2016
DOI: 10.1016/j.ecolmodel.2015.11.016
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Using a new PDP modelling approach for land-use and land-cover change predictions: A case study in the Stubai Valley (Central Alps)

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Cited by 16 publications
(15 citation statements)
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“…In the Unteralptal valley the shrub cover increased by 18.7%, and the shrub cover by 32.6%, resulting in an increase in resistance to soil erosion. Abandonment is also the main factor explaining the regeneration of forests in the Austrian Alps, particularly in the altitudinal range 1500-2150 m (Fondevilla et al 2016).…”
Section: Cytisus Scoparius)mentioning
confidence: 99%
“…In the Unteralptal valley the shrub cover increased by 18.7%, and the shrub cover by 32.6%, resulting in an increase in resistance to soil erosion. Abandonment is also the main factor explaining the regeneration of forests in the Austrian Alps, particularly in the altitudinal range 1500-2150 m (Fondevilla et al 2016).…”
Section: Cytisus Scoparius)mentioning
confidence: 99%
“…Flat and accessible areas are preferred for agricultural use [21], so the added cropland was concentrated around the surrounding cropland and the residential land with flat topography and convenient traffic conditions. The increased arable land also occurred in the southern part of Tekes County with suitable natural and socio-economic conditions for modern agriculture.…”
Section: Characteristics Of Each Scenario Simulationmentioning
confidence: 99%
“…Predicting land use changes can assist in determining the extent of degradation and enabling the managers to control changes in the proper directions [16][17][18]. From a planning and management perspective, it is of great significance to have an explicit understanding of predicted land use change as well as the underlying drivers [19][20][21]. The processes and mechanisms of land use change are complex and largely influenced by natural and socio-economic driving factors [22].…”
Section: Introductionmentioning
confidence: 99%
“…The CLUE family of models allows LULC changes to be visualized more easily, but under greater uncertainties, in that the models do not consider as many key factors as more recent models, such as the PDP (Fondevilla et al, 2016). The SPA-LUCC model (Schirpke et al, 2012) overcomes this limitation with a combination of both integrated visualization functionality and greater LULC model details, thereby supporting more realistic assessments of LULC changes.…”
Section: Gis-based Spatial Allocation Of Lulc Changesmentioning
confidence: 99%