Spatiotemporal changes of landscape pattern in response to urbanization

Weng, Yen-Chu

doi:10.1016/j.landurbplan.2007.01.009

Cited by 454 publications

(211 citation statements)

References 54 publications

(56 reference statements)

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“…Urbanization is a global trend that has been accelerated significantly since the 20th century ) which acts as a major driver of change in landscape structure and functions (Antrop 2004;Banerjee and Srivastava 2013). In contrast, LULCC is one of the most important variables that decide most of the resource planning and control measures (Weng 2007), as well as pollution of many natural resources such as water and soil (Grimm et al 2008;Srivastava et al 2012a). Several landscape pattern scenarios, considering changing environmental conditions for, e.g., climate change, land use change, establishing new road networks etc., have also been found responsible for urbanization by several researchers (Lambin et al 2001;Satterthwaite 2009;Csorba and Szabó 2012;Srivastava et al 2012c;Vaz et al 2012;.…”

Section: Introductionmentioning

confidence: 99%

Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

et al. 2015

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Remote sensing and GIS are important tools for studying land use/land cover (LULC) change and integrating the associated driving factors for deriving useful outputs. This study is based on utilization of Earth observation datasets over the highly urbanized Allahabad district in India. Allahabad district has experienced intense change in LULC in the last few decades. To monitor the changes, advanced techniques in remote sensing and GIS, such as Cellular Automata (CA)-Markov Chain Model (CAMCM) were used to identify the spatial and temporal changes that have occurred in LULC in this area. Two images, 1990 and 2000, were used for calibration and optimization of the Markovian algorithm, while 2010 was used for validating the predictions of CA-Markov using the ground based land cover image. After validating the model, plausible future LULC changes for 2020 were predicted using the CAMCM. Analysis of the LULC pattern maps, achieved through classification of multitemporal satellite datasets, indicated that the socio-economic and biophysical factors have greatly influenced the growth of agricultural lands and settlements in the area. The two urbanization indicators calculated in this study viz. Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used, which indicated a drastic change in the area in terms of urbanization. The predicted LULC scenario for year 2020 provides useful inputs to the LULC planners for effective and pragmatic management of the district and a direction for an effective land use policy making. Further suggestions for an effective policy making are also provided which can be used by government officials to protect this important land resource.

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Section: Introductionmentioning

confidence: 99%

Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

et al. 2015

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“…Therefore, the urbanization can be considered as the observable transformation of the spatial pattern of land use and land cover, such as the transformation of agricultural and forest land uses into built-up area or the gradual transformation of rural landscape into urban forms. The transformation of rural landscape to urban landscape has caused various impacts on ecosystem structure, function, and dynamics [7,8]. Persistent dynamic land use change processes are expected to accelerate in the next several decades.…”

Section: Introductionmentioning

confidence: 99%

“…A wide range of urban remote sensing applications from both sensors (active and passive) is currently available. These include quantifying urban growth and land use dynamics [1,10], landscape pattern analysis [11,16,17], urbanization [8], socioeconomic applications [18], life quality improvement [19], urban infrastructure characterization [7], microclimate and hydrology [20], and topographic mapping [21].…”

Section: Introductionmentioning

confidence: 99%

Examining Spatiotemporal Urbanization Patterns in Kathmandu Valley, Nepal: Remote Sensing and Spatial Metrics Approaches

2009

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This paper examines the spatiotemporal pattern of urbanization in Kathmandu Valley using remote sensing and spatial metrics techniques. The study is based on 33-years of time series data compiled from satellite images. Along with new developments within the city fringes and rural villages in the valley, shifts in the natural environment and newly developed socioeconomic strains between residents are emerging. A highly dynamic spatial pattern of urbanization is observed in the valley. Urban built-up areas had a slow trend of growth in the 1960s and 1970s but have grown rapidly since the 1980s. The urbanization process has developed fragmented and heterogeneous land use combinations in the valley. However, the refill type of development process in the city core and immediate fringe areas has shown a decreasing trend in the neighborhood distances between land use patches, and an increasing trend towards physical connectedness, which indicates a higher probability of homogenous landscape development in the upcoming decades.

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“…The fragmentation in the sub-watershed has been caused by relatively high population density of Batang 2 Toru Hilir of 50-250 individuals (or people) per km (BPS 2011). The FLF is positively related to the degree of urbanization (Weng 2007). Increasing population density is leading to increase of fragmentation (Gonzalez-Abraham et al 2007).…”

Section: Resultsmentioning

confidence: 99%

Analysis of Tropical Forest Landscape Fragmentation in Batang Toru Watershed, North Sumatra

Samsuri

Jaya

Kusmana

et al. 2014

jtfm

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Spatiotemporal changes of landscape pattern in response to urbanization

Cited by 454 publications

References 54 publications

Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

Examining Spatiotemporal Urbanization Patterns in Kathmandu Valley, Nepal: Remote Sensing and Spatial Metrics Approaches

Analysis of Tropical Forest Landscape Fragmentation in Batang Toru Watershed, North Sumatra

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