本文關鍵詞： 草地 波段優(yōu)化 高光譜 生物量　出處：《內蒙古農業(yè)大學》2015年碩士論文　論文類型：學位論文

【摘要】：遙感技術的發(fā)展為草地快速、無損的動態(tài)監(jiān)測提供可能,本文通過對2013-2014年內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單比值算法進行波段選擇,從而提取對生物量變化敏感的波段組合,通過線性和非線性回歸分析進行生物量估算模型的建立,探討不同優(yōu)化算法計算的光譜指數(shù)在估算草地生物量時的魯棒性。結果表明：(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測能力,不存在現(xiàn)有基于紅光位置植被指數(shù)存在的飽和性問題,并且優(yōu)化光譜指數(shù)的波段組合隨著草地類型和牧草品種的不同而不同,其波段組合包括藍光、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性隨著草地類型和生長時期的變化而變化(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).包含藍光和紅邊位置的優(yōu)化光譜指數(shù)NDSI(Normalized difference spectral index)和藍光和紅邊位置簡單比值植被指數(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)出較高的相關性決定系數(shù)R2=0.79,且其建立模型的預測值與實測值之間的關系相比CI(R2=0.76,均方根誤差RMSE=954,平均相對誤差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為估算本實驗生物量的最優(yōu)生物量模型。(3)NDSI更適合不同草地類型、不同生長時期、不同冠層蓋度等復雜環(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性,對生物量變化更敏感,不存在飽和問題,所建模型能夠用于估算復雜環(huán)境下的草地生物量,這不僅為本試驗區(qū)根據(jù)遙感估算草地生物量提供了準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。
[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.
【學位授予單位】：內蒙古農業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：S812

【參考文獻】

相關期刊論文 前10條

1 劉加文;;應對全球氣候變化決不能忽視草原的重大作用[J];草地學報;2010年01期

2 Xi見正坁;李思東;鄭金鈴;;估測放牧草地草本植物生物量的目測法[J];國外畜牧學.草原與牧草;1992年04期

3 趙冰茹,劉闖,劉愛軍,王正興;利用MODIS-NDVI進行草地估產研究——以內蒙古錫林郭勒草地為例[J];草業(yè)科學;2004年08期

4 張連義;張靜祥;賽音吉亞;包紅霞;鐔建國;郭志忠;;典型草原植被生物量遙感監(jiān)測模型——以錫林郭勒盟為例[J];草業(yè)科學;2008年04期

5 周磊;辛曉平;李剛;楊桂霞;張宏斌;;高光譜遙感在草原監(jiān)測中的應用[J];草業(yè)科學;2009年04期

6 張宏斌;楊桂霞;黃青;李剛;陳寶瑞;辛曉平;;呼倫貝爾草甸草原景觀格局時空演變分析——以海拉爾及周邊地區(qū)為例[J];草業(yè)學報;2009年01期

7 張艷楠;牛建明;張慶;楊艷;董建軍;;植被指數(shù)在典型草原生物量遙感估測應用中的問題探討[J];草業(yè)學報;2012年01期

8 范月君;侯向陽;石紅霄;師尚禮;;氣候變暖對草地生態(tài)系統(tǒng)碳循環(huán)的影響[J];草業(yè)學報;2012年03期

9 陳全功，，衛(wèi)亞星，梁天剛;使用NOAA／AVHRR資料進行牧草產量及載畜量監(jiān)測的方法研究[J];草業(yè)學報;1994年04期

10 王樹功,黎夏,周永章;濕地植被生物量測算方法研究進展[J];地理與地理信息科學;2004年05期

相關碩士學位論文 前3條

1 蘇日娜;基于3S技術的錫林浩特市草地動態(tài)監(jiān)測研究[D];內蒙古農業(yè)大學;2012年

2 張小琪;阿勒泰地區(qū)草地生物量遙感反演模型的建立[D];新疆農業(yè)大學;2013年

3 徐君韜;荒漠化草原牧戶飼草料儲備與生產效率研究[D];內蒙古大學;2013年

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