Discussion
Operationalised advantages of satellite
images
Satellites have been used for many years to monitor changes, since the
launch of the first LandSat satellite in 1984 developments have
continued and now make available a vast amount of useable information .
Many websites cater for quick access to processed data for various
purposes, such as the aquamonitor, the forest watch on global scale
(refs? Including the websites). However, the tool presented here is the
first to be actually implemented for the Dutch River Authority (RWS) and
to be used in the regulatory daily operational working process of river
management. As far as we know this is also the first open access tool to
present daily and automatically updating (Sentinel-2) satellite image
analysis for floodplain management to a large group of end-users.
Performance and
implementation
The accuracy of the tool is in line with earlier attempts for
classifying floodplain vegetation in a similar number of classes and of
the ‘traditional’ ecotope maps . The random forest classifier handles
mixed classes such as ‘fields & grass’ unexpectedly well proving its
robustness . Incorporating more images in the classification improved
the result as seen in the overall accuracy of year-maps and day-maps.
Clearly, classes that are separable on difference trough time benefit
from a temporal data. For example, fields are the type of class that has
a specific seasonal signature being bare at the start of the growing
season and harvested as dissimilar stages and dates.
We see some opportunities to improve the classification for year-maps
and single-date maps. Separation of classes in year-maps showing a
seasonal signal could be improved when incorporating pixel-level fitted
parameters on sinusoidal function of yearly NDVI as bands . For single
date (image) maps and year-maps, the inclusion of SAR data could provide
some extra resolution . However, experiments with ‘raw’ Sentinel-1 data
(hh, hv; data not shown) did not show significant improvement (order of
about 1-2% to total accuracy). Another approach could be a combination
of segmentation in super-pixels and the use of texture within those
super-pixels . This can also be a way to incorporate SAR data into the
classification . However, for single date maps the classification speed
determines the user-experience greatly. Therefore, there is a speed
trade-off for incorporating new procedures and data in on-the-fly
classification. The pre-classified year-maps do not have that
disadvantage.
The accuracy was one of the most important topics in the discussion with
the end-users. Beforehand we could estimate the accuracy based on quick
scans and existing knowledge, but these numbers did not mean much to the
end-users. During the first development year results were quickly shared
and maps were used in the field. This experience helped translate the
statistical accuracy in a form of trust in the produced maps. The
vegetation-monitoring team could use the map in discussions with
stakeholders and have no doubt about the map contents.
Development process
Developing a new monitoring tool for operational floodplain management
is a process that is highly interactive and a strong dialogue with
end-users is needed to create a useable tool. Features like GPS location
indication for field use and downloadable images were implemented in a
second iteration of the tool. Also, as the end-users started to download
small AOIs (as in on-screen views) and mosaicking these in GIS, the
download option evolved from downloads of single user selected
classifications of the AOI in the viewer to downloads for total covered
area. We further added the year-maps after different users asked for a
“standard year map” to be able to refer to in reports and other
stakeholders in order to standardize the yearly assessment of the
vegetation state. It is foreseen that there will be additional changes
to the tool out of the continued use and experience in the coming
period. Also, a new or updated fixed map-layers will need to be included
when made available. For instance, a new legal vegetation map will
become available in 2020.
The process of creating this tool has taken several years of intense
discussions between researchers and managers at the national water
board. The interplay between scientific and technical advances, and
management requirements was helped by labelling this project as an
innovative project within the framework of cooperation. The end-user
interaction to come to the here presented tool must not be
underestimated in the creation of an accepted and useful tool. The
availability of Google Earth Engine with its continued up-to-date image
library greatly speeded up and facilitated the prototyping and
operationalizing of the project.
Reflection on use and other
applications
The vegetation monitor is primarily designed as a quick and easy
screening tool to identify those areas in the Dutch floodplains that in
need of maintenance and is always used as a starting point for the
dialogue with the responsible land owners and must be complimented with
field visits for the final check on the correctness of the initial
assessment. Next to this there are many potential possibilities to use
the tool also in a wider context, for example to assess the impact of
the changing lay-outs of vegetation distribution on flood water levels
when these maps are directly used as input for the flood risk models.
Also, the tool can easily be adapted to be useful in other river
systems, providing that some ground truth data is available as test and
training data. Several other initiatives are currently undergoing to
classify the vegetation of the floodplains, but most often this is done
in a GIS environment, making it less accessible to the general audience.
Especially in large scale data-poor areas the technique may give a first
order estimation of the current status and allow also for analysis on
changes over time.
Recommendations
During the creation of the vegetation monitor there was discussion
regarding the absolute accuracy of the individual classification results
and which percentage of accuracy was deemed acceptable for the
evaluation of the status of the vegetation. It took time to acknowledge
that reaching 95% accuracy was not only technically not feasible, but
also not necessary to give an overall judgement of the status of the
vegetation. The annual maps with an average accuracy of between 80 %
and 86 % are now being used and give a sufficient first indication of
the situation. The communication that this tool is a first screening and
that field visits will remain necessary in the final judgement has
helped to accept that a certain level of inaccuracy is unavoidable.
During the stakeholder process to define the terms of reference, it was
clear that different types of users have different requirements. The
stakeholder process is therefore very important in defining the final
features of such tool.
The time series analysis feature has added benefit in judging a single
classification, as it aids in checking for the consistency of the
classification and potential fluctuations and dynamics through time,
e.g. in relation to interannual fluctuations in river discharge and
weather patterns and the response of vegetation to this.