Kaixiong Xing

Dr. Kaixiong Xing

orcid

Research Focus & Metrics

Academic Career Length

11Years (2016-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 October 2017 in Ecosystem Services
Verified
Journal Article
Closed access
Xue-Chao WangXue-Chao Wang
Xue-Chao Wang
XDXiaobin Dong
Xiaobin Dong
HLHuiming Liu
Huiming Liu
HWHejie Wei
Hejie Wei
WFWeiguo Fan
Weiguo Fan
KXKaixiong Xing
Kaixiong Xing

21 August 2021 in New Phytologist
Verified
Journal Article
Closed access
PDF file
KXKaixiong Xing
Kaixiong Xing
Ülo NiinemetsÜlo Niinemets
Ülo Niinemets
Zdenko RengelZdenko Rengel
Zdenko Rengel
YOYusuke Onoda
Yusuke Onoda
JXJiangzhou Xia
Jiangzhou Xia
HLHongbo Li
Hongbo Li

1 February 2017 in Sustainability
Verified
Journal Article
Open access
PDF file
HWHejie Wei
Hejie Wei
Weiguo FanWeiguo Fan
Weiguo Fan
ZDZhenyu Ding
Zhenyu Ding
BWBoqi Weng
Boqi Weng
KXKaixiong Xing
Kaixiong Xing
XDXiaobin Dong
Xiaobin Dong
Accurately identifying the spatiotemporal variations and driving factors of ecosystem services (ES) in ecological restoration is important for ecosystem management and the sustainability of nature conservation strategies. As the Green for Grain project proceeds, food provision, water regulation and climate regulation services in the Northern Shaanxi Loess Plateau (NSLP) are changing and have caused broad attention. In this study, the dynamic pattern of the normalized differential vegetation index (NDVI) and the main drivers of grain production (GP), water yield (WY) and net primary production (NPP) in the NSLP from 2000–2013 are identified by incorporating multiple data and methods, in order to provide a better understanding of how and why ES change during ecological restoration. WY was simulated by hydrological modeling, and NPP was estimated with the Carnegie Ames Stanford Approach (CASA) model. The results show that vegetation restoration continued from 2000–2013, but fluctuated because of the comprehensive influence of climate and human activity. GP and NPP both exhibited significantly increasing trends, while changes in WY occurred in two stages: decline (2000–2006) and growth (2007–2013). Spatially, significantly increasing trends in NPP and WY were detected in 52.73% and 24.76% of the region, respectively, in areas that correspond with the Green for Grain project and high precipitation growth. Correlation and partial correlation analyses show that there were different dominant factors (i.e., natural vs. anthropogenic) driving ES change in the NSLP from 2000–2013. The change in WY was mainly driven by precipitation, while the improvements in GP and NPP can be attributed to investments in natural capital (i.e., chemical fertilizer, agricultural machinery power and afforestation). We also found that vegetation restoration can produce positive effects on NPP, but negative effects on WY by using response analyses of WY or NPP change to NDVI change, demonstrating that additional research on the role of water in vegetation restoration is needed. Our results provide support for ES management and the sustainable development of ecological restoration in the NSLP.

1 July 2018 in Ecological Indicators
Verified
Journal Article
Closed access
NLNachuan Lu
Nachuan Lu
HWHejie Wei
Hejie Wei
WFWeiguo Fan
Weiguo Fan
ZXZihan Xu
Zihan Xu
Xue-Chao WangXue-Chao Wang
Xue-Chao Wang
Sergio UlgiatiSergio Ulgiati
Sergio Ulgiati

3 May 2017 in Sustainability
Verified
Journal Article
Open access
PDF file
YWYuhang Wang
Yuhang Wang
MKMuyi Kang
Muyi Kang
MZMingfei Zhao
Mingfei Zhao
KXKaixiong Xing
Kaixiong Xing
GWGuoyi Wang
Guoyi Wang
FXFeng Xue
Feng Xue
Analyzing spatiotemporal variation of tree cover could enhance understanding of the environment and promote a sustainable resource use of ecosystems. This study investigated the variation in tree cover in the Loess Plateau after an ecological restoration effort called the ‘Grain for Green Project’ (GGP). The results show that the proportion of tree covered area in the Loess Plateau changed from 73% to 88%, with the cumulative tree cover fluctuating from approximately 7% to 11%, and the average annual tree cover increased from 10% in 2000 to 12% in 2014. Based on tree cover values over the course of 15 years, the study area was classified into five regions, which provide much more information for spatial assessment of tree cover change in the Loess Plateau spatially. The increase in tree cover value was mainly in the core part of Loess Plateau, the mountains, and the edge of the mountain areas; whereas the values were stable in 36.21% of the area, and a decrease was noted in 5.63% of the area, primarily located in the low plain areas. Approximately 26.36% of the Loess Plateau will show a sustained increase in tree cover in the future. The results of this study will facilitate us to understand the current conditions and development of the GGP’s effects, and offer a valuable reference for future detection of tree cover change through geographic information system (GIS) and remote sensing (RS) tools.

11 January 2017 in Journal of Mountain Science
Verified
Journal Article
Closed access
PDF file
GWGuo-Yi Wang
Guo-Yi Wang
MZMing-Fei Zhao
Ming-Fei Zhao
MKMu-Yi Kang
Mu-Yi Kang
KXKaixiong Xing
Kaixiong Xing
YWYuhang Wang
Yuhang Wang
CCChen Chen
Chen Chen