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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 objective of this paper is to contribute to improve ETa estimation in semiarid environments by proposing two variations to the TDMT model. These variations are based on the use of MODIS products from TERRA or AQUA satellites and on the use of NDVI instead of EVI, to estimate the fraction of vegetation cover. The proposed changes were validated with the original methodology for the 2012-2014 period with data obtained from two flux towers (ES-LJu and ES-Amo). The best results were obtained when using the alternative methodology (RMSE of 0.64-0.67mm in ES-Lju and of 0.97-1.02mm in ES-Amo). Additionally, a correction of the temporal systematic errors of the model based on RandomForest is proposed. With this correction, RMSE of 0.31-0.35mm in ES-Lju and 0.30-0.34mm in ES-Amo were obtained. The spatial distribution obtained with this corrected model is the most consistent with the characteristics of the study area. [ABSTRACT FROM AUTHOR]

The occurrence of drought is related to complicated interactions between many factors, such as precipitation, temperature, evapotranspiration and vegetation. In this study, the relationships between drought and precipitation, temperature, vegetation and evapotranspiration were investigated with a random forest (RF), and a new combined drought monitoring index (CDMI) was constructed. The effectiveness of the CDMI in monitoring drought in Shaanxi Province was verified by the in situ 1 ∼ 12-month standardized precipitation index (SPI); relative soil moisture (RSM) and four other commonly used remote sensing drought monitoring indices. The results show that CDMI is more correlated with the SPI and RSM than the four indices. Moreover, the spatial distributions of drought for the CDMI and RSM are similar. Therefore, the CDMI can be used to monitor droughts in Shaanxi Province, and machine learning can explore the relationships between various factors and establish a drought index without knowledge of the causal mechanisms of these factors. [ABSTRACT FROM AUTHOR]