The death toll and monetary damages from landslides continue to rise despite advancements in predictive modeling. These models’ performances are limited as landslide databases used in developing them often miss crucial information, e.g., underlying movement types. This study introduces a method of discerning landslide movements, such as slides, flows, and falls, by analyzing landslides’ 3D shapes. By examining landslide topological properties, we discover distinct patterns in their morphology, indicating different movements including complex ones with multiple coupled movements. We achieve 80-94% accuracy by applying topological properties in identifying landslide movements across diverse geographical and climatic regions, including Italy, the US Pacific Northwest, Denmark, Turkey, and Wenchuan in China. Furthermore, we demonstrate a real-world application on undocumented datasets from Wenchuan. Our work introduces a paradigm for studying landslide shapes to understand thei
Enhancing confidence in satellite observations of terrestrial properties like land surface temperature and soil moisture requires advances in validation and data quality assessment practices.
A comparison against classic models. SHISHIR PRIYADARSHI, WAHYUDIN P. SYAM, ANDRÉS ABELARDO GARCÍA ROQUÉ, ALEJANDRO PÉREZ CONESA, GMV GUILLAUME BUSCARLET, RAÜL ORÚS PÉREZ, MICKAEL DALL’ ORSO, EUROPEAN SPACE AGENCY (ESA) Sun is the source of energy needed to ionize the Earth’s atmosphere, which is an
Understanding the effects of cash crop expansion on natural forest is of fundamental importance. However, for most crops there are no remotely sensed global maps1, and global deforestation impacts are estimated using models and extrapolations. Natural rubber is an example of a principal commodity for which deforestation impacts have been highly uncertain, with estimates differing more than fivefold1–4. Here we harnessed Earth observation satellite data and cloud computing5 to produce high-resolution maps of rubber (10 m pixel size) and associated deforestation (30 m pixel size) for Southeast Asia. Our maps indicate that rubber-related forest loss has been substantially underestimated in policy, by the public and in recent reports6–8. Our direct remotely sensed observations show that deforestation for rubber is at least twofold to threefold higher than suggested by figures now widely used for setting policy4. With more than 4 million hecta