Wei Fu

TL;DR: In this article, the first 30 m resolution global land cover maps using Landsat Thematic Mapper TM and enhanced thematic mapper plus ETM+ data were produced. And the authors used four classifiers that were freely available were employed, including the conventional maximum likelihood classifier MLC, J4.8 decision tree classifier, Random Forest RF classifier and support vector machine SVM classifier.

TL;DR: OBIA with machine-learning classifiers is useful for landscape vegetation analyses, however, considerations of spatial scale and image segmentation outcomes are critical in mapping PFTs and should be more thoroughly investigated in future work.

TL;DR: In this paper, the authors identify the spatial temporal patterns and spatial hotspots of land-cover mapping research around the world and find that there is a significant mismatch between hotspot areas of landcover mapping and areas that are either hard to map or rich in biodiversity.

TL;DR: In this paper , the effect of long-term Ni contamination on fungal community composition and co-occurrence patterns was investigated and fungal interaction networks were built to reveal fungal cooccurrence pattern.

TL;DR: In this article , the authors conducted a six-year rainfall manipulation experiment in a semi-arid grassland that consisted of an initial two-year drought (2015-2016), followed by a recovery period (2017-2018), and finally a second two year drought (2019-2020).