Introduction
In the last century we have witnessed increased migration of people from
rural areas to cities. Today, the majority of the human population live in
cities, and this number is estimated to grow constantly and reach a level of
60 % . The process of rapid urbanization required developing
an infrastructure capable of coping with a constantly increasing number of
its users. Accordingly, ensuring water supply for the people is important,
but due to the increased hydrological extremes induced by climate change
, being able to safely direct
stormwater away from populated areas, in order to avoid flooding, is not
least a challenging task. It requires predicting the hydraulic
behaviour of the given drainage
infrastructure using reliable hydrological models . Those
models, apart from detailed rainfall information, call for surface
characteristics such as imperviousness.
Impervious surfaces reduce the infiltration of water into the soil. They can
be directly related to a level of urbanization ,
because in urban environments, impervious surfaces dominate (e.g. rooftops or
roads). Monitoring of the imperviousness level is important as it directly impacts many
environmental processes. An increasing percentage of impervious surfaces
increases surface runoff volume and peak discharge, and decreases soil
moisture compensation and groundwater recharge. Moreover, increased peak
runoff volumes together with an inefficient drainage network can not only
cause urban floods, but also lead to an increased hydraulic stress and
increase the risk of loading waterbeds with sediments, and its associated
constituents (e.g. nutrients, contaminants and micro-pollutants).
An important step towards automation of the processes applied to map
impervious areas was made as a consequence of remote sensing sensors and
classification techniques development (for a detailed review of remote
sensing methods used to map imperviousness, please refer to the Supplement).
In general, most of the studies on extraction of impervious surfaces from
remote sensing data focused on satellite images. During the last decade, a
rapid improvement of imaging sensors gave the end-user an access to very high
spatial resolution (VHR) imagery. Satellite
sensors like Ikonos and QuickBird or
VHR aerial images were quickly adopted for
impervious surfaces mapping. Some studies suggest using highly accurate
methods to quantify landscape changes (land-use and land-cover) using
multi-sensor approaches . In the context
of urban hydrology, attempted to use impervious
surfaces extracted from VHR satellite and aerial imagery as an input to the
urban drainage model, but they did not analyse pipe flow predictions,
focusing only on the surface runoff component. However, modern urban drainage
modelling methods call for up-to-date and detailed input data, which could
also be acquired in an efficient way. Even though VHR satellite images able
to acquire fine-grained image information (WorldView-3 satellite can achieve
up to 0.31 m GSD in panchromatic channel) and have short revisit periods,
are still expensive and vulnerable to cloud cover. VHR aerial imagery on the
other hand, although being able to acquire very detailed imagery, is usually
being updated at most once a year, but usually every third year
. Recently, imaging platforms based on UAVs became very
popular, finding their application in the fields of photogrammetry,
archeology or agriculture .
More recently, investigated the quality of digital
elevation models (DEMs) generated using UAV imagery from urban drainage
modelling applications. In the study, the authors show that the quality of
UAV DEMs is comparable to that of conventional, off-the-shelf height data
sets. However, to our best knowledge no studies exist, that used UAV-based
imagery to extract imperviousness information, and to use it in the field of
urban drainage modelling. In comparison to a standard, off-the-shelf
satellite or aerial remote sensing imagery, UAVs demonstrate greater
flexibility and are more efficient in terms of money and time. Yet, the
classification of UAV VHR imagery, particularly in urban areas, is
challenging, because in this level of detail, many small objects appear, and
fine-grained texture details of larger objects emerge. Thus, describing an
object class using only single raw pixel values is insufficient. Accurate
classification needs additional image features, which would characterize the
contextual information by describing an object's local neighbourhood. The
value of such approach in classification of surface imperviousness has
already been acknowledged . However, what is highly
relevant, but currently unclear, is how to best exploit the rich information,
i.e. the unprecedented level of detail and flexibility to acquire
problem-specific images. And, whether it is feasible to use imagery acquired
with UAVs for urban drainage modelling.
Overall analysis approach (⊖-%imp: model parameter “degree
of imperviousness”; ML: maximum likelihood; RQE: boosting with randomized
quasi-exhaustive feature bank).
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Specifically, we present three key aspects:
We evaluate whether land-use data based on UAV imagery can be used to
assess the performance of urban drainage systems.
We propose a unique workflow based on a randomized quasi-exhaustive (RQE)
feature bank and a boosting classifier. The RQE feature bank consists of a multitude of
multiscale textural features describing both, spectral and height information
. The boosting classifier lends itself to the task to
only choose the optimal features during training (for details, see below).
We perform end-to-end comparison of land use against high-quality sewer
pipe flow data. Although important to correctly interpret the results, this is
not routinely done in remote sensing literature.
The key idea of our study was not to solely base the assessment of the
usefulness of UAV images for urban drainage applications on the performance
of the classifiers. Thus, we demonstrate the usefulness of our approach by
means of a case study in a small urban area in Lucerne, Switzerland, in two
steps (see also Fig. ): first, we compare the UAV
data with standard airborne imagery using a maximum likelihood (ML)
classifier and the RQE method on both image sources (1). Second, we use a
hydrodynamic model to show the consequences of different land-use information
for urban drainage performance indicators, here surface runoff (2) and
in-sewer pipe flow (3).
The remainder of the paper is structured as follows: first we present a
general approach and the case study catchment with related material, such as
the hydrodynamic rainfall–runoff model, rainfall and runoff observations,
and remote sensing data. Then we describe the applied methods, land-use
classification, surface runoff and in-sewer flow modelling, as well as the
suggested performance criteria. Finally we present results and discuss the
potential and limitations of using UAV images in urban hydrology.
Discussion
Classification
In order to fully exploit the advantages coming with high spatial resolution
of an image, one has to use the classification method tailored to the
characteristics of a data set. Thus, the choice of the classifier has a
substantial impact on the overall classification accuracy. While boosting
achieves accuracies between 93.7 and 96.2 % for the UAV data set and
95.6 to 97.4 % for the swisstopo data set, maximum likelihood
yields results which are up to 20 % worse. Furthermore, it can be seen that
the number of target classes strongly influences the results of the ML
method. Classification with three target classes is up to 9 % less accurate
than with two. Moreover, the amount of data used to train the ML classifier
gives inconclusive results. By increasing the number of training samples,
overall accuracy should increase. However, in our case the training appears
to be unstable, and the expected increase only materializes in a single case
(see Table , orthophoto data set, three classes). A
possible explanation is that the class distribution is not unimodal, and thus
not appropriately captured by the Gaussian model.
In contrast to the ML method, the boosting classifier behaves in a stable
manner. Differences in overall accuracy do not exceed 2.5 % per data set.
The changes in boosting performance with varying amounts of training data are
negligible: 1 % (7000 pixels) already yield satisfactory results, i.e. the
effort for annotation as well as the training time remains low. The
efficiency and robustness of boosting used together with features appropriate
for VHR aerial imagery makes this approach a good choice for the task. Also,
overall classification accuracy achieved using a boosting classifier together
with UAV-based imagery shows that in terms of classification accuracy of
impervious surfaces, this new imaging platform gives comparable results to
the off-the-shelf aerial image products.
Moreover, our experiments show that at the level of surface runoff
prediction, the differences between different imaging platforms and between
different processing methods are small. Even though the classification
accuracy between data sets and methods differs by up to 20 %, their
influence on surface runoff characteristics lies within only a few percent on
average. We believe that one of the reasons is the spatial size of our
subcatchments. Each of them consists of hundreds of image pixels, but the
hydrological model disregards the spatial information and only uses
aggregated values, i.e. the sum of all impervious pixels belonging to one
subcatchment. A further observation is that the differences in classification
accuracy are much larger for the three-class case. This is in line with
conventional machine learning wisdom (“only predict what you need to
know”); however, we have not yet constructed an end-to-end study with the
three-class result as an input.
Prediction of surface runoff
Exploratory analysis of surface runoff
While there are substantial differences when the images are compared
pixel-by-pixel (see Figs. and ),
these are largely lost for the predicted surface runoff. In our view, this is
again explained by the SWMM surface runoff model. It is a lumped model, which
aggregates the pixels and thus smoothes out the differences already on this
small scale. This tendency will be even more pronounced for a higher degree
of spatial aggregation, e.g. when modelling larger urban areas, where the
subcatchments equipped with flow measurements will also be larger. Future
experiments that investigate the continuous spatial downsampling of images
may reveal when differences fully disappear.
Model structure as a bottleneck?
Obvious differences in the input data may be smoothed out due to the
simplified, conceptual representation of the surface runoff in SWMM. We do
expect different results for more detailed representation of land use,
e.g. with a separate “roof” land-use or modern pixel-based modelling
approaches for surface runoff. In the future, this might be even more
important considering the increasing popularity of coupled
2-D-overland/1-D-channel flow models including more detailed overland-flow
modelling using raster/pixel-based approaches (cf. ).
Traditional models – as currently used in day-to-day engineering practice –
will probably never be able to fully make use the amount of detail (pixel
basis) provided by such aerial images.
High-resolution images provide added value in urban drainage
The effect on surface runoff and pipe hydraulics using spatially aggregating
models (two land-use classes) may not be as immense. However, in future
investigations, models that allow differentiating between three or more
land-use classes should be further investigated. This may be particularly
relevant for pollutant load modelling, for which detail, accuracy and
actuality of land-use characteristics are highly influential. Relevance of
input data accuracy may even further increase due to the fact that obtaining
adequate pollution load reference data is considered to be very difficult
(cf. ).
Also, other urban drainage tasks would greatly benefit from detailed land-use
maps, e.g. precise and justified stormwater fees due to exactly delineated
types of impervious areas (cf. Figs. and ).
An improved feature (gully pots, sewer inlets, curbstone
structures) identification is expected to further provide valuable input data
for network generation approaches (e.g. as outlined in )
and the coupled 2-D surface runoff/1-D pipe flow model applications. For
this, the RQE method seems to be most promising, although for the runoff
analysis, a simpler method still seems to produce robust results.
The possibility of on-demand image acquisitions through UAV flights allows
almost instantaneous response to land-use developments in dynamic urban
environments. As land use changes become increasingly evident, keeping
hydrological models up-to-date appears to be a key to effectively reduce the
risk of urban flooding. We consider the flexibility of collecting
high-quality images at almost any time (“on-demand”) for spatially
pre-defined urban areas of manageable size as clear benefit, also with regard
to cost efficiency.
Pipe flow predictions
The results from the model calibration show that input data deviations are
nearly fully compensated by the calibration procedure, involving an adaption
of four different calibration parameter sets. The analysis of the final
calibration parameter values however reveals that the best fit for each of
the implementations is achieved by differing parameter sets (see
Fig. S6 in the Supplement). On the one hand side, this
may indicate that, even though the full range of a priori defined
parameter ranges is used during the auto-calibration procedure, for each
implementation a different (local) optimum in the Pareto front is identified.
On the other hand, it may underline that the given model structure is
flexible enough to address different model inputs through different parameter
settings. Here, it becomes clear that the compensation is achieved by
adjusting parameters in a way that involves the risk that some parameters
loose its physically based origin and turn into “conceptual handles”. The
discussion on this particular question is certainly interesting and would
need further analyses, but it cannot be accomplished in this paper
contribution as it would blur the main focus of the paper.
Conclusions
In this study we investigated the possibility to automatically generate
high-resolution imperviousness maps for urban areas from imagery acquired
with UAVs, and for the first time assessed the potential of UAVs for
high-resolution hydrological applications compared with a standard
large-format aerial orthophotos. We proposed an automatic processing pipeline
with modern classification methods to extract accurate imperviousness maps
from high resolution aerial images, and presented an end-to-end comparison,
in which the maps obtained from different sources and processed with
different classification methods were used as input for urban drainage models.
The first part of our analysis indicates that, using a boosting classifier in
conjunction with RQE features, we were able to classify UAV imagery with an
accuracy comparable to standard aerial orthophotos. The proposed
classification method yields more stable results, when compared with those
produced using the maximum likelihood method. This improvement is even more
apparent when classifying three instead of two classes of land use.
In the second part of our analysis we have demonstrated how model input data
variations propagate in the course of the urban drainage modelling exercise,
and how this is reflected in the surface runoff and sewer flow predictions.
Results from uncalibrated model implementations actually show deviations in
the predictions, which can be explained by input data variations. But still
predictions are inaccurate. Conversely, after calibration the performance
analysis shows that the calibration process attenuates variations in the
input data, suggesting that model predictions are insensitive to these
variations. However, the analysis of the resulting model parameter settings
also reveals that apparent robustness is achieved by tweaking the parameter
in a way which involves the risk of leaving valid parameter ranges.
Because model development and calibration in everyday practice is often based
on less accurate information than used in our case study, it is important to
underline reliable input data to reduce overall uncertainty in model predictions.
We note that the conclusions of the study are limited regarding
(i) the small size of the case study catchment, (ii) the degree of
detail in which the catchment has been described (more detail may show a more
pronounced input error propagation, a more lumped description may absorb
input deviations from the start), and (iii) the type of hydrological
modelling concept used. Therefore we suggest conducting further research to
evaluate the impact of the spatial scale, i.e. the degree of spatial
aggregation linked to the hydrological model approach (ensemble modelling).
In the case study presented here we chose a traditional and widely used urban
drainage model (EPA SWMM) to deliberately demonstrate the effect of new image
sources and processing methods for standard engineering practice.
Still, we suggest using imperviousness maps consisting of three land-use
classes as more differentiated input for a more detailed hydrological model,
i.e. a pollution load model, which makes better use of urban land-use
differentiation. Because the proposed boosting classifier showed the largest
accuracy gain for a three-class case, we strongly believe that introducing
this additional information more clearly shows the potential of UAV data sets
and advanced classification methods for more accurate urban drainage and
pollution load modelling.