Geospatial analysis of flooding from hurricane Florence in the coastal South Carolina using Google Earth Engine

Flooding impacts from hurricanes and other natural hazards are an important concern in many areas of the world. The objectives of this study were to: (1) develop a framework to identify flood-affected areas after storm impact; (2) map the flooded areas caused by the hurricane Florence; and (3) assess the major effect of the hurricane on the land cover and agricultural crops in the coastal South Carolina during the flood period. The coastal South Carolina regions are recognized as the most important agricultural area in the state. The developed framework identified and mapped the affected areas during the hurricane season. Based on the results the hurricane-flooded areas were approximately 681 km2, and the major affected counties in both analysis flood frequency and flooded areas are Charleston, Georgetown, Berkeley, Florence, Marlboro, Marion, Horry, Chesterfield, Sumter, Clarendon, and Darlington. These results not only indicate flood risk on the land cover but also demonstrate the advantage of utilizing Google Earth Engine and the public archive database in its platform to track and monitor the natural hazards over time

Evaluating the integrity of forested riparian buffers over a large area using LiDAR data and Google Earth Engine

Spatial and temporal changes in the land cover affect water quality in the streams and other water bodies. Stream riparian areas are increasingly relevant as the human modification of the landscape continue unabated. The objectives of this study were to: (1) determine the classes and the distribution of land cover in stream riparian areas; (2) examine the accuracy of the existing land cover data National Land Cover Database (NLCD) using high-resolution imagery NAIIP and LiDAR data; and (3) evaluate the integrity of forested riparian buffers areas in the Lower Savannah River basin. The land cover map was produced using a Support Vector Machine (SVM) algorithm supervised classification through the cloud-based Google Earth Engine platform with an overall accuracy assessment of 83.66%. LiDAR data analysis were implemented using ArcGIS 10.6. The result of this study demonstrates that LiDAR data can be used to accurately map the vegetation width, height and canopy cover within the riparian buffer over wide areas to support ecological-based management. It is also highlighted that the open-access imagery and the efficient geospatial analysis GEE provides a reliable methodology to remotely monitor forest cover and land use in the riparian buffer areas