A Method of Automatically Extracting Forest Fire Burned Areas Using Gf-1 Remote Sensing Images

Wu, Bin; Liu, Ming; Jia, Dan; Li, Suju; Cong, Qiang; Chao, Wang; Zhu, Yanbo; Yin, Hujun; Zhu, Jun

doi:10.1109/igarss.2019.8900399

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…They are computed using different spectral bands to generate an index highlighting the affected areas of interest. Such techniques are often coupled with thresholding methodologies: either fixed or manually calibrated threshold values are chosen [17], or automatic thresholding algorithms are used [18]. Additional studies evaluate indexbased techniques with additional in-situ information, namely the Composite Burned Area Index (CBI), which indeed provides insightful information but does not represent a scalable solution because in-situ data are incredibly costly to collect.…”

Section: Related Workmentioning

confidence: 99%

CaBuAr: California burned areas dataset for delineation [Software and Data Sets]

Cambrin,

Colomba,

Garza

2023

IEEE Geosci. Remote Sens. Mag.

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Forest wildfires represent one of the catastrophic events that, over the last decades, caused huge environmental and humanitarian damages. In addition to a significant amount of carbon dioxide emission, they are a source of risk to society in both short-term (e.g., temporary city evacuation due to fire) and long-term (e.g., higher risks of landslides) cases. Consequently, the availability of tools to support local authorities in automatically identifying burned areas plays an important role in the continuous monitoring requirement to alleviate the aftereffects of such catastrophic events. The great availability of satellite acquisitions coupled with computer vision techniques represents an important step in developing such tools. This paper introduces a novel open dataset that tackles the burned area delineation problem, a binary segmentation problem applied to satellite imagery. The presented resource consists of pre-and post-fire Sentinel-2 L2A acquisitions of California forest fires that took place starting in 2015. Raster annotations were generated from the data released by California's Department of Forestry and Fire Protection. Moreover, in conjunction with the dataset, we release three different baselines based on spectral indexes analyses, SegFormer, and U-Net models.

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Section: Related Workmentioning

confidence: 99%

CaBuAr: California burned areas dataset for delineation [Software and Data Sets]

Cambrin,

Colomba,

Garza

2023

IEEE Geosci. Remote Sens. Mag.

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“…Gain and offset values needed to be added to the image before calibration. Landsat8 OLI and GF-1 images were set according to the relevant parameters of the downloaded image file; this research uses the RPC module [33,34] in orthorectification, the RPC parameters included with GF-1 WFV data are used for correction, the DEM data required for correction is from ASTER satellite's 30 m spatial resolution GDEM product (https://www.gscloud.cn/ search?kw=GF-1 (accessed on 1 May 2022)), eliminating the geometric distortion caused by the influence of the mountain, and the deformation caused by the camera orientation; the atmospheric correction was performed on Landsat8 OLI and GF-1 WFV through the FLASSH atmospheric correction module [35,36]. As a result, the influence of external factors such as atmosphere and light on the image was eliminated, and a more accurate surface reflectance was obtained.…”

Section: Remote Sensing Image Dataset Preprocessingmentioning

confidence: 99%

A High Spatiotemporal Enhancement Method of Forest Vegetation Leaf Area Index Based on Landsat8 OLI and GF-1 WFV Data

et al. 2023

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The leaf area index (LAI) is a crucial parameter for analyzing terrestrial ecosystem carbon cycles and global climate change. Obtaining high spatiotemporal resolution forest stand vegetation LAI products over large areas is essential for an accurate understanding of forest ecosystems. This study takes the northwestern part of the Inner Mongolia Autonomous Region (the northern section of the Greater Khingan Mountains) in northern China as the research area. It also generates the LAI time series product of the 8-day and 30 m forest stand vegetation growth period from 2013 to 2017 (from the 121st to the 305th day of each year). The Simulated Annealing-Back Propagation Neural Network (SA-BPNN) model was used to estimate LAI from Landsat8 OLI, and the multi-period GaoFen-1 WideField-View satellite images (GF-1 WFV) and the spatiotemporal adaptive reflectance fusion mode (STARFM) was used to predict high spatiotemporal resolution LAI by combining inversion LAI and Global LAnd Surface Satellite-derived vegetation LAI (GLASS LAI) products. The results showed the following: (1) The SA-BPNN estimation model has relatively high accuracy, with R2 = 0.75 and RMSE = 0.38 for the 2013 LAI estimation model, and R2 = 0.74 and RMSE = 0.17 for the 2016 LAI estimation model. (2) The fused 30 m LAI product has a good correlation with the LAI verification of the measured sample site (R2 = 0.8775) and a high similarity with the GLASS LAI product. (3) The fused 30 m LAI product has a high similarity with the GLASS LAI product, and compared with the GLASS LAI interannual trend line, it accords with the growth trend of plants in the seasons. This study provides a theoretical and technical reference for forest stand vegetation growth period LAI spatiotemporal fusion research based on high-score data, and has an important role in exploring vegetation primary productivity and carbon cycle changes in the future.

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“…Satellites collect information across multiple bandwidths which are sensitive to different land features. Therefore, combinations of specific acquisition channels can be used to detect the presence of water bodies [18], vegetation [19], burnt areas [20], snow, and, in principle, any kind of terrain [21][22][23][24] with the application of thresholding strategies [25] such as the Otsu's method [26,27]. These combinations of acquisition channels are known in the literature as indexes.…”

Section: Related Workmentioning

confidence: 99%

“…Severity prediction based on manual or automatic thresholding of these indexes is addressed with a wide range of techniques [25,32,33]. However, dNBR also requires acquisitions taken before the wildfire event, which might be difficult to obtain.…”

Section: Related Workmentioning

confidence: 99%

Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction

et al. 2021

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Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem.

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A Method of Automatically Extracting Forest Fire Burned Areas Using Gf-1 Remote Sensing Images

Cited by 8 publications

References 5 publications

CaBuAr: California burned areas dataset for delineation [Software and Data Sets]

CaBuAr: California burned areas dataset for delineation [Software and Data Sets]

A High Spatiotemporal Enhancement Method of Forest Vegetation Leaf Area Index Based on Landsat8 OLI and GF-1 WFV Data

Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction

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