title: Decadal Trend in Agricultural Abandonment and Woodland Expansion in an Agro-Pastoral Transition Band in Northern China.
abstract: 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.
authors.affiliation: Department of Environmental Sciences, University of Puerto Rico, Rio Piedras, San Juan, Puerto Rico, United States of America.
authors.forename: Chao
authors.initials: C
authors.lastname: Wang
authors.affiliation: Department of Environmental Sciences, University of Puerto Rico, Rio Piedras, San Juan, Puerto Rico, United States of America.
authors.forename: Qiong
authors.initials: Q
authors.lastname: Gao
authors.affiliation: Department of Environmental Sciences, University of Puerto Rico, Rio Piedras, San Juan, Puerto Rico, United States of America.
authors.forename: Xian
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Environmental Sciences, University of Puerto Rico, Rio Piedras, San Juan, Puerto Rico, United States of America.
authors.forename: Mei
authors.initials: M
authors.lastname: Yu
mesh_terms: D000383:Agriculture / Q000379:methods / Q000592:standards / Q000639:trends*; D000465:Algorithms; D000822:Animal Husbandry / Q000379:methods / Q000706:statistics & numerical data / Q000639:trends*; D001499:Bayes Theorem; D002681:China; D019295:Computational Biology / Q000379:methods; D003247:Conservation of Natural Resources / Q000379:methods / Q000706:statistics & numerical data / Q000639:trends*; D017753:Ecosystem; D058735:Environmental Policy / Q000639:trends; D005843:Geography; D060434:Herbivory; D006109:Poaceae / Q000502:physiology; D011157:Population Dynamics; D063809:Satellite Imagery / Q000379:methods; D060388:Support Vector Machine; D013997:Time Factors; D014197:Trees / Q000502:physiology; D035501:Uncertainty; D014507:Urbanization / Q000639:trends*