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Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks.

, , , , and . Int. J. Appl. Earth Obs. Geoinformation, 11 (4): 265-272 (2009)

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Development of 500 meter vegetation continuous field maps using MODIS data., , , , , and . IGARSS, page 264-266. IEEE, (2003)Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks., , , , and . Int. J. Appl. Earth Obs. Geoinformation, 11 (4): 265-272 (2009)Demonstration of Percent Tree Cover Mapping Using Landsat Analysis Ready Data (ARD) and Sensitivity with Respect to Landsat ARD Processing Level., , , , and . Remote. Sens., 10 (2): 209 (2018)Tropical Forest Canopy Structure and Change Assessment Using Landsat, GEDI, and Airborne Lidar Data., , , , , , , and . IGARSS, page 666-669. IEEE, (2021)Wheat production forecasting for Pakistan from satellite data., , , , , , and . IGARSS, page 3239-3242. IEEE, (2013)Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions, , , , , , , , , and 33 other author(s). Remote Sensing in Ecology and Conservation, (2016)A Sample-Based Forest Monitoring Strategy Using Landsat, AVHRR and MODIS Data to Estimate Gross Forest Cover Loss in Malaysia between 1990 and 2005., , , and . Remote. Sens., 5 (4): 1842-1855 (2013)