A5065238728- Remote Sensing in Agriculture
- Fire effects on ecosystems
- Remote Sensing and LiDAR Applications
- Plant Water Relations and Carbon Dynamics
- Atmospheric and Environmental Gas Dynamics
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Landslides and related hazards
- Remote Sensing and Land Use
- Land Use and Ecosystem Services
- Soil Moisture and Remote Sensing
- Hydrology and Drought Analysis
- Species Distribution and Climate Change
- Cryospheric studies and observations
- Fire Detection and Safety Systems
- Remote-Sensing Image Classification
- Fire dynamics and safety research
- Hydrology and Watershed Management Studies
- Climate variability and models
- Leaf Properties and Growth Measurement
- Rangeland and Wildlife Management
- Mobile and Web Applications
- Computational Physics and Python Applications
- Climate change and permafrost
- Urban Heat Island Mitigation
- Automated Road and Building Extraction
Huzhou University
2021-2024
University of Electronic Science and Technology of China
2015-2024
In this study, the Standardized Precipitation Evaporation Index (SPEI) was applied to characterize drought conditions in Southwest China from 1982–2012. The SPEI calculated by precipitation and temperature data for various accumulation periods. Based on SPEI, multi-scale patterns, trend, spatio-temporal extent of were evaluated, respectively. results explicitly showed a drying trend China. mean values at five time scales all decreased significantly. Some moderate severe droughts captured...
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 global scale 500 m pixel resolution from Moderate Resolution Imaging Spectroradiometer (MODIS) radiative transfer models (RTMs) inversion techniques. Firstly, multi-source information parameterized PROSPECT-5 (leaf level), 4SAIL (grass shrub canopy level) GeoSail (tree RTMs to generate three look-up tables (LUTs). Each LUT contained most...
Abstract Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, 2.0 incorporates 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within provides...
Retrieval of vegetation parameters from remotely sensed data using a radiative transfer model is generally hampered by the ill-posed inverse problem, which dramatically decreases precision level retrieved parameters. The purpose this study was to use Bayesian network-based method allow alleviation problem. This achieved introducing correlations between free into their prior joint probability distribution (PJPD), allowing reduction probabilities unrealistic combinations. Three sampling...
Abstract 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 States America. The contains 161,717 individual records based on situ destructive samples used to measure LFMC, representing amount water plant leaves per unit dry matter. primary goal calibrate validate remote sensing algorithms predict LFMC....
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 still remains unexplored over southwest China ecosystem, an area historically vulnerable to wildfires. To this end, we took 10-years dynamics retrieved from Moderate Resolution Imaging Spectrometer (MODIS) reflectance product using physical Radiative Transfer Model (RTM) and events extracted MODIS Burned Area (BA) explore relations...
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 remote sensing data from multiple sensors to derive FMC; however, little attention been devoted C-band Sentinel-1A data. In this study, we aimed test performance for multi-temporal retrieval forest FMC by coupling bare soil backscatter linear model with vegetation water cloud (WCM). This coupled that linked observed directly FMC, was firstly calibrated...
Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which 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 burned area at the large spatial scale. However, daily revisit cycles make them inherently unable extract FSR in near real-time (hourly or less). We argue that Himawari-8, next generation geostationary satellite with...
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+ 8 OLI data retrieval using radiative transfer model (RTM) machine learning method. To this end, GeoSail, SAIL, PROSPECT RTMs were first coupled together...
This study presents a method to assimilate leaf area index (LAI) retrieved from MODIS data using physically based into soil-water-atmosphere-plant (SWAP) model estimate the aboveground dry biomass of grass in Ruoergai grassland, China. The assimilation consists reinitializing with optimal input parameters that allow better temporal agreement between LAI simulated by SWAP and LA! data. minimization is performed four-dimensional variational (4D-VAR) algorithm but which challenged development...
Synthetic Aperture Radar (SAR) texture has been demonstrated to have the potential improve forest biomass estimation using backscatter. However, forests are 3D objects with a vertical structure. The strong penetration of SAR signals means that each pixel contains contributions all scatterers inside canopy, especially for P-band. Consequently, traditional derived from images is affected by heterogeneity, although influence on texture-based not yet explicitly explored. To separate and explore...
In the absence of historical fire records, end-users intend to adopt free satellite-derived products, including global burn area (BA) and active (AF) understand dynamics for better forest management. Previous literature evaluated accuracy these products in regions with different environments, but no study performance fire-prone, cloudy mountainous areas. This contributed filling this gap, through first evaluation four broadly used products: MODIS-based MCD64A1, MCD14ML, VIIRS-based...
In 2018, the megafire episodes on record occurred in California, causing a large number of civilian deaths and damages. As an important part "fire environment triangle," fuel moisture content (FMC) both live (LFMC) dead (DFMC) vegetation were broadly accepted as drivers wildfire ignition spread, but their effects 2018 megafires California less explored. Here, we explored compared LFMC DFMC allowing for highlighting role different types FMC risk assessment. The was collected from global...
Live fuel moisture content (LFMC) is a crucial variable affecting the ignition potential of shrublands. Different remote sensing-based models (either empirical or physical) have been adopted to estimate LFMC in shrublands but with mixed success potentially owing differences vegetation cover (closed vs. open shrublands). This study aimed evaluate and discuss estimation closed using different sensing approaches. For each case, three broadly used radiative transfer (RTMs) (PROSAILH, PROGeoSail,...
The selection of unburned labels is a crucial step in machine learning modelling wildfire occurrence probability. However, the effect different sampling strategies on performance methods has not yet been thoroughly investigated. Additionally, whether ratio burned to should be balanced or imbalanced remains controversial issue. To address these gaps literature, we examined effects four broadly used for label selection: (1) random areas, (2) areas with only one fire event, (3) barren and (4)...
Abstract Wildfire occurrence is attributed to the interaction of multiple factors including weather, fuel, topography, and human activities. Among them, weather variables, particularly temporal characteristics variables in a given period, are paramount predicting probability wildfire occurrence. However, rainfall has large influence on if they derived from fixed introducing additional uncertainties modeling. To solve problem, this study employed continuous nonprecipitation days as...