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.

Since the modern industrial revolution, the speed of urbanization has accelerated, resulting in a series of ecological environment problems. Fujian province will begin to implement the policy of strengthening the provincial capital in October 2021. Therefore, it is crucial to analyze the ecological maintenance status of Fuzhou and provide a relevant reference for Fuzhou to be built into a national central city. This article takes Fuzhou city as the research area, collects land use data in 2000, 2010, and 2020 from Globeland30, combines remote sensing image data, statistical yearbook data, and Digital Elevation Model (DEM) data, and based on Pressure-State-Responses (PSR) model. A total of 15 indicators were selected to construct the ecological security evaluation system of Fuzhou in 2000, 2010, and 2020, and the weight of each indicator was determined by analytic hierarchy process. Spatial autocorrelation was used to analyze the correlation degree of the land use ecological security index in Fuzhou. The concept of ecological maintenance was introduced to analyze the status of ecological maintenance in Fuzhou during the third period. Finally, the diagnostic model of obstacle factors was used to determine the main obstacle factors affecting ecological security in Fuzhou. The results showed that from 2000 to 2020, the area of cultivated land, grassland and bare land decreased year by year, and transferred 320.63 km², 25.34 km² and 9.85 km² to the artificial surface respectively. The high-value ecological security indexes in 2000, 2010 and 2020 were 27.54%, 28.67% and 30.93% respectively. Therefore, the overall level of ecological security in Fuzhou has improved, but there is a large gap in local ecological security. The area with moderate ecological maintenance degree accounted for 60.15%. From the perspective of the whole city, the very poor ecological maintenance degree mainly concentrated in the downtown area and Pingtan Island, while the very good ecological maintenance degree accounted for very little, mainly distributed in the northwest of Fuzhou. The ecological security index showed the characteristics of high and low-value agglomeration, and the distribution of four clusters in three periods was similar. The main obstacle factors of ecological security in three periods are highway density, population density, surface temperature, intensity of human interference, landscape diversity, GDP per capita, and regional development degree. The ecological security evaluation and ecological maintenance of Fuzhou city is beneficial to provide an efficient decision support tool for the ecological environment protection of Fuzhou City.

Despite the recent progress on Ecological Vulnerability studies by developing index models, similar studies on the coastal areas are still rare. Pingtan’s main island of Fujian Province in China and the coastal area of Nile Delta in Egypt, have witnessed unprecedented development associated with fragile coastal areas. SRP model is a comprehensive model, utilized to evaluate the ecological vulnerability to provide a solid reference for the development of SRP model development. Research results indicated that the ecological vulnerability of the coastal areas of Nile Delta was slightly better than that of Pingtan’s main island in 2018, the former was almost at a micro level, while the latter was almost at a mild level (micro and mild vulnerable area accounted for 65.99%). Pingtan’s main island, showed a slight deterioration trend from west to east, and the characteristics of massive regional distribution, with a slight deterioration of the overall ecological environment of the planning area from 2013 to 2018. For the coastal area of Nile Delta, the ecological vulnerability was gradually increasing from the middle of the basin to the periphery, and the extremely vulnerable area was mainly distributed in the northern coastal area and the arid desert areas from 2018 to 2019. Moreover, according to the statistics results about the maximum weight of ecological pressure was 0.4989 on Pingtan’s main island and 0.4960 in the coastal area of Nile Delta, ecological sensitivity and pressure played a vital role in ecological vulnerability and spatial pattern of two study areas, which contain climate (mainly involving temperature, precipitation), vegetation, and human activities (mainly involving land-use, economy, and population). The current study not only confirmed the close relationship between ecological vulnerability, natural attributes, and human activities, also showed the strong applicability of SRP model in the coastal ecological fragile area.

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.

Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit of an optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the random forest (RF) and classification and regression tree (CART). The results indicated that the RF model has good prediction performance with corresponding R² and root-mean-square error (RMSE) values of 0.96 and 0.91 mg·g⁻¹, respectively. The distribution of SOC content showed variability across landforms (CV = 78.67%), land use (CV = 93%), and lithology (CV = 64.67%). Forestland had the highest SOC (13.60 mg·g⁻¹) followed by agriculture (10.43 mg·g⁻¹), urban (9.74 mg·g⁻¹), and water body (4.55 mg·g⁻¹) land uses. Furthermore, soils developed in bauxite and laterite lithology had the highest SOC content (14.69 mg·g⁻¹). The SOC content was remarkably lower in soils developed in sandstones; however, the values obtained in soils from the rest of the lithologies could not be significantly differentiated. The mean SOC concentration was 11.70 mg·g⁻¹, where the majority of soils in the study area were classified as highly humus and extremely humus. The soils with the highest SOC content (extremely humus) were distributed in the mountainous regions of the study area. The biophysical land surface indices, brightness removed vegetation indices, topographic indices, and soil spectral bands were the most influential predictors of SOC in the study area. The spatial variability of SOC may be influenced by landform, land use, and lithology of the study area. Remotely sensed predictors including land moisture, land surface temperature, and built-up indices added valuable information for the prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm subtropical urban regions.