A coastal seabeach sueda target recognition method based on multispectral remote sensing image
By calculating the NDSSI index from multispectral remote sensing images and combining it with a two-level screening strategy, the problem of identifying Suaeda salsa with other vegetation was solved, achieving high-precision monitoring of Suaeda salsa, which is suitable for large-scale ecological monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NORTH CHINA SEA ENVIRONMENTAL MONITORING CENT OF STATE OCEANIC ADMINISTATION
- Filing Date
- 2026-02-06
- Publication Date
- 2026-05-29
AI Technical Summary
Traditional methods are insufficient to accurately identify Suaeda salsa and other salt marsh vegetation. Existing remote sensing technologies lack dedicated identification methods, resulting in low accuracy in identifying Suaeda salsa and failing to meet the needs of ecological monitoring.
Using multispectral remote sensing imagery, the Normalized Diffusion Index (NDSSI) of Suaeda salsa was calculated. A two-stage screening strategy was adopted: first, NDVI was used to exclude non-vegetated areas, and then NDSSI was used to distinguish Suaeda salsa from other vegetation within the vegetated areas.
It enables rapid and accurate identification of Suaeda salsa in saline-alkali land, reduces the false judgment rate, improves the identification accuracy, supports multi-scale monitoring, adapts to different remote sensing image conditions, and has good universality and operability.
Smart Images
Figure CN122116166A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of remote sensing image processing and ecological monitoring technology, specifically to a method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing images. Background Technology
[0002] Suaeda salsa is a typical pioneer plant of saline marsh wetlands, widely distributed in coastal saline-alkali wetlands of my country, especially in the Yellow River Delta and Liao River Delta, forming large-scale distributions. Suaeda salsa is not only an important component of coastal wetland ecosystems but also a key indicator species for assessing wetland ecological health. Suaeda salsa communities have important ecological functions, including carbon sequestration, water purification, and providing habitats and food sources for migratory birds. Therefore, accurately identifying and monitoring the distribution range and area changes of Suaeda salsa is of great significance for coastal wetland ecological protection, biodiversity conservation, and wetland resource management.
[0003] Traditional methods for surveying Suaeda salsa in saline areas mainly rely on field surveys. While this method offers high accuracy, it suffers from drawbacks such as high workload, low efficiency, high cost, and difficulty in covering large areas. Furthermore, the complex terrain and significant tidal influences in coastal salt marsh regions make field surveys less accessible.
[0004] With the development of remote sensing technology, vegetation identification using satellite remote sensing imagery has become an efficient and economical technique. Currently, commonly used vegetation identification methods are mainly based on vegetation indices such as the Normalized Difference Vegetation Index (NDVI), but these methods have limitations in distinguishing different vegetation types in salt marsh wetlands. Coastal salt marsh wetlands have diverse vegetation types, including not only Suaeda salsa but also reeds, alkali grass, and swerts, among others. These vegetation types do not show significant differences in traditional vegetation indices such as NDVI, making it difficult to accurately distinguish Suaeda salsa from other salt marsh vegetation.
[0005] Suaeda salsa possesses unique spectral characteristics; its leaves and stems turn red or purplish-red during its vigorous growth period. This feature gives it reflective properties in the visible light spectrum that distinguish it from other green vegetation. However, current technology lacks a dedicated identification method for this spectral characteristic of Suaeda salsa, making it difficult to achieve high-precision automated identification.
[0006] Therefore, there is an urgent need to develop a remote sensing monitoring method that can fully utilize the unique spectral characteristics of Suaeda salsa in saline-alkali land and achieve efficient and accurate identification.
[0007] In view of this, we propose a target identification method for Suaeda salsa in coastal salt flats based on multispectral remote sensing imagery. Summary of the Invention
[0008] The technical problem to be solved by this invention is: how to use multispectral remote sensing images to achieve rapid and accurate identification of coastal Suaeda salsa based on its unique spectral characteristics, and overcome the technical defects of traditional methods that make it difficult to distinguish Suaeda salsa from other salt marsh vegetation.
[0009] To address the aforementioned technical problems, this invention provides a method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery, comprising the following steps:
[0010] S1. Determine the target area: Based on the needs of the monitoring and investigation work, determine the target area for the monitoring and investigation of Suaeda salsa in coastal salt flats;
[0011] S2. Acquire multispectral remote sensing data: Acquire multispectral remote sensing image data covering the target area, wherein the multispectral remote sensing image data includes blue light band, green light band, red light band and near-infrared band;
[0012] S3. Determine the vegetation coverage area of the salt marsh: Based on the multispectral remote sensing image data, the Normalized Difference Vegetation Index (NDVI) is used to identify the vegetation coverage area of the salt marsh. The formula for calculating the NDVI is as follows:
[0013] In the formula, Near-infrared reflectivity; Red light band reflectivity; areas with NDVI greater than a set threshold are selected as salt marsh vegetation cover areas;
[0014] S4. Calculate the Normalized Diffusion Saline-Alkali Index (NDSSI): Within the saline marsh vegetation cover area, calculate the NDSSI for each pixel. The formula for calculating the NDSSI is as follows:
[0015] In the formula, Blue light band reflectivity; Reflectivity in the green light band.
[0016] S5. Determine the Suaeda salsa coverage area: Within the salt marsh vegetation coverage area, select areas with NDSSI>0 as the Suaeda salsa coverage area, and calculate the area of the Suaeda salsa coverage area.
[0017] Furthermore, in step S1, the upper boundary of the target area is the shoreline, the lower boundary is the 0-meter isobath, and the left and right boundaries are administrative or geographical boundaries.
[0018] Furthermore, in step S2, the multispectral remote sensing image data is multispectral satellite imagery with a spatial resolution better than 2 meters acquired from July to September.
[0019] Furthermore, in step S2, the multispectral remote sensing image data undergoes radiometric correction, geometric correction, image cropping, mosaicking, and / or fusion processing to achieve normalization of remote sensing data quality.
[0020] Furthermore, in step S3, the method for determining the set threshold includes: extracting the NDVI of more than 20 vegetation boundary points in the target area, performing an arithmetic mean on the NDVI, and using the arithmetic mean as the set threshold.
[0021] Furthermore, in step S3, the set threshold is determined independently for each remote sensing image.
[0022] Furthermore, when the multispectral remote sensing image data does not contain the blue light band, the calculation formula for the normalized saline-alkali phytosanitary index in step S4 is replaced by:
[0023] In the formula, Reflectivity in the red light band; Reflectivity in the green light band.
[0024] Furthermore, in step S5, the area with NDSSI>0 is converted into a vector file using a raster-to-vector tool, and the area is calculated after setting the projection coordinate system according to the location of the monitoring area.
[0025] Furthermore, step S6, revision of the Suaeda salsa coverage area: based on business monitoring needs, areas with an area smaller than the set area threshold are removed.
[0026] Furthermore, in step S6, the set area threshold is 600 square meters, and patches with the same ecosystem type and an actual distance between their edges of less than 20 meters are merged into habitat patches of the same type.
[0027] Compared with the prior art, the present invention has the following beneficial effects:
[0028] (1) In view of the unique spectral characteristics of Suaeda salsa exhibiting red or purplish-red color during its vigorous growth period, this invention innovatively proposes the Normalized Suaeda salsa Index (NDSSI). This index utilizes the difference in reflectance between the blue and green light bands to effectively distinguish Suaeda salsa from other green salt marsh vegetation, thus solving the technical problem that traditional vegetation indices are difficult to distinguish between salt marsh vegetation types.
[0029] (2) This invention first uses NDVI to determine the vegetation-covered area of the salt marsh, eliminating interference from non-vegetated areas such as water bodies, bare land, and buildings; then, within the vegetation-covered area, NDSSI is used to identify Suaeda salsa. This two-stage screening strategy can effectively reduce the false positive rate and improve the identification accuracy.
[0030] (3) The present invention adopts a dynamic threshold determination method based on vegetation boundary points for remote sensing images of different regions and different time phases, which avoids the problem of poor adaptability of fixed threshold under different conditions and enhances the universality and stability of the method.
[0031] (4) This invention supports the combined application of satellite remote sensing images and UAV remote sensing images. For key or complex areas that are difficult to identify by satellite images, high-resolution UAV images can be used to supplement them, so as to achieve multi-scale and high-precision monitoring coverage.
[0032] (5) The vegetation index calculation method used in the invention is simple, has high calculation efficiency, and has low requirements for remote sensing image data. It can be achieved with only conventional multispectral images, and has good operability and promotion and application value.
[0033] (6) Each step of the present invention can be automated by remote sensing image processing software or programming, which can quickly process remote sensing image data of a large area and multiple time phases to meet the needs of large-area coastal wetland monitoring and investigation. Attached Figure Description
[0034] Figure 1 This is a flowchart of the method of the present invention;
[0035] Figure 2 This is a schematic diagram of the target area;
[0036] Figure 3 This is a schematic diagram of the preprocessed multispectral remote sensing image of the target area.
[0037] Figure 4 This is a schematic diagram of the NDVI calculation results for the target area;
[0038] Figure 5 A schematic diagram showing the screening results of salt marsh vegetation cover areas in the target region;
[0039] Figure 6 A schematic diagram showing the calculation results of the Suaeda salsa index in the target area's salt marsh vegetation-covered zone;
[0040] Figure 7 A schematic diagram of the target area's salt marsh vegetation cover zone where the saline land saline-alkali phytosanitary index is >0;
[0041] Figure 8 This is a schematic diagram of the results for the target area covered by Suaeda salsa in saline-alkali soil. Detailed Implementation
[0042] The following detailed description, in conjunction with the accompanying drawings, further illustrates this application. Based on the need for refined monitoring and assessment of *Suaeda salsa* by marine ecological authorities, and relying on remote sensing data used in operational work, this paper develops a target identification method for coastal *Suaeda salsa* based on multispectral high-resolution remote sensing imagery, taking into account the multispectral characteristics of salt marsh vegetation and the unique red vegetation features of *Suaeda salsa*. This method can guide technical personnel in conducting refined monitoring of *Suaeda salsa*, enabling accurate classification and area calculation. It also helps solve the problem of boundary identification in mixed-species areas of *Suaeda salsa* and other salt marsh vegetation. Furthermore, it can guide the development of relevant target identification modules for *Suaeda salsa* in domestically produced information software, thereby achieving automation and informatization of *Suaeda salsa* monitoring based on multispectral remote sensing data.
[0043] The specific implementation of this method is described below, taking the joint use of remote sensing image processing software ENVI and geographic information processing software ArcGIS to identify Suaeda salsa targets in saline land as an example.
[0044] Example 1; Refer to Figure 1 This embodiment provides a method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery, including the following steps:
[0045] S1. Determine the target area: Based on the needs of the monitoring and investigation work, determine the target area for the monitoring and investigation of Suaeda salsa in coastal salt flats;
[0046] S2. Acquire multispectral remote sensing data: Acquire multispectral remote sensing image data covering the target area, wherein the multispectral remote sensing image data includes blue light band, green light band, red light band and near-infrared band;
[0047] S3. Determine the vegetation coverage area of the salt marsh: Based on the multispectral remote sensing image data, the Normalized Difference Vegetation Index (NDVI) is used to identify the vegetation coverage area of the salt marsh. The formula for calculating the NDVI is as follows:
[0048] In the formula, Near-infrared reflectivity; Red light band reflectivity; areas with NDVI greater than a set threshold are selected as salt marsh vegetation cover areas;
[0049] S4. Calculate the Normalized Diffusion Saline-Alkali Index (NDSSI): Within the saline marsh vegetation cover area, calculate the NDSSI for each pixel. The formula for calculating the NDSSI is as follows:
[0050] In the formula, Blue light band reflectivity; Reflectivity in the green light band.
[0051] S5. Determine the Suaeda salsa coverage area: Within the salt marsh vegetation coverage area, select areas with NDSSI>0 as the Suaeda salsa coverage area, and calculate the area of the Suaeda salsa coverage area.
[0052] As an embodiment of this application, in step S1, the upper boundary of the target area is the shoreline, the lower boundary is the 0-meter isobath, and the left and right boundaries are administrative or geographical boundaries.
[0053] As an embodiment of this application, in step S2, the multispectral remote sensing image data is multispectral satellite imagery with a spatial resolution better than 2 meters acquired from July to September.
[0054] As an embodiment of this application, in step S2, the multispectral remote sensing image data is subjected to radiometric correction, geometric correction, image cropping, mosaicking and / or fusion processing to achieve remote sensing data quality normalization.
[0055] As an embodiment of this application, in step S3, the method for determining the set threshold includes: extracting the NDVI of more than 20 vegetation boundary points in the target area, performing an arithmetic mean on the NDVI, and using the arithmetic mean as the set threshold.
[0056] As an embodiment of this application, in step S3, the set threshold is determined independently for each remote sensing image.
[0057] As an embodiment of this application, when the multispectral remote sensing image data does not contain the blue light band, the calculation formula for the normalized saline-alkali physalis index in step S4 is replaced with:
[0058] In the formula, Reflectivity in the red light band; Reflectivity in the green light band.
[0059] As an embodiment of this application, in step S5, the area with NDSSI>0 is converted into a vector file using a raster-to-vector tool, and the area is calculated after setting the projection coordinate system according to the location of the monitoring area.
[0060] As an implementation of this application, it also includes step S6, revision of the salt-tolerant land covered area: according to business monitoring needs, areas with an area smaller than the set area threshold are removed.
[0061] As an embodiment of this application, in step S6, the set area threshold is 600 square meters, and patches with the same ecosystem type and an actual distance between the edges of the patches is less than 20 meters are merged into habitat patches of the same type.
[0062] Implement column 2; refer to Figure 1This embodiment provides a method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery. The specific implementation steps are as follows:
[0063] Step S1: Determine the target area;
[0064] Based on the needs of coastal wetland ecological monitoring and survey work, a coastal salt marsh wetland in the Yellow River Delta was identified as the target area. For example... Figure 3 As shown, the boundary determination principles for the target area are as follows: the upper boundary is the shoreline, the lower boundary is the 0-meter isobath, and the left and right boundaries are administrative or natural geographical boundaries. The total area of the target area is approximately 150 square kilometers.
[0065] Step S2: Acquire multispectral remote sensing data; such as Figure 2 As shown, multispectral satellite remote sensing image data covering the target area is acquired. This embodiment uses WFV sensor imagery from the Gaofen-6 (GF-6) satellite acquired on August 15, 2025, with a spatial resolution of 16 meters, including four multispectral bands: blue (450-520nm), green (520-590nm), red (630-690nm), and near-infrared (770-890nm).
[0066] The reason for choosing July to September to acquire images is that this is the peak growing season for Suaeda salsa, when its stems and leaves display a vibrant red or purplish-red color, exhibiting the most distinct spectral characteristics, making it easy to distinguish from other green salt marsh vegetation. For example... Figure 2 As shown, the reflectance curve of Suaeda salsa in the visible light band is significantly different from that of other green vegetation such as reeds and alkali grass, especially in the blue and green light bands.
[0067] The acquired remote sensing image data undergoes preprocessing, including:
[0068] (a) Radiation correction: Convert the original DN value to the top atmospheric reflectivity to eliminate sensor response differences and atmospheric effects;
[0069] (b) Geometric correction: Geometric fine correction is performed using a rational function model and ground control points, with the correction error controlled within one pixel;
[0070] (c) Image cropping: The image is cropped according to the target area boundary determined in step S1;
[0071] (d) Mosaic processing: When the target area needs to be covered by multiple images, image mosaic and color balance processing are performed.
[0072] like Figure 3 As shown, after preprocessing, quality-normalized multispectral remote sensing image data is obtained.
[0073] Step S3: Determine the vegetation cover area of the salt marsh.
[0074] Based on the preprocessed multispectral remote sensing image data, the Normalized Difference Vegetation Index (NDVI) is calculated, such as... Figure 4 As shown, the calculation formula is:
[0075] In the formula, Near-infrared reflectivity; Red light band reflectivity; areas with NDVI greater than a set threshold are selected as salt marsh vegetation cover areas.
[0076] The NDVI ranges from -1 to 1. The NDVI of vegetated areas is usually greater than 0.2, while the NDVI of non-vegetated areas such as water bodies and bare land is usually less than 0.2.
[0077] To accurately determine the segmentation threshold between vegetation and non-vegetation areas, a dynamic threshold determination method was adopted: 25 vegetation boundary points (the junctions between vegetation and non-vegetation areas) were selected within the target area, and the NDVI values of these points were extracted, which were 0.18, 0.21, 0.19, 0.22, 0.20, 0.17, 0.23, 0.19, 0.21, 0.18, 0.20, 0.22, 0.19, 0.21, 0.20, 0.18, 0.21, and 0.20, respectively. The arithmetic mean of these NDVI values was calculated, yielding an average of 0.199. After rounding, the NDVI threshold was set to 0.20.
[0078] Areas with NDVI > 0.20 were selected as salt marsh vegetation cover areas, such as... Figure 5 As shown, the identified salt marsh vegetation cover an area of approximately 45 square kilometers, including various types of salt marsh vegetation such as Suaeda salsa, reeds, alkali grass, and stag sedge.
[0079] Step S4: Calculate the normalized saline-alkali soil Suaeda salsa index;
[0080] like Figure 6 As shown, within the salt marsh vegetation cover area determined in step S3, the Normalized Difference Salt Land Index (NDSSI) for each pixel is calculated using the following formula:
[0081] In the formula, Blue light band reflectivity; Reflectivity in the green light band.
[0082] The NDSSI index is designed based on the unique spectral characteristics of Suaeda salsa: during its vigorous growth period, Suaeda salsa is red or purplish-red, and its leaves and stems contain more betaine and anthocyanins, resulting in different reflectance characteristics in the visible light band compared to ordinary green vegetation. Ordinary green vegetation (such as reeds) has a significantly higher reflectance in the green light band than in the blue light band, therefore its NDSSI value is negative; while Suaeda salsa, due to its red color, has a blue light band reflectance close to or even higher than its green light band reflectance, resulting in a positive NDSSI value or close to zero.
[0083] like Figure 6 As shown, within the salt marsh vegetation cover area, the NDSSI value ranges from -0.16 to 0.21.
[0084] Step S5: Determine the area covered by Suaeda salsa in saline-alkali soil;
[0085] like Figure 7 As shown; within the salt marsh vegetation coverage area, areas with NDSSI>0 were selected as Suaeda salsa coverage areas. The raster-to-vector tool in remote sensing image processing software was used to convert the eligible raster areas into vector polygon files.
[0086] To accurately calculate the area, the projection coordinate system is set to Gauss-Kruger projection based on the geographical location of the target area, and then the area of each vector polygon is calculated.
[0087] like Figure 8 As shown, the total area of the identified Suaeda salsa-covered area is approximately 12.5 square kilometers, mainly distributed in the upper and middle intertidal zone and the edge area of the salt marsh wetland.
[0088] Step S6: Revision of the Suaeda salsa coverage area;
[0089] The identification results have been revised in accordance with the requirements of the ecological monitoring business specifications.
[0090] (a) Remove patches with an area of less than 600 square meters: These small patches may be noise or misjudgment, and have little impact on the overall monitoring results, so they are removed;
[0091] (b) Merging of map patches: adjacent map patches of the same ecosystem type and with an actual distance of less than 20 meters between their edges are merged into the same habitat map patch.
[0092] Before the revision, there were 1256 map patches, which were merged into 358 valid map patches after the revision. The total area of the salt-covered Suaeda salsa area was revised to 12.3 square kilometers.
[0093] Example 3: This example uses an alternative formula to identify Suaeda salsa in remote sensing images that do not contain the blue light band.
[0094] Steps S1 to S3: The target area is located in a coastal wetland, covering an area of approximately 60 square kilometers. Although the acquired Landsat 8OLI imagery includes the blue light band, the data quality is poor due to severe atmospheric scattering. Therefore, this embodiment only uses the green, red, and near-infrared bands for processing. The NDVI threshold is set to 0.22, and the area identified as being covered by salt marsh vegetation is approximately 20 square kilometers.
[0095] Step S4: Since the blue light band is unavailable, the normalized salinity index of Suaeda salsa is calculated using an alternative formula:
[0096] In the formula, Reflectivity in the red light band; The principle behind this alternative formula is that Suaeda salsa is red, and its red light band reflectance is higher than its green light band reflectance, while the green light band reflectance of ordinary green vegetation is higher than its red light band reflectance. Therefore, there is a significant difference in the NDSSI values between the two.
[0097] Steps S5 to S6: Select areas with NDSSI>0 as the Suaeda salsa coverage area, which is revised to 6.2 square kilometers.
[0098] After comparison and verification with the results of field surveys, the identification accuracy of the alternative formula is slightly lower than that of the standard formula (blue-green band), but it can still meet the accuracy requirements of large-scale monitoring and surveys.
[0099] Technical principle explanation;
[0100] 1. Spectral characteristics of Suaeda salsa;
[0101] Suaeda salsa is an annual herb belonging to the genus Suaeda in the family Chenopodiaceae. Unlike most green plants, Suaeda salsa's stems and leaves gradually change from green to red or purplish-red during its growth, especially during its vigorous growth period from July to October. This is mainly due to the accumulation of large amounts of pigments such as betalains and anthocyanins in the plant.
[0102] This unique pigment composition results in a significant difference in the visible light reflectance characteristics of Suaeda salsa compared to ordinary green vegetation:
[0103] (1) Green vegetation: Chlorophyll strongly absorbs blue and red light and reflects green light, thus forming a reflection peak in the green light band and an absorption valley in the blue and red light bands;
[0104] (2) Red Suaeda salsa: Betalains and anthocyanins mainly absorb green and blue-green light and reflect red light, resulting in a decrease in the reflectivity of the green light band, while the reflectivity of the blue and red light bands is relatively high.
[0105] 2. The design basis of the NDSSI index;
[0106] The Normalized Difference in Reflectance Index (NDSSI) fully utilizes the aforementioned differences in spectral characteristics. By calculating the normalized difference in reflectance between the blue and green light bands, it is possible to effectively distinguish between red Suaeda salsa and green salt marsh vegetation.
[0107] Green vegetation: > NDSSI < 0;
[0108] Red Suaeda salsa: ≥ , NDSSI≥0.
[0109] Using NDSSI=0 as the classification threshold has clear physical meaning and good interpretability.
[0110] 3. The necessity of a two-stage screening strategy;
[0111] Directly using NDSSI for identifying Suaeda salsa may lead to misclassification because some non-vegetated features, such as red buildings and exposed red soil, can also have positive NDSSI values. Therefore, this invention employs a two-stage screening strategy: First, NDVI is used to exclude non-vegetated areas, ensuring that subsequent analysis is conducted only within vegetated areas; second, within vegetated areas, NDSSI is used to distinguish Suaeda salsa from other green vegetation. This strategy effectively reduces the misclassification rate and improves identification accuracy.
[0112] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery, characterized in that, Includes the following steps: S1. Determine the target area: Based on the needs of the monitoring and investigation work, determine the target area for the monitoring and investigation of Suaeda salsa in coastal salt flats; S2. Acquire multispectral remote sensing data: Acquire multispectral remote sensing image data covering the target area, wherein the multispectral remote sensing image data includes blue light band, green light band, red light band and near-infrared band; S3. Determine the vegetation coverage area of the salt marsh: Based on the multispectral remote sensing image data, the Normalized Difference Vegetation Index (NDVI) is used to identify the vegetation coverage area of the salt marsh. The formula for calculating the NDVI is as follows: ， In the formula, Near-infrared reflectivity; Red light band reflectivity; areas with NDVI greater than a set threshold are selected as salt marsh vegetation cover areas; S4. Calculate the Normalized Diffusion Saline-Alkali Index (NDSSI): Within the saline marsh vegetation cover area, calculate the NDSSI for each pixel. The formula for calculating the NDSSI is as follows: ， In the formula, Blue light band reflectivity; Reflectivity in the green light band; S5. Determine the Suaeda salsa coverage area: Within the salt marsh vegetation coverage area, select areas with NDSSI>0 as the Suaeda salsa coverage area, and calculate the area of the Suaeda salsa coverage area.
2. The method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery according to claim 1, characterized in that, In step S1, the upper boundary of the target area is the shoreline, the lower boundary is the 0-meter isobath, and the left and right boundaries are administrative or geographical boundaries.
3. The method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery according to claim 1, characterized in that, In step S2, the multispectral remote sensing image data is multispectral satellite imagery with a spatial resolution better than 2 meters acquired from July to September.
4. The method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery according to claim 1, characterized in that, In step S2, radiometric correction, geometric correction, image cropping, mosaicking and / or fusion processing are performed on the multispectral remote sensing image data to achieve normalization of remote sensing data quality.
5. The method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery according to claim 1, characterized in that, In step S3, the method for determining the set threshold includes: extracting the NDVI of more than 20 vegetation boundary points in the target area, performing an arithmetic mean on the NDVI, and using the arithmetic mean as the set threshold.
6. The method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery according to claim 1, characterized in that, In step S3, the set threshold is determined independently for each remote sensing image.
7. The method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery according to claim 1, characterized in that, When the multispectral remote sensing image data does not contain the blue light band, the calculation formula for the normalized saline-alkali phytosanitary index in step S4 is replaced by: ， In the formula, Reflectivity in the red light band; Reflectivity in the green light band.
8. The method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery according to claim 1, characterized in that, In step S5, the area with NDSSI>0 is converted into a vector file using a raster-to-vector tool, and the area is calculated after setting the projection coordinate system according to the location of the monitoring area.
9. The method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery according to any one of claims 1-9, characterized in that, It also includes step S6, revision of the salt flat Suaeda salsa coverage area: based on business monitoring needs, areas with an area smaller than the set area threshold are removed.
10. The method for identifying Suaeda salsa targets in coastal salt flats based on multispectral remote sensing imagery according to claim 9, characterized in that, In step S6, the set area threshold is 600 square meters, and patches with the same ecosystem type and an actual distance between their edges of less than 20 meters are merged into habitat patches of the same type.