Document Type

Article

Publication Version

Version of Record

Publication Date

2017

Keywords

Earth observations, land cover, remote sensing

Description

The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km2 and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003–2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user’s and producer’s classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presented

Publication Title

International Journal of Digital Earth

Volume

10

Issue

1

First Page

54

Last Page

84

DOI of Published Version

10.1080/17538947.2016.1208686

Pages

32

Type

text

Format

application/pdf

Language

en

Publisher

Taylor and Francis

Rights

Copyright © The Author(s)

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Comments

This is the Version of Record published by Taylor and Francis in International Journal of Digital Earth on 2017/07/21, available online: doi: http://dx.doi.org/10.1080/17538947.2016.1208686

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