Dataset Information

Spatially differentiated trends in urbanization, agricultural land abandonment and reclamation, and woodland recovery in Northern China.

ABSTRACT: Uncovering magnitude, trend, and spatial pattern of land cover/land use changes (LCLUC) is crucial for understanding mechanisms of LCLUC and assisting land use planning and conservation. China has been undergoing unprecedented economic growth, massive rural-to-urban migration, and large-scale policy-driven ecological restoration, and therefore encountering enormous LCLUC in recent decades. However, comprehensive understandings of spatiotemporal LCLUC dynamics and underlying mechanisms are still lacking. Based on classification of annual LCLU maps from MODIS satellite imagery, we proposed a land change detection method to capture significant land change hotspots over Northern China during 2001-2013, and further analyzed temporal trends and spatial patterns of LCLUC. We found rapid decline of agricultural land near urban was predominantly caused by urban expansion. The process was especially strong in North China Plain with 14,057?km² of urban gain and -21,017?km² of agricultural land loss. To offset the loss of agricultural land, Northeast China Plain and Xinjiang were reclaimed. Substantial recovery of forests (49,908?km²) and closed shrubland (60,854?km²) occurred in mountainous regions due to abandoned infertile farmland, secondary succession, and governmental conservation policies. The spatial patterns and trends of LCLUC in Northern China provide information to support effective environmental policies towards sustainable development.

SUBMITTER: Wang C

PROVIDER: S-EPMC5118683 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Publications

Spatially differentiated trends in urbanization, agricultural land abandonment and reclamation, and woodland recovery in Northern China.

Wang Chao C Gao Qiong Q Wang Xian X Yu Mei M

Scientific reports 20161122

Uncovering magnitude, trend, and spatial pattern of land cover/land use changes (LCLUC) is crucial for understanding mechanisms of LCLUC and assisting land use planning and conservation. China has been undergoing unprecedented economic growth, massive rural-to-urban migration, and large-scale policy-driven ecological restoration, and therefore encountering enormous LCLUC in recent decades. However, comprehensive understandings of spatiotemporal LCLUC dynamics and underlying mechanisms are still ...[more]

PMID: 27874092

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Project description:Land use land cover (LULC) changes frequently in ecotones due to the large climate and soil gradients, and complex landscape composition and configuration. Accurate mapping of LULC changes in ecotones is of great importance for assessment of ecosystem functions/services and policy-decision support. Decadal or sub-decadal mapping of LULC provides scenarios for modeling biogeochemical processes and their feedbacks to climate, and evaluating effectiveness of land-use policies, e.g. forest conversion. However, it remains a great challenge to produce reliable LULC maps in moderate resolution and to evaluate their uncertainties over large areas with complex landscapes. In this study we developed a robust LULC classification system using multiple classifiers based on MODIS (Moderate Resolution Imaging Spectroradiometer) data and posterior data fusion. Not only does the system create LULC maps with high statistical accuracy, but also it provides pixel-level uncertainties that are essential for subsequent analyses and applications. We applied the classification system to the Agro-pasture transition band in northern China (APTBNC) to detect the decadal changes in LULC during 2003-2013 and evaluated the effectiveness of the implementation of major Key Forestry Programs (KFPs). In our study, the random forest (RF), support vector machine (SVM), and weighted k-nearest neighbors (WKNN) classifiers outperformed the artificial neural networks (ANN) and naive Bayes (NB) in terms of high classification accuracy and low sensitivity to training sample size. The Bayesian-average data fusion based on the results of RF, SVM, and WKNN achieved the 87.5% Kappa statistics, higher than any individual classifiers and the majority-vote integration. The pixel-level uncertainty map agreed with the traditional accuracy assessment. However, it conveys spatial variation of uncertainty. Specifically, it pinpoints the southwestern area of APTBNC has higher uncertainty than other part of the region, and the open shrubland is likely to be misclassified to the bare ground in some locations. Forests, closed shrublands, and grasslands in APTBNC expanded by 23%, 50%, and 9%, respectively, during 2003-2013. The expansion of these land cover types is compensated with the shrinkages in croplands (20%), bare ground (15%), and open shrublands (30%). The significant decline in agricultural lands is primarily attributed to the KFPs implemented in the end of last century and the nationwide urbanization in recent decade. The increased coverage of grass and woody plants would largely reduce soil erosion, improve mitigation of climate change, and enhance carbon sequestration in this region.

Project description:Urbanization is a major contributor to biodiversity declines. However, studies assessing effects of urban landscapes per se (i.e., disentangled from focal habitat effects) on biodiversity across spatial scales are lacking. Understanding such scale-dependent effects is fundamental to preserve habitats along an urbanization gradient in a way that maximizes overall biodiversity. We investigated the impact of landscape urbanization on communities of woodland-breeding bird species in individual (local scale) and across multiple (regional scale) cities, while controlling for the quality of sampled habitats (woodlands). We conducted bird point counts and habitat quality mapping of trees, dead wood, and shrubs in 459 woodlands along an urban to rural urbanization gradient in 32 cities in Sweden. Responses to urbanization were measured as local and regional total diversity (γ), average site diversity (α), and diversity between sites (β). We also assessed effects on individual species and to what extent dissimilarities in species composition along the urbanization gradient were driven by species nestedness or turnover. We found that landscape urbanization had a negative impact on γ-, α-, and β-diversity irrespective of spatial scale, both regarding all woodland-breeding species and red-listed species. At the regional scale, dissimilarities in species composition between urbanization levels were due to nestedness, that is, species were lost with increased landscape urbanization without being replaced. In contrast, dissimilarities at the local scale were mostly due to species turnover. Because there was no difference in habitat quality among woodlands across the urbanization gradient, we conclude that landscape urbanization as such systematically causes poorer and more homogeneous bird communities in adjacent natural habitats. However, the high local turnover and the fact that several species benefited from urbanization demonstrates that natural habitats along the entire urbanization gradient are needed to maintain maximally diverse local bird communities.

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Dataset Information

Spatially differentiated trends in urbanization, agricultural land abandonment and reclamation, and woodland recovery in Northern China.

Publications

Spatially differentiated trends in urbanization, agricultural land abandonment and reclamation, and woodland recovery in Northern China.

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