Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and their sustainability. In addition, they have a strong impact on the global carbon balance and, ultimately, on climate change. This review attempts to provide a comprehensive meta-analysis of studies on remotely sensed methods and data used for estimation of forest burnt area, burn severity, post-fire effects, and forest recovery patterns at the global level by using the PRISMA framework. In the study, we discuss the results of the analysis based on 329 selected papers on the main aspects of the study area published in 48 journals within the past two decades (2000–2020). In the first part of this review, we analyse characteristics of the papers, including journals, spatial extent, geographic distribution, types of remote sensing sensors, ecological zoning, tree species, spectral indices, and accuracy metrics used in the studies. The second part of this review discusses the main tendencies, challenges, and increasing added value of different remote sensing techniques in forest burnt area, burn severity, and post-fire recovery assessments. Finally, it identifies potential opportunities for future research with the use of the new generation of remote sensing systems, classification and cloud performing techniques, and emerging processes platforms for regional and large-scale applications in the field of study.

Land use/cover change (LULCC) is an integral part of global environmental change and is influenced by both natural and socioeconomic factors. This study aims to comprehensively analyze land use and land cover (LULC) in Kwazulu-Natal and Mpumalanga provinces in eastern South Africa from 1995 to 2020 and to identify the driving force behind LULCC. Utilizing Landsat series satellite imagery as a data source and based on the Google Earth Engine (GEE) platform and eCognition software 9.0, two different classification methods, pixel-based classification and object-oriented classification, were adopted to gather LULC data every five years. The spatiotemporal characteristics of the data were then analyzed. Using an optimal parameter-based geodetector (OPGD), this study explored the driving factors of LULCC in this region. The results show the following: (1) Of the two classification methods examined, the object-oriented classification had higher accuracy, with an overall accuracy of 80–90%. The pixel-based classification had lower accuracy, with an overall accuracy of 62.33–72.14%. (2) From 1995 to 2020, the area of farmland in the study area showed a fluctuating increase, while the areas of forest and grassland declined annually. The area of constructed land increased annually, whereas the areas of water and unused land fluctuated over time. (3) Socioeconomic factors generally had greater explanatory power than natural factors, with population growth and economic development being the main drivers of LULCC in the region. This study provides a reliable scientific basis for the formulation of sustainable land resource development strategies in the area, as well as for the management and implementation of urban and rural planning, ecological protection, and environmental governance by relevant departments.

Remote sensing study of soil hazards for Odendaalsrus in the Free State Province