Comparison of simulation models in terms of quantity and allocation of land change

Olmedo, María Teresa Camacho; Pontius, Robert Gilmore; Paegelow, Martin; Mas, Jean-François

doi:10.1016/j.envsoft.2015.03.003

Cited by 120 publications

(55 citation statements)

References 31 publications

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“…Triantakonstantis and Stathakis [32] used MLP for modeling future LULC transition probabilities based on information on past LULC changes and geomorphic drivers such as elevation, slope, and distance variables from specific land features, etc. Similar methods can be seen in a number of studies, although the number and types of driver variables utilized vary [27,[34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 93%

Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines

Iizuka

Johnson

Onishi

et al. 2017

Land

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This study uses a spatially-explicit land-use/land-cover (LULC) modeling approach to model and map the future (2016-2030) LULC of the area surrounding the Laguna de Bay of Philippines under three different scenarios: 'business-as-usual', 'compact development', and 'high sprawl' scenarios. The Laguna de Bay is the largest lake in the Philippines and an important natural resource for the population in/around Metro Manila. The LULC around the lake is rapidly changing due to urban sprawl, so local and national government agencies situated in the area need an understanding of the future (likely) LULC changes and their associated hydrological impacts. The spatial modeling approach involved three main steps: (1) mapping the locations of past LULC changes; (2) identifying the drivers of these past changes; and (3) identifying where and when future LULC changes are likely to occur. Utilizing various publically-available spatial datasets representing potential drivers of LULC changes, a LULC change model was calibrated using the Multilayer Perceptron (MLP) neural network algorithm. After calibrating the model, future LULC changes were modeled and mapped up to the year 2030. Our modeling results showed that the 'built-up' LULC class is likely to experience the greatest increase in land area due to losses in 'crop/grass' (and to a lesser degree 'tree') LULC, and this is attributed to continued urban sprawl.

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Section: Introductionmentioning

confidence: 93%

Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines

Iizuka

Johnson

Onishi

et al. 2017

Land

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“…Therefore, some differences exist in the MOLA algorithm in LCM and in CA_MARKOV (Eastman 2012;Camacho Olmedo et al 2015). The MOLA works only once in the LCM procedure, while, in CA_MARKOV, the MOLA runs once for each chosen iteration, that is the number of time units of the simulation period (t1 to T), and the final result is the overlay of each new simulation map after each MOLA reallocation.…”

Section: Land Change Simulation: the Scenariomentioning

confidence: 99%

“…Besides, using CA transition rules and land use transition is governed by maximum probability transition and will follow the constraint of cell transition that happens only once to a particular land use, which will never be changed further during simulation. For better comparing CA_MARKOVand LCM, in our case study, we used a non-filter (Camacho Olmedo et al 2015), ignoring the cellular automata; therefore, the effect of contiguity disappeared.…”

Section: Land Change Simulation: the Scenariomentioning

confidence: 99%

Scenario of land use and land cover change in the Gaza Strip using remote sensing and GIS models

Abuelaish¹,

Olmedo

2016

Arab J Geosci

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Land use and land cover change is a major global environmental change issue, and projecting changes are essential for the assessment of the environment. The Gaza Strip will have grown over 2.4 million inhabitants by 2023, and the land demands will exceed the sustainable capacity of land use by far. Land use planning is one of the most difficult issues in the Gaza Strip given that this area is too small. Continuous urban and industrial growth will place additional stress on land cover, unless appropriate integrated planning and management actions are instituted immediately. Planners need further statistics and estimation tools to achieve their vision for the future based on sound information. Therefore, this study combines the use of satellite remote sensing with geographic information systems (GISs). The spatial database is developed by using five Landsat images gathered in 1972, 1982, 1990, 2002 and 2013. Three GIS models are selected for simulation by the year 2023: Geomod, CA_Markov and Land Change Modeler using Idrisi Selva. The projected urban area will have undergone an increase of 212.3 km 2 by the year 2023 in the used models, and the percentage of urban area will account for 58.83 % of the Gaza strip by 2023.

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“…Model calibration (1) involves the modeling of a transition potential map (i.e., transition from non-built to built); simulation (2) involves the prediction of the quantities of ULCs (i.e., changes from non-built to built from 2001 to 2014, 2030, and 2050); and validation (3) comprises the allocation of the predicted changes (i.e., using the transition potential map to spatially allocate the predicted quantities of ULCs). The details of these modeling processes can be found in the literature [20,[49][50][51]. More specifically, we used the MLP NN algorithm to model the transition potential map with the following inputs: the land-use/cover maps of 1992 and 2001 and the five driver variables, namely distance to major roads, distance to schools, distance to growth nodes, distance to administrative centers, and distance to existing built-up areas (1992).…”

Section: Urban Land Change Modelingmentioning

confidence: 99%

Spatiotemporal Analysis of Urban Growth Using GIS and Remote Sensing: A Case Study of the Colombo Metropolitan Area, Sri Lanka

Subasinghe

Estoque

Murayama

2016

IJGI

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Abstract:Understanding urban growth spatiotemporally is important for landscape and urban development planning. In this study, we examined the spatiotemporal pattern of urban growth of the Colombo Metropolitan Area (CMA)-Sri Lanka's only metropolitan area-from 1992 to 2014 using remote sensing data and GIS techniques. First, we classified three land-use/cover maps of the CMA (i.e., for 1992, 2001, and 2014) using Landsat data. Second, we examined the temporal pattern of urban land changes (ULCs; i.e., land changes from non-built-up to built-up) across two time intervals (1992-2001 and 2001-2014). Third, we examined the spatial pattern of ULCs along the gradients of various driver variables (e.g., distance to roads) and by using spatial metrics. Finally, we predicted the future urban growth of the CMA (2014-2050). Our results revealed that the CMA's built-up land has increased by 24,711 ha (221%) over the past 22 years (11,165 ha in 1992 to 35,876 ha in 2014), at a rate of 1123 ha per year. The analysis revealed that ULC was more intense or faster during the 2000s (1268 ha per year) than in the 1990s (914 ha per year), coinciding with the trends of population and economic growth. The results also revealed that most of the ULCs in both time intervals occurred in close proximity to roads and schools, while also showing some indications of landscape fragmentation and infill urban development patterns. The ULC modeling revealed that by 2030 and 2050, the CMA's built-up land will increase to 42,500 ha and 56,000 ha, respectively. Most of these projected gains of built-up land will be along the transport corridors and in proximity to the growth nodes. These findings are important in the context of landscape and urban development planning for the CMA. Overall, this study provides valuable information on the landscape transformation of the CMA, also highlighting some important challenges facing its future sustainable urban development.

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Comparison of simulation models in terms of quantity and allocation of land change

Cited by 120 publications

References 31 publications

Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines

Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines

Scenario of land use and land cover change in the Gaza Strip using remote sensing and GIS models

Spatiotemporal Analysis of Urban Growth Using GIS and Remote Sensing: A Case Study of the Colombo Metropolitan Area, Sri Lanka

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