Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world

Tayyebi, Amin; Pijanowski, Bryan C.; Linderman, Marc; Gratton, Claudio

doi:10.1016/j.envsoft.2014.05.022

Cited by 85 publications

(47 citation statements)

References 81 publications

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“…The values p 1 and p 0 are the proportions of correctly classified test pixels of two classifiers under comparison. In addition, receiver operating characteristic (ROC) curves and the corresponding AUC have been calculated; the AUC is an increasingly used accuracy metric in machine-learning and data mining [102][103][104]. The AUC ranges from 0-100%, with 100% representing an error-free classification.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series

Löw¹,

Prishchepov

Waldner

et al. 2018

Remote Sensing

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Abstract:Cropland abandonment is globally widespread and has strong repercussions for regional food security and the environment. Statistics suggest that one of the hotspots of abandoned cropland is located in the drylands of the Aral Sea Basin (ASB), which covers parts of post-Soviet Central Asia, Afghanistan and Iran. To date, the exact spatial and temporal extents of abandoned cropland remain unclear, which hampers land-use planning. Abandoned land is a potentially valuable resource for alternative land uses. Here, we mapped the abandoned cropland in the drylands of the ASB with a time series of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from [2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016]. To overcome the restricted ability of a single classifier to accurately map land-use classes across large areas and agro-environmental gradients, "stratum-specific" classifiers were calibrated and classification results were fused based on a locally weighted decision fusion approach. Next, the agro-ecological suitability of abandoned cropland areas was evaluated. The stratum-specific classification approach yielded an overall accuracy of 0.879, which was significantly more accurate (p < 0.05) than a "global" classification without stratification, which had an accuracy of 0.811. In 2016, the classification results showed that 13% (1.15 Mha) of the observed irrigated cropland in the ASB was idle (abandoned). Cropland abandonment occurred mostly in the Amudarya and Syrdarya downstream regions and was associated with degraded land and areas prone to water stress. Despite the almost twofold population growth and increasing food demand in the ASB area from 1990 to 2016, abandoned cropland was also located in areas with high suitability for farming. The map of abandoned cropland areas provides a novel basis for assessing the causes leading to abandoned cropland in the ASB. This contributes to assessing the suitability of abandoned cropland for food or bioenergy production, carbon storage, or assessing the environmental trade-offs and social constraints of recultivation.

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Section: Accuracy Assessmentmentioning

confidence: 99%

Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series

Löw¹,

Prishchepov

Waldner

et al. 2018

Remote Sensing

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“…Exploring future land use patterns is critical for anticipating possible negative impacts, identifying potential conflicts between competing land functions, and developing sustainable land use strategies to mitigate these (Verburg et al 2006;Fürst et al 2013;Seppelt et al 2013). Land use modeling based on salient, credible, and legitimate storylines is a fundamental tool for exploring possible futures of land use change (Brown et al 2013), and a wide range of models is currently available (Sleeter et al 2012;Mas et al 2014;Tayyebi et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Simulating and delineating future land change trajectories across Europe

Stürck¹,

Levers

Zanden³

et al. 2015

Reg Environ Change

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Explorations of future land use change are important to understand potential conflicts between competing land uses, trade-offs associated with particular land change trajectories, and the effectiveness of policies to steer land systems into desirable states. Most model-based explorations and scenario studies focused on conversions in broad land use classes, but disregarded changes in land management or focused on individual sectors only. Using the European Union (EU) as a case study, we developed an approach to identifying typical combinations of land cover and management changes by combining the results of multimodel simulations in the agriculture and forest sectors for four scenarios from 2000 to 2040. We visualized land change trajectories by mapping regional hotspots of change. Land change trajectories differed in extent and spatial pattern across the EU and among scenarios, indicating trajectory-specific option spaces for alternative land system outcomes. In spite of the large variation in the area of change, similar hotspots of land change were observed among the scenarios. All scenarios indicate a stronger polarization of land use in Europe, with a loss of multifunctional landscapes. We analyzed locations subject to change by comparing location characteristics associated with certain land change trajectories. Results indicate differences in the location conditions of different land change trajectories, with diverging impacts on ecosystem service provisioning. Policy and planning for future land use needs to account for the spatial variation of land change trajectories to achieve both overarching and location-specific targets.

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“…This unprecedented phenomenon is accompanied by massive rural-urban migration [6,7]. According to the National Population Census, the percentage of urban population in China grew from 36.22% to 49.68% between 2000 and 2010 [8]. As a consequence, this country has also witnessed an increase in private vehicle…”

Section: Introductionmentioning

confidence: 99%

Simulating and Predicting the Impacts of Light Rail Transit Systems on Urban Land Use by Using Cellular Automata: A Case Study of Dongguan, China

Lin

Chen²,

Han

2018

Sustainability

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Abstract:The emergence of Light Rail Transit systems (LRTs) could exert considerable impacts on sustainable urban development. It is crucial to predict the potential land use changes since LRTs are being increasingly built throughout the world. While various land use and land cover change (LUCC) models have been developed during the past two decades, the basic assumption for LUCC prediction is the continuation of present trends in land use development. It is therefore unreasonable to predict potential urban land use changes associated with LRTs simply based on earlier trends because the impacts of LRT investment may vary greatly over time. To tackle this challenge, our study aims to share the experiences from previous lines with newly planned lines. Dongguan, whose government decided to build LRTs around 2008, was selected as the study area. First, we assessed the impacts of this city's first LRT (Line R2) on three urban land use types (i.e., industrial development, commercial and residential development, and rural development) at different periods. The results indicate that Line R2 exerted a negative impact on industrial development and rural development, but a positive impact on commercial and residential development during the planning stage of this line. Second, such spatial impacts (the consequent land use changes) during this stage were simulated by using artificial neural network cellular automata. More importantly, we further predicted the potential impacts of Line R1, which is assumed to be a newly planned line, based on the above calibrated model and a traditional method respectively. The comparisons between them demonstrate the effectiveness of our method, which can easily take advantage of the experiences from other LRTs. The proposed method is expected to provide technical support for sustainable urban and transportation planning.

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Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world

Cited by 85 publications

References 81 publications

Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series

Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series

Simulating and delineating future land change trajectories across Europe

Simulating and Predicting the Impacts of Light Rail Transit Systems on Urban Land Use by Using Cellular Automata: A Case Study of Dongguan, China

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