Abstract
Spatial and temporal changes in land cover have direct impacts on the hydrological cycle and stream quality. Techniques for accurately and efficiently mapping these changes are evolving quickly, and it is important to evaluate how useful these techniques are to address the environmental impact of land cover on riparian buffer areas. The objectives of this study were to: (1) determine the classes and distribution of land cover in the riparian areas of streams; (2) examine the discrepancies within the existing land cover data from National Land Cover Database (NLCD) using high-resolution imagery of the National Agriculture Imagery Program (NAIP) and a LiDAR canopy height model; and (3) develop a technique using LiDAR data to help characterize riparian buffers over large spatial extents. One-meter canopy height models were constructed in a high-throughput computing environment. The machine learning algorithm Support Vector Machine (SVM) was trained to perform supervised land cover classification at a 1-m resolution on the Google Earth Engine (GEE) platform using NAIP imagery and LiDAR-derived canopy height models. This integrated approach to land cover classification provided a substantial improvement in the resolution and accuracy of classifications with F1 Score of each land cover classification ranging from 64.88 to 95.32%. The resulting 1-m land cover map is a highly detailed representation of land cover in the study area. Forests (evergreen and deciduous) and wetlands are by far the dominant land cover classes in riparian zones of the Lower Savannah River Basin, followed by cultivated crops and pasture/hay. Stress from urbanization in the riparian zones appears to be localized. This study demonstrates a method to create accurate high-resolution riparian buffer maps which can be used to improve water management and provide future prospects for improving buffer zones monitoring to assess stream health.
Highlights
Rapid population growth in the Savannah River basin region has had dramatic impacts on the natural land cover[7]
The results showed that the total impervious surface areas that were extracted from the National Land Cover Database (NLCD) data exceeded the results from the classified National Agriculture Imagery Program (NAIP) classification at all of the streams levels
This study demonstrates that the availability of historical remotely sensed data as well as the new geospatial technology of Google Earth Engine (GEE) represents a significant improvement for monitoring and evaluating land cover over large areas
Summary
Rapid population growth in the Savannah River basin region has had dramatic impacts on the natural land cover[7] This change is most evident in increasing urbanization and conversion of farmland and forests to urban. A management plan for the Georgia portion of the Savannah River basin was developed using remotely sensed data and indicated that the use of high-resolution imagery (e.g., aerial photography, etc.) provided more accurate results to detect land cover changes in the r egion[14]. High-resolution aerial photos can provide detailed spatial information including texture, color, and shape, as well as certain spectral information[25], but do not provide topography information about trees and other objects on the ground surface This lack of height information can be compensated for by using LiDAR data, which contains detailed three-dimensional data, but has limited spectral information. The integration of high-resolution images and LiDAR data provides the data necessary for extracting building and forest metrics
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