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Evaluation of seasonal water body extents in Central Asia over thepast 27 years derived from medium-resolution remote sensing data

Klein, Igor and Dietz, Andreas and Gessner, Ursula and Galayeva, Anastassiya and Myrzakhmetov , Akhan and Künzer, Claudia (2014) Evaluation of seasonal water body extents in Central Asia over thepast 27 years derived from medium-resolution remote sensing data. International Journal of Applied Earth Observation and Geoinformation (26), pp. 335-349. Elsevier. DOI: 10.1016/j.jag.2013.08.004 ISSN 0303-2434

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Official URL: http://www.sciencedirect.com/science/article/pii/S0303243413000901

Abstract

In this study medium resolution remote sensing data of the AVHRR and MODIS sensors were used for derivation of inland water bodies extents over a period from 1986 till 2012 for the region of Central Asia. Daily near-infrared (NIR) spectra from the AVHRR sensor with 1.1 km spatial resolution and 8-day NIR composites from the MODIS sensor with 250 m spatial resolution for the months April, July and September were used as input data. The methodological approach uses temporal dynamic thresholds for individual data sets, which allows detection of water pixel independent from differing conditions or sensor differences. The individual results are summed up and combined to monthly composites of areal extent of water bodies. The presented water masks for the months April, July, and September were chosen to detect seasonal patterns as well as inter-annual dynamics and show diverse behaviour of static, decreasing, or dynamic water bodies in the study region. The size of the Southern Aral Sea, as the most popular example for an ecologic catastrophe, is decreasing significantly throughout all seasons (R2 0.96 for April; 0.97 for July; 0.96 for September). Same is true for shallow natural lakes in the northern Kazakhstan, exemplary the Tengiz-Korgalzhyn lake system, which have been shrinking in the last two decades due to drier conditions (R2 0.91 for July; 0.90 for September). On the contrary, water reservoirs show high seasonality and are very dynamic within one year in their areal extent with maximum before growing season and minimum after growing season. Furthermore, there are water bodies such as Alakol-Sasykol lake system and natural mountainous lakes which have been stable in their areal extent throughout the entire time period. Validation was performed based on several Landsat images with 30 m resolution and reveals an overall accuracy of 83% for AVHRR and 91% for MODIS monthly water masks. The results should assist for climatological and ecological studies, land and water management, and as input data for different modelling applications.

Item URL in elib:https://elib.dlr.de/84565/
Document Type:Article
Title:Evaluation of seasonal water body extents in Central Asia over thepast 27 years derived from medium-resolution remote sensing data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Klein, Igorigor.klein (at) dlr.deUNSPECIFIED
Dietz, AndreasAndreas.Dietz (at) dlr.deUNSPECIFIED
Gessner, Ursulaursula.gessner (at) dlr.deUNSPECIFIED
Galayeva, AnastassiyaUNSPECIFIEDUNSPECIFIED
Myrzakhmetov , AkhanUNSPECIFIEDUNSPECIFIED
Künzer, ClaudiaClaudia.Kuenzer (at) dlr.deUNSPECIFIED
Date:2014
Journal or Publication Title:International Journal of Applied Earth Observation and Geoinformation
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1016/j.jag.2013.08.004
Page Range:pp. 335-349
Publisher:Elsevier
ISSN:0303-2434
Status:Published
Keywords:Water bodies; Central Asia; Medium resolution satellite data; Time-series
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
Deposited By: Klein, Igor
Deposited On:18 Nov 2013 21:26
Last Modified:06 Sep 2019 15:16

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