Cellular Automata Markov Chain Research Articles

Prediction of Land Cover Changes in Evaluation of Regional Spatial Planning in the Keduang Sub-Watershed, Wonogiri Regency

River watersheds have an important function for water absorption for human life. The Pemudang sub-watershed is a national priority because its condition is increasingly critical due to land degradation. This research aims to carry out spatial modeling of land cover changes in 2031 in the Uangng Sub-watershed which will be compared with the 2011-2031 Regional Spatial Plan scenario. The method used uses the Cellular Automata-Markov Chain method. Based on predictions of land cover in 2031, agricultural areas dominate in the Uangng Sub-watershed covering an area of 14,783.4 ha (37.5%). Agricultural area according to the model in 2031 is estimated to increase by 97.5 ha. The difference between the 2031 model area and the region spatial plan is 5,168.9 ha, which means that the region spatial plan settlements are not expected to be able to accommodate future settlements. The forest area in the 2031 model is 12,351.6 ha, while in the region spatial plan it is 12,300.3 ha with an area difference of 51.2 ha. This shows that controlling the use of space in forests is not in accordance with the policies in the region spatial plan.

Impact of land use/land cover change on surface water hydrology in Akaki river catchment, Awash basin, Ethiopia

Watershed reactions to environmental changes is critical for water resource use and management planning. The change in land use/land cover (LUCC) alters surface water hydrology and water balance, subsequently it affects the availability of water resources. This study examined Akaki River catchment surface hydrological responses to future land use scenarios. The future LULC simulated using Cellular Automata-Markov Chain (CA-Markov) model in IDRISI software for 2031, 2041, and 2051 under Business as Usual (BAU) and Forest Patch Protection (FPP) scenarios. Water yield (WY), sediment export (SE), and soil loss (SL) were simulated using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The findings indicate that under BAU, forest and settlement areas increased 42.64% and 31.47%, whereas water body, grass land, and agriculture dropped 31.99%, 25.72%, and 10.4%, respectively, from 2021 to 2051. The FPP scenario shows a 66.42% increase in forest coverage and a 31.97% decrease in cultivated land between 2021 and 2051. Overall settlement expansion will expand by 31.44%, while water body and grassland will decline by 11.12% and 24.9%. As a result of LULC changes in BAU scenario, the proportions of WY (4.36%), SE (26%), and SL (22.45%) increased. The hydrological components exhibited more gradual variations of 0.55%, 18.8%, and 14.24% for the WY, SE, and SL, respectively, under the FPP scenario. Therefore, the alteration has resulted in a concerning state within the area, necessitating the implementation of comprehensive management techniques to restore the hydrology of the catchment.

Model Prediction of Built-Up Land Use on Flood-Prone Areas (a Systematic Literature Review)

Land use is dynamic due to the influence of population growth in an area. High population growth leads to uncontrolled and unplanned changes in land use in an area, resulting in environmental damage, especially in disaster-prone areas. The aim of this study is to review various models suitable for predicting changes in land use concerning flood-prone areas using a literature review method with PRISM guidelines and visualizing literature results using VosViewer software (1.6.19). The results of this study show that SD (System Dynamic Model) and CA-MC (Cellular Automata – Markov Chain) are the most commonly used models in land use change prediction. Meanwhile, the variables most suitable for land use studies, especially built-up land in relation to flood disasters, are population, growth rate, rainfall, slope, GDP, distance from (roads, rivers, and city centers), and LULC.

Approach maximum likelihood classification and cellular automata markov chain model for land use/land cover change prediction in Nagan Raya Country, Indonesia

Approach maximum likelihood classification and cellular automata markov chain model for land use/land cover change prediction in Nagan Raya Country, Indonesia

Spatio-temporal modeling of lake's ecosystem and dynamism in response to changing environment: a case study of L. Ol Bolossat in Kenya.

Lakes' ecosystems are vulnerable to environmental dynamisms prompted by natural processes and anthropogenic activities happening in catchment areas. The present study aimed at modeling the response of Lake Ol Bolossat ecosystem in Kenya to changing environment between 1992 to 2022 and its future scenario in 2030. The study used temperature, stream power index, rainfall, land use land cover, normalized difference vegetation index, slope, and topographic wetness index as datasets. A GIS-ensemble modeling approach coupling the analytical hierarchical process and principal component analysis was used to simulate the lake's extents between 1992 and 2022. Cellular Automata-Markov chain analysis was used to predict the lake extent in 2030. The results revealed that between 1992 and 2002, the lake extent shrunk by about 18%; between 2002 and 2012, the lake extent increased by about 13.58%; and between 2012 and 2022, the lake expanded by about 26%. The spatial-temporal changes exhibited that the lake has been changing haphazardly depending on prevailing climatic conditions and anthropogenic activities. The comparison between the simulated and predicted lake extents in 2022 produced Kno, Klocation, KlocationStrata, K standard, and average index values of 0.80, 0.81, 1.0, 0.74, and 0.84, respectively, which ascertained good performance of generated prediction probability matrices. The predicted results exhibited there would be an increase in lake extent by about 13% by the year 2030. The research findings provide baseline information which would assist in protecting and conserving the Lake Ol Bolossat ecosystem which is very crucial in promoting tourism activities and provision of water for domestic and commercial use in the region.

Analysis of the Direction and Prediction of Built-Up Land Development in Slawi Sub-district, Tegal Regency, Indonesia in 2045

Abstract Slawi Sub-district is characterized by a high population density as it serves as both the capital of Tegal Regency and the central hub of the Tegal Regency government. The large growth of built-up land threatens the balance of the environment and uncontrolled spatial planning. This study aims to predict the development of built-up land in Slawi Subdistrict in 2045 and determine the direction of built-up land development in Slawi Subdistrict in 2007, 2015, 2023, and 2045. The research approach used Geographic Information System with the prediction of Cellular Automata (CA) Markov Chain prediction model. The inputs of the CA Markov model are the driving factors of land cover change consisting of road network, river network, government, settlement, and slope. The results showed that in 2045 the predicted built-up land area was 1,111.72 ha or 71.41% of the total land cover. The development of built-up land in Slawi Sub-district is closely related to the road distribution pattern, where the north and south are influenced by arterial road and rail road, while the east and west are influenced by collector roads.

Prediction of land cover change in the Belawan watershed using the cellular automata-markov chain model

Belawan Watershed is one of the watersheds that cross Medan City, the capital of North Sumatra province. The spatial distribution of land cover affects the hydrological conditions in this watershed. The purpose of this study is to predict land cover in 2032 so that it can be used as information to mitigate changes in forest land cover. The method used is Cellular Automata-Markov Chain modeling with five change parameters (driving factors): distance from the river, distance from the road, altitude, population density, and slope. Land cover changes in the Belawan watershed used for this modeling include land cover in 2012, 2017, and 2022 and predicted land cover in 2032. The results showed that the predicted land cover is the water body 998.409 ha (1.36%), dryland forest 970.399 ha (1.32%), mangrove forest 3,351.496 ha (4.58%), settlement 11,600.105 ha (15.85%), plantation 38,787.924 ha (53.01%), agriculture 8,674.632 ha (11.85%), rice fields 5,568.183 ha (7.6%) and ponds 3,218.175 ha (4.3%). The area of forests and mangroves tends to continue to decline, so mitigation efforts are needed to reduce the reduction rate in forest and mangrove land cover.

Determining future scenarios of urban areas with cellular automata/Markov Chain Model method; example of Ereğli District Konya-Türkiye (2030–2040)

As a result of the rapid increase in the world population, the earth surface has started to be damaged due to natural and artificial effects. The extent of the damage to nature can be determined by examining the temporal changes of land use and land cover (LULC). In order to offer healthier and more sustainable living spaces, scientists have produced many studies on the changes in nature. Within the scope of this study, 5 basic training classes were created with the help of Landsat satellite images and CORINE data, covering the period of 1985–2018 for Ereğli-Bor Sub-Basin, which is one of the 9 sub-basins of Konya Closed Basin located in the Central Anatolian Region of Türkiye. Landsat Satellite images, Google Earth Program and CORINE data were overlaid to create a basic training class as artificial areas, agricultural areas—pasture areas—forest areas and wetlands and these areas were classified by supervised classification method. The study was carried out on an area of approximately 331057 ha in and around Ereğli district. Modeling was carried out with the Cellular Automata (CA) Markov Chain Model to determine the urban development potential in the region. In order to estimate the modeling accuracy, the 2018 prediction model was created according to the 2018 reference map, and the validation between the two data was analyzed with the kappa statistics. According to kappa statistics values, it was determined that K_location and K_standard values were 0.9301 and 0.8935, respectively. As a result of the validation in sufficient standards, future prediction models were applied; future models and result maps were prepared for the years 2030–2040. According to the modeling results, it is estimated that the artificial area class in Ereğli district will reach 122.74 km2 by 2030 and 142.24 km2 in 2040. In addition, it was expressed in detail with the prediction results and maps that there will be a decrease in pasture, forest and agricultural areas in the region until 2030 and 2040. As a result, it is predicted that the ecological balance in the region will change and agricultural production may decrease as a result of the decline in agricultural pasture and forest areas. For this reason, it has been revealed that it is important for the future of humanity that plans such as environmental layout and master development plans to be made by regional manager in the region for the future should be planned in line with the results to be obtained as a result of future prediction models.

Groundwater Recharge and Surface Runoff Modeling Response to Land Use and Land Cover Dynamics in a Mae Wong Watershed of Thailand

This study aims to develop efficient management strategies and assess the spatiotemporal dynamics of land use and land cover (LULC) on surface runoff modeling response dynamics for the long-term sustainability of watersheds. The soil and water assessment tool (SWAT) model was used to evaluate the LULC dynamics on GRSR in the Mae Wong Watershed (MWW) of Thailand. Using Landsat images, three different LULC maps (2011, 2021, and 2031) were created using the cellular automata markov chain (CA-Markov) model, and TerrSet 2020 geospatial monitoring and modeling software. In the overall MWW, the forestland has undergone deforestation and decreased by 2.10% of the total area and 2.72% of the total area has been transformed into agricultural lands due to human activity and population growth. The soil, LULC, weather, and the digital elevation model (DEM) were all used in the SWAT simulation procedure. To understand the groundwater recharge and surface runoff (GRSR) responses of each hydrologic response units (HRUs), the SWAT model was calibrated and verified using streamflow and the sequential uncertainty fitting (SUFI-2) technique from the SWAT calibration and uncertainty program (SWAT-CUP). The results indicate that there is a good agreement for both the calibration and validation phases of all LULC simulations. The study indicated that groundwater recharge has decreased over the last two decades while surface runoff has increased due to the forest area being converted to agricultural land. Thus, the study can support maximizing water management and strategies for systematically attaining sustainability.

Integrated assessment of future climate and land use changes on urban floods: A Markov chain and PCSWMM-based approach for Hyderabad case study.

This research examines the impact of climate change and urban expansion on urban drainage systems in Hyderabad (Zone-XII, Zone-IV&V), India. It employs a Markov chain-based framework to simulate future climate and land changes. Integrated 1D-2D PCSWMM model is used to assess the hazards posed by these changes. Present and future extreme rainfall event(s) (1-10 days) are simulated to determine maximum flooding hours, valuable for resilience studies. Future rainfall events are simulated under four SSP scenarios using CMIP6 Global Climate Models (GCMs): EC-Earth3-Veg, MPI-ESM-1-2-HR, and MPI-ESM-1-2-LR. The Markov Chain Precipitation Generator (MCPG) model downscales grid-scale precipitation data to station-scale. Future urban land expansion is simulated using the Markov Chain-Cellular Automata (MC-CA) model with Terrset. MCPG model is validated using performance measures, and it showed most increased rainfall events under EC-Earth3-Veg. The MC-CA model obtained a Kappa coefficient of 0.89, indicating an increase in imperviousness in future LULC; 6.1% of vegetation and 29.06% of barren land in 2022 will be urbanized by 2075. A significant increase in extreme flood hazard areas for the 1-day and above 7-day events in the both zones is observed from the PCSWMM results. The study highlighted the importance of Markov chains and event duration in flood hazard assessments.

The Potential of Rice Field Development as a Hinterland for The New Capital City

Rice fields in North Penajam Paser Regency continue to experience conversion. The plan to move the capital city to North Penajam Paser Regency can lead to an expansion of paddy field conversion. Babulu Subdistrict is a sub-district in North Penajam Paser Regency that has the largest area of rice fields, so it has considerable potential for providing food for the new capital city. In order to forecast rice fields in 2031 using cellular automata-markov chain modeling and to examine the potential of rice fields in the hinterland area of the future capital city, this study will look at changes in land use from 2009 to 2020. Land use changes were interpreted using 2009 Landsat 5 TM and 2014 and 2020 Maxar Technologies imagery. The driving factors used in this research are distance from road, distance from settlement, distance from river, slope, and elevation. The findings revealed that the number of rice fields in the Babulu Subdistrict raised in 2014 and declined in 2020. The rice fields’ transformations took place in places with easy access and level terrain. Rice fields are predicted to decline in 2031 because they have turned into oil palm plantations. Priority III dominates the potential for rice fields in Babulu Subdistrict with regard to the suitability of land development directives. Meanwhile, Priority I has the smallest area and is in the lowlands, which tend to be flat, far from roads, close to rivers, and immediate to irrigation networks.

Study on the evaluation system and risk factor traceability of receiving water body

Abstract The risk factor of the receiving water body is one of the important factors that affect the self-purification ability of the water body. Analyzing the concentration and removal rate of the target substances such as suspended solid (SS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) in the receiving water body during the life cycle can trace the risk factor. Based on this, this article takes a sewage treatment plant and its receiving water body in Nanjing as the research object, sets up five sampling points, and calculates the target concentration and removal rate using the cellular automata-Markov chain Monte Carlo method. Thus, risk assessment of water bodies can be achieved. The results show that: (1) the concentration of the target substance in each process stage has obviously decreased during the life cycle, with an average concentration of 3–18 mg/L; (2) in the life cycle, the highest removal rate of target concentration in each process section can reach 85%; (3) the concentration of target substances SS, COD, TN, TN, TP, and TP at the outlet of the sewage treatment plant is relatively high. Practice has proven that this research method can assess the risk factors of receiving water bodies, so as to improve the scientific, refined and standardized environmental management.

Spatio-temporal analysis of land use land cover change and its impact on land surface temperature of Sialkot City, Pakistan

The dynamic interplay between urbanization and its impacts on climate is a subject of recent concern, particularly in rapidly urbanizing cities of Pakistan. This research investigated the spatio-temporal effects of urban growth in terms of Land Use Land Cover changes on the thermal environment (Land Surface Temperature) of the Sialkot city, Pakistan using satellite data spanning four distinct time periods (1989, 2000, 2009 and 2020) and predicted changes for year 2030 by employing Cellular Automata Markov Chain Model. Satellite imagery (Landsat 5, 7 and 8) was processed, and maximum likelihood supervised classification was done to generate LULC maps for each of the aforementioned years. In addition to LULC classification, thermal bands of satellite data (for summer and winter) were processed to compute Land Surface Temperature (LST) of the city. The prediction of LULC changes and LST was done for year 2030 using Cellular Automata Markov Chain Model. The accuracy of classified and prediction maps was checked using Kappa Index. The LULC analysis revealed 4.14% increase in the built-up area and 3.43% decrease in vegetation cover of the city during 1989 to 2020. Both land covers are expected to change in the future (year 2030) by + 1.31% (built-up) and − 1.1% (vegetation). Furthermore, a declining trend in the barren land and water bodies was also observed over time. These LULC changes were found affecting the LST of study area. The transformation of vegetation cover into built-up area resulted in an increase in LST over time. A notable rise of 4.5 °C (summer) and 5.7 °C (winter) in the mean LST of Sialkot was observed during 1989 to 2020 and further increases are anticipated in year 2030. This study calls for attention of the policy makers to reduce human impact on the local climate of the city. The study will also help city developers in analyzing the urban population growth trend, finding suitable location to built new infrastructure by governmental authorities and how the rising temperature can affect energy demand and agriculture production of the city in future.

Spatio-temporal prediction of land use and land cover change in Bahi (Manyoni) Catchment, Tanzania, using multilayer perceptron neural network and cellular automata-Markov chain model.

Evaluation of land use and land cover (LULC) change is among vital tools used for tracking environmental health and proper resource management. Remote sensing data was used to determine LULC change in Bahi (Manyoni) Catchment (BMC) in central Tanzania. Landsat satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used, and support vector machine (SVM) algorithm was applied to classify the features of BMC. The obtained kappa values were 0.74, 0.83 and 0.84 for LULC maps of 1985, 2005 and 2021, respectively, which indicates the degree of accuracy from produced being substantial to almost perfect. Classified maps along with geospatial, socio-economic and climatic drivers with sufficient explanatory power were incorporated into MLP-NN to produce transition potential maps. Transition maps were subsequently used in cellular automata (CA)-Markov chain model to predict future LULC for BMC in immediate-future (2035), mid-future (2055) and far-future (2085). The findings indicate BMC is expected to experience significant expansion of agricultural lands and built land from 31.89 to 50.16% and 1.48 to 9.1% from 2021 to 2085 at the expense of open woodland, shrubland and savanna grassland. Low-yield crop production, water scarcity and population growth were major driving forces for rapid expansion of agricultural lands and overall LULC in BMC. The findings are essential for understanding the impact of LULC on hydrological processes and offer insights for the internal drainage basin (IDB) board to make necessary measures to lessen the expected dramatic changes in LULC in the future while sustaining harmonious balance with livelihood activities.

Analysis of The Quality of The Mamasa Sub-Watershed Using The Land Cover Approach and Land Cover Projections in 2031

The biophysical cover that can be seen on the earth’s surface due to regulation, activity, and human intervention is called land cover. In general, changes in land cover can be observed using spatial data from land cover maps from several points in different years. Changes in land cover in a watershed will directly affect the quality of the watershed. This study aims to analyze land cover changes in 2011 – 2016 and 2016 – 2021, analyze land cover change projections in 2031, and analyze watershed quality using the Percentage of Vegetation Cover approach. The overlay analysis approach was used to conduct an examination of land cover change. Land cover change projections were carried out using the Multi-Layer Perceptron method and Cellular Automata (CA) Markov chain modeling in Idrisi TerraSet Software and watershed quality analysis by calculating the PPV entering the plants permanently. The results of this study note that changes in land cover in 2011 – 2016 experienced a change of 763,53 hectares, and in 2016 – 2021, the most significant change was 802,69 hectares. The biggest change in land cover occurred in the closure of dry land agriculture. The results of the projected land cover in 2031 have changed by 4.472,44 hectares, with the most significant change occurring in dry-land agriculture. Calculating the percentage value of vegetation cover shows that the quality of the Mamasa sub-watershed has decreased from very good to sound due to a decrease in permanent vegetation.

Soil erosion prediction using Markov and CA-Markov chains methods and remote sensing drought indicators

Climate change and reduced rainfall in the past decade have resulted in an alarming increase in soil erosion within arid and semi-arid regions, including southern Iran. Given the significance of this issue, our research aimed to predict soil erosion in the southwest of Fars province, Iran, by utilizing climatic indicators and remote sensing. To determine the key indicators of drought influencing soil erosion, the principal component analysis (PCA) method was employed. Subsequently, Markov chains and Cellular Automata Markov chain (CA-Markov) were utilized to forecast soil erosion and drought conditions in 2040. The findings revealed a steady rise in drought frequency within the region from 2000 to 2020. Notably, meteorological indices and meteorological drought exhibited strong correlations with the Standard Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI) (RSPEI = 0.98 and RSPI = 0.97). The correlation analysis between soil erodibility indices and drought identified Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) as significantly linked to erosion rates. The soil erosion predictions using Markov and CA-Markov chains in 2040 indicated that approximately 38% of the region faces erosion risk based on the NDVI index, while around 47% face risk based on the EVI index. Moreover, the southern and eastern regions are projected to experience increased erosion due to the combination of low rainfall and high drought in the upcoming years. These results underscore the profound impact of drought on critical soil properties, including soil moisture, texture, and the adhesion of soil particles. Consequently, drought-induced soil erosion may lead to greater wastage and ecological repercussions. By leveraging remote sensing drought indicators and their influence on soil erosion, effective land management practices can be implemented to prevent soil erosion and mitigate its adverse effects on the ecological potential of watersheds.

A landscape ecology approach: Modeling forest fragmentation with artificial neural networks and cellular‐automata Markov‐chain for improved environmental policy in the southwestern Brazilian Amazon

AbstractLand degradation and forest fragmentation have been prominent issues in the Amazon since the 1970s, primarily driven by the suppression of primary forests due to land use changes. In this study, we propose an innovative approach by integrating artificial neural networks (ANN) and cellular automata Markov chain (CA‐MA) models to predict future land use and assess the associated forest fragmentation using landscape ecology metrics in the Jamari River Basin. The analysis reveals a significant increase in fragmentation between 1985 and 2018, as evidenced by a rise in the number of fragments from 7162 to 28,170. Moreover, we observed a decrease in the core area from approximately 2.5 million hectares to less than 1 million hectares, accompanied by an increase in edge density from 6 to 18 m.ha−1. Additionally, the average distance between fragments expanded from 66 to 95 m. Highlighting the urgency of addressing this issue, our study emphasizes the necessity of exploring effective strategies within the Brazilian public environmental management system. By utilizing various rural policy planning tools, we aim to tackle unsustainable development and mitigate the adverse impacts of land use changes in the region. In conclusion, this research offers an innovative approach that combines ANN and CA‐MA models to predict land use and assess forest fragmentation, shedding light on the alarming trend of land degradation and providing valuable insights for informed decision‐making and sustainable land management practices.

Uncertainty in forecasting land cover land use at a watershed scale: Towards enhanced sustainable land management

Human-induced environmental stressors represent a major source of recent global changes that are reflected through alterations in the land cover land use (LCLU) that are concomitantly driven by natural and socio-economic factors. While the Cellular Automata Markov Chain (CA-MC) process has been advocated as a promising approach in LCLU predictions, this process excludes the underlying processes that drive behavior in the context of LCLU change. Behavioral analyses can uncover motivations by considering a wider range of factors and shedding light on potential uncertainties associated with the CA-MC process. Therefore, the main objective of this study is to incorporate social behavior in the process of LCLU predictions through a novel comparative framework that allows for uncertainty assessment in predicting LCLU changes. We propose a novel comparative framework to assess uncertainty in predicting LCLU changes. The framework compares a behavioral mental model with a Cellular Automata Markov Chain (CA-MC) model expanded through its multi-criteria decision analyses (MCDA) to cover natural and socio-economic factors under a GIS platform. Both models rely on field survey responses of farmers under climate change stress in the same test area as well as historical data for validation and verification with the aim to improve the LCLU prediction ability. Using past land cover maps, the CA-MC approach exhibited a good similarity between simulated and past observed results (71 %) and predicted future urbanization expansion (93 % in 15 years) being the driving force behind the loss of grasslands (73 %) and forests (5 %). However, when both models are compared, agricultural and urban areas exhibited 17 % and 47 % differences reflecting an appreciable level of uncertainty in LCLU predictions. The outcomes highlight the importance of considering behavioral processes in LCLU predictions towards sustainable land management.

Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia

Land Use/Land Cover (LULC) change has inhibited sustainable development for the last millennia by affecting climate, biological cycles, and ecosystem services and functions. In this regard, understanding the historical and future patterns of LULC change plays a crucial role in implementing effective natural resource management. This study aimed to model and characterize the spatiotemporal trajectories of landscape change between the 1984 and 2060 periods. The satellite image spectral information was segmented into seven LULC classes using a hybrid approach of image spectral recognition. The supervised classification technique of Support Vector Machine (SVM) was used to classify the satellite images, whilst the Land Change Modeler (LCM) Module in TerrSet software was used to assess the historical trend and future simulation of LULC dynamics. To predict future landscape changes, transition potential maps were generated using a Multi-layer Perceptron (MLP) neural network algorithm. The findings of the study demonstrated that the Goang Watershed has experienced significant LULC change since 1984. During the 1984–2001, 2001–2022, and 1984–2022 periods, farmland showed a dramatic increasing trend with 7.5 km2/yr−1, 110.3 km2/yr−1, and 64.3 km2/yr−1, respectively. A similar trend was also observed in built-up areas with 0.5 km2/yr−1, 3.2 km2/yr−1, and 2 km2/yr−1. The expansion of farmland and built-up area was at the expense of forest, shrubland, and grasslands. With a business-as-usual scenario, the extent of farmland will continue to increase between 2022 and 2060 while rapid reduction is expected by forest, shrubland, and grasslands. The alarming rate of farmland and built-up area expansion will put significant pressure on biodiversity and ecosystem services in the area. As a result, eco-friendly conservation approaches should be implemented as soon as possible to maintain ecosystem health and encourage sustainable development.

Projection of urban land surface temperature: An inter- and intra-annual modeling approach

An easily applied and efficiently exploited approach was proposed for the prediction of inter- and intra-annual land surface temperature (LST) in a city's central zone. Chengdu is chosen as the study area to demonstrate the applicability of the novel approach. Built-up areas were extracted using Landsat images in 2008, 2013, and 2019 and projected for 2030 using the cellular automata-Markov chain model, indicating that built-up areas grew nearly 109% during 2008–2019 in Chengdu and will continue to grow up to 2030. Employing the multiple linear regression, the LST of built-up areas in 2019 was predicted using the normalized difference built-up index (NDBI) and the mean LST of its surrounding pixels (LST-R3) in 2013 (R2 = 0.981). The model's intra-annual application for 2019 showed a mean LST deviation of 0.93 °C. According to the model's inter-annual application, the projected LST of the built-up areas in 2030 will increase to 36.5 ± 2.0 °C. The results can contribute to achieving a thermally comfortable urban environment by providing insights into the impact of urban expansion on the urban heat island (UHI) phenomenon under the limited availability of continuous LST data.