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  5. Determination of Microtopography of Low-Relief Tidal Freshwater Forested Wetlands Using LiDAR

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Article
English
2024

Determination of Microtopography of Low-Relief Tidal Freshwater Forested Wetlands Using LiDAR

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English
2024
Remote Sensing
Vol 16 (18)
DOI: 10.3390/rs16183463

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Shenen Chen
Shenen Chen

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Tarini Shukla
Wenwu Tang
Carl Trettin
+2 more

Abstract

The microtopography of tidal freshwater forested wetlands (TFFWs) impacts biogeochemical processes affecting the carbon and nitrogen dynamics, ecological parameters, and habitat diversity. However, it is challenging to quantify low-relief microtopographic features that might only vary by a few tens of centimeters. We assess the high-resolution fine-scale microtopographic features of a TFFW with terrestrial LiDAR and aerial LiDAR to test a method appropriate to quantify microtopography in low-relief forested wetlands. Our method uses a combination of water-level and elevation thresholding (WALET) to delineate hollows in terrestrial and aerial LiDAR data. Close-range remote sensing technologies can be used for microtopography in forested regions. However, the aerial and terrestrial LiDAR technologies have not been used to analyze or compare microtopographic features in TFFW ecosystems. Therefore, the objectives of this study were (1) to characterize and assess the microtopography of low-relief tidal freshwater forested wetlands and (2) to identify optimal elevation thresholds for widely available aerial LiDAR data to characterize low-relief microtopography. Our results suggest that the WALET method can correctly characterize the microtopography in this area of low-relief topography. The microtopography characterization method described here provides a basis for advanced applications and scaling mechanistic models.

How to cite this publication

Tarini Shukla, Wenwu Tang, Carl Trettin, Shenen Chen, Craig Allan (2024). Determination of Microtopography of Low-Relief Tidal Freshwater Forested Wetlands Using LiDAR. Remote Sensing, 16(18), pp. 3463-3463, DOI: 10.3390/rs16183463.

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Publication Details

Type

Article

Year

2024

Authors

5

Datasets

0

Total Files

0

Language

English

Journal

Remote Sensing

DOI

10.3390/rs16183463

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