Method and system for evaluating forest-water composite ecological region

Abstract

The invention relates to an evaluation method and system for a forest-water composite ecological region. The evaluation system for the forest-water composite ecological region comprises the following steps: acquiring image data of the forest-water composite ecological region; the image data comprises rainy season data and non-rainy season image data; performing land type identification on the forest-water composite ecological region based on the image data; obtaining the vegetation frequency of the water body based on the non-rainy season image data; obtaining the water body frequency of the forest land based on the rainy season image data; obtaining the forest-water composite degree of the forest-water composite ecological region; and evaluating and classifying the forest-water composite ecological region based on a threshold evaluation system of vegetation frequency, water body frequency, forest-water composite degree and a zero model. According to the evaluation method for the forest-water composite ecological region, different forest-water composite ecological regions can be accurately evaluated and classified, a scientific basis is provided for fine management of an urban ecological system, and ecological construction and management can be optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological assessment, and particularly to an assessment method and system for a forest-water complex ecological region. Background Art

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

[0003] Different from coastal wetlands, urban forest-water complex ecosystems have unique characteristics: First, their flooding scenarios are irregular and long-term monitoring is required to master their change laws, while coastal wetlands are affected by tides and show regular flooding scenarios; Second, urban forest-water complex areas may exist as vegetations with ground covers during the non-rainy season, while there are scenarios of being submerged for a long time during the rainy season, and this seasonal change poses special requirements for assessment methods; Third, in recent years, the policy demand for improving the water body landscape level in landscape systems such as urban parks has been increasing, and scientific methods are needed to determine the optimization sequence and methods of water body landscapes. Existing technologies are mostly based on the static characteristics of a single time phase and are difficult to meet the special needs of forest-water complex ecosystem assessment.

[0004] How to effectively identify, assess, and optimize forest-water complex habitats is a major challenge faced by urban ecological planning and management. Summary of the Invention

[0005] Based on this, it is necessary to provide an assessment method and system for a forest-water complex ecological region. In view of the particularity of urban forest-water complex ecosystems, the present invention has established a systematic assessment method by comprehensively considering the vegetation frequency of water bodies, the water frequency of forest lands, and the forest-water complex degree, which can provide a scientific basis for the refined management and landscape optimization of urban ecosystems.

[0006] To achieve the above object, in a first aspect, the present invention provides an assessment method for a forest-water complex ecological region, and the assessment method for the forest-water complex ecological region includes:

[0007] Obtain image data of the forest-water complex ecological region; the image data includes rainy season data and non-rainy season image data;

[0008] Perform land type identification on the forest-water complex ecological region based on the image data, and the land types include water bodies and forest lands;

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

[0010] Obtain the water body frequency of the forest land based on the rainy season image data;

[0011] Obtain the forest-water complex degree of the forest-water complex ecological region;

[0012] Evaluate and classify the forest-water complex ecological region based on the vegetation frequency, the water body frequency, the forest-water complex degree and the threshold evaluation system of the null model.

[0013] The above-mentioned evaluation method for the forest-water complex ecological region includes: obtaining the image data of the forest-water complex ecological region; performing land type recognition on the forest-water complex ecological region based on the image data, and the land types include water bodies and forest land; obtaining the vegetation frequency of the water bodies; obtaining the water body frequency of the forest land; obtaining the forest-water complex degree of the forest-water complex ecological region; evaluating and classifying the forest-water complex ecological region based on the vegetation frequency, the water body frequency and the forest-water complex degree. The above-mentioned evaluation method for the forest-water complex ecological region can accurately evaluate and classify different forest-water complex ecological regions, provide a scientific basis for the refined management of urban ecological systems, and can optimize ecological construction and management.

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

[0015] Perform multiple effective observations on the water body to obtain non-rainy season image data of multiple effective observations;

[0016] Obtain the enhanced vegetation index, the normalized vegetation index and the land surface water index based on the non-rainy season image data;

[0017] Determine whether the water body is a vegetation-covered area based on the enhanced vegetation index, the normalized vegetation index and the land surface water index;

[0018] Take the ratio of the number of times the water body is determined to be the vegetation-covered area to the number of effective observations as the vegetation frequency of the water body.

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

[0020] Obtain the enhanced vegetation index 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 light band; BLUE is the reflectance in the blue light band;

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

[0024]

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

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

[0027]

[0028] Wherein, LSWI is the land surface water index; NIR is the reflectance in the near-infrared band; 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 vegetation index, and the land surface water index includes:

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

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

[0032] Conducting multiple effective observations on the forest land to obtain rainy season image data of multiple effective observations;

[0033] Obtaining the enhanced vegetation index, the normalized vegetation index, and the improved normalized difference water index based on the rainy season image data;

[0034] Determining whether the forest land is a water body based on the enhanced vegetation index, the normalized vegetation index, and the improved normalized difference water index;

[0035] Taking the ratio of the number of times the forest land is determined to be a water body to the number of effective observations as the water body frequency of the forest land.

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

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

[0038]

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

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

[0041]

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

[0043] The land surface water index is obtained based on the following formula:

[0044] MNDWI = GREEN-SWIR

[0045] GREEN + SWIR

[0046] Among them, MNDWI is the land surface water index; GREEN is the reflectance in the green band; SWIR is the reflectance in the shortwave infrared band.

[0047] In some embodiments, determining whether the forest land is water based on the enhanced vegetation index, the normalized vegetation index, and the improved normalized difference water index includes:

[0048] If EVI < 0.1, MNDWI > EVI or MNDWI > NDVI, it is determined that the forest land is water.

[0049] In some embodiments, obtaining the forest-water complex degree of the forest-water complex ecological region includes:

[0050] Identifying the shared boundary between the water body and the forest land;

[0051] Obtaining the length of the shared boundary;

[0052] Obtaining the total perimeter of the water body;

[0053] Taking the ratio of the perimeter of the shared boundary to the total perimeter as the forest-water complex degree of the forest-water complex ecological region.

[0054] In some embodiments, evaluating and classifying the forest-water complex ecological region based on the vegetation frequency, the water body frequency, the forest-water complex degree, and the threshold evaluation system of the null model includes:

[0055] A threshold evaluation system based on the zero model determines a preset threshold through threshold sensitivity evaluation, stability evaluation, and classification balance evaluation.

[0056] Water bodies that share a boundary with forest land and have a vegetation frequency not lower than the preset threshold are classified as ecological integration areas; the ecological integration areas represent an ideal forest-water complex state with a complete land-water transition zone and stable vegetation coverage.

[0057] Water bodies that share a boundary with forest land and have a vegetation frequency lower than the preset frequency are classified as basic forest-water junction areas; the basic forest-water junction areas represent a transitional state with forest-water adjacency but insufficient vegetation coverage.

[0058] Water bodies that do not share a boundary with forest land and have a vegetation frequency not lower than the preset threshold, and forest land with a 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 ecological restoration potential areas with siltation bases or poor drainage.

[0059] In a second aspect, the present invention also provides an evaluation system for a forest-water complex ecological region, and the evaluation system for the forest-water complex ecological region includes:

[0060] Data acquisition devices for acquiring image data of the forest-water complex ecological region; the image data includes rainy season data and non-rainy season image data.

[0061] Land type identification modules for performing land type identification on the forest-water complex ecological region based on the image data, and the land types include water bodies and forest land.

[0062] Vegetation frequency acquisition modules for acquiring the vegetation frequency of the water bodies based on the non-rainy season image data.

[0063] Water body frequency acquisition modules for acquiring the water body frequency of the forest land based on the rainy season image data.

[0064] Forest-water complex degree acquisition modules for acquiring the forest-water complex degree of the forest-water complex ecological region.

[0065] Evaluation classification modules for evaluating and classifying the forest-water complex ecological region based on the vegetation frequency, the water body frequency, the forest-water complex degree, and the threshold evaluation system of the zero model.

[0066] The above-mentioned evaluation system for the forest-water complex ecological region includes: data acquisition devices, land type identification modules, vegetation frequency acquisition modules, water body frequency acquisition modules, forest-water complex degree acquisition modules, and evaluation classification modules. The above-mentioned evaluation system for the forest-water complex ecological region can accurately evaluate and classify different forest-water complex ecological regions, providing a scientific basis for the refined management of urban ecological systems and enabling the optimization of ecological construction and management. Description of the Drawings

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0068] Figure 1 It is a flowchart of the evaluation method for the forest-water composite ecological region provided in an embodiment of the present invention;

[0069] Figure 2 It is a curve graph showing the changing trends of the areas of the ecological integration area, the basic forest-water junction area, and the water forest area to be restored in step S60 under different thresholds (including the preset threshold);

[0070] Figure 3 It is the slope of the curve of the water forest area to be restored at different threshold points;

[0071] Figure 4 It is the change rate of the slope of the curve of the water forest area to be restored;

[0072] Figure 5 It is a structural block diagram of the evaluation system for the forest-water composite ecological region provided in another embodiment of the present invention.

[0073] Description of the reference numerals: 10, data acquisition device; 11, land type identification module; 12, vegetation frequency acquisition module; 13, water body frequency acquisition module; 14, forest-water composite degree acquisition module; 15, evaluation classification module. Detailed Embodiments

[0074] In order to make the objectives, technical solutions, and advantages of the present application clearer, the following further details the present application in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0075] The main technical solutions related to the identification and quantification of the potential space for forest-water compound construction may include: (1) The method for extracting wetland vegetation based on remote sensing images, which uses vegetation indices (such as NDVI, EVI) and water body indices (such as MNDWI) to extract wetland vegetation, but it is difficult to accurately identify the 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 of multiple time phases, but has high requirements for data volume and complex processing; (3) Machine learning classification methods, which use technologies such as deep learning to classify remote sensing images and identify forest-water compound areas, but have relatively high requirements for the quality and quantity of training samples; (4) Ecological suitability evaluation, which assesses the suitability of constructing a forest-water compound ecosystem in a region based on factors such as terrain and hydrology, but it is difficult to reflect the dynamic changes of the system; (5) Landscape pattern analysis, which evaluates the degree of forest-water compound by analyzing the spatial relationship between forest land and water bodies, but it is difficult to reflect the internal structure and functions of the system. These methods still have limitations in the precise identification, dynamic monitoring, and ecological function assessment of forest-water compound ecosystems, and it is difficult to provide comprehensive and accurate decision-making support for urban ecological planning and management.

[0076] In one embodiment, please refer to Figure 1 , the present invention provides an evaluation method for a forest-water compound ecological region, and the evaluation method for the forest-water compound ecological region may include the following steps: S10 to S60.

[0077] S10: Obtain the image data of the forest-water compound ecological region; the image data includes rainy season data and non-rainy season image data.

[0078] S20: Conduct land type identification on the forest-water compound ecological region based on the image data, and the land types include water bodies and forest land.

[0079] S30: Obtain the vegetation frequency of the water body based on the non-rainy season image data.

[0080] S40: Obtain the water body frequency of the forest land based on the rainy season image data.

[0081] S50: Obtain the degree of forest-water compound in the forest-water compound ecological region.

[0082] S60: Evaluate and classify the forest-water compound ecological region based on the vegetation frequency, the water body frequency, the degree of forest-water compound, and the threshold evaluation system of the null model.

[0083] The above-mentioned evaluation method for the forest-water complex ecological region includes: obtaining image data of the forest-water complex ecological region; performing land type identification on the forest-water complex ecological region based on the image data, where the land types include water bodies and forest land; obtaining the vegetation frequency of the water bodies; obtaining the water frequency of the forest land; obtaining the forest-water complex degree of the forest-water complex ecological region; and evaluating and classifying the forest-water complex ecological region based on the vegetation frequency, the water frequency, and the forest-water complex degree. The above-mentioned evaluation method for the forest-water complex ecological region can accurately evaluate and classify different forest-water complex ecological regions, provide a scientific basis for the refined management of urban ecological systems, and optimize ecological construction and management.

[0084] The forest-water complex ecosystem targeted by the present invention has unique ecological characteristics. Different from coastal wetlands, the forest-water complex ecosystem may show vegetation with ground cover during the non-rainy season, and may be flooded for a long time during the rainy season. This flooding scenario is irregular, and its change law needs to be accurately grasped through three consecutive years of monitoring data. At the same time, the vegetation in the forest-water complex ecosystem needs to have the ability to adapt to the periodic flooding environment, which puts special requirements on the root structure, physiological characteristics, etc. of the vegetation.

[0085] In step S10, refer to Figure 1 the S10 step in, and obtain the image data of the forest-water complex ecological region; the image data includes rainy season data and non-rainy season image data.

[0086] As an example, Sentinel-2 (Sentinel 2) can be used to obtain L1C-level satellite image data in recent years (for example, three years) as the image data, that is, the image data can be Sentinel-2 L1C image data. L1C-level satellite image data represents atmospherically apparent reflectance data that has been orthorectified and sub-pixel geometrically corrected. The image data can include rainy season image data and non-rainy season image data.

[0087] In step S20, refer to Figure 1 the S20 step in, and perform land type identification on the forest-water complex ecological region based on the image data, where the land types include water bodies and forest land.

[0088] As an example, the land type that appears most frequently on each pixel can be extracted as the land type of the current pixel. Among them, the land type of the flooded vegetation type belongs to the water body.

[0089] As an example, during the process of land type identification, the image data during the rainy season can be focused on, and the maximum water body range can be extracted.

[0090] In step S30, refer to Figure 1In step S30, based on the non-rainy season image data, obtain the vegetation frequency of the water body.

[0091] As an example, in step S30, the obtaining of the vegetation frequency of the water body based on the non-rainy season image data may include the following steps: S301 to S304.

[0092] S301: Conduct multiple effective observations on the water body to obtain non-rainy season image data of multiple effective observations.

[0093] S302: Obtain the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Land Surface Water Index (LSWI) based on the non-rainy season image data.

[0094] S303: Determine whether the water body is a vegetation-covered area based on the Enhanced Vegetation Index, the Normalized Difference Vegetation Index, and the Land Surface Water Index.

[0095] S304: Take the ratio of the number of times the water body is determined to be the vegetation-covered area to the number of effective observations as the vegetation frequency of the water body.

[0096] As an example, in step S302, the obtaining of the Enhanced Vegetation Index, the Normalized Difference Vegetation Index, and the Land Surface Water Index based on the non-rainy season image data may include:

[0097] Obtain the Enhanced Vegetation Index based on the following formula:

[0098]

[0099] where EVI (Enhanced Vegetation Index) is the Enhanced Vegetation Index; NIR is the reflectance of the near-infrared band; RED is the reflectance of the red band; BLUE is the reflectance of the blue band.

[0100] Obtain the Normalized Difference Vegetation Index based on the following formula:

[0101]

[0102] where NDVI (Normalized Difference Vegetation Index) is the Normalized Difference Vegetation Index; NIR is the reflectance of the near-infrared band; RED is the reflectance of the red band.

[0103] Obtain the Land Surface Water Index based on the following formula:

[0104]

[0105] Among them, LSWI (Land Surface Water Index) is the land surface water index; NIR is the reflectance in the near-infrared band; SWIR is the reflectance in the short-wave infrared band.

[0106] Specifically, NIR can be the reflectance in the near-infrared band (Band B8); RED is the reflectance in the red light band (Band B4); BLUE is the reflectance in the blue light band (Band B2); SWIR is the reflectance in the short-wave infrared band (Band B11).

[0107] As an example, in step S303, determining whether the water body is a vegetation-covered area based on the enhanced vegetation index, the normalized vegetation index, and the land surface water index includes: if EVI≥0.1, NDVI≥0.2, and LSWI>0, then it is determined that the water body is the vegetation-covered area. That is, when the three conditions of EVI≥0.1, NDVI≥0.2, and LSWI>0 are simultaneously satisfied, it is determined that the water body is the vegetation-covered area.

[0108] As an example, in step S304, the vegetation frequency can be a percentage, that is, the value obtained by converting the ratio of the number of times the water body is determined to be the vegetation-covered area to the number of effective observations into a percentage is the vegetation frequency; the number of times the water body is determined to be the vegetation-covered area at this time, the number of times the water body in the non-rainy season is determined to be the vegetation-covered area, and the number of effective observations is the number of effective observations in the non-rainy season. The specific formula for the vegetation coverage rate P_veg can be as follows:

[0109]

[0110] Among them, N_veg_non_rainy is the number of times the water body in the non-rainy season is determined to be the vegetation-covered area, and N_total_non_rainy is the number of effective observations in the non-rainy season.

[0111] Special attention is paid to the image data in the non-rainy season because the water level is low in the non-rainy season, and the vegetation coverage in the water body area can be more accurately identified, so as to evaluate the siltation degree and ecological succession stage of the water body.

[0112] Based on meteorological and hydrological data analysis, the months with the top 50% of annual precipitation are defined as the rainy season (such as April - September in Shanghai), and the latter 50% of the months are defined as the non-rainy season (such as October to March of the following year in Shanghai).

[0113] In step S40, please refer to Figure 1 the S40 step in

[0114] As an example, in step S40, obtaining the water body frequency of the forest land based on the rainy season image data may include the following steps: S401 to S404.

[0115] S401: Conduct multiple effective observations on the forest land to obtain rainy season image data of multiple effective observations.

[0116] S402: Obtain an enhanced vegetation index, a normalized vegetation index, and a modified normalized difference water index based on the rainy season image data.

[0117] S403: Determine whether the forest land is a water body based on the enhanced vegetation index, the normalized vegetation index, and the modified normalized difference water index.

[0118] S404: Take the ratio of the number of times the forest land is determined to be a water body to the number of effective observations as the water body frequency of the forest land.

[0119] As an example, in step S402, obtaining the enhanced vegetation index, the normalized vegetation index, and the modified normalized difference water index based on the rainy season image data includes:

[0120] Obtain the enhanced vegetation index based on the following formula:

[0121]

[0122] where EVI is the enhanced vegetation index; NIR is the reflectance of the near-infrared band; RED is the reflectance of the red light band; BLUE is the reflectance of the blue light band;

[0123] Obtain the normalized index index based on the following formula:

[0124]

[0125] where NDVI is the normalized index index; NIR is the reflectance of the near-infrared band; RED is the reflectance of the red light band;

[0126] Obtain the land surface water index based on the following formula:

[0127]

[0128] where MNDWI (Modified Normalized Difference Water Index) is the land surface water index; GREEN is the reflectance of the green light band (B3 band); SWIR is the reflectance of the short-wave infrared band.

[0129] As an example, in step S403, determining whether the forest land is water based on the enhanced vegetation index, the normalized vegetation index, and the modified normalized difference water index may include: if EVI < 0.1, MNDWI > EVI, or MNDWI > NDVI, it is determined that the forest land is water. That is, when any of the conditions EVI < 0.1, MNDWI > EVI, and MNDWI > NDVI is satisfied, it is determined that the forest land is water.

[0130] As an example, in step S404, the water frequency may be a percentage, that is, the value obtained by converting the ratio of the number of times the forest land is determined to be water to the number of effective observations into a percentage is the water frequency. At this time, the number of times the forest land is determined to be water is the number of times the forest land is determined to be water during the rainy season, and the number of effective observations is the number of effective observations during the rainy season. The specific formula for the water frequency P_water can be as follows:

[0131]

[0132] where N_water_rainy is the number of times the forest land is determined to be water during the rainy season, and N_total_rainy is the number of effective observations during the rainy season.

[0133] Particular attention is paid to the image data during the rainy season because the water level is higher during the rainy season, which can more accurately identify the situation where the forest land is covered by water, thereby evaluating the flood tolerance and ecological adaptability of the vegetation. This distinction between the rainy season and the non-rainy season is not just a simple time division, but is based on the response characteristics of the forest-water complex ecosystem to seasonal hydrological changes, and can accurately capture the dynamic interaction relationship between water and forest land.

[0134] It should be noted that in step S30, the obtained vegetation frequency may be the vegetation frequency of each water pixel; in step S40, the obtained water frequency may be the water frequency of each forest land pixel.

[0135] It should be noted that effective observations refer to the observed values that meet the following conditions: located within the study area (such as forest land or water), obtained during the defined period (rainy season / non-rainy season), from images with a cloud cover of less than 20%, and not marked as cloud pixels or cirrus pixels after cloud masking processing. Specifically, first, images with a cloud cover exceeding 20% are excluded, and then the remaining images are subjected to cloud masking processing to mark cloud pixels and cirrus pixels as invalid values, and the remaining observed values are effective observations.

[0136] In step S50, please refer to Figure 1 the S50 step in

[0137] As an example, in step S50, obtaining the forest-water complex degree of the forest-water complex ecological region may include the following steps:

[0138] S501: Identify the shared boundary between the water body and the forest land.

[0139] S502: Obtain the length of the shared boundary.

[0140] S503: Obtain the total perimeter of the water body.

[0141] S504: Take the ratio of the perimeter of the shared boundary to the total perimeter as the forest-water complex degree of the forest-water complex ecological region.

[0142] As an example, in step S50, after obtaining the forest-water complex degree of the forest-water complex ecological region, the relationship between the forest-water complex degree and the characteristics such as the area and shape of the water body can also be analyzed. Specifically, it may include the following steps: Obtain the area and perimeter of each independent water body; Use the perimeter-area ratio to quantify the shape characteristics of the water body; Analyze the correlation between the forest-water complex degree and the area and shape of the water body. Specifically, visual analysis can be carried out through a scatter plot. Since the analysis results show that neither the area and shape of the water body nor the forest-water complex degree show obvious patterns, no further regression analysis is performed on the analysis results.

[0143] It should be noted that in step S50, obtaining the forest-water complex degree is not just a simple boundary ratio calculation, but a key indicator for quantifying the integrity of the ecological transition zone between the forest land and the water body. The forest-water complex degree reflects the contact tightness and mutual influence degree between the water body and the forest land, and is an important parameter for evaluating the quality of ecological connectivity. It is easier to carry out wetland landscape transformation in areas with the condition of forest-water adjacency itself. Therefore, this step must be considered and is a special consideration for the specific property of the forest-water boundary.

[0144] In step S60, please refer to Figure 1 step S60 in, and evaluate and classify the forest-water complex ecological region based on the vegetation frequency, the water body frequency, the forest-water complex degree, and the threshold evaluation system of the null model.

[0145] As an example, in step S60, the forest-water complex ecological region can be divided into an ecological integration area, a basic forest-water boundary area, and a water forest area to be restored.

[0146] As an example, in step S60, the evaluation and classification of the forest-water complex ecological region based on the vegetation frequency, the water body frequency, and the forest-water complex degree may include: based on the threshold evaluation system of the null model, determining a preset threshold through threshold sensitivity evaluation, stability evaluation, and classification balance evaluation; classifying a water body that shares a boundary with a forest land (i.e., is adjacent to the forest land) and has a vegetation frequency not lower than the preset threshold as an ecological integration area; the ecological integration area represents an ideal forest-water complex state with a complete land-water transition zone and stable vegetation cover; classifying a water body that shares a boundary with a forest land and has a vegetation frequency lower than the preset frequency as a basic forest-water junction area; the basic forest-water junction area represents a transitional state with forest-water adjacency but insufficient vegetation cover; classifying a water body that does not share a boundary with a forest land (i.e., is not adjacent to the forest land) and has a vegetation frequency not lower than the preset threshold and a forest land with a water body frequency not lower than the preset threshold as a water forest area to be restored; the water forest area to be restored represents an ecological restoration potential area with siltation foundation or poor drainage.

[0147] Specifically, the ecological integration area is a water body area with forest-water complex attributes and a certain degree of siltation and vegetation cover. The basic forest-water junction area may be a water body area adjacent to the forest land but not including the ecological integration area. The water forest area to be restored may be a water body that is not adjacent to the forest land but has silted vegetation, and forest land with poor drainage conditions.

[0148] The present invention divides the forest-water complex ecological region into three categories, and this classification has clear ecological significance and technological innovation:

[0149] Ecological integration area: A water body that shares a boundary with a forest land and has a vegetation frequency not lower than the preset threshold, representing the ideal state of the forest-water complex ecosystem, with a complete land-water transition zone and rich ecological service functions;

[0150] Basic forest-water junction area: A water body that shares a boundary with a forest land but has a vegetation frequency lower than the preset threshold, which is the basic form of the forest-water ecosystem;

[0151] Water forest area to be restored: A water body that does not share a boundary with a forest land but has a vegetation frequency not lower than the preset threshold and a forest land with a water body frequency not lower than the preset threshold, which is the key area for ecological restoration.

[0152] This classification is not a simple artificial definition, but is based on the scientific characteristics and actual needs of the forest-water complex ecosystem, and has clear ecological basis.

[0153] As an example, the preset threshold may include 50%. Figure 2It can be seen that the 50% threshold is located at the middle position of the curves of 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 one area; secondly, at the 50% threshold, the curve of the water forest area to be restored gradually flattens, increasing the stability of the evaluation classification result. The slope of the area curve of the water forest area to be restored at different threshold points is as Figure 3 shown, and the change rate of the slope of the area curve of the water forest area to be restored is as Figure 4 shown.

[0154] To rigorously evaluate the performance of different thresholds, the present invention establishes a threshold evaluation system of a null model including three dimensions of threshold sensitivity, stability, and classification balance. The evaluation content designed by the threshold evaluation system of the null model includes:

[0155] Threshold sensitivity evaluation:

[0156] S(t) = ∑|A i(t) -A i(t-Δt) | / ∑A i(t)

[0157] where S(t) is the sensitivity coefficient of threshold t, indicating the degree of influence of threshold change on the classification result; A i(t) is the area of the i-th type of region under threshold t; A i(t-Δt) is the area of the i-th type of region under threshold (t - Δt); Δt is the threshold change step, usually taking 5% or 10%. A smaller S(t) value indicates that the threshold change has a smaller impact on the classification result and the classification is more stable.

[0158] Threshold stability evaluation:

[0159] ST(t) = 1 - S(t) / max(S)

[0160] where ST(t) is the stability index of threshold t, and the value range is [0, 1]; max(S) is the maximum value of the sensitivity coefficient among all the investigated thresholds; the closer to 1, the more stable the threshold, that is, the classification result is least affected by the small change of the threshold at this threshold; the threshold with high stability is usually located in the flat area of the area curve.

[0161] Classification balance evaluation:

[0162]

[0163] where B(t) is the classification balance of threshold t, and the value range is [0, 1]; A i(t) / A total is the proportion of the area of the i-th type of region in the total area; A totalis the total area of the evaluation area; 1 / n is the proportion of each type of area in the ideal case (assuming uniform distribution); n is the total number of classification categories. In the present invention, n = 3 (ecological integration area, basic forest-water boundary area, water forest area to be restored); n - 1 is the normalization factor. The closer to 1, the more balanced the area distribution of each type of area, avoiding the situation where a certain type of area is too large or too small.

[0164] Based on the above indicators, the comprehensive score is calculated as follows:

[0165] CS(t) = w1 × ST(t) + w2 × B(t) + w3 × AC(t)

[0166] where CS(t) is the comprehensive score at threshold t; w1, w2, w3 are weight coefficients, representing the importance degrees of stability, balance degree, and accuracy respectively; in the present 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 the field investigation sample points, and the threshold with the highest comprehensive score is selected as the optimal threshold.

[0167] Through null model analysis, the preset threshold is set to 50%, and the reasons are as follows:

[0168] (1) Area balance analysis: At the 50% threshold, the area of the ecological integration area is 128.90 hectares, the area of the basic forest-water boundary area is 447.54 hectares, and the area of the water forest area to be restored is 25.60 hectares. The area distribution of the three types of areas is reasonable;

[0169] (2) Curve stability analysis: At the 50% threshold, the change rate of the area curve of the water forest area to be restored decreases by 18.97%, as Figure 4 shown, indicating that the stability of the classification result is significantly enhanced;

[0170] (3) Component sensitivity analysis: At the 50% threshold, the misjudgment rate is significantly reduced, and the classification reliability is improved.

[0171] As an example, in step S60, the evaluation and classification of the threshold evaluation system based on vegetation frequency, water body frequency, forest-water composite degree, and null model have clear ecological significance:

[0172] 1. The ecological integration area is the ideal state of the forest-water composite ecosystem, with a complete land-water transition zone and rich ecological service functions. It usually shows a high interaction between water bodies and forests, and the water body area has stable vegetation coverage during the non-rainy season;

[0173] 2. The basic forest-water boundary area is the basic form of the forest-water ecosystem. Although the water body is adjacent to the forest, the vegetation coverage in the water body area is insufficient, indicating that the ecological transition zone has not been fully formed;

[0174] 3. The water forest areas to be restored include two situations: one is water bodies that are not adjacent to forest land but have a high vegetation frequency, indicating the basic conditions for siltation and vegetation growth; the other is forest land with a high water body frequency, indicating poor drainage conditions and the need for ecological restoration.

[0175] Compared with the prior art, the evaluation method of the forest-water composite ecological area of the present invention has the following advantages: 1. Strong pertinence: Specifically designed for the urban forest-water composite ecosystem, taking into account its non-regular flooding characteristics and seasonal variation characteristics; 2. Dynamic evaluation: By analyzing the data of the rainy season and non-rainy season for three consecutive years, capturing the dynamic change characteristics of the forest-water composite ecosystem; 3. Comprehensive indicators: Through three key indicators, namely the vegetation frequency of water bodies, the water body frequency of forest land, and the forest-water composite degree, comprehensively evaluating the state and potential of the forest-water composite ecological area; 4. Scientific classification: Based on ecological principles, the forest-water composite area is divided into an ecological integration area, a basic forest-water boundary area, and a water forest area to be restored, providing targeted guidance for the management and restoration of different types of areas; 5. Model innovation: Determining the optimal threshold through the null model evaluation system, improving the scientificity and reliability of classification.

[0176] In another embodiment, please refer to Figure 5 , the present invention also provides an evaluation system for a forest-water composite ecological area. The evaluation system for the forest-water composite ecological area includes: a data acquisition device 10, which is used to acquire image data of the forest-water composite ecological area; the image data includes rainy season data and non-rainy season image data; a land type recognition module 11, which is used to perform land type recognition on the forest-water composite ecological area based on the image data, and the land types include water bodies and forest land; a vegetation frequency acquisition module 12, which is used to acquire the vegetation frequency of the water body based on the non-rainy season image data; a water body frequency acquisition module 13, which is used to acquire the water body frequency of the forest land based on the rainy season image data; a forest-water composite degree acquisition module 14, which is used to acquire the forest-water composite degree of the forest-water composite ecological area; an evaluation and classification module 15, which is used to evaluate and classify the forest-water composite ecological area based on the vegetation frequency, the water body frequency, the forest-water composite degree, and the threshold evaluation system of the null model.

[0177] The above-mentioned evaluation system for forest-water complex ecological regions 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 complex degree acquisition module 14, and an evaluation and classification module 15. The above-mentioned evaluation system for forest-water complex ecological regions can accurately evaluate and classify different forest-water complex ecological regions, providing a scientific basis for the refined management of urban ecological systems and enabling the optimization of ecological construction and management.

[0178] As an example, the evaluation system for the forest-water complex ecological region can be used to execute the evaluation method for the forest-water complex ecological region in the previous embodiment.

[0179] The evaluation method and system for the forest-water complex ecological region of the present invention can have the following beneficial effects:

[0180] 1) It can achieve the precise quantification of the forest-water complex degree. This method fills the gap in the systematic evaluation of the prior art and can provide accurate identification and quantification for different types of forest-water complex regions.

[0181] 2) It can effectively identify different types of forest-water complex ecological regions, helping to deeply understand the spatial distribution characteristics and formation mechanisms of the forest-water complex ecological system. This provides a scientific basis for the refined management of urban ecological systems, thereby optimizing ecological construction and management.

[0182] 3) Long-term monitoring of dynamic changes: It can capture the dynamic changes of the forest-water complex system and can long-term monitor the dynamic changes of the forest-water complex ecological region, which is crucial for understanding the evolution process of the system. The accumulation of long-term data is particularly critical for evaluating the health and changes of the ecological system.

[0183] 4): It can conduct differential evaluations on different types of forest-water complex ecological regions, which helps to accurately formulate targeted management strategies; it can achieve differential evaluation and the formulation of management strategies. By identifying the unique needs and functions of each region, resources can be allocated and managed more effectively.

[0184] 5) It can accurately identify regions with ecological restoration potential, can accurately identify the ecological restoration potential, and provides clear spatial guidance for urban and regional ecological planning. This helps to optimize the design and implementation of ecological restoration projects and improve the efficiency and effectiveness of ecological restoration.

[0185] 6) It is applicable to different urban environments and has broad applicability and promotion value. This enables the framework to be not limited to specific regions but can be widely applied to diverse urban environments, increasing its practicality and universality.

[0186] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope recorded in this specification.

[0187] The above embodiments only express several implementation manners of the present invention, and the description thereof is relatively specific and detailed. However, it should not be construed as a limitation on the scope of the patent application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of this invention patent shall be subject to the appended claims.

Claims

1. An evaluation method for a forest-water composite ecological region, characterized in that, The evaluation method for the forest-water complex ecological region includes: Obtaining image data of the forest-water complex ecological region; the image data includes rainy season data and non-rainy season image data; Conducting land use type identification on the forest-water complex ecological region based on the image data, and the land use types include water bodies and forest land; Obtaining the vegetation frequency of the water body based on the non-rainy season image data; Obtaining the water frequency of the forest land based on the rainy season image data; Obtaining the forest-water complex degree of the forest-water complex ecological region; Evaluating and classifying the forest-water complex ecological region based on the vegetation frequency, the water frequency, the forest-water complex degree, and the threshold evaluation system of the null model.

2. The evaluation method of the forest-water composite ecological region according to claim 1, wherein The obtaining of the vegetation frequency of the water body based on the non-rainy season image data includes: Conducting multiple effective observations on the water body to obtain non-rainy season image data of multiple effective observations; Obtaining the enhanced vegetation index, the normalized vegetation index, and the land surface water index based on the non-rainy season image data; Determining whether the water body is a vegetation-covered area based on the enhanced vegetation index, the normalized vegetation index, and the land surface water index; Taking the ratio of the number of times the water body is determined to be the vegetation-covered area to the number of effective observations as the vegetation frequency of the water body.

3. The evaluation method of the forest-water complex ecological region according to claim 2, wherein The image data includes Sentinel-2 L1C image data; the obtaining of the enhanced vegetation index, the normalized vegetation index, and the land surface water index based on the non-rainy season image data includes: Obtaining the enhanced vegetation index based on the following formula: where EVI is the enhanced vegetation index; NIR is the reflectance of the near-infrared band; RED is the reflectance of the red band; BLUE is the reflectance of the blue band; Obtaining the normalized index index based on the following formula: where NDVI is the normalized index index; NIR is the reflectance of the near-infrared band; RED is the reflectance of the red band; Obtaining the land surface water index based on the following formula: where LSWI is the land surface water index; NIR is the reflectance of the near-infrared band; SWIR is the reflectance of the short-wave infrared band.

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

5. The evaluation method of the forest-water complex ecological region according to claim 1, characterized in that The obtaining of the water frequency of the forest land based on the rainy season image data includes: Conducting multiple effective observations on the forest land to obtain rainy season image data of multiple effective observations; Obtaining the enhanced vegetation index, the normalized vegetation index, and the modified normalized difference water index based on the rainy season image data; Determining whether the forest land is a water body based on the enhanced vegetation index, the normalized vegetation index, and the modified normalized difference water index; Taking the ratio of the number of times the forest land is determined to be a water body to the number of effective observations as the water frequency of the forest land.

6. The evaluation method of the forest-water composite ecological region according to claim 5, characterized in that Obtaining the enhanced vegetation index, normalized vegetation index, and improved normalized difference water index based on the rainy season image data includes: Obtaining the enhanced vegetation index based on the following formula: where EVI is the enhanced vegetation index; NIR is the reflectance of the near-infrared band; RED is the reflectance of the red light band; BLUE is the reflectance of the blue light band; Obtaining the normalized index index based on the following formula: where NDVI is the normalized index index; NIR is the reflectance of the near-infrared band; RED is the reflectance of the red light band; Obtaining the land surface water index based on the following formula: where MNDWI is the land surface water index; GREEN is the reflectance of the green light band; SWIR is the reflectance of the short-wave infrared band.

7. The evaluation method of the forest-water composite ecological region according to claim 5, characterized in that, Determining whether the forest land is water based on the enhanced vegetation index, the normalized vegetation index, and the improved normalized difference water index includes: If EVI < 0.1, MNDWI > EVI or MNDWI > NDVI, then it is determined that the forest land is water.

8. The evaluation method of the forest-water composite ecological region according to claim 1, characterized in that Obtaining the forest-water complex degree of the forest-water complex ecological region includes: Identifying the shared boundary between the water body and the forest land; Obtaining the length of the shared boundary; Obtaining the total perimeter of the water body; Taking the ratio of the perimeter of the shared boundary to the total perimeter as the forest-water complex degree of the forest-water complex ecological region.

9. The evaluation method of the forest-water composite ecological region according to claim 8, wherein Evaluating and classifying the forest-water complex ecological region based on the vegetation frequency, the water body frequency, the forest-water complex degree, and the threshold evaluation system of the null model includes: Based on the threshold evaluation system of the null model, determining the preset threshold through threshold sensitivity evaluation, stability evaluation, and classification balance evaluation; Classifying the water bodies that have a shared boundary with the forest land and whose vegetation frequency is not lower than the preset threshold as the ecological integration area; the ecological integration area represents an ideal forest-water complex state with a complete land-water transition zone and stable vegetation coverage; Classifying the water bodies that have a shared boundary with the forest land and whose vegetation frequency is lower than the preset frequency as the basic forest-water junction area; the basic forest-water junction area represents a transitional state with adjacent forest and water but insufficient vegetation coverage; Classifying the water bodies that have no shared boundary with the forest land and whose vegetation frequency is not lower than the preset threshold and the forest land whose water body frequency is not lower than the preset threshold as the forest-water area to be restored; the forest-water area to be restored represents an ecological restoration potential area with siltation foundation or poor drainage.

10. An evaluation system for a forest-water composite ecological region, characterized in that, The evaluation system of the forest-water complex ecological region includes: Data acquisition equipment for obtaining the image data of the forest-water complex ecological region; the image data includes rainy season data and non-rainy season image data; A land type identification module for identifying the land type of the forest-water complex ecological region based on the image data, and the land types include water bodies and forest land; A vegetation frequency acquisition module for obtaining the vegetation frequency of the water body based on the non-rainy season image data; A water body frequency acquisition module for obtaining the water body frequency of the forest land based on the rainy season image data; A forest-water complex degree acquisition module for obtaining the forest-water complex degree of the forest-water complex ecological region; An evaluation and classification module is used to evaluate and classify the forest-water complex ecological region based on the vegetation frequency, the water body frequency, the degree of forest-water complex, and the threshold of the null model of the system.

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