Global Detection of Long-Term (1982–2017) Burned Area with AVHRR-LTDR Data

Otón, Gonzalo; Ramo, Rubén; Lizundia-Loiola, Joshua; Chuvieco, Emilio

doi:10.3390/rs11182079

Cited by 38 publications

(25 citation statements)

References 45 publications

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“…Most studies conducted on burned area mapping have focused on binary burned area mapping [5,[11][12][13][14][15][16][17][18]21,22,33,52,57,58]. However, in most cases, it is very important to know the land cover types of burned areas.…”

Section: Discussionmentioning

confidence: 99%

Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform

Seydi

Akhoondzadeh

Amani³

et al. 2021

Remote Sensing

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Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products.

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Section: Discussionmentioning

confidence: 99%

Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform

Seydi

Akhoondzadeh

Amani³

et al. 2021

Remote Sensing

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“…We used two burn area (BA) datasets to analyze the spatial and temporal variation of fire BA within and across the south boundary of Amazon basin. The ESA Fire Climate Change Initiative (Fire CCI) Dataset Collection (Otón, Ramo, Lizundia-Loiola, & Chuvieco, 2019) is generated from Advanced Very High Resolution Radiometer images under the land long-term data record project. Fire CCI spans from January 1982 to December 2017 with monthly resolution.…”

Section: Fire Regime Analysismentioning

confidence: 99%

Climate regime shift and forest loss amplify fire in Amazonian forests

Jia

Zhang

et al. 2020

Global Change Biology

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Frequent Amazonian fires over the last decade have raised the alarm about the fate of the Earth's most biodiverse forest. The increased fire frequency has been attributed to altered hydrological cycles. However, observations over the past few decades have demonstrated hydrological changes that may have opposing impacts on fire, including higher basin‐wide precipitation and increased drought frequency and severity. Here, we use multiple satellite observations and climate reanalysis datasets to demonstrate compelling evidence of increased fire susceptibility in response to climate regime shifts across Amazonia. We show that accumulated forest loss since 2000 warmed and dried the lower atmosphere, which reduced moisture recycling and resulted in increased drought extent and severity, and subsequent fire. Extremely dry and wet events accompanied with hot days have been more frequent in Amazonia due to climate shift and forest loss. Simultaneously, intensified water vapor transport from the tropical Pacific and Atlantic increased high‐altitude atmospheric humidity and heavy rainfall events, but those events did not alleviate severe and long‐lasting droughts. Amazonia fire risk is most significant in the southeastern region where tropical savannas undergo long seasonally dry periods. We also find that fires have been expanding through the wet–dry transition season and northward to savanna–forest transition and tropical seasonal forest regions in response to increased forest loss at the “Arc of Deforestation.” Tropical forests, which have adapted to historically moist conditions, are less resilient and easily tip into an alternative state. Our results imply forest conservation and fire protection options to reduce the stress from positive feedback between forest loss, climate change, and fire.

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“…Climate, deforestation, and fires in the Amazon. (a) The tropical rainforest (Af), monsoon (Am), and winter dry (Aw) climate zones according to Köppen–Geiger climate classification (Beck et al, 2018) with overlapping May‐to‐July (MJJ) burned area increase in 2001–2017 relative to 1982–2000 according to the ESA Fire Climate Change Initiative (Fire CCI) Dataset Collection (Otón et al, 2019). The black dots in (a) denote gridcells with cumulative forest loss greater than 10% during 2001–2017 (Hansen et al, 2013).…”

Section: Figurementioning

confidence: 99%

“…However, fire damage and burn scars are clearly visible by satellite sensors. B2020’s fourth point that the two fire products we used (i.e., FireCCI; Otón et al, 2019; and GFEDv4; Randerson et al, 2018) are “inadequate products to assess forest fires” ignores decades of published research developing those products. We note that B2020’s assertion in this regard is unsupported in the literature and therefore insufficient to criticize our use of these fire products for our large‐scale analysis of coupled system responses.…”

Section: Figurementioning

confidence: 99%