Mapping Deciduous Broad-leaved Forested Swamps Using ALOS/Palsar Data  

作　　者：BIAN Hongfeng YAN Tingting ZHANG Zhengxiang HE Chunguang SHENG Lianxi 

机构地区：[1]State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University [2]School of Geographical Sciences, Northeast Normal University

出　　处：《Chinese Geographical Science》2016年第3期352-365,共14页中国地理科学（英文版）

基　　金：Under the auspices of Special Funds of State Environmental Protection Public Welfare Industry(No.2011467032)

摘　　要：Accurate, updated information on the distribution of wetlands is essential for estimating net fluxes of greenhouse gases and for effectively protecting and managing wetlands. Because of their complex community structure and rich surface vegetation, deciduous broad-leaved forested swamps are considered to be one of the most difficult types of wetland to classify. In this research, with the support of remote sensing and geographic information system, multi-temporal radar images L-Palsar were used initially to extract the forest hydrological layer and phenology phase change layer as two variables through image analysis. Second, based on the environmental characteristics of forested swamps, three decision tree classifiers derived from the two variables were constructed to explore effective methods to identify deciduous broad-leaved forested swamps. Third, this study focused on analyzing the classification process between flat-forests, which are the most severely disturbed elements, and forested swamps. Finally, the application of the decision tree model will be discussed. The results showed that: 1) L-HH band(a L band with wavelength of 0–235 m in HH polarization mode; HH means Synthetic Aperture Radars transmit pulses in horizontal polarization and receive in horizontal polarization) in the leaf-off season is shown to be capable of detecting hydrologic conditions beneath the forest; 2) the accuracy of the classification(forested swamp and forest plat) was 81.5% based on hydrologic features, and 83.5% was achieved by combining hydrologic features and phenology response features, which indicated that hydrological characteristics under the forest played a key role. The HHOJ(refers to the band created by the subtraction with HH band in October and HH band in July) achieved by multi-temporal radar images did improve the classification accuracy, but not significantly, and more leaf-off radar images may be more efficient than multi-seasonal radar images for inland forested swamp mapping; 3) the lower separability betweenAccurate, updated information on the distribution of wetlands is essential for estimating net fluxes of greenhouse gases and for effectively protecting and managing wetlands. Because of their complex community structure and rich surface vegetation, deciduous broad-leaved forested swamps are considered to be one of the most difficult types of wetland to classify. In this research, with the support of remote sensing and geographic information system, multi-temporal radar images L-Palsar were used initially to extract the forest hydrological layer and phenology phase change layer as two variables through image analysis. Second, based on the environmental characteristics of forested swamps, three decision tree classifiers derived from the two variables were constructed to explore effective methods to identify deciduous broad-leaved forested swamps. Third, this study focused on analyzing the classification process between flat-forests, which are the most severely disturbed elements, and forested swamps. Finally, the application of the decision tree model will be discussed. The results showed that： 1） L-HH band（a L band with wavelength of 0–235 m in HH polarization mode; HH means Synthetic Aperture Radars transmit pulses in horizontal polarization and receive in horizontal polarization） in the leaf-off season is shown to be capable of detecting hydrologic conditions beneath the forest; 2） the accuracy of the classification（forested swamp and forest plat） was 81.5% based on hydrologic features, and 83.5% was achieved by combining hydrologic features and phenology response features, which indicated that hydrological characteristics under the forest played a key role. The HHOJ（refers to the band created by the subtraction with HH band in October and HH band in July） achieved by multi-temporal radar images did improve the classification accuracy, but not significantly, and more leaf-off radar images may be more efficient than multi-seasonal radar images for inland forested swamp mapping; 3） the lower

关 键 词：forested swamp Palsar radar images forest hydrological characteristics multi-temporal technique decision tree classifier 

分 类 号：S718.5[农业科学—林学]