Search

An accurate and efficient estimation of eucalyptus plantation areas is of paramount significance for forestry resource management and ecological environment monitoring. Currently, combining multidimensional optical and SAR images with machine learning has become an important method for eucalyptus plantation classification, but there are still some challenges in feature selection. This study proposes a feature selection method that combines multi-temporal Sentinel-1 and Sentinel-2 data with SLPSO (social learning particle swarm optimization) and RFE (Recursive Feature Elimination), which reduces the impact of information redundancy and improves classification accuracy. Specifically, this paper first fuses multi-temporal Sentinel-1 and Sentinel-2 data, and then carries out feature selection by combining SLPSO and RFE to mitigate the effects of information redundancy. Next, based on features such as the spectrum, red-edge indices, texture characteristics, vegetation indices, and backscatter coefficients, the study employs the Simple Non-Iterative Clustering (SNIC) object-oriented method and three different types of machine-learning models: Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM) for the extraction of eucalyptus plantation areas. Each model uses a supervised-learning method, with labeled training data guiding the classification of eucalyptus plantation regions. Lastly, to validate the efficacy of selecting multi-temporal data and the performance of the SLPSO–RFE model in classification, a comparative analysis is undertaken against the classification results derived from single-temporal data and the ReliefF–RFE feature selection scheme. The findings reveal that employing SLPSO–RFE for feature selection significantly elevates the classification precision of eucalyptus plantations across all three classifiers. The overall accuracy rates were noted at 95.48% for SVM, 96% for CART, and 97.97% for RF. When contrasted with classification outcomes from multi-temporal data and ReliefF–RFE, the overall accuracy for the trio of models saw an increase of 10%, 8%, and 8.54%, respectively. The accuracy enhancement was even more pronounced when juxtaposed with results from single-temporal data and ReliefF-RFE, at increments of 15.25%, 13.58%, and 14.54% respectively. The insights from this research carry profound theoretical implications and practical applications, particularly in identifying and extracting eucalyptus plantations leveraging multi-temporal data and feature selection. [ABSTRACT FROM AUTHOR]

Wildfire disasters pose a significant threat to the stability and sustainability of ecosystems. The assessment of wildfire risk based on a seasonal dimension has contributed to improving the spatiotemporal targeting of fire prevention efforts. In this study, Nanning, China, was selected as the research area. The wildfire driving factors were chosen from both seasonal and nonseasonal aspects, and the datasets were divided into five periods: all seasons, spring, summer, autumn, and winter. The light gradient boosting machine (LGBM) was employed to construct wildfire danger models for different periods, evaluating the spatial distribution of high-wildfire-danger areas during these periods and the predictive performance differences. The SHapley Additive exPlanations (SHAP) method was utilized to analyze the differential contributions of various factors to wildfire occurrence in different seasons. Subsequently, the remote sensing ecological index (RSEI) was calculated using four indicators, greenness, heat, wetness, and dryness, to assess the ecological vulnerability in different seasons. Finally, by integrating danger and vulnerability information, wildfire risk models were developed to systematically assess the risk of wildfire disasters causing losses to the ecological environment in different seasons. The results indicate that: (1) The evaluation of wildfire danger based on individual seasons effectively compensates for the shortcomings of analyzing danger across all seasons, exhibiting higher predictive performance and richer details. (2) Wildfires in Nanning primarily occur in spring and winter, while the likelihood of wildfires in summer and autumn is relatively lower. In different seasons, NDVI is the most critical factor influencing wildfire occurrence, while slope is the most important nonseasonal factor. The influence of factors varies among different seasons, with seasonal factors having a more significant impact on wildfire danger. (3) The ecological vulnerability in Nanning exhibits significant differences between different seasons. Compared to spring and winter, the ecological environment is more vulnerable to wildfire disasters during summer and autumn. (4) The highest wildfire risk occurs in spring, posing the greatest threat to the ecological environment, while the lowest wildfire risk is observed in winter. Taking into account information on danger and vulnerability in different seasons enables a more comprehensive assessment of the risk differences in wildfire disasters causing ecological losses. The research findings provide a scientific theoretical basis for relevant departments regarding the prevention, control, and management of seasonal wildfires. [ABSTRACT FROM AUTHOR]

Research on wildfire risk can quantitatively assess the risk of wildfire damage to the population, economy, and natural ecology. However, existing research has primarily assessed the spatial risk of wildfires across an entire region, neglecting the impact of different land-use types on the assessment outcomes. The purpose of the study is to construct a framework for assessing wildfire risk in different land-use types, aiming to comprehensively assess the risk of wildfire disasters in a region. We conducted a case study in Central China, collecting and classifying historical wildfire samples according to land-use types. The Light Gradient Boosting Machine (LGBM) was employed to construct wildfire susceptibility models for both overall and individual land-use types. Additionally, a subjective and objective combined weighting method using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) was utilized to build the wildfire vulnerability model. By integrating susceptibility and vulnerability information, we comprehensively assessed the combined risk of wildfire disasters across land-use types. The results demonstrate the following: (1) Assessing wildfire susceptibility based on different land-use types compensated for limitations in analyzing overall wildfire susceptibility, with a higher prediction performance and more detailed susceptibility information. (2) Significant variations in wildfire susceptibility distribution existed among different land-use types, with varying contributions of factors. (3) Using the AHP-EWM combined weighting method effectively addressed limitations of a single method in determining vulnerability. (4) Land-use types exerted a significant impact on wildfire risk assessment in Central China. Assessing wildfire risk for both overall and individual land-use types enhances understanding of spatial risk distribution and specific land use risk. The experimental results validate the feasibility and effectiveness of the proposed evaluation framework, providing guidance for wildfire prevention and control. [ABSTRACT FROM AUTHOR]