Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires

Yin, Changming; He, Bin; Quan, Xingwen; Yebra, Marta; Lai, Gengke

doi:10.3390/rs12213590

Cited by 7 publications

(5 citation statements)

References 52 publications

(83 reference statements)

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“…The most promising method of monitoring forest territories today is remote aerospace monitoring. In most cases, hyperspectral sensors in the visible and near-infrared spectral ranges are used for monitoring forest areas [1][2][3][4][5][6] The initial information for mathematical modeling is the measurements data of vegetation elements spectral reflection coefficients in the broad spectral band from 400 to 2400 nm [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] .…”

Section: Problem Descriptionmentioning

confidence: 99%

Evaluation of the hyperspectral monitoring method capabilities for forests areas

Belov,

Gorodnichev

et al. 2023

29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

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The purpose of the study is to analyze the possibilities of forest areas hyperspectral monitoring. The mathematical modeling of the forest territories elements classification on the created neural network using experimentally measured reflection coefficients is presented. The simulation results show that hyperspectral monitoring in the spectral range of 400-2400 nm allows for classification of forest elements (different species of deciduous and coniferous trees, deadwood, swamps, water bodies, soils without vegetation, different types of mosses and lichens, post-fire areas, different shrubby plants) with the probability of correct classification of more than 0.73 and the probability of incorrect classification of less than 0.037. The use of additional information from the laser altimeter allows to significantly improve the classification. The created neural network, using hyperspectral monitoring data and lidar data on the height of trees, provides the probabilities of correct forest area elements classification of more than 0.8 and the probabilities of incorrect classification of less than 0.025.

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Section: Problem Descriptionmentioning

confidence: 99%

Evaluation of the hyperspectral monitoring method capabilities for forests areas

Belov,

Gorodnichev

et al. 2023

29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

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“…They are a simple and effective empirical measurement method for different features and their characteristic information, and are widely used in remote sensing monitoring of soil parameters, crop growth, environmental changes, etc. [31][32][33][34]. In this study, 14 vegetation indices were selected as indicators for constructing the ET inversion model of the study area according to the requirements of the evapotranspiration inversion, as shown in Table 2.…”

Section: Vegetation Index Constructionmentioning

confidence: 99%

Multi-Temporal Remote Sensing Inversion of Evapotranspiration in the Lower Yangtze River Based on Landsat 8 Remote Sensing Data and Analysis of Driving Factors

et al. 2023

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Analysis of the spatial and temporal variation patterns of surface evapotranspiration is important for understanding global climate change, promoting scientific deployment of regional water resources, and improving crop yield and water productivity. Based on Landsat 8 OIL_TIRS data and remote sensing image data of the lower Yangtze River urban cluster for the same period of 2016–2021, combined with soil and meteorological data of the study area, this paper constructed a multiple linear regression (MLR) model and an extreme learning machine (ELM) inversion model with evapotranspiration as the target and, based on the model inversion, quantitatively and qualitatively analyzed the spatial and temporal variability in surface evapotranspiration in the study area in the past six years. The results show that both models based on feature factors and spectral indices obtained a good inversion accuracy, with the fusion of feature factors effectively improving the inversion ability of the model for ET. The best model for ET in 2016, 2017, and 2021 was MLR, with an R2 greater than 0.8; the best model for ET in 2018–2019 was ELM, with an R2 of 0.83 and 0.62, respectively. The inter-annual ET in the study area showed a “double-peak” dynamic variation, with peaks in 2018 and 2020; the intra-annual ET showed a single-peak cycle, with peaks in July–August. Seasonal differences were obvious, and spatially high-ET areas were mainly found in rural areas north of the Yangtze River and central and western China where agricultural land is concentrated. The net solar radiation, soil heat flux, soil temperature and humidity, and fractional vegetation cover all had significant positive effects on ET, with correlation coefficients ranging from 0.39 to 0.94. This study can provide methodological and scientific support for the quantitative and qualitative estimation of regional ET.

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“…For high burn severity level, most of the overstory is scorched and the substratum can be charcoal or ash depending on the burn efficiency [11,51]. The CBI data (Figure 1) used in this study were obtained based on the algorithm proposed by Yin et al in 2020 [52], which was estimated by introducing tree canopy cover (TCC) into a coupled radiative transfer model. Through the field measured CBI and landscape photos, we verified the quantitative (R 2 = 0.92, RMSE = 0.2) and qualitative CBI estimation accuracy, which demonstrated good performance [52].…”

Section: Burn Severity Data and Random Sample Selectionmentioning

confidence: 99%

“…The CBI data (Figure 1) used in this study were obtained based on the algorithm proposed by Yin et al in 2020 [52], which was estimated by introducing tree canopy cover (TCC) into a coupled radiative transfer model. Through the field measured CBI and landscape photos, we verified the quantitative (R 2 = 0.92, RMSE = 0.2) and qualitative CBI estimation accuracy, which demonstrated good performance [52]. Location of the study area and the burn severity sample plots used in this study.…”

Section: Burn Severity Data and Random Sample Selectionmentioning

confidence: 99%

“…Areas with CBI lower Location of the study area and the burn severity sample plots used in this study. The right map is the spatial distribution map of burn severity, represented by CBI retrieved from remote sensing data and radiative transfer model inversion [52]. The CBI was stratified into 4 categories from 0 to 3, and 500 random samples with CBI greater than 1 were selected to analyze the relationship between burn severity and environmental drivers.…”

Section: Burn Severity Data and Random Sample Selectionmentioning

confidence: 99%

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Relationships between Burn Severity and Environmental Drivers in the Temperate Coniferous Forest of Northern China

et al. 2021

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Burn severity is a key component of fire regimes and is critical for quantifying fires’ impacts on key ecological processes. The spatial and temporal distribution characteristics of forest burn severity are closely related to its environmental drivers prior to the fire occurrence. The temperate coniferous forest of northern China is an important part of China’s forest resources and has suffered frequent forest fires in recent years. However, the understanding of environmental drivers controlling burn severity in this fire-prone region is still limited. To fill the gap, spatial pattern metrics including pre-fire fuel variables (tree canopy cover (TCC), normalized difference vegetation index (NDVI), and live fuel moisture content (LFMC)), topographic variables (elevation, slope, and topographic radiation aspect index (TRASP)), and weather variables (relative humidity, maximum air temperature, cumulative precipitation, and maximum wind speed) were correlated with a remote sensing-derived burn severity index, the composite burn index (CBI). A random forest (RF) machine learning algorithm was applied to reveal the relative importance of the environmental drivers mentioned above to burn severity for a fire. The model achieved CBI prediction accuracy with a correlation coefficient (R) equal to 0.76, root mean square error (RMSE) equal to 0.16, and fitting line slope equal to 0.64. The results showed that burn severity was mostly influenced by flammable live fuels and LFMC. The elevation was the most important topographic driver, and meteorological variables had no obvious effect on burn severity. Our findings suggest that in addition to conducting strategic fuel reduction management activities, planning the landscapes with fire-resistant plants with higher LFMC when possible (e.g., “Green firebreaks”) is also indispensable for lowering the burn severity caused by wildfires in the temperate coniferous forests of northern China.

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Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires

Cited by 7 publications

References 52 publications

Evaluation of the hyperspectral monitoring method capabilities for forests areas

Evaluation of the hyperspectral monitoring method capabilities for forests areas

Multi-Temporal Remote Sensing Inversion of Evapotranspiration in the Lower Yangtze River Based on Landsat 8 Remote Sensing Data and Analysis of Driving Factors

Relationships between Burn Severity and Environmental Drivers in the Temperate Coniferous Forest of Northern China

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