Land-use change simulation and assessment of driving factors in the loess hilly region—a case study as Pengyang County

Zhu, Zhanqiang; Liu, Liming; Chen, Zhantao; Zhang, Junlian; Verburg, Peter H.

doi:10.1007/s10661-009-0880-2

Cited by 40 publications

(20 citation statements)

References 13 publications

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“…In the last decades, numerous studies have focused on land use status, the evolution of landscape pattern, forecasting future land use change, etc. (Zhao et al, 2012;Zhu et al, 2010;Verburg et al, 2002). In China, a number of studies have demonstrated the landscape pattern changes using LUCC data in urban areas (Zhang et al, 2014;Peng et al, 2013;Pan et al, 2012), watersheds (Wang et al, 2014), wetlands (X.…”

Section: Introductionmentioning

confidence: 99%

Landscape pattern changes at a county scale: A case study in Fengqiu, Henan Province, China from 1990 to 2013

Fan

Ding

2016

CATENA

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Section: Introductionmentioning

confidence: 99%

Landscape pattern changes at a county scale: A case study in Fengqiu, Henan Province, China from 1990 to 2013

Fan

Ding

2016

CATENA

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“…Land use change models are usually used to illustrate land use change and its relationship with driving forces. Even so, it is complicated to assess driving forces of land use change [9]. Spatially explicit modeling of Land use changes is an important technique for describing processes of change in quantitative terms and for testing our understanding of these processes [10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Logistic Regression and Geomod Approaches to Forest Change Modeling in the Period of 1988 – 2025

Galdavi

Mohammadzadeh

Mahiny

et al. 2019

AJRAF

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Spatial modelling of land use change is a technique for understanding changes in terms of the location and amount. In this study, logistic regression and Geomod approaches were used for modelling forest change in Gorgan area in Northern Iran in the time period of 1988-2007. To do this, at first, remotely sensed imagery data of the years 1988, 1998 and 2007 were used to produce land use maps. Land use maps accuracy assessments were achieved using Error matrix method and then the maps were used to implement change detection process in two time periods of 1988-1998 and 1998-2007. Results indicated a reduction in forest areas during the mentioned time period. Next, the independent variables were extracted in order to land use change modeling. The Results of the models implementation showed the ability of both models for forest change modeling in this region. Also, the models were used to predict the future condition of forest area in the years 2016 and 2025. The results revealed that the forest area would be associated with a reduction in the future. Comparison of the results of the models using kappa indices showed the successful implementation of both models for forest change modelling in this region. The results of this research reveal the need for appropriate applications of the proper plans to control land use change in order to preserve the environment and ecological balance of the area. Therefore, careful planning can reduce the land use change and its impacts in the future in this region.

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“…CLUE-S is an empirical analysis-based model that considers the influences of geophysical and socioeconomic driving factors on land-use category changes [41][42][43][44][45][46][47][48][49]. The main purpose of the transformation of the land-use type and the spatial allocation in the CLUE-S model was to provide a reference for predicting green manure intercropping.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Spatial Expansion of Green Manure Considering Land Productivity and Implementing Strategies

et al. 2018

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Abstract:In modern sustainable agriculture, green manuring is increasingly emphasized for a reasonable land use management. However, the expansion of green manure is affected by a range of factors, such as soil geophysical properties and human intervention. This paper proposes an approach of spatial modelling to understand the mechanisms that influence green manure expansion and map the future distribution of green manure intercropped in the orchards in the Pinggu District, Beijing, China. We firstly classified the orchards into five grades according to a land productivity evaluation, and then considered two strategies for implementing green manure. Two scenarios were designed to represent the strategies: prioritizing low-productivity orchards to promote green manure intercropping (scenario 1) and prioritizing high-productivity orchards to promote green manure intercropping (scenario 2). The spatial expansion of green manure for 2020 was simulated at a resolution of a 100 × 100 m grid in the CLUE-S (the Conversion of Land Use and its Effects at the Small Region Extent) model. The two strategies led to quite different spatial patterns of green manure, although they were applied to the same areas. As a result, the spatial pattern of green manuring of scenario 1 was more concentrated than that of scenario 2. To summarize, the modelled outcomes identified the driving factors that affect green manure expansion at a grid scale, whereas the implementing strategies directly determined the spatial arrangements of green manuring at a regional scale. Therefore, we argue that the assessment of the driving factors and the prediction of the future distribution of green manuring are crucial for informing an extensive use of green manure.

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Land-use change simulation and assessment of driving factors in the loess hilly region—a case study as Pengyang County

Cited by 40 publications

References 13 publications

Landscape pattern changes at a county scale: A case study in Fengqiu, Henan Province, China from 1990 to 2013

Landscape pattern changes at a county scale: A case study in Fengqiu, Henan Province, China from 1990 to 2013

Comparison of Logistic Regression and Geomod Approaches to Forest Change Modeling in the Period of 1988 – 2025

Modelling the Spatial Expansion of Green Manure Considering Land Productivity and Implementing Strategies

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