Automated Extraction for Water Bodies Using New Water Index from Landsat 8 OLI Images

The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to highlight water bodies in remote sensing images.We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images.Firstly,we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction.Subsequently,we apply KT transformation,LBV transformation,AWEI nsh,and HIS transformation to the preprocessed image to calculate a new water index.Then,we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately.Meanwhile,we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies.Finally,we combine small and large water bodies to get complete water bodies.Compared with other traditional methods,our method has apparent advantages in water extraction,particularly in the extraction of small water bodies.

Fusion of Landsat 8 OLI and PlanetScope Images for Urban Forest Management in Baton Rouge, Louisiana

In recent years image fusion method has been used widely in different studies to improve spatial resolution of multispectral images. This study aims to fuse high resolution satellite imagery with low multispectral imagery in order to assist policymakers in the effective planning and management of urban forest ecosystem in Baton Rouge. To accomplish these objectives, Landsat 8 and PlanetScope satellite images were acquired from United States Geological Survey (USGS) Earth Explorer and Planet websites with pixel resolution of 30m and 3m respectively. The reference images (observed Landsat 8 and PlanetScope imagery) were acquired on 06/08/2020 and 11/19/2020. The image processing was performed in ArcMap and used 6-5-4 band combination for Landsat 8 to visually inspect healthy vegetation and the green spaces. The near-infrared (NIR) panchromatic band for PlanetScope was merged with Landsat 8 image using the Create Pan-Sharpened raster tool in ArcMap and applied the Intensity-Hue-Saturation (IHS) method. In addition, location of urban forestry parks in the study area was picked using the handheld GPS and recorded in an excel sheet. This sheet was converted into Excel (.csv) file and imported into ESRI ArcMap to identify the spatial distribution of the green spaces in East Baton Rouge parish. Results show fused images have better contrast and improve visualization of spatial features than non-fused images. For example, roads, trees, buildings appear sharper, easily discernible, and less pixelated compared to the Landsat 8 image in the fused image. The paper concludes by outlining policy recommendations in the form of sequential measurement of urban forest over time to help track changes and allows for better informed policy and decision making with respect to urban forest management.