本文選題：地上生物量 + 紋理因子��； 參考：《遙感學報》2017年05期

【摘要】：使用高空間分辨率衛(wèi)星WorldView-2的多光譜遙感影像,構建植被指數(shù)和紋理因子等遙感因子與森林地上生物量的關系方程,并計算模型估測精度和均方根誤差,探索高分辨率數(shù)據(jù)的光譜與紋理信息在溫帶森林地上生物量估測應用中的潛力。以黑龍江省涼水自然保護區(qū)溫帶天然林及天然次生林為研究對象,通過灰度共生矩陣(GLCM)、灰度差分向量(GLDV)及和差直方圖(SADH)對高分辨率遙感影像進行紋理信息提取,并利用外業(yè)調查的74個樣地地上生物量與遙感因子建立參數(shù)估計模型。提取的遙感因子包括6種植被指數(shù)(比值植被指數(shù)RVI、差值植被指數(shù)DVI、規(guī)一化植被指數(shù)NDVI、增強植被指數(shù)EVI、土壤調節(jié)植被指數(shù)SAVI和修正的土壤調節(jié)植被指數(shù)MSAVI)以及3類紋理因子(GLCM、GLDV和SADH)。為避免特征變量個數(shù)較多對估測模型造成過擬合,利用隨機森林算法對提取的遙感因子進行特征選擇,將最優(yōu)的特征變量輸入模型參與建模估測。采用支持向量回歸(SVR)進行生物量建模及驗證,結果顯示選入模型的和差直方圖均值(sadh_mean)、灰度共生矩陣方差(glcm_var)和差值植被指數(shù)(DVI)等遙感因子對森林地上生物量有較好的解釋效果;植被指數(shù)+紋理因子組合的模型獲得較精確的AGB估算結果(R2=0.85,RMSE=42.30 t/ha),單獨使用植被指數(shù)的模型精度則較低(R~2=0.69,RMSE=61.13 t/ha)。
[Abstract]:Using the multispectral remote sensing image of high spatial resolution satellite WorldView-2, the relationship equation between vegetation index and texture factor and forest aboveground biomass was established, and the estimation accuracy and root mean square error of the model were calculated.To explore the potential of spectral and texture information of high-resolution data in the estimation of aboveground biomass of temperate forest.Taking temperate natural forest and natural secondary forest in Liangshui Nature Reserve of Heilongjiang Province as the research object, the texture information of high resolution remote sensing image was extracted by GLCM, GLDV and SADH.A parameter estimation model was established by using the aboveground biomass and remote sensing factors of 74 plots investigated in the field.The extracted remote sensing factors include 6 cropping cover indices (ratio vegetation index RVI, differential vegetation index DVI, normalized vegetation index NDVI, enhanced vegetation index SAVI and modified soil regulated vegetation index MSAVI) and 3GLDV and SADH.In order to avoid the overfitting of the estimation model caused by the large number of feature variables, the extracted remote sensing factors were selected by the stochastic forest algorithm, and the optimal feature variables were input into the model to participate in the modeling and estimation.Support vector regression (SVR) was used to model and verify the biomass. The results showed that the sum-difference histogram mean value of the selected model, grey co-occurrence matrix variance glcmvar.) and differential vegetation index (DVI) had better interpretation effect on forest aboveground biomass.The model with texture factor combination of vegetation index obtained more accurate AGB estimation results than that using vegetation index alone, the accuracy of the model was lower than that of the model using vegetation index alone at 61.13 t 路ha ~ (-1) ~ (-1) ~ (-1) ~ (-1) ~ (-1) ~ (-1) 路ha ~ (-1) ~ (-1) ~ (-1) ~ (-1) ~ (-1) ~ (-1) ~ (-1).
【作者單位】： 北京師范大學信息科學與技術學院;中國林業(yè)科學研究院資源信息研究所;
【基金】：國家重點基礎研究發(fā)展計劃(973計劃)(編號:2013CB733406,2013CB733404) 國家高技術研究發(fā)展計劃(863計劃)(編號:2012AA12A306) 中央高校基本科研業(yè)務費專項資金(編號:2015KJJCA12) 中央級公益性科研院所基本科研業(yè)務費專項資金項目(編號:CAFYBB2016ZD004)~~
【分類號】：TP751
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本文編號：1753376