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13 pages, 5522 KiB  
Data Descriptor
Multi-Temporal Surface Water Classification for Four Major Rivers from the Peruvian Amazon
by Margaret Kalacska, J. Pablo Arroyo-Mora, Oliver T. Coomes, Yoshito Takasaki and Christian Abizaid
Data 2022, 7(1), 6; https://doi.org/10.3390/data7010006 - 6 Jan 2022
Cited by 7 | Viewed by 3505
Abstract
We describe a new minimum extent, persistent surface water classification for reaches of four major rivers in the Peruvian Amazon (i.e., Amazon, Napo, Pastaza, Ucayali). These data were generated by the Peruvian Amazon Rural Livelihoods and Poverty (PARLAP) Project which aims to better [...] Read more.
We describe a new minimum extent, persistent surface water classification for reaches of four major rivers in the Peruvian Amazon (i.e., Amazon, Napo, Pastaza, Ucayali). These data were generated by the Peruvian Amazon Rural Livelihoods and Poverty (PARLAP) Project which aims to better understand the nexus between livelihoods (e.g., fishing, agriculture, forest use, trade), poverty, and conservation in the Peruvian Amazon over a 35,000 km river network. Previous surface water datasets do not adequately capture the temporal changes in the course of the rivers, nor discriminate between primary main channel and non-main channel (e.g., oxbow lakes) water. We generated the surface water classifications in Google Earth Engine from Landsat TM 5, 7 ETM+, and 8 OLI satellite imagery for time periods from circa 1989, 2000, and 2015 using a hierarchical logical binary classification predominantly based on a modified Normalized Difference Water Index (mNDWI) and shortwave infrared surface reflectance. We included surface reflectance in the blue band and brightness temperature to minimize misclassification. High accuracies were achieved for all time periods (>90%). Full article
(This article belongs to the Section Spatial Data Science and Digital Earth)
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