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Land-use and land-cover (LULC) mapping in the complex area is a challenging task due to the mixed vegetation patterns, and rough mountains with fast-flowing rivers. In Vietnam, LULC update is not frequently. In this study, we applied a supervised machine learning (Random forest—RF) approach to mapping LULC in Thanh Hoa province, Vietnam from 2011 to 2015 utilizing multitemporal Normalized Difference Vegetation Index (NDVI) data from MODIS, combined with topographic features. Random forest classification (RFC) reached a total prediction accuracy of 91% and Kappa coefficient (K) of 0.89 across eight LULCs. Besides, the results showed that the features extracted from time-series NDVI comprising the mean of yearly NDVI, the sum of NDVI, and the topography were the important variables controlling the LULC classification. For similar studies on the distribution of LULC, the method proposed in this study could be helpful. [ABSTRACT FROM AUTHOR]

The use of remote sensing for examining phenological variation in tropical forests is scarce. The major objectives of the study were to characterize the intra-annual variability of phenological cycle of the Biligiri Ranganathaswamy Temple Tiger Reserve (BRT) and the potentiality of these phenological metrices in defining species assemblages by classifying the forest. Sentinel-2 derived temporal Normalized Difference Vegetation Index (NDVI) data of 2019 was used to extract the vegetation trends and to derive phenological metrics using CropPhenology R package. Seasonal trends revealed that the highest greenness was associated with high NDVI values in September and October. We identified seven vegetation classes in the region and used Random Forest classifier to prepare a community level classification map with an overall classification accuracy of 68.9%. Our results revealed that incorporating the field sampling data and NDVI data can be effectively used for identifying, mapping and monitoring phenology of the BRT landscape.. [ABSTRACT FROM AUTHOR]