Simulate Land Use Change Research Articles

Multi-Scenario Simulation of Land-Use Change and Delineation of Urban Growth Boundaries in County Area: A Case Study of Xinxing County, Guangdong Province

Delineating urban growth boundaries (UGBs) by combining the land-use/land-cover (LULC) change simulation method has become common in recent studies. However, few of the existing studies have integrated multi-source big data to analyze the driving factors of LULC dynamics in the simulation. Moreover, most of previous studies mainly focused on the UGBs delineation in macroscale areas rather than small-scale areas, such as the county area. In this study, taking Xinxing County of Guangdong Province as the study area, we coupled a system dynamics (SD) model and a patch-generating land-use simulation (PLUS) model to propose a framework for the LULC change simulation and UGBs delineation in the county area. Multi-source big data such as points of interest (POIs), night-time light (NTL) data and Tencent user density (TUD) were integrated to analyze the driving forces of LULC change. The validation results indicate that the coupled model received high accuracy both in the land-use demand projection and LULC distribution simulation. The combination of multi-source big data can effectively describe the influence of human socio-economic factors on the expansion of urban land and industrial land. The UGBs delineation results have similar spatial patterns with the LULC change simulation results, which indicates that the proposed UGBs delineation method can effectively transform the LULC simulation results into available UGBs for the county area. It has been proven that the proposed framework in this study is effective for the LULC change simulation and UGBs delineation in the county area, which can provide insight on territorial spatial planning in the county area.

Multi‐scenario simulation of land use change based on MCR‐SD‐FLUS model: A case study of Nanchang, China

AbstractModeling land use/cover change (LUCC) is a helpful approach to simulate land use changes, analyze land use driving mechanisms, and support territorial spatial planning. However, there is no single model that comprehensively represents spatiotemporal changes, impact mechanisms, and ecological conservation of land use. This article focuses on Nanchang City and how the minimum cumulative resistance (MCR) model, the system dynamics (SD) model, and the future land use simulation (FLUS) model can be combined and applied to simulate the trends of land utilization change in different future scenarios. This allows us to explore the environmental protection policies and constraints, as well as to assess the socioeconomic regulation mechanisms and dynamic spatial patterns of land use. The results proved that the integrated model is competent in monitoring and projecting the dynamics of LUCC. The areas suitable for ecological protection are mainly located in the southeast, northeast and Meiling Scenic Area of Nanchang. By 2030, the scale of cultivated land will decrease, while the scale of forestland, grassland, water area, and total construction land will increase. Spatially, some cultivated land will be occupied due to the expansion of urban land, while the rural settlement land will be converted into other construction land. The interconversion of ecological lands also encroaches on part of the cultivated land. This scenario simulation based on the coupled MCR‐SD‐FLUS model provides decision‐making options for land resource planning and ecological environment assessment not only in the study region but also in other similar cities.

Probabilistic Cellular Automata modelling and simulation of land-use changes in Okomu National Park

Land use, land cover (LULC) change technique is essential for measuring ecological quality, environmental sustainability, and uncontrolled development at various spatiotemporal scales. To construct effective land use management plans, the probable future scenario of LULC changes can be easily detected utilizing a simulation technique.This study monitors and models spatiotemporal land-use changes in Okomu National Park over two decades (2000 – 2020) to project forest cover changes for the near future. A probabilistic cellular automata (CA) model was created and used to simulate land-use changes with the aim of predicting future land-use scenarios. Landsat7 ETM+ satellite images for years 2000, 2005, 2010, 2015, and 2020 were classified into Forest and Non–Forest using a maximum likelihood supervised classification algorithm. A probabilistic cellular automata model using Moore’s neighborhood with a Von Neumann extension was used to simulate land-use changes for years 2005, 2010, 2015, and 2020 with the year 2000 as the base year.The overall classification accuracy for the years under study was 98.18%, 97.52%, 96.33%, 91.67%, and 94.61%with overall kappa coefficients of 0.97, 0.96, 0.95, 0.86, and 0.91 respectively. State transition probabilities for 2000–2005, 2005–2010, 2010–2015, and 2015–2020 were calculated from the classified images. Simulation accuracywas 77.46%, 74.1%, 70.98%, and 78.27% for the year 2005, 2010, 2015, and 2020 respectively. Projections were made for years 2025 and 2030 and it shows a 27.41% decline from the base year by 2025 anda 29.90% decline by 2030.The amount of forest cover in the actual and simulated land-use changes shows a gradual drop from 185.15 km2 in the base year 2000 to 136.07 and 135.30 km2 in the year 2020, respectively.Spatial simulation models, which provide a scientific basis for supporting sustainable forest management based on different simulation scenarios alsocontribute significantly to the implementation framework for the United Nations' Reducing Emissions from Deforestation and Forest Degradation (REDD/REDD+) program, as well as reference scenarios for REDD/REDD+ incentive payments.

Comparison of multimodel simulations of land use and land cover change considering integrated constraints - A case study of the Fuxian Lake basin

The simulation and future prediction of spatial and temporal land use and land cover change (LUCC) have long been the focus (and challenge) of research on land use change and sustainable development. However, the existing research lacks empirical comparisons among different simulation models under the same conditions in the same region; hence, the most accurate simulation results cannot be obtained through the optimal model. In this paper, taking the Fuxian Lake basin as an example, the CA-Markov, FLUS, and PLUS models are adopted to explore the optimal model for simulating LUCC in this basin by integrating restricted areas of LUCC. This study shows the following. (1) The simulation results are more accurate under the joint restrictions of open water, prohibited development zones and ecological protection red lines than under a single restriction. (2) All three models can simulate the LUCC in the Fuxian Lake basin. Among these models, the PLUS model has the highest simulation accuracy in simulating LUCC at the basin scale and offers the best match of land types in terms of their quantity and spatial distribution. (3) The PLUS model can accurately predict the changes of LULC in the Fuxian Lake basin. The results of this study should help decision makers evaluate land use development patterns and provide a reference for the formulation of regional sustainable development policies.

Multi-Scenario Simulation of Land Use and Habitat Quality in the Guanzhong Plain Urban Agglomeration, China.

Regional habitat quality is a proxy of biodiversity. Simulating changes in land use and habitat quality in urban agglomerations is the scientific basis for promoting the optimal allocation of land resources and building ecological civilizations in urban agglomerations. Therefore, we established a research framework mainly consisting of the Future Land Use Simulation (FLUS) model with the Integrated Valuation of Environmental Services and Tradeoffs (InVEST) model to predict the spatial and temporal distribution of habitat quality. In addition, we set three scenarios which were a natural development scenario, a cultivated land protection scenario, and an ecological protection scenario to analyze the changes of habitat quality in the Guanzhong Plain urban agglomeration in 2035. The results showed that: (1) the FLUS model had an excellent effect on the simulation of land-use change in the Guanzhong Plain urban agglomeration, with an overall accuracy of 0.952 and a kappa coefficient of 0.924. (2) From 2000 to 2035, the cultivated land area of the study area, which was mainly transferred into construction land and grassland, shrank due to the process of urbanization. (3) The habitat quality score of this region gradually decreased from 2000 to 2020, and it continued to decrease to 0.6921 in 2035 under the natural development scenario, while it increased under the other two scenarios. The low-value areas of habitat quality were mainly located in the middle of this region with Xi’an as the core, whereas the high-value areas were mainly distributed in the southern Qinling Mountains and the northern Loess Plateau. (4) Of the different scenarios, the ecological protection scenario had the highest habitat quality, while the natural development scenario had the lowest. Besides this, we also found that the cultivated protection scenario had high habitat quality, which was mainly because the rate of occupation of ecological land was controlled. The results are expected to provide a scientific basis for optimizing the spatial allocation of land resources and promoting the sustainable use of land space in other ecologically fragile urban agglomerations.

Multi-Scenario Simulation of Land Use Changes with Ecosystem Service Value in the Yellow River Basin

Land use change plays a crucial role in global environmental change. Understanding the mode and land use change procedure is conducive to improving the quality of the global eco-environment and promoting the harmonized development of human–land relationships. Large river basins play an important role in areal socioeconomic development. The Yellow River Basin (YRB) is an important ecological protective screen, economic zone, and major grain producing area in China, which faces challenges with respect to ecological degradation and water and sediment management. Simulating the alterations in ecosystem service value (ESV) owing to land use change in the YRB under multiple scenarios is of great importance to guaranteeing the ecological security of the basin and improve the regional ESV. According to the land use data of 1990, 2000, 2010, and 2018, the alterations in the land use and ESV in the YRB over the past 30 years were calculated and analyzed on the basis of six land use types: cultivated land, forestland, grassland, water area, built-up land, and unused land. The patch-generating land use simulation (PLUS) model was used to simulate the land use change in the study area under three scenarios (natural development, cultivated land protection, and ecological protection in 2026); estimate the ESV under each scenario; and conduct a comparative analysis. We found that the land use area in the YRB changed significantly during the study period. The ESV of the YRB has slowly increased by ~USD 15 billion over the past 30 years. The ESV obtained under the ecological protection scenario is the highest. The simulation of the YRB’s future land use change, and comparison and analysis of the ESV under different scenarios, provide guidance and a scientific basis for promoting ecological conservation and high-quality development of river basins worldwide.

Dynamic simulation of urban growth and land use change using an integrated cellular automata and markov chain models: a case of Bahir Dar city, Ethiopia

Dynamic simulation of urban growth and land use change using an integrated cellular automata and markov chain models: a case of Bahir Dar city, Ethiopia

Forecasting Urban Land Use Change Based on Cellular Automata and the PLUS Model

Nowadays, cities meet numerous sustainable development challenges in facing growing urban populations and expanding urban areas. The monitoring and simulation of land use and land-cover change have become essential tools for understanding and managing urbanization. This paper interprets and predicts the expansion of seven different land use types in the study area, using the PLUS model, which combines the Land use Expansion Analysis Strategy (LEAS) and the CA model, based on the multi-class random patch seed (CARS) model. By choosing a variety of driving factors, the PLUS model simulates urban expansion in the metropolitan area of Hangzhou. The accuracy of the simulation, manifested as the kappa coefficient of urban land, increased to more than 84%, and the kappa coefficient of other land use types was more than 90%. To a certain extent, the PLUS model used in this study solves the CA model’s deficiencies in conversion rule mining strategy and landscape dynamic change simulation strategy. The results show that various types of land use changes obtained using this method have a high degree of accuracy and can be used to simulate urban expansion, especially over short periods.

PENGARUH TATA GUNA LAHAN TERHADAP FLUKTUASI DEBIT EMBUNG KAMPUNG BANJAR DI WILAYAH PERKANTORAN PROVINSI KALIMANTAN SELATAN

This study was conducted to find out the value of the drainage coefficient of the office area in South Kalimantan under the existing conditions, the effect of land use changes on fluctuations in discharge at Embung Kampung Banjar and the green open space that must be available with the existence of Embung Kampung Banjar. The existence of Embung Kampung Banjar is expected to reduce the impact of excessive rainfall which causes various kinds of natural disasters. This study usesd the Rational Method analysis to get the value of rainfall intensity in determining the planned discharge, the method of analysis of the coefficient of flow and the research method used for discharge fluctuations in Embung Kampung Banjar that is simulated with the synthetic unit hydrograph ITB –2 Kirpich, Snyder and Mononobe which all calculations by useng Microsoft Excel . The results shows that the simulation of land use change by adding the area of residential housing and offices are evidence of reduced open areas. The calculation of the maximum discharge coefficient of the existing flow using the Synthetic Unit Hydrograph (HSS) ITB 2 Timelag Kirpich of 7.83 m3/s and the results of flood tracking caused a decrease in peak discharge of 5.4 m3/s the largest land use change was found in the flow coefficient is 0.499 with a maximum discharge of 9.69 m3/s where the housing area is 0.85 km2 and offices are 0.47 km2. In Indonesian Law Number 26 of 2007 on Spatial Planning Article 29 paragraph 3 regarding the proportion of Green Open Space (RTH) about 20 percent of the city area. The calculation results of the peak discharge capacity of Embung Banjar Kampung which is equal to 6.8963 m3/s equal to 15 percent of the flow area of Embung Kampung Banjar which is obtained from the maximum discharge calculation, which is 9.69 m3/s. Then the results of the routing flood that caused a decrease in the discharge amounted to 6.6 m3/s, so the overall result is that the reservoir is still safe if overflow occurs in the reservoir.

Determinants of Fire Impact in the Brazilian Biomes

More and more, wildfires are raging in large parts of the world due to a warmer climate, more frequent and severe droughts, and continued land-use changes. In Brazil, the weakening of public environmental policies has further aggravated wildfires with widespread impacts across the country. Here, we investigated the determinants of the impact of fire in the Brazilian biomes using a dataset of burned areas between 2001 and 2019 to simulate its future impact under alternative policy and climate scenarios. We began by deriving a fire impact index using a principal component (PC) analysis comprising the variables: 1. fire intensity, 2. fire recurrence, 3. burned area size, 4. mean time interval between successive fires, and 5. predominance of fires in the dry season. We considered as High Impact Fires (HIF) those areas whose values of the first PC were above the 90th percentile. HIF occurred in the Amazon, Cerrado, and Pantanal, but not in the Atlantic Forest, Pampa, and Caatinga biomes. As the main drivers of HIF, our spatial autoregressive models (SAR) (Amazônia R2 = 0.66, Pantanal R2 = 0.86 and Cerrado R2 = 0.79) indicated the climate (Amazon, 25%, Pantanal, 53%, and Cerrado, 56%) together with land-use change (Amazon, 75%, Pantanal, 25%, and Cerrado, 38%). Most HIF occurred in native vegetation remnants (NVR) (55% in the Amazon, 86% in the Pantanal and 94% in the Cerrado), especially in places close to areas deforested over the last two decades. Only in Pantanal fuel loads (dry biomass) play a major role in HIF (22% of explanation). In the Cerrado, it only accounted for 4% of the observed variability and in the Amazon, it was not a significant factor. Over the analyzed period, HIF imposed a loss of 23%, on average, on the NDVI response of the native vegetation in the Amazon, 19% in the Cerrado and 16% in the Pantanal, thus indicating physiological stress. Simulations of future climate and land-use change pointed to a dramatic increase in HIF by 2050. Under the RCP4.5 and strong environmental governance scenario, HIF in the Cerrado would expand from the current 3% of the biome to 15%, from 7 to 8% in the Pantanal and from 0.7 to 1.2% in the Amazon. In addition, the impact of fire would intensify in 95% of the Cerrado, 97% of the Amazon and 74% of the Pantanal. Effective public and private policies will be vital to mitigate the growing threat of HIF. In this sense, our spatially explicit models can help direct prevention and firefighting programs.

Simulating mixed land-use change under multi-label concept by integrating a convolutional neural network and cellular automata: a case study of Huizhou, China

ABSTRACT Cellular automata (CA) model is a useful tool for simulating spatiotemporal changes of land-use evolution. However, previous CA models have usually ignored the inhomogeneous and mixed land-use situations and simplified the land grid cell as one single land-use type. This study developed a multi-label (ML) convolutional neural network CA (ML-CNN-CA) model to simulate the complex evolution of mixed land uses. The multi-label learning strategy in the proposed model can formulize mixed land uses by labeling multiple land-use types to each grid cell. The transition rules of CA were modified to accommodate the multi-label learning strategy and mined by coupling a CNN network and three interactive mechanisms, including the neighborhood effect, adaptive inertia coefficient, and random factor. The proposed ML-CNN-CA model was applied and examined in a fast-developing city (Huizhou) in the China’s Pearl River Delta region for the mixed land-use simulation during 2009–2013 and 2013–2020. The ML-CNN-CA model with different CNN architectures and a previous artificial neural network (ANN)-based multi-label CA model were also implemented for comparisons. Results show the capability of the ML-CNN-CA model to simulate fine-scale mixed land-use changes with satisfactory performances. Specifically, the simulated mixed land-use patterns agree well with the actual land uses from the morphological perspective. Furthermore, the quantitative assessments demonstrate the performance of the proposed model, showing an accuracy value of 0.912 for 2009–2013 and 0.896 for 2013–2020, and a hamming loss value of 0.048 for 2009–2013 and 0.055 for 2013–2020. The comparisons also show the best performance of the ML-CNN-CA model with the VGG-based architecture and significant outperformance of the proposed model against the previous ANN-based CA model. Multiple sensitivity analyses were also conducted to investigate the uncertainty of the proposed model. The ML-CNN-CA model proposed in this study can provide a new tool for better simulation of fine-scale mixed land-use changes and is expected to help formulate urban planning guidelines and achieve sustainable urban development.

Simulating multiple urban land use changes by integrating transportation accessibility and a vector-based cellular automata: a case study on city of Toronto

ABSTRACT The accessibility provided by the transportation system plays an essential role in driving urban growth and urban functional land use changes. Conventional studies on land use simulation usually simplified the accessibility as proximities and adopted the grid-based simulation strategy, leading to the insufficiencies of characterizing spatial geometry of land parcels and simulating subtle land use changes among urban functional types. To overcome these limitations, an Accessibility-interacted Vector-based Cellular Automata (A-VCA) model was proposed for the better simulation of realistic land use change among different urban functional types. The accessibility at both local and zonal scales derived from actual travel time data was considered as a key driver of fine-scale urban land use changes and was integrated into the vector-based CA simulation process. The proposed A-VCA model was tested through the simulation of urban land use changes in the City of Toronto, Canada, during 2012–2016. A vector-based CA without considering the driving factor of accessibility (VCA) and a popular grid-based CA model (Future Land Use Simulation, FLUS) were also implemented for comparisons. The simulation results reveal that the proposed A-VCA model is capable of simulating fine-scale urban land use changes with satisfactory accuracy and good morphological feature (kappa = 0.907, figure of merit = 0.283, and cumulative producer’s accuracy = 72.83% ± 1.535%). The comparison also shows significant outperformance of the A-VCA model against the VCA and FLUS models, suggesting the effectiveness of the accessibility-interactive mechanism and vector-based simulation strategy. The proposed model provides new tools for a better simulation of fine-scale land use changes and can be used in assisting the formulation of urban and transportation planning.

Monitoring and Simulation of Land-Use Changes in the Bosomtwe District of Ghana Based on CA-Markov Model

Monitoring and Simulation of Land-Use Changes in the Bosomtwe District of Ghana Based on CA-Markov Model

An integrated approach for simulation and prediction of land use and land cover changes and urban growth (Case study: Sanandaj city in Iran)

One of the growing areas in the west of Iran is Sanandaj city, the center of Kordestan province, which requires the investigation of the city's growth and the estimation of land degradation. Today, the combination of remote sensing data and spatial models is a useful tool for monitoring and modeling land use and land cover (LULC) changes. In this study, LULC changes and the impact of Sanandaj city growth on land degradation in geographical directions during the period 1989 to 2019 were investigated. Also, the accuracy of three models, artificial neural network-cellular automata (ANN-CA), logistic regressioncellular automata (LR-CA), and the weight of evidence-cellular automata (WOE-CA) for modeling LULC changes was evaluated, and the results of these models were compared with the CA-Markov model. According to the results of the study, ANN-CA, LR-CA, and WOE-CA models, with an accuracy of more than 80%, are efficient and effective for modeling LULC changes and growth of urban areas.

Dynamic simulation of land use change and assessment of carbon storage based on climate change scenarios at the city level: A case study of Bortala, China

Exploring future changes in land use and carbon storage (CS) under different climate scenarios is important for optimizing regional ecosystem service functions and formulating sustainable socioeconomic development policies. We proposed a framework that integrates the system dynamics (SD) model, patch-generating land use simulation (PLUS) model, and Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) model to dynamically simulate changes in land use/cover change (LUCC) and CS at the city level based on SSP-RCP scenarios provided by the CMIP6. The simulations were applied to Bortala Mongol Autonomous Prefecture in Xinjiang. Changes in LUCC were similar under the SSP126 and SSP245 scenarios, but woodland expansion was more rapid under the SSP126 scenario. Changes in LUCC under the SSP585 scenario were different from those under the other two scenarios, and this was mainly caused by the continuous reduction in woodland area and the rapid expansion of construction land and cultivated land. By 2050, the simulation results revealed that CS was highest under the SSP126 scenario (193.20 Tg), followed by the SSP245 scenario (192.75 Tg) and SSP585 scenario (185.17 Tg). Overall, the results of this study suggest that increases in CS could be achieved by controlling economic growth and population growth, promoting an energy transition, and expanding woodland in the study area.

Simulation and impact assessment of future land use and land cover changes in two protected areas in Tehran, Iran

Exploring land use and land cover (LULC) changes in protected areas (PAs) is critical for effective environmental management and sustainability. PAs are ecological regions which play a critical role in preserving natural, ecological, economic, and even cultural values of specific areas. This study monitored LULC changes in two PAs within Tehran province in Iran, namely Jajrud and Tangeh Vashi, using Landsat imagery for the years 1989-2019 and simulated a future LULC map for 2050 using the Markov chain-cellular automata model. The simulated map indicated that built-up areas will have the top increasing trend in Jajrud by 2050; however, high-density pasture will decrease because of the heavy dependency and immediate vicinity of Jajrud to Tehran, the largest city and the capital of Iran. In Tangeh Vashi, bare land expansion will have the top increasing trend, whereas the volume of high-density pasture will have the top decreasing trend. According to the results, the LULC changes in Jajrud and Tangeh Vashi are already greater than management plan standards and have led to the destruction of valuable biological habitats. Without proper planning and managerial strategies, the continuation of such changes will further destroy ecosystems and cause species extinction and unsustainability in the studied PAs.

Assessing current and projected soil loss under changing land use and climate using RUSLE with Remote sensing and GIS in the Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia

Erosion of soil in the Lake Tana basin poses a critical economic and environmental crisis due to its impacts on agriculture productivity, food security and reservoir sedimentations. This study assesses soil erosion hazards by considering changes of land use and the climate. Simulation of land use change was conducted using CA_Markov chain model while climate was downscaledusing statistical techniques from CanESM2 model. The integration of the revised universal soil loss equation (RUSLE) with a geographic information system and remote sensing were used to estimate the soil loss. To quantify soil loss, six RUSLE model parameters were used: rainfall erosivity (R), soil erodibility (K), topography (LS), cover management (C), support and conservation practices (P). The average annual loss of soil by the 2080s in the basin is projected to be 7.7, 7.5 and 8.3 tons per hectare per year for changes land use, combined changes in land use and climate, and changes in climate under RCP8.5, respectively. Furthermore, by the end of this century, the soil loss in the basin tends to increase by 10.8%, 3.4% and 2.2% by considering changes in land use pattern, combined changes in land use and climate, and changes in climate, respectively, suggesting the aggravation of soil erosion in the basin. Accordingly, the effects of climate variability on annual average loss of soil are relatively higher than the combined effects of changing climate and land use. Consequently, the integration of RUSLE model with geospatial technology is recommended for an effective soil and water conservation measures.

Simulation and Analysis of Land Use Changes Applying Cellular Automata in the South of Quito and the Machachi Valley, Province of Pichincha, Ecuador

Rapid urban growth has historically led to changes in land use patterns and the degradation of natural resources and the urban environment. Uncontrolled growth of urban areas in the city of Quito has continued to the present day since 1960s, aggravated by illegal or irregular new settlements. The main objective of this paper is to generate spatial predictions of these types of urban settlements and land use changes in 2023, 2028 and 2038, applying the Dinamica EGO cellular automata and multivariable software. The study area was the Machachi Valley between the south of the city of Quito and the rural localities of Alóag and Machachi. The results demonstrate the accuracy of the model and its applicability, thanks to the use of 15 social, physical and climate predictors and the validation process. The analysis of the land use changes throughout the study area shows that urban land use will undergo the greatest net increase. Growth in the south of Quito is predicted to increase by as much as 35% between 2018 and 2038 where new highly vulnerable urban settlements can appear. Native forests in the Andes and forest plantations are expected to decline in the study area due to their substitution by shrub vegetation or agriculture and livestock land use. The implementation of policies to control the land market and protect natural areas could help to mitigate the continuous deterioration of urban and forest areas.

Land Use Scenario Simulation and Ecosystem Service Management for Different Regional Development Models of the Beibu Gulf Area, China

Land use change is an important way for human activities to affect ecosystems. Based on the land use demands and policies, the simulation of future land use changes under different scenarios can test the rationality of socio-economic and policy-oriented land use changes. In this study, we set three scenarios of regular growth, ecological protection, and ecotourism development in 2030 for the Beibu Gulf area, China. We simulated the spatial distribution and evolution characteristics of the future landscape pattern using the Scenario Generator Rule Based Module of InVEST. Meanwhile, the ecosystem service value (ESV) was estimated by the improved unit area value equivalent method to reveal the trend of ESVs under different regional development models. The results indicated that the land use changes in the Beibu Gulf during 1999–2014 showed significant spatial heterogeneity. The farmland was mainly distributed in Beihai, the forestland was located in Fangchenggang, while the orchard was concentrated on Qinzhou. Due to economic construction and urban expansion, construction land and aquaculture land were gradually growing, while farmland and mud flat continued to decrease. Between 2014 and 2030, the total ESV decreased in the regular growth scenario and gradually increased in the ecological protection scenario and ecotourism development scenario. In addition, by comparing the three scenarios, the ecotourism development scenario is a more reasonable model for Guangxi Beibu Gulf area, which realized the trade-off between tourism development and resource conservation. Therefore, regional planners should not only consider maximizing ESVs when planning for ecosystem services, but also strive to maintain a reasonable structure of ecosystem services. Some suggestions were provided in this paper at the macro level and the local development model level respectively, which offered some references for the rational allocation of land resources, ecological environmental protection and ecotourism development in the coastal area of Beibu Gulf.

Multi-scenario Simulation of Land Use Change and Its Eco-environmental Effect in Hainan Island Based on CA-Markov Model

Multi-scenario Simulation of Land Use Change and Its Eco-environmental Effect in Hainan Island Based on CA-Markov Model