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SMOS Vegetation Optical Depth and Ecosystem Functional Properties: Exploring Their Relationships in Tropical Forests., , , , , , , , and . IGARSS, page 5891-5894. IEEE, (2018)Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology., , , , , and . Remote Sensing, 5 (5): 2113-2144 (2013)Nonlinear dimensionality reduction: Alternative ordination approaches for extracting and visualizing biodiversity patterns in tropical montane forest vegetation data., , , and . Ecological Informatics, 2 (2): 138-149 (2007)Predicting Landscapes as Seen from Space from Environmental Conditions., , , , and . IGARSS, page 1768-1771. IEEE, (2018)Maximally Divergent Intervals for Anomaly Detection., , , , , , , and . CoRR, (2016)Predicting Landscapes from Environmental Conditions Using Generative Networks., , , , and . GCPR, volume 11824 of Lecture Notes in Computer Science, page 203-217. Springer, (2019)Identifying multiple spatiotemporal patterns: A refined view on terrestrial photosynthetic activity., , , and . Pattern Recognition Letters, 31 (14): 2309-2317 (2010)Analyzing floristic inventories with multiple maps., , and . Ecological Informatics, (2012)Detection and attribution of large spatiotemporal extreme events in Earth observation data., , , and . Ecological Informatics, (2013)Ranking drivers of global carbon and energy fluxes over land., , , , , , , , , and . IGARSS, page 4416-4419. IEEE, (2015)