Multi-Temporal Remote Sensing Satellite Data Analysis for the 2023 Devastating Flood in Derna, Northern Libya

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Soil organic matter (SOM) is a key soil property used to predict the impacts of land-use changes as well as to indicate soil health status. The vegetative indices (VI) derived from remote-sensing data, such as soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) are important indices reflecting crop growth and biomass. They can be related to soil organic carbon changes in large-scale environments. However, there is little information in the literature about the relationship between VI and soil carbon changes over the transition of different farming practices from degraded grassland to an agroforestry system. This study aimed to determine the relationship between soil C stocks and VI over the transition of degraded grassland (DGL) to agroforestry (AgrfS) in the Brazilian Cerrado. Soil organic stocks, NDVI, SAVI and NDMI were measured from 2011 to 2015, when the area passed from a low-productivity grassland, followed by a crop-pasture intercropping system and an agroforestry system. The transitional phases from degraded grassland to agroforestry system promoted surprisingly large gains in soil carbon stocks, ranging from 41.7 Mg C ha−1 in DGL to 68.4 Mg C ha−1 in AgrfS. Similar to soil carbon stocks, the mean VI values increased over the years from DGL to AgrfS, indicating the importance of vegetation indices to predict soil carbon stocks changes over the restoration of degraded grasslands.

This research offers a fresh understanding of desertification in Turkmenistan by utilizing satellite remote sensing data and machine learning techniques. With 80% of its area covered by desert, Turkmenistan has particular difficulties as a result of the harsh effects of desertification, which are made worse by climate change and irresponsible land use. Despite the fact that desertification has been the subject of numerous studies conducted worldwide, this study is among the first to use a multi-index approach to specifically focus on Turkmenistan. It does this by integrating six important desertification indicators within machine learning models like random forest (RF), eXtreme Gradient Boosting (XGBoost), naïve Bayes (NB), and K-nearest neighbors (KNN). These indicators include the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Moisture Index (NDMI), Bare Soil Index (BSI), Enhanced Vegetation Index (EVI), and land surface temperature (LST). Based on Sentinel-2 satellite data processed by the Google Earth Engine (GEE) platform, the findings show that the country’s northern, central, and eastern regions are undergoing severe desertification. Moreover, RF and XGBoost performed better than the straightforward models like NB and KNN in terms of accuracy (96% and 96.33%), sensitivity (both 100%), and kappa (0.901 and 0.9095). By concentrating on Turkmenistan, this study fills a significant gap and provides a framework for tracking desertification in similar regions around the world.

Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Google Earth Engine

Vegetation indices (VIs) are crucial instruments for evaluating and tracking vegetation temporal changes using satellite data to promote sustainable land and environmental management. Diyala governorate in central Iraq is a strategically significant area that can be used as a model to evaluate vegetation dynamics and environmental change due to its various biological regions and semi-arid weather. Thus, this research aims to monitor vegetation cover in Diyala region of Iraq using satellite images during the period from 2000 to 2024. Two types of Landsat images, Enhanced Thematic Mapper (ETM) and Landsat 8, were used. To extract vegetation cover in the study area, multiple indices were used, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Soil Adjusted Vegetation Index (SAVI), and Transformed Normalized Difference Vegetation Index (TNDVI). The results illustrated that there was an increase in vegetation cover from approximately 12% to 62% during the period from 2000 to 2024, respectively. The water area increased approximately from 2% to 4%, while bare lands decreased approximately from 86% to 34% for the years 2000 and 2024. The percentages of vegetation cover, extracted from VIs, were closer to the results of supervised classification.

Remote sensing of urban heat islands(UHIs) has traditionally used the Normalized Difference Vegetation Index(NDVI) as the indicator of vegetation abundance to estimate the relationship land surface temperature(LST) and vegetation.This study investigates the applicability of Normalized Difference Moisture Index(NDMI) as an alternative indicator.This paper compares the Normalized Difference Vegetation Index(NDVI) and Normalized Difference Moisture Index(NDMI) as indicators of surface urban heat island effects in Landsat imagery by investigating the relationships between the Land Surface Temperature(LST),NDMI,and the NDVI.Landsat Thematic Mapper(TM) and Enhanced Thematic Mapper Plus(ETM+) data were used to estimate the LST through the single window algorithm from three different months for the Zhujiang Delta area.Maps of NDVI and NDMI for three different data were generated from band 3,band 4 and band 5 of TM/ETM+ imageries,respectively.The relationships between the LST,NDMI,and the NDVI were analyzed supported by Geographic Information System(GIS).Our analysis indicates that there is stronger linear relationship between LST and NDMI for all three months,whereas the relationship between LST and NDVI is much weaker and varies by different months.With the change of seasons from summer to autumn,the linear correlation relationship between LST and NDMI was gradually lowered.This result suggests that NDMI provides a complementary metric to the traditionally applied NDVI for analyzing LST quantitatively over the three months for surface urban heat island studies using thermal infrared remote sensing in an urbanized environment.

The rapid detection of landslide phenomena that may be triggered by extreme rainfall events is a critical point concerning timely response and the implementation of mitigation measures. The main goal of the present study is to identify susceptible areas by estimating changes in the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Bare Soil Index (BSI), and Synthetic Aperture Radar (SAR) amplitude ratio before and after extreme rainfall events. The developed methodology was utilized in a case study of Storm Daniel, which struck central Greece in September 2023, with a focus on the Mount Pelion region on the Pelion Peninsula. Using Google Earth Engine, we processed satellite imagery to calculate these indices, enabling the assessment of vegetation health, soil moisture, and exposed soil areas, which are key indicators of landslide activity. The methodology integrates these indices with a Weight of Evidence (WofE) model, previously developed to identify regions of high and very high landslide susceptibility based on morphological parameters like slope, aspect, plan and profile curvature, and stream power index. Pre- and post-event imagery was analyzed to detect changes in the indices, and the results were then masked to focus only on high and very high susceptibility areas characterized by the WofE model. The outcomes of the study indicate significant changes in NDVI, NDMI, BSI values, and SAR amplitude ratio within the masked areas, suggesting locations where landslides were likely to have occurred due to the extreme rainfall event. This rapid detection technique provides essential data for emergency services and disaster management teams, enabling them to prioritize areas for immediate response and recovery efforts.

Rabigh Lagoon, located on the eastern coast of the Red Sea, is an ecologically rich zone in Saudi Arabia, providing habitat to Avicennia marina mangrove trees. The environmental quality of the lagoon has been decaying since the 1990s mainly from sedimentation, road construction, and camel grazing. However, because of remedial measures, the mangrove communities have shown some degree of restoration. This study aims to monitor mangrove health of Rabigh Lagoon during the time it was under stress from road construction and after the road was demolished. For this purpose, time series of EVI (Enhanced Vegetation Index), MSAVI (Modified, Soil-Adjusted Vegetation Index), NDVI (Normalized Difference Vegetation Index), and NDMI (Normalized Difference Moisture Index) have been used as a proxy to plant biomass and indicator of forest disturbance and recovery. Long-term trend patterns, through linear, least square regression, were estimated using 30 m annual Landsat surface-reflectance-derived indices from 1986 to 2019. The outcome of this study showed (1) a positive trend over most of the study region during the evaluation period; (2) most trend slopes were gradual and weakly positive, implying subtle changes as opposed to abrupt changes; (3) all four indices divided the times series into three phases: degraded mangroves, slow recovery, and regenerated mangroves; (4) MSAVI performed best in capturing various trend patterns related to the greenness of vegetation; and (5) NDMI better identified forest disturbance and recovery in terms of water stress. Validating observed patterns using only the regression slope proved to be a challenge. Therefore, water quality parameters such as salinity, pH/dissolved oxygen should also be investigated to explain the calculated trends.

Urban green infrastructure plays a crucial role in enhancing environmental resilience in cities, particularly in arid regions characterized by water scarcity, soil salinity, and high climatic stress. However, arid coastal cities remain insufficiently studied with regard to spatially explicit assessments of the structure and dynamics of green infrastructure. This study evaluates the state and spatial organization of urban green infrastructure in Aktau, Kazakhstan, over the period 2015–2025, with the most recent satellite observations obtained in June 2025. Sentinel-2 satellite imagery was used to calculate seasonal Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) values, and zonal statistics were applied to assess intra-urban differentiation across functional zones. In addition, inventory-based indicators—Green Planting Density (GPD), Structural Composition of Greenery (SCG), and Protective Green Infrastructure (PGI)—were integrated to complement the remote sensing analysis. The results indicate a moderate overall increase in mean NDVI values (from 0.21 to 0.28), with the most significant growth observed in central and coastal areas (ΔNDVI = +0.12; ΔSAVI = +0.21), while industrial and newly developed zones exhibit only limited changes. Despite these localized improvements, the spatial configuration of green infrastructure remains fragmented, reflecting a persistent center–periphery asymmetry in urban greening. These results underline the importance of irrigation practices and spatially targeted greening strategies for improving vegetation conditions in arid urban environments. The proposed integrated approach combining satellite-derived vegetation indices and inventory-based indicators can serve as a useful tool for monitoring urban green infrastructure and supporting evidence-based planning in arid coastal cities.

Discrimination of mangrove stage changes is useful for the conservation of this valuable natural resource. However, present-day optical satellite imagery is not fully reliable due to its high sensitivity to weather conditions and tidal variables. Here, we used the Vertical Transmit—Vertical Receive Polarization (VV) and Vertical Transmit—Horizontal Receive Polarization (VH) backscatter from the same and multiple-incidence angles from Sentinel-1 SAR C-band along with Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), Normalized Difference Red Edge (NDVIRE) and Chlorophyll Index Green (CIGreen) from the optical satellite imageries from Sentinel-2 to discriminate between the changes in disturbance, recovery, and healthy mangrove stages in Samut Songkhram province, Thailand. We found the mean NDVI values to be 0.08 (±0.11), 0.19 (±0.09), and −0.53 (±0.16) for the three stages, respectively. We further found their correlation with VH backscatter from the multiple-incidence angles at about −17.98 (±2.34), −16.43 (±1.59), and −13.40 (±1.07), respectively. The VH backscatter from multiple-incidence angles was correlated with NDVI using Pearson’s correlation (𝑟2 = 0.62). However, Pearson’s correlation of a single plot (ID2) of mangrove stage change from disturbance to recovery, and then on to the healthy mangrove stage, displayed a 𝑟2 of 0.93 (p value is less than 0.0001, n = 34). This indicated that the multitemporal Sentinel-1 C-band backscatter and Sentinel-2 data could be used to discriminate mangrove stages, and that a reduced correlation to significant observations was the result of variations in both optical and SAR backscatter data.

A strong earthquake with magnitude 7.7 that shook the Indian Province of Gujarat on the morning of January 26, 2001 caused wide spread destruction and casualties. Earthquakeinduced ground failures, including liquefaction and lateral spreading, were observed in many areas. Optical remote sensing offers an excellent opportunity to understand the post-earthquake effects both qualitatively and quantitatively. The impact of using conventional indices from Landsat-7 temporal images for the liquefaction is empirically investigated and compared with class-based sensor independent (CBSI) indices, while applying possibilistic fuzzy classification as a soft computing approach via supervised classification. Five spectral indices, namely simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) are investigated to identify liquefaction using temporal multi-spectral images. A soft-computing based fuzzy algorithm, which is independent of statistical distribution data assumption, is used to extract a single land cover class from remote sensing multi-spectral images. The result indicates that appropriately used indices can incorporate temporal variations, while extracting liquefaction with soft computing techniques for coarser spatial resolution with temporal remote sensing data. It is found that CBSI-NDVI with temporal data was good for extraction liquefaction while CBSI-TNDVI with temporal data was good for extraction water bodies.

Irrigation areas in arid regions are vital production areas for grain and cash crops worldwide. Grasping the temporal and spatial evolution of planting configurations across several years is crucial for effective regional agricultural and resource management. In view of problems such as insufficient optical images caused by cloudy weather in arid regions and the unclear spatiotemporal evolution patterns of the planting structures in irrigation areas over the years, in this study, we took the Santun River Irrigation Area, a typical arid region in Xinjiang, China, as an example. By leveraging long time-series remote sensing images from Sentinel-1 and Sentinel-2, the spectral, index, texture, and polarization features of the ground objects in the study area were extracted. When analyzing the index characteristics, we considered several widely used global vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and Global Environment Monitoring Index (GEMI). Additionally, we integrated the vertical–vertical and vertical–horizontal polarization data obtained from synthetic aperture radar (SAR) satellite systems. Machine learning algorithms, including the random forest algorithm (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM), were employed for planting structure classification. The optimal classification model selected was subjected to inter-annual transfer to obtain the planting structures over multiple years. The research findings are as follows: (1) The RF classification algorithm outperforms CART and SVM algorithms in terms of classification accuracy, achieving an overall accuracy (OA) of 0.84 and a kappa coefficient of 0.805. (2) The cropland area classified by the RF algorithm exhibited a high degree of consistency with statistical yearbook data (R2 = 0.82–0.91). Significant differences are observed in the estimated planting areas of cotton, maize, tomatoes, and wheat, while differences in other crops are not statistically significant. (3) From 2019 to 2024, cotton remained the dominant crop, although its proportional area fluctuated considerably, while the areas of maize and wheat tended to remain stable, and those of tomato and melon showed relatively minor changes. Overall, the region demonstrates a cotton-dominated, stable cropping structure for other crops. The newly developed framework exhibits exceptional precision in categorization while maintaining impressive adaptability, offering crucial insights for optimizing agricultural operations and sustainable resource allocation in irrigation-dependent arid zones.

Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC. A total of sixty (n = 60) soil samples were collected within the research area at 30 cm soil depth and measured for SOC content using the Walkley–Black method. From these samples, 80% were used for model training and 21 auxiliary data were included as predictors. The predictors include effective cation exchange capacity (ECEC), base saturation (BS), calcium to magnesium ratio (Ca_Mg), potassium to magnesium ratio (K_Mg), potassium to calcium ratio (K_Ca), elevation, plan curvature, total catchment area, channel network base level, topographic wetness index, clay index, iron index, normalized difference build-up index (NDBI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and land surface temperature (LST). Mean absolute error (MAE), root-mean-square error (RMSE) and R2 were used to determine the model performance. The result showed the mean SOC to be 1.62% with a coefficient of variation (CV) of 47%. The best performing model was RF (R2 = 0.68) followed by the cubist model (R2 = 0.51), SVM (R2 = 0.36), ANN (R2 = 0.36) and MLR (R2 = 0.17). The soil nutrient indicators, topographic wetness index and total catchment area were considered an indicator for spatial prediction of SOC in flat homogenous topography. Future studies should include other auxiliary predictors (e.g., soil physical and chemical properties, and lithological data) as well as cover a broader range of soil types to improve model performance.

As a major disturbance to forest ecosystems, wildfires pose a serious threat to the ecological environment. Monitoring post-fire vegetation recovery is critical to quantifying the effects of wildfire on ecosystems and conducting forest resource management. Most previous studies have analyzed short-term (less than five years) post-fire recovery and limited the driving factors to temperature and precipitation. The lack of long-term and multi-faceted observational analyses has limited our understanding of the long-term effects of fire on vegetation recovery. This study utilized multi-source remote sensing data for a long time series analysis of post-fire vegetation recovery in China based on Google Earth Engine (GEE) cloud computing platform. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Burn Ratio (NBR), and Normalized Difference Moisture Index (NDMI) were selected to quantify the low, moderate, and high severity of burned areas. Ridge Regression Model (RRM) was used to analyze the relationship between 15 driving factors and the vegetation regeneration process. The results show that it took at least 7–10 years for the vegetation index to recover to the pre-fire level after a forest fire. The recovery rate of high severity combustion areas was the fastest within the first two years. From the results of Ridge Regression, it came out that the overall fitting degree of the model with NDVI as the dependent variable was superior than that with EVI. The four variables of temperature, precipitation, soil temperature, and soil moisture were able to explain the change in more detail in vegetation indices. Our study enriches the research cases of global forest fires and vegetation recovery, provides a scientific basis for the sustainable development of forest ecosystems in China, and provides insight into environmental issues and resource management.

Information on the distribution, area and extent of swidden agriculture landscape is necessary for implementing the program of Reducing Emissions from Deforestation and Forest Degradation (REDD), biodiversity conservation and local livelihood improvement. To our knowledge, explicit spatial maps and accurate area data on swidden agriculture remain surprisingly lacking. However, this traditional farming practice has been transforming into other profit-driven land use, like tree plantations and permanent cash agriculture. Swidden agriculture is characterized by a rotational and dynamic nature of agroforestry, with land cover changing from natural forests, newly-cleared swiddens to different-aged fallows. The Operational Land Imager (OLI) onboard the Landsat-8 satellite has visible, near-infrared and shortwave infrared bands, which are sensitive to the changes in vegetation cover, land surface moisture content and soil exposure, and therefore, four vegetation indices (VIs) were calculated, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), the Normalized Burn Ratio (NBR) and the Soil Adjusted Vegetation Index (SAVI). In this study, we developed a multi-step threshold approach that uses a combination of thresholds of four VIs and local elevation range (LER) and applied it to detect and map newly-opened swiddens and different-aged fallows using OLI imagery acquired between 2013 and 2015. The resultant Landsat-derived swidden agriculture maps have high accuracy with an overall accuracy of 86.9% and a Kappa coefficient of 0.864. The results of this study indicated that the Landsat-based multi-step threshold algorithms could potentially be applied to monitor the long-term change pattern of swidden agriculture in montane mainland Southeast Asia since the late 1980s and also in other tropical regions, like insular Southeast Asia, South Asia, Latin America and Central Africa, where swidden agriculture is still common.