Grassland bearing capacity sustainability evaluation method based on current grassland bearing capacity situation and long-term dynamic trend

Abstract

The invention provides a grassland bearing capacity sustainability evaluation method based on a grassland bearing capacity current situation and a long-term dynamic trend, and the method comprises the steps: estimating grassland above-ground biomass which can be used by livestock based on remote sensing data; a grassland bearing capacity interval is generated by combining a grassland utilization rate ecological threshold value and livestock unit daily intake on the premise of sustainable utilization of grassland resources, and a grassland bearing condition index is calculated by using actual livestock data and the grassland bearing capacity interval; calculating the slope of the grassland bearing state index through linear regression based on the time sequence data, obtaining a standardized trend term by using the slope, and generating a grassland bearing capacity warning index by using the standardized trend term and the grassland bearing state index; based on the generated grassland bearing capacity warning index and a set warning threshold value, the grassland development grade is determined, and the sustainable risk of grassland resource utilization is comprehensively evaluated. Remote sensing data and statistical data which are easy to obtain are adopted, and the method has the advantages of being easy and convenient to operate, high in practicability and the like and has good application and popularization value.

Description

Technical Field

[0001] The present invention relates to the technical field of grassland carrying capacity assessment methods, and in particular to a grassland carrying capacity sustainability assessment method based on the current status and long-term dynamic trend of grassland carrying capacity. Background Art

[0002] Grassland ecosystems are important renewable natural resources, playing an irreplaceable role in maintaining ecological balance and supporting the development of animal husbandry. However, with population growth and the expansion of animal husbandry, overgrazing is becoming increasingly serious, leading to a series of ecological problems such as grassland degradation and biodiversity loss. Accurately assessing grassland carrying capacity and alerting to potential risks have become key scientific issues for sustainable grassland management.

[0003] The current grassland carrying capacity assessment system mainly focuses on two dimensions: static indicator analysis and time series evaluation. The core algorithm of the assessment system based on static indicators is to quantitatively represent the intensity of grassland resource utilization in a specific year by establishing a ratio model of "actual carrying capacity / theoretical carrying capacity". This method provides a basis for grass-livestock balance management by setting threshold intervals (such as the Grassland Carrying Condition Index GCSI, light utilization ≤ 0.8, moderate utilization 0.8-1, overload 1-1.2, and severe overload >1.2). However, due to the limitations of static modeling characteristics, this type of method has difficulty describing the impact of dynamic factors such as climate change and grazing activities on grassland carrying capacity, and cannot reveal the long-term evolution of grassland ecosystems.

[0004] As a complement to static methods, the time series assessment system primarily captures the evolution of grassland carrying capacity by constructing a multi-year time series dataset of indicators such as grassland carrying capacity. This assessment method primarily assesses the sustainability risk intensity of grassland carrying capacity through empirical interpretation of grassland carrying capacity trend lines. However, the subjectivity and uncertainty of this empirical interpretation may affect the accuracy and objectivity of sustainability risk assessments.

[0005] Currently, monitoring grassland carrying capacity using remote sensing imagery and livestock statistics is a relatively quick and efficient method. However, due to the limitations of current assessment methods, there is a lack of unified standards for assessing grassland carrying capacity sustainability risks. Therefore, it is necessary to develop a comprehensive index that can directly reflect the current and trend characteristics of grassland carrying capacity, thereby quantitatively assessing the long-term risk to grassland carrying capacity sustainability. However, no reports have been found on a comprehensive index that can directly reflect the current and trend characteristics of grassland carrying capacity.

[0006] Patent application number 202310210619.4 discloses a method for obtaining a grassland ecological carrying capacity index and stocking capacity under the ecological priority objective. The method comprises: obtaining grazing data and species diversity data from different grazing plots at different grazing intensities across different grassland types in a region through grazing gradient experiments; calculating a baseline stocking pressure index for each grazing plot within each grassland type, and performing curve fitting between the baseline stocking pressure index and the normalized species diversity data. The ecological carrying capacity index for each grassland type is determined based on the maximum value of the fitted curve; and the ecological carrying capacity for the region under the ecological priority objective is calculated based on the ecological carrying capacity index of each grassland type, combined with the area and grass yield per unit area of ​​each grassland type. The grassland ecological carrying capacity index obtained by the above invention prioritizes ecological protection and can more accurately assess the suitable carrying capacity of grasslands in protected areas. However, the above invention analyzes the impact of the grassland stocking pressure index on normalized biodiversity and determines its inflection point, thereby assessing the ecological threshold for livestock use of different grassland types over a year under the ecological priority objective. It lacks a quantitative assessment method for the long-term risk of sustainable grassland use. Summary of the Invention

[0007] In response to the technical problem of the lack of unified standards in the existing assessment of grassland carrying capacity sustainability risks, the present invention proposes a grassland carrying capacity sustainability assessment method based on the current status and long-term dynamic trends of grassland carrying capacity. The grassland carrying capacity sustainability is assessed by combining the grassland carrying capacity warning index of the current status and long-term dynamic trends. A grassland carrying capacity warning index that can directly reflect the current status and long-term dynamic trends of grassland carrying capacity is proposed. By integrating the Grassland Carrying Condition Index (GCSI) and its time series trend (slope), it is used to quantify the sustainability risk of grassland resource utilization, i.e., grassland carrying status, on a long-term scale, reflecting the risk level of over-exploitation or inefficient utilization of grassland.

[0008] To achieve the above objectives, the technical solution of the present invention is implemented as follows: a grassland carrying capacity sustainability assessment method based on the current grassland carrying capacity and long-term dynamic trends, the steps of which are as follows:

[0009] Step 1: Estimate the aboveground biomass of grassland available for livestock based on remote sensing data, and generate the grassland carrying capacity interval (GCC) by combining the ecological threshold of grassland utilization based on the premise of sustainable use of grassland resources and the unit daily intake of livestock. I , using actual livestock data and grassland carrying capacity interval GCC I Calculate the grassland carrying condition index GCSI;

[0010] Step 2: Based on the time series data, calculate the slope GCSI of the grassland carrying capacity index through linear regression slope , using the slope GCSI slopeThe standardized trend item is obtained by combining the grassland carrying capacity index GCSI, and the grassland carrying capacity warning index is generated by combining the standardized trend item and the grassland carrying capacity index GCSI.

[0011] Step 3: Determine the grassland development level based on the generated grassland carrying capacity warning index GCCAI and the set warning threshold, and comprehensively assess the sustainability risk of grassland resource utilization; define five levels of warning thresholds based on the value of the grassland carrying capacity warning index GCCAI, including extremely inefficient utilization, inefficient utilization, normal, overdevelopment, and severe overdevelopment.

[0012] Preferably, the remote sensing data is preprocessed according to the African grassland classification data, and the net primary productivity data of vegetation, tree crown cover data, temperature data and terrain data within the grassland of the study area are extracted using the mask extraction method; the grassland classification data, net primary productivity data of vegetation, temperature data, tree crown cover data and terrain data after preprocessing are combined to estimate the aboveground biomass of grassland available to livestock (AGB).

[0013] Preferably, the calculation formula for the aboveground biomass AGB of grassland available to livestock is:

[0014]

[0015] Where AGB is the aboveground biomass of grassland available to livestock; NPP is the net primary productivity of vegetation; f ANPP is the ratio coefficient allocated to aboveground net primary productivity.

[0016] Preferably, the canopy cover coefficient is the ratio of grassland aboveground biomass after excluding the tree canopy biomass in the pixel of vegetation net primary productivity, and the calculation formula is:

[0017]

[0018] Where x is the percentage of tree canopy cover to pixel area;

[0019] Proportional coefficient f ANPP The calculation formula is:

[0020] f ANPP =0.171+0.0129×annual average temperature

[0021] Among them, the annual average temperature is obtained by summing and averaging the monthly average temperature data in a year;

[0022] The terrain coefficient is the proportion of grassland biomass that can be accessed by livestock due to the influence of terrain; when the slope is less than 10%, the terrain coefficient is 1; when the slope is 10%-30%, the terrain coefficient is 0.7; when the slope is 30%-60%, the terrain coefficient is 0.4; when the slope is greater than 60%, the terrain coefficient is 0.

[0023] Preferably, the generated grassland carrying capacity interval GCC I The method is to combine the ecological threshold of grassland utilization rate based on the sustainable use of grassland resources and the daily intake of livestock to estimate the minimum grassland carrying capacity under the minimum grassland utilization rate. and the maximum grassland carrying capacity at maximum grassland utilization Generate grassland carrying capacity interval GCC I =[GCC min , GCC max ], AGB is the aboveground biomass of grassland available to livestock.

[0024] Preferably, the grassland carrying capacity index GCSI is the difference between the actual number of livestock and the grassland carrying capacity GCC. I The grassland carrying capacity index GCSI is: Livestock numbers are obtained from livestock statistics.

[0025] Preferably, the estimated annual grassland carrying capacity index GCSI is arranged in chronological order from front to back, and the slope GCSI is calculated by linear regression based on the time series data. slope , the grassland carrying capacity index GCSI and the standardized trend Add them together to generate the grassland carrying capacity warning index GCCAI, and:

[0026]

[0027] Preferably, the grassland carrying capacity index GCSI represents the grassland utilization status in a year, and the standardized trend item It can show the changing trend of grassland utilization; when the trend item When the trend item is When , it means that the grassland carrying pressure has been gradually decreasing over the years: for high-load countries with grassland carrying capacity index GCSI>1, the reduction of grassland carrying pressure means that the grassland carrying condition is gradually improving, and the actual carrying risk of the grassland is lower than the GCSI assessment result; for countries with grassland carrying capacity index GCSI<0.6, the reduction of grassland carrying pressure means that the efficiency of grassland use continues to decline, grassland resources are excessively wasted, and the actual risk of inefficient use is greater than the grassland carrying capacity index GCSI assessment result; on the contrary, when the trend item When the grassland carrying capacity index (GCSI) is less than 0.6, the grassland utilization efficiency will gradually increase, the waste of grassland resources will be effectively improved, and the risk of inefficient utilization will be alleviated.

[0028] Preferably, the risk intensity of the grassland carrying sustainability includes five levels: extremely inefficient utilization, inefficient utilization, normal, overdevelopment and serious overdevelopment.

[0029] Preferably, the thresholds of the grassland carrying capacity warning index GCCAI are divided into:

[0030] (1)0 <GCCAI max <0.6, the risk intensity of grassland carrying sustainability is extremely inefficient utilization;

[0031] (2)0.6 <GCCAI max <0.8, the risk intensity of grassland carrying sustainability is low efficiency utilization;

[0032] (3)0.8 <GCCAI max GCCAI min <1, the risk intensity of grassland carrying sustainability is normal;

[0033] (4)1 <GCCAI max <1.2, the risk intensity of grassland sustainability is overexploitation;

[0034] (5)GCCAI max >1.2, the risk intensity of grassland carrying sustainability is severe overexploitation.

[0035] Among them, GCCAI max is the maximum value of grassland carrying capacity warning index GCCAI, GCCAI min It is the minimum value of grassland carrying capacity warning index GCCAI.

[0036] Compared with the existing technology, the beneficial effects of the present invention are as follows: the current status of grassland carrying capacity is added to the standardized grassland carrying capacity change trend, and the risk intensity of the current sustainability risk assessment is corrected by the standardized change trend, thereby realizing the assessment of the sustainability risk of long-term utilization of grassland resources. Specifically, when the standardized change trend is greater than zero, it indicates that the grassland carrying pressure is on the rise, the actual sustainability risk increases, and the intensity of its risk assessment also increases accordingly; conversely, when the standardized change trend is less than zero, it reflects that the grassland carrying pressure is reduced, the actual sustainability risk decreases, and the intensity of its risk assessment also decreases accordingly. The present invention quantitatively evaluates the sustainability risk of grassland carrying capacity on a long-term scale by combining the current status of grassland carrying capacity with the long-term dynamic trend, reveals the potential contradiction between grassland resource utilization and animal husbandry development, and provides decision-making support for the sustainable management strategy of grassland resources.

[0037] The present invention integrates the current status of grassland carrying capacity with its long-term dynamic changes, and by quantifying the evolution of grassland carrying capacity over long timescales, it achieves an objective assessment of the sustainability risk of grassland ecosystems. The present invention not only reveals the long-term evolutionary characteristics of grassland ecosystems, but also addresses the lack of a unified quantitative standard for assessing the sustainability risk of grassland carrying capacity, providing a new assessment method for monitoring the health of grassland ecosystems. It also provides a new reference for sustainability assessments in the field of remote sensing monitoring. Due to the use of easily accessible remote sensing data and statistical data, the present invention is easy to operate, highly practical, and has good value for promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 It is the overall technical flow chart of the present invention.

[0040] Figure 2 This is the estimated results of edible aboveground biomass of grasslands in Africa from 2001 to 2020, where: Figure 2 a is the annual average aboveground biomass of African grasslands from 2001 to 2020. Figure 2 b is the changing trend of aboveground biomass of African grasslands from 2001 to 2020.

[0041] Figure 3 The following is the grassland carrying capacity (GCC) of Africa in 2020 and its changing trend from 2001 to 2020. Figure 3 a is a schematic diagram of the spatial distribution and coefficient of variation of the largest GCC in African countries in 2020; Figure 3 b is a schematic diagram of the spatial distribution and coefficient of variation of the minimum GCC in African countries in 2020; Figure 3 c is a schematic diagram of the slope and significance of the maximum GCC changes in African countries from 2001 to 2020; Figure 3 d is a schematic diagram of the slope and significance of the change in the minimum GCC of African countries from 2001 to 2020; Figure 3 The graph of e is the dynamic trend of Africa GCCI from 2001 to 2020.

[0042] Figure 4 The following are the livestock populations in Africa in 2020 and their changing trends from 2001 to 2020. Figure 4 a is a schematic diagram of the spatial distribution and coefficient of variation of livestock populations in African countries in 2020; Figure 4 b is a schematic diagram of the slope and significance of livestock population changes in African countries from 2001 to 2020; Figure 4 c is a curve chart showing the dynamic trends of livestock populations in Africa from 2001 to 2020.

[0043] Figure 5 The 2020 African Grassland Condition Index (GCSI) and its changing trends from 2001 to 2020 are shown below. Figure 5 a is a schematic diagram of the spatial distribution and coefficient of variation of the maximum GCSI in African countries in 2020; Figure 5 b is a schematic diagram of the spatial distribution and coefficient of variation of the minimum GCSI in African countries in 2020; Figure 5 c is a schematic diagram of the maximum GCSI change slope and significance of African countries from 2001 to 2020; Figure 5 d is a schematic diagram of the slope and significance of changes in small and large GCSIs in African countries from 2001 to 2020; Figure 5 The graph of e is a curve showing the dynamic trend of Africa's GCSI from 2001 to 2020.

[0044] Figure 6 The results of the Africa grassland carrying capacity sustainability risk assessment are based on remote sensing data and livestock statistics from 2001 to 2020. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0046] A grassland carrying capacity sustainability assessment method based on the current status and long-term dynamic trends of grassland carrying capacity is provided. This embodiment uses Africa as the study area and utilizes the present invention to assess the grassland carrying capacity sustainability risk in this region from 2001 to 2020. A brief introduction is given below.

[0047] The main technical solutions of the present invention are as follows Figure 1 As shown in the figure, the specific implementation steps are as follows: First, the required remote sensing data is acquired and preprocessed, and the aboveground biomass of grassland available for livestock utilization is estimated using grassland classification maps. Second, based on the ecological threshold for maintaining sustainable grassland ecosystems and the annual intake of animal units, the maximum number of animal units that a unit area of ​​grassland can support without compromising ecological sustainability, i.e., the grassland carrying capacity, is calculated. Subsequently, livestock statistics are combined to estimate the Grassland Carrying Condition Index (GCSI). By integrating the estimated GCSI results over multiple years, its long-term trends are analyzed. Finally, the standardized GCSI trend is added to the current value to obtain the Grassland Carrying Condition Index (GCCAI). This is then graded according to pre-set risk intensity thresholds, enabling a visual assessment of sustainability risk intensity.

[0048] The present invention is specifically introduced as follows, and its steps are:

[0049] Step 1: Estimate the aboveground biomass of grassland available for livestock based on remote sensing data, and generate the grassland carrying capacity interval (GCC) by combining the ecological threshold of grassland utilization based on the premise of sustainable use of grassland resources and the unit daily intake of livestock. I , using actual livestock data and grassland carrying capacity interval GCC I Calculate the grassland carrying condition index GCSI.

[0050] (1) Estimation of aboveground biomass of grassland available to livestock

[0051] Based on the Google Earth Engine (GEE) platform, vegetation net primary productivity (NPP) data and tree canopy cover data from the MODIS dataset, temperature data from the ERA5 dataset, and terrain data from the Geomorpho90m dataset were imported. The imported remote sensing data were preprocessed based on the African grassland classification data provided by the Institute of Space Information Innovation of the Chinese Academy of Sciences. The mask extraction method was used to extract vegetation net primary productivity (NPP) data, tree canopy cover data, temperature data, and terrain data within the grassland of the study area to facilitate the calculation of grassland aboveground biomass in the next step. The aboveground biomass (AGB) of grassland available for livestock was estimated by combining the preprocessed grassland classification data, MODIS NPP data, temperature data, tree canopy cover data, and terrain data. The calculation formula is:

[0052]

[0053] Where AGB is the aboveground biomass of grassland available to livestock; NPP is the net primary productivity of vegetation, which is directly obtained from the MODIS NPP dataset; f aNPP is the proportion coefficient allocated to aboveground net primary productivity (ANPP) by NPP, and the calculation formula is:

[0054] f ANPP =0.171+0.0129×annual average temperature

[0055] The annual mean temperature is obtained by summing and averaging the monthly mean temperature data in the ERA5 dataset over the year.

[0056] The crown cover coefficient is the proportion of grassland aboveground biomass after excluding the tree canopy biomass in the pixel of MODIS NPP data. The calculation formula is:

[0057]

[0058] Where x is the percentage of tree canopy cover to pixel area, which is directly obtained from the Terra MODIS VCF dataset.

[0059] The terrain coefficient is the proportion of aboveground biomass of grassland that can be accessed by livestock due to the influence of terrain. The steeper the terrain, the more difficult it is for livestock to access it, and the flatter the terrain, the easier it is for livestock to access it. When the slope is less than 10%, the terrain coefficient is 1; when the slope is 10%-30%, the terrain coefficient is 0.7; when the slope is 30%-60%, the terrain coefficient is 0.4; when the slope is greater than 60%, the terrain coefficient is 0. Using the terrain coefficient to assess the proportion of grassland biomass consumed by livestock can make the grassland carrying capacity assessment results closer to the actual situation of livestock use of grassland, and improve the accuracy of subsequent grassland carrying capacity assessments. The estimated results of aboveground biomass of grassland available to livestock are as follows: Figure 2 As shown. Figure 2 As can be seen from a, except for the Sahara Desert in northern Africa and the Kalahari Desert in southern Africa, the average annual grassland biomass in other parts of Africa remains at a high level. Figure 2 As can be seen from Figure b, the decrease in grassland biomass in Africa mainly occurred in the eastern and southern coastal areas of the continent, while grassland biomass in other regions mainly increased to varying degrees.

[0060] (2) Estimation of grassland carrying capacity

[0061] Then, the minimum grassland carrying capacity (GCC) under the minimum grassland utilization rate was estimated by combining the ecological threshold of grassland utilization rate and the daily intake of livestock based on the premise of sustainable use of grassland resources. minand the maximum grassland carrying capacity GCC under maximum grassland utilization max , generate grassland carrying capacity interval GCC I Based on relevant research results, the minimum grassland utilization rate for maintaining ecosystem sustainability in this example is 0.3, and the maximum grassland utilization rate is 0.5. The animal unit uses the tropical livestock unit (TLU). A TLU is generally considered to be an animal with a live weight of 250 kg. The daily feed intake of each TLU is 2.5% of its body weight, that is, 6.25 kg / day. Grassland carrying capacity interval GCC I The calculation formula is:

[0062] GCC I =[GCC min , GCC max ]

[0063]

[0064] The estimated results of grassland carrying capacity are as follows: Figure 3 As shown in Figure 2 , grassland carrying capacity across Africa generally exhibits a spatial pattern of high in the southeast and low in the northwest. Overall, grassland carrying capacity across the continent showed a trend of continuous growth between 2001 and 2020, providing more grassland biomass for livestock consumption. However, at the national level, grassland carrying capacity in some coastal countries in the east and west has decreased over the past 20 years. This reduction in grassland biomass may have a negative impact on livestock development in these countries. Furthermore, grassland carrying capacity variability in countries south of the Sahara Desert is greater than in other regions, indicating that grassland ecosystems in this region are more fragile and susceptible to disturbance.

[0065] (3) Estimation of grassland carrying capacity index

[0066] In this example, for ease of calculation, all herbivorous livestock are uniformly converted to tropical livestock units (TLU). Camels are 1 TLU, horses are 0.8 TLU, cattle and mules are 0.7 TLU, donkeys are 0.5 TLU, and goats and sheep are 0.1 TLU. The livestock statistics after conversion to tropical livestock units are as follows: Figure 4 Overall, the number of major herbivorous livestock in Africa maintained a steady growth trend between 2001 and 2020, with an average annual growth rate of 10.43×10 6 TLU / year, reaching 529.19×10 in 2020 6TLU is 1.59 times that of 2001. The stable development of animal husbandry will intensify the consumption of grassland resources. National-scale analysis shows that a higher livestock inventory is concentrated in the central and northern regions of Africa. However, there are significant differences in the spatial distribution of these regions and the areas with high grassland carrying capacity. This spatial imbalance in resource utilization may pose a challenge to the sustainability of Africa's grassland ecosystem. Combining the grassland carrying capacity estimation results and livestock statistics, the grassland carrying status index that reflects grassland utilization and livestock carrying conditions is used to represent the grass-livestock supply status in Africa. The grassland carrying status index GCSI is the difference between the actual livestock number and the grassland carrying capacity interval GCC. I The formula of grassland carrying capacity index is as follows:

[0067]

[0068] Livestock statistics were obtained from the FAOSTAT database of the Food and Agriculture Organization of the United Nations. The estimated results of grassland carrying capacity index are as follows: Figure 5 As shown, the utilization of Africa's grassland resources is clearly extremely uneven. In northern Africa, the number of livestock far exceeds the grassland's capacity, and grassland resources are insufficient to meet livestock consumption. However, grassland resources in southern Africa are underutilized, and the livestock industry in these countries still has significant room for development. If this unbalanced development pattern persists for a long time, it will be detrimental to the sustainable development of Africa's grassland resources and livestock industry.

[0069] Step 2: Based on the time series data, calculate the slope GCSI of the grassland carrying capacity index through linear regression slope , using the slope GCSI slope The standardized trend term is obtained by combining the grassland carrying capacity index (GCSI) and the grassland carrying capacity index (GCSI). The grassland carrying capacity warning index is generated using the standardized trend term and the grassland carrying capacity index (GCSI).

[0070] (4) Grassland carrying capacity sustainability risk assessment

[0071] The estimated annual grassland carrying capacity index GCSI is arranged in chronological order from the beginning to the end, and the slope GCSI is calculated by linear regression based on the time series data. slope , the grassland carrying capacity index GCSI and the standardized trend Add them together to generate the grassland carrying capacity warning index GCCAI, which is calculated as follows:

[0072]

[0073] The Grassland Carrying Capacity Index (GCSI) can indicate the grassland utilization status in a year. It can show the changing trend of grassland utilization. When , it indicates that the current grassland carrying capacity is stable and the grassland livestock pressure has a tendency to maintain the status quo for a long time. When , it means that the grassland carrying pressure has been gradually reduced over the past 20 years. For high-load countries with GCSI>1, the reduction in grassland carrying pressure means that the grassland carrying capacity is gradually improving, and the actual carrying risk of the grassland is lower than the GCSI assessment result. However, for countries with GCSI<0.6, the reduction in grassland carrying pressure means that the efficiency of grassland use continues to decline, grassland resources are excessively wasted, and the actual risk of inefficient use is greater than the GCSI assessment result. Conversely, when When the grassland carrying capacity index (GCSI) is 0.6, the grassland carrying capacity index (GCSI) and the normalized trend index (normalized trend index) are used to measure the grassland carrying capacity. The grassland carrying capacity warning index GCCAI constructed by the sum of the two can effectively quantify the sustainability risks of grassland resource utilization, clarify the potential contradictions between grassland resource utilization and animal husbandry development on a temporal scale, and provide decision-making support for sustainable management strategies of grassland resources.

[0074] Step 3: Determine the grassland development level based on the generated grassland carrying capacity warning index GCCAI and the set warning threshold, and comprehensively assess the sustainability risk of grassland resource utilization; define five levels of warning thresholds based on the value of the grassland carrying capacity warning index GCCAI, including extremely inefficient utilization, inefficient utilization, normal, overdevelopment, and severe overdevelopment.

[0075] According to this method, the grassland carrying capacity warning index GCCAI is an interval, where the maximum value of GCCAI is GCCAI. max , the minimum value is GCCAI min The relationship between the risk intensity and warning threshold of grassland carrying capacity sustainability is shown in Table 1. Specifically, the threshold of grassland carrying capacity warning index GCCAI is divided into:

[0076] (1)0 <GCCAI max <0.6, the risk intensity of grassland carrying sustainability is extremely inefficient utilization;

[0077] (2)0.6 <GCCAI max <0.8, the risk intensity of grassland carrying sustainability is low efficiency utilization;

[0078] (3)0.8 <GCCAI max GCCAImin <1, the risk intensity of grassland carrying sustainability is normal;

[0079] (4)1 <GCCAI max <1.2, the risk intensity of grassland sustainability is overexploitation;

[0080] (5)GCCAI max >1.2, the risk intensity of grassland carrying sustainability is severe overexploitation.

[0081] The risk intensity of grassland carrying capacity sustainability in the region is divided according to the above thresholds, and the risk level map is visualized according to the regional risk level results. Figure 6 shown.

[0082] Table 1 Relationship between risk intensity and warning threshold of grassland carrying capacity sustainability

[0083]

[0084] In summary, the grassland carrying capacity sustainability assessment method based on the grassland bearing capacity warning index GCCAI of the present invention integrates the grassland carrying capacity status and long-term dynamic change characteristics, and realizes the objective assessment of the sustainability risk of grassland ecosystem by quantifying the evolution law of grassland carrying capacity status over a long time scale. It not only reveals the long-term evolution characteristics of grassland ecosystems, but also solves the problem of the lack of a unified quantitative standard for the assessment of grassland carrying capacity sustainability risk, and provides a new assessment method for the health monitoring of grassland ecosystems. It also provides a new reference basis for sustainability assessment in the field of remote sensing monitoring. Due to the use of easily accessible remote sensing data and statistical data, the present invention has the characteristics of simple operation and strong practicality, and has good promotion and application value.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A grassland carrying capacity sustainability assessment method based on the current status and long-term dynamic trend of grassland carrying capacity, characterized by: The steps are as follows: Step 1: Estimate the aboveground biomass of grassland available for livestock based on remote sensing data, and generate the grassland carrying capacity interval (GCC) by combining the ecological threshold of grassland utilization based on the premise of sustainable use of grassland resources and the unit daily intake of livestock. I , using actual livestock data and grassland carrying capacity interval GCC I Calculate the grassland carrying condition index GCSI; Step 2: Based on the time series data, calculate the slope GCSI of the grassland carrying capacity index through linear regression slope , using the slope GCSI slope The standardized trend item is obtained by combining the grassland carrying capacity index GCSI, and the grassland carrying capacity warning index is generated by combining the standardized trend item and the grassland carrying capacity index GCSI. Step 3: Determine the grassland development level based on the generated grassland carrying capacity warning index GCCAI and the set warning threshold, and comprehensively assess the sustainability risk of grassland resource utilization; define five levels of warning thresholds based on the value of the grassland carrying capacity warning index GCCAI, including extremely inefficient utilization, inefficient utilization, normal, overdevelopment, and severe overdevelopment.

2. The grassland carrying capacity sustainability assessment method based on grassland carrying capacity status and long-term dynamic trends according to claim 1, characterized in that: Remote sensing data were preprocessed according to African grassland classification data, and the net primary productivity data, tree canopy cover data, temperature data, and topographic data within the grassland of the study area were extracted using the mask extraction method. The aboveground biomass (AGB) of grassland available to livestock was estimated by combining the preprocessed grassland classification data, net primary productivity data, temperature data, tree canopy cover data, and topographic data.

3. The grassland carrying capacity sustainability assessment method based on grassland carrying capacity status and long-term dynamic trends according to claim 2, characterized in that: The calculation formula for the aboveground biomass of grassland available to livestock, AGB, is: Where AGB is the aboveground biomass of grassland available to livestock; NPP is the net primary productivity of vegetation; f ANPP is the ratio coefficient allocated to aboveground net primary productivity.

4. The grassland carrying capacity sustainability assessment method based on grassland carrying capacity status and long-term dynamic trends according to claim 3, characterized in that: The crown cover coefficient is the ratio of grassland aboveground biomass after excluding the tree canopy biomass in the pixel of vegetation net primary productivity. The calculation formula is: Where x is the percentage of tree canopy cover to pixel area; Proportional coefficient f ANPP The calculation formula is: f ANPP =0.171+0.0129×annual average temperature Among them, the annual average temperature is obtained by summing and averaging the monthly average temperature data in a year; The terrain coefficient is the proportion of grassland biomass that can be accessed by livestock due to the influence of terrain; when the slope is less than 10%, the terrain coefficient is 1; when the slope is 10%-30%, the terrain coefficient is 0.7; when the slope is 30%-60%, the terrain coefficient is 0.4; when the slope is greater than 60%, the terrain coefficient is 0.

5. The method for evaluating grassland carrying capacity sustainability based on grassland carrying capacity status and long-term dynamic trends according to any one of claims 1 to 4, characterized in that: The generated grassland carrying capacity interval GCC I The method is to combine the ecological threshold of grassland utilization rate based on the sustainable use of grassland resources and the daily intake of livestock to estimate the minimum grassland carrying capacity under the minimum grassland utilization rate. and the maximum grassland carrying capacity at maximum grassland utilization Generate grassland carrying capacity interval GCC I =[GCC min , GCC max ], AGB is the aboveground biomass of grassland available to livestock.

6. The grassland carrying capacity sustainability assessment method based on grassland carrying capacity status and long-term dynamic trends according to claim 5, characterized in that: The grassland carrying capacity index GCSI is the difference between the actual number of livestock and the grassland carrying capacity GCC. I The grassland carrying capacity index GCSI is: Livestock numbers are obtained from livestock statistics.

7. The grassland carrying capacity sustainability assessment method based on grassland carrying capacity status and long-term dynamic trends according to claim 6, characterized in that: The estimated annual grassland carrying capacity index GCSI is arranged in chronological order from front to back, and the slope GCSI is calculated by linear regression based on the time series data. slope , the grassland carrying capacity index GCSI and the standardized trend Add them together to generate the grassland carrying capacity warning index GCCAI, and:

8. The method for evaluating grassland carrying capacity sustainability based on grassland carrying capacity status and long-term dynamic trends according to claim 7, characterized in that: The Grassland Carrying Capacity Index (GCSI) represents the grassland utilization status in a year. The standardized trend item It can show the changing trend of grassland utilization; when the trend item When the trend item is When , it means that the grassland carrying pressure has been gradually decreasing over the years: for high-load countries with grassland carrying capacity index GCSI>1, the reduction of grassland carrying pressure means that the grassland carrying condition is gradually improving, and the actual carrying risk of the grassland is lower than the GCSI assessment result; for countries with grassland carrying capacity index GCSI<0.6, the reduction of grassland carrying pressure means that the efficiency of grassland use continues to decline, grassland resources are excessively wasted, and the actual risk of inefficient use is greater than the grassland carrying capacity index GCSI assessment result; on the contrary, when the trend item When the grassland carrying capacity index (GCSI) is less than 0.6, the grassland utilization efficiency will gradually increase, the waste of grassland resources will be effectively improved, and the risk of inefficient utilization will be alleviated.

9. The method for evaluating grassland carrying capacity sustainability based on grassland carrying capacity status and long-term dynamic trends according to claim 7 or 8, characterized in that: The risk intensity of grassland carrying sustainability includes five levels: extremely inefficient utilization, inefficient utilization, normal, overexploitation and severe overexploitation.

10. The grassland carrying capacity sustainability assessment method based on grassland carrying capacity status and long-term dynamic trends according to claim 9, characterized in that: The thresholds of the grassland carrying capacity warning index GCCAI are divided into: (1)0 <GCCAI max <0.6, the risk intensity of grassland carrying sustainability is extremely inefficient utilization; (2)0.6 <GCCAI max <0.8, the risk intensity of grassland carrying sustainability is low efficiency utilization; (3)0.8 <GCCAI max And GCCAI min <1, the risk intensity of grassland carrying sustainability is normal; (4)1 <GCCAI max <1.2, the risk intensity of grassland sustainability is overexploitation; (5)GCCAI max >1.2, the risk intensity of grassland carrying sustainability is severe overexploitation. Among them, GCCAI max is the maximum value of grassland carrying capacity warning index GCCAI, GCCAI min It is the minimum value of grassland carrying capacity warning index GCCAI.

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