Driving Factors and Future Prediction of Land Use and Cover Change Based on Satellite Remote Sensing Data by the LCM Model: A Case Study from Gansu Province, China

Estimating the severity of landscape degradation in future management scenarios based on modeling the dynamics of Hoor Al-Azim International Wetland in Iran-Iraq border

Temporal and spatial changes in land cover in wetland ecosystems indicate the severity of degradation. Understanding such processes in the past, present, and future might be necessary for managing any type of development plan. Therefore, this research has monitored and analyzed the Hoor Al-Azim International Wetland to determine the orientation of its changes in various future scenarios. Wetland status modeling was conducted using developed hybrid approaches and cellular automata along with evaluating the accuracy of the modeled maps. The dynamics of the landscape were simulated using a higher accuracy approach in three scenarios-Water Conservation, Water Decreasing, and Business-as-Usual- to get the level of degradation of the wetland. The results showed that the amount of water in the wetland has decreased in all three periods, and the salt lands and vegetation have undergone drastic changes. The water bodies experienced a reduction of 148,139 ha between 1985 and 2000, followed by a decrease of 9107 ha during the 2000-2015 period. However, based on the results, these developments are expressed better by the developed hybrid approach than the CA-MC approach and are more reliable for future simulation. The figure of merit index, which assesses the hybrid model's accuracy, yielded a value of 18.12%, while the CA-MC model's accuracy was estimated at 14.42%. The assessment of degradation in hexagonal units showed the least degradation in the water conservation scenario compared with the other two scenarios in 2030.

Impact of ecological conservation policies on land use and carbon stock in megacities at different stages of development

Urban expansion, especially the construction of megacities, increases carbon emissions and adversely affects the carbon storage of terrestrial ecosystems. However, scientific land-use management policies can increase carbon storage. This study takes two megacities at different stages of development, Beijing and Tianjin, as examples to explore the impact of different ecological conservation scenarios on both urban land use and carbon storage to provide recommendations for the construction planning of large cities with low-carbon development as the goal. Furthermore, we coupled the patch-generating land use simulation (PLUS) model with the integrated valuation of ecosystem services and tradeoffs (InVEST) model to simulate land use and carbon storage under a natural development scenario, a planned ecological protection scenario (PEPS), and a policy-based ecological restoration scenario (PERS). From 2000 to 2020, both cities had different degrees of construction land expansion and carbon loss, and Tianjin's dynamic degree of construction land was 0.94% higher than Beijing's, with a carbon loss 183,536.19 Mg higher than Beijing's; this trend of reducing carbon reserves will continue under the natural development scenario (NDS). Under the PEPS and PERS, the carbon stock of both cities increases, and the impact on Tianjin is greater, with an increase of 4.51% and 8.04%, respectively. Under PERS, the carbon stock increases the most, but the dynamic degree of construction land use is negative for both cities. Beijing's carbon stock is 0.40% lower than Tianjin's, which deviates slightly from the trend of urban economic development. Megacities in the rapid development stage can refer to Tianjin, strictly following the ecological protection land planning scope and vigorously implementing ecological restoration policies to effectively increase regional carbon stock. Megacities in the mature stage of development can refer to Beijing, and flexibly implement ecological restoration policies to increase regional carbon stock without affecting the city's economic development.

Dynamics and future prediction of LULC on Pare River basin of Arunachal Pradesh using machine learning techniques

Anthropogenic disturbances caused by increasing population densities are a significant concern as they accelerate climate change. Thus, regular monitoring of land use/land cover (LULC) is essential to mitigate these effects. Pare River basin of Arunachala Pradesh situated in the foothills of Eastern Himalayas was selected for this study. Landsat-5 TM and Landsat-8 OLI data from 2000 (T₁), 2015 (T₂), and 2020 (T₃) were used to prepare the LULC map. A support vector machine (SVM) classifier in the Google Earth Engine (GEE) environment was utilized for classification of LULC, while the TerrSet software environment was used for change analysis and projection using the CA-MC model. The SVM classifier produced overall all classification accuracies of 0.91, 0.85, and 0.91 with kappa values of 0.88, 0.82, and 0.89 for T₁, T₂, and T₃, respectively. The CA-MC model, which combines Markov chain and hybrid cellular automata, was calibrated with various predictor variables, including natural, proximity, and demographic variables along with T₁ and T₂ LULC and validated using T₃ LULC. The MLP was used for calibration, and an accuracy rate of above 0.70 was employed to generate transition potential maps (TPMs). The TPMs were used to project future LULC for 2030, 2040, and 2050. Validation analysis produced satisfactory results, with K_no, K_location, K_quality, and K_standard values of 0.96, 0.95, 0.95, and 0.93, respectively. Receiver operating characteristics (ROC) analysis showed an excellent area under the curve (AUC) value of 0.87. The findings of this study provide important insights to decision-makers and stakeholders in addressing the impacts of LULC changes.

Analysis of the Future Evolution of Biocapacity and Landscape Characteristics in the Agro-Pastoral Zone of Northern China

The Agro-Pastoral Zone of Northern China (AZNC) is an ecologically fragile zone. It is a challenge to create scientifically sound plans for environmental conservation and agro-pastoral development due to the lack of future evolution prediction, and analysis of biocapacity (BC) and landscape characteristics. Using the Globeland30 dataset from 2000 to 2020, this study simulated 2030 land use/land cover (LULC) scenarios, and analyzed the future evolution of BC and landscape patterns. The results show that: (1) The Logistic and CA-Markov models can reasonably simulate the LULC changes in the research area, with ROC indices over 0.9 and Kappa approaching 0.805, after considering the driving factors such as physical geography, regional climate, and socio-economic development. (2) From 2000 to 2030, the spatial distribution pattern of LULC does not change significantly, and cultivated land, grassland, and forest are still the dominant land types in the research area. The regional BC exhibits an increasing trend (+4.55 × 10⁶ gha/a), and the spatial distribution pattern of BC is similar to that of LULC. (3) Changes in land miniaturization, landscape fragmentation, and decreased aggregation can be seen in the entire AZNC and specific land categories, including cultivated land, grassland, and forest. The study provides suggestions for formulating the AZNC's future ecological protection and agro-pastoral development strategies, and guidance for the LULC simulation in other agro-pastoral zones.

Territorial Pattern Evolution and Its Comprehensive Carrying Capacity Evaluation in the Coastal Area of Beibu Gulf, China

Changes in the territorial pattern of the Beibu Gulf, an environmentally sensitive and ecologically fragile area in China, will directly or indirectly affect the regional ecological environment, while profoundly influencing economic development and human well-being. Therefore, it is significant to understand the ecological response in the process of territorial space changes in the Beibu Gulf to promote the coordination between sea and land and sustainable regional development. This paper used remote sensing image interpretation to generate land-use maps in 2000, 2010 and 2020, and then analyzed the spatial and temporal evolution of the territorial pattern of the Beibu Gulf from 2000 to 2020. Finally, this paper proposed a comprehensive carrying capacity evaluation system and explored the spatial functional zones of the coastal areas of the Beibu Gulf. The results showed that the demand for urban development and ecological protection between 2000 and 2020 increased built-up land and forestland by 386.71% and 25.56%, respectively, and reduced farmland by 28.33%. There was significant spatial heterogeneity in various land-use types. Where forestland is mainly distributed in the west, farmland is mainly distributed in the east, wetland is mainly distributed in the south, and orchards are spread throughout the whole area. The evaluation results of land resources, water resources and ecological conditions in the Beibu Gulf area showed that its comprehensive carrying capacity was high in the south and low in the north, and high in the west and low in the east. On this basis, this paper considered the actual situation of natural resources, ecological conditions, socio-economic development, protection and development in coastal areas; divided the study area into four categories: developed areas, priority development areas, ecological reserve areas and coastal reserve areas; and put forward corresponding control suggestions. The results of this paper could provide a scientific basis for regional development and territorial spatial planning in the coastal areas.

Integrated model for land-use transformation analysis based on multi-layer perception neural network and agent-based model

The efficacy of land-use changes on aquatic ecosystems has been extensively studied in recent decades. Water resource management needs to understand the relationship between land-use change patterns and water quality, especially in urban areas. Hence, recognizing spatial-temporal changes in land use is required for sustainable development and proper water resource management. This research has developed an integrated model based on agent-based model (ABM) and multi-layer perceptron (MLP) neural network technique to predict the future land-use transformation tested on the North Ahvaz watershed, Iran. Random forest-supervised classification technique was applied to derive the land-use maps using Landsat 1989, 2004, and 2019 images in the Google Earth Engine (GEE) platform. The overall accuracy of classified land-use images was 0.82, 0.81, and 0.84, respectively, with the kappa coefficient of 0.74, 0.72, and 0.78. Land-use change analysis and generating transition potential maps were carried out in land change modeler (LCM) through MLP based on seven driving factors. Then, the land-use map for 2019 (for validation) and 2040 was simulated using the transition potential map and an agent-based approach. The ABM scenario was farmers' and urban landowners' decisions to convert undeveloped and unprotected lands to residential lands. The results showed that residential areas and pasture lands would grow by 67.96 km² and 64.63 km², and agricultural and barren lands would degrade about 84.19 km² and 47.98 km² during 2019-2040, respectively. Predicting land-use change through the integrated MLP-ABM model may be used to evaluate the effects of land-use change coming out of human decision-making.

Forecasting Rice Status for a Food Crisis Early Warning System Based on Satellite Imagery and Cellular Automata in Malang, Indonesia

The increasing population in Indonesia is challenging rice production to feed more people while rice fields are being converted to other land-use land cover (LULC). This study analyzes land use in 2015, 2017, 2019, 2021, and 2025 using an artificial neural network cellular automata (ANN-CA) and rice data from Statistics Indonesia to predict future rice status in Malang Districts, Indonesia. The primary LULC change driver was the rapid conversion of rice fields, which had their area reduced by 18% from 2019 to 2021 and 2% from 2021 to 2025. Rice fields are mainly being converted to settlements and buildings. The Kappa coefficient of simulation achieved 88%, with 91 accuracies. The model predicted a 2% lower rate of rice production but a 3% higher demand in 2025 compared to 2021. Lower rice production and higher demand are predicted to reduce the rice surplus by 57% in 2025, suggesting that the Malang district might lower its supply of rice to other areas by 2025. Our study provides a food crisis early warning system that decision makers can use to form adequate strategic plans and solutions to combat food insecurity.

Spatio-Temporal Patterns of Land-Use Changes and Conflicts between Cropland and Forest in the Mekong River Basin during 1990–2020

The Mekong River Basin (MRB) has experienced drastic and extensive land-use and land-cover changes (LULCCs) since the 1990s, including the conflicts between cropland and forest, yet remain quantitatively uninvestigated. With three decades (1990–2020) of land-use products, here we reveal the characteristics of LULCCs and the conflicts between cropland and forest in the MRB and its three sub-basins, i.e., upstream area (UA), midstream area (MA), and downstream area (DA). The four main results are as follows: (1) Since 1990, the dominated features are forest loss and cropland expansion in the MRB and show obvious sub-basin differences. (2) The LULCC was most active before 2000, with a comprehensive dynamic degree of almost 2%. Among them, construction land has the highest single dynamic degree (5%), especially in the DA, reaching 12%. (3) The key features of land-use transfer are the interconversions of forest and cropland, as well as cropland converted into construction land. About 18% (63,940 km2) of forest was reclaimed as cropland, and 17% (45,967 km2) of cropland was returned to forest in the past 31 years. (4) The conflict between cropland and forest was the most dominant LULCC, accounting for 86% of the MRB area. Overall, cropland expansion and forest loss (CEFL) were more dominant in the DA, while cropland fallow and forest restoration (CFFR) had an advantage in the MA. Indeed, CEFL was mainly seen in the plains below a 200 m elevation level, while CFFR tended to occur in the highlands. Our basin-scale study can enrich the existing pan-regional results of LULCCs, and facilitates the understanding of the dynamics and related mechanisms of CFER and CFFR in the tropics.

Land Use Dynamic Changes in an Arid Inland River Basin Based on Multi-Scenario Simulation

The Tarim River Basin is the largest inland river basin in China. It is located in an extremely arid region, where agriculture and animal husbandry are the main development industries. The recent rapid rise in population and land demand has intensified the competition for urban land use, making the water body ecosystem increasingly fragile. In light of these issues, it is important to comprehensively grasp regional land structure changes, improve the degree of land use, and reasonably allocate water resources to achieve the sustainable development of both the social economy and the ecological environment. This study uses the CA-Markov model, the PLUS model and the gray prediction model to simulate and validate land use/cover change (LUCC) in the Tarim River Basin, based on remote sensing data. The aim of this research is to discern the dynamic LUCC patterns and predict the evolution of future spatial and temporal patterns of land use. The study results show that grassland and barren land are currently the main land types in the Tarim River Basin. Furthermore, the significant expansion of cropland area and reduction in barren land area are the main characteristics of the changes during the study period (1992–2020), when about 1.60% of grassland and 1.36% of barren land converted to cropland. Over the next 10 years, we anticipate that land-use types in the basin will be dominated by changes in grassland and barren land, with an increasing trend in land area other than for cropland and barren land. Grassland will add 31,241.96 km2, mainly in the Dina River and the lower parts of the Weigan-Kuqu, Kashgar, Kriya, and Qarqan rivers, while barren land will decline 2.77%, with significant decreases in the middle and lower reaches of the Tarim River Basin. The findings of this study will provide a solid scientific basis for future land resource planning.

Simulating Future LUCC by Coupling Climate Change and Human Effects Based on Multi-Phase Remote Sensing Data

Future land use and cover change (LUCC) simulations play an important role in providing fundamental data to reveal the carbon cycle response of forest ecosystems to LUCC. Subtropical forests have great potential for carbon sequestration, yet their future dynamics under natural and human influences are unclear. Zhejiang Province in China is an important distribution area for subtropical forests. For forest management, it is of great significance to explore the future dynamic changes of subtropical forests in Zhejiang. As a popular LUCC spatial simulation model, the cellular automata (CA) model coupled with machine learning and LUCC quantitative demand models such as system dynamics (SD) can achieve effective LUCC simulation. Therefore, we first integrated a back propagation neural network (BPNN), a CA, and a SD model as a BPNN_CA_SD (BCS) coupled model for future LUCC simulation and then designed a slow development scenario (SD_Scenario), a harmonious development scenario (HD_Scenario), a baseline development scenario (BD_Scenario), and a fast development scenario (FD_Scenario), combining climate change and human disturbance. Thirdly, we obtained future land-use patterns in Zhejiang Province from 2014 to 2084 under multiple scenarios, and finally, we analyzed the temporal and spatial changes of land use and discussed the subtropical forest dynamics of the future. The results showed the following: (1) The overall accuracy was approximately 0.8, the kappa coefficient was 0.75, and the figure of merit (FOM) value was over 28% when using the BCS model to predict LUCC, indicating that the model could predict the consistent change of LUCC accurately. (2) The future evolution of the LUCC under different scenarios varied, with the growth of bamboo forests and the decline of coniferous forests in the FD_Scenario being prominent among the forest dynamics changes. Compared with 2014, the bamboo forest in 2084 will increase by 37%, while the coniferous forest will decrease by 25%. (3) Comparing the area and spatial change of the subtropical forests, the SD_Scenario was found to be beneficial for the forest ecology. These results can provide an important decision-making reference for land-use planning and sustainable forest development in Zhejiang Province.

Land Use/Land Cover Change and Their Driving Factors in the Yellow River Basin of Shandong Province Based on Google Earth Engine from 2000 to 2020

As the convenient outlet to the Bo Sea and the major region of economic development in the Yellow River Basin, Shandong Province in China has undergone large changes in land use/land cover (LULC) in the past two decades with rapid urbanization and population growth. The analysis of the LULC change patterns and its driving factors in the Shandong section of the Yellow River Basin can provide a scientific basis for rational planning and ecological protection of land resources in the Shandong section of the Yellow River Basin. In this manuscript, we analyzed the spatial pattern of LULC and its spatial and temporal changes in the Shandong section of the Yellow River Basin in 2000, 2010, and 2020 by using the random forest classification algorithm with the Google Earth Engine platform and multi-temporal Landsat TM/OLI data. The driving factors of LULC changes were also quantified by the factor detector and interaction detector in the geodetector. Results show that in the past two decades, the LULC types in the study area are mainly farmland and construction land, among which the proportion of farmland area has decreased and the proportion of construction land area has increased from 19.4% to 29.7%. Based on the results of factor detector, it can be concluded that elevation, slope, and soil type are the key factors affecting LULC change in the study area. The interaction between elevation and slope, slope and soil type, and temperature and precipitation has strong explanatory power for the spatial variation of LULC change in the study area. The research results can provide data support for ecological environmental protection, sustainable, and high-quality development of the Shandong section of the Yellow River Basin, and help local governments take corresponding measures to achieve coordinated and sustainable socioeconomic and environmental development.

Land Use Change in the Cross-Boundary Regions of a Metropolitan Area: A Case Study of Tongzhou-Wuqing-Langfang

Since the 1980s, metropolitan areas have increased worldwide due to urbanization and regionalization. While the spatial integration of the labor and housing markets has benefitted the development of cities within metropolitan areas, they have also brought great challenges for land governance; this is particularly evident in cross-boundary regions due to the complex relations between the markets and the regulations and between governments at different levels. Extensive research has been conducted on the city-level analysis of socioeconomic integration, land use development, and urban governance within metropolitan areas; yet, it is insufficient for understanding the intricate interplay between the various forces in such regions. This study aims to reveal the dynamics of land use change from 1990–2020 and its driving forces in the recent decade in the Tongzhou-Wuqing-Langfang (TWL) region—a typical cross-boundary area between Beijing, Tianjin, and the Hebei Metropolitan Area—using Landsat imagery. We employed the land-use dynamic degree, kernel density analysis, principal component analysis, and multiple linear regression to explore the spatiotemporal patterns of land use change and its driving factors at the district/county level. The results show that the general land use changes from cultivated and forest land to urban and rural construction land across the region. The speed of the trend varies considerably over time between different areas as the land use policies and regulations of each local government change. The population growth and the tertiary and secondary industry growth are the main driving factors for the change in construction land across the whole TWL region, while the urbanization rate and fixed asset investment have different impacts across the cross-boundary region. The results suggest that expanding the integration of land use policies and regulations in the cross-boundary region is urgently required.

Simulation and Spatio-Temporal Variation Characteristics of LULC in the Context of Urbanization Construction and Ecological Restoration in the Yellow River Basin

The Yellow River Basin (YRB), located in the northern region of China, has a fragile ecological environment. With the construction of urbanization and ecological restoration projects, the YRB LULC has undergone significant change. In this study, we used the coupled Markov-FLUS model by combining natural and social driver factors to predict and simulate the LULC of the YRB in 2030, and then the LULC transfer matrix was used to analyze the characteristics of LULC change in the YRB from 1990 to 2030. The results of the study are as follows. (1) For the simulated result of LULC compared with the same period observed result, the Kappa coefficient is 0.92, indicating the coupled Markov-FLUS model has good applicability in the YRB. (2) The LULC in the YRB shows significant spatial autocorrelation. The cropland is mainly distributed in the eastern region, which is dominated by plain; woodland is mainly distributed in the central region; grassland is mainly distributed in the northern, central, and western region; waterbody is mainly distributed in the western region; built-up land is mainly distributed in the northern, south-central, and eastern region; unused land is mainly distributed in the central, northern, and western region. (3) From 1990 to 2000, the area of cropland transferred in significantly and the area of grassland transferred out significantly; from 2000 to 2015, the area of construction land transferred in significantly and the area of cultivated land transferred out significantly; from 2015 to 2030, the amount of cropland transferred out will be large, and the conversion of each other LULC type will be not significant compared with the previous periods, and the conversion structure of LULC will tend to be stable. This study is a crucial reference value for the high-quality development of the Yellow River Basin.

An Integrated CNN Model for Reconstructing and Predicting Land Use/Cover Change: A Case Study of the Baicheng Area, Northeast China

Land use and land cover change (LUCC) modeling has continuously been a major research theme in the field of land system science, which interprets the causes and consequences of land use dynamics. In particular, models that can obtain long-term land use data with high precision are of great value in research on global environmental change and climate impact, as land use data are important model input parameters for evaluating the effect of human activity on nature. However, the accuracy of existing reconstruction and prediction models is inadequate. In this context, this study proposes an integrated convolutional neural network (CNN) LUCC reconstruction and prediction model (CLRPM), which meets the demand for fine-scale LUCC reconstruction and prediction. This model applies the deep learning method, which far exceeds the performance of traditional machine learning methods, and uses CNN to extract spatial features and provide greater proximity information. Taking Baicheng city in Northeast China as an example, we verify that CLRPM achieved high-precision annual LUCC reconstruction and prediction, with an overall accuracy rate 9.38% higher than that of the existing models. Additionally, the error rate was reduced by 49.5%. Moreover, this model can perform multilevel LUCC classification category reconstructions and predictions. This study casts light on LUCC models within the high-precision and fine-grained LUCC categories, which will aid LUCC analyses and help decision-makers better understand complex land-use systems and develop better land management strategies.

Land Degradation and Development Processes and Their Response to Climate Change and Human Activity in China from 1982 to 2015

Land degradation and development (LDD) has become an urgent global issue. Quick and accurate monitoring of LDD dynamics is key to the sustainability of land resources. By integrating normalized difference vegetation index (NDVI) and net primary productivity (NPP) based on the Euclidean distance method, a LDD index (LDDI) was introduced to detect LDD processes, and to explore its quantitative relationship with climate change and human activity in China from 1985 to 2015. Overall, China has experienced significant land development, about 45% of China’s mainland, during the study period. Climate change (temperature and precipitation) played limited roles in the affected LDD, while human activity was the dominant driving force. Specifically, LDD caused by human activity accounted for about 58% of the total, while LDD caused by climate change only accounted for 0.34% of the total area. Results from the present study can provide insight into LDD processes and their driving factors and promote land sustainability in China and around the world.

Integrating Remote Sensing and a Markov-FLUS Model to Simulate Future Land Use Changes in Hokkaido, Japan

As the second largest island in Japan, Hokkaido provides precious land resources for the Japanese people. Meanwhile, as the food base of Japan, the gradual decrease of the agricultural population and more intensive agricultural practices on Hokkaido have led its arable land use to change year by year, which has also caused changes to the whole land use pattern of the entire island of Hokkaido. To realize the sustainable use of land resources in Hokkaido, past and future changes in land use patterns must be investigated, and target-based land use planning suggestions should be given on this basis. This study uses remote sensing and GIS technology to analyze the temporal and spatial changes of land use in Hokkaido during the past two decades. The types of land use include cultivated land, forest, waterbody, construction, grassland, and others, by using the satellite images of the Landsat images in 2000, 2010, and 2019 to achieve this goal to make classification. In addition, this study used the coupled Markov-FLUS model to simulate and analyze the land use changes in three different scenarios in Hokkaido in the next 20 years. Scenario-based situational analysis shows that the cultivated land in Hokkaido will drop by about 25% in 2040 under the natural development scenario (ND), while the cultivated land area in Hokkaido will remain basically unchanged in cultivated land protection scenario (CP). In forest protection scenario (FP), the area of forest in Hokkaido will increase by 1580.8 km2. It is believed that the findings reveal that the forest land in Hokkaido has been well protected in the past and will be protected well in the next 20 years. However, in land use planning for future, Hokkaido government and enterprises should pay more attention to the protection of cultivated land.

Simulation of Biocapacity and Spatial-Temporal Evolution Analysis of Loess Plateau in Northern Shaanxi Based on the CA–Markov Model

Biocapacity evaluation is an important part of sustainable development research, but quantitative and spatial evaluation and future scenario analysis still have model and methodological difficulties. Based on the high-resolution Globeland30 dataset, the authors analyzed the characteristics of land use/cover changes of the Loess Plateau in Northern Shaanxi from 2000 to 2020. Then, comprehensively considering the driving factors of social development, topography, climatic conditions, and spatial distance, the logistic regression method and the CA–Markov model were used to simulate the land use scenario in 2030. Finally, the biocapacity model was used to describe the spatial distribution and spatial-temporal evolution of the regional biocapacity in detail. The results showed the following: (1) Biocapacity was jointly restricted by land use types, yield factors, and equivalence factors. The high values were mainly distributed in the riparian areas of the central and eastern regions, the ridges and valleys of the central and western regions, and the farmland patches of the southern valleys; the median values were mainly distributed in the forest of the southern mountains; the low values were mainly distributed in the grassland and unused land in the hilly and gully areas of the central and northern regions. (2) The biocapacity of Loess Plateau in Northern Shaanxi increased by 9.98% from 2000 to 2010, and decreased by 4.14% from 2010 to 2020, and the total amount remained stable. It is predicted that by 2030, the regional biocapacity will continue to increase by 0.03%, reaching 16.52 × 106 gha.

A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery

Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract informative features, they require large amounts of sample data. As a result, the design of more interpretable DL models with lower sample demand is highly important. In this study, a novel multi-level output-based deep belief network (DBN-ML) model was developed based on Ziyuan-3 imagery, which was applied for fine classification in an open-pit mine area of Wuhan City. First, the last DBN layer was used to output fine-scale land cover types. Then, one of the front DBN layers outputted the first-level land cover types. The coarse classification was easier and fewer DBN layers were sufficient. Finally, these two losses were weighted to optimize the DBN-ML model. As the first-level class provided a larger amount of additional sample data with no extra cost, the multi-level output strategy enhanced the robustness of the DBN-ML model. The proposed model produces an overall accuracy of 95.10% and an F1-score of 95.07%, outperforming some other models.

Analysis of the Current and Future Prediction of Land Use/Land Cover Change Using Remote Sensing and the CA-Markov Model in Majang Forest Biosphere Reserves of Gambella, Southwestern Ethiopia

This study aimed to evaluate land use/land cover changes (1987–2017), prediction (2032–2047), and identify the drivers of Majang Forest Biosphere Reserves. Landsat image (TM, ETM+, and OLI-TIRS) and socioeconomy data were used for the LU/LC analysis and its drivers of change. The supervised classification was also employed to classify LU/LC. The CA-Markov model was used to predict future LU/LC change using IDRISI software. Data were collected from 240 households from eight kebeles in two districts to identify LU/LC change drivers. Five LU/LC classes were identified: forestland, farmland, grassland, settlement, and waterbody. Farmland and settlement increased by 17.4% and 3.4%, respectively; while, forestland and grassland were reduced by 77.8% and 1.4%, respectively, from 1987 to 2017. The predicted results indicated that farmland and settlement increased by 26.3% and 6.4%, respectively, while forestland and grassland decreased by 66.5% and 0.8%, respectively, from 2032 to 2047. Eventually, agricultural expansion, population growth, shifting cultivation, fuel wood extraction, and fire risk were identified as the main drivers of LU/LC change. Generally, substantial LU/LC changes were observed and will continue in the future. Hence, land use plan should be proposed to sustain resource of Majang Forest Biosphere Reserves, and local communities' livelihood improvement strategies are required to halt land conversion.

Evaluation of Ecological Environment Effect of Villages Land Use and Cover Change: A Case Study of Some Villages in Yudian Town, Guangshui City, Hubei Province

Rapid economic development has a significant negative impact on the rural ecological environment. Evaluating the ecological environmental effect of land use and its change trend at the village scale has important practical significance for maintaining ecological functions and ensuring ecological safety. Taking a typical village in Yudian Town as an example, we applied a land-use ecological environment effect evaluation and the CA-Markov change trend prediction model and constructed an index of ecological environmental effect status. Based on the land use, resource environment, and social economic data from 2014 and 2019, we evaluated the ecological environmental effects of land use in each village, simulated the land-use change in each village in two different scenarios, i.e., the developmental orientation (DO) and ecological orientation (EO), in 2030, and analyzed the corresponding change trend of the land-use effect. The ecological environmental effect of land use showed obvious characteristic differentiation in villages with different development levels. For example, villages with poor natural geographic background conditions and slower economic development had a good level of ecological environmental effect, whereas villages with better resource and environmental endowments but faster economic development had lower levels of ecological environmental effect. Village land-use management methods have had a certain effect on improving ecological security, but the effect has been slow. In conclusion, the research results portray the relationship between rural land use and ecological environmental effects in low hilly areas in northern Hubei at a small scale and have reference value for land resource allocation and spatial pattern optimization in similar regions.

Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran

Exact land cover inventory data should be extracted for future landscape prediction and seismic hazard assessment. This paper presents a comprehensive study towards the sustainable development of Tabriz City (NW Iran) including land cover change detection, future potential landscape, seismic hazard assessment and municipal performance evaluation. Landsat data using maximum likelihood (ML) and Markov chain algorithms were used to evaluate changes in land cover in the study area. The urbanization pattern taking place in the city was also studied via synthetic aperture radar (SAR) data of Sentinel-1 ground range detected (GRD) and single look complex (SLC). The age of buildings was extracted by using built-up areas of all classified maps. The logistic regression (LR) model was used for creating a seismic hazard assessment map. From the results, it can be concluded that the land cover (especially built-up areas) has seen considerable changes from 1989 to 2020. The overall accuracy (OA) values of the produced maps for the years 1989, 2005, 2011 and 2020 are 96%, 96%, 93% and 94%, respectively. The future potential landscape of the city showed that the land cover prediction by using the Markov chain model provided a promising finding. Four images of 1989, 2005, 2011 and 2020, were employed for built-up areas’ land information trends, from which it was indicated that most of the built-up areas had been constructed before 2011. The seismic hazard assessment map indicated that municipal zones of 1 and 9 were the least susceptible areas to an earthquake; conversely, municipal zones of 4, 6, 7 and 8 were located in the most susceptible regions to an earthquake in the future. More findings showed that municipal zones 1 and 4 demonstrated the best and worst performance among all zones, respectively.

A Novel Model Integrating Deep Learning for Land Use/Cover Change Reconstruction: A Case Study of Zhenlai County, Northeast China

In recent decades, land use/cover change (LUCC) due to urbanization, deforestation, and desertification has dramatically increased, which changes the global landscape and increases the pressure on the environment. LUCC not only accelerates global warming but also causes widespread and irreversible loss of biodiversity. Therefore, LUCC reconstruction has important scientific and practical value for studying environmental and ecological changes. The commonly used LUCC reconstruction models can no longer meet the growing demand for uniform and high-resolution LUCC reconstructions. In view of this circumstance, a deep learning-integrated LUCC reconstruction model (DLURM) was developed in this study. Zhenlai County of Jilin Province (1986–2013) was taken as an example to verify the proposed DLURM. The average accuracy of the DLURM reached 92.87% (compared with the results of manual interpretation). Compared with the results of traditional models, the DLURM had significantly better accuracy and robustness. In addition, the simulation results generated by the DLURM could match the actual land use (LU) map better than those generated by other models.

Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture

As an important production base for livestock and a unique ecological zone in China, the northeast Tibetan Plateau has experienced dramatic land use/land cover (LULC) changes with increasing human activities and continuous climate change. However, extensive cloud cover limits the ability of optical remote sensing satellites to monitor accurately LULC changes in this area. To overcome this problem in LULC mapping in the Ganan Prefecture, 2000–2018, we used the dense time stacking of multi-temporal Landsat images and random forest algorithm based on the Google Earth Engine (GEE) platform. The dynamic trends of LULC changes were analyzed, and geographical detectors quantitatively evaluated the key driving factors of these changes. The results showed that (1) the overall classification accuracy varied between 89.14% and 91.41%, and the kappa values were greater than 86.55%, indicating that the classification results were reliably accurate. (2) The major LULC types in the study area were grassland and forest, and their area accounted for 50% and 25%, respectively. During the study period, the grassland area decreased, while the area of forest land and construction land increased to varying degrees. The land-use intensity presents multi-level intensity, and it was higher in the northeast than that in the southwest. (3) Elevation and population density were the major driving factors of LULC changes, and economic development has also significantly affected LULC. These findings revealed the main factors driving LULC changes in Gannan Prefecture and provided a reference for assisting in the development of sustainable land management and ecological protection policy decisions.