Meinan Zhang

TL;DR: In this article, the authors reported the first all-season training and validation sample sets for global land cover classification with Landsat-8 data, and compared the performances of training samples in different seasons using Random Forest algorithm.

TL;DR: This study developed a set of continuous annual land cover mapping product at 30 m resolution using multi-temporal Landsat images and demonstrated the maps could capture the important land cover conversion processes, such as urbanization, forest logging activities, and agricultural expansion.

TL;DR: Bamboo is an important plant not only because of its vital role in supporting biodiversity and land restoration, but also due to its contribution to poverty eradication as discussed by the authors, although remote sensing has not yet been used for this purpose.

TL;DR: This study proposed an automatic approach in which the tile-based model was used on each tile (defining an extent of 1° × 1° as a tile) for mapping land cover in Madagascar, and enables reduce the misclassifications between several land cover types, including impervious land, grassland and wetland.