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Sustainability 2017, 9(10), 1780;

Detecting Historical Vegetation Changes in the Dunhuang Oasis Protected Area Using Landsat Images

College of Earth Environmental Sciences, Lanzhou University, No. 222, Tianshui South Road, Chengguan District, Lanzhou 730000, China
School of Civil Engineering, Lanzhou University of Technology, No. 287, Langongping Road, Qilihe District, Lanzhou 730050, China
Key Laboratory of Western China’s Environmental systems (Ministry of Education), Lanzhou University, Lanzhou 730000, China
Author to whom correspondence should be addressed.
Received: 25 July 2017 / Revised: 27 September 2017 / Accepted: 28 September 2017 / Published: 30 September 2017
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Abstract: Given its proximity to an artificial oasis, the Donghu Nature Reserve in the Dunhuang Oasis has faced environmental pressure and vegetation disturbances in recent decades. Satellite vegetation indices (VIs) can be used to detect such changes in vegetation if the satellite images are calibrated to surface reflectance (SR) values. The aim of this study was to select a suitable VI based on the Landsat Climate Data Record (CDR) products and the absolute radiation-corrected results of Landsat L1T images to detect the spatio-temporal changes in vegetation for the Donghu Reserve during 1986–2015. The results showed that the VI difference (ΔVI) images effectively reduced the changes in the source images. Compared with the other VIs, the soil-adjusted vegetation index (SAVI) displayed greater robustness to atmospheric effects in the two types of SR images and was more responsive to vegetation changes caused by human factors. From 1986 to 2015, the positive changes in vegetation dominated the overall change trend, with changes in vegetation in the reserve decreasing during 1990–1995, increasing until 2005–2010, and then decreasing again. The vegetation changes were mainly distributed at the edge of the artificial oasis outside the reserve. The detected changes in vegetation in the reserve highlight the increased human pressure on the reserve. View Full-Text
Keywords: remote sensing; CDR; vegetation index; Dunhuang remote sensing; CDR; vegetation index; Dunhuang

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Zhang, X.; Xie, Y. Detecting Historical Vegetation Changes in the Dunhuang Oasis Protected Area Using Landsat Images. Sustainability 2017, 9, 1780.

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