Prediction Of Land Use Change Research Articles

Simulation and Prediction of Land Use Change and Carbon Emission under Multiple Development Scenarios at the City Level: A Case Study of Xi’an, China

Studying urban land use and its impact on carbon emissions is crucial for achieving China’s dual carbon goals. This research utilized the Shared Socio-economic Pathways (SSPs) scenarios 126, 245, and 585 from the Sixth International Coupled Model Intercomparison Project (CMIP6), along with a coupled System Dynamics (SD) and Patch-generating Land Use Simulation (PLUS) model and a carbon emission coefficient method to simulate and predict Xi’an’s land use carbon emissions from 2020 to 2040. The results indicate the following: (1) Cultivated and forest lands are the predominant land use types in Xi’an, with cultivated and grassland areas projected to decline under all three SSP scenarios by 2040. The most significant expansion of construction land, primarily at the expense of farmland, is projected under the SSP585 scenario, with an increase of 515.92 km2 by 2040. (2) Land use carbon emissions increased from 414.15 × 104 t in 2000 to 2376.10 × 104 t in 2020, with construction land being the primary source of emissions and forest land serving as the main carbon sink. However, the carbon sink capacity remained low at only 21.38 × 104 t in 2020. (3) Carbon emissions are expected to continue increasing under all scenarios through 2030 and 2040, though at a decreasing rate. The SSP126 scenario predicts the lowest emissions, reaching 9186.00 × 104 t by 2040, while SSP585 predicts the highest at 14,935.00 × 104 t. The findings of this study provide theoretical support for future low-carbon and high-quality urban development strategies.

Simulation and multi-scenario prediction of land-use change in the Gansu section of the Yellow River Basin, China

Introduction: The Gansu section of the Yellow River Basin is an important water resource conservation and replenishment area for the entire Yellow River Basin. With urbanization and socio-economic development, it is urgent to study the characteristics of land-use change and its future simulation in order to realize the coordinated ecological and economic development.Methods: Based on the patch-generating land-use simulation (PLUS) model, this paper investigated the main drivers of land-use type expansion with a comprehensive consideration of natural and socio-economic aspects; moreover, the study simulated land-use change in 2030 under the four scenarios of natural development, cultivated land protection, ecological priority, and economic construction.Results: The results showed the following: 1) the prediction of land-use types continued the historical evolution since 1980. Grassland, cultivated land, and forest land were still the dominant land types, accounting for more than 87% of the basin’s total area. Water bodies and wetlands remained relatively stable, and there was an obvious increase of approximately 20% in construction land. 2) Construction land and grassland were primarily driven by the social factor of the distance from the primary road and the distance from the secondary road, respectively. The cultivated land was greatly affected by the economic factor of population density. 3) The cultivated land protection scenario was the only one of the four scenarios that could make the cultivated land area increase positively, with an increase rate of 0.5%. This scenario also restricted effectively the conversion of cultivated land into construction land. The ecological priority scenario can expand grassland obviously with a proportion of 1.82% and slow down oasis desertion. The economic construction scenario can increase the construction land area the most by a rate of 25.5% to accelerate the economic development of specific regions in the study area.Discussion: Therefore, implementing policies on the basis of choosing suitable scenarios in different areas was significant for optimizing the land-use structure, promoting the efficient use of land resources and ecological environment in the Gansu section of the Yellow River Basin.

Assessment of Uncertainties in Ecological Risk Based on the Prediction of Land Use Change and Ecosystem Service Evolution

Assessment of Uncertainties in Ecological Risk Based on the Prediction of Land Use Change and Ecosystem Service Evolution

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

For sustainable water resource management within dam catchments, accurate knowledge of land-use and land-cover change (LULCC) and the relationships with dam water variability is necessary. To improve LULCC prediction, this study proposes the use of a random forest regression (RFR) model, in comparison with logistic regression–cellular automata (LR-CA) and artificial neural network–cellular automata (ANN-CA), for the prediction of LULCC (2019–2030) in the Gaborone dam catchment (Botswana). RFR is proposed as it is able to capture the existing and potential interactions between the LULC intensity and their nonlinear interactions with the change-driving factors. For LULCC forecasting, the driving factors comprised physiographic variables (elevation, slope and aspect) and proximity-neighborhood factors (distances to water bodies, roads and urban areas). In simulating the historical LULC (1986–2019) at 5-year time steps, RFR outperformed ANN-CA and LR-CA models with respective percentage accuracies of 84.9%, 62.1% and 60.7%. Using the RFR model, the predicted LULCCs were determined as vegetation (−8.9%), bare soil (+8.9%), built-up (+2.49%) and cropland (−2.8%), with water bodies exhibiting insignificant change. The correlation between land use (built-up areas) and water depicted an increasing population against decreasing dam water capacity. The study approach has the potential for deriving the catchment land–water nexus, which can aid in the formulation of sustainable catchment monitoring and development strategies.

Simulation and prediction of land use change in Dongguan of China based on ANN cellular automata - Markov chain model

Simulation and prediction of land use cover change (LUCC) in cities can effectively identify trends in land conversion and support the decision-making process regarding future urban development. To identify urban LUCC and the effects of its inherent climatic, geographical, and socioeconomic drivers, this study developed a hybrid method for predicting future land type area based on a Markov chain model and simulating the spatial distribution of land use based on an artificial neural network, the cellular automata approach, and the Land Expansion Analysis Strategy. The proposed method was shown effective in the following aspects: (1) identification of land use variation, (2) simulation of future land use change distribution, and (3) analysis of the contribution of different driving factors to LUCC. The approach was applied to the city of Dongguan. The land use patterns changed in a balanced manner, according to the movement distances of gravity center during 2010–2020. Compared with the land use pattern in 2020, the area of ecological land was projected to diminish by 0.67 %, 1.42 %, and 2.07 % in 2025, 2030, and 2035, respectively. Based on the simulation and prediction of the land-use areas in Dongguan, the area of woodland would reduce by 7.42 %, and the growth rate of building land would be slow. It was recommended that a conservation district should be established, especially in the central mountainous region of Dongguan.

Analysis of Regional Spatial Regulation Policies Pesawaran District

Objective: Formulate philosophical, sociological, juridical considerations or foundations for the formation of draft laws or draft regional regulations related to regional spatial planning. Methodology: Formulate the objectives to be realized, the scope of regulation, the reach, and direction of regulation in the draft Regional Regulation researchers use a type of descriptive – qualitative research. Results: The 2031 land change prediction shows that the largest projected land use change is dryland agriculture at 21830 ha (17.2%). This prediction is made following historical patterns that have occurred between 2016, 2019 and 2022. The prediction of land use change in 2022-2031 in Pesawaran District shows that wetland agriculture will decrease by 17.91%. This is due to improved infrastructure in rice field land use areas, this reduction is also caused by an increase in population growth that occurs in Pesawaran Regency. Conclusion: Strategic policies that can be carried out are increasing the fulfillment of green open space (RTH), developing habitable residential areas; preserving production forest areas to maintain ecosystem balance; developing community plantation areas to improve community welfare; optimization of space utilization in accordance with its designation.

Future carbon storages of ecosystem based on land use change and carbon sequestration practices in a large economic belt.

Assessments of ecosystem carbon storage are needed to form the scientific basis for carbon policies. Due to lack of data, there are few accurate, large-scale, and long-term predictions of ecosystem carbon storage. This study used the Distributed Land-Use Change Prediction(DLUCP) model with ten socioeconomic and two climate change scenarios for a total of 20 combinations that take into account population increase, technology innovation, climate change, and Grain for Green Project to make high-resolution predictions of land use change in the Yangtze River Economic Belt. Low and high carbon sequestration practices were considered to predict future carbon densities. Land use change data, carbon densities data, and the InVEST model were used to predict changes in ecosystem carbon storage from now to 2070. The results show a slight increase (1.88-4.17%) in carbon storage in the study area only based on land use change. Grain for Green Project has the largest impact on carbon storage among population increase, technology innovation, climate scenarios, and Grain for Green Project, which increases carbon storage by 4.17%. After the implementation of carbon sequestration practices, there is an increase in carbon storages from 28.51 to 56.77% in the study area from now to 2070, and increasing carbon storages of forest in each stream and carbon storage of cropland in downstream are efficient ways to achieve carbon neutralization.

Future land-use change predictions using Dyna-Clue to support mosquito-borne disease risk assessment

Mosquitoes are known vectors for viral diseases in Canada, and their distribution is driven by climate and land use. Despite that, future land-use changes have not yet been used as a driver in mosquito distribution models in North America. In this paper, we developed land-use change projections designed to address mosquito-borne disease (MBD) prediction in a 38 761 km2 area of Eastern Ontario. The landscape in the study area is marked by urbanization and intensive agriculture and hosts a diverse mosquito community. The Dyna-CLUE model was used to project land-use for three time horizons (2030, 2050, and 2070) based on historical trends (from 2014 to 2020) for water, forest, agriculture, and urban land uses. Five scenarios were generated to reflect urbanization, agricultural expansion, and natural areas. An ensemble of thirty simulations per scenario was run to account for land-use conversion uncertainty. The simulation closest to the average map generated was selected to represent the scenario. A concordance matrix generated using map pair analysis showed a good agreement between the simulated 2020 maps and 2020 observed map. By 2050, the most significant changes are predicted to occur mainly in the southeastern region’s rural and forested areas. By 2070, high deforestation is expected in the central west. These results will be integrated into risk models predicting mosquito distribution to study the possibility of humans’ increased exposure risk to MBDs.

Impacts and prediction of land use change on ecosystem carbon sequestration in Zhejiang Province, China.

It is crucial to clarify the impact of land use change on ecosystem carbon sequestration service for exploring natural-based carbon neutral solutions. We used InVEST and FLUS models to analyze land use change and its impacts on ecosystem carbon sequestration service in Zhejiang Province from 2000 to 2021, and predict ecosystem carbon sequestration service potential and its economic value of land use pattern in 2035 and 2050 under three scenarios of natural development, ecological protection, and cultivated land protection. The results showed that the area of cultivated land, forest, grassland, and water was continuously reducing and the area of construction land was continuously increasing from 2000 to 2021 in Zhejiang Province. The total amount of ecosystem carbon sequestration service decreased by 29.9658 million t, and the net income of carbon sequestrqtion service was -170.7184 million dollars. The distribution of ecosystem carbon sequestration services showed a spatial pattern of high in the southwest and low in the northeast. In the future, the total amount of ecosystem carbon sequestration services in Zhejiang Province would be the highest under the ecological protection scenario, followed by the cultivated land protection scenario, and the lowest under the natural development scenario. From 2021 to 2035 and 2050, ecosystem carbon sequestration services in Zhejiang Province would increase by 3.2326 million and 4.73 million t respectively under the ecological protection scenario, generating carbon sequestration service benefits of 77.0786 million and 111.8391 million dollars, respectively. Under the cultivated land protection scenario, it would be reduced by 10.1318 million and 16.1611 million t, and the net loss of carbon sequestration service value would be 241.3849 million and 381.9109 million dollars, respectively. Under the natural development scenario, it would be reduced by 11.6490 million and 16.1651 million t, resulting in a net loss of carbon sequestration service value of 277.5393 million and 382.0063 million dollars, respectively. In the context of actively addressing climate change and striving to achieve the goal of carbon neutrality, Zhejiang Province should focus on protecting ecological land such as woodlands, grasslands, and water, expand the scale of ecological land, optimize the spatial structure of ecological land, and continuously enhance carbon sequestration and sink enhancement functions of ecological land.

Predicting Urban Expansion to Assess the Change of Landscape Character Types and Its Driving Factors in the Mountain City

The urban landscape is being affected by rapid urbanization, leading to a complexity of land features and a fragmentation of patches. However, many studies have focused on the prediction of land-use change with a lack of research on the landscape character types which have more integrated descriptions of land features. Hence, this study predicts and identifies landscape character types (LCTs) in different periods based on the PLUS model and the K-Medoids algorithm, taking the central city of Chongqing as an example, to reveal the differences in the influence of driving factors on LCTs. The results show that (1) the urban landscape characteristic types present a gradient change from the built-up area to the outward expansion. (2) The SHDI and LPI of landscape character types decreased significantly with the expansion of construction land. (3) Nighttime light, distance from water bodies, and distance from the motorways are the main factors affecting the change of landscape character types. This study predicts and identifies urban landscape character types and quantifies the impact of urban expansion on landscape character. It can be used to guide urban planning and help governments to make more informed decisions on sustainable urban development and ecological conservation.

Prediction of Land Use Change in Catchment Area of Buttu Batu DAM, South Sulawesi Indonesia

Land use change is generally caused for social, economic, and cultural reasons. Still, it has occurred not only in urban areas but almost all areas, such as upstream watersheds or catchment areas. Land use is a natural resource influenced by soil, climate, topography, and geology that determine the potential for various uses such as natural water sources, agriculture, transportation, and renewable energy. So this study aims to look at changes in land use and factors that influence these changes. The regional boundary is in the form of the Batu-Batu catchment area as the location of the hydropower dam with a capacity of 2x100 megawatts. It is planned to be built. The geographic information system (gis) is used to see land use changes. Land use projections use cellular automata analysis by looking atgrowth trends and targets on landusesimsoftware.landusesim begins with the neighborly process by entering and managing landuse data as an initial transition map into a potential transition map.The catchment areabuttu batu,which affects the dam plan, has an area of 371,191.77 ha. The existing catchment area of buttu batu is dominated by dryland agricultural land in the form of seasonal crops. Land use projections for 100 years see changes in land use area in several classes, the largest occurring in the land use of dryland agricultural areas with an area of 26,092.24 ha or an increase of 94.78%. Landuse in the form of settlements also increased by 3,223.44 ha or with a percentage change of 5.22 percent. Factors influencing land change in the catchment area include road access, settlement, and riverdistance.

Detection and prediction of land use changes and population dynamics in the Gorganrud River basin, Iran

AbstractAnalyzing land use/land cover change is a fundamental tool for evaluating the environmental consequences of human activities. This research was conducted to detect and predict likely land use changes in the Gorganrud River basin, Iran, and to estimate past and future population growth as a driving force in land use change and degradation. First, land use maps for 1999, 2009, and 2017 were prepared. Then, the likely land use changes for 2030 and 2040 were predicted using the Land Change Modeler (LCM) in TERRSET software. Results indicate that the percentage of changes in agricultural and residential areas, bare lands, and semi‐dense forests from 1999 to 2017 were +4.2, +0.62, +1.76, and +3.15, respectively, while the percentage of changes in rangelands, dense forests, and water bodies were −8.7, −0.37, and −0.63. Analysis of changes from 2017 to 2040 indicates that the percentage of changes in croplands, dense forests, and bare lands may reach −4.42, −2.35, and −2.74, respectively. Conversely, the area of rangelands, semi‐dense forests, water bodies, and residential areas would likely increase by +7.78%, +1.02%, +0.04%, and + 0.7%, respectively. The population density in 2011 and 2016 was 94 and 97 persons/km2, respectively, whereas the 5‐year population growth rate was 3.5%. Better conservation practices to prevent deforestation and inappropriate growth of residential areas, in line with forest replantation to prevent the conversion of semi‐dense forests into rangelands, are some of the management strategies required in the study area. Population control and redistribution are other prescribed actions based on the research findings.

Evolution and prediction of land use around metro stations

Metro stations are considered high-quality resources for promoting urban development, which have great influences on the surrounding land use changes. The simulation and prediction of land use change can provide a scientific basis for urban land planning. In this work, the cellular automata (CA)-Markov model was adopted by taking into account point of interest (POI) kernel density and station accessibility as driving factors to predict the land use change of station surrounding areas. Then, the land type compositions of different years, temporal and spatial evolution of landscape patterns, and strategies of different metro stations were explored. The results show that the Kappa coefficients of the Zoo Station and the Lu Xiao Station are 87% and 79%, respectively, indicating that the improved CA-Markov model can predict land use changes more accurately by considering POI kernel density and station accessibility. Finally, different optimized strategies based on systematic predictions of land use landscape patterns according to the spatial and temporal distribution of metro stations were proposed. The work provides important references for predicting the impact of new metro stations on land use in the future and guides the adjustment and optimization of land use policy planning.

Identification and prediction of land use changes based on artificial neural network and CA-Markov for sustainable land-use planning

Identification and prediction of land use changes based on artificial neural network and CA-Markov for sustainable land-use planning

Agriculture and aquaculture land-use change prediction in five central coastal provinces of Vietnam using ANN, SVR, and SARIMA models

Abstract Vietnam’s economy with agriculture and aquaculture still account for roughly 26% of the country’s gross domestic product, and nearly 70% of the Vietnamese population lives in rural areas; therefore, agriculture and aquaculture land use play a crucial role in the development process of Vietnam. Rapidly increasing population and infrastructure in rural areas and industrial zones lead to these land-use changes. Hence, these land-use change predictions are crucial for local authorities and the local people to make land-resource funds and set up planning. This article suggests support vector regression (SVR), artificial neural network (ANN), and seasonal autoregressive integrated moving average (SARIMA) methods to predict land-use change. By comparing the three models, the results indicate that almost all of the SVR models improve the accurate performance more than ANN and SARIMA in Quangtri, ThuThienHue, Danang, and Quảngnam provinces. Furthermore, the ANN model indicates more accurate forecasting than the SVR and SARIMA models in Quan Binh province. The result may be support for the Ministry of Natural Resources and Environment to conduct the land-use inventory and upgrade agriculture and aquaculture land-use change maps every 5 years. Afterward, the Department of Natural Resources and Environment’s provinces use the estimating database and update it manually.

Spatial Dynamic Analysis of Changes in Land Use Applying Markov Chain and Cellular Automata

Changes in land use in the Alo Pohu watershed have concerned the government in preventing the siltation of Limboto Lake, along with floods and slides. On that ground, the present study was conducted to analyze the spatial prediction of land-use changes in the Alo Pohu watershed, Gorontalo Regency, Indonesia, in the 2000-2017 period for the 2030 land use prediction. The method and analysis of spatial data to predict land-use changes involved the Powersim Version 10 and Idrisi apps. Further, the data consisted of two maps, i.e., the Indonesian topographical map and land use map. The Markov chain model applied to predict the land in the site area for the year of 2030 has an excellent suitability/agreement. Based on the validation test with the Kappa Index of Agreement, the value of K-standard gets 0.8 from 0-1 scale. Another validation using Google Earth is also employed, in which it shows an 83% of suitability level. Confirmed by the integration of the dynamic system simulation model, the map of land use resulting from the modeling is therefore scientifically acceptable. The study concluded that land conversion in the Alo Pohu watershed in Gorontalo Province has occurred.

Prediction of land use changes at a metropolitan city using integrated cellular automata: past and future

ABSTRACT In the cities of developing countries like India, rapid and uncontrolled urbanization has been taking place due to continuous population growth in last few decades. As a result, land use/land cover (LU/LC) is changing very fast in the cities of developing countries. Therefore, this study aims to examine the changes in LU/LC pattern in Delhi during 1991–2018 and simulate the future LU/LC pattern of Delhi for 2030. The LU/LC pattern mapping was done from Landsat datasets using k-means clustering technique. The cellular automata (CA) technique was integrated with artificial neural network (ANN) for simulating the future LU/LC patterns. The projected LU/LC pattern shows that Delhi’s built-up area will increase to nearly 60% of the total area of city, while cropland and open land will decrease to 19.86 and 0.15%, respectively. The highest increase in built-up area was observed in the northern, western, and south-western sub-districts of Delhi. Outcomes of the study may be used for future land use planning in Delhi and other cities. In addition, they can also provide valuable insights for the development of transportation network and other facilities and amenities in the areas of future urban expansion.

PREDICTING OF URBAN EXPANSION USING CONVOLUTIONAL LSTM NETWORK MODEL: THE CASE OF SEOUL METROPOLITAN AREA, KOREA

Abstract. As urbanization progresses, many studies about the analysis and prediction of land-use change and urban sprawl have been conducted recently. As the sprawl phenomenon progresses rapidly, the urban expansion phenomenon became uncontrolled and it has affected negatively on the city's environment and transportation finally. So, it is essential to identify lands likely to be urbanized in the future because it aids in establishing land use plans and policies pre-acting the negative impact of spatially urban expansion the sprawl by determining factors affecting the urban sprawl. Previous studies based on statistical models are limited to identifying determining factors, so the prediction performance is low compared to deep learning. On the other hand, existing studies using machine learning and deep learning overlook selecting specific region-focused variables. Therefore, this study aims to analyze and predict changes in the Seoul Metropolitan Area's sprawl in Korea using the Convolutional Long Short-Term Memory Network (ConvLSTM) with factors at the city scale and neighboring factors at the local scale in the Seoul Metropolitan Area (SMA). ConvLSTM is a type of combination model: combining Recurrent Neural Network(RNN) and Convolutional Neural Network(CNN). This study showed that ConvLSTM with factors at the city and neighboring factors at the local scale predicted the urbanized land. The determinants contain population and roads ratio at the city scale, and neighboring urban lands, distance to the nearest subway stations, slope, and elevation at the local scale. The results reveal that predicted urban lands in 2020 increase over the entire region. In particular, the expected urban lands in 2020 increase by reducing farmlands in the southern part of the SMA. It is consistent with the trend of urbanized lands from 1980 to 2010. In addition, urbanization occurs in areas adjacent to Seoul due to the well-established urban infrastructure. The results of this study can be used as evidence to establish sustainable land use plans and regulations in the future.

Evaluation and prediction of land use change impacts on ecosystem service values in Nanjing City from 1995 to 2030.

Land use changes have a considerable impact on ecosystem services (ESs). In recent years, land use changes caused by urban expansion in Nanjing City have been obvious and are expected to further change in the future. Therefore, it is urgent to quantitatively assess ecosystem service value (ESV) changes caused by previous land use changes and future potential changes in Nanjing. In this study, land use data products based on remote sensing images, Dyna-CLUE model, and the ESV equivalent coefficient method were applied to assess the impact of land use changes on ESVs in Nanjing City over the past 23 years (1995-2018), and to forecast the changes of ESV in 2030. The results indicated that the total ESVs of Nanjing City displayed a trend of first increasing and then declining in 1995-2018. From the land use classification, the water area had the largest ESV in Nanjing, followed by arable land. Additionally, the regulating service value was the highest among the four primary ESs in Nanjing from 1995 to 2030, with the highest value of 13.73 billion yuan in 2015. Among the three forecast scenarios, the ecological protection scenario had the highest total ESV and was followed by the urban expansion and business as usual scenarios. These findings may assist for the scientific decision-making of sustainable land use and ecosystem management in Nanjing City.

Land Use and Land Cover Change Assessment and Future Predictions in the Matenchose Watershed, Rift Valley Basin, Using CA-Markov Simulation

Land use and land cover change (LULC) is known worldwide as a key factor of environmental modification that significantly affects natural resources. The aim of this study was to evaluate the dynamics of land use and land cover in the Matenchose watershed from the years 1991, 2003, and 2020, and future prediction of land use changes for 2050. Landsat TM for 1991, ETM+ for 2003, and Landsat-8 OLI were used for LULC classification for 2020. A supervised image sorting method exhausting a maximum likelihood classification system was used, with the application using ERDAS Imagine software. Depending on the classified LULC, the future LULC 2050 was predicted using CA-Markov and Land Change Models by considering the different drivers of LULC dynamics. The 1991 LULC data showed that the watershed was predominantly covered by grassland (35%), and the 2003 and 2020 LULC data showed that the watershed was predominantly covered by cultivated land (36% and 52%, respectively). The predicted results showed that cultivated land and settlement increased by 6.36% and 6.53%, respectively, while forestland and grassland decreased by 63.76% and 22.325, respectively, from 2020 to 2050. Conversion of other LULC categories to cultivated land was most detrimental to the increase in soil erosion, while forest and grassland were paramount in reducing soil loss. The concept that population expansion and relocation have led to an increase in agricultural land and forested areas was further reinforced by the findings of key informant interviews. This study result might help appropriate decision making and improve land use policies in land management options.