（1）在Toolbox工具箱中，双击Spectral/Vegetation/NDVI工具，在文件输入对话框中，选择Landsat8 OLI大气校正结果。提示：覃志豪（2004）提出使用原始的DN值图像计算NDVI对反演结果影响不大。（2）在NDVI Calculaton parameters对话框中，选择NDVI计算波段：Red：4，Near IR：5。（3）选择输出文件名和路径。（4）在Too...
(1)在Toolbox工具箱中,双击Spectral/Vegetation/NDVI工具,在文件输入对话框中,选择Landsat8 OLI大气校正结果。 提示:覃志豪(2004)提出使用原始的DN值图像计算NDVI对反演结果影响不大。 (2)在NDVI Calculaton parameters对话框中,选择NDVI计算波段:Red:4,Near IR:5。 (3)选择输出文件名和路径。 (4)在Toobox中,...
主要内容就是使用BandMath工具计算公式(15.2)和公式(15.3),处理流程如图15.8所示。 图15.8 基于大气校正法的TIRS反演流程图 15.4.3 图像辐射定标和大气校正 在主界面中,选择File→Open,在文件选择对话框中选择“txt”文件,ENVI自动按照波长分为五个数据集:多光谱数据(1-7波段),全色波段数据(8波段),卷云波段数据(...
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...
This derived SST can be used for monitoring salinity variation, chl-a accumulation, coral bleaching, coastal plumes, thermal pollution by factories and power plants, and so on. It is noticed that the Landsat 8 TIRS image with the combination of the visible and near-infrared (VNIR) and short...
Price K P,Guo X,Stiles J M. Optimal Landsat TM band combinations and vegetation indices for discrimination of six grassland types in eastern Kansas[J].International Journal of Remote Sensing,2002,(23):5031-5042.doi:10.1080/01431160210121764....
In the present investigation, lineament mapping was first conducted using Landsat-8 OLI/SRTM images. To enhance the image, various techniques were used, including band combination (BC), PCA, color composition (CC), and spatial directional filtering (SDF). The boundaries between the light and dark...
The comparison results showed that the MLC classification of band ratio images of 5:2 and 6:7 yielded a classification accuracy of 56.4% and kappa coefficient of 0.36, which was higher than those of other machine learning methods and band combinations. The combination of L...
The most important band of Landsat TM and ETM for detecting water is band 5 (1150–1250nm), and for MSS, band 4 (800–1100nm), which can be used to differentiate between vegetation and soil-moisture levels (Jensen, 2007; Ozesmi and Bauer, 2002). Because deep, pure water absorbs nearl...
This animation (right) shows a Landsat 8 Surface Reflectance image, along with the Surface Reflectance-derived Spectral Indices created from it. Normalized Difference Vegetation Index (NDVI): quantify vegetation greeness. It is useful in understanding vegetation density and assessing changes in plant he...