【摘要】：葉面積指數(shù)(Leaf Area Index,LAI)是指單位水平地表上方所有葉片面積的總和,是衡量生物圈與大氣圈能量中物質(zhì)循環(huán)和能量流通、植被生長態(tài)勢(shì)和監(jiān)控全球氣候變化的一個(gè)重要指標(biāo)。傳統(tǒng)直接測(cè)量方法雖然在精度上能夠滿足實(shí)際應(yīng)用的需要,但該方法具有很大的破壞性且人力物力的投入過大,因而對(duì)于大面積LAI數(shù)據(jù)獲取還存在很大難度;遙感監(jiān)測(cè)屬于非接觸間接測(cè)量,以其能夠大面積、高效、全天候、非破壞性測(cè)量等優(yōu)勢(shì)被廣泛應(yīng)用。在遙感影像數(shù)據(jù)獲取的過程中,地表反射率往往作為反演地物理化及生物特性的重要參量而被廣泛使用,但高精度地表反射率產(chǎn)品通常與傳感器成像幾何姿態(tài)以及大氣環(huán)境密切相關(guān)。本文研究和討論了太陽天頂角和傳感器成像姿態(tài)對(duì)植被光譜的影響,提出一種基于植被冠層BRDF效應(yīng)的LAI反演方法,該方法應(yīng)用PROSAIL模型獲取模擬數(shù)據(jù),并將此作為樣本集輸入到BP神經(jīng)網(wǎng)絡(luò)(Back Propagation Artificial Neural Network,BP-ANN)進(jìn)行訓(xùn)練構(gòu)建反演模型。文章主要工作和相應(yīng)結(jié)論如下:(1)、根據(jù)實(shí)測(cè)數(shù)據(jù)、參考資料及先驗(yàn)知識(shí),構(gòu)建植被輻射傳輸模型多角度參數(shù)數(shù)據(jù)庫,在參數(shù)數(shù)據(jù)庫基礎(chǔ)之上模擬植被冠層光譜數(shù)據(jù),根據(jù)模擬數(shù)據(jù)角度信息,計(jì)算半經(jīng)驗(yàn)Ross-Li(Ross thick-Li transit)模型核參數(shù)。(2)、設(shè)計(jì)RVI、DVI、NDVI、MSR、EVI1、EVI2、RDVI、ARVI、SAVI、OSAVI、MSAVI、NLI 12個(gè)植被指數(shù),分別與LAI建立經(jīng)驗(yàn)統(tǒng)計(jì)模型。利用模擬驗(yàn)證樣本和實(shí)測(cè)小麥數(shù)據(jù)對(duì)各指數(shù)模型進(jìn)行精度驗(yàn)證,發(fā)現(xiàn)基于NDVI的統(tǒng)計(jì)模型在LAI反演過程中具有最高精度,實(shí)測(cè)小麥數(shù)據(jù)驗(yàn)證結(jié)果為:R2=0.78,MAE=0.380505,RMSE=0.486441。(3)、根據(jù)神經(jīng)網(wǎng)絡(luò)的超強(qiáng)容錯(cuò)、非線性映射能力,提出基于BP神經(jīng)網(wǎng)絡(luò)的LAI反演方法,即依據(jù)健康植被對(duì)電磁波的反射特性,以植被冠層多光譜數(shù)據(jù)為輸入、LAI為輸出構(gòu)建神經(jīng)網(wǎng)絡(luò)反演模型。文章構(gòu)建兩個(gè)4層BP神經(jīng)網(wǎng)絡(luò)模型針對(duì)是否考慮植被冠層方向性特征的情況進(jìn)行建模分析,其中神經(jīng)網(wǎng)絡(luò)1忽略冠層的幾何光學(xué)特性,而神經(jīng)網(wǎng)絡(luò)2考慮了植被冠層BRDF特性,即將Ross Li核參數(shù)作為幾何光學(xué)特性的表征參數(shù)應(yīng)用于神經(jīng)網(wǎng)絡(luò)的訓(xùn)練。利用模擬數(shù)據(jù)和實(shí)測(cè)小麥數(shù)據(jù)對(duì)兩種網(wǎng)絡(luò)進(jìn)行精度驗(yàn)證,忽略植被冠層幾何光學(xué)特性的神經(jīng)網(wǎng)絡(luò)1對(duì)實(shí)測(cè)數(shù)據(jù)的驗(yàn)證精度為:R2=0.80859,MAE=0.45998,RMSE=0.34936;顧及植被冠層BRDF效應(yīng)的神經(jīng)網(wǎng)絡(luò)2精度為:R2=0.82973,MAE=0.44297,RMSE=0.33886。文章得出結(jié)論:(1)、在基于經(jīng)驗(yàn)統(tǒng)計(jì)模型反演LAI時(shí),NDVI在12種植被指數(shù)中具有更高的反演精度,更好的魯棒性,反演結(jié)果更可靠;(2)、基于植被冠層反射率光譜建立的神經(jīng)網(wǎng)絡(luò)反演模型,以其特有的非線性擬合能力使得其反演結(jié)果明顯優(yōu)于NDVI統(tǒng)計(jì)模型;(3)、考慮植被冠層BRDF效應(yīng)的神經(jīng)網(wǎng)絡(luò)反演精度優(yōu)于不考慮該效應(yīng)的神經(jīng)網(wǎng)絡(luò)。故文章提出一種基于植被冠層光譜BRDF模型的神經(jīng)網(wǎng)絡(luò)LAI反演方法,該方法在一定程度上可以提高LAI的反演精度。
[Abstract]:Leaf area Index (Leaf Area Index,LAI) refers to the sum of all leaf areas above the surface per unit level, which measures the material circulation and energy flow in the energy of the biosphere and the atmosphere. Vegetation growth situation and an important index to monitor global climate change. Although the traditional direct measurement method can meet the needs of practical application in accuracy, it is very destructive and the investment of human and material resources is too large, so it is still very difficult to obtain large area LAI data. Remote sensing monitoring is a non-contact indirect measurement, which is widely used because of its large area, high efficiency, all-weather, non-destructive measurement and so on. In the process of remote sensing image data acquisition, surface reflectivity is often widely used as an important parameter to retrieve the physical and biological characteristics of the ground. However, high precision surface reflectivity products are usually closely related to sensor imaging geometric attitude and atmospheric environment. In this paper, the influence of solar zenith angle and sensor imaging attitude on vegetation spectrum is studied and discussed, and a LAI inversion method based on vegetation canopy BRDF effect is proposed, which uses PROSAIL model to obtain simulation data. This model is input into BP neural network (Back Propagation Artificial Neural Network,BP-ANN as sample set for training to construct inversion model. The main work and corresponding conclusions of this paper are as follows: (1) according to the measured data, reference materials and prior knowledge, the multi-angle parameter database of vegetation radiation transfer model is constructed, and the spectral data of vegetation canopy are simulated on the basis of parameter database. According to the angle information of simulation data, the kernel parameters of semi-empirical Ross-Li (Ross thick-Li transit) model are calculated. (2) 12 vegetation indices of RVI,DVI,NDVI,MSR,EVI1,EVI2,RDVI,ARVI,SAVI,OSAVI,MSAVI,NLI are designed. The empirical statistical model was established with LAI. The accuracy of each exponential model is verified by simulation samples and measured wheat data. It is found that the statistical model based on NDVI has the highest accuracy in the process of LAI inversion. The verification results of measured wheat data are as follows: R2 鈮，

本文編號(hào)：2491222