Remote sensing in urban sprawl modeling: Scenario and way forward in developing countries

In recent years, the developing countries was deal the use of computer based models of land use changes and urban sprawl which have greatly increased and tend to become important tools in supporting urban planning and management. The modeling recently used in various planning specialization such as economics, transportation, spatial planning, urbanization, ecology, and other social science aspects. However, modeling sprawl phenomena which convergence to remote sensing data has not fully demonstrated lack of common ground and testable concepts. Remote sensing data products have often been incorporated into urban modeling applications as additional sources of spatial data primarily for historical land use history. The objectives of this study to identify recent scenario and way forward of remote sensing tools in urban sprawl modeling based on reviewed of previously studied and urban planning situation in developing countries and Malaysia contexts specificall

Land cover change detection analysis on urban green area loss using GIS and remote sensing techniques

The loss of green area has been rising all over the world particularly in big cities. For a number of decades, urban sprawl and developments have changed the natural landscapes of urban areas where areas with green areas have been converted into built up developments and other land uses. Thus this research intends to study the changes of green areas in Kuala Lumpur based on land use detection analysis approach where 3 series of remote sensing images namely SPOT2, SPOT4 and IKONOS for year 1990, 2001 and 2010 have been used to acquire the data on the green area changes aided by ERDAS IMAGINE 2011 and ARGIS 9.2. The finding of the study shows that there is a decrease in the size of green area in Kuala Lumpur from year 1990-2010 due to pressure of urban developments. Two significant factors which contribute to the changes of green area in Kuala Lumpur have been identified in the study, which are the increase in built up areas and sprawl development pattern

Land Use-Transportation Interaction: Lessons Learned from an Experimental Model using Cellular Automata and Artificial Neural Networks

Land use and transportation interact to produce large urban concentrations in most major cities that create tremendous sprawl, noise, congestion, and environmental concerns. The desire to better understand this relationship has led to the development of land use–transport (LUT) models as an extension of more general urban models. The difficulties encountered in developing such models are many as local actions sum to form global patterns of land use change, producing complex interrelationships. Cellular automata (CA) simplify LUT model structure, promise resolution improvement, and effectively handle the dynamics of emergent growth. Artificial Neural Networks (ANN) can be used to quantify the complex relationships present in historical land use data as a means of calibrating a CA-LUT model. This study uses an ANN, slope, historical land use, and road data to calibrate a CA-LUT model for the I-140 corridor of Knoxville, TN. The resulting model was found to require a complex ANN, produce realistic emergent growth patterns, and shows promising simulation performance in several significant land classes such as single-family residential. Problems were encountered as the model was iterated due to the lack of a mechanism to extend the road network. The presence of local roads in the model’s configuration strengthened ability of the model to simulate historical development patterns. Shortcomings in certain aspects of the simulation performance point to the need for the addition of a socio-economic sub-model to assess demand for urban area and/or an equilibrium mechanism to arbitrate the supply of developable land. The model constructed in this study was found to hold considerable potential for local-scale simulation and scenario testing given suitable modification to its structure and input parameters

Urban metabolism and land use modeling for urban designers and planners: A land use model for the Integrated Urban Metabolism Analysis Tool

Predicting the resource consumption in the built environment and its associated environmental consequences (urban metabolism analysis) is one of the core challenges facing policy-makers and planners seeking to increase the sustainability of urban areas. There is a critical need for a single integrated framework to analyze the consequences of urban growth and eventually predict the impacts of sustainable policies on the urbanscape. This dissertation presents the development of an Integrated Urban Metabolism Analysis Tool (IUMAT) – an analytical framework that simulates urban metabolism by integrating urban subsystems in a single comprehensive computational environment. It reviews the existing literature on urban sustainability, urban metabolism, as well as introducing the general framework for IUMAT. IUMAT uses three separate models for quantifying environmental impacts of land-use transition, consumption of resources, and transportation. This work outlines the development of IUMAT Land-Use Model that uses Remote Sensing, GIS, and Artificial Neural Networks (ANNs) to predict land use change patterns. By using Density-Based Spatial Clustering and normal equations, this dissertation introduces a method for generating building-form variables from Light Detection and Ranging (LIDAR) data, which can be used as a new determinant factor in land-use change modeling. The proposed Land-use Model, within IUMAT or other analytical models, can be useful to local planning officials in understanding the complexity of land-use change and developing enhanced land-use policies

Land-Cover and Land-Use Study Using Genetic Algorithms, Petri Nets, and Cellular Automata

Recent research techniques, such as genetic algorithm (GA), Petri net (PN), and cellular automata (CA) have been applied in a number of studies. However, their capability and performance in land-cover land-use (LCLU) classification, change detection, and predictive modeling have not been well understood. This study seeks to address the following questions: 1) How do genetic parameters impact the accuracy of GA-based LCLU classification; 2) How do image parameters impact the accuracy of GA-based LCLU classification; 3) Is GA-based LCLU classification more accurate than the maximum likelihood classifier (MLC), iterative self-organizing data analysis technique (ISODATA), and the hybrid approach; 4) How do genetic parameters impact Petri Net-based LCLU change detection; and 5) How do cellular automata components impact the accuracy of LCLU predictive modeling. The study area, namely the Tickfaw River watershed (711mi²), is located in southeast Louisiana and southwest Mississippi. The major datasets include time-series Landsat TM / ETM images and Digital Orthophoto Quarter Quadrangles (DOQQ’s). LCLU classification was conducted by using the GA, MLC, ISODATA, and Hybrid approach. The LCLU change was modeled by using genetic PN-based process mining technique. The process models were interpreted and input to a CA for predicting future LCLU. The major findings include: 1) GA-based LCLU classification is more accurate than the traditional approaches; 2) When genetic parameters, image parameters, or CA components are configured improperly, the accuracy of LCLU classification, the coverage of LCLU change process model, and/or the accuracy of LCLU predictive modeling will be low; 3) For GA-based LCLU classification, the recommended configuration of genetic / image parameters is generation 2000-5000, population 1000, crossover rate 69%-99%, mutation rate 0.1%-0.5%, generation gap 25%-50%, data layers 16-20, training / testing data size 10000-20000 / 5000-10000, and spatial resolution 30m-60m; 4) For genetic Petri nets-based LCLU change detection, the recommended configuration of genetic parameters is generation 500, population 300, crossover rate 59%, mutation rate 5%, and elitism rate 4%; and 5) For CA-based LCLU predictive modeling, the recommended configuration of CA components is space 6025 * 12993, state 2, von Neumann neighborhood 3 * 3, time step 2-3 years, and optimized transition rules

Role of Geographical Information System (GIS) for Eco-city Planning: A Review

The rapid expansion of cities expected to rise within population and global economic growth is increasing additional demand on natural resource that leads to land-use changes in particularly cities. In the present scenario, cities are becoming the center of human activities. Cities in developing countries become overpopulated and over-crowded as a result of the migration of population from rural to urban due to job opportunities, educational facilities, availability of health facilities etc. This has resulted in ever-growing size of cities, informal settlements environmental pollution, destruction of ecological structure and scarcity of natural resources and also leads to traffic congestion, housing shortage, unaffordable housing prices, crowded streets, degraded ecosystem, increasing demands for waste disposal and many others. Eco-cities have a strong potential to solve the urban challenges and derive to manage the environment and natural resources. This review article presents the brief on innovative uses of GIS techniques for eco-city planning. The purpose of this article is to provide information about GIS tool for planners, engineers and others to think about the impact of urban sprawl and to develop eco-city for sustainable development

Modeling non-stationary urban growth: The SPRAWL model and the ecological impacts of development

Urban development is a principal driver of landscape change affecting the integrity of ecological systems and the capacity of the landscape to support species. We developed an urban growth model (SPRAWL), evaluated it with hindcasting, and used it to simulate urban growth across the northeastern United States between 2010 and 2080 under four alternative scenarios. In the model, urban growth is constrained by demand for new development for each time step at the subregional scale. Demand is subsequently allocated to local application panes (5 km on a side within 15 km window) using a unique landscape context matching algorithm, such that the more historical development that occurred in the matched training windows the higher the proportion of future demand assigned to the pane. Lastly, demand in each pane is allocated among development types and then allocated to individual patches based on suitability surfaces unique to that landscape context. SPRAWL has a multi-level, multi-scale structure that captures urban growth drivers operating at multiple scales and, when combined with the unique matching and suitability algorithms, induces non-stationarity in urban growth across time and space. Our evaluation indicated that SPRAWL was highly discriminatory, well-calibrated, and highly predictive of new development, but performed weakly for redevelopment transitions. We evaluated the ecological impacts of four alternative urban growth scenarios varying in total demand for new development and “sprawliness” of new development relative to historical patterns using an ecological integrity index. The results were consistent with expectations and demonstrated the potential of SPRAWL for scenario analysis

PREDICTING TROPICAL RAINFOREST DEFORESTATION USING MACHINE LEARNING, REMOTE SENSING & GIS: CASE STUDY OF THE CROSS RIVER NATIONAL PARK, NIGERIA

Population growth, urban sprawl, agricultural expansion, and illegal logging has led to losses in forested land in most parts of the world, especially in a highly populated country like Nigeria. The Cross River National Park (CRNP) in southeastern Nigeria with an area just above 4000km2 is designated a biodiverse hotspot and one of the oldest rainforests in Africa. As with all other tropical forests spread across the globe the CRNP is not immune to these factors that threaten its existence. The focus of this study is to analyze the change of forest cover at the Oban division of the Cross River National Park using multi-temporal remotely sensed data to predict and model the future probability of deforestation within the area of interest. This study made use of the Landsat West Africa Land Use/Land Cover Time Series dataset for the years 1975, 2000 and 2013 and Landsat 8 operational land imager (OLI) imagery for the year 2020 in a post classification change detection model to determine the extent of change in forest cover classes. Random forest decision tree machine learning algorithm was used to predict the future risk of forest cover loss using the datasets produced from the post classification change detection. The model related deforestation probabilities with several physical and anthropogenic factors such as elevation, slope angle, solar radiation, aspect, topographic roughness, soil type, distance from roads, distance from towns, distance from rivers, distance from plantations and population density. The results from the change detection analysis showed that from 1975 to 2020 the forest cover declined by 1909km2 a rate of 42km2 per year. The random forest regression analysis predicted areas of the forest with modest to high deforestation probabilities and indicated that socio-economic factors are major drivers of deforestation in the region rather than physical factors