本文選題：NDVI + LAI�。� 參考：《南京大學(xué)》2017年碩士論文

【摘要】：歸一化植被指數(shù)(NDVI)是遙感領(lǐng)域中應(yīng)用最為廣泛的植被指數(shù)之一,用于作物長勢監(jiān)測、農(nóng)業(yè)估產(chǎn)、旱情預(yù)測、精準(zhǔn)農(nóng)業(yè)等方面的應(yīng)用。NDVI能夠在一定程度上消除大氣、陰影、傳感器定標(biāo)、觀測角度等方面的影響,但是土壤背景的混入干擾使NDVI產(chǎn)生較大誤差。生長于不同土壤類型背景條件下的相同長勢冬小麥農(nóng)田NDVI有很大差異,也一直影響著利用NDVI進(jìn)行小麥長勢有效監(jiān)測和精確評價(jià)�，F(xiàn)有NDVI 土壤影響的去除方法,多為發(fā)展和改進(jìn)現(xiàn)有的NDVI模型,但這些新植被指數(shù)多依賴研究區(qū)土壤線特征,無法像NDVI形成產(chǎn)品進(jìn)而廣泛應(yīng)用于大尺度、寬覆蓋地區(qū)的作物長勢監(jiān)測。因此研究NDVI的土壤背景影響去除方法依然是當(dāng)今的熱點(diǎn)和難點(diǎn)。論文在以典型土壤類型為農(nóng)田背景對不同植被覆蓋度冬小麥NDVI的影響模擬分析的基礎(chǔ)上,基于混合光譜理論,提出基于混合光譜理論的兩種土壤背景影響去除模型(NDVIT)。以安徽省滁州地區(qū)的冬小麥農(nóng)田為研究區(qū),以水稻土和黃褐土等農(nóng)田土壤背景的拔節(jié)前期冬小麥為研究對象,采用實(shí)測冬小麥冠層光譜及葉面積指數(shù)(LAI)數(shù)據(jù),利用傳統(tǒng)的相片估算法求算植被覆蓋度,研究分析兩種模型的土壤背景影響去除能力。同時(shí)以山東省濟(jì)寧地區(qū)冬小麥農(nóng)田為研究區(qū),以褐土、潮土、水稻土和砂姜黑土等農(nóng)田土壤背景的拔節(jié)后期冬小麥為研究對象,結(jié)合Landsat-8 OLI衛(wèi)星多光譜遙感數(shù)據(jù),驗(yàn)證與評價(jià)土壤背景影響去除模型適用性和有效性。主要研究內(nèi)容與結(jié)論如下:(1)結(jié)合典型土壤類型和植被覆蓋度,研究土壤背景對冬小麥農(nóng)田NDVI信息的影響,結(jié)果表明,不同類型土壤背景對冬小麥農(nóng)田NDVI造成很大差異,且造成冬小麥農(nóng)田NDVI對植被覆蓋度的敏感性也存在明顯差異,為不同類型土壤背景的各小麥生長期遙感NDVI信息估算頻次選擇提供依據(jù)。(2)基于線性混合光譜理論,構(gòu)建基于NDVI的土壤背景影響去除模型NDVI1T,以及對基于像元二分理論對NDVI1T進(jìn)行簡化得到簡約模型NDVI2T。驗(yàn)證了模型的適用性和有效性,同時(shí)采用信噪比的分析方法定量研究兩種模型抵抗土壤噪聲影響的能力,分析發(fā)現(xiàn)NDVI1T提取植被信息抵抗土壤噪聲能力更佳。在中低LAI(LAI=3)環(huán)境條件下,兩種模型更適用于植被葉片覆蓋程度較為均勻,或植被類型單一的情況;兩種土壤背景影響去除模型和NDVI的擬合關(guān)系良好,相關(guān)關(guān)系R2均達(dá)到0.9以上,表明了利用NDVIT模型可實(shí)現(xiàn)修正地面實(shí)驗(yàn)計(jì)算的NDVI 土壤背景影響。(3)基于Landsat-8 OLI衛(wèi)星遙感影像對土壤背景影響去除模型(NDVIT)進(jìn)行分析驗(yàn)證,研究結(jié)果表明,在對應(yīng)OLI影像的研究區(qū)具有4種土壤背景類型的情形下,通過土壤背景影響去除模型(NDVIT)和基于影像計(jì)算NDVI擬合公式,依然可實(shí)現(xiàn)修正大尺度NDVI產(chǎn)品的土壤背景影響。
[Abstract]:The normalized vegetation index (NDVI) is one of the most widely used vegetation indices in remote sensing. It can be used in crop growth monitoring, agricultural yield estimation, drought forecasting, precision agriculture, and so on. NDVI can eliminate the atmosphere and shadow to a certain extent. Sensor calibration, observation angle and other aspects of the impact, but the mixing of soil background interference caused a large error in NDVI. The NDVI of the same growing winter wheat field under different soil type background conditions is very different and has been affecting the effective monitoring and accurate evaluation of wheat growth using NDVI. Most of the existing NDVI soil impact removal methods are to develop and improve the existing NDVI models. However, these new vegetation indices depend on the characteristics of soil lines in the study area, so they can not be used in large scale as NDVI products. Crop growth monitoring in wide coverage areas. Therefore, it is still a hot and difficult point to study the soil background removal method of NDVI. On the basis of simulating and analyzing the effects of typical soil types on NDVI of winter wheat with different vegetation cover, two soil background removal models based on mixed spectral theory are proposed in this paper. The winter wheat field in Chuzhou area of Anhui Province was used as the research area, and the winter wheat in the early jointing stage of paddy soil and yellow cinnamon soil was studied. The data of canopy spectrum and leaf area index (Lai) of winter wheat were measured. The traditional photo estimation method was used to calculate the vegetation coverage and the soil background removal ability of the two models was studied and analyzed. At the same time, the winter wheat field in Jining area of Shandong Province was used as the research area, and the winter wheat in the late jointing stage was studied in the soil background of cinnamon soil, tidal soil, paddy soil and shajiang black soil, and Landsat-8 OLI satellite multispectral remote sensing data were used. Validation and evaluation of the applicability and effectiveness of the soil background impact removal model. The main contents and conclusions are as follows: (1) combined with typical soil types and vegetation coverage, the effects of soil background on NDVI information of winter wheat farmland were studied. The sensitivity of NDVI to vegetation coverage in winter wheat farmland is also different, which provides a basis for the frequency selection of NDVI information estimation in different types of soil background, which is based on the theory of linear mixed spectrum. The NDVI1T model based on NDVI and the simplified NDVI1T model based on pixel dichotomy were constructed. The applicability and validity of the model were verified, and the ability of two models to resist soil noise was quantitatively studied by using the method of SNR analysis. It was found that NDVI1T extraction of vegetation information was better than that of NDVI1T in resisting soil noise. The two models are more suitable for the condition that the vegetation leaf cover degree is more uniform or the vegetation type is single, and the fitting relationship between the two soil background impact removal models and NDVI is good, and the correlation between the two models is more than 0.9. The results show that the NDVI soil background effect calculated by the NDVIT model can be modified and verified based on Landsat-8 OLI satellite remote sensing image. The research results show that the NDVI soil background impact removal model is based on Landsat-8 OLI satellite remote sensing image. In the case of four soil background types in the study area corresponding to Oli images, the soil background effect of large-scale NDVI products can still be corrected by removing the soil background effect model and calculating the NDVI fitting formula based on the image.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S127

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