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.

The unprecedented regional urbanization has brought great pressure on the ecological environment. Building an ecological security pattern and guide regional land and space development is an important technique to ensure regional ecological security and stability to achieve sustainable development. In this study, the Pingtan Island of China and the Durban city of South Africa were chosen as case study area for a comparative study of different scales. The importance of ecosystem services and ecological sensitivity were evaluated, respectively. The core area of landscape which is vital for ecological function maintenance was extracted by morphological spatial pattern analysis (MSPA) and landscape connectivity analysis. Furthermore, the ecological sources were determined by combining the results of ecological protection redline delimitation and core area landscape extraction. The potential ecological corridors were identified based on the minimum cumulative resistance model, and the ecological security pattern of study areas was constructed. The results showed that the ecological protection redline areas of Pingtan and Durban were 42.78 km² and 389.07 km², respectively, which were mainly distributed in mountainous areas with good habitat quality. Pingtan ecological security pattern is composed of 15 ecological sources, 16 ecological corridors, 10 stepping stone patches and 15 ecological obstacle points. The total length of corridors is 112.23 km, which is radially distributed in the form of “one ring, three belts”. The ecological security pattern of Durban is composed of 15 ecological sources, 17 ecological corridors, 11 stepping stone patches and 18 ecological obstacle points. The total length of corridors is 274.25 km, which is radially distributed in the form of “two rings and three belts”. The research results can provide an important reference for the land space construction planning and ecological restoration projects in Pingtan and Durban.

Forests of Middle Povolzhje in the Russian Federation were seriously affected by severe wildfires in 2010. The importance of accurate estimation of burn severity levels is because fire-affected areas may have important societal, ecological and economic consequences to the region. The aim of the study was to explore the performance of the differenced Normalized Burn Ratio (dNBR) and Composite Burn Index (CBI) to evaluate the burnt forest areas of the 2010 wildfires in the Republics of Mari El and Chuvashia of the Russian Federation with the use of Landsat-5 Thematic Mapper (TM) time series images. In total, 625 forest sites were sampled for ground-based burn severity data following the CBI protocol. Both datasets for Mari El and Chuvashia were statistically similar with correlation coefficients of 0.87, which confirms pooling them into a single dataset for the forests of Middle Povozhje. A non-linear third-degree polynomial model form of third order best represented the relationship (R2 = 0.79) between the dNBR and CBI data. Our model estimates that the total forest burnt area on the study area was 113 000 ha in 2010, mostly in coniferous stands. Almost half (45.9% or 51 900 ha) of the burnt forest areas were classified in the high-severity category. The accuracy assessment shows that severity classification results were accurate for estimating burn severity levels, with both producer’s and user’s accuracies higher than 80% for the unburned, low and high classes. Lower user and producer accuracies were noticed for the moderate class of burn severity.

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.

Healthy ecosystems are the basis of social and economic development. It is of great significance to conduct ecological security assessments in rapidly urbanization areas. Based on the driving forces, pressure, state, impact, and response (DPSIR) model, five years (1995, 2000, 2005, 2010 and 2015) of remote sensing images, social and economic statistics, and field survey data were used to establish an ecological security assessment index system. The ecological security assessment of central Yunnan Province (CYP) urban agglomeration was conducted at the 1 km × 1 km pixel scale and at the county scale based on the multilevel weighted comprehensive index method. The results showed that: (1) With 2005 as the turning point, the ecological security situation in CYP first decreased and then increased. (2) The ecological security at the county scale was mainly categorized as unsafe. At the pixel scale, ecologically unsafe and relatively unsafe areas were mainly distributed in central, northern, and western CYP. (3) The ecological security deterioration and strengthened spatial distribution differences were caused by habitat fragmentation, different physical geographical conditions, and population agglomeration. These results can provide a basis for the coordination and sustainability of economic development and environmental protection in urban agglomerations with rapid urbanization.