本文關(guān)鍵詞： 草地 波段優(yōu)化 高光譜 生物量　出處：《內(nèi)蒙古農(nóng)業(yè)大學(xué)》2015年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：遙感技術(shù)的發(fā)展為草地快速、無(wú)損的動(dòng)態(tài)監(jiān)測(cè)提供可能,本文通過(guò)對(duì)2013-2014年內(nèi)蒙古天然草地和人工草地生物量及其冠層光譜數(shù)據(jù)分析,通過(guò)概念模型和從350nm-1150 nm所有可能兩兩波段組合的歸一化算法和簡(jiǎn)單比值算法進(jìn)行波段選擇,從而提取對(duì)生物量變化敏感的波段組合,通過(guò)線性和非線性回歸分析進(jìn)行生物量估算模型的建立,探討不同優(yōu)化算法計(jì)算的光譜指數(shù)在估算草地生物量時(shí)的魯棒性。結(jié)果表明：(1)相比現(xiàn)有常用估算草地生物量的植被指數(shù),優(yōu)化光譜指數(shù)顯著提高了生物量的預(yù)測(cè)能力,不存在現(xiàn)有基于紅光位置植被指數(shù)存在的飽和性問(wèn)題,并且優(yōu)化光譜指數(shù)的波段組合隨著草地類(lèi)型和牧草品種的不同而不同,其波段組合包括藍(lán)光、紅光、紅邊位置和近紅外位置�，F(xiàn)有植被指數(shù)與草地生物量的相關(guān)性隨著草地類(lèi)型和生長(zhǎng)時(shí)期的變化而變化(R2=0.00-0.77),優(yōu)化光譜指數(shù)克服了基于紅光的光譜指數(shù)在高生物量條件下的飽和問(wèn)題。(2)包含綠光和紅邊位置的優(yōu)化光譜指數(shù)CI(Chlorophyll index).包含藍(lán)光和紅邊位置的優(yōu)化光譜指數(shù)NDSI(Normalized difference spectral index)和藍(lán)光和紅邊位置簡(jiǎn)單比值植被指數(shù)RSI(Simple spectral index)目比現(xiàn)有植被指數(shù)都表現(xiàn)出與生物量較好的相關(guān)性(R2=0.36-0.80),其中波段組合為(R696-R652)/(R696+R652)的NDSI相比CI和RSI與所有生物量表現(xiàn)出較高的相關(guān)性決定系數(shù)R2=0.79,且其建立模型的預(yù)測(cè)值與實(shí)測(cè)值之間的關(guān)系相比CI(R2=0.76,均方根誤差RMSE=954,平均相對(duì)誤差RE=52.0%)和RSI(R2=0.80,RMSE=926,RE=35.8%)具有最小的偏差(R2=0.77,RMSE=756,RE=37.1％),故選取其建立的非線性二次回歸方程y=23288.0x2+16116.0x+422.2為估算本實(shí)驗(yàn)生物量的最優(yōu)生物量模型。(3)NDSI更適合不同草地類(lèi)型、不同生長(zhǎng)時(shí)期、不同冠層蓋度等復(fù)雜環(huán)境草地生物量的估算,它提高了光譜指數(shù)與生物量之間的相關(guān)性,對(duì)生物量變化更敏感,不存在飽和問(wèn)題,所建模型能夠用于估算復(fù)雜環(huán)境下的草地生物量,這不僅為本試驗(yàn)區(qū)根據(jù)遙感估算草地生物量提供了準(zhǔn)確的生物量估算模型,也為遙感植被指數(shù)的理論研究提供了更深入的理論基礎(chǔ)。
[Abstract]:The development of remote sensing technology provides the possibility for rapid and non-destructive dynamic monitoring of grassland. This paper analyzes the biomass and canopy spectral data of natural grassland and artificial grassland in Inner Mongolia from 2013 to 2014. The spectral bands that are sensitive to biomass change are extracted by the conceptual model and the normalized algorithm and simple ratio algorithm from all possible combinations of two bands at 350 nm to 1150 nm. The biomass estimation model was established by linear and nonlinear regression analysis. The robustness of spectral indices calculated by different optimization algorithms in estimating grassland biomass was discussed. The optimized spectral index can significantly improve the prediction ability of biomass, and there is no saturation problem based on the red position vegetation index. And the band combination of the optimized spectral index is different with the grassland type and forage variety. The band combination includes blue light and red light. Red edge position and near infrared position. The correlation between the existing vegetation index and grassland biomass changes with the change of grassland type and growth period. The optimized spectral index overcomes the saturation problem of the spectral index based on red light under the condition of high biomass. Chlorophyll index.Optimum spectral index NDSI with blue and red edge positions. Normalized difference spectral index and simple ratio of blue light and red edge vegetation index (RSI). Simple spectral index showed a better correlation with biomass than the existing vegetation index (R2P 0.36-0.80). The correlation coefficient between CI and RSI was higher than that of R696-R652 / R696R652 (R _ (696-R _ (652) / R _ (696) R _ (652))). The coefficient of correlation between CI and RSI was 0.79. The relationship between the predicted value and the measured value of the model is compared with that of CIR _ 2N _ (0.76) and the root mean square error (RMSE=954). The mean relative error (RE52.0) and RSI R2N 0.80 RMSE 926RE35.8) have the minimum deviation of R2 / 0.77. RMSE 756 and RMSE 37.1). Therefore, the nonlinear quadratic regression equation yang23288.0x216116.0x422.2 was chosen as the optimal biomass model for estimating the experimental biomass. NDSI is more suitable for different grassland types. The estimation of grassland biomass in different growth period and different canopy coverage increased the correlation between spectral index and biomass, and was more sensitive to biomass change and had no saturation problem. The model can be used to estimate grassland biomass in complex environment, which not only provides an accurate model for estimating grassland biomass based on remote sensing. It also provides a deeper theoretical basis for the theoretical study of remote sensing vegetation index.
【學(xué)位授予單位】：內(nèi)蒙古農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：S812

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