Over the last few decades, anthropogenic activities have triggered the rate of change in the function of mangrove ecosystems in coastal urban areas. Satellite imagery provides valuable information for mangrove mapping and monitoring. Metrics derived from the linear regression analysis of spectral indices (SIs) derived from satellites are commonly used for change analysis. This study examines the robustness of the widely used SIs derived from Landsat satellite image to distinguish mangroves and non-mangrove features and identify the non-linear changes over mangrove forest using the polynomial trend analysis. Airborne Visible and Infra-Red Imaging Spectrometer Next Generation (AVIRIS-NG) data was resampled to simulate the spectral-response of the Landsat sensor. One-way analysis of variance (ANOVA) and mutual information (MI) were applied to the simulated data to identify the optimal SIs. Based on the statistical analysis, modular mangrove recognition index (MMRI), modified normalized difference water index (MNDWI) and normalized difference built-up index (NDBI) were identified to delineate the mangrove, inundated and built-up region. Finally, time-series profiles of the identified spectral indices were generated using Landsat-8 (L8) and Landsat-7 (L7) data to analyse the change dynamics of the mangrove vegetation from 2002 to 2019. The proposed methodology was applied to study the changes in mangroves in the coastal areas of Kochi (Ernakulam District, Kerala State, India). The temporal-non-linear changes in the mangrove area were identified based on polynomial regression that revealed abrupt decline (42% decrease) in the mangrove area due to large-scale infrastructure development projects. The proposed approach is easy to implement, which enables the frequent monitoring of extensive mangrove forests.

在过去的几十年里，人为活动引发了沿海城市地区红树林生态系统功能的变化速度。卫星图像为红树林测绘和监测提供了宝贵的信息。从卫星频谱指数 (SI) 的线性回归分析得出的指标通常用于变化分析。本研究检验了广泛使用的源自 Landsat 卫星图像的 SI 的稳健性，以区分红树林和非红树林特征，并使用多项式趋势分析识别红树林的非线性变化。对下一代机载可见光和红外成像光谱仪 (AVIRIS-NG) 数据进行重新采样，以模拟 Landsat 传感器的光谱响应。对模拟数据应用单向方差分析 (ANOVA) 和互信息 (MI) 以确定最佳 SI。在统计分析的基础上，确定了模块化红树林识别指数（MMRI）、修正归一化差异水指数（MNDWI）和归一化差异集聚指数（NDBI），以划定红树林、淹没区和集聚区。最后，利用 Landsat-8 (L8) 和 Landsat-7 (L7) 数据生成已识别光谱指数的时间序列剖面，以分析 2002 年至 2019 年红树林植被的变化动态。高知沿海地区（印度喀拉拉邦 Ernakulam 区）红树林的变化。红树林地区的时间非线性变化是基于多项式回归确定的，该回归揭示了由于大规模基础设施开发项目导致红树林地区的突然减少（减少了 42%）。所提出的方法易于实施，可以频繁监测广泛的红树林。