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Land Surface Changes (LSC) are significantly impacted by dynamic environmental variations, particularly armed conflicts and associat-ed failures, according to a natural perspective. Regarding the current understanding of their relationship, on the other hand, human activi-ties on land usage or land covers may also increase the fluctuations. With a focus on the impacts of environmental restrictions like as land cover, temperature, air pollution, and sustainable water management, this study examines LSC in various metropolitan regions of developing nations. Recently, land cover has changed due to rapid urbanization and environmental pressures such socioeconomic and climatic shifts. We assess patterns of temporal LSC in the central urban regions of under-developing nations using Remote Sensing (RS) technologies, satellite data, and Geographic Information System (GIS). For example, Pakistan’s urbanized land surface is Karachi. We measure and identify the major changes in urban landscapes, as well as the factors related environmental variables, by using Machine Learning-enabled Random Forest Classification (RFC) for image classification and Support Vector Machine for change detection patterns. Critical trends in urban sprawl and deforestation are revealed by the simulation results of the proposed model, underscoring the necessity of sustainable planning for industrialized cities. Furthermore, the outcomes highlight the uniqueness of combining RFC and SVM to categories patterns in terms of change detections from 2000 to 2023, reaching 26.91% and 19.73% higher than previously state-of-the-art techniques.

Synthetic Aperture Radar (SAR) was developed to map the terrain without the use of large antennas. Combined array is one of the radar methods that is applied from an aircraft or a space platform and in which an effective aperture of the antenna is created in a combined manner. The images obtained by these radars are very accurate. On the other hand, the surface of the earth always changes due to various factors. Accurate identification of these changes can be used in many applications, especially in Iraq due to its semidesert structure. In this research, an improved surface change detection algorithm based on morphological transformation, two-stage center-constrained FCM algorithm (TCCFCM) clustering and deep learning is presented. The simulation of the method of this research on MATLAB software shows that the proposed method is fast and inexpensive. The accuracy was 99.7%.

Highland ecologies are the most susceptible to climate change, often experiencing intensified impacts. Due to climate change and human activities, there were dramatic changes in the alpine domain of the China–Pakistan Economic Corridor (CPEC), which is a vital project of the Belt and Road Initiative (BRI). The CPEC is subjected to rapid infrastructure expansion, which may lead to potential land surface susceptibility. Hence, focusing on sustainable development goals, mainly SDG 9 (industry, innovation, and infrastructure) and SDG 13 (climate action), to evaluate the conservation and management practices for the sustainable and regenerative development of the mountainous region, this study aims to assess change detection and find climatic conditions using multispectral indices along the mountainous area of Gilgit and Hunza-Nagar, Pakistan. It has yielded practical and highly relevant implications. For sustainable and regenerative ecologies, this study utilized 30 × 30 m Landsat 5 (TM), Landsat 7 (ETM+), and Landsat-8/9 (OLI and TIRS), and meteorological data were employed to calculate the aridity index (AI). The results of the AI showed a non-significant decreasing trend (−0.0021/year, p > 0.05) in Gilgit and a significant decreasing trend (−0.0262/year, p < 0.05) in Hunza-Nagar. NDVI distribution shows a decreasing trend (−0.00469/year, p > 0.05), while NDWI has depicted a dynamic trend in water bodies. Similarly, NDBI demonstrated an increasing trend, with rates of 79.89%, 87.69%, and 83.85% from 2008 to 2023. The decreasing values of AI mean a drying trend and increasing drought risk, as the study area already has an arid and semi-arid climate. The combination of multispectral indices and the AI provides a comprehensive insight into how various factors affect the mountainous landscape and climatic conditions in the study area. This study has practical and highly relevant implications for policymakers and researchers interested in research related to land use and land cover change, environmental and infrastructure development in alpine regions.

Runoff variations were influenced by climate variability and land surface change, but the mechanism was not clear, and quantification of their effects was not mature. To find a reliable method, we selected four sub-watersheds in Luanhe watershed in which runoff series had significant downward trends. We first calibrated and validated the Soil and Water Assessment Tool (SWAT) hydrological model by using the hydrometeorological data in reference period in the four sub-watersheds. The simulated runoff series agreed well with the observed ones, which demonstrated the model performed well. Then we reconstructed the runoff series in impaired period under 1970 and 2000 land use conditions without check dams. According to the simulation results, the contributions of precipitation variability, land use change and construction of large number of check dams were quantified by traditional method. It was found that precipitation variability was the main cause of runoff decrease. In this study, we proposed a new perception for distinguishing the contributions of land surface change and precipitation variability, and got different results from traditional method. Precipitation variability was the main factor for runoff decrease in Luanhe and Wuliehe sub-watersheds, and land surface change contributed more in Yixunhe and Liuhe sub-watersheds. The combined effects were not the summation of single effect of precipitation variability and land surface change. It is of great necessity to do further research on this issue, and will provide reliable information for water resources managers.

Rapid and uncontrolled urbanization is one of the drivers responsible for land cover Change dynamics in Ethiopia. In most of the cities and towns of Ethiopia, the proportion of different types of urban land surface cover and trends of change in each are unknown and thus hindering environmentally sustainable urban planning. This study describes different land surface cover types and their dynamics of change, and subsequent influence on the Land Surface Temperature of Hawassa city between 2011 and 2021. The i-Tree canopy and Landsat 5 (TM) and Landsat 8 (OLI) images were used for 2011 and 2021 to analyze the land surface cover and surface temperature change, respectively. The results show that bare soil is the dominant land surface cover type (23.4%), followed by tree canopy cover (21.4%), while impervious roads occupied the smallest land surface area cover (3.4%) along with water bodies (1%). In 10 years most of the land surface cover types increased positively, including tree covers by + 9.8%. The only exceptions were bare soil and herbaceous cover, which decreased drastically by − 34.6% and − 2.8%, respectively. As a result of tree cover increment, the average and maximum Land Surface Temperature showed a declining trend between the two periods. This study shows that increasing tree cover in line with the expansion of urbanization is inversely correlated with the land surface temperature which implies that integrating green coverage along with the built-up area can reduce heatwave in the fast growing urban areas. Therefore, the Hawassa city administration should set tree cover targets to achieve the desired balance between green and grey infrastructure and enhance the climate resilience level of the study area.

In this paper, we propose a secure blockchain-aware framework for distributed data management and monitoring. Indeed, images-based data are captured through drones and transmitted to the fog nodes. The main objective here is to enable process and schedule, to investigate individual captured entity (records) and to analyze changes in the blockchain storage with a secure hash-encrypted (SH-256) consortium peer-to-peer (P2P) network. The proposed blockchain mechanism is also investigated for analyzing the fog-cloud-based stored information, which is referred to as smart contracts. These contracts are designed and deployed to automate the overall distributed monitoring system. They include the registration of UAVs (drones), the day-to-day dynamic captured drone-based images, and the update transactions in the immutable storage for future investigations. The simulation results show the merit of our framework. Indeed, through extensive experiments, the developed system provides good performances regarding monitoring and management tasks.

Although desertification has greatly increased across the Mongolian Plateau during the last decades of the 20th century, recent satellite records documented increasing vegetation growth since the 21st century in some areas of the Mongolian Plateau. Compared to the study of desertification, the opposite characteristics of land use and vegetation cover changes and their different effects on regional land–atmosphere interaction factors still lack enough attention across this vulnerable region. Using long-term time-series multi-source satellite records and regional climate model, this study investigated the climate feedback to the observed land surface changes from the 1990s to the 2010s in the Mongolia Plateau. Model simulation suggests that vegetation greening induced a local cooling effect, while the warming effect is mainly located in the vegetation degradation area. For the typical vegetation greening area in the southeast of Inner Mongolia, latent heat flux increased over 2 W/m2 along with the decrease of sensible heat flux over 2 W/m2, resulting in a total evapotranspiration increase by 0.1~0.2 mm/d and soil moisture decreased by 0.01~0.03 mm/d. For the typical vegetation degradation area in the east of Mongolia and mid-east of Inner Mongolia, the latent heat flux decreased over 2 W/m2 along with the increase of sensible heat flux over 2 W/m2 obviously, while changes in moisture cycling were spatially more associated with variations of precipitation. It means that precipitation still plays an important role in soil moisture for most areas, and some areas would be at potential risk of drought with the asynchronous increase of evapotranspiration and precipitation.

Spatial–temporal changes of land surface parameters (land cover change, net primary production, and vegetation phenology) affect the characteristics of atmospheric dust. This phenomenon subsequently will influence the climate parameters (radiative forcing, temperature, cloud, and precipitation). This research uses time-series data (2005–2015) from Regional Climate Model (RegCM) dust module simulation, WRF-Chem, meteorological data, aerosol optical depth, soil moisture, MODIS-based vegetation phenology, and net primary production to investigate the impacts of land surface parameter change on dust emission and the influence of dust on radiative forcing, heat fluxes, cloud formation, and the amount of precipitation over Southwest Asia. As the findings suggest, the southern regions of the Arabian Peninsula, southeastern Iraq, southwestern Iran, northern Arabian Peninsula, and southeastern Iran are subject to the most potent regional concentration of atmospheric dust. Our results revealed: (i) 12% of the land cover classes in southeastern Iraq and southwestern Iran were transformed into barren lands from 2005 to 2015 and subsequently these areas experienced the highest rate of dust formation, (ii) land surface changes and their consequences for dust formation have affected dust radiative forcing up to 10%, (iii) net-radiative forcing of dust always induces a state of cooling temperatures at surface, (iv) longwave forcing of dust causes a rise in heating impacts at top-of-atmosphere (TOA), so, dust aerosols have a cooling effect at the surface and heating effect at TOA, and (v) dust seems to reinforce cloud cover and cloud perceptible water, while reducing precipitation over Southwest Asia. Graphic abstract

The Yellow River Basin faces water scarcity and ecological fragility. Changes on the land surface, characterized by large‐scale soil and water conservation measures, have a significant impact on river runoff and ecological environment. However, there are still great uncertainties in the scientific understanding of the mechanisms by which multiple driver impact eco‐hydrological processes due to the diversity of land surfaces and the complexity of the coupling processes. As an international scientific frontier on interdisciplinary studies in climatology, hydrology, ecology, and other related fields, it is significant to study the mechanisms and assess the impacts of land surface change on eco‐hydrological risk to support ecological restoration plan and sustainable water resources utilization in the Yellow River Basin. Taking the Yellow River Basin as the study area, this study proposes several important research initiatives, focusing on addressing the ecological and water resources problems in the Loess Plateau. These initiatives include (1) to quantify the individual effect of land surface elements (e.g., vegetation, terraces, and check dam) and reveal the nonlinear driving mechanisms of multiple drivers on eco‐hydrological processes; (2) to construct a distributed eco‐hydrological model that couples dynamic land surface features, and simulate eco‐hydrological processes in a changing environment; (3) to improve the ecological risk assessment indicator system and methods for assessing the impacts of land surface changes on eco‐hydrological synergistic functions and ecological risk; (4) to establish an ecological regulation model based on multiobjective game theory and adopt an adaptive regulation mode for ecological risk management. The research could enrich the scientific understanding and theory of eco‐hydrology, and prompt disciplinary studies of ecology, hydrology, climatology, and other fields. The expected academic achievements will innovate eco‐hydrological simulation and assessment techniques in a changing environment, and strongly support the implementation of the national strategy for ecological protection and high‐quality development in the Yellow River Basin.

The serious soil erosion problems and decreased runoff of the Loess Plateau may aggravate the shortage of its local water resources. Understanding the spatiotemporal influences on runoff changes is important for water resource management. Here, we study this in the largest tributary of the Yellow River, the Weihe River Basin. Data from four hydrological stations (Lin Jia Cun (LJC), Xian Yang (XY), Lin Tong (LT), and Hua Xian (HX)) and 10 meteorological stations from 1961–2014 were used to analyze changes in annual runoff. The Mann–Kendall test and Pettitt abrupt change point test diagnosed variations in runoff in the Weihe River basin; the time periods before and after abrupt change points are the base period (period I) and change period (period II), respectively. Within the Budyko framework, the catchment properties (ω in Fu’s equation) represent land surface changes; climate variability comprises precipitation (P) and potential evapotranspiration (ET0). All the stations showed a reduction in annual runoff during the recording period, of which 22.66% to 50.42% was accounted for by land surface change and 1.97% to 53.32% by climate variability. In the Weihe River basin, land surface changes drive runoff variation in LT and climate variability drives it in LJC, XY, and HX. The contribution of land surface changes to runoff reduction in period I was less than that in period II, indicating that changes in human activity further decreased runoff. Therefore, this study offers a scientific basis for understanding runoff trends and driving forces, providing an important reference for social development, ecological construction, and water resource management.