文献类型：期刊文献

英文题名：Mangrove mapping in China using Gaussian mixture model with a novel mangrove index (SSMI) derived from optical and SAR imagery

作者：Chen, Zhaojun[1,2,3] Zhang, Huaiqing[4] Zhang, Meng[1,2,3] Wu, Yehong[1,2,3] Liu, Yang[4]

第一作者：Chen, Zhaojun

通信作者：Zhang, M[1];Zhang, M[2];Zhang, M[3]

机构：[1]Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China;[2]Key Lab State Forestry Adm Forest Resources Manage, Changsha 410004, Peoples R China;[3]Key Lab Forestry Remote Sensing Based Big Data & E, Changsha 410004, Peoples R China;[4]Chinese Acad Forestry, Res Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2024

卷号：218

起止页码：466-486

外文期刊名：ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

收录：;EI(收录号：20244017124653);Scopus(收录号：2-s2.0-85204994844);WOS:【SCI-EXPANDED(收录号:WOS:001327981500001)】；

基金：This study was funded by the National Natural Science Foundation of China (41901385) , the Natural Science Foundation of Hunan Province of China (2024JJ8321) .

语种：英文

外文关键词：Mangrove mapping; Mangrove index; Optical; SAR; Gaussian mixture model

摘要：As an important shoreline vegetation and highly productive ecosystem, mangroves play an essential role in the protection of coastlines and ecological diversity. Accurate mapping of the spatial distribution of mangroves is crucial for the protection and restoration of mangrove ecosystems. Supervised classification methods rely on large sample sets and complex classifiers and traditional thresholding methods that require empirical thresholds, given the problems that limit the feasibility and stability of existing mangrove identification and mapping methods on large scales. Thus, this paper develops a novel mangrove index (spectral and SAR mangrove index, SSMI) and Gaussian mixture model (GMM) mangrove mapping method, which does not require training samples and can automatically and accurately map mangrove boundaries by utilizing only single-scene Sentinel-1 and single-scene Sentinel-2 images from the same time period. The SSMI capitalizes on the fact that mangroves are differentiated from other land cover types in terms of optical characteristics (greenness and moisture) and backscattering coefficients of SAR images and ultimately highlights mangrove forest information through the product of three expressions (f(S) = red egde/SWIR1, f(B) = 1/(1 + e(-VH)), f(W)=(NIR-SWIR1)/(NIR+SWIR1)). The proposed SSMI was tested in six typical mangrove distribution areas in China where climatic conditions and mangrove species vary widely. The results indicated that the SSMI was more capable of mapping mangrove forests than the other mangrove indices (CMRI, NDMI, MVI, and MI), with overall accuracys (OA) higher than 0.90 and F1 scores as high as 0.93 for the other five areas except for the Maowei Gulf (S5). Moreover, the mangrove maps generated by the SSMI were highly consistent with the reference maps (HGMF_2020,LASAC_2018 and IMMA). In addition, the SSMI achieves stable performance, as shown by the mapping results of the other two classification methods (K-means and Otsu's algorithm). Mangrove mapping in six typical mangrove distribution areas in China for five consecutive years (2019-2023) and experiments in three Southeast Asian countries with major mangrove distributions (Thailand, Vietnam, and Indonesia) demonstrated that the SSMIs constructed in this paper are highly stable across time and space. The SSMI proposed in this paper does not require reference samples or predefined parameters; thus, it has great flexibility and applicability in mapping mangroves on a large scale, especially in cloudy areas.