Coastal zone ecological environment quality high-precision remote sensing evaluation method based on ecological partition
By employing ecological zoning and principal component analysis, the applicability of traditional remote sensing indices in coastal complex ecosystems has been addressed, enabling high-precision assessment of ecological and environmental quality and improving the accuracy and reliability of the assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-29
- Publication Date
- 2026-04-03
AI Technical Summary
Traditional remote sensing ecological indices are not sufficiently applicable to coastal complex ecosystems, and are difficult to accurately reflect the differences in ecological zones of land, wetlands and water bodies, resulting in incomplete evaluation scope and misjudgment.
Based on the ecological zoning method, the coastal zone is divided into terrestrial, wetland and aquatic ecological zones. A remote sensing index system with zoning adaptability is constructed, and the index is integrated and spatially integrated through principal component analysis (PCA) to generate a coastal zone ecological environment quality map.
It has improved the accuracy and objectivity of coastal ecological environment quality assessment, reduced misjudgments, and enhanced the ecological explanatory power and stress response capacity of complex ecosystems.
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Figure CN121789054A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of ecological and environmental remote sensing technology, and in particular to a high-precision remote sensing evaluation method for coastal ecological and environmental quality based on ecological zoning. Background Technology
[0002] Coastal zones are characterized by the strongest coupling between land and sea processes, the most complex ecological functions, and the most intensive human activities. Under the multiple pressures of urbanization, resource development, and climate change, ecological problems such as vegetation degradation, wetland shrinkage, coastal erosion, and exacerbated eutrophication and salinization are becoming increasingly prominent, significantly weakening ecosystem service functions. Against this backdrop, conducting accurate ecological environment quality assessments is crucial. Traditional assessment methods, based on single ecological indicators or subjectively weighted comprehensive indices, are insufficient to meet the needs of the highly heterogeneous coastal environment. The Remote Sensing Ecological Index (RSEI), by integrating greenness, humidity, aridity, and heat and utilizing PCA (principal component analysis) to achieve objective weighting, has improved the assessment efficiency of terrestrial ecosystems and has been widely applied in urban areas, forests, and watersheds. Although subsequent studies have attempted to incorporate air quality, nighttime light, and human activity intensity, or replace some indicators, a unified indicator system is still generally used, failing to effectively adapt to the coastal environment with its significant ecological gradients and complex interplay of ecological elements.
[0003] In coastal complex ecosystems, the applicability of the Relationship between Ecosystems and Sediments (RSEI) is even more pronounced. Terrestrial, wetland, and aquatic ecozones differ significantly in structure, dominant stresses, and ecological response characteristics, making it difficult for a unified indicator system to accurately reflect their spatial variations. Humidity indicators, primarily derived from vegetation-soil water content, cannot effectively characterize the ecological state of open water bodies or shallow seas, leading to the frequent masking of water bodies in practical applications and resulting in incomplete evaluation scope. Wetlands are highly sensitive to stresses such as salinization and seawater intrusion, but the RSEI often misjudges these stresses due to distorted indicator responses, weakening or ignoring key degradation signals. Therefore, although the RSEI performs well in terrestrial ecosystems, its indicator system matching, ecological response direction, and explanatory power are significantly limited in coastal zones with interwoven ecological types and diverse stress mechanisms. Summary of the Invention
[0004] The purpose of this invention is to provide a high-precision remote sensing evaluation method for the ecological environment quality of coastal zones based on ecological zoning, thereby solving the problems mentioned in the background art.
[0005] To achieve the above objectives, this invention provides a high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning, comprising the following steps: S1. Based on multi-source land use and cover data, the coastal zone is divided into terrestrial ecological zone, wetland ecological zone and aquatic ecological zone. S2. Based on the differences in ecosystem composition and key environmental stress factors in each ecological zone, construct a remote sensing index system with zonal adaptability; S3. After normalizing the indicators of each ecological zone, principal component analysis (PCA) is used to synthesize the indicators, and the results of each ecological zone are spatially integrated to form an ecological environment quality map covering the entire coastal zone.
[0006] Preferably, the terrestrial ecological zone in S1 includes construction land, farmland and terrestrial vegetation, the wetland ecological zone is an area affected by tidal inundation or groundwater exchange, including mudflats, swamps and mangroves, and the aquatic ecological zone includes rivers, lakes, reservoirs and nearshore shallow sea waters.
[0007] Preferably, the remote sensing indices in the remote sensing index system in S2 include Normalized Difference Vegetation Index (NDVI), Humidity Index (WET), Building Bare Soil Index (NDBSI), Land Surface Temperature (LST), Cyanobacteria and Aquatic Plant Index (CMI), Turbid Water Index (TWI), and Salinity Index (SI-T), which respectively reflect vegetation status, water conditions, surface aridity, thermal environment, degree of water quality deterioration, and degree of soil salinization.
[0008] Preferably, the remote sensing indices used for terrestrial ecoregions include NDVI, WET, NDBSI, and LST.
[0009] Preferably, the remote sensing indices used in wetland ecosystems include NDVI, SI-T, and LST.
[0010] Preferably, the remote sensing indices used in aquatic ecological zones include CMI and TWI.
[0011] Preferably, the specific steps of S3 are as follows: S31. The Min-Max standardization method is used to uniformly map the original indicators in each ecological zone to the [0, 1] interval to eliminate the influence of the difference in indicator dimensions on the PCA results. S32. By extracting the first principal component PC1, the multi-index synthesis is completed; S33. Perform Min-Max standardization on the PC1 data of each ecological zone, and uniformly map the PC1 values to the [0, 1] interval; S34. Generate a continuous CARSEEI spatial distribution covering the entire coastal zone through spatial integration; S35. Based on the equal interval method, CARSIEI is classified into levels to complete the ecological environment quality assessment.
[0012] Preferably, the classification in S35 divides CARSEI into five levels, specifically: First level: Excellent, PC1 value is [0.8-1.0]; Second grade: Good, PC1 value is [0.6-0.8]; Level 3: Medium, PC1 value is [0.4-0.6]; Level 4: General, PC1 value is [0.2-0.4]; Fifth level: Poor, PC1 value is [0-0.2].
[0013] Therefore, the present invention employs the above-mentioned high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning, which has the following beneficial effects: (1) Through PCA principal component analysis, the information redundancy of multidimensional remote sensing data can be effectively reduced, and the information of multiple related indicators can be concentrated in a few principal components.
[0014] (2) PCA automatically determines the weight of each indicator through a data-driven approach, avoiding subjective bias caused by human weighting and enhancing the objectivity and repeatability of the evaluation results.
[0015] (3) It can effectively solve the evaluation blind spots and misjudgments caused by the traditional remote sensing ecological index "unified modeling", improve the robust characterization ability, stress response ability and ecological explanatory power of the spatial differentiation of the ecological environment quality of the coastal complex ecosystem, reduce misjudgments, and provide a more reliable tool for the monitoring and management of the coastal ecological environment.
[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description
[0017] Figure 1 This is a flowchart illustrating the construction of the coastal remote sensing ecological index, as an embodiment of the high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning of the present invention. Figure 2 This is a schematic diagram of coastal zone ecological zoning, representing an embodiment of the high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning of the present invention. Figure 3 This invention provides an example of a high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning, using GLC_FCS30 data to delineate the ecological zoning of a study area. Figure 4 The spatial differentiation pattern and local comparison of the CARSEI and RSEI evaluation results of the high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning in this invention are shown in (a) remote sensing image comparison map, (b) CARSEI comparison map, and (c) RSEI comparison map. Figure 5This is a visual comparison of CARSEI and RSEI of a verification point in a river delta in 2022, based on the embodiment of the high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning of the present invention. (a) is the terrestrial ecological zone, (b) is the wetland ecological zone, (c) is the aquatic ecological zone, and (d) is the distribution of the verification point. Figure 6 This document presents the quantitative accuracy evaluation of CARSIEI and RSEI at a river delta verification point in 2015, 2020, and 2022, based on the embodiment of the high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning of this invention. Detailed Implementation
[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.
[0019] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.
[0020] Example Please see Figures 1-6 This invention provides a high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning, comprising the following steps: 1. Coastal ecological zoning.
[0021] The coastal zone defined in this invention refers to a broad coastal zone, encompassing core terrestrial ecosystem types such as built-up land and farmland. This invention comprehensively references Chinese standards and international norms, including the *Classification of Wetlands in China* (GB / T24708-2009) (https: / / openstd.samr.gov.cn / bzgk / gb / newGbInfo?hcno=F01ACC268139897A3FC284E5ABD85793&refer=outter), the *Guidelines for Land and Sea Use Classification in Territorial Spatial Survey, Planning, and Land Use Control* (https: / / www.gov.cn / zhengce / zhengceku / 202311 / content_6917279.htm), and the Ramsar Convention wetland classification system. It divides the land use / cover type of the study area into three major ecological zones, providing a clear spatial framework for the subsequent construction of a remote sensing indicator system. Figure 2 As shown, from left to right, the ecological zones are terrestrial, wetland, and aquatic, with the aquatic zone encompassing river, lake, and nearshore seawater. The three ecological zones are as follows: (1) Terrestrial ecological zone: including construction land, farmland and terrestrial vegetation, mainly reflecting the terrestrial ecological process cycle under the influence of human activities, and its ecological environment quality is jointly regulated by urbanization, agricultural development and climate factors.
[0022] (2) Wetland ecological zone: including mudflats, swamps, mangroves and other areas that are periodically affected by tidal inundation or groundwater exchange. It has typical water-land interaction characteristics, and its ecological environment quality is dominated by the survival ability of vegetation under salinity stress and the physical and chemical environment of water-land interaction.
[0023] (3) Aquatic ecological zone: including rivers, lakes, reservoirs and nearshore shallow sea waters, which undertake important ecological functions such as nutrient transport, primary production and carbon sink regulation. The ecological environment quality of the water body is dominated by the optical characteristics, eutrophication degree and suspended matter status of the water body.
[0024] Guided by the aforementioned zoning framework, this invention integrates and merges GLC_FCS30 (Global 30-meter Land Cover Fine Classification Product) data to generate ecological zoning, such as... Figure 3 As shown, green represents terrestrial ecological zones, orange represents wetland ecological zones, and blue represents aquatic ecological zones.
[0025] 2. Construction of a remote sensing indicator system based on the "regional adaptation" strategy.
[0026] This invention constructs an index system comprising seven remote sensing indices to adapt to the characteristics of different ecological zones in the coastal zone, including: Normalized Difference Vegetation Index (NDVI), Wetness Index (WET), Narrow Building Soil Index (NDBSI), Land Surface Temperature (LST), Cyanobacteria and Aquatic Plant Index (CMI), Turbidity Index (TWI), and Salinity Index (SI-T) (Table 1). These indices reflect vegetation status, water conditions, thermal environment, surface aridity, water quality deterioration, and soil salinization, respectively.
[0027] In terrestrial ecological zones, the four core ecological and environmental remote sensing indicators—NDVI, WET, NDBSI, and LST—are used. This combination has been widely validated for characterizing the comprehensive ecological and environmental quality of multi-scale, multi-type terrestrial ecosystems.
[0028] In wetland ecozones, these areas are often subject to alternating periods of flooding and drying, making them susceptible to seawater intrusion and resulting in soil salinization. Therefore, in addition to retaining the NDVI (Natural Density Index) which reflects vegetation status, the SI-T (Soil Salinity Index) is introduced to characterize the degree of surface soil salt accumulation. SI-T, based on the sensitive response of the salt crust to the shortwave infrared band, has been proven to be useful for remote sensing monitoring of coastal saline soils. Furthermore, although land surface temperature (LST) is generally negatively correlated with ecological environment quality in terrestrial areas, moderate thermal conditions in wetlands can promote photosynthetic activity and community expansion of wetland and marsh vegetation. Therefore, LST may exhibit a positive ecological environment effect in this region. For this reason, this invention retains LST as a thermal environmental factor in the WLEZ (Wetland Ecozone) and automatically identifies its direction of influence on ecological environment quality using PCA (Potentially Analytical Analysis).
[0029] In the aquatic ecosystem, this invention introduces spectral indices specifically for the aquatic environment. Among them, the CMI (Chlorophylland Macrophyte Index), based on the difference in reflectance between cyanobacteria and submerged / floating-leaved plants in the near-infrared band, can effectively identify the distribution of algal blooms and indirectly reflect the eutrophication level and primary productivity status of the water body. The TWI (Turbid Water Index), by enhancing the suspended sediment signal through the ratio of red light to short-wave infrared light, is used to quantify water turbidity and reveal its inhibitory effect on light penetration depth and photosynthesis in aquatic ecosystems.
[0030] 3. Construction of CARSEEI (Coastal Remote Sensing Ecological Index) based on PCA and "spatial integration" strategy.
[0031] Principal Component Analysis (PCA) effectively reduces information redundancy in multidimensional remote sensing data by concentrating information from multiple related indicators into a few principal components. The first principal component (PC1) typically contains the maximum variance information of the original variables and can be considered the best linear representation of overall ecological environment quality. More importantly, PCA automatically determines the weights of each indicator through a data-driven approach, avoiding subjective bias caused by manual weighting and enhancing the objectivity and repeatability of the evaluation results.
[0032] Therefore, this invention selects PCA as the method for constructing CARSEI.
[0033] First, the Min-Max standardization method is used to uniformly map the original indicators in each ecological zone to the [0, 1] interval to eliminate the influence of the difference in indicator dimensions on the PCA results.
[0034] Then, the first principal component (PC1) is extracted to achieve multi-index synthesis, as shown in Table 1 below: Table 1. Indicator System and Calculation Method
[0035] In Table 1, Red, Blue, Green, NIR, S1, S2, λGreen, λBlue, and λSWIR represent the reflectance values of Landsat remote sensing images at the center wavelengths of the red, blue, green, near-infrared, shortwave infrared 1, shortwave infrared 2, green, blue, and short-infrared bands, respectively. The LST (Land Surface Temperature) was calculated using an atmospheric correction algorithm. In the LST formula, T represents the sensor's calorimetry, λ represents the center wavelength of the thermal infrared band, and ε represents the surface emissivity. Both represent the comprehensive function of principal component analysis. When the direction of the eigenvector of the index is consistent with the ecological connotation, =PC1 ,otherwise =1-PC1 .
[0036] To facilitate cross-regional comparison and visualization, the PC1 data of each partition were further standardized by Min-Max to the range of [0, 1]. The higher the value, the better the ecological environment quality.
[0037] Based on this, a continuous CARSIE spatial distribution covering the entire coastal zone is generated through spatial integration.
[0038] Finally, based on the equal interval method, CARSIEI is divided into five levels: poor [0-0.2], fair [0.2-0.4], moderate [0.4-0.6], good [0.6-0.8], and excellent [0.8-1.0], thus realizing the evaluation of ecological environment quality.
[0039] Therefore, the present invention adopts the above-mentioned high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning, which can effectively solve the evaluation blind spots and misjudgments caused by the "unified modeling" of traditional remote sensing ecological indices, improve the robust characterization ability, stress response ability and ecological explanatory power of spatial differentiation of coastal complex ecosystem ecological environment quality, reduce misjudgments, and provide a more reliable tool for coastal ecological environment monitoring and management.
[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.
Claims
1. A high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning, characterized in that, Includes the following steps: S1. Based on multi-source land use and cover data, the coastal zone is divided into terrestrial ecological zone, wetland ecological zone and aquatic ecological zone. S2. Based on the differences in ecosystem composition and key environmental stress factors in each ecological zone, construct a remote sensing index system with zonal adaptability; S3. After normalizing the indicators of each ecological zone, principal component analysis (PCA) is used to synthesize the indicators, and the results of each ecological zone are spatially integrated to form an ecological environment quality map covering the entire coastal zone.
2. The high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning as described in claim 1, characterized in that: The terrestrial ecological zone in S1 includes construction land, farmland and terrestrial vegetation; the wetland ecological zone is the area affected by tidal inundation or groundwater exchange, including mudflats, swamps and mangroves; and the aquatic ecological zone includes rivers, lakes, reservoirs and nearshore shallow sea waters.
3. The high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning as described in claim 1, characterized in that: The remote sensing indexes in the remote sensing index system in S2 include the Normalized Difference Vegetation Index (NDVI), Humidity Index (WET), Building Bare Soil Index (NDBSI), Land Surface Temperature (LST), Cyanobacteria and Aquatic Plant Index (CMI), Turbid Water Index (TWI), and Salinity Index (SI-T), which respectively reflect vegetation status, water conditions, surface aridity, thermal environment, degree of water quality deterioration, and degree of soil salinization.
4. The high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning as described in claim 3, characterized in that: The remote sensing indices used for terrestrial ecoregions include NDVI, WET, NDBSI, and LST.
5. The high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning as described in claim 3, characterized in that: The remote sensing indices used in wetland ecosystems include NDVI, SI-T, and LST.
6. The high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning according to claim 3, characterized in that: The remote sensing indices used in aquatic ecological zones include CMI and TWI.
7. The high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning according to claim 1, characterized in that, The specific steps of S3 are as follows: S31. The Min-Max standardization method is used to uniformly map the original indicators in each ecological zone to the [0, 1] interval to eliminate the influence of the difference in indicator dimensions on the PCA results. S32. By extracting the first principal component PC1, the multi-index synthesis is completed; S33. Perform Min-Max standardization on the PC1 data of each ecological zone, and uniformly map the PC1 values to the [0, 1] interval; S34. Generate a continuous CARSEEI spatial distribution covering the entire coastal zone through spatial integration; S35. Based on the equal interval method, CARSIEI is classified into levels to complete the ecological environment quality assessment.
8. The high-precision remote sensing evaluation method for coastal ecological environment quality based on ecological zoning according to claim 7, characterized in that, The classification in S35 divides CARSEI into five levels, specifically: First level: Excellent, PC1 value is [0.8-1.0]; Second grade: Good, PC1 value is [0.6-0.8]; Level 3: Medium, PC1 value is [0.4-0.6]; Level 4: General, PC1 value is [0.2-0.4]; Fifth level: Poor, PC1 value is [0-0.2].