Method and system for evaluating forest-water complex ecological region

Abstract

The present application relates to a kind of forest water compound ecological region evaluation method and system.The forest water compound ecological region evaluation system includes: obtaining the image data of forest water compound ecological region;Image data includes rain season data and non-rain season image data;Forest water compound ecological region is identified based on image data;Based on the vegetation frequency of water body based on non-rain season image data;Based on the water body frequency of forest land based on rain season image data;The forest water compound degree of forest water compound ecological region is obtained;Based on the threshold value evaluation system of vegetation frequency, water body frequency, forest water compound degree and zero model, forest water compound ecological region is evaluated and classified.The above-mentioned forest water compound ecological region evaluation method can accurately evaluate and classify different forest water compound ecological regions, provide scientific basis for the fine management of urban ecological system, and can optimize ecological construction and management.

Description

Technical Field

[0001] This invention relates to the field of ecological assessment technology, and in particular to an assessment method and system for forest-water complex ecological areas. Background Technology

[0002] Global wetland area has decreased by nearly 20% over the past three centuries, and the degradation and loss of wetland ecosystems has become a global environmental problem. Forest-water complex ecosystems (such as aquatic forests and riparian woodlands) are considered important vehicles for improving the development and protection of urban ecological spaces, playing a crucial role in maintaining biodiversity, improving water quality, regulating microclimates, and enhancing urban livability.

[0003] Unlike coastal wetlands, urban forest-water complex ecosystems have unique characteristics: First, their flooding patterns are irregular, requiring long-term monitoring to understand their variations, whereas coastal wetlands are affected by tides and exhibit regular flooding patterns. Second, urban forest-water complex areas may exist as ground cover vegetation during the non-rainy season, but experience prolonged flooding during the rainy season; this seasonal variation places special demands on assessment methods. Third, in recent years, the policy demand for improving the water landscape level of urban parks and other landscape systems has been increasing, requiring scientific methods to determine the order and methods of water landscape optimization. Existing technologies are mostly based on static characteristics of a single time phase, which is insufficient to meet the special needs of assessing forest-water complex ecosystems.

[0004] Effectively identifying, assessing, and optimizing forest-water composite habitats is a major challenge for urban ecological planning and management. Summary of the Invention

[0005] Therefore, it is necessary to provide an assessment method and system for forest-water composite ecological areas. This invention, considering the unique characteristics of urban forest-water composite ecosystems, establishes a systematic assessment method by comprehensively considering the vegetation frequency of water bodies, the water frequency of forest land, and the degree of forest-water integration. This method can provide a scientific basis for the refined management and landscape optimization of urban ecosystems.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a method for assessing forest-water composite ecological zones, the method comprising:

[0007] Acquire image data of forest-water complex ecological areas; the image data includes rainy season data and non-rainy season image data.

[0008] Based on the image data, land use classification is performed on the forest-water composite ecological area, where land use includes water bodies and forest land.

[0009] The vegetation frequency of the water body is obtained based on the non-rainy season image data;

[0010] The water frequency of the forest land is obtained based on the rainy season image data;

[0011] Obtain the degree of forest-water integration in the aforementioned forest-water composite ecological region;

[0012] The forest-water composite ecological region is assessed and classified based on the vegetation frequency, the water body frequency, the degree of forest-water integration, and a zero-model threshold assessment system.

[0013] The aforementioned assessment method for forest-water composite ecological areas includes: acquiring image data of the forest-water composite ecological area; identifying land use types (including water bodies and forest land) based on the image data; acquiring vegetation frequencies of the water bodies; acquiring water body frequencies of the forest land; acquiring the degree of forest-water integration in the forest-water composite ecological area; and assessing and classifying the forest-water composite ecological area based on the vegetation frequencies, water body frequencies, and the degree of forest-water integration. This assessment method for forest-water composite ecological areas can accurately assess and classify different forest-water composite ecological areas, providing a scientific basis for the refined management of urban ecosystems and optimizing ecological construction and management.

[0014] In some embodiments, obtaining the vegetation frequency of the water body based on the non-rainy season image data includes:

[0015] Multiple effective observations were conducted on the water body to obtain non-rainy season image data from multiple effective observations;

[0016] Based on the non-rainy season image data, the enhanced vegetation index, normalized vegetation index, and land surface moisture index were obtained.

[0017] The enhanced vegetation index, the normalized vegetation index, and the land surface moisture index are used to determine whether the water body is a vegetation-covered area.

[0018] The ratio of the number of times the water body was identified as a vegetation-covered area to the number of valid observations is taken as the vegetation frequency of the water body.

[0019] In some embodiments, the image data includes Sentinel-2 L1C image data; obtaining the enhanced vegetation index, normalized difference vegetation index, and land surface moisture index based on the non-rainy season image data includes:

[0020] The enhanced vegetation index is obtained based on the following formula:

[0021]

[0022] Wherein, EVI is the enhanced vegetation index; NIR is the reflectance in the near-infrared band; RED is the reflectance in the red band; and BLUE is the reflectance in the blue band.

[0023] The normalized vegetation index is obtained based on the following formula:

[0024]

[0025] Wherein, NDVI is the normalized vegetation index; NIR is the reflectance in the near-infrared band; and RED is the reflectance in the red band.

[0026] The land surface moisture index is obtained based on the following formula:

[0027]

[0028] Wherein, LSWI is the land surface moisture index; NIR is the reflectance in the near-infrared band; and SWIR is the reflectance in the short-wave infrared band.

[0029] In some embodiments, determining whether the water body is a vegetation-covered area based on the enhanced vegetation index, the normalized difference vegetation index, and the land surface moisture index includes:

[0030] If EVI ≥ 0.1, NDVI ≥ 0.2 and LSWI > 0, then the water body is determined to be the vegetation-covered area.

[0031] In some embodiments, obtaining the water body frequency of the woodland based on the rainy season image data includes:

[0032] Multiple effective observations were conducted on the woodland to obtain rainy season image data from multiple effective observations;

[0033] Based on the aforementioned rainy season image data, the enhanced vegetation index, normalized vegetation index, and improved normalized differential water index were obtained.

[0034] The forest land is determined to be a water body based on the enhanced vegetation index, the normalized vegetation index, and the improved normalized differential water body index.

[0035] The ratio of the number of times the forest land was identified as a water body to the number of valid observations is taken as the water body frequency of the forest land.

[0036] In some embodiments, obtaining the enhanced vegetation index, normalized vegetation index, and improved normalized differential water index based on the rainy season image data includes:

[0037] The enhanced vegetation index is obtained based on the following formula:

[0038]

[0039] Wherein, EVI is the enhanced vegetation index; NIR is the reflectance in the near-infrared band; RED is the reflectance in the red band; and BLUE is the reflectance in the blue band.

[0040] The normalized vegetation index is obtained based on the following formula:

[0041]

[0042] Wherein, NDVI is the normalized vegetation index; NIR is the reflectance in the near-infrared band; and RED is the reflectance in the red band.

[0043] The improved normalized differential water body index is obtained based on the following formula:

[0044]

[0045] Wherein, MNDWI is the improved normalized differential water index; GREEN is the reflectance in the green light band; and SWIR is the reflectance in the shortwave infrared band.

[0046] In some embodiments, determining whether the forest land is a water body based on the enhanced vegetation index, the normalized vegetation index, and the improved normalized differential water index includes:

[0047] If EVI < 0.1, MNDWI > EVI, or MNDWI > NDVI, then the woodland is determined to be a water body.

[0048] In some embodiments, obtaining the degree of forest-water integration in the forest-water integrated ecological region includes:

[0049] Identify the shared boundary between the water body and the woodland;

[0050] Obtain the length of the shared boundary;

[0051] Obtain the total circumference of the water body;

[0052] The ratio of the perimeter of the shared boundary to the total perimeter is used as the degree of forest-water integration in the forest-water integrated ecological region.

[0053] In some embodiments, the threshold assessment system based on the vegetation frequency, the water body frequency, the degree of forest-water integration, and the null model assesses and classifies the forest-water integrated ecological region, including:

[0054] The threshold evaluation system based on the null model determines the preset threshold through threshold sensitivity evaluation, stability evaluation, and classification balance evaluation.

[0055] Water bodies that share boundaries with forest land and have a vegetation frequency not lower than a preset threshold are classified as ecological integration zones; the ecological integration zone represents an ideal forest-water composite state with a complete water-land transition zone and stable vegetation cover.

[0056] Water bodies that share a boundary with forest land and whose vegetation frequency is lower than the preset threshold are classified as basic forest-water boundary zones; the basic forest-water boundary zones represent a transitional state with adjacent forest and water but insufficient vegetation cover.

[0057] Water bodies that do not share boundaries with forest land and whose vegetation frequency is not lower than the preset threshold, and forest land with water body frequency not lower than the preset threshold, are classified as water-forest areas to be restored; the water-forest areas to be restored represent areas with siltation foundations or poor drainage and ecological restoration potential.

[0058] Secondly, the present invention also provides an assessment system for forest-water composite ecological regions, the assessment system comprising:

[0059] Data acquisition equipment is used to acquire image data of forest-water complex ecological areas; the image data includes rainy season data and non-rainy season image data;

[0060] The land use identification module is used to identify land use types in the forest-water composite ecological area based on the image data, wherein the land use types include water bodies and forest land.

[0061] A vegetation frequency acquisition module is used to acquire the vegetation frequency of the water body based on the non-rainy season image data.

[0062] A water frequency acquisition module is used to acquire the water frequency of the forest land based on the rainy season image data.

[0063] The forest-water composite degree acquisition module is used to acquire the forest-water composite degree of the forest-water composite ecological area;

[0064] The assessment and classification module is used to assess and classify the forest-water composite ecological area based on the vegetation frequency, the water body frequency, the degree of forest-water integration, and a threshold assessment system of the null model.

[0065] The aforementioned assessment system for forest-water composite ecological areas includes: data acquisition equipment, a land type identification module, a vegetation frequency acquisition module, a water body frequency acquisition module, a forest-water composite degree acquisition module, and an assessment and classification module. This system can accurately assess and classify different forest-water composite ecological areas, providing a scientific basis for the refined management of urban ecosystems and optimizing ecological construction and management. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a flowchart of an assessment method for a forest-water composite ecological area provided in one embodiment of the present invention;

[0068] Figure 2 A graph showing the changing trend of the area of ​​the ecological integration zone, the basic forest-water boundary zone, and the water-forest zone to be restored, as divided in step S60 under different thresholds (including the preset threshold);

[0069] Figure 3 The slope of the curve representing the area of ​​the water-forest region to be restored at different threshold points;

[0070] Figure 4 The rate of change of the slope of the curve representing the area of ​​the water and forest region to be restored;

[0071] Figure 5 This is a structural block diagram of an assessment system for a forest-water composite ecological region provided in another embodiment of the present invention.

[0072] Figure labeling: 10, Data acquisition equipment; 11, Land use identification module; 12, Vegetation frequency acquisition module; 13, Water body frequency acquisition module; 14, Forest-water composite degree acquisition module; 15, Assessment and classification module. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0074] The main technical solutions related to the spatial identification and quantification of the potential for forest-water composite construction can include: (1) Wetland vegetation extraction methods based on remote sensing images, which use vegetation indices (such as NDVI, EVI) and water body indices (such as MNDWI) to extract wetland vegetation, but it is difficult to accurately identify vegetation covered by water bodies; (2) Multi-temporal remote sensing data analysis, which captures the seasonal changes of vegetation and water bodies by analyzing remote sensing images from multiple temporal phases, but it requires a large amount of data and is complex to process; (3) Machine learning classification methods, which use deep learning and other technologies to classify remote sensing images and identify forest-water composite areas, but it requires high quality and quantity of training samples; (4) Ecological suitability assessment, which assesses the suitability of a region for constructing a forest-water composite ecosystem based on factors such as topography and hydrology, but it is difficult to reflect the dynamic changes of the system; (5) Landscape pattern analysis, which assesses the degree of forest-water composite by analyzing the spatial relationship between forest land and water bodies, but it is difficult to reflect the internal structure and function of the system. These methods still have limitations in the accurate identification, dynamic monitoring and ecological function assessment of forest-water composite ecosystems, and are difficult to provide comprehensive and accurate decision support for urban ecological planning and management.

[0075] In one embodiment, see Figure 1 The present invention provides a method for assessing forest-water composite ecological areas, which may include the following steps: S10~S60.

[0076] S10: Acquire image data of the forest-water complex ecological area; the image data includes rainy season data and non-rainy season image data.

[0077] S20: Based on the image data, land use classification is performed on the forest-water composite ecological area, where the land use includes water bodies and forest land.

[0078] S30: Obtain the vegetation frequency of the water body based on the non-rainy season image data.

[0079] S40: Obtain the water frequency of the forest land based on the rainy season image data.

[0080] S50: Obtain the degree of forest-water integration in the forest-water integrated ecological zone.

[0081] S60: The forest-water composite ecological region is assessed and classified based on the vegetation frequency, the water body frequency, the degree of forest-water integration, and the threshold assessment system of the null model.

[0082] The aforementioned assessment method for forest-water composite ecological areas includes: acquiring image data of the forest-water composite ecological area; identifying land use types (including water bodies and forest land) based on the image data; acquiring vegetation frequencies of the water bodies; acquiring water body frequencies of the forest land; acquiring the degree of forest-water integration in the forest-water composite ecological area; and assessing and classifying the forest-water composite ecological area based on the vegetation frequencies, water body frequencies, and the degree of forest-water integration. This assessment method for forest-water composite ecological areas can accurately assess and classify different forest-water composite ecological areas, providing a scientific basis for the refined management of urban ecosystems and optimizing ecological construction and management.

[0083] The forest-water complex ecosystem targeted by this invention possesses unique ecological characteristics. Unlike coastal wetlands, forest-water complex ecosystems may exhibit ground cover vegetation during the non-rainy season, but may be submerged for extended periods during the rainy season. This submersion scenario is irregular, requiring three consecutive years of monitoring data to accurately grasp its changing patterns. Furthermore, the vegetation in the forest-water complex ecosystem must possess the ability to adapt to periodic submersion environments, which places special demands on the root structure and physiological characteristics of the vegetation.

[0084] In step S10, please refer to Figure 1 In step S10, image data of the forest-water complex ecological area is acquired; the image data includes rainy season data and non-rainy season image data.

[0085] As an example, Sentinel-2 imagery data from recent years (e.g., three years) can be used as the imagery data; that is, the imagery data can be Sentinel-2 L1C imagery data. L1C imagery data represents atmospheric apparent reflectance data after orthorectification and sub-pixel geometric correction. The imagery data can include rainy season imagery data and non-rainy season imagery data.

[0086] In step S20, please refer to Figure 1 In step S20, land use classification is performed on the forest-water composite ecological area based on the image data, and the land use includes water bodies and forest land.

[0087] As an example, the most frequently occurring land type in each cell can be extracted as the land type for the current cell. Among them, the land type of submerged vegetation belongs to water bodies.

[0088] As an example, during the land cover identification process, the focus can be on the image data during the rainy season, and the maximum water body extent can be extracted.

[0089] In step S30, please refer to Figure 1In step S30, the vegetation frequency of the water body is obtained based on the non-rainy season image data.

[0090] As an example, step S30, which involves obtaining the vegetation frequency of the water body based on the non-rainy season image data, may include the following steps: S301~S304.

[0091] S301: Perform multiple effective observations on the water body to obtain non-rainy season image data from multiple effective observations.

[0092] S302: Based on the non-rainy season image data, the enhanced vegetation index, normalized vegetation index, and land surface moisture index are obtained.

[0093] S303: Determine whether the water body is a vegetation-covered area based on the enhanced vegetation index, the normalized vegetation index, and the land surface moisture index.

[0094] S304: The ratio of the number of times the water body is identified as a vegetation-covered area to the number of valid observations is taken as the vegetation frequency of the water body.

[0095] As an example, step S302, which involves obtaining the enhanced vegetation index, normalized difference vegetation index, and land surface moisture index based on the non-rainy season image data, may include:

[0096] The enhanced vegetation index is obtained based on the following formula:

[0097]

[0098] Wherein, EVI (Enhanced Vegetation Index) is the enhanced vegetation index; NIR is the reflectance in the near-infrared band; RED is the reflectance in the red band; and BLUE is the reflectance in the blue band.

[0099] The normalized vegetation index is obtained based on the following formula:

[0100]

[0101] Wherein, NDVI (Normalized Difference Vegetation Index) is the normalized vegetation index; NIR is the reflectance in the near-infrared band; and RED is the reflectance in the red band.

[0102] The land surface moisture index is obtained based on the following formula:

[0103]

[0104] Wherein, LSWI (Land Surface Water Index) is the land surface water index; NIR is the reflectance in the near-infrared band; and SWIR is the reflectance in the short-wave infrared band.

[0105] Specifically, NIR can be the reflectance of the near-infrared band (B8 band); RED is the reflectance of the red light band (B4 band); BLUE is the reflectance of the blue light band (B2 band); and SWIR is the reflectance of the short-wave infrared band (B11 band).

[0106] As an example, in step S303, determining whether the water body is a vegetation-covered area based on the enhanced vegetation index, the normalized difference vegetation index, and the land surface moisture index includes: if EVI ≥ 0.1, NDVI ≥ 0.2, and LSWI > 0, then the water body is determined to be a vegetation-covered area. That is, when all three conditions are met simultaneously, the water body is determined to be a vegetation-covered area.

[0107] As an example, in step S304, the vegetation frequency can be a percentage, that is, the ratio of the number of times the water body is identified as a vegetation-covered area to the number of valid observations converted into a percentage is the vegetation frequency; here, the number of times the water body is identified as a vegetation-covered area refers to the number of times the water body is identified as a vegetation-covered area during the non-rainy season, and the number of valid observations refers to the number of valid observations during the non-rainy season. The specific formula for the vegetation coverage rate P_veg can be as follows:

[0108]

[0109] Among them, N_veg_non_rainy represents the number of times the water body was identified as a vegetation-covered area during the non-rainy season, and N_total_non_rainy represents the number of valid observations during the non-rainy season.

[0110] Special attention is paid to imagery data from the non-rainy season because the water level is lower during the non-rainy season, which allows for a more accurate identification of vegetation cover in water bodies, thereby assessing the degree of siltation and the stage of ecological succession.

[0111] Based on meteorological and hydrological data analysis, the months with the highest annual precipitation are defined as the rainy season (e.g., April to September in Shanghai), and the months with the lowest precipitation are defined as the non-rainy season (e.g., October to March of the following year in Shanghai).

[0112] In step S40, please refer to Figure 1 In step S40, the water frequency of the forest land is obtained based on the rainy season image data.

[0113] As an example, step S40, which involves obtaining the water frequency of the forest land based on the rainy season image data, may include the following steps: S401~S404.

[0114] S401: Conduct multiple effective observations of the forest land to obtain multiple effective observations of rainy season image data.

[0115] S402: Based on the rainy season image data, obtain the enhanced vegetation index, normalized vegetation index, and improved normalized differential water index.

[0116] S403: Determine whether the forest land is a water body based on the enhanced vegetation index, the normalized vegetation index, and the improved normalized differential water body index.

[0117] S404: The ratio of the number of times the forest land was identified as a water body to the number of valid observations is taken as the water body frequency of the forest land.

[0118] As an example, in step S402, obtaining the enhanced vegetation index, normalized vegetation index, and improved normalized differential water index based on the rainy season image data includes:

[0119] The enhanced vegetation index is obtained based on the following formula:

[0120]

[0121] Wherein, EVI is the enhanced vegetation index; NIR is the reflectance in the near-infrared band; RED is the reflectance in the red band; and BLUE is the reflectance in the blue band.

[0122] The normalized vegetation index is obtained based on the following formula:

[0123]

[0124] Wherein, NDVI is the normalized vegetation index; NIR is the reflectance in the near-infrared band; and RED is the reflectance in the red band.

[0125] The improved normalized differential water body index is obtained based on the following formula:

[0126]

[0127] Wherein, MNDWI (Modified Normalized Difference Water Index) is the modified normalized difference water index; GREEN is the reflectance in the green light band (B3 band); and SWIR is the reflectance in the shortwave infrared band.

[0128] As an example, in step S403, determining whether the forest land is a water body based on the enhanced vegetation index, the normalized difference vegetation index, and the improved normalized difference water index may include: if EVI < 0.1, MNDWI > EVI, or MNDWI > NDVI, then the forest land is determined to be a water body. That is, when any one of the conditions EVI < 0.1, MNDWI > EVI, and MNDWI > NDVI is met, the forest land is determined to be a water body.

[0129] As an example, in step S404, the water body frequency can be a percentage, that is, the ratio of the number of times the forest land was identified as a water body to the number of valid observations converted into a percentage is the water body frequency. In this case, the number of times the forest land was identified as a water body refers to the number of times the forest land was identified as a water body during the rainy season, and the number of valid observations refers to the number of valid observations during the rainy season. The specific formula for the water body frequency P_water can be as follows:

[0130]

[0131] Wherein, N_water_rainy represents the number of times the woodland was identified as a water body during the rainy season, and N_total_rainy represents the number of valid observations during the rainy season.

[0132] Special attention is paid to imagery data from the rainy season because higher water levels during this period allow for more accurate identification of forest land being covered by water, thus enabling an assessment of vegetation's flood tolerance and ecological adaptability. This distinction between rainy and non-rainy seasons is not merely a simple temporal division, but rather based on the response characteristics of the forest-water complex ecosystem to seasonal hydrological changes, enabling precise capture of the dynamic interaction between water bodies and forest land.

[0133] It should be noted that, in step S30, the vegetation frequency obtained can be the vegetation frequency of each water cell; in step S40, the water frequency obtained can be the water frequency of each forest cell.

[0134] It should be noted that valid observations refer to observations that meet the following conditions: located within the study area (e.g., woodland or water body), acquired during the defined period (rainy season / non-rainy season), from images with cloud cover below 20%, and not marked as cloud pixels or cirrus cloud pixels after cloud masking. Specifically, images with cloud cover exceeding 20% ​​are first removed, then cloud masking is performed on the remaining images to mark cloud pixels and cirrus cloud pixels as invalid values; the remaining observations are the valid observations.

[0135] In step S50, please refer to Figure 1 In step S50, the degree of forest-water integration in the forest-water integrated ecological area is obtained.

[0136] As an example, step S50, obtaining the degree of forest-water integration in the forest-water composite ecological area, may include the following steps:

[0137] S501: Identify the shared boundary between the water body and the woodland.

[0138] S502: Obtain the length of the shared boundary.

[0139] S503: Obtain the total circumference of the water body.

[0140] S504: The ratio of the perimeter of the shared boundary to the total perimeter is taken as the degree of forest-water integration in the forest-water integrated ecological zone.

[0141] As an example, in step S50, after obtaining the degree of forest-water integration in the forest-water composite ecological area, the relationship between the degree of forest-water integration and the area and shape of the water bodies can be analyzed. Specifically, this can include the following steps: obtaining the area and perimeter of each independent water body; quantifying the shape characteristics of the water bodies using the perimeter-area ratio; and analyzing the correlation between the degree of forest-water integration and the area and shape of the water bodies. Specifically, this can be visualized using scatter plots. Since the analysis results show that the water body area and shape do not exhibit a clear pattern with the degree of forest-water integration, no further regression analysis is performed on the results.

[0142] It should be noted that in step S50, obtaining the degree of forest-water integration is not merely a simple calculation of the boundary ratio, but a key indicator for quantifying the integrity of the ecological transition zone between forest land and water bodies. The degree of forest-water integration reflects the closeness of contact and mutual influence between water bodies and forest land, and is an important parameter for assessing the quality of ecological connectivity. Areas that already have forest-water adjacency are easier to transform into wetland landscapes; therefore, this step is essential and specifically addresses the unique characteristics of the forest-water boundary.

[0143] In step S60, please refer to Figure 1 In step S60, the forest-water composite ecological region is assessed and classified based on the vegetation frequency, the water body frequency, the degree of forest-water integration, and a threshold assessment system of the null model.

[0144] As an example, in step S60, the forest-water composite ecological area can be divided into an ecological integration area, a basic forest-water boundary area, and a water-forest area to be restored.

[0145] As an example, in step S60, the assessment and classification of the forest-water composite ecological region based on the vegetation frequency, the water body frequency, and the degree of forest-water integration may include: determining a preset threshold based on the zero-model threshold assessment system through threshold sensitivity assessment, stability assessment, and classification balance assessment; classifying water bodies that share a boundary with forest land (i.e., are adjacent to forest land) and whose vegetation frequency is not lower than the preset threshold as ecological integration zones; the ecological integration zones represent an ideal forest-water composite state with a complete water-land transition zone and stable vegetation cover; classifying water bodies that share a boundary with forest land and whose vegetation frequency is lower than the preset threshold as basic forest-water boundary zones; the basic forest-water boundary zones represent a transitional state with forest-water adjacency but insufficient vegetation cover; classifying water bodies that do not share a boundary with forest land (i.e., are not adjacent to forest land) and whose vegetation frequency is not lower than the preset threshold, as well as forest land with a water body frequency not lower than the preset threshold, as water-forest areas to be restored; the water-forest areas to be restored represent areas with siltation foundations or poor drainage and potential for ecological restoration.

[0146] Specifically, the ecological integration zone is a water body area with combined forest and water attributes, and a certain degree of siltation and vegetation cover. The basic forest-water boundary zone can be a water body area adjacent to forest land but not including the ecological integration zone. The water-forest area to be restored can be a water body with silted vegetation that is not adjacent to forest land, as well as forest land with poor drainage conditions.

[0147] This invention classifies forest-water composite ecological zones into three categories, a classification that has clear ecological significance and technological innovation:

[0148] Ecological integration zone: Water bodies that share boundaries with forest land and have a vegetation frequency not lower than a preset threshold represent the ideal state of a forest-water complex ecosystem, with a complete water-land transition zone and rich ecological service functions;

[0149] Basic forest-water boundary zone: Water bodies that share a boundary with forest land but have a vegetation frequency below a preset threshold are the basic form of forest-water ecosystem;

[0150] Water-forest areas awaiting restoration: Water bodies that do not share boundaries with forest land but whose vegetation frequency is not lower than a preset threshold, and forest land whose water body frequency is not lower than a preset threshold, are key areas for ecological restoration.

[0151] This classification is not a simple, arbitrary definition, but rather based on the scientific characteristics and practical needs of forest-water complex ecosystems, and has a clear ecological basis.

[0152] As an example, the preset threshold may include 50%. Figure 2It can be seen that the 50% threshold lies in the middle of the curves for the basic forest-water boundary area, the ecological integration area, and the water-forest area to be restored, avoiding the risk of overestimating or underestimating any area. Secondly, at the 50% threshold, the curve for the water-forest area to be restored gradually flattens, increasing the stability of the assessment and classification results. The slope of the area curve for the water-forest area to be restored at different threshold points is as follows: Figure 3 As shown, the rate of change of the slope of the curve for the area of ​​the water-forest region to be restored is as follows: Figure 4 As shown.

[0153] To rigorously evaluate the performance of different thresholds, this invention establishes a zero-model threshold evaluation system comprising three dimensions: threshold sensitivity, stability, and classification balance. The evaluation content of the zero-model threshold evaluation system includes:

[0154] Threshold sensitivity assessment:

[0155]

[0156] Where S(t) is the sensitivity coefficient of the threshold t, representing the degree of influence of threshold changes on the classification result; A i(t) It is the area of ​​the i-th class region under threshold t; A i(t-Δt) S(t) represents the area of ​​the i-th class region at the threshold (t-Δt); Δt is the threshold change step size, usually taken as 5% or 10%. A smaller S(t) value indicates that the threshold change has less impact on the classification result, and the classification is more stable.

[0157] Threshold stability assessment:

[0158]

[0159] Where ST(t) is the stability index of threshold t, with a value range of [0,1]; max(S) is the maximum value of the sensitivity coefficient among all the thresholds examined; the closer to 1, the more stable the threshold is, that is, the classification result is least affected by small changes in the threshold at this threshold; thresholds with high stability are usually located in the flat region of the area curve.

[0160] Classification balance assessment:

[0161]

[0162] Where B(t) is the classification balance of the threshold t, with a value range of [0,1]; A i(t) / A total It is the proportion of the area of ​​the i-th type region to the total area; A total1 / n represents the total area of ​​the assessment region; 1 / n is the ideal proportion of each type of region (assuming uniform distribution); n is the total number of categories, which is 3 in this invention (ecological integration zone, basic forest-water boundary zone, and water-forest zone to be restored); n-1 is the normalization factor. The closer to 1, the more balanced the area distribution of each type of region, avoiding situations where a certain type of region is too large or too small.

[0163] Based on the above indicators, calculate the overall score:

[0164] CS(t)=w1×ST(t)+w2×B(t)+w3×AC(t)

[0165] Where CS(t) is the comprehensive score of threshold t; w1, w2, w3 are weighting coefficients, representing the importance of stability, balance and accuracy, respectively; in this invention, w1=0.3, w2=0.3, w3=0.4, indicating that accuracy is slightly more important than the other two indicators; AC(t) is the classification accuracy at threshold t, obtained by comparing with field survey samples, and the threshold with the highest comprehensive score is selected as the optimal threshold.

[0166] Based on zero-model analysis, the preset threshold was set to 50% for the following reasons:

[0167] (1) Area balance analysis: At the 50% threshold, the area of ​​the ecological integration zone is 128.90 hectares, the area of ​​the basic forest-water boundary zone is 447.54 hectares, and the area of ​​the water-forest zone to be restored is 25.60 hectares. The area distribution of the three types of areas is reasonable.

[0168] (2) Curve stability analysis: At the 50% threshold, the rate of change of the area curve of the water-forest area to be restored decreased by 18.97%, such as... Figure 4 As shown, this indicates that the stability of the classification results is significantly enhanced;

[0169] (3) Component sensitivity analysis: At the 50% threshold, the misjudgment rate is significantly reduced and the classification reliability is improved.

[0170] As an example, in step S60, the assessment classification based on vegetation frequency, water body frequency, forest-water composite degree, and the null model threshold assessment system has clear ecological significance:

[0171] 1. An ecological integration zone is the ideal state of a forest-water complex ecosystem, with a complete water-land transition zone and rich ecological service functions. It is usually characterized by a high degree of interaction between water bodies and forest land, and the water area has stable vegetation cover during the non-rainy season.

[0172] 2. The basic forest-water boundary zone is the basic form of the forest-water ecosystem. Although the water body is adjacent to the forest land, the vegetation cover in the water body area is insufficient, indicating that the ecological transition zone has not yet been fully formed.

[0173] 3. Water-forest areas awaiting restoration include two situations: one is water bodies with a high vegetation frequency that are not adjacent to forest land, indicating that there are already basic conditions for siltation and vegetation growth; the other is forest land with a high water frequency, indicating poor drainage conditions and the need for ecological restoration.

[0174] Compared with existing technologies, the assessment method for forest-water composite ecological zones of the present invention has the following advantages: 1. Highly targeted: Specifically designed for urban forest-water composite ecosystems, taking into account their irregular flooding characteristics and seasonal variations; 2. Dynamic assessment: Capturing the dynamic changes of forest-water composite ecosystems by analyzing three consecutive years of rainy and non-rainy season data; 3. Comprehensive indicators: Comprehensively assessing the status and potential of forest-water composite ecological zones through three key indicators: vegetation frequency of water bodies, water frequency of forest land, and degree of forest-water integration; 4. Scientific classification: Dividing forest-water composite zones into ecological integration zones, basic forest-water boundary zones, and water-forest zones requiring restoration based on ecological principles, providing targeted guidance for the management and restoration of different types of zones; 5. Innovative model: Determining the optimal threshold through a zero-model assessment system improves the scientific rigor and reliability of the classification.

[0175] In another embodiment, please refer to Figure 5 The present invention also provides an assessment system for forest-water composite ecological areas, comprising: a data acquisition device 10 for acquiring image data of the forest-water composite ecological area, the image data including rainy season data and non-rainy season image data; a land type identification module 11 for identifying land types of the forest-water composite ecological area based on the image data, the land types including water bodies and forest land; a vegetation frequency acquisition module 12 for acquiring the vegetation frequency of the water bodies based on the non-rainy season image data; a water body frequency acquisition module 13 for acquiring the water body frequency of the forest land based on the rainy season image data; a forest-water composite degree acquisition module 14 for acquiring the forest-water composite degree of the forest-water composite ecological area; and an assessment and classification module 15 for assessing and classifying the forest-water composite ecological area based on the vegetation frequency, the water body frequency, the forest-water composite degree, and a threshold assessment system using a null model.

[0176] The aforementioned assessment system for forest-water composite ecological areas includes: a data acquisition device 10, a land type identification module 11, a vegetation frequency acquisition module 12, a water body frequency acquisition module 13, a forest-water composite degree acquisition module 14, and an assessment and classification module 15. This system can accurately assess and classify different forest-water composite ecological areas, providing a scientific basis for the refined management of urban ecosystems and optimizing ecological construction and management.

[0177] As an example, the assessment system for forest-water complex ecological zones can be used to perform the assessment method for forest-water complex ecological zones as described in the previous embodiment.

[0178] The method and system for assessing forest-water composite ecological zones of the present invention can have the following beneficial effects:

[0179] 1) It enables precise quantification of the degree of forest-water integration. This method fills the gap in existing technologies for systematic assessment, and can provide accurate identification and quantification for different types of forest-water integration areas.

[0180] 2) It can effectively identify different types of forest-water composite ecological zones, helping to deepen the understanding of the spatial distribution characteristics and formation mechanisms of forest-water composite ecosystems. This provides a scientific basis for the refined management of urban ecosystems, thereby optimizing ecological construction and management.

[0181] 3) Long-term monitoring of dynamic changes: This allows for the capture of dynamic changes in the forest-water complex system and long-term monitoring of the dynamic changes in the aforementioned forest-water complex ecological region, which is crucial for understanding the system's evolution. The accumulation of long-term data is particularly critical for assessing the health and changes of the ecosystem.

[0182] 4) It enables differentiated assessment of different types of forest-water complex ecological zones, which helps to accurately formulate targeted management strategies; it allows for differentiated assessment and management strategy development. By identifying the unique needs and functions of each zone, resource allocation and management can be carried out more effectively.

[0183] 5) It can accurately identify areas with ecological restoration potential, providing clear spatial guidance for urban and regional ecological planning. This helps optimize the design and implementation of ecological restoration projects, improving the efficiency and effectiveness of ecological restoration.

[0184] 6) Applicable to different urban environments, with broad applicability and promotional value. This makes the framework not limited to specific regions, but can be widely applied to diverse urban environments, increasing its practicality and universality.

[0185] 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 of 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.

[0186] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for assessing forest-water composite ecological zones, characterized in that, The assessment methods for the forest-water composite ecological zone include: Acquire image data of forest-water complex ecological areas; the image data includes rainy season data and non-rainy season image data. Based on the image data, land use classification is performed on the forest-water composite ecological area, where land use includes water bodies and forest land. The vegetation frequency of the water body is obtained based on the non-rainy season image data; The water frequency of the forest land is obtained based on the rainy season image data; Obtain the degree of forest-water integration in the aforementioned forest-water composite ecological area; The forest-water composite ecological region is assessed and classified based on the vegetation frequency, the water body frequency, the degree of forest-water integration, and a null model threshold assessment system; wherein, the assessment content designed by the null model threshold assessment system includes: Threshold sensitivity assessment: Where S(t) is the sensitivity coefficient of the threshold t, representing the degree of influence of threshold changes on the classification result; A i(t) It is the area of ​​the i-th class region under threshold t; A i(t-Δt) It is the area of ​​the i-th class region under the threshold (t-Δt); Δt is the threshold change step size, which is 5% or 10%; Threshold stability assessment: Where ST(t) is the stability index of threshold t, with a value range of [0,1]; max(S) is the maximum value of the sensitivity coefficient among all the thresholds examined; Classification balance assessment: Where B(t) is the classification balance of the threshold t, with a value range of [0,1]; A i(t) / A total It is the proportion of the area of ​​the i-th type region to the total area; A total is the total area of ​​the assessment region; 1 / n is the ideal proportion of each type of region; n is the total number of categories; n-1 is the normalization factor; Based on the above indicators, calculate the comprehensive score: CS(t)=w1×ST(t)+w2×B(t)+w3×AC(t) Where CS(t) is the comprehensive score of threshold t; w1, w2, w3 are weight coefficients, representing the importance of stability, balance and accuracy respectively; AC(t) is the classification accuracy at threshold t, which is obtained by comparing with the field survey samples, and the threshold with the highest comprehensive score is selected as the optimal threshold.

2. The assessment method for forest-water composite ecological areas according to claim 1, characterized in that, The step of obtaining the vegetation frequency of the water body based on the non-rainy season image data includes: Multiple effective observations were conducted on the water body to obtain non-rainy season image data from multiple effective observations; Based on the non-rainy season image data, the enhanced vegetation index, normalized vegetation index, and land surface moisture index were obtained. The enhanced vegetation index, the normalized vegetation index, and the land surface moisture index are used to determine whether the water body is a vegetation-covered area. The ratio of the number of times the water body was identified as a vegetation-covered area to the number of valid observations is taken as the vegetation frequency of the water body.

3. The assessment method for forest-water composite ecological areas according to claim 2, characterized in that, The image data includes Sentinel-2 L1C image data; the process of obtaining enhanced vegetation index, normalized difference vegetation index, and land surface moisture index based on the non-rainy season image data includes: The enhanced vegetation index is obtained based on the following formula: Wherein, EVI is the enhanced vegetation index; NIR is the reflectance in the near-infrared band; RED is the reflectance in the red band; and BLUE is the reflectance in the blue band. The normalized vegetation index is obtained based on the following formula: Wherein, NDVI is the normalized vegetation index; NIR is the reflectance in the near-infrared band; and RED is the reflectance in the red band. The land surface moisture index is obtained based on the following formula: Wherein, LSWI is the land surface moisture index; NIR is the reflectance in the near-infrared band; and SWIR is the reflectance in the short-wave infrared band.

4. The assessment method for forest-water composite ecological areas according to claim 3, characterized in that, The step of determining whether a water body is a vegetation-covered area based on the enhanced vegetation index, the normalized difference vegetation index, and the land surface moisture index includes: If EVI ≥ 0.1, NDVI ≥ 0.2 and LSWI > 0, then the water body is determined to be the vegetation-covered area.

5. The assessment method for forest-water composite ecological areas according to claim 1, characterized in that, The process of obtaining the water body frequency of the forest land based on the rainy season image data includes: Multiple effective observations were conducted on the woodland to obtain rainy season image data from multiple effective observations; Based on the aforementioned rainy season image data, the enhanced vegetation index, normalized vegetation index, and improved normalized differential water index were obtained. The forest land is determined to be a water body based on the enhanced vegetation index, the normalized vegetation index, and the improved normalized differential water body index. The ratio of the number of times the forest land was identified as a water body to the number of valid observations is taken as the water body frequency of the forest land.

6. The assessment method for forest-water composite ecological areas according to claim 5, characterized in that, The enhanced vegetation index, normalized difference vegetation index, and improved normalized difference water index obtained based on the rainy season image data include: The enhanced vegetation index is obtained based on the following formula: Wherein, EVI is the enhanced vegetation index; NIR is the reflectance in the near-infrared band; RED is the reflectance in the red band; and BLUE is the reflectance in the blue band. The normalized vegetation index is obtained based on the following formula: Wherein, NDVI is the normalized vegetation index; NIR is the reflectance in the near-infrared band; and RED is the reflectance in the red band. The improved normalized differential water body index is obtained based on the following formula: Wherein, MNDWI is the improved normalized differential water index; GREEN is the reflectance in the green light band; and SWIR is the reflectance in the shortwave infrared band.

7. The assessment method for forest-water composite ecological areas according to claim 5, characterized in that, The determination of whether the forest land is a water body based on the enhanced vegetation index, the normalized vegetation index, and the improved normalized differential water index includes: If EVI < 0.1, MNDWI > EVI, or MNDWI > NDVI, then the woodland is determined to be a water body.

8. The assessment method for forest-water composite ecological areas according to claim 1, characterized in that, The process of obtaining the degree of forest-water integration in the forest-water integrated ecological region includes: Identify the shared boundary between the water body and the woodland; Obtain the length of the shared boundary; Obtain the total circumference of the water body; The ratio of the perimeter of the shared boundary to the total perimeter is used as the degree of forest-water integration in the forest-water integrated ecological region.

9. The assessment method for forest-water composite ecological areas according to claim 8, characterized in that, The threshold assessment system based on the vegetation frequency, the water body frequency, the degree of forest-water integration, and the null model assesses and classifies the forest-water integrated ecological region, including: The threshold evaluation system based on the null model determines the preset threshold through threshold sensitivity evaluation, stability evaluation, and classification balance evaluation. Water bodies that share boundaries with forest land and have a vegetation frequency not lower than the preset threshold are classified as ecological integration zones; the ecological integration zone represents an ideal forest-water composite state with a complete water-land transition zone and stable vegetation cover. Water bodies that share a boundary with forest land and whose vegetation frequency is lower than the preset threshold are classified as basic forest-water boundary zones; the basic forest-water boundary zones represent a transitional state with adjacent forest and water but insufficient vegetation cover. Water bodies that do not share boundaries with forest land and whose vegetation frequency is not lower than the preset threshold, and forest land with water body frequency not lower than the preset threshold, are classified as water-forest areas to be restored; the water-forest areas to be restored represent areas with siltation foundations or poor drainage and ecological restoration potential.

10. An assessment system for forest-water complex ecological zones, characterized in that, The assessment system for forest-water composite ecological zones is used to perform the assessment method for forest-water composite ecological zones as described in any one of claims 1 to 9; the assessment system for forest-water composite ecological zones includes: Data acquisition equipment is used to acquire image data of forest-water complex ecological areas; the image data includes rainy season data and non-rainy season image data; The land use identification module is used to identify land use types in the forest-water composite ecological area based on the image data, wherein the land use types include water bodies and forest land. A vegetation frequency acquisition module is used to acquire the vegetation frequency of the water body based on the non-rainy season image data. A water frequency acquisition module is used to acquire the water frequency of the forest land based on the rainy season image data. The forest-water composite degree acquisition module is used to acquire the forest-water composite degree of the forest-water composite ecological area; The assessment and classification module is used to assess and classify the forest-water composite ecological area based on the vegetation frequency, the water body frequency, the degree of forest-water integration, and a threshold assessment system of the null model.

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