1、OLI Band5(0.845–0.885 μm),排除了0.825μm处水汽吸收特征; 2、OLI全色波段Band8波段范围较窄,这种方式可以在全色图像上更好区分植被和无植被特征; 3、新增两个波段:海蓝波段 (band 1; 0.433–0.453 μm) 主要应用海岸带观测;短波红外波段,又称卷云波段(band 9; 1.360–1.390 μm) 包含水汽强吸收特...
This band combination is also called the near-infrared (NIR) composite. It uses near-infrared (5), red (4), and green (3). Because chlorophyll reflects near-infrared light, this band composition is useful for analyzing vegetation. In particular, areas in red have better vegetation health. D...
表2 波段组合变量计算公式Table 2 Band combination variable calculation expression 1.4.2 生物量建模方法 本研究拟采用多元线性回归(multivariable liner regression,MLR)法、K最邻近(K-nearest neighbor,KNN)算法、随机森林(random forest,RF)...
KewordsveetationinformationextractionLandsat8imaebandcombinationtexturefeatures y g g 1, 1, 2, 1, 1( Z T J G D X L Z W Y 1 HAI ianlin INui ENG ianzhen I haohua ANG uan .FacultofRe - g g y s , , , ; ourcesandEnvironmentalScienceHubeiUniversitWuhan430062China2.InstituteofGeorahic ...
Computes a simple cloud-likelihood score in the range [0,100] using a combination of brightness, temperature, and NDSI. This is not a robust cloud detector, and is intended mainly to compare multiple looks at the same point for *relative* cloud likelihood. ...
Keywords: vegetation information extraction; Landsat 8; band combination; texture features 0 引言 植被是覆盖地表的植物群落的总称,包括森林、灌丛、草地与农作物等多种类型的植被,是地理环境中的重要组成部分,在地球生态系统中扮演着非常重要的角色,对于改善城市生态环境、维持城市生态平衡、促进人类健康、提高文化...
landsat8;land use/cover;optimum band combination;OIF;Enshi近 20 年来,土地利用/覆被变化(land use/cover change,LUCC)已是地球表层系统研究的重要内容,得到了国际地圈生物圈计划(IGBP)和国际全球环境变化人文因素计划(IHDP)的合力支持[1] ,已成为研究热点.作为获取土地利用/覆被信息的主要数据源,遥感数据种类...
selected as the test image data.Based on the statistics and analysis of spectral characteristics of each band, the optimum index factor ( OIF) was calculated,and finally according to the typical spectral image feature curve in research area, the selection of optimum band ...
Computes a simple cloud-likelihood score in the range [0,100] using a combination of brightness, temperature, and NDSI. This is not a robust cloud detector, and is intended mainly to compare multiple looks at the same point for *relative* cloud likelihood. ...
Computes a simple cloud-likelihood score in the range [0,100] using a combination of brightness, temperature, and NDSI. This is not a robust cloud detector, and is intended mainly to compare multiple looks at the same point for *relative* cloud likelihood. ...