Controlling the Urban Physical Development in Karawang and Purwakarta Regencies using Quantitative Zoning Approach

Jakarta and Bandung metropolitan areas in Indonesia are experiencing urban expansion, which makes these two metropolitan areas increasingly connected by corridors to become one mega-urban. Karawang and Purwakarta Regencies are part of the Jakarta-Bandung corridor area which then triggers the urban physical development. This study aims to 1) Determine the level of service facilities in Karawang and Purwakarta Regencies; 2) Identify the changes in built-up and paddy fields Land Use/Land Cover (LULC) of Karawang and Purwakarta Regency based on existing and future conditions, and 3) Propose recommendations to control the urban physical development in Karawang and Purwakarta Regency. Analysis of level service facilities was carried out by using the scalogram method. Changes of built-up and paddy fields LULC in the existing and future conditions (projected using the CA-Markov method) are based on LULC of 2005, 2010 and 2018. Recommendations are given based on the grouping of villages with the same characteristics using the quantitative zoning method. Results showed the village development index in 2018 as the level of service facilities indicators, has a high or more developed value in the area around the connecting accessibility route between Jabodetabek and Greater Bandung metropolitan area. Changes in built-up and paddy fields LULC also the same trend as the village development index that is characterized by a fairly large increase in the area of built-up LULC in Karawang and Purwakarta Regencies. Recommendations are given to address specific problems that exist in each village group formed based on the spatial clustering method result

Forest canopy density analysis of Sokpomba Forest Reserve, Edo State

Forest is a dynamic landscape especially in the tropics as a result of high anthropogenic activities. This study therefore, attempts to evaluate the changes in forest canopy density sequel to the interaction between man and forest ecosystem in Sokpomba Forest Reserve from 1990 to 2020. Relevant Remote Sensing and GIS algorithms were used at different levels of this study. Landsat images formed the major input data for the analysis. In addition to the satellite images, ground control points (GCP) picked with the aid of Global Positioning System (GPS) were used to calculate the accuracy assessment of the Forest Canopy Density (FCD) analysis. The high canopy density (HD) decreased from 320.82km2 in 1990 to 292.82km2 in 2020. Conversely, the low canopy density (LD) increased from 171.12km2 in 1990 to 282.82km2 in 2020. The transitioning of the different Forest Canopy Densities from one category to another was also captured in this study. For instance between 2005 and 2020, about 37 km² changed from low density (LD) to no forest (NF). The accuracy assessment shows that the image classification is good in the sense that the Overall Accuracy figures are 69% (1990), 84% (2005) and 85% (2020). This forest modeling technique is very apt when it comes to the monitoring of forest cover dynamics, forest disturbance and ways of mitigating them. Key words: Geographic Information System, Remote sensing, Forest changes, Landsat, FCD, classification, anthropogenic and  urbanization

URBAN SPACE CHANGE AND FUTURE PREDICTION OF KANPUR NAGAR, UTTAR PRADESH USING EO DATA

Urban Land use changes, measurements, and the analysis of rate trends of growth would help in resources management and planning, etc. In this study, we analyze the urban change dynamics using a support vector machine model. This method derives the urban and rural land-use change and various components, such as population growth, built-up areas, and other utilities. Urban growth increases rapidly due to exponential growth of population, industrial growth, etc. The population growth also affects the availability of various purposes in its spatial distribution. In this present study, we carried out using multi-temporal satellite remote sensing data Landsat MSS (Multispectral scanner), ETM+ (Enhanced thematic mapper), OLI (Operational land imager) for the analysis of urban change dynamics between years 1980-1990, 1990-2003, 2012-2020 in Kanpur Nagar city in the state of Uttar Pradesh in India. In our study, we used SVM (Support Vector Machine) Model to analyze the urban change dynamics. A support vector machine classification technique was applied to generate the LULC maps using Landsat images of the years 1980, 1990, 2003, and 2020. Envi and ArcGIS software had used to identify the land cover changes and the applying urban simulation model (CA- Markov model) in Idrisi selva edition 17.0 software. The LULC maps of 2003 and 2020 were used to simulate the LULC projected map for 2050 using (Cellular automata) CA- Markov based simulation model

Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest

We used the Cellular Automata Markov (CA-Markov) integrated technique to study land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab, Pakistan. We plotted the distribution of the LULC throughout the desert terrain for the years 1990, 2006 and 2022. The Random Forest methodology was utilized to classify the data obtained from Landsat 5 (TM), Landsat 7 (ETM+) and Landsat 8 (OLI/TIRS), as well as ancillary data. The LULC maps generated using this method have an overall accuracy of more than 87%. CA-Markov was utilized to forecast changes in land usage in 2022, and changes were projected for 2038 by extending the patterns seen in 2022. A CA-Markov-Chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2038. Analysis of urban sprawl was carried out by using the Random Forest (RF). Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 8.12 to 12.26 km2 and from 18.10 to 28.45 km2 in 2022 and 2038, as inferred from the changes occurred from 1990 to 2022. The LULC projected for 2038 showed that there would be increased urbanization of the terrain, with probable development in the croplands westward and northward, as well as growth in residential centers. The findings can potentially assist management operations geared towards the conservation of wildlife and the eco-system in the region. This study can also be a reference for other studies that try to project changes in arid are as undergoing land-use changes comparable to those in this study

Urban Growth Prediction Using Cellular Automata Markov: A Case Study Using Sulaimaniya City in the Kurdistan Region of North Iraq

Many cities in the Kurdistan Region have witnessed a rapid change in land use during the last two decades. Geographic information systems (GIS) and remote sensing have been broadly utilized to monitor and detect urban growth prediction. In this paper, three Landsat TM 5 and one Landsat 8 of Sulaimaniya city were used to identify and develop an urban growth map for 1991, 1998, 2006 and 2014. A supervised classification approach was applied; in order to predict urban growth, the Markov chain and CA-Markov models were used. The result demonstrates that validation of CA-Markov to forecast 2006 land cover map is ineffective in reasonably predicting land coverage for this time period; however this model had significant validation for the year 2014 and also has a good forecast power for 2024. Keywords Land Use Change/Cover, Urban Growth Prediction, Supervised Classification, Markov Chain, CA-Markov, Validation

Land Use Conflict Detection and Multi-Objective Optimization Based on the Productivity, Sustainability, and Livability Perspective

Land use affects many aspects of regional sustainable development, so insight into its influence is of great importance for the optimization of national space. The book mainly focuses on functional classification, spatial conflict detection, and spatial development pattern optimization based on productivity, sustainability, and livability perspectives, presenting a relevant opportunity for all scholars to share their knowledge from the multidisciplinary community across the world that includes landscape ecologists, social scientists, and geographers. The book is systematically organized into the optimization theory, methods, and practices for PLES (production–living–ecological space) around territorial spatial planning, with the overall planning of PLES as the goal and the promotion of ecological civilization construction as the starting point. Through this, the competition and synergistic interactions and positive feedback mechanisms between population, resources, ecology, environment, and economic and social development in the PLES system were revealed, and the nonlinear dynamic effects among subsystems and elements in the system identified. In addition, a series of optimization approaches for PLES is proposed

Urban Growth Prediction of Special Economic Development Zone in Mae Sot District, Thailand

Since the ASEAN Economic Community (AEC) was activated in 2015, eight subdistricts of Mae Sot district in Tak province, Thailand have been regarded as special economic development zones (SEZ) due to their situation on the border of Thailand near the pathway of the East-West Economic Corridor project (EWEC). Thus, the Thai Government is developing many infrastructure projects there, and the urban areas are likely to expand, as the population is increasing dramatically. The study of land use could aid in more efficient decision-making in urban planning, and could mitigate the effects of uncontrolled urban development. Based on this background, land use change monitoring was performed based on Remote Sensing and Geographic Information System (GIS) using high-resolution satellite images. The images were captured by QuickBird in 2006, and by Thaichote in 2011 and 2016. The object-based classification considers not only the reflectance of the pixels but also the size, shape, color, smoothness, and compactness of the objects. This technique will bring higher accuracy to land use classification. The eCognition Developer was employed in this study for object-based classification. The mean and standard deviation of the original band was used for principle component analysis (PCA), and the normalized difference vegetation index (NDVI) was also applied to land use classification. The types of land use were divided into five categories that followed the definitions given by the Land Development Department of Thailand (LDD): agricultural area, forest, urban area, water body, and miscellaneous land. The results of land use classification showed that urban areas increased drastically year by year. The GIS dataset for land use compiled by the LDD was employed to evaluate the accuracy of our results. The overall accuracies based on the images captured in 2006 and 2011 were 86.00% and 79.88%, respectively. To evaluate urban growth in 2015, the states of land use in 2006 and 2011 were applied to a Markov Chain and Cellular Automata model (CA-Markov), which is a model for the prediction of land use change from one period to another. The Markov model evaluates the transition probability matrix to project future change, while CA-Markov performs the spatial variations in cell time transition and neighborhood based on its element cell space, cell states, time steps, transition rules, and neighbors. The accuracy of the land use prediction obtained from CA-Markov in 2016 was evaluated by comparing it with land use classification from the object-based classification of the image captured by Thaichote in 2016. The overall accuracy was 68.45%. The pattern of land use change detected from both the projection map and the classification map showed that the urban area would spread following the development of transportation infrastructure, and would encroach on the agricultural areas, while forest areas would become agricultural areas

An AI framework for Change Analysis and Forecast Modelling of Temporal Series of Satellite Images

The study focuses on change analysis and predicting future LULC map of capital city of Karnataka state, India. The chosen study area is more prone to urbanisation and greatly affected by population in recent years. Spatial-temporal data from 1989-2019 are considered. LULC classes comprise of Water bodies, Urban, Forest, Vegetation and Openland. An optimal LULC maps from 1989 to 2019 obtained by deep neural network technique are used to perform change analysis which would mainly give the change LULC map with number and percentage of change pixels. According to the analysis performed major change as environmental affecting factor was noticed between 2009 and 2019 where in urban with the area of 189.3861 sq. km remain unchanged and noticeable transitions from other LULC classes to urban. Later, time series classification was performed using Cellular Automata, Cellular Automata-Neural Networks, techniques to predict the LULC map of 2024. Among these CA-NN outperformed with an average kappa coefficient of 0.83. Also, this was validated with projected LULC map of 2024 provided by USGS

Evaluation of the Extent of Land Use-Land Cover Changes of Benin City, Edo State, Nigeria from 1987-2019

Human growth and development occur at the expense of our natural resources. The ancient city of Benin, the capital of Edo State, Nigeria has been experiencing a surge in population hence this study was initiated to evaluate the extent of land use-land cover (LULC) changes over a 32-year period (1987-2019), using remote sensing and Geographic Information Systems (GIS) techniques. USGS Landsat data were acquired for 1987, 2002, and 2019, pre-processed and classified using ENVI 5.2 software and exported into ARC-GIS platform for further analysis. The results of the 1987-2019 LULC classifications were also used to forecast Benin City's LULC for 2050 using the Markov and CA-Markov models in TerrSet 17.0 software. The results showed that 284.56 km2 of forest lands were lost over a 32-year period (1987-2019), while built-up and barren lands increased rapidly by 153.96km2 and 81.58km2, respectively. By 2050, the built-up area is expected to increase by 236.92km2, while barren land is expected to maintain its percentage cover. Grassland increased by 52.16 km2, while water decreased by 3.60 km2, both of these classes are expected to decrease by 157.58km2 and 0.45km2 by 2050, respectively. The increase in population and built-up areas in Benin City contributes to deforestation and increased urban heat, as well as reduced ecosystem services and biodiversity loss. As a result, it is recommended that the Benin City Urban Planning Authority encourage the planting of ornamental trees, shrubs, and lawns in order to restore more carbon sequestration to the ecosystem and thus reduce global warming

Land Use Change from Non-urban to Urban Areas

This reprint is related to land-use change and non-urban and urban relationships at all spatiotemporal scales and also focuses on land-use planning and regulatory strategies for a sustainable future. Spatiotemporal dynamics, socioeconomic implication, water supply problems and deforestation land degradation (e.g., increase of imperviousness surfaces) produced by urban expansion and their resource requirements are of particular interest. The Guest Editors expect that this reprint will contribute to sustainable development in non-urban and urban areas