Retrieval of Mangrove leaf area index and its response to typhoon based on WorldView-3 image

Abstract

In recent decades, mangroves have become one of the world's most threatened and vulnerable ecosystems due to human disturbance and climate change. Remote sensing techniques have helped to obtain biophysical parameters of forests on a large scale. However, to our knowledge, mangrove leaf area index (LAI) inversion using texture features and its scale dependency is rarely discussed. Whether the dynamics of remotely sensed LAI could reveal the responses of mangrove species to typhoon disturbance remain unknown. In this study, we classified mangrove species, estimated the LAI of each species based on high-resolution WorldView-3 (WV-3) images, and evaluated the performance of different object-based classifiers and the effects of texture features and scale dependency on the LAI model. We characterized typhoon disturbance on different species using interannual LAI variation. Random forest (RF) has the highest accuracy in mangrove species classification, with an overall accuracy of 82.29% after post-classification, compared with naive Bayes (NB), classification and regression trees (CART), k-nearest neighbor (KNN), and support vector machines (SVM). Texture features significantly increased the accuracy of LAI estimates, and the optimal prediction result was generated at a 2-m resampling rate (R2 = 0.52; MSE = 0.56). The mean LAIs of B. gymnorrhiza and S. apetala assemblages were significantly higher than those of K. obovata, S. caseolaris, and A. marina. LAI variation demonstrates a disturbance pattern caused by Typhoon Mangkhut. This typhoon had the greatest impact on K. obovata, while S. apetala and S. caseolaris showed good resistance to wind damage. We concluded that RF was the best classifier for mapping mangrove species distribution using object-based classification algorithms with WV-3 image. The texture features play a key role in constructing the estimation model of mangrove LAI with scale dependency.