Mapping disturbance in mangrove ecosystems: Incorporating landscape metrics and PCA-based spatial analysis

Mangrove ecosystems are of high ecological value and occur in tropical and subtropical coastal areas. They consist of salt-tolerant woody plants and are highly complex and productive. Here, a novel approach was developed to map disturbances and evaluate the ecological status of Iranian mangroves based on multispectral WorldView-2 and Sentinel-2 and remote sensing techniques. A land cover map was generated that serves as basis to assess changes in the composition and structure of mangrove ecosystems. The spatial disturbance index (SDI) was calculated based on landscape metrics and principal component analysis (PCA) in order to detect mangroves degraded on Qeshm Island, Iran. The approach comprised: (I) selection of landscape metrics, (II) definition of the hexagon cell scale, (III) completion of a PCA of the standardized values of eight selected metrics, and (IV) modeling of the SDI based on eigenvalues and a loading factor. For validation, the variance inflation factor (VIF) was used. The results indicate that the best set of key metrics to quantify SDI comprises the eight metrics patch density (PD), total edge (TE), mean patch size (MPS), mean shape index (MSI), Euclidean nearest neighbor distance (ENND), contagion (CONT), splitting index (SPLIT), and Shannon's diversity index (SHDI). The first three components of the PCA explained in total 87% (47%, 27%, and 13%, respectively) of the variance of information distribution, demonstrating the effectiveness of the PCA. Then, based on the disturbance index, five spatial levels, ranging from very low to very high disturbances, were identified. This approach enabled to detect the zonation of disturbance that has occurred in the region and supports the sustainable management of mangrove ecosystems and future planning efforts, thus making their conservation more realistic.

Automated summary

A novel approach based on multispectral remote sensing techniques was developed to evaluate the ecological status and disturbances of Iranian mangroves. The approach involved calculating a spatial disturbance index (SDI) using landscape metrics and principal component analysis (PCA). The results showed that the selected metrics could effectively quantify the SDI and identify different levels of disturbances. This approach enables the detection and management of disturbances in mangrove ecosystems.