Forest degeneration degree, degeneration type and degeneration process identification method

Abstract

The invention discloses a forest degeneration degree, degeneration type and degeneration process identification method, and relates to the field of forest resource monitoring and ecological environment evaluation.The method comprises the steps that multi-source data obtained in a research area is preprocessed, and the preprocessed multi-source data is determined; selecting multi-dimensional indexes from three aspects of forest degradation structure, composition and function, and constructing a multi-dimensional index system according to the preprocessed multi-source data; the multi-dimensional indexes comprise forest coverage rate, crushing degree, tree variety diversity, aboveground biomass and net primary productivity; generating a forest degradation index according to each index in the multi-dimensional index system; determining a forest degeneration degree according to the forest degeneration index, generating a forest degeneration type graph in combination with land coverage data to reveal degeneration differences of different forest types, analyzing a dynamic change track of a forest degeneration region in combination with a normalized combustion index time sequence, and identifying a forest degeneration process; according to the method, the multi-dimensional characteristics of degradation can be comprehensively revealed.

Description

Technical Field

[0001] This application relates to the field of forest resource monitoring and ecological environment assessment, and in particular to a method for identifying the degree, type and process of forest degradation. Background Technology

[0002] Forests are the largest terrestrial ecological carbon sink on Earth, playing a vital role in climate regulation, water conservation, and biodiversity protection. However, forest degradation has become a major concern in global climate governance. In developing countries, forest degradation is considered a significant contributor to greenhouse gas emissions, accounting for a substantial proportion of global greenhouse gas emissions caused by land use and cover change. International organizations have demonstrated that curbing forest loss and degradation, along with strengthening sustainable forest management, protection, and restoration, can significantly enhance the carbon sink function of forests, possessing enormous emission reduction potential. Despite the widespread existence of forest degradation, it receives relatively less attention compared to deforestation. This is because forest degradation typically manifests as a partial reduction in biomass and carbon storage, rather than the complete removal of vegetation, thus its carbon emissions are often more insidious. However, while less severe than deforestation, forest degradation can have more extensive and long-term negative impacts on ecosystems, including the continued loss of biodiversity, weakened material cycling, and a decline in ecosystem services.

[0003] Currently, although researchers have recognized the problem of forest degradation and are attempting to restore forest carbon storage through various measures, significant challenges remain in effectively monitoring forest degradation. Existing methods lack precision in identifying degradation types, analyzing degradation processes, and quantifying degradation intensity, making it difficult to support accurate forest quality assessments and scientific forest management decisions. Therefore, there is an urgent need to develop a new technology that can integrate multi-source data and time series analysis to achieve accurate identification of the degree, type, and process of forest degradation.

[0004] In recent years, forest degradation has gradually become a core issue in forest ecology and global change research, with related studies mainly focusing on degradation identification methods, degradation severity, degradation mechanisms, and their ecological impacts. Traditionally, forest degradation is identified by establishing fixed sample plots and conducting field surveys to determine the degraded area and rate within each plot, and then extrapolating this to the entire study area. However, this method is not only resource-intensive, but fixed sample plots, due to long-term monitoring, often receive more protection than surrounding forest land, potentially underestimating the actual degree of degradation and affecting the representativeness and accuracy of the assessment.

[0005] With the development of science and technology, remote sensing technology, with its high efficiency, accuracy, and wide monitoring range, has been widely used in identifying the degree, type, and process of forest degradation. Constructing multiple vegetation indices using optical remote sensing imagery can effectively identify typical degradation patterns such as fire, drought, and insect infestations, and reveal their driving factors. Calculating vegetation indices based on long-term MODIS and Landsat data allows for large-scale dynamic monitoring of degradation, validating the applicability and advantages of remote sensing methods at regional scales. Meanwhile, lidar and radar remote sensing offer high accuracy in acquiring structural dimension information and can be combined with optical remote sensing to improve the accuracy of degradation identification. In existing studies, many methods primarily rely on single indicators (such as changes in vegetation indices) for degradation detection, often only identifying severe degradation and failing to comprehensively reveal the multidimensional characteristics of degradation. Forest degradation, as a complex ecological process, involves not only reduced vegetation cover but also decreased carbon storage, loss of species diversity, and intensified landscape fragmentation. Therefore, there is an urgent need to develop an integrated method based on multi-source data and multi-indicator system to comprehensively identify degradation characteristics from multiple dimensions of forest structure, composition and function, and combine time series information to achieve dynamic characterization of degradation process, thereby achieving accurate grading and classification of degradation degree. Summary of the Invention

[0006] The purpose of this application is to provide a method for identifying the degree, type, and process of forest degradation, in order to solve the problem that traditional methods rely on a single indicator for forest degradation detection, which makes it difficult to fully reveal the multidimensional characteristics of degradation.

[0007] To achieve the above objectives, this application provides the following solution: This application provides a method for identifying the degree, type, and process of forest degradation, including: The multi-source data obtained from the study area were preprocessed to determine the preprocessed multi-source data. Multidimensional indicators were selected from three aspects of forest degradation structure, composition and function, and a multidimensional indicator system was constructed based on the preprocessed multi-source data; wherein the multidimensional indicators include forest coverage, fragmentation, tree species diversity, aboveground biomass and net primary productivity. Based on the various indicators in the multidimensional indicator system, a forest degradation index is generated; The degree of forest degradation is determined based on the forest degradation index. Combined with land cover data, a forest degradation type map is generated to reveal the degradation differences of different forest types. Furthermore, by combining the normalized rate of combustion index time series, the dynamic change trajectory of forest degradation areas is analyzed to identify the forest degradation process.

[0008] According to the specific embodiments provided in this application, this application has the following technical effects: This application preprocesses multi-source data obtained from the study area and selects key indicators such as forest coverage, fragmentation, tree species diversity, aboveground biomass, and net primary productivity from different aspects of forest structure, composition, and function to construct a multi-dimensional indicator system. It uses multiple different indicators to generate forest degradation indicators to generate a forest degradation type map, identify the degradation patterns and processes of different forest types, and, compared to the limitations of traditional single-indicator degradation detection, uses multiple indicators for degradation detection to comprehensively reveal the multi-dimensional characteristics of degradation. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating a method for identifying the degree, type, and process of forest degradation in one embodiment of this application. Figure 2 This is a diagram showing the results of the spatiotemporal fusion model. Figure 3 Spatial distribution map of changes in key indicators of forest degradation from 2010 to 2020; Figure 4 This is a degradation process classification model accuracy map and degradation degree map in the embodiments of this application; Figure 5 This is a classification diagram of the degradation process in the embodiments of this application; Figure 6 This is a precision diagram of the degradation process classification model in the embodiments of this application; Figure 7 This is a schematic diagram of a method for identifying the degree, type, and process of forest degradation in one embodiment of this application. Detailed Implementation

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] To make the objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0013] like Figure 1As shown in the embodiments of this application, a method for identifying the degree, type, and process of forest degradation is provided, including: S1: Preprocess the multi-source data obtained from the study area to determine the multi-source data to be preprocessed.

[0014] S2: Select multidimensional indicators from three aspects of forest degradation structure, composition and function, and construct a multidimensional indicator system based on the preprocessed multi-source data; wherein, the multidimensional indicators include forest coverage, fragmentation, tree species diversity, aboveground biomass and net primary productivity.

[0015] S3: Generate a forest degradation index based on the various indicators in the multidimensional indicator system.

[0016] S4: Determine the degree of forest degradation based on the forest degradation index, generate a forest degradation type map by combining land cover data to reveal the degradation differences of different forest types, and analyze the dynamic change trajectory of forest degradation areas by combining the normalized burn index time series to identify the forest degradation process.

[0017] In an exemplary embodiment, S1 specifically includes: The multi-source data includes optical imagery data, radar imagery data, topographic data, climate data, and forest resource survey data.

[0018] S11: Fill in the missing positions in the optical image data based on different spatiotemporal fusion models, and determine the filled optical image data.

[0019] S12: Filter, remove speckles and perform geometric correction on the radar image data to determine the processed radar image data.

[0020] S13: The terrain data is resampled and projected in a unified manner to obtain terrain factor data with the same spatial resolution as other data; the other data includes optical image data, radar image data and climate data.

[0021] S14: Resample, reproject, and time-match the climate data (including temperature, precipitation, and solar radiation) to obtain climate variable data that is time-consistent with the optical image data. S15: Standardize and format the forest resource survey data to extract auxiliary information; the auxiliary information includes forest stand type, tree species, tree height, diameter at breast height, age group, and interference records.

[0022] S16: Based on the filled optical image data, processed radar image data, terrain factor data, climate variable data and auxiliary information, determine the preprocessed multi-source data.

[0023] In practical applications, multi-source data are used, including Landsat surface reflectance products, long-term optical image data from the Moderate-resolution Imaging Spectroradiometer (MODIS), ALOS-2 PALSAR radar image data, climate data, topographic data, and forest resource survey data. After preprocessing the optical images, three spatiotemporal fusion models are compared: Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and Flexible Spatiotemporal Data Fusion (FSDAF). The STARFM model, which performs best, is selected to fill in the gaps in the optical images of the study area. SAR images undergo preprocessing such as filtering, speckle removal, and geometric correction. Forest resource survey data are standardized and formatted to extract auxiliary information such as stand type, tree species, tree height, diameter at breast height (DBH), age group, and interference records.

[0024] Data acquisition and preprocessing specifically included: screening Landsat series surface reflectance products and MODIS imagery covering the study area within the research period. Landsat data included imagery acquired by three sensors: Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI). For MODIS data, MCD43A4 data was selected as a long-term series surface reflectance product, providing daily reflectance observations corrected using the Bidirectional Reflectance Distribution Function (BRDF). Simultaneously, the Normalized Difference Vegetation Index (NDVI) band from the MODIS monthly vegetation index product MOD13Q1 (spatial resolution 250 m) was selected for dynamic monitoring of vegetation cover. The selected images were masked with cloud / shadow / snow using the CFmask algorithm; the selected images were then cropped and stitched together according to the boundaries of the study area using the ee.mosaic and ee.clip functions in the cloud geospatial analysis platform (Google Earth Engine, GEE).

[0025] Download ALOS-2 PALSAR radar images covering the study area, and perform preprocessing operations such as radiometric calibration, geometric correction, and speckle noise suppression on the images to obtain radar intensity images with a projection coordinate system and spatial range consistent with the optical data.

[0026] The downloaded climate data includes temperature, precipitation, and solar radiation data. Temperature and precipitation data were downloaded from the National Tibetan Plateau Scientific Data Center and downscaled to a spatial resolution of 1 km. Solar radiation data was sourced from the GEE platform's ECMWF / ERA5_LAND / MONTHLY_AGGR product. This climate data underwent reprojection and resampling to ensure consistency with optical remote sensing imagery in spatial resolution and coordinate system, providing input for the Carnegie-Ames-Stanford Approach (CASA) model to estimate net primary productivity (NPP).

[0027] The downloaded terrain data uses SRTMGL1_003 global digital elevation data with a spatial resolution of 30m, and slope and aspect data are derived from it.

[0028] The land cover data product uses the GLC_FCS30D global 30m land cover dataset, which is constructed based on multi-source remote sensing image fusion and deep learning methods, and can provide detailed information on the distribution of land use / cover types.

[0029] Furthermore, Tier 2 reflectivity data from Collection 2 scenes of Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI were obtained based on the GEE cloud platform; MODIS data included MCD43A4 and MOD13Q1 data; ALOS-2 PALSAR SAR data; climate data including precipitation, temperature, and solar radiation; topographic data including elevation, slope, and aspect; and concurrent forest resource inventory data.

[0030] Furthermore, based on forest resource survey data, forest cover (FC) of the sample plots was calculated using plot area and canopy closure information; tree species diversity (TSD) was calculated using the Shannon–Wiener Index based on the tree species composition and number of individuals in the sample plots; and above-ground biomass (AGB) of the sample plots was calculated based on the established allometric growth equation, taking into account the diameter at breast height (DBH), height, and age of the dominant tree species.

[0031] In an exemplary embodiment, after preprocessing the optical image, three spatiotemporal fusion models—STARFM, ESTARFM, and FSDAF—are used to fill in the missing locations in the Landsat image. The performance of these three models in terms of temporal consistency, spatial detail preservation, and filling accuracy is compared using qualitative and quantitative indicators. The comprehensive evaluation results show that the STARFM model performs best in this application area, effectively recovering missing or cloud-covered Landsat optical images. S11 specifically includes: S111: Three spatiotemporal fusion models, STARFM, ESTARFM, and FSDAF, are used to fill in the missing positions in the optical image data, and the filling results are compared.

[0032] S112: Select the spatiotemporal fusion model with the best filling effect; the best spatiotemporal fusion model is STARFM; the best model corresponding to STARFM is: in, Optical image data filled in by STARFM; MODIS optical imagery for the predicted time; MODIS optical image at reference time; Landsat optical image at reference time; () represents the pixel position; The image acquisition times are the baseline time and the predicted time, respectively. For the search window size; () indicates the center cell of the search window; i,j For spatial location index; k For paired cell indexes; n The number of similar pixels; weight The contribution of each neighboring pixel to the estimated reflectance of the center pixel.

[0033] S113: Determine the infilled optical image data based on the optimal spatiotemporal fusion model.

[0034] In practical applications, the ESTARFM model: in, n This indicates the number of similar pixels of the same type within the moving window. For the first i The ratio of the reflectance change of fine image pixels to the reflectance change of mixed coarse image pixels.

[0035] FSDAF model: in, They are in and Constantly observed at coarse resolution pixel position The first in j Each fine resolution pixel value The weights corresponding to similar pixels. This indicates the amount of change in land cover type over time for pixels in a Landsat fine-resolution image.

[0036] In this application, the STARFM model was ultimately selected to fill in the optical image, as it provided the best filling effect. Figure 2 As shown, where, Figure 2 (a) in the image is the Landsat predicted date image. Figure 2 (b) in the image is the MODIS predicted date image. Figure 2 (c) in the figure represents the STARFM fusion result. Figure 2 (d) in the figure represents the ESTARFM fusion result. Figure 2 (e) in the figure represents the FSDAF fusion result.

[0037] In an exemplary embodiment, S2 specifically includes: Starting from the multidimensional characteristics of forest degradation, this study selects five key indicators—forest cover (FC), forest fragmentation (FFI), tree species diversity (TSD), aboveground biomass (AGB), and net primary productivity (NPP)—from three aspects: structure, composition, and function, to construct a comprehensive Forest Degradation Index (FDI). Specifically, FC is obtained by reclassifying forest land types and performing area statistics based on the China Annual Thematic Classification Dataset (CATCD); FFI is obtained by comprehensively calculating landscape pattern indices such as edge density, patch density, and average patch area from land cover data; TSD is based on tree species information from sample plots, using the InceptionTime deep learning architecture to build a predictive model and achieve spatial estimation; AGB inversion is based on dominant tree species information from sample plot surveys, using feature variables extracted from multi-source remote sensing data to construct a machine learning regression model to obtain the spatial distribution results of biomass at the regional scale; and NPP is calculated pixel-by-pixel using the CASA model based on light energy utilization theory. The above methods form a multi-indicator system encompassing structure, composition, and function, providing data support for subsequent comprehensive evaluation and process identification of forest degradation.

[0038] In practical applications, the forest structure degradation index FC is processed using CATCD data. CATCD data is resampled at the plot scale, and correlation analysis is performed between the FC values ​​at each plot and the plot canopy closure. The results show low correlation in non-forest areas (e.g., correlation < 0.3). Therefore, in subsequent processing, land cover data is used to divide forest and non-forest areas, and the CATCD data values ​​for non-forest areas are uniformly assigned to 0 to eliminate the interference of non-forest pixels on the FC estimation results.

[0039] Forest structure degradation index (FFI) is used to quantitatively characterize the spatial integrity of forest landscapes and is an important indicator for measuring the degree of forest ecosystem degradation. This application selects three landscape pattern indices—Edge Density (ED), Patch Density (PD), and Mean Patch Area (MPA)—to comprehensively quantify forest fragmentation from three dimensions: boundary complexity, patch number, and patch size. The calculation method is as follows: Edge density ED: .

[0040] Patch density (PD): .

[0041] Mean plaque area (MPA): .

[0042] in, The total side length (unit: m). For the number of plaques, A Total landscape area (unit: square meters, with grid units considered as landscape). AREA [ patch ij [] represents the area of ​​each patch (in hectares). First, the land cover data is reclassified into a binary layer (Category 0: non-forest, Category 1: forest); then, using the "landscapemetrics" package in R, the three landscape pattern indices are calculated; finally, the obtained index values ​​are summed to obtain the overall fragmentation index of the forest landscape.

[0043] Forest structure degradation was characterized using the Shannon Diversity Index (TSD). Tree species information was extracted from plot survey data, and the Shannon diversity index was calculated as the TSD value. An InceptionTime deep learning model was used, with Landsat image features as input and the TSD value as the training objective, for model training and optimization. The trained model was applied to remote sensing imagery of the study area to achieve spatial prediction of tree species diversity, generate a Shannon index distribution map, and thus obtain the TSD index. The TSD index effectively reflects the complexity and stability of forest structure, providing important support for the quantitative identification of degradation levels.

[0044] The forest function degradation index (AGB) was calculated based on forest resource survey data from 2010 and 2020. Individual tree AGB was calculated using allometric growth equations for dominant tree species (mainly birch, Scots pine, and Dahurian larch). The AGB per unit area (t / ha) was obtained by summing the biomass of all trees in the sample plot and dividing by the plot area. This AGB was then used as training and validation data for the model. The calculation method for the allometric growth equations of dominant tree species is as follows: Birch: .

[0045] Pinus sylvestris: .

[0046] Larch: .

[0047] in, D The data represents the diameter at breast height (DBH) of a single tree. Subsequently, remote sensing features were extracted from the remote sensing imagery, including: original single-band reflectance, vegetation index, texture features calculated based on a 5×5 sliding window, and topographic and climatic features.

[0048] The Recursive Feature Elimination (RFE) algorithm is used to filter all candidate features and retain the optimal feature variables for modeling.

[0049] Subsequently, the AGB data from the sample plots were randomly divided into a training set (80%) and a test set (20%). AGB inversion modeling was performed using the Random Forest (RF) algorithm in the R language environment. The model hyperparameters (including the number of trees and node depth) were optimized through grid search to obtain the optimal model parameters and generate a spatial distribution map of the AGB in the study area.

[0050] The forest function degradation index, NPP, is calculated pixel-by-pixel using the CASA model based on light energy use efficiency (LUE) theory. The NPP calculation method is as follows: Net primary productivity (NPP): in, It represents the actual light energy utilization rate (i.e. the efficiency of converting unit radiation energy into plant carbon), and the unit is (gof C / MJ). It is calculated from two factors: the maximum conversion efficiency specific to the biological community and the effects of temperature and water on plant photosynthesis; APAR The photosynthetically active radiation absorbed by vegetation is expressed as follows: Photosynthetically active radiation: in, SOL It is expressed as total solar radiation, and the unit (MJ / m²) is derived from the Era5 dataset in GEE. FPAR The value ranges from 0 to 1, depending on the vegetation type and vegetation cover. In the CASA model... FPAR The solar radiation (NPP) was calculated using the Normalized Difference Vegetation Index (NDVI); a coefficient of 0.5 was used to convert solar radiation into photosynthetically active radiation (PAR). Finally, the spatial distribution of NPP in the study area was calculated pixel-by-pixel to reflect the degree of forest function degradation. All index calculation results are as follows: Figure 3 As shown.

[0051] In an exemplary embodiment, visual interpretation results of ESRI high-resolution imagery are used as a reference. The correlation between each indicator and the interpretation results is used to replace the subjective scoring step in the traditional AHP method, ensuring the objectivity and scientific nature of the weight determination.

[0052] A key step in integrating the selected indicators is assigning weights to each indicator to quantify their relative contribution to identifying forest degradation. The Analytic Hierarchy Process (AHP) is used for weighting. First, negative indicators (FFIs) are positiveized to unify their dimensions and direction. Then, the correlation coefficients between each candidate indicator and the visual interpretation results are calculated, replacing the expert scoring step to reduce subjectivity. Next, a pairwise comparison judgment matrix is ​​constructed, scoring the indicators based on their relative importance. Finally, the Consistency Ratio (CR) is used to verify the reasonableness of the weights, thus obtaining the Forest Degradation Index (FDI). S3 specifically includes: The analytic hierarchy process (AHP) is used to standardize and assign weights to each indicator in the multidimensional indicator system to generate a forest degradation index.

[0053] in, Assigning weights to each indicator. The values ​​represent the corresponding indicators. A CR < 0.1 indicates a successful consistency check, demonstrating that the weight allocation is scientifically sound and suitable for FDI construction. FDI values ​​are normalized to 0–2; higher values ​​indicate gradual forest recovery, while lower values ​​indicate gradual forest degradation. The FDI threshold is determined using a visual interpretation method.

[0054] In the classification process, the determination of the FDI threshold mainly considers two aspects: First, actual image changes. By visually interpreting the high-resolution images of the sample points during the study period, the FDI distribution intervals corresponding to sample points with no change, positive change, and negative change are determined to ensure that the threshold classification is consistent with the actual observation. Second, the optimization of classification accuracy. The confusion matrix is ​​calculated among multiple candidate thresholds and the overall accuracy (OA) and Kappa coefficient are compared. The threshold combination that maximizes the accuracy index is selected to improve the reliability and stability of degradation and restoration classification.

[0055] In one exemplary embodiment, S4 specifically includes: S411: When the forest degradation index is less than 0.6, the forest degradation level is determined to be severe degradation. S412: When the forest degradation index is in the range of [0.6, 0.9), the forest degradation level is determined to be of the mild degradation type.

[0056] S413: When the forest degradation index is in the range of [0.9, 1.1], the forest degradation level is determined to be of the "no change" type.

[0057] S414: When the forest degradation index is in (1.1, 1.4], the forest degradation level is determined to be of the mild recovery type.

[0058] S415: When the forest degradation index is greater than 1.4, the forest degradation level is determined to be of the complete restoration type.

[0059] S416: Combining land cover data, the degradation types of forests in the study area are identified according to different degrees of forest degradation, and a forest degradation type map is generated to reveal the degradation differences of different forest types.

[0060] Forest state identification: First, the study area was divided into a uniform grid, and all grid points were extracted as samples, resulting in 72 sample points. Due to the lack of clear images for some sample points, 71 valid sample points were ultimately retained. Subsequently, based on high-resolution imagery provided by Google Earth and Esri, combined with visual interpretation technology, the forest changes at each sample point were analyzed and classified into three categories: Degradation: The sample sites showed significant negative changes during the study period, including reduced trees, fragmented canopies, decreased biomass, and increased disturbance, without showing a significant recovery trend. Degradation was further subdivided into severe degradation and mild degradation.

[0061] No change: The forest conditions at the sample sites remained basically stable during the study period, with no significant changes.

[0062] Recovery: The sample sites showed positive changes during the study period, including an increase in the number of trees, an increase in forest canopy density, and an increase in biomass; recovery was further subdivided into mild recovery and complete recovery.

[0063] The classification accuracy of the ensemble model is calculated using the partitioned validation set. The precision, recall, and overall accuracy of each class are calculated to measure the model's classification performance.

[0064] Accuracy verification results show that this application performs well overall in identifying the three forest states: degraded, stable, and restored. Of the 25 samples in the degraded category, 23 were correctly identified, with a producer's accuracy (PA) of 0.90 and a user's accuracy (UA) of 0.92. Overall, 61 out of 71 verification samples were correctly classified. Further analysis of area statistics shows that the areas of degraded, stable, and restored forests in the study area are 2589.0 km², 4321.6 km², and 966.8 km², respectively, accounting for 33%, 55%, and 12% of the total area.

[0065] In an exemplary embodiment, S4 specifically includes: based on the LandTrendr algorithm, calling the built-in interface of the GEE platform to perform segmented fitting of the normalized combustion index time series in the study area, analyzing the dynamic change trajectory of the forest degradation area, and identifying the forest degradation process.

[0066] In practical applications, the process of generating forest degradation type maps by combining FDI and land cover data is as follows: First, the GLC_FCS30 land cover data classification product is used, and the land cover data for the corresponding area is masked according to the vector boundary of the study area. Then, land cover classification layers for the start and end years of the study period are extracted. Finally, using the statistical module in ArcGIS, the main vegetation cover type with the highest pixel count in each year is obtained.

[0067] Next, the main vegetation categories of the two years are combined to construct a vegetation classification map of the study area. Finally, the degradation degree classification map and the vegetation cover classification map are overlaid to generate a comprehensive degradation type classification map of the study area. In the specific implementation process, each pixel in the degradation degree classification map is traversed. If the pixel belongs to the main vegetation cover type in the vegetation classification map, it is reclassified as "XX Forest (severe degradation / mild degradation / recovery)" according to its degradation level. If the pixel does not belong to any of the main vegetation categories of the study area, it is assigned a NoData value (255) to mark it as a non-main vegetation area. The result is as follows. Figure 4 As shown.

[0068] Combining the Lantr forest disturbance detection algorithm with the location map identified by FDI, forest degradation processes are identified. The specific process is as follows: A time series is constructed using the Normalized Burn Ratio (NBR). Leveraging its sensitivity to disturbances such as fires, windbreaks, and pests and diseases, the dynamic changes in degraded areas are characterized. The NBR index is calculated as follows: NBR Index: in: NIR It is the near-infrared band (Band 4 in Landsat TM / ETM+ imagery, Band 5 in OLI). SWIR It is the shortwave infrared band (Band 7 in Landsat imagery).

[0069] The LandTrendr algorithm was used to fit the time series data. First, the built-in LandTrendr API was called on the GEE platform. The runLT function was used to perform piecewise regression fitting on the original NBR time series images of the study area and automatically detect inflection points. To ensure fitting accuracy and stability, the LandTrendr parameters were set as follows: maximum number of segments (maxSegments) = 6, spikeThreshold = 0.9, vertexCountOvershoot = 3, recoveryThreshold = 0.25, minimum number of observations (minObservationsNeeded) = 6, preventOneYearRecovery = true, significance level threshold (pvalThreshold) = 0.05, and bestModelProportion = 0.75.

[0070] For the forest degradation areas identified by FDI, the LandTrendr algorithm is further applied to perform NBR time series fitting to obtain the change trajectory of degradation locations and quantify the degradation process.

[0071] Taking the Tuqiang Forestry Bureau in the Greater Khingan Mountains as an example, the methods for analyzing the degree, type, and process of forest degradation were applied to the Tuqiang area, resulting in a map of the degree of forest degradation in the Tuqiang area (e.g., Figure 4 (As shown), a map of forest degradation types (such as...) Figure 5 (as shown) and a map of forest degradation (as shown) Figure 6 (as shown in (a)-(c)).

[0072] like Figure 7As shown, this application can efficiently identify the spatial location and degree of forest degradation within a large-scale area, and further obtain the degradation type and degradation process. First, by introducing a spatiotemporal fusion method to fill in high-quality long-term optical imagery, and combining multi-source data such as SAR, climate, topography, and forest inventory, a feature set covering spectral, texture, topographic, and environmental factors is constructed to provide stable data support for the calculation and estimation of degradation indices. Second, key indicators such as forest cover (FC), fragmentation (FFI), tree species diversity (TSD), aboveground biomass (AGB), and net primary productivity (NPP) are selected from three dimensions: forest structure, composition, and function. The AHP method is used to assign weights based on the correlation of interpretation results, and a comprehensive forest degradation index (FDI) is generated to effectively achieve quantitative characterization and spatial inversion of the degree of degradation. Third, a degradation type map is generated by combining FDI and land cover data to intuitively reveal the degradation patterns of different forest types. Finally, the LandTrendr algorithm is used to fit the NBR time series to characterize the dynamic change trajectory of the degradation area and realize dynamic monitoring of the degradation process. The overall technical solution breaks through the limitations of traditional methods that rely on a single index, improves the comprehensiveness, refinement and accuracy of degradation identification, provides scientific basis and data support for the monitoring, evaluation and restoration of degraded forests, and has good practicality and prospects for promotion.

[0073] Compared with traditional algorithms for identifying the degree, type, and process of forest degradation, this application comprehensively selects forest cover, fragmentation, tree species diversity, aboveground biomass, and net primary productivity as key indicators from three dimensions: forest structure, composition, and function. This overcomes the limitations of traditional methods that rely on only a single indicator for degradation identification, enabling multi-dimensional, comprehensive, and refined identification of the degree, type, and process of forest degradation. By introducing a spatiotemporal fusion method to effectively fill in high-quality long-term optical imagery, and combining SAR, climate, and forest resource survey data, a multi-source feature set is constructed and feature selection is performed, ensuring the stability and accuracy of indicator inversion and spatial estimation. Deep learning and machine learning algorithms are used to improve the spatial estimation accuracy of key indicators. The correlation of visual interpretation results is introduced into the weight allocation, avoiding the uncertainty of subjective scoring in traditional AHP methods, making the degradation index construction more objective and reliable. Ultimately, this application enables the quantitative characterization of forest degradation, the spatial identification of degradation types, and the dynamic monitoring of degradation processes. It is particularly suitable for cold-temperate forest areas with frequent disturbances, providing a scientific basis for forest ecosystem health assessment, carbon storage accounting, and sustainable management.

[0074] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0075] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for identifying the degree, type and process of forest degradation, characterized in that, The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device.

2. The method of claim 1, wherein the method further comprises: The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device.

3. The method of claim 2, wherein the forest degradation level, type and process are identified by, The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device.

4. The method of claim 3, wherein the forest degradation level, type and process are identified by, The application relates to a forest degradation index generation method and device. where, is the filled optical image data for STARFM; is the MODIS optical image at the prediction time; is the MODIS optical image at the reference time; is the Landsat optical image at the reference time; is the pixel position; is the image acquisition time at the reference time and the prediction time, respectively; is the search window size; is the center pixel of the search window; The application relates to a forest degradation index generation method and device. is the spatial position index; k is the paired pixel index; n is the number of similar pixels; weight is the contribution of each neighboring pixel pair to the estimated reflectance of the center pixel. 5.The method of claim 1, wherein, The application relates to a forest degradation index generation method and device. 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The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation index generation method and device. The application relates to a forest degradation Based on the above-mentioned aboveground biomass information of the sample plot, a random forest model is used to carry out inversion and spatial estimation of the aboveground biomass, so as to determine the aboveground biomass; Based on the MODIS NDVI data, precipitation, air temperature and solar radiation data, the CASA model is used to calculate the net primary productivity pixel by pixel. 6.The method of claim 1, wherein, According to each index in the multi-dimensional index system, a forest degradation index is generated, specifically including: The analytic hierarchy process is used to standardize and distribute the weights of each index in the multi-dimensional index system, and a forest degradation index is generated. 7.The method of claim 1, wherein, According to the forest degradation index, the forest degradation degree is determined, and the land cover data is combined to generate a forest degradation type map to reveal the degradation differences of different forest types, specifically including: When the forest degradation index is less than 0.6, the forest state is determined to be severely degraded; When the forest degradation index is in [0.6, 0.9), the forest state is determined to be slightly degraded; When the forest degradation index is in [0.9, 1.1], the forest state is determined to be unchanged; When the forest degradation index is in (1.1, 1.4], the forest state is determined to be recovered; When the forest degradation index is greater than 1.4, the forest degradation degree is determined to be completely recovered type. In combination with the land cover data, the forests in the study area are identified according to different forest degradation degrees to generate a forest degradation type map to reveal the degradation differences of different forest types. 8.The method of claim 1, wherein, In combination with the time series of the normalized combustion index, the dynamic change trajectory of the forest degradation area is analyzed, and the forest degradation process is identified, specifically including: Based on the LandTrendr algorithm, the built-in interface of the GEE platform is called to segmentally fit the time series of the normalized combustion index in the study area, analyze the dynamic change trajectory of the forest degradation area, and identify the forest degradation process. 9.The method of claim 8, wherein, The parameters in the process of quantifying the forest degradation position include maxSegments=6, spikeThreshold=0.9, vertexCountOvershoot=3, recoveryThreshold=0.25, minObservationsNeeded=6, preventOneYearRecovery=true, pvalThreshold=0.05 and bestModelProportion=0.75.

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