Target area resource analysis method and system based on regional remote sensing technology

Abstract

The present invention proposes a target area resource analysis method and system based on regional remote sensing technology, the method comprising: collecting regional remote sensing images, performing feature recognition on the regional remote sensing images to obtain one or more target area resource feature blocks; collecting environmental variable data and ecosystem data of the target area resource feature blocks, and evaluating the regional value of the target area resource feature blocks based on the environmental variable data and ecosystem data; performing risk assessment on the target area resource feature blocks according to the environmental variable data and ecosystem data to obtain risk assessment results, and analyzing to obtain expected change trends, and generating optimization suggestions and decisions according to regional value, risk assessment results and expected change trends. The present invention not only comprehensively considers various included analysis factors, but can also better model and simulate the real natural resource environment, and finally obtain more accurate analysis results.

Description

Technical Field

[0001] The present invention relates to the technical field of natural resource analysis, and in particular to a target area resource analysis method and system based on regional remote sensing technology. Background Art

[0002] Natural resource ecological big data analysis refers to the collection, storage, processing and analysis of various data on natural resources in order to achieve monitoring, management and decision-making support for natural resources; its data sources include: satellite remote sensing, UAV remote sensing, forest resource surveys, ecological monitoring stations, sensor networks, smart devices and forest product market data, etc.

[0003] However, existing methods for analyzing natural resources mainly focus on processing low-dimensional data, and the model factors considered are relatively simple. They are unable to model and simulate the real natural resource environment well, and the final analysis results are not particularly accurate.

[0004] Based on this, it is necessary to propose a target area resource analysis method based on regional remote sensing technology to solve the above technical problems. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a target area resource analysis method and system based on regional remote sensing technology to solve the problems existing in the above-mentioned background technology.

[0006] The present invention is achieved by a method for analyzing target area resources based on regional remote sensing technology, the method comprising the following steps:

[0007] Collecting regional remote sensing images, and performing feature recognition on the regional remote sensing images to obtain one or more target regional resource feature blocks, wherein the target regional resource feature blocks include forest feature blocks, grassland feature blocks, farmland feature blocks, and water feature blocks;

[0008] Obtain environmental variable data and ecosystem data of the target area resource characteristic blocks. Environmental variable data include climate regulation, flood regulation and water conservation. Ecosystem data include ecosystem diversity and oxygen release and carbon fixation. Calculate the regional value of the target area resource characteristic blocks based on climate regulation, flood regulation, water conservation and oxygen release and carbon fixation.

[0009] A risk assessment is conducted on the resource characteristic blocks in the target area based on the environmental variable data and the ecosystem data to obtain the risk assessment results, and the expected change trends are analyzed to generate optimization recommendation decisions based on the regional value, risk assessment results and expected change trends.

[0010] Another object of the present invention is to provide a target area resource analysis system based on regional remote sensing technology, the system comprising:

[0011] A regional feature recognition module is used to collect regional remote sensing images, perform feature recognition on the regional remote sensing images to obtain one or more target regional resource feature blocks, wherein the target regional resource feature blocks include forest feature blocks, grassland feature blocks, farmland feature blocks and water feature blocks;

[0012] The regional resource assessment module is used to obtain the environmental variable data and ecosystem data of the target regional resource characteristic blocks. The environmental variable data include climate regulation, flood regulation and water conservation. The ecosystem data include ecosystem diversity and oxygen release and carbon fixation. The regional value of the target regional resource characteristic blocks is calculated based on climate regulation, flood regulation, water conservation and oxygen release and carbon fixation.

[0013] The regional optimization module conducts risk assessment on the resource characteristic blocks in the target area based on environmental variable data and ecosystem data to obtain risk assessment results, and analyzes the expected change trends, and generates optimization recommendation decisions based on the regional value, risk assessment results and expected change trends.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] 1. The present invention combines environmental variable data and ecosystem data for resource analysis. By considering factors such as climate regulation, flood regulation, water conservation, oxygen release and carbon fixation, the value of resources in the target area can be more comprehensively evaluated. Comprehensive analysis can provide more accurate resource evaluation results and provide a more comprehensive basis for decision-making.

[0016] 2. The present invention analyzes the risk situation of the characteristic blocks of resources in the target area and obtains risk assessment results. This helps to understand the vulnerability and sustainability of resources in the target area and ensure that the subsequent resource management plan can achieve better results. The present invention also analyzes the expected change trend of the characteristic blocks of resources in the target area, can predict possible changes in advance, and provide corresponding optimization suggestions for decision makers. It helps decision makers to formulate more reasonable resource management plans and effectively respond to future changes and risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The flowchart is a method for analyzing target area resources based on regional remote sensing technology.

[0018] Figure 2 The present invention is a flow chart for obtaining characteristic blocks of target area resources in a target area resource analysis method based on regional remote sensing technology.

[0019] Figure 3The present invention is a flowchart for evaluating the regional value of characteristic blocks of target area resources in a target area resource analysis method based on regional remote sensing technology.

[0020] Figure 4 It is a structural diagram of a target area resource analysis system based on regional remote sensing technology. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0023] like Figure 1 As shown, an embodiment of the present invention provides a target area resource analysis method based on regional remote sensing technology, and the method includes the following steps:

[0024] S100, collecting a regional remote sensing image, and performing feature recognition on the regional remote sensing image to obtain one or more target regional resource feature blocks, wherein the target regional resource feature blocks include forest feature blocks, grassland feature blocks, farmland feature blocks, and water feature blocks;

[0025] S200, obtaining environmental variable data and ecosystem data of the target area resource characteristic block, the environmental variable data including climate regulation, flood regulation and water conservation, the ecosystem data including ecosystem diversity and oxygen release and carbon fixation, and calculating the regional value of the target area resource characteristic block based on climate regulation, flood regulation, water conservation and oxygen release and carbon fixation;

[0026] S300, performing risk assessment on the resource characteristic blocks in the target area according to the environmental variable data and the ecosystem data to obtain the risk assessment results, and analyzing to obtain the expected change trend, and generating an optimization suggestion decision according to the regional value, the risk assessment results and the expected change trend.

[0027] In an embodiment of the present invention, a regional remote sensing image is first acquired through a satellite or an unmanned aerial vehicle, and feature recognition is performed on the regional remote sensing image to obtain one or more target area resource feature blocks, wherein the target area resource feature blocks include forest feature blocks, grassland feature blocks, farmland feature blocks, and water feature blocks, thereby automatically and accurately dividing the resource feature blocks of different target areas to facilitate subsequent regional value assessment, and then assessing the regional value of the target area resource feature blocks based on factors such as climate regulation, flood regulation, water conservation, and oxygen release and carbon fixation.

[0028] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of performing feature recognition on the regional remote sensing image to obtain one or more target regional resource feature blocks specifically includes:

[0029] S101, preprocessing a regional remote sensing image, segmenting the preprocessed remote sensing image into a plurality of regional objects, wherein the segmentation method is a threshold-based segmentation or an edge-based segmentation;

[0030] S102, extracting spectral features, texture features and shape features from the segmented region objects, wherein the spectral features include reflectance and brightness value, and the texture features include gray-level co-occurrence matrix and local binary pattern;

[0031] S103, according to the extracted features and the random forest classification algorithm, the pixel objects in the remote sensing image are divided into different categories to obtain resource feature blocks of each target area.

[0032] In the embodiment of the present invention, in order to accurately identify the features of the regional remote sensing image to obtain one or more target area resource feature blocks, the regional remote sensing image needs to be preprocessed first, specifically including: performing radiation correction on the regional remote sensing image, adjusting the image data to eliminate the influence of the atmosphere and the solar altitude angle factors on the image; performing geometric correction to correct the geometric distortion in the image; performing image enhancement, improving the image quality through histogram equalization and filtering methods, and improving the distinguishability of different types of objects. Then, the preprocessed remote sensing image is segmented into multiple regional objects, and these regions or objects have similar features in terms of spectrum, texture, shape, etc. The segmentation method is threshold-based segmentation or edge-based segmentation; spectral features, texture features and shape features are extracted from the segmented regional objects, the spectral features include reflectance and brightness value, and the texture features include grayscale co-occurrence matrix and local binary pattern; select a suitable classification algorithm to classify the extracted features. Commonly used classification algorithms include supervised classification (such as maximum likelihood classification, support vector machine, random forest, etc.) and unsupervised classification (such as K-means clustering, ISODATA clustering, etc.). Finally, based on the extracted features and the selected classification algorithm, the pixels or objects in the remote sensing image are divided into different categories, namely, characteristic resource blocks of target areas such as forests, grasslands, farmlands and waters.

[0033] As a preferred embodiment of the present invention, the preprocessing of the regional remote sensing image, segmenting the preprocessed remote sensing image into multiple regional objects, the segmentation method is a threshold-based segmentation or edge-based segmentation step, specifically including:

[0034] Convert the regional remote sensing image into a grayscale image, apply a Gaussian blur operation to the grayscale image, and obtain a blurred grayscale image;

[0035] Use LBP to encode the relationship between each pixel and its neighboring pixels in the blurred grayscale image to obtain LBP features;

[0036] Use the Sobel operator to calculate the gradients of the blurred grayscale image in the horizontal and vertical directions respectively, and combine the gradients in the two directions to get the gradient amplitude of the image;

[0037] The blurred grayscale image, LBP features and gradient amplitude are combined to form a multi-dimensional feature vector, where each pixel corresponds to a multi-dimensional feature vector;

[0038] The KMeans clustering algorithm is used to divide the multidimensional feature vector into several categories to obtain the category label of each pixel;

[0039] Remap the category label of each pixel to each position of the regional remote sensing image to form a segmented image;

[0040] The Otsu threshold segmentation operation is used to find the optimal threshold for segmenting the image, and the segmented image is binarized using the optimal threshold to obtain the threshold segmented image;

[0041] The threshold segmentation image is closed to fill the small holes in the image, and then opened to remove the residual noise in the image to obtain a refined segmentation image.

[0042] As a preferred embodiment of the present invention, according to the extracted features and the random forest classification algorithm, the pixel objects in the remote sensing image are divided into different categories to obtain the resource feature blocks of each target area. The corresponding process has the following relationship:

[0043] ;

[0044] in, Indicates that the pixel object is classified as t The probability of the class, Indicates t category, The feature vector of the pixel object formed by spectral features, texture features and shape features, represents the number of trees in the random forest, Indicates j The adaptive weight of each tree, represents the exponential function, represents the number of features, Indicates k The weight coefficient of the feature, Expressing the j The tree i Pixel objects are transformed to perform feature transformation operations. Indicatesk The weight coefficient of the global feature, Indicates the global feature transformation operation on the feature vector of the pixel object. Indicates j The tree i The classification decision output of pixel objects, Indicates bias;

[0045] In the above process of classifying pixel objects in remote sensing images into different categories, each tree has an adaptive weight , reflecting the importance of the tree in the overall classification. Adjusting these weights during the training process can make more effective trees have a greater impact on the final decision, thereby improving the overall classification accuracy; and through feature transformation and global feature transformation operations, features can be processed nonlinearly to capture complex patterns that were not recognized in the original feature space. This transformation can make the model more flexible in responding to different features, which helps to improve classification performance.

[0046] In order to capture the complex interaction between multiple features, the present invention introduces global feature weights and bias , and set weights for each feature , the contribution of each feature to the classification result can be adjusted more finely. This configuration helps capture richer feature information and improve classification accuracy. Finally, the weighted decision sum of all trees is used to form the final classification result. Such an ensemble method can reduce the bias and variance of a single tree, thereby improving the stability and accuracy of the model. The contribution of each tree is adjusted by its adaptive weight. to make adjustments to ensure that valid trees have a greater impact on the final result.

[0047] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of obtaining environmental variable data and ecosystem data of the characteristic block of resource in the target area, wherein the environmental variable data includes climate regulation, flood regulation and water conservation, and the ecosystem data includes ecosystem diversity and oxygen release and carbon fixation, and calculating the regional value of the characteristic block of resource in the target area based on climate regulation, flood regulation, water conservation and oxygen release and carbon fixation, specifically includes:

[0048] S201, collect climate regulation index, flood regulation index, water conservation index and oxygen release and carbon fixation index of forests, grasslands, farmlands and waters;

[0049] S202, automatically identifying the area of ​​the resource characteristic blocks in each target area, and evaluating the regional value based on the area of ​​the resource characteristic blocks in each target area and the corresponding climate regulation index, flood regulation index, water conservation index and oxygen release and carbon fixation index.

[0050] In an embodiment of the present invention, relevant data need to be collected in advance to calculate the climate regulation index, flood regulation index, water conservation index and oxygen release and carbon fixation index of forests, grasslands, farmlands and waters, and then automatically identify the area of ​​resource characteristic blocks in each target area, and evaluate the regional value based on the area of ​​resource characteristic blocks in each target area and the corresponding climate regulation index, flood regulation index, water conservation index and oxygen release and carbon fixation index to obtain a specific comprehensive regional value.

[0051] As a preferred embodiment of the present invention, the regional value is evaluated based on the area of ​​the resource characteristic blocks of each target area and the corresponding climate regulation index, flood regulation index, water conservation index and oxygen release and carbon fixation index, and the corresponding process has the following relationship:

[0052] ;

[0053] in, represents the total area value, Indicates the number of resource feature blocks in the target area. Indicates n The area of ​​the resource characteristic blocks in the target area, Indicates n The climate regulation index of the resource characteristic blocks in the target area, Indicates n The flood regulation index of the resource characteristic blocks in the target area, Indicates n Water conservation index of resource characteristic blocks in target areas, Indicates n Oxygen release and carbon fixation index of resource characteristic blocks in target areas, They represent the weights of climate regulation index, flood regulation index, water conservation index, and oxygen release and carbon fixation index, respectively. They respectively represent the fuzzy membership values ​​corresponding to the climate regulation index, flood regulation index, water conservation index, and oxygen release and carbon fixation index.

[0054] In the above-mentioned process of evaluating regional value, the present invention uses the area of ​​each target regional resource characteristic block as It is used to weight the contribution of each index to the regional value to reflect the proportion of the area of ​​different blocks in the total value, and comprehensively consider the impact of each index on the regional value by weighted average of different indexes. In addition, due to the complexity and uncertainty of the ecosystem itself, the present invention converts each index into a fuzzy membership value by introducing a fuzzy membership function, and can effectively capture this characteristic through fuzzy logic, thereby improving the accuracy and robustness of the evaluation results.

[0055] As a preferred embodiment of the present invention, risk assessment is performed on the resource characteristic blocks of the target area according to the environmental variable data and the ecosystem data to obtain the risk assessment results, and the expected change trend is analyzed to obtain the expected change trend, and the optimization suggestion decision is generated according to the regional value, the risk assessment results and the expected change trend, which specifically includes:

[0056] Normalizing and standardizing the environmental variable data and the ecosystem data in turn to obtain preprocessed environmental variable data and ecosystem data;

[0057] According to the preset scoring rules, weights are assigned to the preprocessed environmental variable data and ecosystem data;

[0058] Calculate and sum the weighted values ​​of the preprocessed environmental variable data and ecosystem data to obtain a comprehensive risk assessment value;

[0059] Obtain historical data of the target area, including historical risk assessment values, historical environmental variable data, and historical ecosystem data;

[0060] A spatiotemporal prediction model is constructed using a long short-term memory network, and the spatiotemporal prediction model is trained using historical data of the target area to obtain a trained spatiotemporal prediction model;

[0061] Input the current risk assessment value, environmental variable data and ecosystem data into the trained spatiotemporal prediction model to obtain the predicted value of future change trend;

[0062] Construct a multi-objective optimization model with ecosystem health, economic benefits and social impact factors as optimization objectives and land management measures and water resource management measures as input variables;

[0063] According to the regional value, comprehensive risk assessment value and future change trend forecast value, combined with land management measures and water resources management measures, the optimization target value of each resource characteristic block in the target area is calculated through a multi-objective optimization method to obtain the optimal land management measures and water resources management measures.

[0064] like Figure 4 As shown, an embodiment of the present invention further provides a target area resource analysis system based on regional remote sensing technology, the system comprising:

[0065] The regional feature recognition module 100 is used to collect regional remote sensing images, perform feature recognition on the regional remote sensing images to obtain one or more target regional resource feature blocks, wherein the target regional resource feature blocks include forest feature blocks, grassland feature blocks, farmland feature blocks and water feature blocks;

[0066] The regional resource assessment module 200 is used to obtain environmental variable data and ecosystem data of the target regional resource characteristic block, the environmental variable data includes climate regulation, flood regulation and water conservation, the ecosystem data includes ecosystem diversity and oxygen release and carbon fixation, and calculate the regional value of the target regional resource characteristic block based on climate regulation, flood regulation, water conservation and oxygen release and carbon fixation;

[0067] The regional optimization module 300 is used to conduct risk assessment on the resource characteristic blocks in the target area based on the environmental variable data and the ecosystem data to obtain the risk assessment results, and analyze the expected change trend, and generate optimization recommendation decisions based on the regional value, risk assessment results and expected change trend.

[0068] As a preferred embodiment of the present invention, the region feature recognition module 100 includes:

[0069] A remote sensing image segmentation unit is used to preprocess the regional remote sensing image and segment the preprocessed remote sensing image into a plurality of regional objects, wherein the segmentation method is threshold-based segmentation or edge-based segmentation;

[0070] A feature extraction unit, used to extract spectral features, texture features and shape features from the segmented regional objects, wherein the spectral features include reflectance and brightness value, and the texture features include gray-level co-occurrence matrix and local binary pattern;

[0071] The target area resource feature block unit is used to classify the pixel objects in the remote sensing image into different categories according to the extracted features and the random forest classification algorithm to obtain the resource feature blocks of each target area.

[0072] As a preferred embodiment of the present invention, the regional resource assessment module 200 includes:

[0073] Parameter index collection unit, used to collect climate regulation index, flood regulation index, water conservation index and oxygen release and carbon fixation index of forests, grasslands, farmlands and waters;

[0074] The above only describes in detail the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0075] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0076] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0077] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A target area resource analysis method based on regional remote sensing technology, characterized in that: The method comprises the following steps: Collecting regional remote sensing images, and performing feature recognition on the regional remote sensing images to obtain one or more target regional resource feature blocks, wherein the target regional resource feature blocks include forest feature blocks, grassland feature blocks, farmland feature blocks, and water feature blocks; Obtain environmental variable data and ecosystem data of the target area resource characteristic blocks. Environmental variable data include climate regulation, flood regulation and water conservation. Ecosystem data include ecosystem diversity and oxygen release and carbon fixation. Calculate the regional value of the target area resource characteristic blocks based on climate regulation, flood regulation, water conservation and oxygen release and carbon fixation. Conduct risk assessment on the resource characteristic blocks in the target area based on environmental variable data and ecosystem data to obtain risk assessment results, analyze and obtain expected change trends, and generate optimization recommendations and decisions based on the regional value, risk assessment results and expected change trends; The step of performing feature recognition on the regional remote sensing image to obtain one or more target regional resource feature blocks specifically includes: Preprocessing the regional remote sensing image, segmenting the preprocessed remote sensing image into multiple regional objects, the segmentation method is threshold-based segmentation or edge-based segmentation; Extracting spectral features, texture features and shape features from the segmented regional objects, wherein the spectral features include reflectance and brightness value, and the texture features include gray-level co-occurrence matrix and local binary pattern; According to the extracted features and random forest classification algorithm, the pixel objects in the remote sensing image are divided into different categories to obtain the resource feature blocks of each target area; According to the extracted features and the random forest classification algorithm, the pixel objects in the remote sensing image are divided into different categories to obtain the resource feature blocks of each target area. The corresponding process has the following relationship: ； in, Indicates that the pixel object is classified as t The probability of the class, Indicates t category, The feature vector of the pixel object formed by spectral features, texture features and shape features, represents the number of trees in the random forest, Indicates j The adaptive weight of each tree, represents the exponential function, represents the number of features, Indicates k The weight coefficient of the feature, Expressing the j The tree i Pixel objects are transformed to perform feature transformation operations. Indicates k The weight coefficient of the global feature, Indicates the global feature transformation operation on the feature vector of the pixel object. Indicates j The tree i The classification decision output of pixel objects, Indicates bias.

2. The target area resource analysis method based on regional remote sensing technology according to claim 1 is characterized in that: The preprocessing of the regional remote sensing image, segmenting the preprocessed remote sensing image into a plurality of regional objects, wherein the segmentation method is a threshold-based segmentation or an edge-based segmentation step, specifically includes: Convert the regional remote sensing image into a grayscale image, apply a Gaussian blur operation to the grayscale image, and obtain a blurred grayscale image; Use LBP to encode the relationship between each pixel and its neighboring pixels in the blurred grayscale image to obtain LBP features; Use the Sobel operator to calculate the gradients of the blurred grayscale image in the horizontal and vertical directions respectively, and combine the gradients in the two directions to get the gradient amplitude of the image; The blurred grayscale image, LBP features and gradient amplitude are combined to form a multi-dimensional feature vector, where each pixel corresponds to a multi-dimensional feature vector; The KMeans clustering algorithm is used to divide the multidimensional feature vector into several categories to obtain the category label of each pixel; Remap the category label of each pixel to each position of the regional remote sensing image to form a segmented image; The Otsu threshold segmentation operation is used to find the optimal threshold for segmenting the image, and the segmented image is binarized using the optimal threshold to obtain the threshold segmented image; The threshold segmentation image is closed to fill the small holes in the image, and then opened to remove the residual noise in the image to obtain a refined segmentation image.

3. The target area resource analysis method based on regional remote sensing technology according to claim 1 is characterized in that: The steps of evaluating the regional value of the resource characteristic blocks in the target area based on climate regulation, flood regulation, water conservation, and oxygen release and carbon fixation specifically include: Collect climate regulation index, flood regulation index, water conservation index and oxygen release and carbon fixation index of forests, grasslands, farmlands and waters; Automatically identify the area of ​​resource characteristic blocks in each target area, and evaluate the regional value based on the area of ​​resource characteristic blocks in each target area and the corresponding climate regulation index, flood regulation index, water conservation index and oxygen release and carbon fixation index.

4. The target area resource analysis method based on regional remote sensing technology according to claim 3 is characterized in that: The regional value is evaluated based on the area of ​​the resource characteristic blocks of each target area and the corresponding climate regulation index, flood regulation index, water conservation index and oxygen release and carbon fixation index. The corresponding process has the following relationship: ； in, represents the total area value, Indicates the number of resource feature blocks in the target area. Indicates n The area of ​​the resource characteristic blocks in the target area, Indicates n The climate regulation index of the resource characteristic blocks in the target area, Indicates n The flood regulation index of the resource characteristic blocks in the target area, Indicates n Water conservation index of resource characteristic blocks in target areas, Indicates n Oxygen release and carbon fixation index of resource characteristic blocks in target areas, They represent the weights of climate regulation index, flood regulation index, water conservation index, and oxygen release and carbon fixation index, respectively. They respectively represent the fuzzy membership values ​​corresponding to the climate regulation index, flood regulation index, water conservation index, and oxygen release and carbon fixation index.

5. The target area resource analysis method based on regional remote sensing technology according to claim 4 is characterized in that: Conduct risk assessment on the resource characteristic blocks in the target area based on environmental variable data and ecosystem data to obtain risk assessment results, and analyze the expected change trend. Generate optimization recommendations based on the regional value, risk assessment results and expected change trend, including: Normalizing and standardizing the environmental variable data and the ecosystem data in turn to obtain preprocessed environmental variable data and ecosystem data; According to the preset scoring rules, weights are assigned to the preprocessed environmental variable data and ecosystem data; Calculate and sum the weighted values ​​of the preprocessed environmental variable data and ecosystem data to obtain a comprehensive risk assessment value; Obtain historical data of the target area, including historical risk assessment values, historical environmental variable data, and historical ecosystem data; A spatiotemporal prediction model is constructed using a long short-term memory network, and the spatiotemporal prediction model is trained using historical data of the target area to obtain a trained spatiotemporal prediction model; Input the current risk assessment value, environmental variable data and ecosystem data into the trained spatiotemporal prediction model to obtain the predicted value of future change trend; Construct a multi-objective optimization model with ecosystem health, economic benefits and social impact factors as optimization objectives and land management measures and water resource management measures as input variables; According to the regional value, comprehensive risk assessment value and future change trend forecast value, combined with land management measures and water resources management measures, the optimization target value of each resource characteristic block in the target area is calculated through a multi-objective optimization method to obtain the optimal land management measures and water resources management measures.

6. The target area resource analysis method based on regional remote sensing technology according to claim 5 is characterized in that: The step of preprocessing the regional remote sensing image specifically includes: Perform radiation correction on regional remote sensing images and adjust image data to eliminate the influence of atmosphere and solar altitude angle on images; Perform geometric correction to correct the geometric distortion in the image; Perform image enhancement to improve image quality through histogram equalization and filtering methods, and increase the distinguishability of different types of objects.

7. A target area resource analysis system based on regional remote sensing technology, the system is applied to the target area resource analysis method based on regional remote sensing technology according to any one of claims 1 to 6, characterized in that: The system comprises: A regional feature recognition module is used to collect regional remote sensing images, perform feature recognition on the regional remote sensing images to obtain one or more target regional resource feature blocks, wherein the target regional resource feature blocks include forest feature blocks, grassland feature blocks, farmland feature blocks and water feature blocks; The regional resource assessment module is used to obtain the environmental variable data and ecosystem data of the target regional resource characteristic blocks. The environmental variable data include climate regulation, flood regulation and water conservation. The ecosystem data include ecosystem diversity and oxygen release and carbon fixation. The regional value of the target regional resource characteristic blocks is calculated based on climate regulation, flood regulation, water conservation and oxygen release and carbon fixation. The regional optimization module conducts risk assessment on the resource characteristic blocks in the target area based on environmental variable data and ecosystem data to obtain risk assessment results, and analyzes the expected change trends, and generates optimization recommendation decisions based on the regional value, risk assessment results and expected change trends.

8. The target area resource analysis system based on regional remote sensing technology according to claim 7 is characterized in that: The regional feature recognition module comprises: A remote sensing image segmentation unit is used to preprocess the regional remote sensing image and segment the preprocessed remote sensing image into a plurality of regional objects, wherein the segmentation method is threshold-based segmentation or edge-based segmentation; A feature extraction unit, used to extract spectral features, texture features and shape features from the segmented regional objects, wherein the spectral features include reflectance and brightness value, and the texture features include gray-level co-occurrence matrix and local binary pattern; The target area resource feature block unit is used to classify the pixel objects in the remote sensing image into different categories according to the extracted features and the random forest classification algorithm to obtain the resource feature blocks of each target area.

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