Determining socioeconomic drivers of urban forest fragmentation with historical remote sensing images

Gong, Chongfeng; Yu, Shixiao; Joesting, Heather M.; Chen, Jiquan

doi:10.1016/j.landurbplan.2013.04.009

Cited by 126 publications

(75 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In contrast, the correlations between the proportional cover of developed land and the mean patch size of forested land was no longer significant in the BTH and WH megaregions after controlling for the patch density of developed land, indicating that the impact of spatial pattern of developed land is more important in the BTH and WH megaregions. The different roles of patch density on forest fragmentation might be related to the differentiated morphological types of urban expansion in different megaregions [40,42,50,51], as dispersed urban expansion can lead to increased forest fragmentation [42,50]. For example, the PRD was dominated by expansion of infilling, the YRD was dominated by infilling and edge-expansion, and the BTH had the highest proportion of edge-expansion from 2000 to 2010 [40].…”

Section: Discussionmentioning

confidence: 99%

Effects of Urban Expansion on Forest Loss and Fragmentation in Six Megaregions, China

Zhou

Zhang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:Urban expansion has significant effects on forest loss and fragmentation. Previous studies mostly focused on how the amount of developed land affected forest loss and fragmentation, but neglected the impacts of its spatial pattern. This paper examines the effects of both the amount and spatial pattern of urban expansion on forest loss and fragmentation. We conducted a comparison study in the six largest urban megaregions in China-Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Wuhan (WH), Chengdu-Chongqing (CY), and Changsha-Zhuzhou-Xiangtan (CZT) urban megaregions. We first quantified both the magnitude and speed of urban expansion, and forest loss and fragmentation from 2000 to 2010. We then examined the relationships between urban expansion and forest loss and fragmentation by Pearson correlation and partial correlation analysis using the prefecture city as the analytical unit. We found: (1) urban expansion was a major driver of forest loss in the CZT, PRD, and CY megaregions, with 34.05%, 22.58%, and 19.65% of newly-developed land converted from forests. (2) Both the proportional cover of developed land and its spatial pattern (e.g., patch density) had significant impacts on forest fragmentation at the city level. (3) Proportional cover of developed land was the major factor for forest fragmentation at the city level for the PRD and YRD megaregions, but the impact of the spatial pattern of developed land was more important for the BTH and WH megaregions.

show abstract

Section: Discussionmentioning

confidence: 99%

Effects of Urban Expansion on Forest Loss and Fragmentation in Six Megaregions, China

Zhou

Zhang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Furthermore, many of the studies that have applied classical regression approaches to understand the drivers of forest cover changes (e.g. Jaimes et al 2010;Gao and Li 2011;Freitas et al 2013;Gong et al 2013) may have had to use a restricted number of factors to be able to satisfy the requirements of normality, which could have hindered the analyses, whereas the flexibility and robustness of RF overcomes such limitations.…”

Section: Comparisons Of Rf With Stepmentioning

confidence: 99%

“…Some studies have used relatively simplistic approaches, such as Mann-Whitney and Kruskal-Wallis tests (Quezada et al 2013), or correlation analyses (Beilin et al 2014). Others have applied more robust approaches, combining or comparing different methods, such as statistical redundancy analyses (RDA) (Parcerisas et al 2012); ordinary least squares regression (OLS) and geographically weighted regression (GWR) (Jaimes et al 2010;Gao and Li 2011); canonical correspondence analysis (CCA), OLS, GWR and spatial clustering procedures (Freitas et al 2013); and stepwise multiple regression models (Gong et al 2013). Most of these studies considered a limited number of potential independent factors that had normal distributions, as this is the basic requirement for using parametric techniques.…”

mentioning

confidence: 99%

How well does random forest analysis model deforestation and forest fragmentation in the Brazilian Atlantic forest?

et al. 2017

View full text Add to dashboard Cite

We assessed the value of applying random forest analysis (RF) to relating metrics of deforestation (DF) and forest fragmentation (FF) to socioeconomic (SE) and biogeophysical (BGP) factors, in the Brazilian Atlantic Forest of Minas Gerais, Brazil. A vegetation-monitoring project provided land cover maps, from which we derived DF and FF metrics. An ecologic-economical zoning project provided more than 300 SE and BGP factors. We used RF to identify relationships between these sets of variables and compared its performance in this task to that of a more traditional multiple linear regression approach. We found that RF modelled relatively-well variance in all metrics used (the rate of deforestation, the amount of forest and the density and isolation of forest patches), presenting a better performance when compared to the classical approach. RF also identified geographical location and topographic factors as being most closely associated with patterns of DF and FF metrics. Both analyses found factors associated with economic productivity, social institutions, accessibility and exploration to have little relationship with forest metrics. RF was better at explaining variations in rates of deforestation, remaining forest and patch patterns, than the multiple linear regression approach. We conclude that RF provides a promisHandling Editor: Pierre Dutilleul. Electronic supplementary materialThe online version of this article (https://doi.org/10.1007/s10651-017-0389-8) contains supplementary material, which is available to authorized users.

show abstract

“…This statistical analysis has been widely used to discard highly correlated variables (Gong et al, 2013;Ren et al, 2013;Schwarz, 2010). Considering their level and date, one test per group of metrics was performed.…”

Section: Correlation Analysismentioning

confidence: 99%

“…Factor analysis and PCA are widely used to reduce a multitude of metrics to a meaningful subset, but also to create new synthetic indices by interpreting each component (Gong et al, 2013;Plexida et al, 2014;Schwarz, 2010). This method was used to select not only FI, FIC and MI metrics, but also to interpret components as new indicators derived from the original data.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Analysing Relationships Between Urban Land Use Fragmentation Metrics and Socio-Economic Variables

Sapena

Ruiz

Gisbert

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Analysing urban regions is essential for their correct monitoring and planning. This is mainly accounted for the sharp increase of people living in urban areas, and consequently, the need to manage them. At the same time there has been a rise in the use of spatial and statistical datasets, such as the Urban Atlas, which offers high-resolution urban land use maps obtained from satellite imagery, and the Urban Audit, which provides statistics of European cities and their surroundings. In this study, we analyse the relations between urban fragmentation metrics derived from Land Use and Land Cover (LULC) data from the Urban Atlas dataset, and socio-economic data from the Urban Audit for the reference years 2006 and 2012. We conducted the analysis on a sample of sixty-eight Functional Urban Areas (FUAs). One-date and two-date based fragmentation indices were computed for each FUA, land use class and date. Correlation tests and principal component analysis were then applied to select the most representative indices. Finally, multiple regression models were tested to explore the prediction of socio-economic variables, using different combinations of land use metrics as explanatory variables, both at a given date and in a dynamic context. The outcomes show that demography, living conditions, labour, and transportation variables have a clear relation with the morphology of the FUAs. This methodology allows us to compare European FUAs in terms of the spatial distribution of the land use classes, their complexity, and their structural changes, as well as to preview and model different growth patterns and socio-economic indicators.

show abstract

Determining socioeconomic drivers of urban forest fragmentation with historical remote sensing images

Cited by 126 publications

References 47 publications

Effects of Urban Expansion on Forest Loss and Fragmentation in Six Megaregions, China

Effects of Urban Expansion on Forest Loss and Fragmentation in Six Megaregions, China

How well does random forest analysis model deforestation and forest fragmentation in the Brazilian Atlantic forest?

Analysing Relationships Between Urban Land Use Fragmentation Metrics and Socio-Economic Variables

Contact Info

Product

Resources

About