TY  - JOUR
A1  - Heim, Wieland
A1  - Trense, Daronja
A1  - Sokolova, Galina V.
A1  - Kitagawa, Tamaki
T1  - Increased populations of endangered cranes after Amur River flood
JF  - Waterbirds
N2  - Dam construction on the Zeya River, which is an important tributary of the Amur River in Far East Russia, has caused significant declines in water levels and frequency of floods in the adjacent floodplains since 1980. However, an extreme flood event occurred in 2013. Populations of six crane species were monitored before and after these drastic water level changes at Muraviovka Park in Far East Russia, an important breeding and stop-over site. Individuals were counted by territory mapping during the breeding season (2000-2015) and by roosting site counts during autumn migration (2006-2015). The objective of this study was to evaluate whether changes in water levels had a significant impact on local and migratory crane populations. We found a positive effect of flooding on numbers of breeding Red-crowned Cranes (Grus japonensis) and White-naped Cranes (Antigone vipio), as well as on numbers of roosting Hooded Cranes (Grus monacha) in autumn. Siberian Cranes (Leucogeranus leucogeranus) were only observed after the wetlands were flooded. The results of this study highlight the importance of elevated Amur River water levels for crane populations of global importance.
KW  - Antigone vipio
KW  - cranes
KW  - dam construction
KW  - East Asian flyway
KW  - floodplain
KW  - Grus japonensis
KW  - Grus monacha
KW  - Leucogeranus leucogeranus
KW  - population trend
KW  - water level
Y1  - 2017
U6  - https://doi.org/10.1675/063.040.0309
SN  - 1524-4695
SN  - 1938-5390
VL  - 40
SP  - 282
EP  - 288
PB  - Waterbirds SOC
CY  - Washington
ER  - 
TY  - GEN
A1  - Malinowski, Radosław
A1  - Groom, Geoff
A1  - Schwanghart, Wolfgang
A1  - Heckrath, Goswin
T1  - Detection and delineation of localized flooding from WorldView-2 multispectral data
N2  - Remote sensing technology serves as a powerful tool for analyzing geospatial characteristics of flood inundation events at various scales. However, the performance of remote sensing methods depends heavily on the flood characteristics and landscape settings. Difficulties might be encountered in mapping the extent of localized flooding with shallow water on riverine floodplain areas, where patches of herbaceous vegetation are interspersed with open water surfaces. To address the difficulties in mapping inundation on areas with complex water and vegetation compositions, a high spatial resolution dataset has to be used to reduce the problem of mixed pixels. The main objective of our study was to investigate the possibilities of using a single date WorldView-2 image of very high spatial resolution and supporting data to analyze spatial patterns of localized flooding on a riverine floodplain. We used a decision tree algorithm with various combinations of input variables including spectral bands of the WorldView-2 image, selected spectral indices dedicated to mapping water surfaces and vegetation, and topographic data. The overall accuracies of the twelve flood extent maps derived with the decision tree method and performed on both pixels and image objects ranged between 77% and 95%. The highest mapping overall accuracy was achieved with a method that utilized all available input data and the object-based image analysis. Our study demonstrates the possibility of using single date WorldView-2 data for analyzing flooding events at high spatial detail despite the absence of spectral bands from the short-waveform region that are frequently used in water related studies. Our study also highlights the importance of topographic data in inundation analyses. The greatest difficulties were met in mapping water surfaces under dense canopy herbaceous vegetation, due to limited water surface exposure and the dominance of vegetation reflectance.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 348 
KW  - decision tree
KW  - floodplain
KW  - inundation
KW  - localized flooding
KW  - object-based image analysis
KW  - wetlands
KW  - WorldView-2
Y1  - 2017
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-400149
ER  -