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A River 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

Wohlfart, Christian and Liu, Gaohuan and Huang, Chong and Künzer, Claudia (2016) A River 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. Remote Sensing, 8 (3), pp. 1-25. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs8030186 ISSN 2072-4292

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Official URL: http://www.mdpi.com/2072-4292/8/3/186

Abstract

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.

Item URL in elib:https://elib.dlr.de/103230/
Document Type:Article
Title:A River 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
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Wohlfart, Christianchristian.wohlfart (at) dlr.deUNSPECIFIED
Liu, GaohuanIGSNRRUNSPECIFIED
Huang, ChongIGSNRRUNSPECIFIED
Künzer, ClaudiaClaudia.Kuenzer (at) dlr.deUNSPECIFIED
Date:February 2016
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:8
DOI :10.3390/rs8030186
Page Range:pp. 1-25
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:land cover change; phenology; MODIS; random forest; time series analysis; Yellow River Basin
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Fernerkundung der Landoberfläche (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface
Deposited By: Wohlfart, Christian
Deposited On:07 Mar 2016 09:34
Last Modified:08 Mar 2018 18:50

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