A planning method for habitat protection and restoration of terrestrial wildlife under the influence of hydropower development

Abstract

The present application relates to the technical field of ecological environment protection, and specifically discloses a planning method for habitat protection and restoration of terrestrial wild animals under the influence of water and electricity development. In view of the problems of lack of exclusive interference factors in existing habitat evaluation of water and electricity development basin, insufficient scientificity in core habitat identification, poor operability of optimization scheme, etc., the present application integrates the maximum entropy model, the landscape morphological spatial pattern analysis model, the circuit theory model, the geographic detector and the neural network model to form an integrated planning system through six steps of data collection and preprocessing, construction of an evaluation index system containing water and electricity development factors, habitat suitability evaluation and classification, identification of core habitat and ecological key nodes, core driving factor analysis and habitat precise optimization. The present application realizes precise evaluation of habitat suitability in water and electricity development area, scientific definition of core habitat and targeted optimization of habitat restoration, and provides systematic and operable technical support for habitat protection and restoration of terrestrial wild animals at the basin scale.

Description

Technical Field

[0001] This invention relates to the field of ecological and environmental protection technology, and in particular to a planning method for the protection and restoration of habitats of terrestrial wild animals under the influence of hydropower development. It is applicable to the habitat protection, restoration and systematic planning of terrestrial wild animals (such as black leaf monkeys) in hydropower development basins (such as the Wujiang River Basin). Background Technology

[0002] Hydropower development is an important source of clean energy, but it alters river basin habitats and threatens biodiversity. A scientific balance between these two factors is crucial for ensuring energy security and ecosystem integrity, avoiding irreversible biological losses, and achieving a win-win situation for sustainable hydropower development and biodiversity conservation. The Wujiang River basin is an important distribution area for terrestrial wildlife (such as the black leaf monkey). Cascade hydropower development has led to changes in the terrain, increased flooding, and habitat fragmentation, seriously threatening the survival and reproduction of wild animals. Habitat suitability assessment is fundamental to species conservation. Existing technologies have established assessment systems that incorporate factors such as topography, vegetation, and climate, but their application in hydropower development basins has significant shortcomings.

[0003] Existing habitat suitability assessments do not include hydropower development-specific factors (such as the proportion of flooded area and distance from power stations / dams), resulting in significant discrepancies between the assessment results and the actual habitat conditions in the watershed, and a lack of specificity. Core habitat identification relies on empirical threshold methods, and the artificially set area standards lack adaptability to species ecological habits and regional specificity, are highly subjective and lack scientific rigor, making it difficult to accurately define the core areas where wild animals truly need protection, and the estimation bias is relatively large.

[0004] Habitat optimization only provides general protection and restoration suggestions, and cannot accurately obtain the location of obstacle points and the optimal vegetation type of the planable area, resulting in a lack of operability of restoration plans and an inability to achieve precise improvement of habitat suitability; it has not formed a complete closed loop of "evaluation-identification-optimization", the identification of key areas is disconnected from subsequent optimization measures, it is impossible to simulate the whole process, and it is difficult to form a precise assessment and planning of systematic protection at the watershed scale.

[0005] Therefore, there is an urgent need to develop a set of scientific, precise, and operable planning methods for the protection and restoration of terrestrial wildlife habitats that are adapted to the characteristics of hydropower development basins and can achieve accurate assessment. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a planning method for the protection and restoration of terrestrial wildlife habitats under the influence of hydropower development. Specifically, it aims to achieve the following objectives: construct a habitat suitability evaluation index system that includes factors influencing hydropower development, thereby improving the accuracy and relevance of evaluation results; establish a scientific method for identifying core suitable habitats to accurately define the core survival areas of wildlife; establish a technical process of "core driving factor identification - vegetation type simulation - precise optimization" to clarify the optimal vegetation types for obstacle points and planable areas, and provide operable habitat optimization schemes; and integrate multiple models to form an integrated planning technology system of "suitability evaluation - core habitat identification - key node analysis - precise optimization," providing comprehensive scientific decision support for the protection and planning of terrestrial wildlife habitats in hydropower development basins.

[0007] The technical solution adopted by this invention to achieve the above objectives is: a planning method for the protection and restoration of terrestrial wildlife habitats under the influence of hydropower development, comprising the following steps:

[0008] Step 1: Data Collection and Preprocessing: Acquire various data within the watershed, including topography, vegetation, climate, human activities, land use, and hydropower development; unify the data coordinate system and raster resolution to eliminate errors caused by differences in data format and scale.

[0009] Step 2: Construct a habitat suitability evaluation index system: Based on the ecological habits of terrestrial wild animals and the characteristics of hydropower development basins, construct a multi-dimensional evaluation index system that includes topography, vegetation, climate, intensity of human activities, land use, and hydropower development. Preprocess the data of each index.

[0010] Step 3: Habitat suitability assessment and classification: Using the preprocessed index data as environmental variables, combined with the field distribution data of wild animals, the habitat suitability index is calculated through the maximum entropy model, and the suitability index is classified into four levels: highly unsuitable, unsuitable, suitable, and highly suitable.

[0011] Step 4: Identification of core habitats and key ecological nodes: Based on the suitable and highly suitable areas identified in Step 3 as foreground landscapes, core suitable habitats are identified using a landscape morphology spatial pattern analysis model; based on the core suitable habitats and the ecological resistance surfaces constructed based on indicator data, ecological corridors and obstacle points are identified using a circuit theory model.

[0012] Step 5: Identification of core driving factors: Using the habitat suitability index obtained in Step 3 as the dependent variable and the evaluation index as the independent variable, the core driving factors with the greatest impact on habitat suitability are screened using a geographic detector.

[0013] Step Six: Precise Habitat Optimization: For the obstacle area consisting of all the obstacle points identified in Step Four, and using the core driving factors determined in Step Five as the target, multiple sets of vegetation transformation scenario simulations are carried out using an artificial neural network model. The improvement of habitat suitability under different scenarios is compared, the optimal vegetation type and transformation range are selected, and a habitat restoration and recovery plan is formed.

[0014] The various types of data mentioned in Step 1 include: terrain data, vegetation data, climate data, human activity data, and hydropower development data; all data are uniformly implemented using the WGS84 coordinate system to convert each type of data into data based on the same coordinate system; the resolution of the raster data is adjusted to 30m×30m.

[0015] The evaluation index system includes 18 indicators in 6 categories: Topography: altitude, slope, aspect, distance from water source; Vegetation: NPP, NDVI, vegetation type; Climate: average annual temperature, average annual precipitation; Human activity intensity: population density, nighttime light intensity, GDP; Land use related: distance from road, distance from town, distance from farmland; Hydropower development related: distance from hydropower station, distance from dam, percentage of flooded area.

[0016] Step 4: Identification of core habitats and key ecological nodes, the specific steps are as follows:

[0017] (4.1) The moderately suitable and moderately suitable areas of the target species habitat were obtained by using the maximum entropy model as foreground elements, and the unsuitable and highly unsuitable areas as background elements. The data were then converted into raster format. Subsequently, based on the raster data after foreground and background classification, core area patches were extracted as core suitable habitats by the landscape morphology spatial pattern analysis model and matched and verified with the actual distribution points of the target species. If the proportion of the points was lower than the threshold, the simulation settings of the landscape morphology spatial pattern analysis model were adjusted and the process returned to step (4.1). Otherwise, the core area patches that were successfully matched and verified were retained.

[0018] (4.2) Based on land use data, habitat quality assessment is completed by combining habitat suitability, distance of threat source impact and attenuation mode. Then, ecological resistance surface is constructed by reverse reclassification of habitat quality and neighborhood smoothing. Subsequently, based on core suitable habitat and ecological resistance surface, current connectivity is simulated by pairwise mode of circuit theory model. Through high current density grid threshold extraction and vectorization and patch fusion optimization, ecological corridors that maintain core habitat connectivity are finally identified.

[0019] (4.3) Using the circuit theory model, with the core habitat as the source and the ecological resistance surface as the basis, the circuit theory model is used to identify the ecological pinch points where the flow is concentrated and the obstacles that hinder species migration in the ecological corridor.

[0020] In step five, the core driving factor is vegetation type, which is determined by calculating the explanatory power of the factor and ranking it using a geographic detector.

[0021] Step Six, details are as follows:

[0022] For the identified obstacles, the core driving factors are targeted, and multiple sets of vegetation transformation scenario simulations are performed using a neural network model:

[0023] For each vegetation scenario, based on the area formed by all obstacle points, the index data preprocessed in step two and the suitability index obtained in step three will be imported into the neural network model to obtain the probability of vegetation type in the obstacle point area.

[0024] Based on the probability of different vegetation types in different scenarios obtained by the neural network model, the current suitability index corresponding to the probability of each vegetation type in each scenario is obtained through step three; the improvement of habitat suitability under different scenarios is compared; the vegetation type with the highest improvement is selected as the optimal vegetation type, and the obstacle point area corresponding to the optimal vegetation type is taken as the conversion range of the optimal vegetation. The optimal vegetation type and the conversion range form a habitat restoration and recovery plan.

[0025] The neural network model is trained as follows:

[0026] The sample is composed of the preprocessed index data in step two and the suitability index of each region in step three. It is input into the neural network model and outputs the vegetation type as the core driving factor to train the neural network model.

[0027] The vegetation transformation scenario includes converting farmland and bare land into native dominant vegetation types suitable for target wildlife, including evergreen broad-leaved forests and deciduous broad-leaved forests.

[0028] A planning system for the protection and restoration of terrestrial wildlife habitats under the impact of hydropower development, comprising:

[0029] The data collection and preprocessing unit is used to acquire various data within the watershed, including topography, vegetation, climate, human activities, land use, and hydropower development, and to unify the data coordinate system and raster resolution to eliminate errors caused by differences in data format and scale.

[0030] A habitat suitability evaluation index system unit is constructed to build a multi-dimensional evaluation index system based on the ecological habits of terrestrial wild animals and the characteristics of hydropower development basins. This system includes topography, vegetation, climate, intensity of human activities, land use, and hydropower development. The data of each index are preprocessed.

[0031] The habitat suitability assessment and grading unit is used to take the preprocessed indicator data as environmental variables, combine it with the field distribution data of wild animals, calculate the habitat suitability index through the maximum entropy model, and divide the suitability index into four levels: highly unsuitable, unsuitable, suitable, and highly suitable.

[0032] The core habitat and key ecological node identification unit is used to identify core suitable habitats based on the divided suitable and highly suitable areas as foreground landscapes, and to identify ecological corridors and barrier points based on core suitable habitats and ecological resistance surfaces constructed based on indicator data, using a circuit theory model.

[0033] The core driving factor identification unit is used to screen the core driving factors that have the greatest impact on habitat suitability by using the habitat suitability index as the dependent variable and the evaluation index as the independent variable through a geographic detector.

[0034] The habitat precision optimization unit is used to target the obstacle area consisting of all identified obstacle points, using core driving factors as the target, and employing an artificial neural network model to simulate multiple vegetation transformation scenarios. It compares the improvement in habitat suitability under different scenarios, selects the optimal vegetation type and transformation range, and forms a habitat restoration and recovery plan.

[0035] A computer-readable storage medium storing a computer program that, when executed by a processor, implements a planning method for the protection and restoration of terrestrial wildlife habitats under the influence of hydropower development.

[0036] The present invention has the following beneficial effects and advantages:

[0037] 1. Improved accuracy of assessment: By adding new indicators specific to hydropower development (proportion of flooded area, distance from power station / dam, etc.), the shortcomings of existing technologies in not considering special disturbance factors in the watershed are made up for, making the habitat suitability assessment more in line with the actual situation of the hydropower development area; and improving the accuracy of the adaptability assessment.

[0038] 2. Scientific identification of core habitats: The landscape morphology analysis model technology is used to replace the traditional experience threshold method, which avoids the subjectivity of setting thresholds by humans. The identification results are more in line with the ecological habits of wild animals and the accuracy is significantly improved.

[0039] 3. The optimized plan is feasible: By simulating the entire process, a closed-loop process of "core factor identification + vegetation transformation simulation" is realized, which clarifies the specific restoration area, the optimal vegetation type and configuration plan, and upgrades habitat restoration from general suggestions to an implementation plan that can achieve accurate assessment and implementation.

[0040] 4. Complete technical system: It integrates multiple models such as the maximum entropy model, landscape morphology analysis model, circuit theory, geographic detector, and neural network model to form a full-chain technical system of "interference factor inclusion - accurate evaluation - functional identification - targeted optimization". Through the simulation of each part and the entire process, it realizes accurate assessment and planning of systematic protection at the watershed scale. At the same time, it fills the gap of lack of systematic technical support for species habitat protection in hydropower development watersheds. It can be widely applied to the protection and restoration planning of terrestrial wildlife habitats in similar watersheds. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the technical process of the present invention;

[0042] Figure 2 A spatial distribution map of suitable habitats for black leaf monkeys in the Wujiang River Basin;

[0043] Figure 3 A schematic diagram showing the distribution of the core habitat and key ecological nodes of the black leaf monkey in the Wujiang River Basin. Detailed Implementation

[0044] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0045] A planning method for the protection and restoration of terrestrial wildlife habitats under the impact of hydropower development, comprising the following steps:

[0046] Step 1: Data Collection and Preprocessing: Collect relevant data on topography, vegetation, climate, human activities, land use and hydropower development within the watershed; unify the data coordinate system and raster resolution; and eliminate errors caused by differences in data format and scale.

[0047] Step 2: Construct a habitat suitability evaluation index system: Based on the ecological habits of terrestrial wild animals and the characteristics of hydropower development basins, construct a multi-dimensional evaluation index system that includes topography, vegetation, climate, intensity of human activities, land use, and hydropower development. Assign positive or negative values ​​to each index and standardize the data.

[0048] Step 3: Habitat suitability assessment and classification: Using the preprocessed indicators as environmental variables, combined with the field distribution data of wild animals, the habitat suitability index is calculated through the maximum entropy model. The natural breakpoint method is used to classify the suitability index into four levels: highly unsuitable, unsuitable, suitable, and highly suitable.

[0049] Step 4: Identification of core habitats and key ecological nodes: The "suitable area" and "highly suitable area" divided in Step 3 are used as foreground landscapes. Core suitable habitats are identified through landscape morphology spatial pattern analysis model. Ecological resistance surfaces are constructed based on evaluation indicators. Ecological corridors are identified by combining circuit theory model. Ecological pinch points and obstacle points are identified by using circuit theory model.

[0050] Step 5: Identification of core driving factors: Using the habitat suitability index obtained in Step 3 as the dependent variable and the evaluation index as the independent variable, the core driving factors with the greatest impact on habitat suitability are screened using a geographic detector.

[0051] Step Six: Precise Habitat Optimization: Targeting the obstacle points and planable areas identified in Step Four, and using the core driving factors determined in Step Five as the target, multiple vegetation transformation scenarios are simulated using a habitat network model. The improvement in habitat suitability under different scenarios is compared, the optimal vegetation type and transformation range are selected, and a habitat restoration and recovery plan is formed.

[0052] The data mentioned in Step 1 includes: topographic data (elevation, slope, aspect, distance from water source), vegetation data (net primary productivity, normalized difference vegetation index, vegetation type distribution map), climate data (multi-year mean annual temperature, mean annual precipitation raster data), human activity data (population density, nighttime light intensity, spatial distribution data of GDP, road / town / farmland distribution map), and hydropower development data (distribution data of hydropower station and dam locations, remote sensing interpretation data of flooded area at different times); the data are uniformly based on the WGS84 coordinate system, and the raster data resolution is adjusted to 30m×30m.

[0053] The evaluation index system described in Step Two specifically includes 18 indicators in 6 categories: topography (altitude, slope, aspect, distance from water source), vegetation (NPP, NDVI, vegetation type), climate (average annual temperature, average annual precipitation), human activity intensity (population density, nighttime light intensity, GDP), land use related (distance from road, distance from town, distance from farmland), and hydropower development related (distance from hydropower station, distance from dam, percentage of flooded area).

[0054] In step three, the maximum entropy model is set with a training set ratio of 75% and a test set ratio of 25%. It is run 10 times and the average value is taken to obtain the habitat suitability index, which has a value range of 0-1.

[0055] The ecological resistance surface described in step four is constructed using the Invest model. The identification of core suitable habitats abandons the traditional empirical threshold method and adopts the landscape morphological spatial pattern analysis model (MSPA) to perform landscape morphological processing on the "suitable area" and "highly suitable area" to identify core habitat areas with complete ecological functions.

[0056] The core driving factor mentioned in step five is vegetation type, which is determined by calculating the explanatory power (q-value) of the factor using a geographic detector and ranking it.

[0057] The vegetation conversion scenario described in step six includes converting farmland and bare land into native dominant vegetation types suitable for target wildlife, including evergreen broad-leaved forests and deciduous broad-leaved forests.

[0058] like Figure 1 As shown, the technical solution of the present invention is as follows, and its process includes six steps: data collection and preprocessing, construction of an evaluation index system, habitat suitability evaluation and classification, identification of core habitats and key ecological nodes, identification of core driving factors, and precise habitat optimization, as detailed below:

[0059] (a) Data collection and preprocessing

[0060] 1. Species data

[0061] Species data were derived from published literature and the Global Biodiversity Information Facility (GBIF) dataset. QGIS was used to remove duplicates (distance less than 1 km), anomalies, and inaccurately geolocated records. Meanwhile, the Spatial Sparsity Presence Toolbox ensured the spatial independence of the existence data and mitigated overfitting caused by environmental bias.

[0062] 2. Environmental variable data

[0063] The environmental dataset contains 18 environmental factors across 5 categories: climate, topography, vegetation, human disturbance, and hydropower development; all variables correspond to the same year (2025), as shown in Table 1.

[0064] Table 1 Environmental Variable Information Table

[0065]

[0066] Data Sources: Digital Elevation Model (DEM) data is sourced from the Earth Resources Data Cloud Platform (www.gis5g.com). Basic environmental and socioeconomic data include climate variables (annual precipitation and annual mean surface temperature), population density, Gross Domestic Product (GDP), vegetation type, nighttime light, land use, road distribution, and administrative village boundaries. All data are from the Resource and Environmental Science Data Registry and Publication System (http: / / www.resdc.cn). Hydropower Station and Dam Distribution Data: This data was acquired in two steps. First, the names and approximate locations (township level) of hydropower stations were determined through a literature review. Subsequently, the precise geographic coordinates of these hydropower stations and dams were determined through visual interpretation of high-resolution satellite imagery. Remote Sensing Data: Normalized Differential Vegetation Index (NDVI) and Net Primary Productivity (NPP) were calculated using Sentinel-2 satellite data.

[0067] Data Processing: Topographic Variable Extraction: Topographic variables, including elevation, slope, and aspect, were extracted from the DEM data using ArcGIS 10.8 software. Euclidean Distance Variable Calculation: Euclidean distance analysis was performed based on the corresponding land cover and feature data to calculate the following variables: distance to towns, distance to roads / railways, distance to farmland, distance to hydroelectric power stations, and distance to dams. Inundation Percentage: This refers to the proportion of inundated area (water bodies in the 2020 land cover minus water bodies in the 1985 land cover) within each administrative village. Vegetation Index and Productivity Calculation: NDVI and NPP were calculated using Sentinel-2 satellite data. Specifically, NPP was estimated using the Carnegie-Ames-Method-Stanford (CASA) model (Potter et al., 1993), which quantifies it as the product of absorbed photosynthetically active radiation (APAR) and actual light energy utilization efficiency (ε).

[0068] All data adopts the WGS84 coordinate system, and the resolution of raster data is adjusted to 30m×30m to eliminate errors caused by differences in data format and scale.

[0069] (II) Habitat suitability assessment and classification

[0070] The Habitat Suitability Index (HSI) of the target species was simulated using the Maxent 3.4.4 model. This model is particularly suitable for small sample sizes of species distribution data and exhibits high accuracy in fitting species-environment relationships; therefore, it has been widely applied to habitat suitability assessment for terrestrial vertebrates. Specific parameter settings and validation methods are as follows:

[0071] Feature type selection: Linear (L) + quadratic (Q) features are used to prevent model overfitting caused by higher-order features, which is consistent with the optimal feature combination recommended for small sample datasets.

[0072] Iteration and convergence criteria: The number of iterations is set to 1000, and the convergence threshold is 1×10 -5 , to ensure the stability of the model.

[0073] Cross-validation: Ten-fold cross-validation is implemented to reduce the random error caused by single-sample partitioning and improve the reliability of simulation results.

[0074] Two metrics are used to comprehensively evaluate the model accuracy: the area under the receiver operating characteristic curve (AUC) and the true skill statistic (TSS). The evaluation criteria are as follows: AUC > 0.9 indicates excellent model accuracy, 0.7 < AUC ≤ 0.9 indicates good accuracy, and TSS > 0.7 indicates excellent model performance. According to the HSI value, the habitat is divided into four grades (highly suitable area, moderately suitable area, unsuitable area, and highly unsuitable area) using the natural breaks classification method (Jenks), and a spatial distribution map of "habitat suitability of target species" is generated.

[0075] (III) Identification of core habitats and ecological key nodes

[0076] Identification using core suitable habitats: Based on the highly suitable area and moderately suitable area layers in the "habitat suitability of target species" generated by the Maxent model as the basic data sources, determine the foreground elements in QGIS. Assign a value of 2 to the highly suitable area and moderately suitable area as foreground elements, and a value of 1 to the unsuitable area and highly unsuitable area as background elements. Convert the data to raster format and save it as a.tif format; then import this raster data into the Guidostoolbox, set 4 core parameters (foreground connection default 8, edge width default 1, conversion default 1, Intext default 1). After the parameter settings are completed, perform the analysis. Finally, extract the core area patches after analysis as core suitable habitats, retain the patches with qualified area and good connectivity, and perform matching verification with the actual distribution points of the target species. If the point ratio is lower than 70%, adjust the parameters or foreground classification criteria.

[0077] Ecological corridor identification: First, based on land use data, rely on the habitat quality module of the InVEST model, and complete the habitat quality assessment by combining parameters such as habitat suitability, threat source impact distance, and attenuation method. Then, construct an ecological resistance surface through habitat quality reverse reclassification and neighborhood smoothing processing; subsequently, extract the centroids of high habitat quality patches as core habitat nodes, and use the paired mode of the Circuitscape circuit theory model to carry out full-node pair current connectivity simulation based on the ecological resistance surface. Extract through the high current density raster threshold (divide the current density raster into 3 categories "low, medium, high" using the natural breaks method, and set the boundary point between medium and high as the threshold. The threshold in this embodiment is 0.005.), and after vectorization and patch fusion optimization, finally identify the ecological corridors that maintain the connectivity of core habitats.

[0078] Among them, parameters such as habitat suitability, distance of stress source influence and attenuation mode can be obtained from similar ecosystem research literature, or verified by the barrier point results obtained from the ecological resistance surface based on the InVEST model; as shown in Tables 2 and 3 in this embodiment.

[0079] Table 2 Habitat suitability parameters

[0080]

[0081] Table 3 Stress Source Parameter Table

[0082]

[0083] Ecological pinch point and obstacle point identification: Using the Circuitscape circuit theory model, the core habitat is taken as the "source area" and the ecological resistance surface is taken as the basis. The current, resistance and other parameters are calculated by the circuit theory model to identify the ecological pinch points with concentrated flow in the ecological corridor and the obstacle points that hinder species migration.

[0084] (iv) Identification of core driving factors

[0085] The habitat suitability index obtained in step (II) was used as the dependent variable, and 18 environmental variables were used as independent variables, and input into the geographic detector software. By calculating and ranking the explanatory power (q-value) of each indicator, the core driving factors with the greatest impact on wildlife habitat suitability (empirically proven to be vegetation type) were screened out, providing a targeted basis for subsequent habitat optimization.

[0086] (V) Precise optimization of habitat

[0087] For the obstacle points and planarable areas within the watershed identified in step (III), habitat optimization was carried out using the GeoSOS-FLUS model with "vegetation type" as the core driving factor: First, the basic data (foreground elements consisting of highly suitable and moderately suitable areas) were preprocessed (unified coordinate system and 30m×30m raster resolution; reclassified and assigned values ​​to each area in QGIS: farmland and bare land in planarable areas were assigned 3, obstacle point areas were assigned 4, native suitable vegetation areas were assigned 2, and unplanarable areas were assigned 1, all converted to .tif format raster data); then the model was imported and basic parameters were set (raster size 30m×30m, simulation period 10 years, number of iterations 10, areas with no data ignored, output format .tif); then the core parameters were set: neural network parameters (learning rate 0.01, number of hidden layer nodes 10, number of training iterations 1000, error threshold 0.001, activation function Sigmoid), and transition rule parameters (farmland in planarable areas). The transfer probabilities of fields to evergreen broad-leaved forests and deciduous broad-leaved forests are 0.6 and 0.4 respectively, and the transfer probabilities of bare land to both types of vegetation are 0.5. The transfer probability of obstacle point areas is increased by 0.2, the transfer probability of prohibited conversion areas is 0, and the transfer probability of preserved vegetation areas is 0.05. The transfer cost is 1 within 500m of the obstacle point, 3 in other planned areas, and 10 in non-planned areas. The constraint parameters are as follows: obstacle points are mandatory constraint areas and must be converted, planned areas are flexible constraint areas, core habitat nodes and ecological corridors are protected constraint areas within 100m, and the transfer probability of high / medium suitability areas is reduced by 0.1. Then, three sets of vegetation conversion scenarios (single evergreen broad-leaved forest, single deciduous broad-leaved forest, and mixed configuration) are constructed for simulation. The parameters are adjusted and the simulation is repeated with reference to the verification standards in step (three) (site proportion ≥70%, suitability index increase ≥15%). Finally, the scenario effects are compared to select the optimal solution, clarify the restoration area, vegetation configuration and implementation priority, and form a feasible habitat restoration plan.

[0088] The following section uses the habitat protection and restoration of the black leaf monkey in the Wujiang River Basin as an example to further illustrate the specific implementation of this invention:

[0089] (I) Data collection and construction of evaluation index system

[0090] We collected topographic data (elevation, slope, aspect, distance from water source), vegetation data (net primary productivity, normalized difference vegetation index, vegetation type map), climate data (multi-year mean annual temperature and mean annual precipitation from 1980 to 2020), human activity data (population density, nighttime light intensity, GDP, road / town / farmland distribution map), and hydropower development data (location of hydropower stations and dams within the basin, remote sensing interpretation data of flooded areas in three periods from 1990 to 2020). All data were uniformly converted to the WGS84 coordinate system, and the raster resolution was adjusted to 30m×30m.

[0091] (II) Habitat suitability assessment and classification

[0092] 120 field distribution sites of black-leaf monkeys in the Wujiang River Basin were collected, with 90 sites used as the training set and 30 as the test set, and input into the maximum entropy model. After the model was run 10 times, the average habitat suitability index of black-leaf monkeys was obtained, and the natural breakpoint method was used to classify them into four levels: highly unsuitable, unsuitable, suitable, and highly suitable.

[0093] like Figure 2 As shown, suitable habitats for black leaf monkeys are mainly concentrated in areas such as Wuchuan Gelao and Miao Autonomous County, Yanhe Tujia Autonomous County, Dejiang County, Songtao Miao Autonomous County, and Yinjiang Tujia and Miao Autonomous County in northeastern Guizhou Province, with only scattered small areas of suitable habitats in the southwest. In terms of area, the unsuitable habitat area is 56,056.06 square kilometers, while the suitable habitat area is only 3,613.12 square kilometers, indicating an extremely low proportion of suitable habitat and a localized, concentrated distribution.

[0094] (III) Identification of core habitats and key nodes

[0095] like Figure 3 As shown, using the "suitable area" and "highly suitable area" as foreground landscapes, nine core habitat regions were identified through a landscape morphological analysis model (MSPA, Morphological Spatial Pattern Analysis), with a total area of ​​3422.62 km². 2 Based on the Invest model, an ecological resistance surface was constructed. Using the circuit theory model, 14 active migration corridors with a total length of 316.59 km were identified, and 2 inactive migration corridors with a total length of 163.861 km were identified. Eight ecological pinch points and 15 obstacle points were identified through the circuit theory model.

[0096] (iv) Identification of core driving factors

[0097] The results of the geographic detector calculations show that the q value of vegetation type is 0.68 (ranked first), which has been identified as the core driving factor for the habitat suitability of black leaf monkeys.

[0098] (V) Precise optimization of habitat

[0099] Three vegetation transformation scenarios were set up for 15 obstacle points: Scenario 1 (converting to evergreen broad-leaved forest), Scenario 2 (converting to deciduous broad-leaved forest), and Scenario 3 (mixed forest). Simulation results from the GeoSOS-FLUS artificial neural network (ANN) model showed that Scenario 2 (deciduous broad-leaved forest) showed the greatest improvement in habitat suitability (average improvement of 0.32). The optimal solution was ultimately determined to be: converting 12 obstacle points (covering an area of ​​42 km²) into evergreen broad-leaved forest. 2The area was converted to deciduous broad-leaved forest, with 3 obstacle points (covering an area of ​​18 km²). 2 The existing conditions will be preserved and an ecological buffer zone will be set up to form an implementation plan for the restoration of the black leaf monkey habitat.

Claims

1. A planning method for the protection and restoration of terrestrial wildlife habitats under the impact of hydropower development, characterized in that, Includes the following steps: Step 1: Data Collection and Preprocessing: Acquire various data within the watershed, including topography, vegetation, climate, human activities, land use, and hydropower development; unify the data coordinate system and raster resolution to eliminate errors caused by differences in data format and scale. Step 2: Construct a habitat suitability evaluation index system: Based on the ecological habits of terrestrial wild animals and the characteristics of hydropower development basins, construct a multi-dimensional evaluation index system that includes topography, vegetation, climate, intensity of human activities, land use, and hydropower development. Preprocess the data of each index. Step 3: Habitat suitability assessment and classification: Using the preprocessed index data as environmental variables, combined with the field distribution data of wild animals, the habitat suitability index is calculated through the maximum entropy model, and the suitability index is classified into four levels: highly unsuitable, unsuitable, suitable, and highly suitable. Step 4: Identification of core habitats and key ecological nodes: Based on the suitable and highly suitable areas identified in Step 3 as foreground landscapes, core suitable habitats are identified using a landscape morphology spatial pattern analysis model; based on the core suitable habitats and the ecological resistance surfaces constructed based on indicator data, ecological corridors and obstacle points are identified using a circuit theory model. Step 5: Identification of core driving factors: Using the habitat suitability index obtained in Step 3 as the dependent variable and the evaluation index as the independent variable, the core driving factors with the greatest impact on habitat suitability are screened using a geographic detector. Step Six: Precise Habitat Optimization: For the obstacle area consisting of all the obstacle points identified in Step Four, and using the core driving factors determined in Step Five as the target, multiple sets of vegetation transformation scenario simulations are carried out using an artificial neural network model. The improvement of habitat suitability under different scenarios is compared, the optimal vegetation type and transformation range are selected, and a habitat restoration and recovery plan is formed.

2. The planning method for the protection and restoration of terrestrial wildlife habitats under the impact of hydropower development according to claim 1, characterized in that, The various types of data mentioned in Step 1 include: terrain data, vegetation data, climate data, human activity data, and hydropower development data; all data are uniformly implemented using the WGS84 coordinate system to convert each type of data into data based on the same coordinate system; the resolution of the raster data is adjusted to 30m×30m.

3. The planning method for the protection and restoration of terrestrial wildlife habitats under the impact of hydropower development according to claim 1, characterized in that, The evaluation index system includes 6 categories and 18 indicators: Topographical features: elevation, slope, aspect, distance from water source; Vegetation categories: NPP, NDVI, vegetation type; Climate-related: average annual temperature, average annual precipitation; Human activity intensity categories: population density, nighttime light intensity, and gross domestic product; Land use related categories: distance from roads, distance from towns, distance from farmland; Hydropower development related categories: distance from hydropower station, distance from dam, and percentage of flooded area.

4. The planning method for the protection and restoration of terrestrial wildlife habitats under the impact of hydropower development according to claim 1, characterized in that, Step 4: Identification of core habitats and key ecological nodes, the specific steps are as follows: (4.1) The moderately suitable and moderately suitable areas of the target species habitat were obtained by using the maximum entropy model as foreground elements, and the unsuitable and highly unsuitable areas as background elements. The data were then converted into raster format. Subsequently, based on the raster data after foreground and background classification, core area patches were extracted as core suitable habitats by the landscape morphology spatial pattern analysis model and matched and verified with the actual distribution points of the target species. If the proportion of the points was lower than the threshold, the simulation settings of the landscape morphology spatial pattern analysis model were adjusted and the process returned to step (4.1). Otherwise, the core area patches that were successfully matched and verified were retained. (4.2) Based on land use data, habitat quality assessment is completed by combining habitat suitability, distance of threat source impact and attenuation mode. Then, ecological resistance surface is constructed by reverse reclassification of habitat quality and neighborhood smoothing. Subsequently, based on core suitable habitat and ecological resistance surface, current connectivity is simulated by pairwise mode of circuit theory model. Through high current density grid threshold extraction and vectorization and patch fusion optimization, ecological corridors that maintain core habitat connectivity are finally identified. (4.3) Using the circuit theory model, with the core habitat as the source and the ecological resistance surface as the basis, the circuit theory model is used to identify the ecological pinch points where the flow is concentrated and the obstacles that hinder species migration in the ecological corridor.

5. The planning method for the protection and restoration of terrestrial wildlife habitats under the impact of hydropower development according to claim 1, characterized in that, In step five, the core driving factor is vegetation type, which is determined by calculating the explanatory power of the factor and ranking it using a geographic detector.

6. The planning method for the protection and restoration of terrestrial wildlife habitats under the impact of hydropower development according to claim 1, characterized in that, Step Six, the specific steps are as follows: For the identified obstacles, the core driving factors are targeted, and multiple sets of vegetation transformation scenario simulations are performed using a neural network model: For each vegetation scenario, based on the area formed by all obstacle points, the index data preprocessed in step two and the suitability index obtained in step three will be imported into the neural network model to obtain the probability of vegetation type in the obstacle point area. Based on the probability of different vegetation types in different scenarios obtained by the neural network model, the current suitability index corresponding to the probability of each vegetation type in each scenario is obtained through step three; the improvement of habitat suitability under different scenarios is compared; the vegetation type with the highest improvement is selected as the optimal vegetation type, and the obstacle point area corresponding to the optimal vegetation type is taken as the conversion range of the optimal vegetation. The optimal vegetation type and the conversion range form a habitat restoration and recovery plan.

7. The planning method for the protection and restoration of terrestrial wildlife habitats under the impact of hydropower development according to claim 6, characterized in that, The neural network model is trained as follows: The sample is composed of the preprocessed index data in step two and the suitability index of each region in step three. It is input into the neural network model and outputs the vegetation type as the core driving factor to train the neural network model.

8. The planning method for the protection and restoration of terrestrial wildlife habitats under the impact of hydropower development according to claim 6, characterized in that, The vegetation transformation scenario includes converting farmland and bare land into native dominant vegetation types suitable for target wildlife, including evergreen broad-leaved forests and deciduous broad-leaved forests.

9. A planning system for the protection and restoration of terrestrial wildlife habitats under the impact of hydropower development, characterized in that, include: The data collection and preprocessing unit is used to acquire various data within the watershed, including topography, vegetation, climate, human activities, land use, and hydropower development, and to unify the data coordinate system and raster resolution to eliminate errors caused by differences in data format and scale. A habitat suitability evaluation index system unit is constructed to build a multi-dimensional evaluation index system based on the ecological habits of terrestrial wild animals and the characteristics of hydropower development basins. This system includes topography, vegetation, climate, intensity of human activities, land use, and hydropower development. The data of each index are preprocessed. The habitat suitability assessment and grading unit is used to take the preprocessed indicator data as environmental variables, combine it with the field distribution data of wild animals, calculate the habitat suitability index through the maximum entropy model, and divide the suitability index into four levels: highly unsuitable, unsuitable, suitable, and highly suitable. The core habitat and key ecological node identification unit is used to identify core suitable habitats based on the divided suitable and highly suitable areas as foreground landscapes, and to identify ecological corridors and barrier points based on core suitable habitats and ecological resistance surfaces constructed based on indicator data, using a circuit theory model. The core driving factor identification unit is used to screen the core driving factors that have the greatest impact on habitat suitability by using the habitat suitability index as the dependent variable and the evaluation index as the independent variable through a geographic detector. The habitat precision optimization unit is used to target the obstacle area consisting of all identified obstacle points, using core driving factors as the target, and employing an artificial neural network model to simulate multiple vegetation transformation scenarios. It compares the improvement in habitat suitability under different scenarios, selects the optimal vegetation type and transformation range, and forms a habitat restoration and recovery plan.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements a planning method for the protection and restoration of terrestrial wildlife habitats under the influence of hydropower development, as described in any one of claims 1-8.

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