Lessons and Challenges in Land Change Modeling Derived from Synthesis of Cross-Case Comparisons

Pontius, Robert Gilmore; Castella, Jean-Christophe; Nijs, Ton de; Duan, Zengqiang; Fotsing, Éric; Goldstein, Noah; Kok, Kasper; Koomen, E.; Lippitt, Christopher D.; McConnell, William J.; Sood, Alias Mohd; Pijanowski, Bryan C.; Verburg, Peter H.; Veldkamp, A.

doi:10.1007/978-3-319-52522-8_8

Cited by 31 publications

(24 citation statements)

References 53 publications

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“…Moreover, Pijanowski et al (2002) reported that even when 10 predictor variables were used on 100-m pixel resolution in the Land Transformation Model to assess future urban expansion, the model's prediction ability was 46% [81]. Furthermore, by comparing 13 applications of 9 different popular peer-reviewed land change models, Pontius et al (2018) proved that, for 6 of the 13 models, the ratio of the intersection of the observed change and predicted change to the union of the observed change and predicted change was less than 0.15 [29].…”

Section: Discussionmentioning

confidence: 99%

“…However, there are three main stream approaches for LUC models: models which rely primarily on economic models and data [17][18][19], models that rely equally on multiple approaches and data to determine cause and effect relationships [20][21][22] and models that are role-based analyses, where a norm is established to allocate LUC-related impacts [23][24][25][26].The objectives of the present paper are: firstly, to use three reference land use maps of the Elaionas catchment for the years 1979, 1996 and 2007 in the Land Transformation Model (LTM) [27,28], so as to forecast the urban sprawl for the year 2027; secondly, based on both the recorded land use changes and the future urban sprawl scenarios developed by the LTM, to examine the effects of current and future urban expansion on the design hydrograph, and, thirdly, to estimate the impact of urban sprawl on the associated sediment yield generated by the latter storm events.The Land Transformation Model (LTM) used for forecasting future LUC was created by the Human-Environment Modelling and Analysis Laboratory of Purdue University [28] and is based on the coupling of an Artificial Neural Network (ANN) with a Geographic Information System (GIS), in which the latter was used to provide the inputs for the ANN. Although there are many machine learning models such as extreme learning machine, support vector machine, adaptive neuro-fuzzy inference system and deep learning models the ANN was selected in this study because the LTM predefined tool box is broadly used worldwide so as to forecast land use evolution [29]. These inputs are used to train and test the neural network in order to generate a numerical solution between the input and the output, expressed as a future urban area prediction map for the year 2027.…”

mentioning

confidence: 99%

“…The Land Transformation Model (LTM) used for forecasting future LUC was created by the Human-Environment Modelling and Analysis Laboratory of Purdue University [28] and is based on the coupling of an Artificial Neural Network (ANN) with a Geographic Information System (GIS), in which the latter was used to provide the inputs for the ANN. Although there are many machine learning models such as extreme learning machine, support vector machine, adaptive neuro-fuzzy inference system and deep learning models the ANN was selected in this study because the LTM predefined tool box is broadly used worldwide so as to forecast land use evolution [29]. These inputs are used to train and test the neural network in order to generate a numerical solution between the input and the output, expressed as a future urban area prediction map for the year 2027.…”

mentioning

confidence: 99%

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Forecasting the Urban Expansion Effects on the Design Storm Hydrograph and Sediment Yield Using Artificial Neural Networks

Myronidis

Ιωάννου²

2018

Water

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Urban expansion substantially alters the impervious areas in a catchment, which in turn affects surface runoff and sediment yield in the downstream areas. In this study, the Land Transformation Model (LTM) was used to forecast the urban land expansion in a catchment, whilst future land use maps were employed according to the Soil Conservation Service Curve Number method (SCS-CN) and the Modified Universal Soil Loss Equation (MUSLE) model, so as to examine the urbanization effects on runoff and sediment yield production respectively. Compared to pristine conditions, urban land is anticipated to increase from 6% in 1979 to 31% by 2027. The latter expansion pointed to an increase of peak discharge by 2.2-2.6 times and of flood volume by 1.6-2.1 times, with the sediment yield ranging between 0.47 to 1.05 t/ha for the upcoming 2027 period. Furthermore, the urban sprawl effects on all the latter variables were more profound during short duration storm events. Forecasting urban expansion through integrated artificial neural networks (ANN) and geographic information system (GIS) techniques, in order to calculate the associated design storm hydrograph and sediment yield, is of great importance, in order to properly plan and design hydraulic works that can sustain future urban development.have been negatively impacted by the degree of urbanization [5] and the hydrologic cycle and in stream process have been altered [6]. These changes could seriously impact the mountain rivers sediment production and transport [7] with significant changes in the bed morphology [8] and floods with a high content of transported sediments and debris floods can occur [9]. Moreover, the temporal and spatial scales of these phenomena may vary greatly during intense precipitation events with respect to normal conditions [10].In recent decades, a water budget or hydrologic model have been commonly utilized [11,12] to evaluate the effects of the recorded urban expansion on the hydrology of catchments, so as to model the effects of different land use change scenarios on runoff and sediment yield [13,14]. However, since land use planners and policy makers are more and more interested in predicting urban expansion and its effect on natural resources, much of the research in recent years has focused on the coupling of hydrologic models with sophisticated spatial land use change (LUC) models, that are able to project LUC changes in the near future. LUC models can be used as tools for studying the human effect on the Earth's surface and to forecast land change into the future [15].LUC models use a variety of techniques, including linear extrapolation, suitability mapping, genetic algorithms, neural networks, scenario analysis, expert opinions, public participation and agent-based modelling, in order to produce a prediction map for the near future [16]. However, there are three main stream approaches for LUC models: models which rely primarily on economic models and data [17][18][19], models that rely equally on multiple approaches and data to determine ca...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Forecasting the Urban Expansion Effects on the Design Storm Hydrograph and Sediment Yield Using Artificial Neural Networks

Myronidis

Ιωάννου²

2018

Water

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“…Both single and multiresolution techniques were used [48,49]. The multiresolution evaluation has been suggested in the literature but has seldom been used [50]. The key distinction between single resolution and multiresolution approaches is that the latter is forgiving of near misses.…”

Section: Validationmentioning

confidence: 99%

If They Come, Where will We Build It? Land-Use Implications of Two Forest Conservation Policies in the Deep Creek Watershed

Anputhas

Janmaat

Nichol

et al. 2019

Forests

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Research Highlights: Forest conservation policies can drive land-use change to other land-use types. In multifunctional landscapes, forest conservation policies will therefore impact on other functions delivered by the landscape. Finding the best pattern of land use requires considering these interactions. Background and Objectives: Population growth continues to drive the development of land for urban purposes. Consequently, there is a loss of other land uses, such as agriculture and forested lands. Efforts to conserve one type of land use will drive more change onto other land uses. Absent effective collaboration among affected communities and relevant institutional agents, unexpected and undesirable land-use change may occur. Materials and Methods: A CLUE-S (Conversion of Land Use and its Effects at Small Scales) model was developed for the Deep Creek watershed, a small sub-basin in the Okanagan Valley of British Columbia, Canada. The valley is experiencing among the most rapid population growth of any region in Canada. Land uses were aggregated into one forested land-use type, one urban land-use type, and three agricultural types. Land-use change was simulated for combinations of two forest conservation policies. Changes are categorized by location, land type, and an existing agricultural land policy. Results: Forest conservation policies drive land conversion onto agricultural land and may increase the loss of low elevation forested land. Model results show where the greatest pressure for removing land from agriculture is likely to occur for each scenario. As an important corridor for species movement, the loss of low elevation forest land may have serious impacts on habitat connectivity. Conclusions: Forest conservation policies that do not account for feedbacks can have unintended consequences, such as increasing conversion pressures on other valued land uses. To avoid surprises, land-use planners and policy makers need to consider these interactions. Models such as CLUE-S can help identify these spatial impacts.

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“…At present, China and Russia participate in extensive strategic cooperative activities, such as political and economic activities, with complementary advantages and win-win collaborations. Under the background of the "Belt and Road" initiative of China, the Russian Eurasian Economic Alliance and the Mongolian Prairie Silk Road strategy, the Irkutsk region is becoming a key node city for the construction of the China-Mongolia-Russia Economic Corridor [17]. The study period is during the hot and short summer in Russia when the air is dry and there are few clouds and low precipitation.…”

Section: Overview Of the Study Areamentioning

confidence: 99%

Land-Use/Cover Change and Driving Mechanism on the West Bank of Lake Baikal from 2005 to 2015—A Case Study of Irkutsk City

Yang

et al. 2018

Sustainability

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Lake Baikal is located on the southern tableland of East Siberian Russia. The west coast of the lake has vast forest resources and excellent ecological conditions, and this area and the Mongolian Plateau constitute an important ecological security barrier in northern China. Land-use/cover change is an important manifestation of regional human activities and ecosystem evolution. This paper uses Irkutsk city, a typical city on the West Bank of Lake Baikal, as a case study area. Based on three phases of Landsat remote-sensing image data, the land-use/cover change pattern and change process are analyzed and the natural factors and socioeconomic factors are combined to reveal driving forces through the partial least squares regression (PLSR) model. The results show the following: (1) From 2005 to 2015, construction land expanded, and forestland was converted into construction land and woodland. In addition, grass land, bare land, and cultivated land were converted into construction land, and the woodland area increased. The annual changes in land use from 2005 to 2010 were dramatic and then slowed down from 2010 to 2015. (2) The main reasons for the change in land-use types were urban expansion and nonagricultural development caused by population migration. The process of urbanization from external populations to urban agglomeration and the process of reverse urbanization from a central urban population to urban suburbs jointly expanded urban construction land area. As a result, forestland, grass land and bare land areas on the outskirts of cities were continuously reduced. After the disintegration of the Soviet Union, land privatization led to a decline in the farm economy, the emergence of agricultural land reclamation and urban expansion; in addition, the implementation of the “one-hectare land policy” intensified development in suburban areas, resulting in a reduction of forestland and grass land areas. The process of constructing the China-Mongolia-Russia Economic Corridor has intensified human activities in the region, and the prevention of drastic changes in land cover, coordination of human-land relations, and green development are necessary.

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Lessons and Challenges in Land Change Modeling Derived from Synthesis of Cross-Case Comparisons

Cited by 31 publications

References 53 publications

Forecasting the Urban Expansion Effects on the Design Storm Hydrograph and Sediment Yield Using Artificial Neural Networks

Forecasting the Urban Expansion Effects on the Design Storm Hydrograph and Sediment Yield Using Artificial Neural Networks

If They Come, Where will We Build It? Land-Use Implications of Two Forest Conservation Policies in the Deep Creek Watershed

Land-Use/Cover Change and Driving Mechanism on the West Bank of Lake Baikal from 2005 to 2015—A Case Study of Irkutsk City

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