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Dr. Xingwen Quan is major in the retrieval of the fuel variables, i.e. fuel moisture content (FMC) and fuel load (or biomass) from remote sensing data and the wildfire danger assessment and early-warning based on these satellites derived fuel variables and machine learning methods.
Fuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver. This study presents the first daily FMC product at a global scale and 500 m pixel resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and radiative transfer models (RTMs) inversion techniques. Firstly, multi-source information parameterized the PROSPECT-5 (leaf level), 4SAIL (grass and shrub canopy level) and GeoSail (tree canopy level) RTMs to generate three look-up tables (LUTs). Each LUT contained the most realistic model inputs range and combination, and the corresponding simulated spectra. Secondly, for each date and location of interest, a global landcover map classified fuels into three classes: grassland, shrubland and forest. For each fuel class, the best LUT-based inversion strategy based on spectral information, cost function, percentage of solutions, and central tendency determined the optimal model for the global FMC product. Finally, 3,034 FMC measurements from 120 worldwide sites validated the statistically significant results (R2 = 0.62, RMSE = 34.57%, p < 0.01). Filtering out low quality field measurements achieved better accuracy (R2 = 0.71, RMSE = 32.36%, p < 0.01, n = 2008). It is anticipated that this global FMC product can assist in wildfire danger modeling, early prediction, suppression and response, as well as improve awareness of wildfire risk to life and property.
Xingwen Quan; Marta Yebra; David Riaño; Binbin He; Gengke Lai; Xiangzhuo Liu. Global fuel moisture content mapping from MODIS. International Journal of Applied Earth Observation and Geoinformation 2021, 101, 102354 .
AMA StyleXingwen Quan, Marta Yebra, David Riaño, Binbin He, Gengke Lai, Xiangzhuo Liu. Global fuel moisture content mapping from MODIS. International Journal of Applied Earth Observation and Geoinformation. 2021; 101 ():102354.
Chicago/Turabian StyleXingwen Quan; Marta Yebra; David Riaño; Binbin He; Gengke Lai; Xiangzhuo Liu. 2021. "Global fuel moisture content mapping from MODIS." International Journal of Applied Earth Observation and Geoinformation 101, no. : 102354.
Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually explored. Optical and SAR data are expected to be sensitive to different types of fuel loads because of their different imaging mechanisms. Optical data mainly captures the characteristics of leaf and forest canopy, while the latter is more sensitive to forest vertical structures due to its strong penetrability. This study aims to explore the performance of Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data as well as their combination on estimating three different types of fuel load—stem fuel load (SFL), branch fuel load (BFL) and foliage fuel load (FFL). We first analyzed the correlation between the three types of fuel load and optical and SAR data. Then, the partial least squares regression (PLSR) was used to build the fuel load estimation models based on the fuel load measurements from Vindeln, Sweden, and variables derived from optical and SAR data. Based on the leave-one-out cross-validation (LOOCV) method, results show that L-band SAR data performed well on all three types of fuel load (R2 = 0.72, 0.70, 0.72). The optical data performed best for FFL estimation (R2 = 0.66), followed by BFL (R2 = 0.56) and SFL (R2 = 0.37). Further improvements were found for the SFL, BFL and FFL estimation when integrating optical and SAR data (R2 = 0.76, 0.81, 0.82), highlighting the importance of data selection and combination for fuel load estimation.
Yanxi Li; Xingwen Quan; Zhanmang Liao; Binbin He. Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data. Remote Sensing 2021, 13, 1189 .
AMA StyleYanxi Li, Xingwen Quan, Zhanmang Liao, Binbin He. Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data. Remote Sensing. 2021; 13 (6):1189.
Chicago/Turabian StyleYanxi Li; Xingwen Quan; Zhanmang Liao; Binbin He. 2021. "Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data." Remote Sensing 13, no. 6: 1189.
Foliage fuel load (FFL) is a critical factor affecting crown fire intensity and rate of spread. Satellite observations provide the potential for monitoring FFL dynamics across large areas. Previous studies commonly used empirical methods to estimate FFL, which potentially lacks reproducibility. This study applied Landsat 7 ETM+ and 8 OLI data for FFL retrieval using radiative transfer model (RTM) and machine learning method. To this end, the GeoSail, SAIL and PROSPECT RTMs were firstly coupled together to model the near realistic scenario of a two-layered forest structure. Secondly, available ecological information was applied to constrain the coupled RTM modeling phases, in order to decrease the probability of generating unrealistic simulations. Thirdly, the coupled RTMs were linked to three machine learning models - random forest, support vector machine and multi-layer perceptron - as well as a traditional look-up table. Finally, the performance of each method was validated by FFL measurements from Southwest China and Sweden. The resulting multi-layer perceptron (R2 = 0.77, RMSE = 0.13 and rRMSE = 0.43) outperformed the other three methods. Evaluation of applicability of the FFL estimates was conducted in a southwest China forest where two forest fires occurred in 2014 and 2020. The FFL dynamics from 2013 through 2020 showed that fire was likely to occur when the FFL accumulated to a critical point (around 27106 kg), highlighting the relevance of remote sensing-derived FFL estimates for understanding potential fire occurrence.
Xingwen Quan; Yanxi Li; Binbin He; Geoffrey Cary; Gengke Lai. Application of Landsat ETM+ and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and Machine Learning Method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.
AMA StyleXingwen Quan, Yanxi Li, Binbin He, Geoffrey Cary, Gengke Lai. Application of Landsat ETM+ and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and Machine Learning Method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.
Chicago/Turabian StyleXingwen Quan; Yanxi Li; Binbin He; Geoffrey Cary; Gengke Lai. 2021. "Application of Landsat ETM+ and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and Machine Learning Method." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.
Burn severity mapping is critical to quantifying fire impact on key ecological processes and post-fire forest management. Satellite remote sensing has the advantages of high spatial-temporal resolution and large-scale monitoring and provides a more efficient way to evaluate forest fire burn severity than traditional field or aerial surveys. However, the proportion of tree canopy cover (TCC) affects the spectral signal received by remote sensing sensors from the background charcoal and ash. Consequently, not considering this factor normally leads a spectral confusion in burn severity retrieval. In this study, the burn severity of two Qinyuan forest fires was estimated using a coupled Radiative Transfer Model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) reflectance data. A two-layer Canopy Reflectance Model (ACRM) RTM was coupled with the GeoSail RTM by replacing the spectra of the background input of GeoSail RTM to simulate the spectra of the three-layered forests for burn severity retrieval measured as the Composite Burn Index (CBI). The TCC data was then served to RTM parameterization and constrain the backward inversion procedure of the coupled RTM to alleviate spectral confusion. Finally, the inversion retrievals were evaluated using 163 field measured CBI. The coupled RTM can simulate the radiative transfer characteristics of three-layer vegetation and has greater potential to accurately estimate burn severity worldwide. To evaluate the merit of our proposed method, the CBI was estimated through coupled RTM inversion with TCC constraint (CP_RTM+TCC), coupled RTM inversion with global optimal search (CP-RTM+GOS), Forest Reflectance and Transmittance (FRT) RTM inversion with TCC constraint (FRT+TCC), and random forest (RF) algorithm. The results showed that the method proposed in this study (CP_RTM+TCC) yielded the highest estimation accuracy (R2 = 0.92, RMSE = 0.2) among the four methods used as benchmark, indicating its reasonable ability to assist forest managers to better understand post-fire vegetation regeneration and forest management.
Changming Yin; Binbin He; Xingwen Quan; Marta Yebra; Gengke Lai. Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires. Remote Sensing 2020, 12, 3590 .
AMA StyleChangming Yin, Binbin He, Xingwen Quan, Marta Yebra, Gengke Lai. Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires. Remote Sensing. 2020; 12 (21):3590.
Chicago/Turabian StyleChangming Yin; Binbin He; Xingwen Quan; Marta Yebra; Gengke Lai. 2020. "Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires." Remote Sensing 12, no. 21: 3590.
The authors wish to make the following corrections to this paper [1]: 1
Long Wang; Xingwen Quan; Binbin He; Marta Yebra; Minfeng Xing; Xiangzhuo Liu. Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568. Remote Sensing 2020, 12, 206 .
AMA StyleLong Wang, Xingwen Quan, Binbin He, Marta Yebra, Minfeng Xing, Xiangzhuo Liu. Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568. Remote Sensing. 2020; 12 (2):206.
Chicago/Turabian StyleLong Wang; Xingwen Quan; Binbin He; Marta Yebra; Minfeng Xing; Xiangzhuo Liu. 2020. "Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568." Remote Sensing 12, no. 2: 206.
Burn severity mapping greatly informs fire management and can be used to predict post-fire vegetation recovery. Satellite remote sensing is a cost-effective method for estimating burn severity, providing a comprehensive spatially explicit view of whole landscapes. However, the proportion of tree canopy cover (TCC) affects the reflectance signal, obscuring background char and ash. Consequently, traditional optical satellite remote sensing methods that do not account for variation in TCC misclassify burn severity, especially in areas with extremely low or high TCC. In this study, TCC data served to parameterize and constrain the inversion of the Forest Reflectance and Transmittance (FRT) radiative transfer model (RTM) to alleviate spectral confusion when retrieving burn severity. The methodology was evaluated using field measurements of burn severity for a series of wildfires in the fire-prone tropical savannas of northern Australia and the western United States. Burn severity classes were used for Australia while the Composite Burn Index (CBI) for US. Reflectance data from Sentinel-2A Multi-Spectral Instrument (MSI) and Landsat-5 Thematic Mapper (TM) corresponding to post-fire field survey dates were used to retrieve burn severity using FRT RTM (with and without using TCC information in its parameterization and inversion) and two standard empirical burn indices, dNBR and RdNBR, for comparison. Using FRT RTM without TCC constraint produced an overestimation for low burn severity in regions with low TCC and an underestimation for moderate and high burn severity in regions with high TCC. Burn severity estimation accuracy significantly improved by integrating TCC in the parameterization and inversion of FRT RTM. The overall accuracy in northern Australia increased from 65% to 81%, and the kappa coefficient increased from 0.35 to 0.55. In the western United States, R2 between estimated and observed CBI, increased from 0.33 to 0.54, root mean square error (RMSE) reduced from 0.53 to 0.43, and in all instances, the method performed better than dNBR and RdNBR. The method used in this study achieved more accurate burn severity mapping, thus assisting land managers to better understand post-fire vegetation resilience and forest management.
Changming Yin; Binbin He; Marta Yebra; Xingwen Quan; Andrew C. Edwards; Xiangzhuo Liu; Zhanmang Liao. Improving burn severity retrieval by integrating tree canopy cover into radiative transfer model simulation. Remote Sensing of Environment 2019, 236, 111454 .
AMA StyleChangming Yin, Binbin He, Marta Yebra, Xingwen Quan, Andrew C. Edwards, Xiangzhuo Liu, Zhanmang Liao. Improving burn severity retrieval by integrating tree canopy cover into radiative transfer model simulation. Remote Sensing of Environment. 2019; 236 ():111454.
Chicago/Turabian StyleChangming Yin; Binbin He; Marta Yebra; Xingwen Quan; Andrew C. Edwards; Xiangzhuo Liu; Zhanmang Liao. 2019. "Improving burn severity retrieval by integrating tree canopy cover into radiative transfer model simulation." Remote Sensing of Environment 236, no. : 111454.
Previous studies have shown that Live Fuel Moisture Content (LFMC) is a crucial driver affecting wildfire occurrence worldwide, but the effect of LFMC in driving wildfire occurrence still remains unexplored over the southwest China ecosystem, an area historically vulnerable to wildfires. To this end, we took 10-years of LFMC dynamics retrieved from Moderate Resolution Imaging Spectrometer (MODIS) reflectance product using the physical Radiative Transfer Model (RTM) and the wildfire events extracted from the MODIS Burned Area (BA) product to explore the relations between LFMC and forest/grassland fire occurrence across the subtropical highland zone (Cwa) and humid subtropical zone (Cwb) over southwest China. The statistical results of pre-fire LFMC and cumulative burned area show that distinct pre-fire LFMC critical thresholds were identified for Cwa (151.3%, 123.1%, and 51.4% for forest, and 138.1%, 72.8%, and 13.1% for grassland) and Cwb (115.0% and 54.4% for forest, and 137.5%, 69.0%, and 10.6% for grassland) zones. Below these thresholds, the fire occurrence and the burned area increased significantly. Additionally, a significant decreasing trend on LFMC dynamics was found during the days prior to two large fire events, Qiubei forest fire and Lantern Mountain grassland fire that broke during the 2009/2010 and 2015/2016 fire seasons, respectively. The minimum LFMC values reached prior to the fires (49.8% and 17.3%) were close to the lowest critical LFMC thresholds we reported for forest (51.4%) and grassland (13.1%). Further LFMC trend analysis revealed that the regional median LFMC dynamics for the 2009/2010 and 2015/2016 fire seasons were also significantly lower than the 10-year LFMC of the region. Hence, this study demonstrated that the LFMC dynamics explained wildfire occurrence in these fire-prone regions over southwest China, allowing the possibility to develop a new operational wildfire danger forecasting model over this area by considering the satellite-derived LFMC product.
Kaiwei Luo; Xingwen Quan; Binbin He; Marta Yebra. Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China. Forests 2019, 10, 887 .
AMA StyleKaiwei Luo, Xingwen Quan, Binbin He, Marta Yebra. Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China. Forests. 2019; 10 (10):887.
Chicago/Turabian StyleKaiwei Luo; Xingwen Quan; Binbin He; Marta Yebra. 2019. "Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China." Forests 10, no. 10: 887.
Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health. Machine-accessible metadata file describing the reported data (ISA-Tab format)
Marta Yebra; Gianluca Scortechini; Abdulbaset Badi; María Eugenia Beget; Matthias M. Boer; Ross Bradstock; Emilio Chuvieco; F. Mark Danson; Philip Dennison; Victor Resco De Dios; Carlos M. Di Bella; Greg Forsyth; Philip Frost; Mariano Garcia; Abdelaziz Hamdi; Binbin He; Matt Jolly; Tineke Kraaij; M. Pilar Martín; Florent Mouillot; Glenn Newnham; Rachael Nolan; Grazia Pellizzaro; Yi Qi; Xingwen Quan; David Riaño; Dar Roberts; Momadou Sow; Susan Ustin. Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications. Scientific Data 2019, 6, 1 -8.
AMA StyleMarta Yebra, Gianluca Scortechini, Abdulbaset Badi, María Eugenia Beget, Matthias M. Boer, Ross Bradstock, Emilio Chuvieco, F. Mark Danson, Philip Dennison, Victor Resco De Dios, Carlos M. Di Bella, Greg Forsyth, Philip Frost, Mariano Garcia, Abdelaziz Hamdi, Binbin He, Matt Jolly, Tineke Kraaij, M. Pilar Martín, Florent Mouillot, Glenn Newnham, Rachael Nolan, Grazia Pellizzaro, Yi Qi, Xingwen Quan, David Riaño, Dar Roberts, Momadou Sow, Susan Ustin. Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications. Scientific Data. 2019; 6 (1):1-8.
Chicago/Turabian StyleMarta Yebra; Gianluca Scortechini; Abdulbaset Badi; María Eugenia Beget; Matthias M. Boer; Ross Bradstock; Emilio Chuvieco; F. Mark Danson; Philip Dennison; Victor Resco De Dios; Carlos M. Di Bella; Greg Forsyth; Philip Frost; Mariano Garcia; Abdelaziz Hamdi; Binbin He; Matt Jolly; Tineke Kraaij; M. Pilar Martín; Florent Mouillot; Glenn Newnham; Rachael Nolan; Grazia Pellizzaro; Yi Qi; Xingwen Quan; David Riaño; Dar Roberts; Momadou Sow; Susan Ustin. 2019. "Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications." Scientific Data 6, no. 1: 1-8.
Fuel moisture content (FMC) is a crucial variable affecting fuel ignition and rate of fire spread. Much work so far has focused on the usage of remote sensing data from multiple sensors to derive FMC; however, little attention has been devoted to the usage of the C-band Sentinel-1A data. In this study, we aimed to test the performance of C-band Sentinel-1A data for multi-temporal retrieval of forest FMC by coupling the bare soil backscatter linear model with the vegetation backscatter water cloud model (WCM). This coupled model that linked the observed backscatter directly to FMC, was firstly calibrated using field FMC measurements and corresponding synthetic aperture radar (SAR) backscatters (VV and VH), and then a look-up table (LUT) comprising of the modelled VH backscatter and FMC was built by running the calibrated model forwardly. The absolute difference (MAEr) of modelled and observed VH backscatters was selected as the cost function to search the optimal FMC from the LUT. The performance of the presented methodology was verified using the three-fold cross-validation method by dividing the whole samples into equal three parts. Two parts were used for the model calibration and the other one for the validation, and this was repeated three times. The results showed that the estimated and measured forest FMC were consistent across the three validation samples, with the root mean square error (RMSE) of 19.53% (Sample 1), 12.64% (Sample 2) and 15.45% (Sample 3). To further test the performance of the C-band Sentinel-1A data for forest FMC estimation, our results were compared to those obtained using the optical Landsat 8 Operational Land Imager (OLI) data and the empirical partial least squares regression (PLSR) method. The latter resulted in higher RMSE between estimated and measured forest FMC with 20.11% (Sample 1), 26.21% (Sample 2) and 26.73% (Sample 3) than the presented Sentinel-1A data-based method. Hence, this study demonstrated that the good capability of C-band Sentinel-1A data for forest FMC retrieval, opening the possibility of developing a new operational SAR data-based methodology for forest FMC estimation.
Long Wang; Xingwen Quan; Binbin He; Marta Yebra; Minfeng Xing; Xiangzhuo Liu. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11, 1568 .
AMA StyleLong Wang, Xingwen Quan, Binbin He, Marta Yebra, Minfeng Xing, Xiangzhuo Liu. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing. 2019; 11 (13):1568.
Chicago/Turabian StyleLong Wang; Xingwen Quan; Binbin He; Marta Yebra; Minfeng Xing; Xiangzhuo Liu. 2019. "Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation." Remote Sensing 11, no. 13: 1568.
Fuel biomass burning plays an important role in shaping many ecosystems worldwide and produces gaseous emissions which ultimately alter global climatic processes. As for grass, we assumed the fuel biomass could be approximately estimated from the total dry weight of aboveground grass live and dead organs. Remote sensing techniques adapted methods for estimating the aboveground biomass (the live organs) were wildly explored, yet limited studies used the remote sensing technique to estimate the dry weight of aboveground dead organs. For this end, a method of assimilating leaf area index (LAI) derived from radiative transfer model into the WOrld FOod STudies (WOFOST) model was presented to simultaneously estimate the total dry weight of aboveground grass live and dead organs (i.e., the grass fuel biomass). Validation between measured and estimated fuel biomass showed that the estimated fuel biomass presents a reasonable accuracy with the R 2 = 0.77 and the RMSE = 223.07 gm -2 .
Yang Zhang; Qidi Shu; Long Wang; Xingwen Quan; Xiangzhuo Liu; Biao Lu. Estimation of Fuel Biomass for Grasslands Using Data Assimilation Technique. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium 2019, 9988 -9991.
AMA StyleYang Zhang, Qidi Shu, Long Wang, Xingwen Quan, Xiangzhuo Liu, Biao Lu. Estimation of Fuel Biomass for Grasslands Using Data Assimilation Technique. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. 2019; ():9988-9991.
Chicago/Turabian StyleYang Zhang; Qidi Shu; Long Wang; Xingwen Quan; Xiangzhuo Liu; Biao Lu. 2019. "Estimation of Fuel Biomass for Grasslands Using Data Assimilation Technique." IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium , no. : 9988-9991.
In this study, the burn severity of several wildfires ignited at northern Australian tropical savannas area were estimated using the Forest Reflectance and Transmittance (FRT) radiative transfer model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) satellite data. To alleviate the spectral confusion between severe (SV) and not-severe (NSV) burnt levels caused by sparse tree distribution, the MODIS Vegetation Continuous Fields (VCF) tree cover percentage data was used to constrain the inversion. The results showed that the accuracy of burn severity estimation significantly improves when considering the tree coverage, with overall accuracy for two study sites increasing from 65% to 81% and kappa coefficient from 0.35 to 0.55. Future work will focus on extending the methodology to other ecosystems.
Changming Yin; Binbin He; Marta Yebra; Xingwen Quan; Andrew C. Edwards; Xiangzhuo Liu; Zhanmang Liao; Kaiwei Luo. Burn Severity Estimation in Northern Australia Tropical Savannas Using Radiative Transfer Model and Sentinel-2 Data. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium 2019, 6712 -6715.
AMA StyleChangming Yin, Binbin He, Marta Yebra, Xingwen Quan, Andrew C. Edwards, Xiangzhuo Liu, Zhanmang Liao, Kaiwei Luo. Burn Severity Estimation in Northern Australia Tropical Savannas Using Radiative Transfer Model and Sentinel-2 Data. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. 2019; ():6712-6715.
Chicago/Turabian StyleChangming Yin; Binbin He; Marta Yebra; Xingwen Quan; Andrew C. Edwards; Xiangzhuo Liu; Zhanmang Liao; Kaiwei Luo. 2019. "Burn Severity Estimation in Northern Australia Tropical Savannas Using Radiative Transfer Model and Sentinel-2 Data." IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium , no. : 6712-6715.
The objective of this study is to investigate the potential of multitemporal remote sensing images for crop classification. Multi-temporal Landsat 8 OLI/TIRS C1 Level-1 images were acquired. The surface reflectance of visible and near infrared bands was used to represent the characteristics of crops. A time series model of surface reflectance was constructed for crop classification. Cloud cover is critical for the accuracy of classification. In order to remove the influence of clouds, the cloud pixels were neglected by setting a constant. Pearson correlation coefficient was used in the time series model of surface reflectance to classify the crop type. Finally, the overall accuracy reaches 78.26% and Kappa reaches 71.33%. Therefore, the method has the operational potential for crop classification even in the special area with cloudy or foggy weather.
Jingduo Song; Minfeng Xing; Yichuan Ma; Long Wang; Kaiwei Luo; Xingwen Quan. Crop Classification Using Multitemporal Landsat 8 Images. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium 2019, 2407 -2410.
AMA StyleJingduo Song, Minfeng Xing, Yichuan Ma, Long Wang, Kaiwei Luo, Xingwen Quan. Crop Classification Using Multitemporal Landsat 8 Images. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. 2019; ():2407-2410.
Chicago/Turabian StyleJingduo Song; Minfeng Xing; Yichuan Ma; Long Wang; Kaiwei Luo; Xingwen Quan. 2019. "Crop Classification Using Multitemporal Landsat 8 Images." IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium , no. : 2407-2410.
Spatiotemporal monitoring of fuel moisture content (FMC) is vital to assessing the wildfire risk and its behavior. Optical remote sensing data-based FMC estimation have been wildly explored in previous studies. However, limited studies focused on FMC retrieval from the active microwave technique represented by synthetic aperture radar (SAR) data, which processes the advantage of higher sensitivity to surface moisture and better all-weather and all-time work capability than optical data. This is the first study to assess the performance of time series dual-polarization Sentinel-1A data for FMC estimation from coupled the bare soil backscatter Linear Model and the vegetation backscatter Water Cloud Model. The results show that the simulated backscattering coefficients and FMC are in line with the measured Sentinel-1A data and FMC with R 2 and RMSE are 0.549, 0.354 dB, and 0.543, 13.579 %, respectively.
Long Wang; Binbin He; Xingwen Quan; Minfeng Xing; Hongguo Zhang. First Assessment of Dual Polarization Sentinel-1A Data for Fuel Moisture Content Retrieval. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium 2019, 9902 -9905.
AMA StyleLong Wang, Binbin He, Xingwen Quan, Minfeng Xing, Hongguo Zhang. First Assessment of Dual Polarization Sentinel-1A Data for Fuel Moisture Content Retrieval. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. 2019; ():9902-9905.
Chicago/Turabian StyleLong Wang; Binbin He; Xingwen Quan; Minfeng Xing; Hongguo Zhang. 2019. "First Assessment of Dual Polarization Sentinel-1A Data for Fuel Moisture Content Retrieval." IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium , no. : 9902-9905.
Fuel moisture content (FMC) is a critical variable in assessing wildfire risk and its behavior. Previous studies normally focused on the methodologies based on optical remote sensing data for FMC retrieval. However, active microwave technique, which processes the advantage of high sensitivity to surface moisture, all-weather and all-time work capability and strong penetrability, attracted more attention in surface parameter monitoring, particularly for the polarimetric SAR which provides more sufficient object scatter characteristic. In this paper, we retrieved the FMC for a grassland based on the multiple linear regression analysis of polarimetric decomposition parameters from Radarsat-2 data. The results show that the correlation coefficient (R) and root mean square error (RMSE) reached to 0.658 and 30.319% when compared to the measured FMC. Finally, the presented method was used for spatial and temporal mapping of FMC in the target study area.
Long Wang; Binbin He; Xingwen Quan; Minfeng Xing; Xiangzhuo Liu. Estimation of Fuel Moisture Content Based on Quad Polarimetric Decomposition Parameters of Radarsat-2 Data. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium 2019, 9934 -9937.
AMA StyleLong Wang, Binbin He, Xingwen Quan, Minfeng Xing, Xiangzhuo Liu. Estimation of Fuel Moisture Content Based on Quad Polarimetric Decomposition Parameters of Radarsat-2 Data. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. 2019; ():9934-9937.
Chicago/Turabian StyleLong Wang; Binbin He; Xingwen Quan; Minfeng Xing; Xiangzhuo Liu. 2019. "Estimation of Fuel Moisture Content Based on Quad Polarimetric Decomposition Parameters of Radarsat-2 Data." IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium , no. : 9934-9937.
This paper studies the applicability of multi-temporal synthetic aperture radar (SAR) images in detecting urban land use types change. In this paper, the study area is 128×358 pixels covers Shuangliu International Airport, Chengdu, China. Nine scenes of ALOS-PALSAR HV images from July 2007 to October 2010 were collected. the logarithmic ratio operator was used to generate the intensity and texture feature difference images. Texture features were extracted by the gray level co-occurrence matrix (GLCM). Then the redundant difference information was compressed by PCA transformation. The index of dynamic change image was generated to represent the change in land use type in Chengdu Shuangliu International Airport.
Qiwen Yu; Minfeng Xing; Xiaofang Liu; Long Wang; Kaiwei Luo; Xingwen Quan. Detection of Land Use Type Using Multitemporal SAR Images. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium 2019, 1534 -1537.
AMA StyleQiwen Yu, Minfeng Xing, Xiaofang Liu, Long Wang, Kaiwei Luo, Xingwen Quan. Detection of Land Use Type Using Multitemporal SAR Images. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. 2019; ():1534-1537.
Chicago/Turabian StyleQiwen Yu; Minfeng Xing; Xiaofang Liu; Long Wang; Kaiwei Luo; Xingwen Quan. 2019. "Detection of Land Use Type Using Multitemporal SAR Images." IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium , no. : 1534-1537.
With the upcoming BIOMASS mission, P-band PolInSAR is expected to provide new perspectives on global forest aboveground biomass (AGB). However, its performance has not yet been fully evaluated for dense tropical forests with complex structure and very high biomass. Based on the TropiSAR campaign in French Guiana, we explored the challenges of the three most commonly used PolInSAR measures to capture AGB in tropical forests; coherence magnitude, interferometric phase, and backscatter. An improved AGB estimation approach was developed by integrating multiple information derived from single-baseline PolInSAR data. The approach involves ground-volume backscatter decomposition and combines volume backscatter with the retrieved forest height. Volume backscatter from the forest canopy was the best predictor of AGB for tropical forests, whereas the ground backscatter contribution was affected by the complex underlying surface and terrain slope. Both LiDAR- and PolInSAR-derived forest heights showed limited correlation with high AGB due to the varying forest basal area. The linear combination of PolInSAR-derived forest height and volume backscatter complemented each other and produced improved AGB estimates. Comparing three different PolInSAR data pairs, the proposed method produced an AGB map with an average R2 of 0.7 and RMSE of 34 tons/ha (relative RMSE of 9.4%) at a spatial resolution of 125 × 125 m2 for biomass between 250–500 tons/ha.
Zhanmang Liao; Binbin He; Xingwen Quan; Albert I.J.M. Van Dijk; Shi Qiu; Changming Yin. Biomass estimation in dense tropical forest using multiple information from single-baseline P-band PolInSAR data. Remote Sensing of Environment 2018, 221, 489 -507.
AMA StyleZhanmang Liao, Binbin He, Xingwen Quan, Albert I.J.M. Van Dijk, Shi Qiu, Changming Yin. Biomass estimation in dense tropical forest using multiple information from single-baseline P-band PolInSAR data. Remote Sensing of Environment. 2018; 221 ():489-507.
Chicago/Turabian StyleZhanmang Liao; Binbin He; Xingwen Quan; Albert I.J.M. Van Dijk; Shi Qiu; Changming Yin. 2018. "Biomass estimation in dense tropical forest using multiple information from single-baseline P-band PolInSAR data." Remote Sensing of Environment 221, no. : 489-507.
Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which is especially helpful for wildfire rescue and management. Historically, images obtained from sun-orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) were used to detect active fire and burned area at the large spatial scale. However, the daily revisit cycles make them inherently unable to extract FSR in near real-time (hourly or less). We argue that the Himawari-8, a next generation geostationary satellite with a 10-min temporal resolution and 0.5–2 km spatial resolution, may have the potential for near real-time FSR extraction. To that end, we propose a novel method (named H8-FSR) for near real-time FSR extraction based on the Himawari-8 data. The method first defines the centroid of the burned area as the fire center and then the near real-time FSR is extracted by timely computing the movement rate of the fire center. As a case study, the method was applied to the Esperance bushfire that broke out on 17 November, 2015, in Western Australia. Compared with the estimated FSR using the Commonwealth Scientific and Industrial Research Organization (CSIRO) Grassland Fire Spread (GFS) model, H8-FSR achieved favorable performance with a coefficient of determination (R2) of 0.54, mean bias error of –0.75 m/s, mean absolute percent error of 33.20% and root mean square error of 1.17 m/s, respectively. These results demonstrated that the Himawari-8 data are valuable for near real-time FSR extraction, and also suggested that the proposed method could be potentially applicable to other next generation geostationary satellite data.
Xiangzhuo Liu; Binbin He; Xingwen Quan; Marta Yebra; Shi Qiu; Changming Yin; Zhanmang Liao; Hongguo Zhang. Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data. Remote Sensing 2018, 10, 1654 .
AMA StyleXiangzhuo Liu, Binbin He, Xingwen Quan, Marta Yebra, Shi Qiu, Changming Yin, Zhanmang Liao, Hongguo Zhang. Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data. Remote Sensing. 2018; 10 (10):1654.
Chicago/Turabian StyleXiangzhuo Liu; Binbin He; Xingwen Quan; Marta Yebra; Shi Qiu; Changming Yin; Zhanmang Liao; Hongguo Zhang. 2018. "Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data." Remote Sensing 10, no. 10: 1654.
Fuel moisture content (FMC) is a critical factor in assessing wildfire risk and its behaviour. Traditional field measurement of this variable is time-consuming and is impossible to extend to large-scale and dynamic applications. The canopy water has strong absorption characteristic in near and shortwave infrared spectra, allowing the near-real-time, multi-temporal and -spatial estimation of the FMC from remotely sensed data available. During last decade, numerous statistic- or physical model-based studies were carried out for the estimation of this variable. As FMC is responsive to weather variations, diurnal determination of this variable is essential for wildfire early-warning. With the launch of Himawari-8 in 2014, 10 mins images are available from this satellite, making real-time retrieval of the FMC achievable. Thus, this is the first study to retrieve diurnal FMC from Himawari-8 images, with the purpose for real-time wildfire risk assessment in near future.
Xingwen Quan; Binbin He; Marta Yebra; Xiangzhuo Liu; Xiaofang Liu; Xiaodong Zhung; Hui Cao. Retrieval of Fuel Moisture Content from Himawari-8 Product: Towards Real-Time Wildfire Risk Assessment. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 7660 -7663.
AMA StyleXingwen Quan, Binbin He, Marta Yebra, Xiangzhuo Liu, Xiaofang Liu, Xiaodong Zhung, Hui Cao. Retrieval of Fuel Moisture Content from Himawari-8 Product: Towards Real-Time Wildfire Risk Assessment. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():7660-7663.
Chicago/Turabian StyleXingwen Quan; Binbin He; Marta Yebra; Xiangzhuo Liu; Xiaofang Liu; Xiaodong Zhung; Hui Cao. 2018. "Retrieval of Fuel Moisture Content from Himawari-8 Product: Towards Real-Time Wildfire Risk Assessment." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 7660-7663.
We present the first continental-scale methodology for estimating Live Fuel Moisture Content (FMC) and flammability in Australia using satellite observations. The methodology includes a physically-based retrieval model to estimate FMC from MODIS (Moderate Resolution Imaging Spectrometer) reflectance data using radiative transfer model inversion. The algorithm was evaluated using 363 observations at 33 locations around Australia with mean accuracy for the studied land cover classes (grassland, shrubland and forest) close to those obtained elsewhere (r 2 =0.57, RMSE = 40%) but without site-specific calibration. Logistic regression models were developed to predict a flammability index, trained on fire events mapped in the MODIS burned area product and four predictor variables calculated from the FMC estimates. The selected predictor variables were actual FMC corresponding to the 8-day and 16-day period before burning; the same but expressed as an anomaly from the long-term mean for that date; and the FMC change between the two successive 8-day periods before burning. Separate logistic regression models were developed for grassland, shrubland and forest, obtaining performance metrics of 0.70, 0.78 and 0.71, respectively, indicating reasonable skill in fire risk prediction.
M Yebra; Xingwen Quan; D Riano; P Rozas Larraondo; Albert Van Dijk; Geoffrey J. Cary. Mapping Live Fuel Moisture Content and Flammability for Continental Australia Using Optical Remote Sensing. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 5903 -5906.
AMA StyleM Yebra, Xingwen Quan, D Riano, P Rozas Larraondo, Albert Van Dijk, Geoffrey J. Cary. Mapping Live Fuel Moisture Content and Flammability for Continental Australia Using Optical Remote Sensing. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():5903-5906.
Chicago/Turabian StyleM Yebra; Xingwen Quan; D Riano; P Rozas Larraondo; Albert Van Dijk; Geoffrey J. Cary. 2018. "Mapping Live Fuel Moisture Content and Flammability for Continental Australia Using Optical Remote Sensing." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 5903-5906.
Fire Spread Rate (FSR) is one of the key factors for fire rescue and prevention. Remote sensing images have an advantage of acquiring intuitive information timely. To achieve extracting real-time FSR from remote sensing data, a method based on the movement rate of burned area centroid is presented in this study. The FSR extracted from geostationary Himawari-8 (H-8) data in two bushfires outbroke in Esperance, Western Australia in 2015. And the FSR estimated from CSIRO (Commonwealth Scientific and Industrial Research Organization) Grassland Fire Spread Model (CGFSM) were set as the benchmark to assess the presented approach. The results illustrated that the proposed method yield a promising accuracy by comparing with the FSR from CGFSM in that two fires, with the coefficient of determination (R2) reaches to 0.76 and root-mean-square error (RMSE) is 0.50 m·s -1 . Furthermore, this study provides a potential application of geostationary satellite in extracting real-time wildfire behavior.
Xiangzhuo Liu; Binbin He; Xingwen Quan; Chongbo Wen; Xiaofang Liu. Estimation of Wildfire Spread Rate from Geostationary Satellite Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 5457 -5460.
AMA StyleXiangzhuo Liu, Binbin He, Xingwen Quan, Chongbo Wen, Xiaofang Liu. Estimation of Wildfire Spread Rate from Geostationary Satellite Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():5457-5460.
Chicago/Turabian StyleXiangzhuo Liu; Binbin He; Xingwen Quan; Chongbo Wen; Xiaofang Liu. 2018. "Estimation of Wildfire Spread Rate from Geostationary Satellite Data." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 5457-5460.