Global Analysis of Burned Area Persistence Time with MODIS Data
نویسندگان
چکیده
منابع مشابه
Global estimation of burned area using MODIS active fire observations
We present a method for estimating monthly burned area globally at 1 spatial resolution using Terra MODIS data and ancillary vegetation cover information. Using regression trees constructed for 14 different global regions, MODIS active fire observations were calibrated to burned area estimates derived from 500-m MODIS imagery based on the assumption that burned area is proportional to counts of...
متن کاملMapping burned area in Alaska using MODIS data: a data limitations-driven modification to the regional burned area algorithm
With the recently observed and projected trends of growing wildland fire occurrence in high northern latitudes, satellite-based burned area mapping in these regions is becoming increasingly important for scientific and fire management communities. Coarseand moderate-resolution remotely sensed data products are the only viable source of comprehensive and timely estimates of burned area in remote...
متن کاملGlobal estimation of burned area
Global estimation of burned area using MODIS active fire observations L. Giglio, G. R. van der Werf, J. T. Randerson, G. J. Collatz, and P. Kasibhatla Science Systems and Applications, Inc., NASA Goddard Space Flight Center, Greenbelt, Maryland, USA Department of Hydrology and Geo-Environmental Sciences, Vrije Universiteit, Amsterdam, The Netherlands Department of Earth System Science, Universi...
متن کاملDeveloping a Random Forest Algorithm for MODIS Global Burned Area Classification
This paper aims to develop a global burned area (BA) algorithm for MODIS BRDF-corrected images based on the Random Forest (RF) classifier. Two RF models were generated, including: (1) all MODIS reflective bands; and (2) only the red (R) and near infrared (NIR) bands. Active fire information, vegetation indices and auxiliary variables were taken into account as well. Both RF models were trained ...
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2018
ISSN: 2072-4292
DOI: 10.3390/rs10050750