Wildfire spread is affected by various factors including weather, fuel, topography, and human intervention. Previous studies have focused on wildfire probability modeling for purposes of wildfire management, with less attention paid to potential wildfire behavior characteristics such as wildfire speed and intensity. Remote sensing technology has excellent advantages in deriving the characteristics and fuel variables. This study aimed to model these characteristics for wildfire hazard assessment in the Yunnan Province of China. The random forest (RF) model and the extreme gradient boosting (XGBoost) model, were selected to establish the potential wildfire behavior characteristics (PWBC) models based on explanatory variables. The results verified that elevation, fuel moisture content, and infrastructure variables played a more significant role in the models. The RF-based models performed better than the XGBoost-based ones with higher overall accuracy (≥0.83) and kappa coefficient (≥0.79), indicating the effectiveness of predicting potential wildfire behavior characteristics to assess wildfire hazards.

Accurate estimation of potential fire intensity (PFI) can improve wildfire management. The PFI can be simulated by fire spread models, but with immeasurable uncertainties. There are also some difficulties in estimating PFI with multi-source drivers, since the fire spread is limited by fire suppression. This study aimed to estimate the probability density of PFI over southwestern China, using time-series fuel and weather data as well as topographic data. The Quantile Regression and Bi-directional Long Short-Term Memory were selected to establish the prediction model of PFI. The results showed that the QR-BiLSTM performed best at the 90% confidence level. The modal PFI values extracted from the estimated probability density were closer to the observed values. This study suggests the potential of probability density estimation of PFI with artificial intelligence, for which improves wildfire risk assessment.

Dead fuel moisture content (DFMC) was usually involved and being an important part in predicting ignition potential, fireline intensity, flame length, and rate of spread. Previous studies focused on model development and paid little attention to large-scale DFMC forecasting using these models, especially process-based models. In this study, we forecast spatial 1-h (fuel with a diameter less than 0.635 cm) and 10-h (fuel with a diameter between 0.635 cm and 2.54 cm) DFMC in 16 days with meteorological variables interpolated from Global Forecast System (GFS). First, we interpolated meteorological variables including air temperature (Tair), relative humidity (RH), wind speed (Ws) and precipitation (P) to 2 km from GFS data (0.25°) for each site with DFMC measurement in Liangshan Yi Autonomous Prefecture. Then, we forecasted DFMC hourly for 96 sites with three process-based models (Simard, Nelson and fuel stick moisture model (FSMM)). Our results show that the FSMM forecasted more accurate DFMC values (1-h: R2=0.54, RMSE=6.58%, MAE=5.63%; 10-h: R2=0.73, RMSE=3.7%, MAE=2.7%) compared to the Nelson and Simard model. Our results suggest that accurate DFMC forecasts from GFS data based on our methods can be used for fire risk assessment and fire behavior prediction.

As climate change intensifies, the increasing frequency of wildfires is attracting attention worldwide. Changes in live fuel moisture content (LFMC), a key factor influencing fire occurrence and spread, are closely related to wildfires. Here, we presented a study to explore the global LFMC dynamic trend between 2000 and 2018 based on the satellite-derived LFMC dataset. We adopted the Mann-Kendall trend test, a non-parametric trend test commonly used in hydrological and climatic analysis, to analyze global LFMC changes. The experiment is divided into two parts: a global analysis of MK trends at the pixel level and regional LFMC annual variation curves. The results show an upward trend in global LFMC from June to August, but a larger area of no clear trend exists in the other months.

Live fuel moisture content (LFMC) is a key variable affecting fire occurrence and is an important precondition for building a fire risk forecasting system. Meteorological indices and soil moisture are commonly used variables for estimating LFMC, however, few studies have focused on their long-term cumulative and lagged effects on LFMC. In this study, we first (1) assessed the lagged effect of meteorology and soil moisture on LFMC, then (2) extracted the time characteristics from the long-term time series of them for estimating LFMC, (3) and ultimately establish an empirical model to achieve the LFMC estimation in the western U.S. The accuracy of the model developed in this study reached 0.57 for overall R2and 27.20% for RMSE. Three different types of vegetation cover were classified and R2and RMSE were 0.61 and 25.94% for shrublands, 0.45 and 25.86% for savanna and 0.57 and 27.62% for grassland.

Rice false smut (RFS) is a late fungal disease mainly occurring on rice panicle in recent years. This research was based on the unmanned aerial vehicle (UAV) hyperspectral remote sensing data. On the basis of genetic algorithm combined with partial least squares to select the feature bands, the correlation coefficient method and Instability Index between Classes method were used to further select the feature bands, which further eliminated 27.78% of the feature bands when the model monitoring accuracy was improved overall. The prediction accuracy of Gradient Boosting Decision Tree model and Random Forest model was the best, which were 85.62% and 84.10% respectively, and the monitoring accuracy was improved by 2.22% and 2.4% compared with that before optimization. Then, based on the UAV hyperspectral data and the characteristic bands, the sensitive band ranges of rice false smut monitoring were determined, which were 698nm-750nm and 974nm-984nm.

Automatic extraction of crop row centerline is an important technology for agricultural automation, and it has a wide range of applications in automated operations, such as automatic agricultural navigation, automatic harvesting, automatic weeding and automatic seedling replenishment. In this study, the method of row centerline detection is proposed by combining image segmentation and the technique of feature point extraction, and it is applied to the extraction of corn missing seedling locations. Firstly, image segmentation is performed by combining the improved vegetation index ExGG and a double-threshold algorithm (the OTSU method combined with the Particle Swarm Optimization algorithm), and most of the pseudo-feature points are removed using median filtering to initially separate corn seedlings from weeds and soil. Then, the number of crop rows is obtained using the vertical projection method; the micro-region of interest(micro-ROI) is used to find the center of mass and extract the feature points. Finally, the remaining pseudo-feature points are removed by the location clustering method, and the crop row centerline is fitted using the linear regression method of least squares. This study extracts the location and number of missing seedlings of corn based on the information from the row centerline, providing technical support for the subsequent seedling replenishment operation. The experimental results show that the accuracy of the proposed method for detecting the centerline of corn seedling rows is 0.016°, which is better than the Hough transform.

Understanding the extent to which climate affects interannual wildfire variability is key to project and mitigate wildfire. However, obtaining a robust quantification of the climate-wildfire relationship at large spatial scales remains challenging. This study employed hierarchical Bayesian framework to estimate the effect of drought on forest wildfire frequency in subtropical China. We quantified the drought-wildfire relationship across the subtropical China that the probability of excess wildfire (i.e., above normal wildfire activity level) shown disproportionate growth from 2.4% to 76.7% when vapor pressure deficit (VPD) increased from -3 to 3 (z-score). The extreme wildfires only occurred when VPD exceeds 0.5 (z-score). Our results suggest that Bayesian hierarchical model performs better in quantifying the impact of drought on wildfire by taking into account spatiotemporal heterogeneity of climate-wildfire relationship.

Dead fuel moisture content (DFMC) is of important significance for estimating and predicting forest wildfire risk, in which 1-h dead fuel is most critical as the easiest fuel to ignite. Current methodologies based on empirical and physical models rely heavily on meteorological data from uneven and sparse stations. In contrast, satellite data become a better choice since its continuous surface observations. Of all satellites, Himawari-8 is the most appropriate data to meet the rapid changes of DFMC throughout the day owing to its high time resolution. Thus, this study explored the application of 1-h DFMC predicting using machine learning from Himawari -8 data. Random forest was selected for the prediction and linear regression was used for comparison. The results showed that random forest has a satisfactory performance with higher R2 (0.53) and lower RMSE (3.15%) than that of linear regression (R2=0.21, RMSE=5.47%). The research suggested that predicting 1-h DFMC at regional scales using machine learning from Himawari -8 data is promising.