A novel fire index-based burned area change detection approach using Landsat-8 OLI data

Liu, Sicong; Zheng, Yongjie; Dalponte, Michele; Tong, Xiaohua

doi:10.1080/22797254.2020.1738900

Cited by 74 publications

(41 citation statements)

References 21 publications

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“…Vegetation indices were also necessary due to diversity of vegetation over the entire study area and the similarity of the burned vegetation response with those of croplands. Many research studies have argued that the burned indices improved the accuracy of burned area mapping [15,32,33].…”

Section: Feature Extractionmentioning

confidence: 99%

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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

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: Feature Extractionmentioning

confidence: 99%

“…Change detection is one of the most important applications of RS and has been widely used for estimating burned areas using pre-and post-fire datasets [32]. In this study, image differencing was used as the change detection algorithm.…”

Section: Change Detectionmentioning

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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“…Remote sensing (RS) is one of the most important contact-free sensing means for Earth observation (EO) to extract relevant information about the physical properties of the Earth and environment system from spaceborne and airborne platforms. With the ever-growing availability of RS data sources from both satellite and airborne sensors on a large scale and even global scale, multimodal RS image processing and analysis techniques have been garnering growing attention in various EO-related tasks( Schmitt and Zhu, 2016 ), such as land cover change detection ( Liu et al, 2017 , Liu et al, 2019 ), disaster monitoring and management ( Zhu et al, 2019 , Liu et al, 2020 ), urban planning ( Weng, 2009 , Xie and Weng, 2017 ), mineral exploration ( Hong et al, 2019b , Siebels et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model

Hong

Yao

et al. 2021

ISPRS Journal of Photogrammetry and Remote Sensing

193

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As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing interest in the RS and geoscience community. However, due to the gap between different modalities in terms of imaging sensors, resolutions, and contents, embedding their complementary information into a consistent, compact, accurate, and discriminative representation, to a great extent, remains challenging. To this end, we propose a shared and specific feature learning (S2FL) model. S2FL is capable of decomposing multimodal RS data into modality-shared and modality-specific components, enabling the information blending of multi-modalities more effectively, particularly for heterogeneous data sources. Moreover, to better assess multimodal baselines and the newly-proposed S2FL model, three multimodal RS benchmark datasets, i.e., Houston2013 – hyperspectral and multispectral data, Berlin – hyperspectral and synthetic aperture radar (SAR) data, Augsburg – hyperspectral, SAR, and digital surface model (DSM) data, are released and used for land cover classification. Extensive experiments conducted on the three datasets demonstrate the superiority and advancement of our S2FL model in the task of land cover classification in comparison with previously-proposed state-of-the-art baselines. Furthermore, the baseline codes and datasets used in this paper will be made available freely at https://github.com/danfenghong/ISPRS_S2FL .

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“…Engelbrecht et al (2017) proposed a Normalized Difference Alpha-Angle Index for burned area identification by using multi-polarisation C-band SAR. Liu et al (2020) proposed a new burned area change detection approach by using Landsat-8 OLI data based on the fire index and Otsu algorithm. More research on burned area mapping based on fire index can be found in (Chuvieco et al, 2002;Roteta et al, 2019;Woźniak et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Method Based on Fire Index Enhancement and GRNN for Automated Burned Area Mapping From Single-Period Remote Sensing Imagery

Zhang

Xiao

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In the current situation of frequent forest fires, the study of forest burned area mapping is important. However, there is still room for improvement in the accuracy of existing forest burning area mapping methods. Therefore, in this paper, an unsupervised method based on fire index enhancement and GRNN (General Regression Neural Network) is proposed for automated forest burned area mapping from single-date post-fire remote sensing imagery. The proposed method first uses adaptive spatial context information to enhance the generated fire index to improve its ability to indicate the burned areas. Then the uncertainty analysis is performed on the enhanced fire index to extract reliable burned samples and non-burned samples for subsequent classifier training. Finally, the improved GRNN model considering the spatial correlation of pixels is used as a classifier to binarize the enhanced fire index to generate the final burned area map. Based on two commonly used fire indexes, NBR (Normalized Burn Ratio) and BAI (Burned Area Index), this paper conducts burned area mapping experiments on a post-fire image of a forest area in Inner Mongolia, China to test the effectiveness of the proposed method, and two commonly used threshold methods (Otsu and Kmeans clustering) are also used to conduct burned area mapping based on threshold segmentation of fire index for comparison experiments. The experimental results prove the effectiveness and superiority of the proposed method. The proposed method is unsupervised and automated, so it has high application value and potential under the current situation of frequent forest fires.

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A novel fire index-based burned area change detection approach using Landsat-8 OLI data

Cited by 74 publications

References 21 publications

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

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

Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model

An Unsupervised Method Based on Fire Index Enhancement and GRNN for Automated Burned Area Mapping From Single-Period Remote Sensing Imagery

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