A River Basin over the Course of Time: Multi-Temporal Analyses of Land Surface Dynamics in the Yellow River Basin (China) Based on Medium Resolution Remote Sensing Data

被引:31
作者
Wohlfart, Christian [1 ]
Liu, Gaohuan [2 ]
Huang, Chong [2 ,3 ]
Kuenzer, Claudia [4 ]
机构
[1] Co Remote Sensing & Environm Res SLU, Kohlsteiner Str 5, D-81243 Munich, Germany
[2] Chinese Acad Sci, IGSNRR, State Key Lab Resources & Environm Informat Syst, 11A Da Tun Rd, Beijing 100101, Peoples R China
[3] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
[4] German Remote Sensing Data Ctr DFD, German Aerosp Ctr DLR, Munchner Str 20, D-82234 Oberpfaffenhofen, Germany
关键词
Yellow River Basin; MODIS; land cover change; random forest; time series analysis; phenology; COVER CLASSIFICATION; AGRICULTURAL INTENSIFICATION; VEGETATION COVER; LATIN-AMERICA; LOESS PLATEAU; RANDOM FOREST; SERIES DATA; MODIS; ACCURACY; REGION;
D O I
10.3390/rs8030186
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The Yellow River Basin is one of China's most densely-populated, fastest growing and most dynamic regions, with abundant natural resources and intense agricultural production. Major land policies have recently resulted in remarkable landscape modifications throughout the basin. The availability of precise regional land cover change information is crucial to better understand the prevailing dynamics and underlying factors influencing the current processes in such a complex system and can additionally serve as a valuable component for modeling and decision making. Such comprehensive and detailed information is lacking for the Yellow River Basin so far. In this study, we derived land cover characteristics and dynamics from the complete last decade based on optical high-temporal MODIS Normalized Differenced Vegetation Index (NDVI) time series for the whole Yellow River Basin. After filtering and smoothing for noise reduction with the use of the adaptive Savitzky-Golay filter, the processed time series was used to derive a large variety of phenological and annual metrics. The final classifications for the basin (2003 and 2013) were based on a random forest classifier, trained by reference samples from very high-resolution imagery. The accuracy assessment for all 18 thematic classes, which was based on a 30% reference data split, yielded an overall accuracy of 87% and 84% for 2003 and 2013, respectively. Major land cover and land use changes during the last decade have occurred on the Loess Plateau, where land and conservation reforms triggered large-scale recovery of grassland and shrubland habitat that had been previously covered by agriculture or sparse vegetation. Agricultural encroachment and urban area expansion are other processes influencing the dynamics in the basin. The necessity for regionally-adapted land cover maps becomes obvious when our land cover products are compared to existing global products, where thematic accuracy remains low, particularly in a heterogeneous landscape, such as the Yellow River Basin. The basin-wide novel land cover and land use products of the Yellow River Basin hold a large potential for climate, hydrology and biodiversity modelers, as well as river basin and regional governmental authorities and will be shared upon request.
引用
收藏
页数:25
相关论文
共 81 条
[1]   Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods [J].
Adelabu, Samuel ;
Mutanga, Onisimo ;
Adam, Elhadi .
GEOCARTO INTERNATIONAL, 2015, 30 (07) :810-821
[2]   Mapping abandoned agriculture with multi-temporal MODIS satellite data [J].
Alcantara, Camilo ;
Kuemmerle, Tobias ;
Prishchepov, Alexander V. ;
Radeloff, Volker C. .
REMOTE SENSING OF ENVIRONMENT, 2012, 124 :334-347
[3]  
[Anonymous], 2009, ASSESSING ACCURACY R
[4]  
[Anonymous], 2020, R PACKAGE VERSION 6
[5]  
[Anonymous], 2013, CHIN STAT DAT
[6]   GLC2000:: a new approach to global land cover mapping from Earth observation data [J].
Bartholomé, E ;
Belward, AS .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2005, 26 (09) :1959-1977
[7]   Fragmentation effects of oil wells and roads on the Yellow River Delta, North China [J].
Bi, Xiaoli ;
Wang, Bin ;
Lu, Qingshui .
OCEAN & COASTAL MANAGEMENT, 2011, 54 (03) :256-264
[8]  
Bicheron P., 2010, GLOBCOVER PRODUCTS D
[9]   A land cover map of Latin America and the Caribbean in the framework of the SERENA project [J].
Blanco, Paula D. ;
Colditz, Rene R. ;
Saldana, Gerardo Lopez ;
Hardtke, Leonardo A. ;
Llamas, Ricardo M. ;
Mari, Nicolas A. ;
Fischer, Angeles ;
Caride, Constanza ;
Acenolaza, Pablo G. ;
del Valle, Hector F. ;
Lillo-Saavedra, Mario ;
Coronato, Fernando ;
Opazo, Sergio A. ;
Morelli, Fabiano ;
Anaya, Jesus A. ;
Sione, Walter F. ;
Zamboni, Pamela ;
Barrena Arroyo, Victor .
REMOTE SENSING OF ENVIRONMENT, 2013, 132 :13-31
[10]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32