Spatiotemporal Change of Sparse Vegetation Coverage in Northern China
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Spatiotemporal Change of Sparse Vegetation Coverage in Northern China Lili Feng1,2 • Zhiqing Jia2 • Qingxue Li2 • Anzhou Zhao1 • Yuling Zhao1 • Zhaojiang Zhang1 Received: 15 November 2017 / Accepted: 13 November 2018 Ó Indian Society of Remote Sensing 2018
Abstract Vegetation coverage is an important biophysical parameter of terrestrial ecosystems. In this study, MODIS data were used to estimate sparse vegetation coverage using an improved dimidiate pixel model in northern China. Specifically, Normalized Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR) and Leaf Area Index (LAI) products acquired from MOD09A1 and MOD15A2 were selected to acquire the sparse vegetation coverage in northern China. When 1 \ LAI \ 3, the no-dense model was used and dense model was used under other conditions. The sparse vegetation coverage results from 2001 to 2015 were used to analyze the spatiotemporal change in this area. In this region, vegetation degradation, vegetation non-alteration and vegetation restoration cover 19.18%, 37.91% and 42.91%, respectively. In recent 5 years, vegetation situation is deteriorated, especially in local area. Although vegetation situation is improved on a long time and large scale, we should pay much attention to vegetation degradation in recent years and local areas. Keywords Sparse vegetation coverage Spatiotemporal change MODIS Northern China
Introduction Vegetation plays an important role in energy exchange, ecological and hydrological cycling processes on the land surface as an indicator to study global changes. Vegetation coverage is an important parameter for describing the land surface vegetation as a comprehensive quantitative variable for plant community and a basic data for characterizing ecosystems (Zhang et al. 2013; Wu et al. 2014; Song et al. 2017). For example, the vegetation coverage is an important parameter for characterizing land surface vegetation conditions and establishing land surface models (Jia et al. 2016; Yang et al. 2014). It is significant to estimate the vegetation coverage in China, especially in northern China. & Zhiqing Jia [email protected] & Zhaojiang Zhang [email protected] 1
College of Mining and Geomatics, Hebei University of Engineering, Handan 056038, Hebei, China
2
Institute of Desertification Studies, Qinghai Gonghe Desert Ecosystem Research Station, Chinese Academy of Forestry, Beijing 100091, China
Currently, many methods are used to estimate vegetation coverage, which mainly include empirical methods, pixel unmixing models and physical model-based methods (Jia et al. 2016; Xiao et al. 2016). The vegetation coverage estimation approaches roughly include ground survey, remote sensing and a combination of the two. Remote sensing technique has become a general way to estimate the vegetation coverage because it is fast and requires little labor compared to conventional methods. With the development of digital camera, vegetation coverage estimation becomes faster and more accurate. Although the digi
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