Land utilization information rapid calculation and key protection area discrimination method and system
By integrating lidar and interferometric synthetic aperture radar data to analyze canopy height changes and combining them with an ecological factor scoring model, the problem of identifying the risk of degradation of the internal structure of forest land was solved, enabling efficient monitoring of forest land status and accurate delineation of key protected areas.
Patent Information
- Application Number
- CN202511181527.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-22
- Publication Date
- 2025-12-05
AI Technical Summary
Existing technologies are insufficient to identify the three-dimensional changes, subtle decline trends, and potential ecological risks within forest land, leading to the neglect of forest ecological degradation processes, biased assessments of forest carbon sequestration capacity, and omissions and delays in the delineation of key protected areas.
By fusing lidar waveform data and interferometric synthetic aperture radar elevation image data, a time-series fused elevation dataset is constructed to analyze the trend of canopy height changes. Combined with ecological factor data, a multi-factor scoring model is established to identify key protected areas.
It enables efficient identification of subtle forest degradation trends, improves the efficiency of rapid calculation of forest status changes, dynamically identifies and extracts the boundaries of key protected areas, and enhances the accuracy of forest carbon sequestration capacity assessment and the precision of protected areas.
Smart Images
Figure CN121074644A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of land use planning technology, specifically to a method and system for rapid calculation of land use information and identification of key protected areas. Background Technology
[0002] With the continuous advancement of ecological protection policies and the increasing demand for refined supervision of natural resources, the dynamic acquisition of land use information and the accurate identification of key ecological areas have become one of the important foundational tasks in the fields of land spatial planning and forestry management. In particular, in forest land, the health status of forest land is directly related to the implementation effect of national strategic goals such as carbon sequestration accounting, biodiversity maintenance, and ecological compensation. Rapidly acquiring information on structural changes in forest areas through remote sensing technology, and on this basis, carrying out high-timeliness and high-precision identification of key protected areas, is a key technological breakthrough direction for ecological monitoring and land management.
[0003] Existing land use information identification technologies mostly rely on optical remote sensing images combined with land surface classification algorithms. They mainly focus on the two-dimensional spatial distribution of land types. However, they neglect the three-dimensional changes, slight decline trends, and potential ecological risks of the canopy structure within forest land, as well as the structural degradation risks caused by changes in canopy height. This leads to the long-term neglect of forest ecological degradation processes, biases in the assessment of forest carbon sequestration capacity, and omissions and delays in the delineation of key protection areas. Summary of the Invention
[0004] The purpose of this invention is to provide a method and system for rapid calculation of land use information and identification of key protected areas, in order to solve the problems mentioned in the background art, such as the three-dimensional changes, slight decline trends and potential ecological risks of the canopy structure within forest land, as well as the structural degradation risks brought about by changes in canopy height, which can lead to the long-term neglect of forest ecological degradation processes, deviations in the assessment of forest carbon sequestration capacity, and omissions and delays in the delineation of key protected areas.
[0005] To achieve the above objectives, the technical solution of the present invention is: a method for rapid calculation of land use information and identification of key protected areas, comprising:
[0006] S1. Acquire multi-source remote sensing data of forest areas and preprocess them to obtain a time-series fused elevation dataset;
[0007] S2. Based on the time-series fused elevation dataset, the canopy elevation change data is constructed using the time series method. The trend of canopy height change is determined by the time-series elevation difference analysis, and the forest land change areas are classified and labeled to obtain structural change identification data.
[0008] S3. Based on structural change identification data and ecological factor data, establish a multi-factor scoring model to conduct a comprehensive ecological score of forest areas and obtain ecological score values for different forest areas.
[0009] S4. Identify key protected forest areas based on the ecological score values of different regions.
[0010] Preferably, in step S1, the multi-source remote sensing data includes lidar waveform data and interferometric synthetic aperture radar (ICSR) elevation image data; the preprocessing includes performing canopy elevation inversion processing on the lidar waveform data, performing terrain elevation extraction processing on the ICSR elevation image data; and performing spatial registration and rasterization fusion in the same coordinate system on the lidar waveform data and the ICSR elevation image data to obtain a temporal fused elevation dataset covering the forest area.
[0011] Preferably, the specific method for spatial registration and rasterization fusion of lidar waveform data and interferometric synthetic aperture radar elevation image data in the same coordinate system is as follows:
[0012] Resampling is performed according to the preset spatial resolution, and a weighted average algorithm is used for fusion at the pixel level. The data fusion weights in the weighted average algorithm are dynamically allocated based on the confidence levels of the lidar waveform data and the interferometric synthetic aperture radar elevation image data. The temporal fusion elevation dataset includes data timestamps and canopy spatial location information.
[0013] Preferably, in step S2, the canopy height change data for consecutive time periods is obtained by performing interpolation on the fused elevation dataset according to the time series, and the forest land change areas are classified and labeled according to a preset height change threshold, and divided into three types of forest land change areas: slightly declining areas, significantly declining areas, and stable areas.
[0014] Among them, the three types of forest land change areas obtained by classification and labeling are output as structural change identification data layers in a raster encoding manner.
[0015] Preferably, the preset height change threshold is set according to the height change distribution of the historical time series in the fused elevation dataset, and the height change values below the 5th percentile and above the 95th percentile are extracted using the quantile statistics method as the judgment boundary for significant decline and slight decline, respectively.
[0016] The classification tag value of each pixel in the structural change identifier data layer is stored as an unsigned 8-bit integer value.
[0017] Preferably, in S3, the multi-factor scoring model takes structural change identification data as input and introduces ecological factor data, and uses a weighted overlay method to comprehensively score the three types of forest land change areas;
[0018] Among them, ecological factor data includes vegetation index data, slope data, and habitat connectivity data.
[0019] Preferably, the weight coefficients of the ecological factor data are determined using the analytic hierarchy process (AHP), as follows:
[0020] Construct pairwise comparison judgment matrices between vegetation index data, slope data, and habitat connectivity data; calculate the eigenvectors of the judgment matrices to obtain the relative weights of each ecological factor; normalize the relative weights of each ecological factor to obtain the weight coefficients of each ecological factor data.
[0021] Preferably, the ecological score is output in a spatial gridded form and encoded in a floating-point raster data structure, with each score pixel maintaining the same spatial resolution as the original fused elevation dataset.
[0022] Preferably, in step S4, key protected forest areas are identified based on the ecological score values of different regions, as follows:
[0023] Based on the ecological score, spatial aggregation analysis is performed on different forest areas to identify contiguous forest areas with ecological scores higher than a set threshold and areas larger than the minimum protection unit area. In the identified contiguous forest areas, a boundary contour extraction algorithm is used to extract their spatial boundary information, and this boundary information is used to generate a key protection zone boundary layer in vector format for dynamic updating of key forest protection areas.
[0024] On the other hand, the present invention provides a land use information quick calculation and key protection zone identification system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the above-described land use information quick calculation and key protection zone identification method.
[0025] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial technical effects:
[0026] 1. In this invention, a time-series fused elevation dataset under a unified coordinate system is constructed based on the fusion processing of lidar waveform data and interferometric synthetic aperture radar elevation image data. This enables continuous analysis of the canopy structure in forest areas at multiple temporal scales, improving the ability to identify subtle forest degradation trends and the efficiency of rapid calculation of forest state changes.
[0027] 2. In this invention, by jointly modeling structural change identification data and ecological factor data, an ecological scoring model is established and spatial aggregation analysis is performed to achieve dynamic identification and boundary extraction of key protected forest areas. Attached Figure Description
[0028] Figure 1 This is a flowchart of one embodiment of the present invention. Detailed Implementation
[0029] Example 1, as Figure 1 As shown, the specific implementation steps of the land use information rapid calculation and key protection zone identification method proposed in this invention are as follows:
[0030] S1. Acquire multi-source remote sensing data of forest areas and preprocess them to obtain a time-series fused elevation dataset;
[0031] In this embodiment S1, the multi-source remote sensing data includes lidar waveform data and interferometric synthetic aperture radar (ISSAR) elevation image data; the preprocessing includes performing canopy elevation inversion processing on the lidar waveform data and performing terrain elevation extraction processing on the ISAR elevation image data; and performing spatial registration and rasterization fusion in the same coordinate system on the lidar waveform data and the ISAR elevation image data to obtain a temporal fused elevation dataset covering the forest area.
[0032] In this embodiment, spatial registration and rasterization fusion in the same coordinate system are performed on lidar waveform data and interferometric synthetic aperture radar elevation image data. The specific method is as follows:
[0033] Resampling is performed according to the preset spatial resolution, and a weighted average algorithm is used for fusion at the pixel level. The data fusion weights in the weighted average algorithm are dynamically allocated based on the confidence levels of the lidar waveform data and the interferometric synthetic aperture radar elevation image data. The temporal fusion elevation dataset includes data timestamps and canopy spatial location information.
[0034] In this embodiment, lidar waveform data is mainly used for vertical structure inversion from the top of the canopy to the ground, providing information on canopy height distribution; interferometric synthetic aperture radar (ISAR) elevation image data is used for terrain surface elevation modeling, possessing strong regional coverage and penetration capabilities. To achieve effective integration of these two heterogeneous data sources, the lidar waveform data is first processed for canopy elevation inversion, extracting the distance between the top of the canopy and the ground for each waveform echo, forming canopy height point cloud data with spatial positioning capabilities; subsequently, the IAR elevation image data is processed for terrain elevation extraction, obtaining a large-scale continuous surface elevation grid. Since the two types of data differ in spatial resolution, coordinate system, and time acquisition period, they need to be processed uniformly under the same coordinate reference system.
[0035] In this embodiment, a spatial registration and rasterization fusion method is adopted. First, the two types of data are resampled and unified to a preset spatial resolution of 30 meters. Then, the map boundaries and pixel centers of the two data sources are spatially aligned using a two-way registration algorithm. After fusion, the data is organized in a raster structure to form a temporal fusion elevation dataset covering the target forest area.
[0036] S2. Based on the time-series fused elevation dataset, the canopy elevation change data is constructed using the time series method. The trend of canopy height change is determined by the time-series elevation difference analysis, and the forest land change areas are classified and labeled to obtain structural change identification data.
[0037] In this embodiment S2, by performing interpolation on the fused elevation dataset according to the time series, the data on the change in canopy height over a continuous period is obtained, and the forest land change areas are classified and labeled according to the preset height change threshold, and divided into three types of forest land change areas: slightly declining area, significantly declining area, and stable area.
[0038] Among them, the three types of forest land change areas obtained by classification and labeling are output as structural change identification data layers in a raster encoding manner.
[0039] In this embodiment, the preset height change threshold is set according to the height change distribution of the historical time series in the fused elevation dataset. The height change values below the 5th percentile and above the 95th percentile are extracted using the quantile statistical method as the judgment boundary between significant decline and slight decline.
[0040] The classification tag value of each pixel in the structural change identifier data layer is stored as an unsigned 8-bit integer value.
[0041] In this embodiment, height change values below the 5th percentile indicate significant degradation; height change values above the 95th percentile indicate slight degradation; therefore, each pixel is classified into the following three categories according to its change value:
[0042] A height change below the 5th percentile is considered a significant decline zone; a height change between the 5th and 95th percentiles is considered a slight decline zone; and a height change above the 95th percentile is considered a stable zone.
[0043] S3. Based on structural change identification data and ecological factor data, establish a multi-factor scoring model to conduct a comprehensive ecological score of forest areas and obtain ecological score values for different forest areas.
[0044] In this embodiment S3, the multi-factor scoring model takes structural change identification data as input and introduces ecological factor data, and uses a weighted overlay method to comprehensively score the three types of forest land change areas;
[0045] Among them, ecological factor data includes vegetation index data, slope data, and habitat connectivity data.
[0046] In this embodiment, the weight coefficients of the ecological factor data are determined using the analytic hierarchy process (AHP), as follows:
[0047] Construct pairwise comparison judgment matrices between vegetation index data, slope data, and habitat connectivity data; calculate the eigenvectors of the judgment matrices to obtain the relative weights of each ecological factor; normalize the relative weights of each ecological factor to obtain the weight coefficients of each ecological factor data.
[0048] In this embodiment, the ecological score is output in a spatial gridded form and encoded in a floating-point raster data structure, with each score pixel maintaining the same spatial resolution as the original fused elevation dataset.
[0049] S4. Identify key protected forest areas based on the ecological score values of different regions;
[0050] In this embodiment S4, key protected forest areas are identified based on the ecological score values of different regions, as detailed below:
[0051] Based on the ecological score, spatial aggregation analysis is performed on different forest areas to identify contiguous forest areas with ecological scores higher than a set threshold and areas larger than the minimum protection unit area. In the identified contiguous forest areas, a boundary contour extraction algorithm is used to extract their spatial boundary information, and this boundary information is used to generate a key protection zone boundary layer in vector format for dynamic updating of key forest protection areas.
[0052] Example 2: The land use information quick calculation and key protection zone identification system proposed in this invention is applied to the land use information quick calculation and key protection zone identification method proposed in Example 1. It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the land use information quick calculation and key protection zone identification method in Example 1.
[0053] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.
Claims
1. A method for rapid calculation of land use information and identification of key protection areas, characterized in that, The method comprises the following steps: S1, acquiring multi-source remote sensing data of forest regions and performing preprocessing to obtain a time-series fusion height data set; S2, based on the time-series fusion height data set, using a time-series method to construct forest canopy height change data, determining the forest canopy height change trend through time-series height difference analysis, and classifying and marking the forest change regions to obtain structure change identification data; S3, based on the structure change identification data and ecological factor data, establishing a multi-factor scoring model to perform ecological comprehensive scoring on the forest regions to obtain ecological score values of different forest regions; S4, identifying key protected forest regions according to the ecological score values of different regions.
2. The method according to claim 1, wherein: In S1, the multi-source remote sensing data comprises laser radar waveform data and interferometric synthetic aperture radar height image data; the preprocessing comprises performing forest canopy height inversion processing on the laser radar waveform data, and performing terrain height extraction processing on the interferometric synthetic aperture radar height image data; and the laser radar waveform data and the interferometric synthetic aperture radar height image data are subjected to spatial registration and rasterization fusion under the same coordinate system to obtain the time-series fusion height data set covering the forest regions.
3. The method according to claim 2, wherein: The spatial registration and rasterization fusion of the laser radar waveform data and the interferometric synthetic aperture radar height image data are performed according to the following method: Resampling processing is performed according to a preset spatial resolution, and weighted average algorithm is used for fusion at the pixel level; wherein the data fusion weight in the weighted average algorithm is dynamically allocated according to the confidence of the laser radar waveform data and the interferometric synthetic aperture radar height image data; and the time-series fusion height data set comprises data time stamp and forest canopy spatial position information.
4. The method according to claim 3, wherein: In S2, the fusion height data set is subjected to difference operation according to the time sequence to obtain forest canopy height change data in continuous time periods, and the forest change regions are classified and marked according to a preset height change threshold to divide into three forest change regions of slight recession area, significant recession area and stable area; The three forest change regions classified and marked are output as a structure change identification data layer in the form of grid coding.
5. The method according to claim 4, wherein: The preset height change threshold is set according to the height change distribution of the historical time sequence in the fusion height data set, and the quantile statistical method is used to extract the height change values below the 5th percentile and above the 95th percentile as the judgment boundaries of significant recession and slight recession; The classification mark value of each pixel in the structure change identification data layer is stored in the form of unsigned 8-bit integer value.
6. The method according to claim 5, wherein: In S3, the multi-factor scoring model takes the structure change identification data as input, introduces ecological factor data, and uses weighted superposition to perform comprehensive scoring on the three forest change regions; The ecological factor data comprises vegetation index data, slope data and habitat connectivity data.
7. The method according to claim 6, wherein: The weight coefficients of the ecological factor data are determined by using the analytic hierarchy process, and the specific method is as follows: A pairwise comparison judgment matrix is constructed among the vegetation index data, slope data and habitat connectivity data; a characteristic vector of the judgment matrix is calculated to obtain the relative weight of each ecological factor; and the relative weight of each ecological factor is normalized to obtain the weight coefficient of each item of ecological factor data.
8. The method according to claim 7, wherein: The ecological score value is output in a spatial gridding form and encoded in a floating-point grid data structure, and the spatial resolution of each score pixel is consistent with that of the original fused elevation data set.
9. The method according to claim 8, wherein: In the S4, according to the ecological score values of different regions, a key protected forest region is identified, and the specific process is as follows: According to the ecological score values, spatial aggregation analysis is performed on different forest regions to identify a continuous forest region with an ecological score value higher than a set threshold and an area greater than a minimum protection unit area; in the identified continuous forest region, a boundary contour extraction algorithm is used to extract spatial boundary information, and the boundary information is generated in a vector format to generate a key protection area boundary layer, which is used for dynamically updating the key protection area of the forest land.
10. A land use information rapid calculation and key protection area discrimination system, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The processor executes a computer program to implement the land use information rapid calculation and key protection area identification method of any one of claims 1-9.