CAJ | 학술논문

为了分析高分一号卫星数据在稀疏植被信息提取方面的能力，该文选取浑善达克沙地及其周边为研究区，以 GF-1和 Landsat TM 为数据源，结合地面同步实测数据，比较了两个传感器在荒漠化地区植被覆盖度和地上生物量估算方面的能力与差异。结果表明：在该区域，两种数据基于 NDVI 建立的对数模型可用于植被覆盖度的估测（GF-1：R 2＝0．7966，RMSEP＝0．0908；Landsat 8：R 2＝0．8080，RMSEP ＝0．0871），GF-1基于 SAVI 和Landsat 8基于 NDVI 建立的乘幂模型进行地上生物量的估测效果最好（R 2＝0．4866，0．3715；RMSEP＝143．46，130．71）。其次，在该区域，经过修正的土壤调节植被指数 MSAVI 相对于没有经过修正的土壤调节植被指数SAVI，与植被覆盖度和植被生物量的相关性并没有多大提高。第三，两种数据通过引入蓝色、绿色波段的多元回归模型估算植被覆盖度相比单一植被指数植被要好，尤其是对于 Landsat 影像改进效果更为明显，R 2提高了0．3。总之，GF-1的16m 数据具有相对较高的质量，可以代替 Landsat 8多光谱数据，而且其具有更高的分辨率、重访周期和覆盖范围。
위료분석고분일호위성수거재희소식피신식제취방면적능력，해문선취혼선체극사지급기주변위연구구，이 GF-1화 Landsat TM 위수거원，결합지면동보실측수거，비교료량개전감기재황막화지구식피복개도화지상생물량고산방면적능력여차이。결과표명：재해구역，량충수거기우 NDVI 건립적대수모형가용우식피복개도적고측（GF-1：R 2＝0．7966，RMSEP＝0．0908；Landsat 8：R 2＝0．8080，RMSEP ＝0．0871），GF-1기우 SAVI 화Landsat 8기우 NDVI 건립적승멱모형진행지상생물량적고측효과최호（R 2＝0．4866，0．3715；RMSEP＝143．46，130．71）。기차，재해구역，경과수정적토양조절식피지수 MSAVI 상대우몰유경과수정적토양조절식피지수SAVI，여식피복개도화식피생물량적상관성병몰유다대제고。제삼，량충수거통과인입람색、록색파단적다원회귀모형고산식피복개도상비단일식피지수식피요호，우기시대우 Landsat 영상개진효과경위명현，R 2제고료0．3。총지，GF-1적16m 수거구유상대교고적질량，가이대체 Landsat 8다광보수거，이차기구유경고적분변솔、중방주기화복개범위。
With the wide use of GF-1 data,the ability of sparse vegetation information estimation of the data needs to be further analyzed.Based on the data of domestic satellite GF-1and Landsat 8 as well as the simultaneous field survey of vegetation cover and above-ground biomass,the research was implemented on Otindag sandy land and its surrounding areas,in which abilities of two sensors to estimate vegetation physiological parameters in desertification areas were compared with each other.It was shown that,firstly,in study region,logarithmic functions which were established on NDVI of GF-1 data (R 2 =0.7966,RMSEP=0.0841 )and Landsat 8 data (R 2 = 0.8080,RMSEP = 0.0871 )could be used to estimate the vegetation coverage perfectly,the power functions which were established on NDVI of GF-1and SAVI of Landsat 8 have the best estimation effect (R 2 =0.4866,0.3715;RMSEP=143.46,130.71 ).Secondly,compared with the unrevised vegetation index SAVI,the correlation of MSAVI with vegetation cover and above-ground biomass was not significantly improved.Thirdly,blue and green band were introduced into multiple regression models,which were supposed to enhance the ability of estimating vegetation coverage,especially for Landsat,and R 2 was improve 0.3.In general,GF-1 has a relatively high data quality,it can replace Landsat 8 data in vegetation parameter inversion,and it has a higher resolution,shorter revisit cycle and wider coverage.