elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Bathymetry Estimates and Bottom Classification using Hyperspectral Data in the Baltic Sea

Wenzl, Martina (2018) Bathymetry Estimates and Bottom Classification using Hyperspectral Data in the Baltic Sea. Master's, TU Munich.

[img] PDF
108MB

Abstract

The water depth of the coastal areas is subject to change due to erosion and sediment accumulation processes and the effects of the sea level rise. Knowing the accurate water depth is not only essential for scientific research and the shipping industry but also for water authorities and decision makers. Remote sensing derived bathymetry can fill the data gaps with better coverage than conventional methods. Especially physical based methods allow for the estimation of water depth, in addition to water constituents and the bottom cover. This master thesis focuses on the determination of the bathymetry and bottom cover of coastal areas in the German Baltic Sea by inverting airborne hyperspectral imagery (HySpex VNIR-1600) and simulated EnMAP data with a semi-analytical inversion program based on a radiative-transfer model. Furthermore, the expected accuracy of the water depth retrieval and the bottom classification was quantified based on simultaneously acquired in situ sonar measurements and a customized spectral database of the bottom cover types in the study area. The validation of the retrieved water depths was conducted for three sites which vary in their bottom cover types and exhibit different water depth distributions. The first (1) and second (2) site yield water depth estimates for the HySpex imagery with (1): up to +/- 15 % mean systematic error for water depths up to 6 m when calculating the mean water depth difference with a 50 cm binning interval and with (2): up to +/- 25 % systematic error for water depths up to 10 m. The corresponding Mean Absolute Percentage Error M APE were derived with (1): 14 % and (2): 19 % for water depths smaller than the averaged Secchi depth of 5.7 m. The inversion of HySpex image covering the third (3) site failed probably due to the dark bottom cover. The corresponding errors of the water depths were (3): up to + 240 % for the mean error and 192 % M APE for water depths up to 7 m, and Secchi depth respectively. The bathymetry results for the EnMAP simulated scene were evaluated for the same sites: site (1) yields systematic errors up to +/- 18 % for binning intervals of 1 m up to 6 m water depth. Site (2) exhibits a mean systematic water depth error from - 45 % to 0 % up to 10 m water depth. The mean systematic error for the third site (3) is in order of + 120% for water depths up to 7 m. The corresponding M APE for water depths up to the averaged Secchi depth are (1): 13 %, (2): 15% and (3): 100 %. The bottom classification using HySpex and simulated EnMAP imagery could not be validated since independent measurements were not available. The comparison with true-colour images derived from HySpex yielded plausible results for sand in all study sites up to the Secchi depth. Other bottom covers including sea grass and mussels were used during the inversion but could not be verified since these could not be distinguished in the true-colour images. To conclude, the bathymetry estimates and bottom classification determined in the scope of this master thesis showed convenient results for water depth over shallow and bright areas but not over dark areas. The results for the EnMAP simulated scene displayed similar accuracies as for the HySpex imagery.

Item URL in elib:https://elib.dlr.de/126033/
Document Type:Thesis (Master's)
Title:Bathymetry Estimates and Bottom Classification using Hyperspectral Data in the Baltic Sea
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Wenzl, Martinamartina.wenzl (at) dlr.deUNSPECIFIED
Date:20 December 2018
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:86
Status:Published
Keywords:Remote sensing, shallow water, bathymetry, water depth, hyperspectral, HySpex, EnMAP
Institution:TU Munich
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):Vorhaben Spectroscopic Methods in Remote Sensing
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Experimental Methods
Deposited By: Gege, Dr.rer.nat. Peter
Deposited On:18 Jan 2019 11:19
Last Modified:31 Jul 2019 20:23

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.