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Straw burning is a significant source of atmospheric pollutants, releasing particulate matter and trace gases. Capturing the characteristics of straw burning and understanding its influencing factors are important prerequisites for regulating straw burning. Based on the fire points detected by the Moderate-resolution Imaging Spectroradiometer (MODIS) in Hunan province, China, from 2010 to 2020, this study analyzed the spatiotemporal variations of straw burning and employed Geographically Weighted Regression (GWR) models to investigate the underlying forces driving straw burning. The results show that the spatiotemporal characteristics of straw burning in Hunan Province can be categorized into two distinct periods: 2010 to 2014 and 2015 to 2020. The period from 2010 to 2014 witnessed a rapid increase, while the period from 2015 to 2020 experienced fluctuating decreases. The shift is closely linked to the implementation of the straw burning ban policy in 2015. Spatially, the areas with a high number of fire points are primarily located in the southern regions, especially in the border regions between Chenzhou, Yongzhou, Hengyang, and Shaoyang cities. A significant change was found in the impact of economic development and human activity factors on straw burning before and after 2015. These factors include crop yield, Gross Domestic Product (GDP), and road network development. From the implementation of the straw burning ban policy, increases in GDP and settlement density will have a dampening effect on straw burning in a region. Straw burning locations may shift towards regions with relatively slow economic development. The results could serve as a foundation for decision-making to address the issue of straw burning. [ABSTRACT FROM AUTHOR]

• The spatio-temporal characteristics of forest disturbance in Hunan Province. • The overall accuracy of forest disturbance monitoring in Hunan Province was 86.39%. • Suitable spectral combination for disturbance detection was B5, B7, NBR, TCA and B3. • The disturbance events with patch area greater than 0.45 ha were increasing. Forest disturbance has a profound impact on the ecological function of forests. Although there are already global forest disturbance products, how accurate and whether they can be further improved remains to be seen. Moreover, due to the scarcity of disturbance characteristics (location, size, severity) data, the uncertainty of carbon estimation and the bottleneck of sustainable forest management are formed. In this study, Hunan was used as the research area to explore the most suitable detection methods for subtropical forest disturbance. We used the LandTrendr algorithm to generate a forest disturbance dataset from 1991 to 2021 and analyzed their spatio-temporal variation and disturbance characteristics. The overall accuracy of forest disturbance monitoring in Hunan Province was 86.39%, which was higher than that of Global Forest Change (GFC) products (82.89%). A total of 11103.25 km2 of forest has been disturbed in Hunan Province over the past 30 years, which represents 10.54% of the total forest area. Over the period 1991–2021, the disturbance area generally increased first and then decreased but varies greatly across regions. The maximum disturbance rate of Changsha city occurred in the period from 2006 to 2010 at 0.95%, while that of Zhangjiajie City is only 0.15%, appeared in the period from 2011 to 2015. Both patch size and severity of forest disturbances have shown a gradual upward trend, and the proportion of disturbance events with large areas and high severity is increasing. This study explored suitable spectral index combination for change detection, and then revealed the spatio-temporal characteristics of forest disturbance in the study area, providing important spatio-temporal information of disturbance regime change that is critical for near-real-time adaptive forest management. [ABSTRACT FROM AUTHOR]