Spatial modelling of wildfire hotspots and their key drivers across districts of Zimbabwe, Southern Africa

Shekede, Munyaradzi Davis; Gwitira, Isaiah; Mamvura, Chenai

doi:10.1080/10106049.2019.1629642

Cited by 15 publications

(15 citation statements)

References 76 publications

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“…The study sites are savannah environments and wildfire hotspots, hence their use in this study [35,36]. Wildfires are not permitted in both study sites with the exception of management fires.…”

Section: Study Sitesmentioning

confidence: 99%

“…In developing countries such as Zimbabwe, wildfires have been reported to affect socio-economic aspects of the country's development. These fires have been reported to be concentrated in the northern region of Zimbabwe [35,36] and accurately mapping these wildfires allows an understanding of their drivers. About the drivers, from Chinamatira, et al [37] we know that arson and human negligence account for 86% and natural phenomena account for 14%.…”

Section: Introductionmentioning

confidence: 99%

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

Ngadze¹,

Mpakairi

Kavhu

et al. 2020

PLoS ONE

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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.

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Section: Study Sitesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ngadze¹,

Mpakairi

Kavhu

et al. 2020

PLoS ONE

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“…This local knowledge should inform mitigation strategies as well. Spatial regression analyses have been shown to help specify which specific variables may be contributing the most to wildfire occurrences around the world (Koutsias et al 2010;Rodrigues et al 2014;Shekede et al 2019) and can therefore validate (or invalidate) assumptions found in any strategies developed by jurisdictions to reduce their risk.…”

Section: Discussionmentioning

confidence: 99%

“…Using wildfire data from Washington Department of Natural Resources (DNR) (Dozic 2020;DNR 2020), we employed a variety of statistical tools in a geographic information system (GIS) (ArcGIS Pro version 2.7) to (1) determine the location of apparent clusters of wildfire activity in Washington; (2) determine whether identified clusters are statistically significant hot spots; and (3) identify any spatiotemporal variation in wildfire locations and/or acres burned. These and other similar methods have been used to identify hot spot trends in wildfire activity in regions around the globe, including in Honduras using the 2009 version of the built-in hot spot analysis tool in the ArcGIS software (Caceres 2011), in Portugal using spacetime permutation scan statistics (Pereira et al 2015), and in Florida using directional distribution analysis (McLemore 2017) (see Mohd Said et al 2017;Shekede et al 2019 for further examples). Previous research using spatial statistical methods in the Pacific Northwest used only data on a 25-year period (Wing and Long 2015).…”

Section: Introductionmentioning

confidence: 99%

Optimized Hot Spot and Directional Distribution Analyses Characterize the Spatiotemporal Variation of Large Wildfires in Washington, USA, 1970−2020

Zerbe¹,

Polit²,

McClain³

et al. 2022

Int J Disaster Risk Sci

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Spatiotemporal analysis of fire activity is vital for determining why wildfires occur where they do, assessing wildfire risks, and developing locally relevant wildfire risk reduction strategies. Using various spatial statistical methods, we determined hot spots of large wildfires (> 100 acres) in Washington, the United States, and mapped spatiotemporal variations in large wildfire activity from 1970 to 2020. Our results found that all hot spots are located east of the crest of the Cascade Range. Our spatiotemporal analysis found that the geographic area wherein most of the state’s acres burned has shrunk considerably since 1970 and has become concentrated over the north-central portion of the state over time. This concentration of large wildfire activity in north-central Washington was previously unquantified and may provide important information for hazard mitigation efforts in that area. Our results highlight the advantages of using spatial statistical methods that could aid the development of natural hazard mitigation plans and risk reduction strategies by characterizing previous hazard occurrences spatially and spatiotemporally.

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“…Forest and land fires are included in the geographical data, so they can be analyzed using Getis-Ord G * i analysis and kernel density analysis. e analysis helps reveal the spatial pattern of forest and land fires so that strategies for fire management, mitigation, and prevention can be designed [28,29]. Unfortunately, studies on spatial patterns of land and forest fires and fire density at various national and regional scales are still rarely carried out [30], including in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns of Burned Areas Based on the Geographic Information System for Fire Risk Monitoring

Arisanty

Muhaimin

Rosadi

et al. 2021

International Journal of Forestry Research

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Forest and land fires occur every year in Indonesia. Efforts to handle forest and land fires have not been optimal because fires occur in too many places with unclear patterns and densities. The study analyzed the spatiotemporal patterns of burned areas and fire density in fire-prone areas in Indonesia. Data of burned areas were taken from http://sipongi.menlhk.go.id/. The website collected its data from NOAA (National Oceanic and Atmospheric Administration) images. Data were analyzed using the hot spot analysis to determine the spatiotemporal patterns of the burned areas and the kernel density analysis to examine the density of land fires. Findings showed that the spatiotemporal pattern from 2016 to 2019 formed a hot spot value in the peatland area with a confidence level of 90–99%, meaning that land fires were clustered in that area. In addition, the highest density of land fires also occurred in the peatland areas. Clustered burned areas with high fire density were found in areas with low–medium vegetation density—they were the peatland areas. The peatland areas must become the priority to prevent and handle forest and land fires to reduce fire risks.

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Spatial modelling of wildfire hotspots and their key drivers across districts of Zimbabwe, Southern Africa

Cited by 15 publications

References 76 publications

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

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

Optimized Hot Spot and Directional Distribution Analyses Characterize the Spatiotemporal Variation of Large Wildfires in Washington, USA, 1970−2020

Spatiotemporal Patterns of Burned Areas Based on the Geographic Information System for Fire Risk Monitoring

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