Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data

被引:257
作者
Martinez, Jean-Michel
Le Toan, Thuy
机构
[1] Univ Toulouse 3, IRD, LMTG, F-31400 Toulouse, France
[2] Univ Toulouse 3, CESBIO, CNRS, IRD, F-31401 Toulouse 9, France
关键词
wetland; floodplain; vegetation; flood; temporal dynamic; radar; classification;
D O I
10.1016/j.rse.2006.11.012
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper presents the use of time series of SAR images to map the flood temporal dynamics and the spatial distribution of vegetation over a large Amazonian floodplain. The region under study (3500 km(2)) presents a diversity of landscape units with open lakes, bogs, large meadows, savannahs, alluvial forests and terra firma forest, covered by 21 images acquired by J-ERS between 1993 and 1997. Ground data include in situ observations of vegetation structure and flood extent as well as water level records. Image analysis demonstrates that temporal variations of the radar backscatter can be used to monitor efficiently the flood extent regardless of the landscape units. Also, analysis of the backscatter temporal variation greatly reduces the confusion between smooth surfaces (e.g. open water bodies, bare soils) inherent to L-band backscatter. The mapping method is based on decision rules over two decision variables: 1) the mean backscatter coefficient computed over the whole time series; 2) the total change computed using an "Absolute Change" estimator. The first variable provides classification into rough vegetation types while the second variable yields a direct estimate of the intensity of change that is related to flood dynamics. The classifier is first applied to the whole time series to map the maximum and minimum flood extent by defining 3 flood conditions: never flooded (NF); occasionally flooded (OF); permanently flooded (PF). It also furnishes the broad land cover type: open water/bare soils/low vegetation/forest. The accuracy of the flood extent mapping shows a kappa value of 0.82. Then, the classifier is run iteratively on the OF pixels to monitor flood stages during which the occasionally flooded areas get submerged. The mapping accuracy is assessed on one intermediate flood stage, showing a precision in excess of 90%. The importance of the time sampling for flood mapping is discussed along with the influence of SAR backscatter accuracy and the number of images. Then general guidelines for floodplain mapping are presented. By combining water level reports with maps of different flood stages the flooding pattern can be retrieved along with the vegetation succession processes. It is shown that the spatial distribution of vegetation communities is governed by flood stress and can be modelled as a function of the mean annual exposure to floods. (C) 2006 Elsevier Inc. All rights reserved.
引用
收藏
页码:209 / 223
页数:15
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