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Land use and cover change (LUCC) is an important issue affecting the global environment, climate change, and sustainable development. Detecting and predicting LUCC, a dynamic process, and its driving factors will help in formulating effective land use and planning policy suitable for local conditions, thus supporting local socioeconomic development and global environmental protection. In this study, taking Gansu Province as a case study example, we explored the LUCC pattern and its driving mechanism from 1980 to 2018, and predicted land use and cover in 2030 using the integrated LCM (Logistic-Cellular Automata-Markov chain) model and data from satellite remote sensing. The results suggest that the LUCC pattern was more reasonable in the second stage (2005 to 2018) compared with that in the first stage (1980 to 2005). This was because a large area of green lands was protected by ecological engineering in the second stage. From 1980 to 2018, in general, natural factors were the main force influencing changes in land use and cover in Gansu, while the effects of socioeconomic factors were not significant because of the slow development of economy. Landscape indices analysis indicated that predicted land use and cover in 2030 under the ecological protection scenario would be more favorable than under the historical trend scenario. Besides, results from the present study suggested that LUCC in arid and semiarid area could be well detected by the LCM model. This study would hopefully provide theoretical instructions for future land use planning and management, as well as a new methodology reference for LUCC analysis in arid and semiarid regions.

Since the 1950s, noteworthy farmland abandonment has been occurring in many developed countries and some developing countries. This global land use phenomenon has fundamentally altered extensive rural landscapes. A review of global farmland abandonment under the headings of ＂land use change - driving mechanisms - impacts and consequences - policy responses＂ found the following： （1） Farmland abandonment has occurred primarily in developed countries in Europe and North America, but the extent of abandonment has varied significantly. （2） Changing socio-economic factors were the primary driving forces for the farmland abandonment. And land marginalization was the fundamental cause, which was due to the drastic increase of farming opportunity cost, while the direct factor for abandonment was the shrink of agricultural labor forces. （3） Whether to abandon, to what extent and its spatial distributions were finally dependent on integrated effect from the physical conditions, laborer attributes, farming and regional socio-economic conditions at the village, household and parcel scales. With the exception of Eastern Europe, farmland abandonment was more likely to occur in mountainous and hilly areas, due to their unfavorable farming conditions. （4） A study of farmland abandonment should focus on its ecological and environmental effects, while which is more positive or more negative are still in dispute. （5） Increasing agricultural subsidies will be conductive to slowing the rate of farmland abandonment, but this is not the only measure that needs to be implemented. Due to China＇s rapid urbanization, there is a high probability that the rate of abandonment will increase in the near future. However, very little research has focused on this rapid land-use trend in China, and, as a result, there is an inadequate understanding of the dynamic mechanisms and consequences of this phenomenon. This paper concludes by suggesting some future directions for further research in China. These directions include monitoring regional and national abandonment dynamics, analyzing trends, assessing the risks and socio-economic effects of farmland abandonment, and informing policy making.

Dust cycle is actively involved in the Earth’s climate and environmental systems. However, the spatiotemporal pattern and recent trend of dust emission from the drylands in East Asia remain unclear. By calculating dust aerosol optical depth (DOD) from the newly released moderate resolution imaging spectrometer aerosol products, we obtain a relatively long satellite-based time series of dust activity from 2001 to 2020 over China and Mongolia. We identify pronounced interannual variability of dust activity that is consistent with ground-based meteorological observations in the study area. A substantial reduction in spring dust activity in northern China is also found, which seems in accordance with the long-term weakening trend since the 1970s that has been attributed to the wind speed decline by previous studies. However, the spatial pattern of the trends in both annual mean and seasonal dust activity during the last 20 years is divergent, and the most significant dust diminishing is found over north-central China where large-scale vegetation restoration projects have been implemented. It indicates that in addition to the potential contribution of wind speed change, land-use change also plays an important role in the recent inhibition of dust emission. The current results show that dust activity occurs most intensively in spring, followed by summer and relatively weaker in autumn and winter. However, dust activity in autumn and winter has increased significantly in NW China despite the overall decreasing trend in other two seasons, probably associated with different seasonal atmospheric and land surface conditions. Finally, the DOD distribution reveals that the Tarim Basin, Gobi and Qaidam Basin Deserts are three major dust sources in East Asia. Compared to ground observations which are spatially limited and distributed unevenly, remote sensing provides an important complement, and it can serve as reference for identification of dust sources using other methods such as geochemical fingerprint and modeling.

Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has become an underlying cause of LUCC, and more than 55% of the world’s population resides in cities. The speedy growth, development and expansion of urban centers, rapid inhabitant’s growth, land insufficiency, the necessity for more manufacture, advancement of technologies remain among the several drivers of LUCC around the globe at present. In this study, the urban expansion or sprawl, together with spatial dynamics of Hyderabad, Pakistan over the last four decades were investigated and reviewed, based on remotely sensed Landsat images from 1979 to 2020. In particular, radiometric and atmospheric corrections were applied to these raw images, then the Gaussian-based Radial Basis Function (RBF) kernel was used for training, within the 10-fold support vector machine (SVM) supervised classification framework. After spatial LUCC maps were retrieved, different metrics like Producer’s Accuracy (PA), User’s Accuracy (UA) and KAPPA coefficient (KC) were adopted for spatial accuracy assessment to ensure the reliability of the proposed satellite-based retrieval mechanism. Landsat-derived results showed that there was an increase in the amount of built-up area and a decrease in vegetation and agricultural lands. Built-up area in 1979 only covered 30.69% of the total area, while it has increased and reached 65.04% after four decades. In contrast, continuous reduction of agricultural land, vegetation, waterbody, and barren land was observed. Overall, throughout the four-decade period, the portions of agricultural land, vegetation, waterbody, and barren land have decreased by 13.74%, 46.41%, 49.64% and 85.27%, respectively. These remotely observed changes highlight and symbolize the spatial characteristics of “rural to urban transition” and socioeconomic development within a modernized city, Hyderabad, which open new windows for detecting potential land-use changes and laying down feasible future urban development and planning strategies.

Rapid and effective access to the spatiotemporal patterns and evolutionary trends of the regional eco-environment is key to regional environment protection and planning. Based on the Google Earth Engine platform, we use 165 Landsat images from the summer and autumn seasons (May–November) of 2000, 2010, and 2018 as data sources to calculate the RSEI, which represents the quality of the eco-environment, and then analyze the factors influencing the spatial heterogeneity of the eco-environment and the relationship between eco-environment and land-use changes based on RSEI. The results showed the following: (1) From 2000 to 2018, the overall ecological environment quality of the Taihu Lake Basin showed a stage of rapid decline (2000–2010) and a stage of slow decline (2010–2018). (2) The factors were ranked in order of their explanatory power for the spatial heterogeneity of the RSEI: land-use (0.594) > population density (0.418) > slope (0.309) > elevation (0.308) > GDP (0.304) > temperature (0.233) > precipitation (0.208). An interactive effect was found for each factor of the RSEI, which is mainly represented by a mutual enhancement. (3) From 2000 to 2010, the rapid urban expansion was the main reason for the deterioration of ecological quality. From 2010 to 2018, urban expansion slowed down, and the trend of ecological quality deterioration was effectively curbed. Therefore, promoting the intensive use of land and reducing construction land expansion are key to ensuring sustainable regional socio-economic development.

Cunninghamia lanceolata (Lamb.) Hook. (Chinese fir) is one of the main timber species in Southern China, which has a wide planting range that accounts for 25% of the overall afforested area. Moreover, it plays a critical role in soil and water conservation; however, its suitability is subject to climate change. For this study, the appropriate distribution area of C. lanceolata was analyzed using the MaxEnt model based on CMIP6 data, spanning 2041–2060. The results revealed that (1) the minimum temperature of the coldest month (bio6), and the mean diurnal range (bio2) were the most important environmental variables that affected the distribution of C. lanceolata; (2) the currently suitable areas of C. lanceolata were primarily distributed along the southern coastal areas of China, of which 55% were moderately so, while only 18% were highly suitable; (3) the projected suitable area of C. lanceolata would likely expand based on the BCC-CSM2-MR, CanESM5, and MRI-ESM2-0 under different SSPs spanning 2041–2060. The increased area estimated for the future ranged from 0.18 to 0.29 million km2, where the total suitable area of C. lanceolata attained a maximum value of 2.50 million km2 under the SSP3-7.0 scenario, with a lowest value of 2.39 million km2 under the SSP5-8.5 scenario; (4) in combination with land use and farmland protection policies of China, it is estimated that more than 60% of suitable land area could be utilized for C. lanceolata planting from 2041–2060 under different SSP scenarios. Although climate change is having an increasing influence on species distribution, the deleterious impacts of anthropogenic activities cannot be ignored. In the future, further attention should be paid to the investigation of species distribution under the combined impacts of climate change and human activities.

Climate change and human activities both have a considerable impact on runoff depth, which are important parts of a changing ecosystem. Nevertheless, the main focus of hydrological response research has been on investigating the impact of climate change on the depth of runoff. In contrast, there has been limited emphasis on comprehending the precise mechanisms through which changes in land use, in relation to human activities, influence runoff depth. This paper employs the MIKE SHE/MIKE 11 model to simulate surface runoff in the study area from 1980 to 2020, assesses the effects of climate change and land use change on runoff depth using the runoff reduction method, and quantifies the influence of land use change on runoff depth through a spatio-temporal geographically weighted regression model. This study indicates that during the past 40 years, the average runoff depth in the Songnen Plain was 36.26 mm, exhibiting a tendency of ‘increasing-decreasing-increasing’. The impact of climate change on surface runoff depth is more substantial than that of land use change. During the impact period 1, the runoff depth diminished by 19.07 mm, with climate change contributing to a decrease of 15.89 mm (83.31% contribution). In the impact period 2, the runoff depth increased by 7.49 mm relative to the baseline period, with climate change leading to an increase of 12.73 mm (70.84% contribution). Changes in various land types within the watershed can be used to observe the influence of human activities on runoff depth. More precisely, a 10% rise in the rate of change of construction land, dry land, and unoccupied land results in an increase in runoff depth of 6.21 mm, 2.45 mm, and 1.14 mm, respectively. Conversely, a 10% rise in the rate of alteration of marsh, paddy, and forest land leads to a reduction in the depth of runoff by 9.49 mm, 6.46 mm, and 3.07 mm, respectively. This research can contribute to improving the efficiency of water and land resource utilization and optimizing land resource governance.

The significant spatial heterogeneity among river basin ecosystems makes it difficult for local governments to carry out comprehensive governance for different river basins in a special administrative region spanning multi-river basins. However, there are few studies on the construction of a comprehensive governance mechanism for multi-river basins at the provincial level. To fill this gap, this paper took Henan Province of China, which straddles four river basins, as the study region. The chord diagram, overlay analysis, and carbon emission models were applied to the remote sensing data of land use to analyze the temporal and spatial patterns of carbon storage caused by land-use changes in Henan Province from 1990 to 2018 to reflect the heterogeneity of the contribution of the four basins to human activities and economic development. The results revealed that food security land in the four basins decreased, while production and living land increased. Ecological conservation land was increased over time in the Yangtze River Basin. In addition, the conversion from food security land to production and living land was the common characteristic for the four basins. Carbon emission in Henan increased from 134.46 million tons in 1990 to 553.58 million tons in 2018, while its carbon absorption was relatively stable (1.67–1.69 million tons between 1990 and 2018). The carbon emitted in the Huai River Basin was the main contributor to Henan Province’s total carbon emission. The carbon absorption in Yellow River Basin and Yangtze River Basin had an obvious spatial agglomeration effect. Finally, considering the current need of land spatial planning in China and the goal of carbon neutrality by 2060 set by the Chinese government, we suggested that carbon sequestration capacity should be further strengthened in Yellow River Basin and Yangtze River Basin based on their respective ecological resource advantages. For future development in Hai River Basin and Huai River Basin, coordinating the spatial allocation of urban scale and urban green space to build an ecological city is a key direction to embark upon.

Land Use/Cover Change (LUCC) plays a crucial role in sustainable land management and regional planning. However, contemporary feature extraction approaches often prove inefficient at capturing critical data features, thereby complicating land cover categorization. In this research, we introduce a new feature extraction algorithm alongside a Segmented and Stratified Principal Component Analysis (SS-PCA) dimensionality reduction method based on correlation grouping. These methods are applied to UAV LiDAR and UAV HSI data collected from land use types (e.g., residential areas, agricultural lands) and specific species (e.g., tree species) in urban, agricultural, and natural environments to reflect the diversity of the study area and to demonstrate the ability of our methods to be applied in different classification scenarios. We utilize LiDAR and HSI data to extract 157 features, including intensity, height, Normalized Digital Surface Model (nDSM), spectral, texture, and index features, to identify the optimal feature subset. Subsequently, the best feature subset is inputted into a random forest classifier to classify the features. Our findings demonstrate that the SS-PCA method successfully enhances downscaled feature bands, reduces hyperspectral data noise, and improves classification accuracy (Overall Accuracy = 91.17%). Additionally, the CFW method effectively screens appropriate features, thereby increasing classification accuracy for LiDAR (Overall Accuracy = 78.10%), HIS (Overall Accuracy = 89.87%), and LiDAR + HIS (Overall Accuracy = 97.17%) data across various areas. Moreover, the integration of LiDAR and HSI data holds promise for significantly improving ground fine classification accuracy while mitigating issues such as the ‘salt and pepper noise’. Furthermore, among individual features, the LiDAR intensity feature emerges as critical for enhancing classification accuracy, while among single-class features, the HSI feature proves most influential in improving classification accuracy.

Land use/Land cover (LULC) change seriously affects ecosystem services and ecosystem functions. In order to maintain ecological security and orderly social development, habitat quality assessment based on Land use/Land cover change is worth exploring. Based on multi-source land use data and Google remote sensing data from 1985 to 2017, land use transfer matrix and habitat quality index were used to study land use change, spatial-temporal evolution of habitat quality, and driving factors influencing habitat quality change in Sanjiang Plain. The results showed that Land use/Land cover changed significantly from 1985 to 2017, especially paddy land increased by 22,184.92 km 2 , while unutilized land decreased by 11,533.53 km 2 . The increase of construction land was mainly at the expense of dry land. There was a polarization in habitat quality, and the high intensity of land use utilization and development resulted in a significant decrease in habitat quality. From 1985 to 2017, the largest change in habitat quality was grassland, which decreased from 0.99 to 0.91.