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

Madeleine M Bolick,Elizabeth A Saldo,Christopher J Post,Hamdi A Zurqani,Andrew P Grunwald,Elena A Mikhailova

doi:10.3390/rs13204172

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.

Highlights

In riparian areas, tree canopy shade management is becoming a more frequent technique as climate change puts increased stressors, such as increased temperatures, tree disease, and extreme storm events on riverine systems [1,2,3]
When the deciduous tree and evergreen tree land cover classes were combined into a single tree cover class, the overall accuracies increased to 96.7% (2011) and 95.3% (2019), with Kappa accuracies of 0.951 (2011) and 0.935 (2019) (Table 4)
When the deciduous tree and evergreen tree land cover classes were combined into8 aofsin19 gle tree cover class, the overall accuracies increased to 96.7% (2011) and 95.3% (2019), with

Summary

Introduction

Tree canopy shade management is becoming a more frequent technique as climate change puts increased stressors, such as increased temperatures, tree disease, and extreme storm events on riverine systems [1,2,3]. In recent studies, combining high-resolution imagery with deep learning techniques has led to over 90% land cover classifications accuracies [12,13]. Previous land cover classification research has relied on the use of medium spatial resolution images with pixel sizes ranging from 10–50 m because this data were more widely available and accessible [14,15]. At this coarse scale, more subtle changes in land cover are more difficult to capture as each pixel often contains more than one land cover class [16]. With the recent advent of high-resolution imagery, such as 1 m pixel imagery from the National

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