Identification and temporally-spatial quantification of geomorphic relevant changes by construction projects in loess landscapes: case study Lanzhou City, NW China. Issue 4 (2nd October 2019)

• Record Type: Journal Article
• Authors: Fuchs, Michael; Torizin, Jewgenij; Wang, Lichao; Tong, Bin; Balzer, Dirk; Chen, Liang; Kuhn, Dirk; Li, Ang; Wan, Liqin
• Is Part Of: Big earth data. Volume 3:Issue 4(2019)
• Page Start: 395
• Page End: 410
• Publication Date: 2019-10-02
• Subjects: Change detection -- principal component analysis -- Chinese loess plateau -- economic development zone; Earth sciences -- Periodicals; Earth sciences -- Research -- Periodicals; Geographic information systems Periodicals; 550
• DOI: 10.1080/20964471.2019.1640412
• Languages: English
• ISSNs: 2096-4471

Abstract

ABSTRACT: Sustainable development of urban areas demands, among others, a holistic approach for identification and monitoring of environmental changes caused by human activities. In this study, the human-made impact on the geo-environment is studied through the application of the change detection study based on Principal Component Analysis on Landsat imageries and comparison of the digital elevation models taken at different times. The analysis is set up to identify and quantify the anthropogenic geomorphological changes in the loess landscape in Lanzhou city, Northwest China. Since 2002 Lanzhou has been undergoing a rapid economic development associated with construction boom. Due to limited flat building ground in the narrowed Yellow River Valley and subsequent expansion into the surrounding loess mountains, massive earthworks are conducted for reclamation of the suitable building ground. The results of the change detection analysis show that approximately 10% of the semi-natural study area corresponding to 35 km 2 has been reshaped by leveling and terracing since 1994. In particular, the geomorphology was significantly changed in these and adjacent areas. For the single developing area Taipingyang, a moving volume of up to 57 million m 3 was roughly estimated.