發(fā)布時間：2018-09-01 16:24

【摘要】：臥龍自然保護區(qū)(世界自然遺產(chǎn)地)是大熊貓最主要的棲息地之一。結(jié)合雷達遙感全天時、全天候觀測優(yōu)勢,以及森林覆蓋對棲息地生境評價的重要性,開展多時相、雙極化雷達數(shù)據(jù)森林精細成圖研究就顯得尤為重要。本研究首先對雷達數(shù)據(jù)進行輻射地形校正;然后選用5個時相ALOS PALSAR數(shù)據(jù),采用支持向量機(support vector machine,SVM)方法進行森林精細成圖。研究選取了5個多時相、雙極化典型特征信息參與初始訓練和分類,即HH_m,HV_m,TSD,HH_m-HV_m和HH_m/HV_m;接著通過對不同信息組合分類精度的試驗與對比,獲取了最優(yōu)特征組合HH_m,HV_m,TSD,HH_m-HV_m。對應(yīng)分類總體精度、森林及非森林類別用戶精度分別為86.90%,82.34%和92.83%,顯著優(yōu)于單時相單極化數(shù)據(jù)分類結(jié)果(分類總體精度55.47%)。研究結(jié)果驗證了多時相、雙極化雷達遙感數(shù)據(jù)在自然遺產(chǎn)地森林精細成圖中的有效性,并揭示了雷達遙感在多云多雨地區(qū)生境監(jiān)測與評價中的潛力與應(yīng)用價值。
[Abstract]:Wolong Nature Reserve is one of the most important habitats for giant pandas. Combined with the advantages of radar remote sensing all day, all weather observation and the importance of forest cover to habitat evaluation, it is very important to study the precise mapping of forest with multi-phase and dual-polarization radar data. In this study, the radar data were first corrected by radiometric topography, and then five time-phase ALOS PALSAR data were selected, and the support vector machine (support vector machine,SVM) method was used for fine forest mapping. In this study, five multi-phase and two-polarization typical feature information were selected to participate in the initial training and classification. Then, HH_m,HV_m,TSD,HH_m-HV_m and HH_m/HV_m; obtained the optimal feature combination HH_m,HV_m,TSD,HH_m-HV_m. by the experiment and comparison of the classification accuracy of different information combinations. The accuracy of forest and non-forest users was 86.90% and 92.83% respectively, which was significantly better than that of single time and single polarization data (total classification accuracy was 55.47%). The results verify the validity of multi-temporal and dual-polarization radar remote sensing data in the fine forest mapping of natural heritage areas, and reveal the potential and application value of radar remote sensing in monitoring and evaluation of habitats in cloudy and rainy areas.
【作者單位】： 中國科學院遙感與數(shù)字地球研究所數(shù)字地球重點實驗室;中國科學院大學;聯(lián)合國教科文組織國際自然與文化遺產(chǎn)空間技術(shù)中心;山東科技大學測繪科學與工程學院;
【基金】：國家國際科技合作專項項目“全球變化對世界遺產(chǎn)影響空間精細觀測與認知”(編號:2013DFG21640) 中國科學院“百人計劃”項目“雷達遙感考古機理與方法示范研究”(編號:Y5YR0300QM)共同資助
【分類號】：TN957.52

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