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\begin{document}

\title{Modern sensors and airborne remote sensing for the mapping of vegetation
and hydromorphology along Federal waterways in Germany}



\author[1]{Katharina Fricke}%
\author[1]{Björn Baschek}%
\author[2]{Maryam Asgari}%
\author[2]{Jens Bongartz}%
\author[3]{Andreas Burkart}%
\author[4]{Filip Dzunic}%
\author[1]{Maike Heuner}%
\author[2]{Alexander Jenal}%
\author[2]{Caspar Kneer}%
\author[4]{Tina Mölter}%
\author[3]{Paul Näthe}%
\author[1]{Ina Quick}%
\author[4]{Gilles Rock}%
\author[1]{Edvinas Rommel}%
\author[1]{Uwe Schröder}%
\author[2]{Immanuel Weber}%
\author[1]{Svenja Wick}%
\affil[1]{German Federal Institute of Hydrology (BfG)}%
\affil[2]{University of Applied Sciences Koblenz}%
\affil[3]{JB Hyperspectral Devices}%
\affil[4]{Geocoptix}%


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  \date{May 27, 2024}


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\begin{abstract}
Up-to-date information about vegetation types and hydromorphological
structures and features are essential for the management of waterways.
They are e.g. used for the monitoring and reporting of riparian statuses
and their changes e.g. after river restoration and consequently,
numerous man-days are spent on field surveys. To allow for an effective
survey of vegetation and hydromorphology in large or even inaccessible
areas, a data acquisition and processing workflow is being developed
complementing in-situ methods with remote sensing techniques. This is
part of the joint research project ``mDRONES4rivers'' funded by the
German Federal Ministry of Transport and Digital Infrastructure
(19F2054A). Aerial surveys by unmanned aerial systems (UAS) and a
gyrocopter are combined with ground measurements of hyperspectral
reflectance signatures as well as with field mapping of vegetation types
and hydromorphological structures and features. The remote sensing data
is classified with an object based image analysis and classification
algorithm. The mobile and (at selected sites) permanent measurements of
hyperspectral field data and the typical field surveys provide data for
calibration. Contrary to other approaches that focus on what can be
detected and classified with certain sensor systems and datasets, the
project addresses equally the user needs to obtain certain classes for
monitoring and reporting. The intended results are (i) data acquisition,
correction and classification workflow combining remote sensing and
field data, identification and change detection (ii) of important
vegetation and biotope types and (iii) of hydromorphological structures
and substrate as well as indicators necessary for the evaluation of the
hydromorphological quality. The preliminary results to be presented
include datasets from UAS, gyrocopter, and field surveys, an outline of
processing workflow and classification algorithm based on Python scripts
and eCognition software and first vegetation and hydromorphological
classification results from spring and summer datasets. In conclusion,
procedures and algorithms are developed to use remote sensing in
combination with and for the reduction of time-consuming traditional
field surveys as a future operational tool for monitoring riparian
vegetation and structures.%
\end{abstract}%



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