Suaeda Salsa Hyperspectral Index (SSHI) for mapping S. salsa in coastal wetlands using hyperspectral satellite imagery: case studies in northern coastal China

作者：Mengyao Zhang, Yinghai Ke, Kun Shang, Zhaojun Zhuo, Liu Han, Peng Li, Nana Zhao, Jinghan Sha, Jingyuan Li · 发表于：GIScience & Remote Sensing · 年份：2025 · DOI：10.1080/15481603.2025.2500793 · 被引用次数：4 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and Land Use、Remote-Sensing Image Classification

Suaeda Salsa (S. salsa), a pioneer species with short and red-purplish plants in the intertidal zones, has significant ecological, economic, recreational and tourism values. Timely monitoring of S. salsa is crucial for understanding its dynamics and sustainable management of coastal wetlands. Hyperspectral satellite offers valuable opportunities due to its detailed spectral information. This study proposed a Suaeda Salsa Hyperspectral Index (SSHI) for S. salsa mapping based on hyperspectral satellite imagery. SSHI was developed by considering the large within-class spectral variations of cover types at coastal wetlands, accounting for the spectral correlations in hyperspectral data, and employing dynamic band selection on a per-pixel basis to optimize the separation of S. salsa from other land covers. SSHI=Sgnρ620−maxρi∗ρ860−minρj ρ860+ρ620+L∗1+L, where i∈[499 nm, 611 nm], j∈[628 nm, 851 nm], L=0.5. We applied SSHI on ZY1-02D/E AHSI images over Yellow River Delta (YRD) and Liao River Delta (LRD), China during 2021–2023. To evaluate the performance of SSHI, a simple thresholding method and a random forest (RF) model were used to map S. salsa, respectively. Our results showed that the overall accuracies of S. salsa maps achieved 87.96~89.43% (YRD) and 92.03~93.36% (LRD) using thresholds, and 92.62~94.05% (YRD) and 94.74~94.79% (LRD) using RF. For RF, incorporating SSHI improves S. salsa producer’s accuracies (user’s accuracies) by 1.02~6.85% (2.26~6.25%) compared to those witho...