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.

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.

With the development of remote sensing techniques and the increasing need for soil contamination monitoring, we estimated soil heavy metal zinc (Zn) content using hyperspectral imaging. Geographically weighted regression (GWR), an extension of the ordinary least squares (OLS) regression framework, was proposed. By estimating a set of parameters for any number of locations in a study area, GWR can probe the spatial heterogeneity in data relationships, whereas the regression parameters of an OLS model are global and aspatially-varied. The objectives of this study were: (1) To find the possible relationships between hyperspectral data and soil Zn content, and (2) to investigate the existence of their spatial heterogeneity. In this study, 67 soil samples collected from Pingtan Island, Fujian Province, China, were used to conduct laboratory hyperspectral modeling for soil Zn content estimation. Four transformations of square root, logarithm, reciprocal of logarithm, and reciprocal, as well as the fractional-order differential operations were applied to increase the amount of reflectance data in which the effective variables for modeling might be involved, and to enhance the spectral characteristics of soil Zn content. To find sensitive variables and to remove redundancy and multicollinearity in the spectra, a data sifting process was applied by selecting wavelengths with local maximum in the absolute values of the correlation coefficients with Zn content in one type of spectral data and by employing Variance Inflation Factors. Since a modeling sample size of 46 is insufficient to construct the appropriate OLS and GWR models, four methods are proposed using all 67 samples to choose explanatory variables. A random process to select 57 samples for modeling and 10 samples for validation was applied to assess model performance, in which the mean verification R² (R_v²) was used as an indicator. The results show that GWR stepwise regression is the most effective method to select better variables. As the mean R_v² converges toward the OLS value when the bandwidth of the GWR model increases, the four variables selected by the GWR stepwise regression were used to establish the representative OLS and GWR models. The representative OLS model has the best mean verification effect among all studied models, which had a mean R_v² value that is 44.6% higher than the OLS model constructed using OLS stepwise regression.