Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

When wildfires are controlled, they are integral to the existence of savannah ecosystems and play an intrinsic role in maintaining their structure and function. Ample studies on wildfire detection and severity mapping are available but what remains a challenge is the accurate mapping of burnt areas...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2020-05, Vol.15 (5), p.e0232962
Main Authors: Ngadze, Fiona, Mpakairi, Kudzai Shaun, Kavhu, Blessing, Ndaimani, Henry, Maremba, Monalisa Shingirayi
Format: Article
Language:English
Subjects:
Accuracy
Band spectra
Biology and Life Sciences
Carbon
Classification
Computer and Information Sciences
Control
Ecology and Environmental Sciences
Ecosystems
Emission analysis
Emissions (Pollution)
Environmental science
Fire damage
Forest & brush fires
Forest fire control
Forest management
Landsat
Landsat satellites
Landscape
Mapping
Methods
People and Places
Physical Sciences
Remote sensing
Research and Analysis Methods
Satellite imagery
Savannahs
Sensors
Spectral bands
Structure-function relationships
Studies
Wildfires
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:When wildfires are controlled, they are integral to the existence of savannah ecosystems and play an intrinsic role in maintaining their structure and function. Ample studies on wildfire detection and severity mapping are available but what remains a challenge is the accurate mapping of burnt areas in heterogenous landscapes. In this study, we tested which spectral bands contributed most to burnt area detection when using Sentinel-2 and Landsat 8 multispectral sensors in two study sites. Post-fire Sentinel 2A and Landsat 8 images were classified using the Random Forest (RF) classifier. We found out that, the NIR, Red, Red-edge and Blue spectral bands contributed most to burned area detection when using Landsat 8 and Sentinel 2A. We found out that, Landsat 8 had a higher classification accuracy (OA = 0.92, Kappa = 0.85 and TSS = 0.84)) in study site 1 as compared to Sentinel-2 (OA = 0.86, Kappa = 0.74 and TSS = 0.76). In study site 2, Sentinel-2 had a slightly higher classification accuracy (OA = 0.89, Kappa = 0.67 and TSS = 0.64) which was comparable to that of Landsat 8 (OA = 0.85, Kappa = 0.50 and TSS = 0.41). Our study adds rudimentary knowledge on the most reliable sensor allowing reliable estimation of burnt areas and improved post-fire ecological evaluations on ecosystem damage and carbon emission.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0232962