CAJ | 학술논문

为了精准地估测荒漠化地区的稀疏植被信息，选取内蒙古苏尼特右旗为研究区，以天宫一号高光谱数据为数据源，结合野外实地调查数据，通过归一化植被指数（normalized difference vegetation index ， NDVI）和土壤调节植被指数（soil adjusted vegetation index ，SAVI）对研究区内的植被覆盖度和生物量进行反演，并对比两种植被指数的优劣。首先，分析了每种波段组合下的植被指数与覆盖度、生物量的相关性，确定了最大相关的波段组合。覆盖度和生物量与NDVI的最大相关系数可达0.7左右，而与SAVI的最大相关系数可达0.8左右。NDVI的最佳波段组合的红光波段中心波长为630nm，近红外波段的中心波长为910nm，而SAVI的组合为620和920nm。其次，分别构建了两种植被指数与覆盖度、生物量之间的线性回归模型，所建模型的R2均能达到0.5以上。SAVI所建模型R2要比NDVI略高，其中植被覆盖度的反演模型R2高达0.59。经留一交叉验证，SAVI所建模型的均方根误差RMSE也比基于NDVI的模型小。结果表明：天宫一号高光谱数据丰富的光谱信息能有效地反映地表植被的真实情况，并且SAVI比NDVI更能较为精准地估测荒漠化地区的稀疏植被信息。
위료정준지고측황막화지구적희소식피신식，선취내몽고소니특우기위연구구，이천궁일호고광보수거위수거원，결합야외실지조사수거，통과귀일화식피지수（normalized difference vegetation index ， NDVI）화토양조절식피지수（soil adjusted vegetation index ，SAVI）대연구구내적식피복개도화생물량진행반연，병대비량충식피지수적우렬。수선，분석료매충파단조합하적식피지수여복개도、생물량적상관성，학정료최대상관적파단조합。복개도화생물량여NDVI적최대상관계수가체0.7좌우，이여SAVI적최대상관계수가체0.8좌우。NDVI적최가파단조합적홍광파단중심파장위630nm，근홍외파단적중심파장위910nm，이SAVI적조합위620화920nm。기차，분별구건료량충식피지수여복개도、생물량지간적선성회귀모형，소건모형적R2균능체도0.5이상。SAVI소건모형R2요비NDVI략고，기중식피복개도적반연모형R2고체0.59。경류일교차험증，SAVI소건모형적균방근오차RMSE야비기우NDVI적모형소。결과표명：천궁일호고광보수거봉부적광보신식능유효지반영지표식피적진실정황，병차SAVI비NDVI경능교위정준지고측황막화지구적희소식피신식。
In order to estimate the sparse vegetation information accurately in desertification region ,taking southeast of Sunite Right Banner ,Inner Mongolia ,as the test site and Tiangong-1 hyperspectral image as the main data ,sparse vegetation coverage and biomass were retrieved based on normalized difference vegetation in-dex(NDVI) and soil adjusted vegetation index (SAVI) ,combined with the field investigation data .Then the advantages and disadvantages between them were compared .Firstly ,the correlation between vegetation inde-xes and vegetation coverage under different bands combination was analyzed ,as well as the biomass .Secondly , the best bands combination was determined when the maximum correlation coefficient turned up between vege-tation indexes (VI) and vegetation parameters .It showed that the maximum correlation coefficient between vegetation parameters and NDVI could reach as high as 0.7 ,while that of SAVI could nearly reach 0.8 .The center wavelength of red band in the best bands combination for NDVI was 630nm ,and that of the near infra-red(NIR) band was 910 nm .Whereas ,when the center wavelength was 620 and 920 nm respectively ,they were the best combination for SAVI .Finally ,the linear regression models were established to retrieve vegeta-tion coverage and biomass based on Tiangong-1 VIs .R2 of all models was more than 0.5 ,while that of the model based on SAVI was higher than that based on NDVI ,especially ,the R2 of vegetation coverage retrieve model based on SAVI was as high as 0.59 .By intersection validation ,the standard errors RMSE based on SA-VI models were lower than that of the model based on NDVI .The results showed that the abundant spectral information of Tiangong-1 hyperspectral image can reflect the actual vegetaion condition effectively ,and SAVI can estimate the sparse vegetation information more accurately than NDVI in desertification region .