Cellular Automata Markov Research Articles

Hydrological Response to Climate and Land Use Changes in the Dry–Warm Valley of the Upper Yangtze River

The hydrological process in the dry–warm valley of the mountainous area of southwestern China has unique characteristics and has attracted scientific attention worldwide. Given that this is an area with fragile ecosystems and intensive water resource conflicts in the upper reaches of the Yangtze River, a systematic identification of its hydrological responses to climate and land use variations needs to be performed. In this study, MIKE SHE was employed and calibrated for the Anning River Basin in the dry–warm valley. Subsequently, a deep learning neural network model of the long short-term memory (LSTM) and a traditional multi-model ensemble mean (MMEM) method were used for an ensemble of 31 global climate models (GCMs) for climate projection. The cellular automata–Markov model was implemented to project the spatial pattern of land use considering climatic, social, and economic conditions. Four sets of climate projections and three sets of land use projections were generated and fed into the MIKE SHE to project hydrologic responses from 2021 to 2050. For the calibration and first validation periods of the daily simulation, the coefficients of determination ( R ) were 0.85 and 0.87 and the Nash–Sutcliffe efficiency values were 0.72 and 0.73, respectively. The advanced LSTM performed better than the traditional MMEM method for daily temperature and monthly precipitation. The average monthly temperature projection under representative concentration pathway 8.5 (RCP8.5) was expected to be slightly higher than that under RCP4.5; this is contrary to the average monthly precipitation from June to October. The variations in streamflow and actual evapotranspiration (ET) were both more sensitive to climate change than to land use change. There was no significant relationship between the variations in streamflow and the ET in the study area. This work could provide general variation conditions and a range of hydrologic responses to complex and changing environments, thereby assisting with stochastic uncertainty and optimizing water resource management in critical regions.

Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach

Considering urbanization can lead to irreversible land transformations, it is crucial to provide city managers, environmental resources managers, and even people with accurate predicting land use/land cover (LULC) to accomplish sustainable development goals. Although many methods have been used to predict land use/land cover (LULC), few studies have compared them. Therefore, by analyzing the results of various prediction models and, consequently, recognizing the most accurate and reliable ones, we can assist city managers, environmental resources managers, and researchers.. In this regard, this research compares Cellular Automata–Markov Chain and Artificial Neural Network (ANN) as frequently used models to overcome this gap and help those concerned about sustainable development to predict urban sprawl with the most reliable accuracy. In the first step, Landsat satellite images acquired in 2000, 2010, and 2020 were classified with Maximum Likelihood Classification (MLC), and LULC maps were prepared for each year. In the second step, to investigate the LULC prediction, validation of the CA–Markov and ANN methods was performed. In this way, the LULC simulation map of 2020 was prepared based on the LULC map of 2000 and 2010; next, the predicted LULC map of 2020 and the actual LULC map for 2020 were compared using correctness, completeness, and quality indices. Finally, the LULC map for 2030 was generated using both algorithms, and the corresponding change map was extracted, showing a reduction in soil and vegetation areas (respectively, 39% and 12%) and an expansion (58%) in built-up regions. Moreover, the validation test of the methods showed that the two algorithms were closer to each other; however, ANN had the highest completeness (96.21%) and quality (93.8%), while CA–Markov had the most correctness (96.47%). This study showed that the CA–Markov algorithm is more accurate in predicting the future of larger areas with higher allocations (urban and vegetation cover) while the ANN algorithm is more accurate in predicting the future of small areas with fewer allocations (soil and rock).

Geospatial modeling to assess the past and future land use-land cover changes in the Brahmaputra Valley, NE India, for sustainable land resource management.

Satellite remote sensing and geographic information system (GIS) have revolutionalized the mapping, quantifying, and assessing the land surface processes, particularly analyzing the past and future land use-land cover (LULC) change patterns. Worldwide river basins have observed enormous changes in the land system dynamics as a result of anthropogenic factors such as population, urbanization, development, and agriculture. As is the scenario of various other river basins, the Brahmaputra basin, which falls in China, Bhutan, India, and Bangladesh, is also witnessing the same environmental issues. The present study has been conducted on the Brahmaputra Valley in Assam, India (a sub-basin of the larger Brahmaputra basin) and assessed its LULC changes using a maximum likelihood classification algorithm. The study also simulated the changing LULC pattern for the years 2030, 2040, and 2050 using the GIS-based cellular automata Markov model (CA-Markov) to understand the implications of the ongoing trends in the LULC change for future land system dynamics. The current rate of change of the LULC in the region was assessed using the 48years of earth observation satellite data from 1973 to 2021. It was observed that from 1973 to 2021, the area under vegetation cover and water body decreased by 19.48 and 47.13%, respectively. In contrast, cultivated land, barren land, and built-up area increased by 7.60, 20.28, and 384.99%, respectively. It was found that the area covered by vegetation and water body has largely been transitioned to cultivated land and built-up classes. The research predicted that, by the end of 2050, the area covered by vegetation, cultivated land, and water would remain at 39.75, 32.31, and 4.91%, respectively, while the area covered by built-up areas will increase by up to 18.09%. Using the kappa index (ki) as an accuracy indicator of the simulated future LULCs, the predicted LULC of 2021 was validated against the observed LULC of 2021, and the very high ki observed validated the generated simulation LULC products. The research concludes that significant LULC changes are taking place in the study area with a decrease in vegetation cover and water body and an increase of area under built-up. Such trends will continue in the future and shall have disastrous environmental consequences unless necessary land resource management strategies are not implemented. The main factors responsible for the changing dynamics of LULC in the study area are urbanization, population growth, climate change, river bank erosion and sedimentation, and intensive agriculture. This study is aimed at providing the policy and decision-makers of the region with the necessary what-if scenarios for better decision-making. It shall also be useful in other countries of the Brahmaputra basin for transboundary integrated river basin management of the whole region.

Spatio-temporal assessment of land use land cover based on trajectories and cellular automata Markov modelling and its impact on land surface temperature of Lahore district Pakistan.

This research aims to assess the urban growth and impact on land surface temperature (LST) of Lahore, the second biggest city in Pakistan. In this research, various geographical information system (GIS) and remote sensing (RS) techniques (maximum likelihood classification (MLC)) LST, and different normalized satellite indices have been implemented to analyse the spatio-temporal trends of Lahore city; by using Landsat for 1990, 2004, and 2018. The development of integrated use of RS and GIS and combined cellular automata-Markov models has provided new means of assessing changes in land use and land cover and has enabled the projection of trajectories into the future. Results indicate that the built-up area and bare area increased from 15,541 (27%) to 23,024 km2 (40%) and 5756 km2 (10%) to 13,814 km2 (24%). Meanwhile, water area and vegetation were decreased from 2302 km2 (4%) to 1151 km2 (2%) and 33,961 km2 (59%) to 19,571 km2 (34%) respectively. Under this urbanization, the LST of the city was also got affected. In 1990, the mean LST of most of the area was between 14 and 28 ℃, which rose to 22-28 ℃ in 2004 and 34 to 36 ℃ in 2018. Because of the shift of vegetation and built-up land, the surface reflectance and roughness of each land use land cover (LULC) class are different. The analysis established a direct correlation between Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI) with LST and an indirect correlation among Soil Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI), and Built-up Index (BI) with LST. The results are important for the planning and development department since they may be used to guarantee the sustainable utilization of land resources for future urbanization expansion projects.

Spatial optimization of ecological ditches for non-point source pollutants under urban growth scenarios.

Non-point source (NPS) pollution is regarded as the major threat to water quality worldwide, and ecological ditches (EDs) are considered an important and widely used method to collect and move NPS pollutants from fields to downstream water bodies. However, few studies have been conducted to optimize the spatial locations of EDs, particularly when the watershed experiences urbanization and rapid land-use changes. As land-use patterns change the spatial distribution of NPS loads, this study used a cellular automata-Markov method to simulate future land-use changes in a typical agricultural watershed. Three scenarios are included as follows: historical trend, rapid urbanization, and ecological protection scenarios. The spatial distributions of particulate phosphorus loads were simulated using the revised universal soil loss equation and sediment transport distribution model. The results suggested that the total particulate phosphorus (TP) load in the Zhuxi watershed decreased by 10,555.2kg from 2000 to 2020, primarily because the quality and quantity of forests in Zhuxi County improved over the last 20years. The TP load in Zhuxi watershed would be 2588.49, 2639.15, and 2553.32kg in 2040 in historical trend, rapid urbanization, and ecological protection scenarios, respectively, compared with 2308.1kg in 2020. This indicated that urban expansion increases the TP load, and the faster the expansion rate, the more the TP load. Consequently, the optimal locations of EDs were determined based on the intercepted loads and the period during which they existed during land-use changes. The results suggested that rapid urbanization would consequently reduce the space available for building EDs and also increase the cost of building EDs to control the NPS pollution in the watershed.

Modeling spatio-temporal assessment of land use land cover of Lahore and its impact on land surface temperature using multi-spectral remote sensing data.

Urban sprawl, also widely known as urbanization, is one of the significant problems in the world. This research aims to assess and predict the urban growth and impact on land surface temperature (LST) of Lahore as well as land use and land cover (LULC) with a cellular automata Markov chain (CA-Markov chain). LULC and LST distributions were mapped using Landsat (5, 7, and 8) data from 1990, 2004, and 2018. Long-term changes to the landscape were simulated using a CA-Markov model at 14-year intervals from 2018 to 2046. Results indicate that the built-up area was increased from 342.54 (18.41%) to 720.31 (38.71%) km2. Meanwhile, barren land, water, and vegetation area was decreased from 728.63 (39.16%) to 544.83 (29.28%) km2, from 64.85 (3.49%) to 34.78 (1.87%) km2, and from 724.53 (38.94%) to 560.63 (30.13%) km2, respectively. In addition, urban index, a non-vegetation index, accurately predicted LST, showing the maximum correlation R2 = 0.87 with respect to retrieved LST. According to CA-Markov chain analysis, we can predict the growth of built-up area from 830.22 to 955.53 km2 between 2032 and 2046, based on the development from 1990 to 2018. As urban index as the predictor anticipated that the LST 20-23°C and 24-27°C, regions would all decline in coverage from 5.30 to 4.79% and 15.79 to 13.77% in 2032 and 2046, while the temperature 36-39°C regions would all grow in coverage from 15.60 to 17.21% of the city. Our results indicate severe conditions, and the authorities should consider some strategies to mitigate this problem. These findings are significant for the planning and development division to ensure the long-term usage of land resources for urbanization expansion projects in the future.

Integration of LULC change/prediction and hydrological modeler for assessment of the effect of LULC Change on peak discharge in Sari Watershed, Sumbawa Island, Indonesia

Flood is the main hazard that frequently occurs in Sari Watershed, Sumbawa Island, West Nusa Tenggara Province, Indonesia. The Land Use Land Cover (LULC) change, especially from the forest into non-forest coverage, may have contributed to the increased surface runoff and flooding frequency downstream. Because of the contradicting effects that these processes have on the risk of flooding, it is difficult to predict what the long-term impact of LULC changes will be on flooding in Sari Watershed. This study evaluated the effect of LULC change and prediction on peak discharge in the Sari watershed from 1990 to 2030. LULC maps of 1990, 1995, 2000, 2005, 2010, 2015, and 2020 were obtained by Landsat images classification, whereas 2025 and 2030 were from prediction. LULC change and prediction analysis used the ANN (Artificial Neural network)-CA (Cellular Automata) Markov model. Peak discharge for each year was calculated using the HEC-HMS model. The relationship between LULC change and peak discharge was analyzed with correlation analysis. The result showed that the trend of peak discharge dynamic was consistent with the tendencies of LULC change, particularly from forest coverage to non-forest coverage. Based on correlation analysis, the peak discharge dynamic correlated with the shift in LULC during the analysis period. The study result may provide a reference for flood risk reduction and LULC planning in Sari Watershed.

Estimation of Water Balance for Anticipated Land Use in the Potohar Plateau of the Indus Basin Using SWAT

Land Use/Land Cover (LU/LC) change is among the dominant driving factors that directly influence water balance by transforming hydrological responses. Consequently, a thorough comprehension of its impacts is imperative for sustainable water resource planning and development, notably in developing worlds such as Pakistan, where agriculture is a major livelihood. This research intends to assess the continuing changes in LU/LC and evaluate their probable repercussions on the hydrological regime of the Potohar Plateau. The maximum likelihood classification (MLC) algorithm for land use classification of the high-resolution satellite imageries, the Cellular-Automata Markov Chain Model (CA-MCM) for the projection of LU/LC maps, and the Soil and Water Assessment Tool (SWAT) in tandem with SWAT-CUP for hydrological modeling were employed in this research. The high-resolution climatic dataset (10 × 10 km) was used in SWAT. The LU/LC analysis revealed a continual propagation of agricultural and built-up lands at the detriment of forest and barren land during the last three decades, which is anticipated to continue in the future, too. Hydrological model calibrations and validations were performed on multi-basins, and the performance was evaluated using different statistical coefficients, e.g., the coefficient of determination (R2), Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS). The results yielded that the model performed very well and demonstrated the model’s robustness in reproducing the flow regime. The water balance study revealed that the anticipated LU/LC changes are projected to decrease the mean annual surface runoff, water yield, and streamflow due to an increase in percolation, lateral flow, sub-surface flow, and evapotranspiration. More significant variations of the water balance components were observed at the sub-basin level, owing to the heterogeneous spatial distribution of LU/LC, than at the basin level. The outcome of this study will provide pragmatic details to legislative bodies to develop land and water management ameliorative strategies to harness hydrological changes sustainably.

Occurrence Prediction of Pine Wilt Disease Based on CA–Markov Model

Pine wilt disease (PWD) has become a devastating disease that impacts China’s forest management. It is of great significance to accurately predict PWD on a geospatial scale to prevent its spread. Using the Cellular Automata (CA)–Markov model, this study predicts the occurrence area of PWD in Anhui Province in 2030 based on PWD-relevant factors, such as weather, terrain, population, and traffic. Using spatial autocorrelation analysis, direction analysis and other spatial analysis methods, we analyze the change trend of occurrence data of PWD in 2000, 2010, 2020 and 2030, reveal the propagation law of PWD disasters in Anhui Province, and warn for future prevention and control direction and measures. The results show the following: (1) the overall accuracy of the CA–Markov model for PWD disaster prediction is 93.19%, in which the grid number accuracy is 95.19%, and the Kappa coefficient is 0.65. (2) In recent 20 years and the next 10 years, the occurrence area of PWD in Anhui Province has a trend of first decreasing and then increasing. From 2000 to 2010, the occurrence area of disasters has a downward trend. From 2010 to 2020, the disaster area has increased rapidly, with an annual growth rate of 140%. In the next 10 years, the annual growth rate of disasters will slow down, and the occurrence area of PWD will reach 270,632 ha. (3) In 2000 and 2010, the spatial aggregation and directional distribution characteristics of the map spots of the PWD pine forest were significant. In 2020 and 2030, the spatial aggregation is still significant after the expansion of the susceptible area, but the directional distribution is no longer significant. (4) The PWD center in Anhui Province shows a significant trend of moving southward. From 2010 to 2020, the PWD center moved from Chuzhou to Anqing. (5) PWD mainly occurs in the north slope area below 700 m above sea level and below 20° slope in Anhui Province. The prediction shows that the PWD disaster will break through the traditional suitable area in the next 10 years, and the distribution range will spread to high altitude, high slope, and sunny slope. The results of this study can provide scientific support for the prevention and control of PWD in the region and help the effective control of PWD in China.

Comparative framework for spatially explicit urban growth modeling for monitoring urban land-use efficiency and sustainable urban development (SDG 11.3.1): a study on Kolkata metropolitan area, India

The present study attempts to model the spatiotemporal urban growth of the Kolkata metropolitan area (KMA), India, in a comparative modeling framework using three (remote sensing and geographic information system) RS-GIS integrated techniques, namely stochastic-choice Markov-chain (STCHOICE), cellular automata-Markov (CA-MARKOV), and multi-layer perceptron neural network (MLPNN) coupled with Markov-chain approaches intending to monitor land-use efficiency defined by United Nations for sustainable urban development in KMA. In order to find out the best modeling approach, each of the three techniques is engaged in modeling KMA’s growth, and the model thus obtained is employed to predict future growth. The MLPNN (Kappa = 0.9025) appears to be a considerably better approach as compared to the other two approaches: CA-MARKOV (Kappa = 0.6941) and STCHOICE (Kappa = 0.5392). The MLPNN simulated urban growth for 2036 reveals that the urban built-up cover is expected to be about 55% from that of 31% in 2016 due to the significant conversion of other land covers. The study reveals that urban and peri-urban areas do not have a similar pattern of land consumption and land-use efficiency in KMA. The central KMA reflects better land-use efficiency due to the compact built-up growth compared to the periphery reflecting leapfrog growth as a result of rapid urban sprawling. HIGHLIGHTS Predicted future urban growth in Kolkata Metropolitan Area (KMA) using comparative modeling framework Monitored urban land use efficiency (SDG 11.3.1) for sustainable urban development in KMA The study reveals that urban and peri-urban land consumption and land use efficiency are different Identified most influential driving factors from the selected factors responsible for the spatial pattern of urban expansion in KMA

Land Use Land Cover Change Analysis for Urban Growth Prediction Using Landsat Satellite Data and Markov Chain Model for Al Baha Region Saudi Arabia

Land Use Land Cover Change (LULCC) and urban growth prediction and analysis are two of the best methods that can help decision-makers for better sustainable management and planning of socioeconomic development in the countries. In the present paper, the growth of urban land use was analyzed and predicted in all districts of the El Baha region (Kingdom of Saudi Arabia) based on high-resolution Landsat, 5, 7, and 8 satellite imagery during the period of study between 1985–2021. Using remote sensing techniques, the LULCC were obtained based on the maximum likelihood classification (MLC), where the geographic information system (GIS) had been used for mapping LULCC classes. Furthermore, Markov cellular automata (MCA) in Idrisi TerrSet was applied for assessing the future growth of urban land use between 2021–2047. The findings of the LULCC analysis based on the MLC indicate great socioeconomic development during the study period and that the urban expansion was at the expense of rangeland, forest and shrubland, and barren land and sand areas, with the contribution of each in the built-up area estimated to be around 9.1% (179.7 km2), 33.4% (656.3 km2) and 57.5% (1131.5 km2), respectively. The simulation of the future LULCC period 2021–2047 revealed a loss in rangeland, forest and shrubland, and barren land and sand by 565, 144 and 105 km2, respectively, where rangeland is the most influenced, its land cover will decrease from 4002 to 3437 km2. From the obtained results based on MCA, urban growth is predicted to be large and it is estimated at around 2607 km2 until the year 2047 with a net increase of 811 km2. The results obtained from this study may provide information to help decision-makers to implement efficient practices for future planning and management of the growth of urban land use, especially Saudi vision 2030.

Prediksi Perubahan Tutupan Lahan di Kecamatan Sirimau, Kota Ambon Menggunakan Celular Automata- Markov Chain

Sirimau District has the largest population in Ambon City which has an impact on the need for higher residential land, this will certainly affect uncontrolled land cover changes and have an impact on land conversion and environmental damage. This study aims to analyze land cover changes in Sirimau District in 2012, 2017, and 2022, and predict land cover in 2031. This study uses the Cellular Automata Markov Chains (CA-MC) method. The results showed that in the period 2012-2031 the area of residential land cover and open land continued to increase along with the increase in population and the high need for built-up land. In contrast to the land cover of agricultural areas and non-agricultural areas, which experienced a decrease in area. The results of the research are expected to be used as a basis for making policies related to the arrangement and good use of space in the future

Spatio-Temporal Classification and Prediction of Land Use and Land Cover Change in Finima Nature Park Bonny Island, Nigeria

Land Use Land Cover (LULC) is naturally dynamic, thus change is inevitable. However, its changes have greatly increased to a frightening proportion as a result of high rate of anthropogenic and natural processes. Consequently, land use and land cover classes of Finima Nature Park were classified and changes were observed within the last 33 years (1987 - 2021) and predicted for the next 33 years (2021 – 2054).
 Remote sensing and Geographic Information System (GIS) were used to achieve the goal. Coordinate points were collected from the various land use and land cover classes as a reference data for accuracy assessment of the classification. Landsat imageries of 1987, 1999, 2010 and 2021 were acquired from The United States Geological Survey (USGS). The imageries were pre-processed, processed and classified into various LULC classes using Maximum Likelihood Classification in Idrisi and ArcGis10.5. Confusion matrix and Cellular Automata (CA) Markov Chain algorithm were used for accuracy assessment and prediction of LULC respectively.
 Results showed that dense vegetation, sparse vegetation, bare land, and water body were the main LULC class in 1987 and 1999, while dense vegetation, sparse vegetation, bare land, water body and built up were observed in the years 2010, 2021 and 2054. The Kappa Coefficient values were 93%, 81%, 83% and 90% for 1987, 1999, 2010 and 2021 respectively; an indication of strong accuracy of the classification. Generally, there were changes in land use and land cover within the study periods. However, changes were mostly observed in the areas of water body and bare lands closer to the sea coast. Hence, the sea was implicated as the major driver of land use and land cover change in the park. The slight decrease in dense vegetation and sparse vegetation from 1999 to 2021, and 2021 to 2054 underscores the importance and benefit of conservation.

Responses of water balance component to land use/land cover and climate change using geospatial and hydrologic modeling in the Gidabo watershed, Ethiopia

The impact of climate change (CC) and land-use/land-cover (LULC) change on water resources poses a major threat globally. This study investigates the separate and combined responses of CC and LULC change on water balance in Gidabo watershed, Ethiopia, using Soil and Water Assessment Tool (SWAT), Cellular Automata-Markov Chain, and regional climate models. CC projection under RCP 4.5 and hydrological modeling show a significant decline in mean annual surface runoff (Q), baseflow (BF), water yield (WY), percolation (PC), and evapotranspiration (ET) during mid-century (2027–2056) as well as late-century (2061–2090) compared to the baseline period (1988–2018) data, largely due to decline in rainfall. Likewise, under RCP 8.5 scenario, the modeling results show a decrease in Q, BF, WY, PC, and ET during these periods. The LULC change (often associated with an expansion in cultivated and urban areas and a reduction in evergreen forest and grassland) alone leads to a positive synergy with an increase in mean annual Q and WY and a negative synergy with a decrease in BF, PC and ET during the period 2018 to 2075. The combined effects of climate and LULC changes have also shown a decline in Q, BF, WY, PC and ET. Overall, the climate change impacts significantly the future Q and WY. However, the combined effect of climate and LULC changes on BF, PC, and ET is more prominent than their separate impacts and it underlines the significance of this study.

Digital mapping and predicting the urban growth: integrating scenarios into cellular automata—Markov chain modeling

Digital mapping and predicting the urban growth: integrating scenarios into cellular automata—Markov chain modeling

Integrating ANNs and Cellular Automata–Markov Chain to Simulate Urban Expansion with Annual Land Use Data

Accurately simulating and predicting the urban expansion process, especially in expeditious urbanization areas, is an important aspect of managing limited land resources and adjusting flawed land use policies. This research was conducted on the basis of a high-temporal-resolution land use dataset to precisely model urban expansion in a rapidly developing zone by integrating the Artificial Neural Network (ANN), cellular automata (CA), and Markov Chain (MC). An urban suitability index (USI) map was created using ANN and fed to CA–MC to identify possible changed-to-urban cells. Two ANNs, multiple-layer perceptron (MLP) and long short-term memory network (LSTM), were implemented as simulation models for comparison. Due to its ability to capture more temporal information, LSTM outperformed MLP in modeling urban expansion dynamics over a short temporal interval. The simulated results were validated by (fuzzy) kappa simulation and the results revealed that the combination of ANN and CA–MC can precisely model the urban development locations due to its strength in revealing the nonlinear relationship between the expansion process and its drivers. The same model was applied to southern Auckland, and the compared results show that the most simulated variance is caused by the land use policies implemented by different types of governments.

Modeling of Cellular Automata Markov Chain for Predicting the Carrying Capacity of Ambon City

Ambon is a city with the highest economic and population growth in Maluku Province, which makes built-up land have high and rapid growth so that it can reduce the carrying capacity of the city's land. This can lead to an imbalance between population and available housing and may result in higher population pressure on available areas. Prediction of spatial modeling is needed as a preventive measure to prevent excessive land cover changes in the future. This study aims to analyze the carrying capacity of residential areas and spatial modeling of land cover changes in Ambon City in 2010, 2015, and 2020 using Cellular Automata Markov Chain (CAMC) and identify settlement patterns based on population density in 2031 in Ambon city. The results of the Cellular Automata Markov Chain (CAMC) analysis show that the residential area in 2031 has increased to 7,910.03 ha and the development of residential areas in 2031 in Ambon City is centered on Sirimau District.

Modeling of carbon sequestration with land use and land cover in the northeastern part of the Nile Delta, Egypt

The world is currently concerned about the phenomenon of climate change and global warming because of the natural disasters that threaten the lives of living organisms on the surface of the earth. Despite this, there is a dearth of studies dealing with reducing carbon emissions in Egypt. We needed to cover this defect in an important area of Egypt, which is the eastern delta region including Manzala Lake. Land use and land cover (LU/LC) represents a vital role in carbon regulation in coastal areas. Our study uses three Landsat images in 2001, 2013, and 2021 to estimate the amount of carbon storage and sequestration and used Land Change Modeler (LCM) with Cellular Automata Markov Chain (CA-MC) model to predict land cover changes in 2031. Results show that in the period between 2013 and 2021, about half of the floating plants have been destroyed and removed. Our results demonstrate that the increased area for agricultural lands from 2013 to 2021 (2306 Mg C/km2) leads to an increased amount of carbon stoke but the decreased area for floating plants (-4508 Mg C/km2) leads to a decrease in carbon storage. Results showed that a remarkable decrease in carbon storage in the future was attributed to anthropogenic activities in the study area and also as a result of the un exploiting of environmental resources. Finally, the total cost of carbon was 1323.4, 1437.9, 1184.8, and 1296.5 $US/ha for 2001, 2013, 2021, and 2032, respectively. Results showed that the minimum cost for 2021 that a noticeable shortage due to the removal of vegetation and an increase the area of lake water. These conversions have to some extent affected the capacity of coastal habitats to sequester and store CO2. For carbon storage, we recommend afforestation of desert areas. The current design for carbon storage and sequestration is a good guide for government and decision makers for suitable sustainable development, climatic regulation, and carbon budget.

MODEL TUTUPAN LAHAN DI DAERAH ALIRAN SUNGAI KOTA AMBON TAHUN 2031MODEL TUTUPAN LAHAN DI DAERAH ALIRAN SUNGAI KOTA AMBON TAHUN 2031: STUDI KASUS DAS WAI BATU GANTUNG, WAI BATU GAJAH, WAI TOMU, WAI BATU MERAH DAN WAI RUHU

Changes in land use/land cover (LULC) in watersheds of Ambon City are influenced by human activities,mostly driven by socio-economic factors. Changes in land use/land cover (LULC) in the watershed in thecenter of Ambon City have the potential to trigger land-use change which will have an impact on landdegradation, water pollution, flooding, and erosion which will increase in the future. Therefore, theutilization and efficiency of land cover in the watershed area must be increased based on rational land cover planning. The objectives of this study were to analyze land cover changes in watersheds in Ambon City in 2012, 2017, and 2022 and predict land cover in 2031. This study used Cellular Automata Markov Chan (CA-MC) and six factors driving the development of settlements. The results showed that from 2012, 2017, and 2022, the land cover for settlements and open land in the watershed in Ambon City continued to increase, while the land cover for non-agricultural areas and agricultural areas decreased in the area. In 2031, residential land has an area of 1,863.34 ha; this is because the residential land cover will continue to increase along with population growth and high demand for land in watersheds.

ANALISIS DAN PREDIKSI PERUBAHAN TUTUPAN LAHAN MENGGUNAKAN MODEL CELULAR AUTOMATA-MARKOV CHAIN DI DAS WAE RUHU KOTA AMBON

The geographical location of the Wae Ruhu watershed is in Sirimau District, which is the sub-district with the largest population in Ambon City and is also the center of economic, educational, and industrial activities; this creates economic and population growth that has the potential to trigger land conversion around this area. This study aimed to analyze land cover changes in Ambon City in 2012, 2017, 2022 and predict land cover in 2031. This study used Cellular Automata Markov Chains modeling to predict land cover changes in 2031. The results showed that the types of land cover are built and land cover. The open area continues to increase in area, while agricultural and non-agricultural areas continue to experience a decrease in area, and water bodies do not experience a decrease or increase in area. The results of land cover predictions in 2031 showed that the built-up area is 345.79 ha, open land is 121.18 ha, agricultural land is 657.35 ha, non-agricultural land is 507.65 ha, and water bodies are 11.46 ha. The results of this study are expected to be used as a reference in making policies related to spatial planning and utilization, especially the Wae Ruhu watershed and can optimize sustainable watershed management as the first step in efforts to mitigate natural disasters.