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

Seydi, Seyd Teymoor; Akhoondzadeh, Mehdi; Amani, Meisam; Mahdavi, Sahel

doi:10.3390/rs13020220

Cited by 97 publications

(66 citation statements)

References 106 publications

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“…The RF achieved an overall accuracy of 98%. These results are comparable with Seydi et al [55], who detected burned areas with accuracies ranging from 82% to 91% within the GEE environment.…”

Section: Accuracy Assessmentsupporting

confidence: 90%

“…Our results successfully showed potential for GEE resources to estimate burned land use/cover classes with great fidelity. This is comparable to Seydi et al [55], who quantified burned classed using the same platform. The mapping of forest fires contributes a major component of forest fire management, and the rich information recorded by Sentinel's constellation of sensors allows much-improved characterization and identification of forest fires than previously, particularly when implemented in the GEE environment, which can be easily accessed and adjusted to select dates that best suit the ecosystem of interest.…”

Section: Variable Contributionsupporting

confidence: 86%

“…Their results showed superior classification accuracies of 97-100% for classification regression tree (CART) and RF algorithms, while the support vector machine (SVM) achieved slightly lower accuracy. Similarly, Seydi et al [55], used Sentinel-2 and machine learning algorithms within the GEE platform to detect wildfire damage in Australia, and they detected burned areas with accuracies from 82% to 92%. Konkathi and Shetty [53] also compared post-fire burn severity indices from Sentinel-2 and Landsat-8 and achieved comparable results in mapping burn severity for both sensors; the relativized NBR showed high accuracy over heterogeneous landscapes followed by dNBR and the relativized burn ratio performed relatively weaker.…”

Section: Introductionmentioning

confidence: 99%

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

Xulu

Mbatha

Peerbhay

2021

IJGI

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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.

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Section: Accuracy Assessmentsupporting

confidence: 90%

Section: Variable Contributionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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

Xulu

Mbatha

Peerbhay

2021

IJGI

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“…KNN is a data classification algorithm based on the premise that similar data exist in close proximity to each other according to some metric [80]. It has been used for burned area mapping in France [81], fire occurrence prediction [82], and wildfire damage assessment [83]. The parameters used in the experiments are 49 neighbors, Minkowski metric, and uniform weight.…”

Section: Ml-based Approachesmentioning

confidence: 99%

Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

Ban

Nascetti

2021

Remote Sensing

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Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

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“…Google Earth Engine (GEE), a web-based open source platform for geospatial analysis, has been widely used for various mapping tasks, including global land cover mapping, forest cover change, crop yield estimation, and biomass estimation [35,36]. The GEE has a large catalog of archived remote sensing data and state-of-the-art cloud-computing and storage capabilities, which enables researchers and the scientific community to work on petabytes of various freely available remote sensing data [37].…”

Section: Introductionmentioning

confidence: 99%

A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine

Jiang

Chen

Li³

et al. 2021

Remote Sensing

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Urban forest is an important component of terrestrial ecosystems and is highly related to global climate change. However, because of complex city landscapes, deriving the spatial distribution of urban forest carbon density and conducting accuracy assessments are difficult. This study proposes a novel spatial simulation method, optimized geographically weighted logarithm regression (OGWLR), using Landsat 8 data acquired by the Google Earth Engine (GEE) and field survey data to map the forest carbon density of Shenzhen city in southern China. To verify the effectiveness of the novel method, multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF) and geographically weighted regression (GWR) models were established for comparison. The results showed that OGWLR achieved the highest coefficient of determination (R2 = 0.54) and the lowest root mean square error (RMSE = 13.28 Mg/ha) among all estimation models. In addition, OGWLR achieved a more consistent spatial distribution of carbon density with the actual situation. The carbon density of the forests in the study area was large in the central and western regions and coastal areas and small in the building and road areas. Therefore, this method can provide a new reference for urban forest carbon density estimation and mapping.

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

Cited by 97 publications

References 106 publications

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

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

Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine

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