Evaluation of Riparian Tree Cover and Shading in the Chauga River Watershed Using LiDAR and Deep Learning Land Cover Classification

Abstract

River systems face negative impacts from development and removal of riparian vegetation that provide critical shading in the face of climate change. This study used supervised deep learning to accurately classify the land cover, including shading, of the Chauga River watershed, located in Oconee County, South Carolina, for 2011 and 2019. The study examined the land cover differences along the Chauga River and its tributaries, inside and outside the Sumter National Forest. LiDAR data were incorporated in solar radiation calculations for the Chauga River inside and outside the National Forest. The deep learning classifications produced land cover maps with high overall accuracy (97.09% for 2011; 97.58% for 2019). The most significant difference in land cover was in tree cover in the 50 m buffer of the tributaries inside the National Forest compared to the tributaries outside the National Forest (2011: 95.39% vs. 81.84%, 2019: 92.86% vs. 82.06%). The solar radiation calculations also confirmed a difference between the area inside and outside the National Forest, with the mean temperature being greater outside the protected area (outside: 455.845 WH/m2; inside: 416,770 WH/m2). This study suggests that anthropogenic influence in the Chauga River watershed is greater in the areas outside the Sumter National Forest, which could cause damage to the river ecosystem if left unchecked in the future as development pressures increase. This study demonstrates the accurate application of deep learning for high-resolution classification of river shading combined with the use of LiDAR data to estimate solar radiation reaching the Chauga River. Techniques to monitor riparian zones and shading at high spatial resolutions are critical for the mitigation of the negative impacts of warming climates on aquatic ecosystems.

Removal of Fecal Indicator Bacteria by River Networks

“…However, there are substantial reductions in solar radiation in small streams (due to riparian shading) [89] or large rivers (due to water depth) [85]. Although canopy cover can reduce solar radiation exposures of rivers [90], especially in forest-dominated watersheds, the water column removal in shallow streams might be underestimated without considering solar radiation [85]. Interaction with sediments, suspended particles and organic debris can result in persistence of fecal-borne bacteria [84].…”

Remote Sensing and Machine Learning for Riparian Vegetation Detection and Classification

“…MLAs generally exploit multispectral or hyperspectral data as input features, and attempt to predict some riparian vegetation classes. Scholars generally exploit supervised MLAs, which implies having a ground truth to be exploited for evaluating the algorithms [8]- [11]. Unsupervised MLAs are less studied in riparian vegetation mapping as they are considered less accurate than supervised approaches [12].…”