Mangrove forests, as important ecological and economic resources, have suffered a loss in the area due to natural and human activities. Monitoring the distribution of and obtaining accurate information on mangrove species is necessary for ameliorating the damage and protecting and restoring mangrove forests. In this study, we compared the performance of UAV Rikola hyperspectral images, WorldView-2 (WV-2) satellite-based multispectral images, and a fusion of data from both in the classification of mangrove species. We first used recursive feature elimination‒random forest (RFE-RF) to select the vegetation’s spectral and texture feature variables, and then implemented random forest (RF) and support vector machine (SVM) algorithms as classifiers. The results showed that the accuracy of the combined data was higher than that of UAV and WV-2 data; the vegetation index features of UAV hyperspectral data and texture index of WV-2 data played dominant roles; the overall accuracy of the RF algorithm was 95.89% with a Kappa coefficient of 0.95, which is more accurate and efficient than SVM. The use of combined data and RF methods for the classification of mangrove species could be useful in biomass estimation and breeding cultivation.

Geographically isolated islands are under great stress due to global climate change, as well as the social and environmental issues relating to human activities. It is necessary to monitor and analyze the spatial–temporal changes of the land surface environment in species-rich tropical islands in order to realize the sustainable development and protection of island areas. In this study, we extracted the land cover and coastline information of three tropical islands from 1990 to 2020 based on the Google Earth Engine platform and the Random Forest algorithm. The results showed that: (1) different tropical islands have similar characteristics in terms of land surface environment changes, with the amount of artificial surface and cultivated land increasing, the forest and mangrove areas decreasing, and the amount of artificial coastline increasing; (2) human disturbance plays an important role in changes in the land surface environment. Population growth, immigration policies, food security, and human activities related to achieving economic profits are likely responsible for these land cover changes; and (3) the main factors driving coastline changes include natural processes (topography, ecological ecosystems, sea-level rise, sea waves, and storms) and human activities (sand mining, tourism, port construction, aquaculture expansion, and mangrove deforestation). Understanding these changes will help tropical islands and coastal zones to make suitable policies for land management and respond to climate change and sustainable development challenges.