Land Change Research Articles

Simulating future cultivated land using a localized SSPs-RCPs framework: A case study in Yangtze River Economic Belt

Simulating cultivated land changes is vital for improving decision-making in sustainable agriculture. Compared to previous studies, the Shared Socioeconomic Pathways and Representative Concentration Pathways (SSPs-RCPs) scenarios provide a comprehensive framework for integrating climate and socioeconomic factors, offering deeper insights into future cultivated land dynamics. Taking the Yangtze River Economic Belt (YREB) as the study area, we developed localized SSPs-RCPs scenarios and adopted Global Change Assessment Model (GCAM) and Future Land Use Simulation (FLUS) model to simulate cultivated land changes under multiple scenarios. The findings indicate that cultivated land is predicted to decrease under all scenarios, with the SSP1-2.6 and SSP3-7.0 scenarios showing the highest and lowest decrease of 39.24% and 8.32%, respectively. By 2050, the loss of cultivated land in the upper reaches is expected to be significantly greater than that in the middle and lower reaches. Furthermore, grasslands, forests, and urban areas are the main types of land that replacing cultivated land in the YREB. The hotspots of cultivated land loss are in mountainous areas such as Sichuan, Yunnan, and Guizhou. Additionally, the gravity center of cultivated land distribution moved along the southeast to northeast, with the center of gravity shifting from Guizhou to Hunan under scenarios SSP1-2.6 and SSP5-8.5. This study offers insights into the future development of cultivated land in the YREB and highlights the significance of implementing and improving policies for its protection.

Urban expansion and agricultural land loss: a GIS-Based analysis and policy implications in Hawassa city, Ethiopia

This study investigated the historical and future trends of urban expansion and its subsequent impact on agricultural land-use in Hawassa city, Ethiopia. A time-series of remote-sensing imageries from Landsat Thematic Mapper for the years 1984, 1990, 2000, and 2010 and Operational Land Imager for 2021 were used to extract the LULC information from the study area. Seven major land-cover classes’ waterbody, built-up, agricultural land, wetland, grassland, woody vegetation, and agroforestry were identified with visual image interpretation along with supervised image classification techniques using the maximum-likelihood algorithm for the study years. The urban and agricultural lands were then extracted from the original LULC data to quantify the extent, rates, and number of area conversion between the two. The Land Change Modeler module of TerrSet software was used to predict the spatial extents of built-up and agricultural lands in 2030 and 2050. The results showed that there have been significant changes between the LULC types in Hawassa city within the past 37 years, from which built-up and agricultural land have shown the most prevalent changes. It showed that built-up land has increased from 584.73 ha in 1,984–3,939.03 ha in 2021, representing a 573.65% increase at an annual growth rate of 15.50%. However, agricultural land decreased from 8,324.64 ha to 3,595.68 ha in the respective years, with a 56.81% decrease at a rate of −1.54% each year. A total of 3,148.74 ha (37.82%) of agricultural land was converted into built-up land within the past 37 years (85.10 ha per year, a rate of 1.02%. The built-up land is projected to increase to 5,009.85 ha and 6,794.73 ha from 2021 to 2030 and 2050, with annual growth rates of 3.02% and 2.50%, respectively. In the same years, agricultural land will decrease to 2,849.58 ha and 2033.46 ha by 2.31% and 1.50% annually, respectively, from which 64.76 ha (1.80%) and 48.41 ha (1.35%) will be converted into built-up land, respectively. Future planning and development in the city should consider the rapid increase in built-up land toward agricultural land areas and develop appropriate adaptation mechanisms for the local community, which is highly dependent on agriculture.

Transforming agricultural land into agrotourism area: Environmental, economic and socio-cultural perspectives

This study investigates the impacts of converting agricultural land into agrotourism areas on environmental, socio-cultural, and economic perspectives within Batukliang District, Central Lombok Regency, Indonesia. With a case study approach, this qualitative descriptive research employed interviews with three target groups: local farmers, residents, and tourism actors. The findings revealed seven key points identified as influences affecting the socio-cultural aspects of land change, including community impact, cultural preservation, cultural identity loss, community dynamics change, local cultural commercialization, cultural heritage loss, and traditional livelihoods. The results also unveiled nine financial impacts, 8 of which were associated with economic implications such as economic challenges, risk management, brand building, costs and investments, market access, increased revenue, and income diversity, which contribute positively to local economic development. The study concluded that integrating community involvement empowerment strategies, income diversification, sustainable farming promotion, and land-use regulation is crucial for developing a successful sustainable agrotourism destination.

Assessing climate change threats and urbanization impacts on surface runoff in Gdańsk (Poland): insights from remote sensing, machine learning and hydrological modeling

AbstractThis study investigates the impacts of Land Use/Land Cover (LULC) changes and climate change on surface runoff in Gdańsk, Poland, which is crucial for local LULC planning and urban flood risk management. The analysis employs two primary methodologies: remote sensing and hydrological modeling. Remote sensing was conducted using Google Earth Engine and Land Change Modeler in IDRISI Terrset software to analyze historical (1985–2022) and future (2050–2100) LULC. Hydrological modeling was performed using the Natural Resources Conservation Service curve number method to assess the overall impact of LULC changes on Gdańsk’s hydrology at the local scale. The Orunia basin, a critical area due to intensive LULC development, was selected for detailed hydrological analysis using the Hydrologic Modeling System (HEC-HMS). The analysis encompassed three scenarios: LULC changes, climate change, and combined LULC and climate change effects. The LULC analysis revealed a marked increase in urban area, a shift in forest and vegetation cover, and a reduction in agricultural land. HEC-HMS simulations showed an increase in the runoff coefficient across selected decades, which was attributed to the combined effect of LULC and climate change. The projected increases under the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios for 2050 and 2100 are projected to surpass those observed during the baseline period. The findings highlight that the synergistic effects of LULC and climate change have a more significant impact on Gdańsk’s hydrology at both local and basin scales than their separate effects. These insights into LULC shifts and urban hydrological responses hold implications for sustainable urban planning and effective flood risk management in Gdańsk and similar urban settings.

Using Google Earth Engine open-source code for land surface temperature estimation from Landsat data in Chuong My district, Hanoi city, Vietnam

Land surface temperature (LST) in association with urbanization has significantly influenced on local climates and terrestrial surface processes. By using multi-temporal Landsat data in the GEE platform, the study has found that there have been changes in land covers due to the main drivers of industrial development, urban and built-up expansion. This study estimated land surface temperature (LST) in Chuong My district from Landsat 5, 7, 8 and 9 thermal infrared sensors, using different surface emissivity sources. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, offers the estimation of LST products, covering the time period from 2000 to 2024 in study site. RF algorithm for land cover classification and mapping is suggested to be applied in Chuong My district, with overall accuracy of land cover mapping in 2024 being 91.9% with Kappa coefficient of 0.89. The period 2000-2010 showed that 327.0 ha of land were converted to land for industrial development, urban and residential land, while the period 2010-2024 continued to witness a continued conversion of land to other land use purposes, with a decrease in agricultural land area (65.7 ha) and an increase in industrial land use (65.2 ha), but the rate of conversion was much lower than the previous period (2000-2024). The periods of 2000-2010 and 2010-2024 showed that there have been significant changes in land surface temperature. Most of the surface temperature in the entire region increased by over 20C, especially in the period 2003-2015 when a total area of 1424 ha had the temperature increased by above 30C. This is a consequence of the process of urbanization and industrialization, which began to take place in 2008, along with the process of changing the land cover status of the study area. Further studies are needed to better understand the thermal conductivity of surface materials and to plan how to reduce LST in such areas.

Analisis Perubahan Penggunaan Lahan dan Daya Dukung Lahan Pertanian Sawah di Kecamatan X Koto Tahun 2005-2023

The objectives of this study are: (1) To analyze the extent of changes in the use of rice field agricultural land in X Koto District in the period of 2005-2014 and the period of 2014-2023, (2) To analyze the carrying capacity of rice field agricultural land in X Koto District for the period of 2005-2014 and the period of 2014-2023. This type of research uses a quantitative descriptive method with secondary analysis. The data used are secondary data in the form of Landsat 7 ETM Satellite Imagery in 2005, Landsat 8 OLI Imagery in 2014 and 2023, as well as data on the area of rice harvested land, the number of population, and the average rice production in X Koto District for the period of 2005-2014 and the period of 2014-2023. The data analysis technique uses Supervised Classification. The analysis of changes in rice field agricultural land in this study uses the overlay technique. The researcher found: (1) The area of rice field agricultural land in 2005 was 1973.40 ha, then in 2014 it decreased to 1513.15 ha with a reduction rate of 23.32%. In 2023, the area of paddy field agricultural land will decrease again to 1265.76 ha with a reduction rate of 16.34%. (2) In the period of 2005-2014, the carrying capacity of rice field agricultural land in X Koto District was 1.74 and the period of 2014-2023 was 1.89. X Koto District is an area with food self-sufficiency, where the area is able to meet the basic needs of the community, but has not been able to provide a decent life for its residents.

Combined effects of urbanization and climate variability on water and carbon balances in a rice paddy-dominated basin in southern China

Abstract Urbanization is known to elevate storm runoff, but how it influences carbon cycle and ecosystem productivity through altering the evapotranspiration (ET) process is less clear. We examined the combined effects of urbanization including change in impervious surface area (ISA) and climate variability on the water and carbon balances of the Qinhuai River Basin (QRB) over 2001–2018. QRB represents a typical rice paddy-dominated region that experienced rapid urbanization in southern China. We improved a monthly scale water supply stress index ecosystem service model by integrating local eddy flux measurements and high-resolution remote sensing data. We found a significant downward trend in both ET (−4.6 mm yr−1, p < 0.05) and gross primary productivity (GPP) (−10.4 gC m−2 yr−1, p < 0.05) but a significant upward trend in water yield (Q) (+28.6 mm yr−1, p < 0.05). These ecosystem function changes coincided with a 96% increase in urban areas, 1.9-fold increase in ISA, and a 37% reduction in rice paddy fields. The mean annual watershed GPP decreased from 1048 gC m−2 to 998 gC m−2 while the annual Q increased from 284 mm to 669 mm from 2001 to 2018. Scenario modeling experiments suggested that the negative impacts of loss of rice paddy fields and increase in ISA on ET and GPP overwhelmed the positive impacts of climate warming. The reduction in GPP and increase in Q were largely attributed to the increases in ISA, not necessarily due to changes in land use types (e.g. urban area). The expansion of urban area, increase in ISA and reduction in leaf area index, and increase in precipitation explained the increase in Q. Our research offers insight about the interactions of carbon and water cycles through the critical ET processes under a changing climate and land surface characteristics at a watershed level. Our modeling tool and analysis provides land managers and policy makers information for designing effective ‘Urban Nature-based Solutions’ to mitigate the negative environmental effects of urbanization on carbon and water resources.

The protein transition—environmental footprints of farmed fish, microalgae and chicken for food, a case study in Kenya

AbstractChallenges of land scarcity, climate change and water shortage are contributing to escalating the shortage of protein rich food in sub-Saharan Africa. In Kenya a total of 27.8% of the population was undernourished, and a total of 72.3% were categorised as moderately or severe food insecure in 2021–2022. The need to investigate alternative protein sources with less impacts on land, water and climate change, which potentially could replace, at least some of the current production of livestock as the main protein source, is getting increasingly urgent. The main aim of this study is therefore to conduct an environmental footprint analysis of favourable protein production systems in Kenya to evaluate environmental impacts on land, water, and climate. The methodological approach used in this study is referred to as environmental footprint analysis, which is applied to assess; (1) farmed fish in ponds, (2) farmed fish in solar based affordable recirculation aquaculture systems (A-RAS), (3) dried spirulina (Arthrospira Platensis), and (4) chicken from semi-intensive farms. The results show that the most promising alternatives to cattle production are dried spirulina produced in ponds, followed by fish produced in affordable recirculation systems (A-RAS) run on solar energy. Spirulina fully replacing beef production would reduce water usage with 34.7 billion m3/year, land/water surface area with 34.8 thousand km2/year, and GHG emission with 28.2 megaton CO2eq/year. For fish farmed in A-RAS, such replacement would reduce water, land and emissions with 30.7 billion m3/year, 32.0 thousand km2/ year and 23.0 megaton CO2eq/year, respectively. This study illustrates that it is possible to tackle urgent challenges of food and nutrition security for millions of people in sub-Saharan Africa and worldwide with a sustainable protein transition towards a more sustainable, inclusive production system of small-scale farmers, if making use of alternative protein sources, such as from solar based A-RAS and microalgae.

Landslide Rapid Impact Assessment at Hydro Dam via THyCAS and UAS

The TNB Hydro Catchment Area Surveillance (THyCAS) is a web-based geographic information system (GIS) used to monitor hydropower dam catchment areas in Peninsular Malaysia. It can also be used for landslide rapid impact assessment by monitoring the surrounding landslide area. However, during the rainy season, satellite images are unable to provide the information required by users due to full cloud cover. Therefore, an Unmanned Aircraft System (UAS) is used to provide the data required by THyCAS by mapping the surrounding area affected by landslides and uploading the processed data in THyCAS. Thus, the user can get the required information regarding the landslide incident. Furthermore, with UAS Full Motion Video (FMV) data, the location of the collapsed power transmission towers can be acquired. In addition, any information on land changes that could jeopardise the surrounding dam area is gathered. Therefore, with the information from the rapid impact assessment provided by THyCAS and UAS data, a preliminary risk assessment plan can be established and implemented by the relevant parties. The results obtained from the rapid assessment can also be used as additional information to find out the actual cause of landslides in the area.

Space-for-time substitutions exaggerate urban bird-habitat ecological relationships.

North American bird abundance has declined by 29% over the last 50 years. These continental population dynamics interact with local landscape changes to affect local bird diversity. Mitigating local declines in cities is particularly significant because (a) such declines greatly impact human-bird relationships since most people live in cities and (b) cities provide levers to create bird-friendly habitat, such as managing yards and gardens, street trees, and urban parks. Yet, the potential for cities to modify habitats to mitigate broader bird declines remains unclear. Studies have been stymied by the difficulty of assembling mutidecadal habitat-bird population datasets. Instead, studies have substituted space for time (e.g. used habitat associations across space at one time point to project future species abundance due to changing land use), but this method may fail amidst nonstationary environments of the Anthropocene. Here, we test the validity of space-for-time substitutions for explaining changes in bird abundance in a North American city over the past two decades by examining the degree to which these changes are explainable by changes in local landcover at multiple spatial scales. Specifically, we use longitudinal urban bird surveys of Metro Vancouver, BC, Canada from 1997 and 2020; deep learning models of remote sensing data to classify contemporaneous landcover; out-of-sample prediction and boosted regression trees to identify multiple spatial scales of landcover that best explained bird abundance (i.e. optimal scale of effect for each species by each habitat); and Bayesian multispecies abundance models in Stan to determine relationships between changes in landcover and bird abundance. We found that total bird abundance declined by 26% over the last two decades. Landcover measured at both 50 m and optimal scales explained spatial variation in bird abundance, but only landcover at the optimal scale explained temporal changes, and only partially. These results suggest that space-for-time substitutions overemphasize habitat-bird ecological relationships, urban habitats only partially determine bird abundance, and measuring habitat at the appropriate scale is important for capturing the most relevant changes in landscapes.

Hydrological response to future changes in climate and land use/land cover in the Hanjiang River Basin

Hydrological response to future changes in climate and land use/land cover in the Hanjiang River Basin

Global spatial and temporal dynamics of the green water coefficient and analysis of factor from 1992 to 2020

In recent years, the impacts of climate change have variably affected the global ecological environment, exacerbating water scarcity and related issues. Meantime, as part of soil evapotranspiration and plant transpiration in water resources, green water is of great significance in stabilizing the global water cycle and promoting the sustainable development of agriculture. Therefore, the green water coefficient in this study calculates the conversion rate of precipitation to green water, which is important for the balance between blue and green water and the stability of the ecological environment. Based on trend analysis methods and Geodetector, this study analyses the spatial and temporal characteristics and driving mechanisms of the green water coefficient on the globe and on each land-use type from 1992 to 2020. The findings revealed that: (1) The global green water coefficient is clearly spatially differentiated, showing certain latitudinal distribution differences, with the lowest near the equator. The areas with higher green water coefficients are mainly in northern and southern Africa, central Asia, Oceania and northern and western North America, while the areas with lower green water coefficients are concentrated in northern South America and Southeast Asia. (2) The global average value of green water coefficient decreases before increasing. Except for water, the green water coefficient of other land use types (crop land, forest land, grassland, urban land, unused land) changes significantly and there are fewer abrupt change points. Areas where the green water coefficient is too low and changes drastically are not suitable for rainfed agriculture. (3) Different influential factors act on the green water coefficients of different land use types, and there is a two-factor enhancement and non-linear enhancement relationship between them, with a combination of factors affecting the green water coefficient. By studying the characteristics of the green water coefficient globally and across different land use types, we quantified the influence of climate factors such as precipitation, temperature, wind speed, evapotranspiration, radiation, etc., on the green water coefficient, thereby providing some information for future global rain-fed agriculture and water resources management.

DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change Detection.

Declining cultivated land poses a serious threat to food security. However, existing Change Detection (CD) methods are insufficient for overcoming intra-class differences in cropland, and the accumulation of irrelevant features and loss of key features leads to poor detection results. To effectively identify changes in agricultural land, we propose a Difference-Directed Multi-scale Attention Mechanism Network (DDAM-Net). Specifically, we use a feature extraction module to effectively extract the cropland's multi-scale features from dual-temporal images, and we introduce a Difference Enhancement Fusion Module (DEFM) and a Cross-scale Aggregation Module (CAM) to pass and fuse the multi-scale and difference features layer by layer. In addition, we introduce the Attention Refinement Module (ARM) to optimize the edge and detail features of changing objects. In the experiments, we evaluated the applicability of DDAM-Net on the HN-CLCD dataset for cropland CD and non-agricultural identification, with F1 and precision of 79.27% and 80.70%, respectively. In addition, generalization experiments using the publicly accessible PX-CLCD and SET-CLCD datasets revealed F1 and precision values of 95.12% and 95.47%, and 72.40% and 77.59%, respectively. The relevant comparative and ablation experiments suggested that DDAM-Net has greater performance and reliability in detecting cropland changes.

Peri-Urban Floodscapes: Identifying and Analyzing Flood Risk Areas in North Bhubaneswar in Eastern India

Peri-urban catchment areas are increasingly susceptible to floods due to rapid land use transformations and unplanned urban expansion. This study comprehensively examines flood vulnerability in the rapidly developing peri-urban areas of North Bhubaneswar, focusing on significant changes in Land Use/Land Cover (LULC) and hydrological dynamics from 2004 to 2024, utilizing Geographic Information System (GIS) tools. The analysis reveals substantial shifts in land use patterns, with the urban footprint expanding by 71.8%, cropland decreasing by 21.7%, and forest areas by 13.6%. These changes have led to increased impervious surfaces, resulting in higher surface runoff and decreased groundwater recharge, thereby exacerbating flood risks in the region. The GRID-based vulnerability analysis classifies 90 villages within the catchment area based on their vulnerability levels, identifying 20 villages as high-risk areas requiring urgent attention, 44 villages as medium vulnerable, and 26 villages as low vulnerable. These classifications are based on factors such as proximity to drainage networks, slope, geomorphology, and LULC characteristics, with areas near drainage channels and low-lying regions being prone to flooding. The analysis integrates multiple factors to provide a comprehensive assessment of flood risk, highlighting the need for sustainable land use planning, conservation of vegetated areas, and the implementation of advanced flood prevention strategies in the peri-urban areas. Extending this research to other fringe regions could offer further valuable insights, guiding flood prevention and sustainable development strategies for areas undergoing significant land use transformations to effectively mitigate future flood risks.

How to Achieve the Ecological Sustainability Goal of Ecologically Fragile Areas on the Qinghai-Tibet Plateau: A Multi-Scenario Simulation of Lanzhou-Xining Urban Agglomerations

The future of the ecologically fragile areas on the Qinghai-Tibet Plateau (QTP) is a matter of concern. With the implementation of the Western Development Strategy, the Lanzhou-Xining Urban Agglomeration (LXUA) has encountered conflicts and compromises between urban expansion, ecological protection, and farmland protection policies in the rapid development of the past 2 decades. These deeply affect the land use layout, making the ecological sustainable development of the ecologically fragile areas of the QTP a complex and urgent issue. Exploring the impact of different policy-led land use patterns on regional ecosystem services is of great significance for the sustainable development of ecologically fragile areas and the formulation of relevant policies. Following the logical main line of “history-present-future”, the Patch-level Land Use Simulation (PLUS) model, which explores potential factors of historical land use, and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model were used to construct three future scenarios for the modernization stage in 2031 dominated by different land use policies in this study. These scenarios include the Business-as-Usual Scenario (BS), the Cropland Protection Scenario (CP), and the Ecological Protection Scenario (EP). The study analyzed and predicted land use changes in the LXUA from 2001 to 2031 and assessed carbon storage, habitat quality at different time points, and water yield in 2021. The results indicated that land use changes from 2001 to 2021 reflect the impacts and conflicts among the Western Development Strategy, ecological protection policies, and cropland preservation policies. In 2031, construction land continues to increase under all three scenarios, expanding northwards around Lanzhou, consistent with the actual “northward expansion” trend of Lanzhou City. Changes in other land uses are in line with the directions guided by land use policy. By 2031, carbon storage and habitat quality decline under all scenarios, with the highest values observed in the EP scenario, the lowest carbon storage in the BS scenario, and the lowest habitat quality in the CP scenario. Regarding water yield, the LXUA primarily relies on alpine snowmelt, with construction land overlapping high evapotranspiration areas. Based on the assessment of ecosystem services, urban expansion, delineation of ecological red lines, and improvement of cropland quality in the LXUA were proposed. These findings and recommendations can provide a scientific basis for policy makers and planning managers in the future.

Projection of land use and land cover changes based on land change modeler and integrating both land use land cover and climate change on the hydrological response of Big Creek Lake Watershed, South Alabama

Changing land use/land cover (LULC) and climate substantially affect the hydrological components of a watershed. This study explored the future impact of the hydrological responses due to the changing LULC and climate on the Big Creek Lake watershed in Alabama, USA, from 2021 to 2050 using the Soil and Water Assessment Tool (SWAT). Five climate model datasets were used under the moderate scenario (Representative Concentrative Pathways 4.5) and the extreme scenario (Representative Concentrative Pathways 8.5), and the datasets were downscaled and bias-corrected. In addition, changing the LULC of five categories was predicted by Cellular Automata Markov (CA- Markov). With these data combined with the elevation (Digital Elevation Model), soils, and weather data, the SWAT model was calibrated and validated for the studied watershed to quantify how climate change will affect streamflow, nitrogen, and phosphorus. Our results indicate streamflow will increase due to the 50-acre increase in urban LULC. As streamflow increases, the percolation, surface runoff, lateral flow, groundwater flow, and water yield will also increase because the streamflow impacts these hydrological components. Moreover, the increase rate in streamflow is the same for all the components for January, February, and March. Therefore, there is a strong correlation between these months. On the contrary, evaporation will be high in May, June, and July because of the increasing temperature and streamflow. However, the changes in the water hydrological parameters and total nitrogen and phosphorus will be more intense in RCP8.5 than in RCP4.5.

Effects of land use change on runoff depth in the Songnen Plain, China.

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 36.26mm, 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 19.07mm, with climate change contributing to a decrease of 15.89mm (83.31% contribution). In the impact period 2, the runoff depth increased by 7.49mm relative to the baseline period, with climate change leading to an increase of 12.73mm (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 6.21mm, 2.45mm, and 1.14mm, 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 9.49mm, 6.46mm, and 3.07mm, respectively. This research can contribute to improving the efficiency of water and land resource utilization and optimizing land resource governance.

The Impact of Toll Road Development on Urbanization Typology and Economic Structure (Case Study: Palikanci Toll Road in Ciledug District, Cirebon Regency)

This study examines the impact of toll roads on regional growth, specifically focusing on the Ciledug District, which has been crossed by the Palikanci Toll Road since 2009. The research identifies the typology of urbanization and assesses changes in the area’s economic structure due to the toll road. A mixed method approach was used, combining qualitative and quantitative data collection, land change analysis via Google Earth and ArcGis 13.1, and field observations of land use. The findings reveal gentrification in the northern part of Ciledug, marked by the growth of business centers in Jatiseeng and rising land prices. This has led to the expansion of settlements toward the southern area, particularly in Jatiseeng Kidul. The toll road and the industrial estate in North Ciledug have accelerated the transformation of the local economy, with residents diversifying their livelihoods to adapt to urbanization. However, the study also notes a lack of improvement in essential infrastructure services, suggesting that regional development policies have not yet adapted to the gentrification, posing risks of urban sprawl.

Assessment of Carbon Stock and Sequestration Dynamics in Response to Land Use and Land Cover Changes in a Tropical Landscape

Quantitative analysis of LULC changes and their effects on carbon stock and sequestration is important for mitigating climate change. Therefore, this study examines carbon stock and sequestration in relation to LULC changes using the Land Change Modeler (LCM) and Ecosystem Services Modeler (ESM) in tropical dry deciduous forests of West Bengal, India. The LULC for 2006, 2014, and 2021 were classified using Google Earth Engine (GEE), while LULC changes and predictions were analyzed using LCM. Carbon stock and sequestration for present and future scenarios were estimated using ESM. The highest carbon was stored in forest land (124.167 Mg/ha), and storage outside the forest declined to 13.541 Mg/ha for agricultural land and 0–8.123 Mg/ha for other lands. Carbon stock and economic value decreased from 2006 to 2021, and are likely to decrease further in the future. Forest land is likely to contribute to 94% of future carbon loss in the study region, primarily due to its conversion into agricultural land. The implementation of multiple-species plantations, securing tenure rights, proper management practices, and the strengthening of forest-related policies can enhance carbon stock and sequestration. These spatial-temporal insights will aid in management strategies, and the methodology can be applied to broader contexts.

Urban Street Landscape Design System Driven by Optimized Genetic Algorithm

With the acceleration of urbanization in China, urban and rural construction land continues to expand. In order to avoid “urban street view disease” in the process of urbanization, dynamic simulation of urban street view land is of great significance. The dynamic model of urban street view land can simulate the dynamic expansion process of street view concretely and effectively, and predict the distribution and form of urban street view land in the future more accurately. How to improve the accuracy of urban street view dynamic model based on geographic cellular automata has always been the direction of unremitting exploration in academic circles. In view of the limitations of the traditional genetic algorithm model, this study constructs a genetic algorithm-logistic regression model, and takes Chengdu-Chongqing Economic Zone as a case, and optimizes the best regression coefficient of the logistic regression model through genetic algorithm fitting, so as to realize the accurate simulation of the dynamic change of urban street view land.