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Article

Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform

1
Department of Geography, University of the Free State, Phuthaditjhaba 9869, South Africa
2
Department of Geography and Environmental Studies, University of Zululand, KwaDlangezwa 3886, South Africa
3
School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4000, South Africa
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz, Palaiologos Palaiologou and Kostas Kalabokidis
ISPRS Int. J. Geo-Inf. 2021, 10(8), 511; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10080511
Received: 10 May 2021 / Revised: 9 July 2021 / Accepted: 21 July 2021 / Published: 28 July 2021
(This article belongs to the Special Issue The Use of Geo-Spatial Tools in Forestry)
Planted forests in South Africa have been affected by an increasing number of economically damaging fires over the past four decades. They constitute a major threat to the forestry industry and account for over 80% of the country’s commercial timber losses. Forest fires are more frequent and severe during the drier drought conditions that are typical in South Africa. For proper forest management, accurate detection and mapping of burned areas are required, yet the exercise is difficult to perform in the field because of time and expense. Now that ready-to-use satellite data are freely accessible in the cloud-based Google Earth Engine (GEE), in this study, we exploit the Sentinel-2-derived differenced normalized burned ratio (dNBR) to characterize burn severity areas, and also track carbon monoxide (CO) plumes using Sentinel-5 following a wildfire that broke over the southeastern coast of the Western Cape province in late October 2018. The results showed that 37.4% of the area was severely burned, and much of it occurred in forested land in the studied area. This was followed by 24.7% of the area that was burned at a moderate-high level. About 15.9% had moderate-low burned severity, whereas 21.9% was slightly burned. Random forests classifier was adopted to separate burned class from unburned and achieved an overall accuracy of over 97%. The most important variables in the classification included texture, NBR, and the NIR bands. The CO signal sharply increased during fire outbreaks and marked the intensity of black carbon over the affected area. Our study contributes to the understanding of forest fire in the dynamics over the Southern Cape forestry landscape. Furthermore, it also demonstrates the usefulness of Sentinel-5 for monitoring CO. Taken together, the Sentinel satellites and GEE offer an effective tool for mapping fires, even in data-poor countries. View Full-Text
Keywords: forest fire; burned area; NBR; Sentinel-2; Sentinel-5; GEE; time series analysis forest fire; burned area; NBR; Sentinel-2; Sentinel-5; GEE; time series analysis
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MDPI and ACS Style

Xulu, S.; Mbatha, N.; Peerbhay, K. Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform. ISPRS Int. J. Geo-Inf. 2021, 10, 511. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10080511

AMA Style

Xulu S, Mbatha N, Peerbhay K. Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform. ISPRS International Journal of Geo-Information. 2021; 10(8):511. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10080511

Chicago/Turabian Style

Xulu, Sifiso, Nkanyiso Mbatha, and Kabir Peerbhay. 2021. "Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform" ISPRS International Journal of Geo-Information 10, no. 8: 511. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10080511

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