Next Article in Journal
Monitoring Water Surface and Level of a Reservoir Using Different Remote Sensing Approaches and Comparison with Dam Displacements Evaluated via GNSS
Next Article in Special Issue
Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency
Previous Article in Journal
Spatiotemporal Evaluation of GNSS-R Based on Future Fully Operational Global Multi-GNSS and Eight-LEO Constellations
Previous Article in Special Issue
Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(1), 69;

Mapping Burned Areas in Tropical Forests Using a Novel Machine Learning Framework

Department of Computer Science and Engineering, University of Minnesota, 4-192 Keller Hall, Minneapolis, MN 55455, USA
NASA Ames Research Center, Moffett Field, Mountain View, CA 94035, USA
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 21 November 2017 / Revised: 27 December 2017 / Accepted: 3 January 2018 / Published: 6 January 2018
(This article belongs to the Special Issue Machine Learning Applications in Earth Science Big Data Analysis)
Full-Text   |   PDF [12176 KB, uploaded 6 January 2018]   |  


This paper presents an application of a novel machine-learning framework on MODIS (moderate-resolution imaging spectroradiometer) data to map burned areas over tropical forests of South America and South-east Asia. The RAPT (RAre Class Prediction in the absence of True labels) framework is able to build data adaptive classification models using noisy training labels. It is particularly suitable when expert annotated training samples are difficult to obtain as in the case of wild fires in the tropics. This framework has been used to build burned area maps from MODIS surface reflectance data as features and Active Fire hotspots as training labels that are known to have high commission and omission errors due to the prevalence of cloud cover and smoke, especially in the tropics. Using the RAPT framework we report burned areas for 16 MODIS tiles from 2001 to 2014. The total burned area detected in the tropical forests of South America and South-east Asia during these years is 2,071,378 MODIS (500 m) pixels (approximately 520 K sq. km.), which is almost three times compared to the estimates from collection 5 MODIS MCD64A1 (783,468 MODIS pixels). An evaluation using Landsat-based reference burned area maps indicates that our product has an average user’s accuracy of 53% and producer’s accuracy of 55% while collection 5 MCD64A1 burned area product has an average user’s accuracy of 61% and producer’s accuracy of 27%. Our analysis also indicates that the two products can be complimentary and a combination of the two approaches is likely to provide a more comprehensive assessment of tropical fires. Finally, we have created a publicly accessible web-based viewer that helps the community to visualize the burned area maps produced using RAPT and examine various validation sources corresponding to every detected MODIS pixel. View Full-Text
Keywords: MODIS; burned area mapping; tropical forests; machine learning MODIS; burned area mapping; tropical forests; machine learning

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Mithal, V.; Nayak, G.; Khandelwal, A.; Kumar, V.; Nemani, R.; Oza, N.C. Mapping Burned Areas in Tropical Forests Using a Novel Machine Learning Framework. Remote Sens. 2018, 10, 69.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top