It is a core tool for carbon sequestration and ecological assessment in the field of natural resources.

[0059] The CASA model is essentially a two-step method of "light energy absorption - efficiency conversion," which quantifies the photosynthetically active radiation (APAR) absorbed by vegetation and the light energy conversion efficiency (ε), as shown in the following formula:

[0060]

[0061] The spatial variable x represents the spatial location of the study area (such as a grid cell); the temporal variable t is usually on a monthly scale (to balance accuracy and data availability, it can also be refined to an 8-day or daily scale); GPP(x,t) represents the total primary productivity at location x over time t, in g C / (m²·month); APAR(x,t) represents the photosynthetically active radiation absorbed by vegetation at location x over time t, in MJ / (m²·month); ε(x,t) represents the actual light energy utilization rate of vegetation at location x over time t, in g C / MJ.

[0062] Based on this, the NPP of each pixel within the monitoring area in the base year is calculated, and an NPP raster layer for the base year is formed; the NPP of each pixel within the monitoring area in the warning year is calculated, and an NPP raster layer for the warning year is formed; due to autotrophic respiration consumption R... a These values ​​are generally very small and not very meaningful for the large-scale monitoring of this invention. Therefore, considering the portability and efficiency of the model, this invention equates GPP with NPP, and the NPP calculation formula for each pixel is:

[0063]

[0064] In the formula, NPP(x,t) represents the light and effective radiation absorbed by pixel x during time period t, ε(x,t) represents the actual light energy utilization rate of pixel during time period t, and APAR(x,t) represents the photosynthetically active radiation absorbed by vegetation in pixel x during time period t.

[0065] The NPP growth rate of each pixel within the monitoring area from the base year to the warning year is expressed as:

[0066]

[0067] In the formula, y i This represents the NPP growth rate of the i-th pixel within the monitoring area. This represents the pixel value of the i-th pixel in the NPP raster layer for the year of the warning. This represents the pixel value of the i-th pixel in the NPP raster layer for the base year.

[0068] In some embodiments, in the secondary partitioning module, for each unmarked cell in the primary partitioning module, if its NPP growth rate is greater than 0, it is marked as a potential value-preserving area; otherwise, it is not marked for the time being.

[0069] In some embodiments, the population pressure density of natural resources in the monitored area is used as a third indicator of resource pressure quality to assess the loss of natural resource assets. This invention uses the number of tourists to measure the quality of natural resources in a given area; the more tourists, the more people are willing to pay for the natural resources there, and the higher their quality.

[0070] The population pressure density calculation module specifically includes:

[0071] The natural resources and landscapes within the monitoring area are spatialized according to point elements.

[0072] Specifically, spatializing natural resource landscapes as point elements refers to mapping the specific location of each natural landscape onto an electronic map as a point. A common example is building icons on a navigation map, which are mapped from their specific spatial locations. The spatialization process may include:

[0073] Collect the two-dimensional coordinates of all natural landscapes to form a coordinate table.

[0074] Use tools such as ArcGIS to plot all the points in the coordinate table onto the map.

[0075] The annual number of tourists for each natural resource landscape in the base year and the warning year is obtained; for areas other than natural resource landscapes in the monitoring area, spatial interpolation method is used to obtain the natural resource population pressure density raster layer of the monitoring area in the base year and the natural resource population pressure density raster layer of the monitoring area in the warning year.

[0076] The natural resource-population pressure density growth rate of each pixel within the monitoring area from the base year to the warning year is calculated and expressed as:

[0077]

[0078] In the formula, y i This represents the population pressure density growth rate of natural resources in the i-th pixel within the monitoring area. This represents the pixel value of the i-th pixel in the natural resource population pressure density raster layer for the year of the warning. This represents the pixel value of the i-th pixel in the natural resources-population pressure density raster layer for the base year.

[0079] In some embodiments, for each unmarked cell in the secondary partitioning module, if its natural resource population pressure density growth rate is greater than 0, it is marked as a potential value preservation area; if its natural resource population pressure density growth rate is not greater than 0, it is marked as a potential loss area.

[0080] In some embodiments, the early warning module specifically includes:

[0081] Obtain land use change patches within the monitoring area, and identify potential loss areas, potential value preservation areas, and potential value appreciation areas after processing by a three-stage segmentation module;

[0082] Intersect analysis is performed between land use change patches and potential loss areas. The intersection results are used as early warning clue patches. The early warning clue patches are marked with red alert, indicating that the probability of asset loss risk is the highest.

[0083] An orange alert is issued for areas outside the warning clue patches within the potential loss area, indicating that the probability of asset loss is the second highest.

[0084] A yellow alert is issued for potential areas of value preservation, indicating that the probability of asset loss is the lowest.

[0085] No warnings will be issued for areas with potential for growth.

[0086] In particular, the calculation of the total natural resource value raster layer, the NPP raster layer, and the natural resource population pressure density raster layer in this invention is carried out independently, but the three correspond one-to-one with the monitoring area in terms of spatial location.

[0087] For example, in this embodiment, the monitoring area is set in a city in Southwest China, and the monitoring requirement is the change of natural resources between 2020 and 2024. Therefore, the value vector maps of agricultural land (NYDZYZC), construction land (JSYDZYZC), reserve land (WQDSYQRJSYD), forest (SLZYZC), and grassland (CYZYZC) in the asset inventory results of the base year 2020 and the warning year 2024 are extracted according to the layer element codes.

[0088] The layer processing module processes each value vector layer, including:

[0089] The projected image was obtained using the CGCS2000 Gauss-Krüger projector;

[0090] A new "Geometric Area" field is added to each polygon, and its attribute value is calculated from the geometric area of ​​the polygon after projection.

[0091] A new "Value per Unit Area" field is added to each map patch. Its attribute value is calculated using the "Economic Value" field (JJJZ) and the "Geometric Area" field in the value vector layer, i.e., "Value per Unit Area" = "Economic Value" / "Geometric Area".

[0092] The Dissolve_Fields parameter is set to the "Value per Unit Area" field, and the Statistics Fields parameter is set to "mean". This operation merges adjacent features with the same attribute values, reducing the number of features and improving the processing speed of the subsequent vector-to-raster conversion. For example, two adjacent feature features, A and B, both have a "Value per Unit Area" of 500 yuan. If the Statistics Fields parameter is set to "mean", their value per unit area remains 500 yuan. However, if the summation parameter is selected, their value per unit area becomes 1000 yuan, which is clearly incorrect because the "Value per Unit Area" does not increase with the size of the feature.

[0093] A new "unit pixel value" field is added to each merged patch, which is calculated as p=J*R. 2 , where p represents “value per unit pixel”, J represents “value per unit area”, and R represents the cell size of the vector-to-raster conversion.

[0094] The fused value vector layer is rasterized based on the "unit pixel value" field. The rasterization range is consistent with the monitoring area, and the pixel values ​​of empty values ​​are set to 0. In this embodiment, to ensure sufficient accuracy, the size of the rasterized pixels is 1m.

[0095] After multiple rounds of segmentation, the final warning situation is as follows: Figure 2 As shown.

[0096] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "setting," "connection," "fixing," "rotation," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Unless otherwise explicitly limited, those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0097] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A natural resource asset loss monitoring and early warning system based on comprehensive quality assessment, characterized in that, include: The data retrieval module is used to retrieve the natural resource asset inventory results of the monitoring area in the base year and extract the value vector layer of the required natural resources from it, as well as to retrieve the natural resource asset inventory results of the monitoring area in the early warning year and extract the value vector layer of the required natural resources from it. The layer processing module is used to calculate the total natural resource value raster layer for the base year and the warning year based on the value vector layer, and then calculate the natural resource value growth rate of each pixel in the monitoring area based on the total natural resource value raster layer. The resource value quality index acquisition module is used to select the minimum value a and the maximum value b from the natural resource value growth rate of all pixels to form the extreme value boundary [a,b], and to calculate the decimal based on the natural resource value growth rate of all pixels. The primary segmentation module is used to label the region type of each cell based on the extreme value boundary [a,b] and the decimal place; the region types include potential loss regions, potential value preservation regions, and potential value-added regions. The primary segmentation module performs region type labeling on each pixel, including: S21. Determine whether the natural resource value growth rate of this pixel is less than 0 and lies in the interval [a, Q]. 10 If so, mark the cell as a potential loss region; otherwise, proceed to step S22; where Q 10 It is the 10th percentile of the growth rate of natural resource value for all pixels; S22. Determine whether the natural resource value growth rate of this pixel is greater than 0 and lies in the interval [Q]. 90 If Q is a potential value-added region, then the cell is marked as such; otherwise, it is not marked. 90 It is the 90th percentile of the growth rate of natural resource value for all pixels; The NPP calculation module is used to calculate the NPP growth rate of each pixel in the monitoring area from the base year to the warning year. The secondary partitioning module is used to label the unlabeled pixels in the primary partitioning module with region types based on the NPP growth rate. The population pressure density calculation module is used to calculate the population pressure density growth rate of each pixel in the monitoring area from the base year to the warning year; The tertiary segmentation module is used to label the unlabeled pixels in the secondary segmentation module with region types based on the population pressure density growth rate. The early warning module is used to provide early warnings about the probability of asset loss in the monitored area.

2. The natural resource asset loss monitoring and early warning system based on comprehensive quality assessment according to claim 1, characterized in that, The required natural resources include farmland, construction land, reserve land, forests, and grasslands.

3. The natural resource asset loss monitoring and early warning system based on comprehensive quality assessment according to claim 1, characterized in that, The layer processing module specifically includes: A single-layer processing unit is used to rasterize each value vector layer to obtain a value raster layer. The overlay processing unit is used to overlay the value raster layers of all natural resources corresponding to the base year to obtain the total natural resource value raster layer of the base year, and to overlay the value raster layers of all natural resources corresponding to the warning year to obtain the total natural resource value raster layer of the warning year. The growth rate processing unit is used to calculate the growth rate of natural resource value for each pixel within the monitoring area from the base year to the warning year, based on the total natural resource value raster layer.

4. A natural resource asset loss monitoring and early warning system based on comprehensive quality assessment according to claim 3, characterized in that, The process of a single-layer processing unit processing any value vector layer includes: S11. The value vector layer is projected into two dimensions using the CGCS2000 Gauss-Kruger projection to obtain the projected image; S12. Calculate the geometric area of ​​each patch after projection in the value vector layer; S13. The unit area value of a patch is obtained by dividing the economic value of each patch in the value vector layer by its projected geometric area. S14. Use Dislove to merge the patches with the same unit area value in the value vector layer to obtain the merged value vector layer; S15. The unit pixel value of each patch is obtained by multiplying the unit area value of each patch in the fused value vector layer by the square of the pixel size. S16. The value raster layer is obtained by rasterizing the fused value vector layer according to the size of the monitoring area and the pixel size. The pixel value of each pixel in the value raster layer is the unit pixel value of its corresponding patch. If the pixel does not belong to any patch, its pixel value is 0.

5. A natural resource asset loss monitoring and early warning system based on comprehensive quality assessment according to claim 3, characterized in that, The growth rate processing unit calculates the natural resource value growth rate of each pixel as follows: In the formula, x i This represents the growth rate of the natural resource value of the i-th pixel within the monitoring area. The pixel value of the i-th pixel in the raster layer representing the total natural resource value for the warning year. The pixel value of the i-th pixel in the raster layer represents the total natural resource value of the base year.

6. A natural resource asset loss monitoring and early warning system based on comprehensive quality assessment according to claim 1, characterized in that, The NPP calculation module specifically includes: Calculate the NPP of each pixel within the monitoring area in the base year and form an NPP raster layer for the base year; calculate the NPP of each pixel within the monitoring area in the warning year and form an NPP raster layer for the warning year; the NPP calculation formula for each pixel is: In the formula, NPP(x,t) represents the light and effective radiation absorbed by pixel x during time period t, ε(x,t) represents the actual light energy utilization rate of pixel x during time period t, and APAR(x,t) represents the photosynthetically active radiation absorbed by vegetation in pixel x during time period t. The NPP growth rate of each pixel within the monitoring area from the base year to the warning year is expressed as: In the formula, y i This represents the NPP growth rate of the i-th pixel within the monitoring area. This represents the pixel value of the i-th pixel in the NPP raster layer for the year of the warning. This represents the pixel value of the i-th pixel in the NPP raster layer for the base year.

7. A natural resource asset loss monitoring and early warning system based on comprehensive quality assessment according to claim 1, characterized in that, The population pressure density calculation module specifically includes: The natural resource landscape within the monitoring area is spatialized according to point elements; The annual number of tourists for each natural resource landscape in the base year and the warning year is obtained, and the natural resource population pressure density raster layer in the base year and the natural resource population pressure density raster layer in the warning year are obtained by spatial interpolation method. Calculate the growth rate of natural resource population pressure density for each pixel within the monitoring area from the base year to the warning year.

8. A natural resource asset loss monitoring and early warning system based on comprehensive quality assessment according to claim 1, characterized in that, The early warning module specifically includes: Obtain land use change patches within the monitoring area, and identify potential loss areas, potential value preservation areas, and potential value appreciation areas after processing by a three-stage segmentation module; Intersect analysis is performed between land use change patches and potential loss areas. The intersection results are used as early warning clue patches. The early warning clue patches are marked with red alert, indicating that the probability of asset loss risk is the highest. An orange alert is issued for areas outside the warning clue patches within the potential loss area, indicating that the probability of asset loss is the second highest. A yellow alert is issued for potential areas of value preservation, indicating that the probability of asset loss is the lowest.

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