基于植被指數(shù)的新疆和靜縣草原地上生物量模擬
發(fā)布時(shí)間：2019-06-12 00:50
【摘要】：草原地上生物量的研究是草原生態(tài)學(xué)與生態(tài)經(jīng)濟(jì)學(xué)研究中的一個(gè)重要方面,實(shí)時(shí)準(zhǔn)確的獲取草地植被覆蓋動(dòng)態(tài)變化以及草原地上生物量的分布狀況,是合理利用及保護(hù)草地資源的前提。以新疆和靜縣草原為研究區(qū),草原地上生物量為研究對(duì)象,Landsat影像為主要遙感數(shù)據(jù),采用野外實(shí)測(cè)采樣點(diǎn)地上生物量數(shù)據(jù)為建模數(shù)據(jù),利用像元二分模型,分級(jí)反演四個(gè)不同時(shí)期草地植被覆蓋度,結(jié)合重心遷移模型,研究其變化特征;基于植被指數(shù)NDVI、RVI與草原地上生物量實(shí)測(cè)值間的相關(guān)分析,分別建立一元線性模型、指數(shù)模型、二次多項(xiàng)式模型,通過(guò)SPSS統(tǒng)計(jì)分析,進(jìn)行模型精度檢驗(yàn),確定適用研究區(qū)草原地上生物量反演的最優(yōu)模型,利用最優(yōu)模型進(jìn)行草原地上生物量的遙感反演。1.基于像元二分模型的草地植被覆蓋度估測(cè)值與野外實(shí)測(cè)值之間進(jìn)行線性相關(guān)分析,得到相關(guān)系數(shù)為0.7267,表明運(yùn)用此方法對(duì)新疆和靜縣草地植被覆蓋度研究是可行的。通過(guò)面積加權(quán)計(jì)算出研究區(qū)2000、2005、2010、2015年平均植被覆蓋度分別為4.2851、4.3042、4.4252、3.7524,平均植被覆蓋度呈現(xiàn)先增加再增加后減少的趨勢(shì)。2000~2015年平均植被覆蓋度整體減少0.5327,其中,2015年較2010年植被覆蓋度減少最多,減少15.20%。2.2000~2015年,五個(gè)不同等級(jí)植被覆蓋度由低到高轉(zhuǎn)化率依次為54.82%,60.08%,5.52%,266.82%,772.37%,三級(jí)植被覆蓋轉(zhuǎn)化率最小,轉(zhuǎn)化率為5.52%,五級(jí)植被覆蓋轉(zhuǎn)化率最大,轉(zhuǎn)化率為772.37%。2000~2015年,一級(jí)植被覆蓋面積及占比減少最多,減少了798164.10hm2,2015年覆蓋度面積比2010年減少57.23%;二級(jí)植被覆蓋面積占比呈現(xiàn)持續(xù)上升趨勢(shì)且增加最多,增加473682.07hm2,2015年覆蓋度面積是2000年的159.75%。2000~2015年,研究區(qū)植被覆蓋表現(xiàn)為退化,退化面積為1376348.49hm2,占研究區(qū)面積49.42%,較植被覆蓋度增加面積多出1261164.41hm2,是植被覆蓋度增加面積的11.95倍。2000~2015年,一級(jí)植被覆蓋重心向西北方向遷移49.78km,三級(jí)植被覆蓋重心向東北方向遷移了4.24km,二級(jí)、四級(jí)和五級(jí)植被覆蓋重心均向西南方向遷移,遷移距離分別為38.07km、53.00km、83.35km。3.基于NDVI和RVI兩種植被指數(shù),與實(shí)測(cè)采樣點(diǎn)地上生物量數(shù)據(jù)進(jìn)行統(tǒng)計(jì)分析,建立了NDVI-線性模型、NDVI-指數(shù)模型、NDVI-二次多項(xiàng)式模型、RVI-線性模型、RVI-指數(shù)模型、RVI-二次多項(xiàng)式模型共6種回歸模型,其中RVI-二次多項(xiàng)式模型與草原地上生物量的相關(guān)性最高,相關(guān)系數(shù)達(dá)到0.911,預(yù)測(cè)決定系數(shù)為0.830,預(yù)估精度為85.31%;其次是RVI-線性模型,相關(guān)系數(shù)為0.908,預(yù)測(cè)決定系數(shù)為0.829,預(yù)估精度為78.52%;NDVI-二次多項(xiàng)式模型,相關(guān)系數(shù)為0.907,預(yù)測(cè)決定系數(shù)為0.822,預(yù)估精度為81.22%;NDVI-線性模型,相關(guān)系數(shù)為0.903,預(yù)測(cè)決定系數(shù)為0.814,預(yù)估精度為77.01%;NDVI-指數(shù)模型,相關(guān)系數(shù)為0.877,預(yù)測(cè)決定系數(shù)為0.768,預(yù)估精度為72.86%;RVI-指數(shù)模型,相關(guān)系數(shù)為0.854,預(yù)測(cè)決定系數(shù)為0.728,預(yù)估精度為70.11%;均通過(guò)P0.001檢驗(yàn)。RVI-二次多項(xiàng)式模型是所建模型中相關(guān)系數(shù)、決定系數(shù)以及預(yù)測(cè)模型精度都高于其它5種模型,是研究區(qū)草原地上生物量遙感反演的最優(yōu)模型,模型為:y=62.121x2+1146.7x-377.66,R2=0.830,n=67,預(yù)估精度為85.31%,可以較好的反映研究區(qū)草原地上生物量特征。
[Abstract]:The study of the aboveground biomass of the grassland is an important aspect in the study of the grassland ecology and the ecological economics, and the dynamic change of the vegetation cover and the distribution of the aboveground biomass of the grassland in real time are the prerequisite for the rational use and protection of the grassland resources. Taking the grassland of Xinjiang and Jingxian as the research area, the aboveground biomass of the grassland is the research object, the Landsat image is the main remote sensing data, the ground biomass data in the field actually measured sampling point is the modeling data, Based on the correlation analysis between the vegetation index NDVI, RVI and the measured value of the aboveground biomass of the grassland, a univariate linear model, an exponential model and a quadratic polynomial model are set up, and the model accuracy test is carried out by using the statistical analysis of SPSS. The optimal model of the aboveground biomass inversion of the grassland in the applicable study area is determined, and the remote sensing inversion of the aboveground biomass of the grassland is carried out by using the optimal model. The correlation coefficient of 0.7267 is obtained based on the linear correlation between the estimated value of the vegetation coverage of the grassland and the field measured value, and it is proved that the method can be used to study the vegetation coverage of the grassland in XinJiang and Jingxian. The average vegetation coverage of the study area was 4.2851, 4.3042, 4.4252, and 3.7524, respectively. The average vegetation coverage of 2000-2015 was decreased by 0.5327, among which, the vegetation coverage in the year of 2015 was the most, the decrease of 15.20%. The vegetation coverage of the five different grades is 54.82%, 60.08%, 5.52%, 266.82%, 772.37%, and the third-level vegetation cover conversion is the least, the conversion rate is 5.52%, the five-stage vegetation cover conversion rate is the largest and the conversion rate is 772.37%. The coverage area of secondary vegetation decreased by 57.23% in 2010, the coverage area of secondary vegetation increased by 57.23% in 2010, the coverage area of secondary vegetation increased by up to 473682.07 hm2, and the coverage area in 2015 was 159.75% in 2000. In 2000-2015, the vegetation cover of the study area was degraded and the degradation area was 1376348.49 hm2, accounting for 49.42% of the area of the study area. The increase of vegetation coverage is 1261164.41hm2, which is 11.95 times that of the increase of vegetation coverage. In the period 2000-2015, the center of gravity of the first-level vegetation covers the north-west direction for 49.78 km, the center of gravity of the three-level vegetation covers the northeast, 4.24km, the second, the fourth and the five-level vegetation cover the center of gravity to the south of the west, The migration distance is 38.07 km, 53.00 km and 83.35 km respectively. The NDVI-linear model, the NDVI-index model, the NDVI-quadratic polynomial model, the RVI-linear model, the RVI-exponential model, the RVI-quadratic polynomial model and the six regression models are established based on the NDVI and RVI vegetation indices. The correlation coefficient of the RVI-quadratic polynomial model and the aboveground biomass of the grassland is the highest, the correlation coefficient is 0.911, the prediction coefficient is 0.830, the prediction accuracy is 85.31%, the second is the RVI-linear model, the correlation coefficient is 0.908, the prediction determination coefficient is 0.829, and the prediction accuracy is 78.52%; The NDVI-quadratic polynomial model has a correlation coefficient of 0.907, a prediction determination coefficient of 0.822, an estimated accuracy of 81.22%, an NDVI-linear model, a correlation coefficient of 0.903, a prediction determination coefficient of 0.814, an estimated accuracy of 77.01%, an NDVI-index model, a correlation coefficient of 0.877 and a prediction determination coefficient of 0.768, The prediction accuracy is 72.86%, the RVI-index model, the correlation coefficient is 0.854, the prediction determination coefficient is 0.728, the estimated accuracy is 70.11%, and all passes the P0.001 test. The RVI-quadratic polynomial model is the best model for the remote sensing inversion of the aboveground biomass in the study area. The model is: y = 62.121x2 + 1146.7 x-377.66, R2 = 0.830, n = 67, and the estimated accuracy is 85.31%. The aboveground biomass of the grassland in the study area can be better reflected.
【學(xué)位授予單位】：新疆師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：Q948.1
本文編號(hào)：2497587
[Abstract]:The study of the aboveground biomass of the grassland is an important aspect in the study of the grassland ecology and the ecological economics, and the dynamic change of the vegetation cover and the distribution of the aboveground biomass of the grassland in real time are the prerequisite for the rational use and protection of the grassland resources. Taking the grassland of Xinjiang and Jingxian as the research area, the aboveground biomass of the grassland is the research object, the Landsat image is the main remote sensing data, the ground biomass data in the field actually measured sampling point is the modeling data, Based on the correlation analysis between the vegetation index NDVI, RVI and the measured value of the aboveground biomass of the grassland, a univariate linear model, an exponential model and a quadratic polynomial model are set up, and the model accuracy test is carried out by using the statistical analysis of SPSS. The optimal model of the aboveground biomass inversion of the grassland in the applicable study area is determined, and the remote sensing inversion of the aboveground biomass of the grassland is carried out by using the optimal model. The correlation coefficient of 0.7267 is obtained based on the linear correlation between the estimated value of the vegetation coverage of the grassland and the field measured value, and it is proved that the method can be used to study the vegetation coverage of the grassland in XinJiang and Jingxian. The average vegetation coverage of the study area was 4.2851, 4.3042, 4.4252, and 3.7524, respectively. The average vegetation coverage of 2000-2015 was decreased by 0.5327, among which, the vegetation coverage in the year of 2015 was the most, the decrease of 15.20%. The vegetation coverage of the five different grades is 54.82%, 60.08%, 5.52%, 266.82%, 772.37%, and the third-level vegetation cover conversion is the least, the conversion rate is 5.52%, the five-stage vegetation cover conversion rate is the largest and the conversion rate is 772.37%. The coverage area of secondary vegetation decreased by 57.23% in 2010, the coverage area of secondary vegetation increased by 57.23% in 2010, the coverage area of secondary vegetation increased by up to 473682.07 hm2, and the coverage area in 2015 was 159.75% in 2000. In 2000-2015, the vegetation cover of the study area was degraded and the degradation area was 1376348.49 hm2, accounting for 49.42% of the area of the study area. The increase of vegetation coverage is 1261164.41hm2, which is 11.95 times that of the increase of vegetation coverage. In the period 2000-2015, the center of gravity of the first-level vegetation covers the north-west direction for 49.78 km, the center of gravity of the three-level vegetation covers the northeast, 4.24km, the second, the fourth and the five-level vegetation cover the center of gravity to the south of the west, The migration distance is 38.07 km, 53.00 km and 83.35 km respectively. The NDVI-linear model, the NDVI-index model, the NDVI-quadratic polynomial model, the RVI-linear model, the RVI-exponential model, the RVI-quadratic polynomial model and the six regression models are established based on the NDVI and RVI vegetation indices. The correlation coefficient of the RVI-quadratic polynomial model and the aboveground biomass of the grassland is the highest, the correlation coefficient is 0.911, the prediction coefficient is 0.830, the prediction accuracy is 85.31%, the second is the RVI-linear model, the correlation coefficient is 0.908, the prediction determination coefficient is 0.829, and the prediction accuracy is 78.52%; The NDVI-quadratic polynomial model has a correlation coefficient of 0.907, a prediction determination coefficient of 0.822, an estimated accuracy of 81.22%, an NDVI-linear model, a correlation coefficient of 0.903, a prediction determination coefficient of 0.814, an estimated accuracy of 77.01%, an NDVI-index model, a correlation coefficient of 0.877 and a prediction determination coefficient of 0.768, The prediction accuracy is 72.86%, the RVI-index model, the correlation coefficient is 0.854, the prediction determination coefficient is 0.728, the estimated accuracy is 70.11%, and all passes the P0.001 test. The RVI-quadratic polynomial model is the best model for the remote sensing inversion of the aboveground biomass in the study area. The model is: y = 62.121x2 + 1146.7 x-377.66, R2 = 0.830, n = 67, and the estimated accuracy is 85.31%. The aboveground biomass of the grassland in the study area can be better reflected.
【學(xué)位授予單位】：新疆師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：Q948.1
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