Figure 5 - uploaded by Andreas Roncat
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(a) Bathymetric LiDAR measurement principle, (b) shaded relief map and water depth, (c) deposition and erosion of material due to a 30-year flood in the main channel and temporary side channels during the flood.
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In this article the principle of laser scanning is recapitulated starting from the LiDAR equation and the measurement possibilities, especially beyond the range measurement, are explained. This includes the radiometric measurement, bathymetric LiDAR, waveform capturing, new possibilities from UAV platforms, and single photon counting detection. It...
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... hits the river or ocean ground surface, and travels the same way back to the sensor. Over dry land reflections are caused by surfaces. However, the different reflection properties at green light (e.g. 532nm) in comparison to the standard topographic wavelengths (e.g. 1064nm and 1550nm) need to be considered (see Fig. 1). As illustrated in Fig. 5, the river channel and the alluvial area can be acquired simultaneously with airborne laser scanning using a green LiDAR (Mandlburger et al., 2015a). This requires automatic classification of echoes into dry ground, wet ground, water surface, and vegetation (and other objects). This is intertwined with modeling of the water surface ...
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... Due to its ability to resolve vegetation structure and texture in high resolution, ALS is rapidly emerging as a sensor for vegetation mapping (Eitel et al. 2016) (Pfeifer et al. 2015). Several recent wetland vegetation analysis methods concentrate on the combined use of ALS and multispectral Earth observation. ...
Wetlands play a major role in Europe’s biodiversity. Despite their importance, wetlands are suffering from constant degradation and loss, therefore, they require constant monitoring. This article presents an automatic method for the mapping and monitoring of wetlands based on the fused processing of laser scans and multispectral satellite imagery, with validations and evaluations performed over an area of Lake Balaton in Hungary. Markov Random Field models have already been shown to successfully integrate various image properties in several remote sensing applications. In this article, we propose the multi-layer fusion Markov Random Field model for classifying wetland areas, built into an automatic classification process that combines multi-temporal multispectral images with a wetland classification reference map derived from airborne laser scanning (ALS) data acquired in an earlier year. Using an ALS-based wetland classification map that relied on a limited amount of ground truthing proved to improve the discrimination of land-cover classes with similar spectral characteristics. Based on the produced classifications, we also present an unsupervised method to track temporal changes of wetland areas by comparing the class labellings of different time layers. During the evaluations, the classification model is validated against manually interpreted independent aerial orthoimages. The results show that the proposed fusion model performs better than solely image-based processing, producing a non-supervised/semi-supervised wetland classification accuracy of 81–93% observed over different years.
Artificial drainage networks sculpt landscapes as they result from the constant
efforts of farmers adjusting landscapes to the needs and constraints imposed by
agriculture. As structural landscape elements, they play a role in flood control
and biogeochemical cycles. Furthermore, they are ecological hotspots and
enhance connectivity within the landscape. Spatially distributed process models
that estimate rainfall-runoff, predict flood levels or assess water resources
increasingly rely on spatially explicit drainage network data. To increase
confidence in model outputs, more complete and detailed input data on the
drainage network is required. These detailed input data encompass the location
and characteristics of the entire drainage network, including smaller watercourses
such as ditches.
Hydrographic reference data particularly lacks data on agricultural ditches.
The emergence of airborne laser data with high spatial resolution and accuracy,
offers the potential for developing innovative methods to detect and characterise
drainage ditches. The general objective of this dissertation was to develop
and evaluate automated methods for detecting ditches and extracting their
morphologic characteristics. The most recent (2014/2015) acquired airborne
LiDAR point cloud data and derived digital elevation models (DEM) were used
in combination with multispectral aerial data. The developed methods allow to
detect and characterise functional agricultural ditches and remnants of such
ditches in former agricultural areas.
Two approaches were followed to detect ditches. The first one is based on
the derived rasterised DEMs by calculating a relative elevation attribute. The
second one is based on the original LiDAR point clouds using trained random
forest classifiers. The detection methods resulted in completenesses of 95%
and 97% respectively, and this with a positional accuracy of less than 0.4m.
The DEM based method turned out to be simpler and less time-consuming to
implement and does not require any training data. A disadvantage common to
both methods is that the hydrological connectivity is not accounted for.
Ditch drainage networks consist of connected linear features, such as culverts but
which may also be blocked due to poor maintenance. As no geographic ancillary
data on culverts were available, the probabilities of candidate network segments
to be true connecting segments were calculated with a logistic regression model.
The independent variables were derived from the characteristics of the ditch
centre lines and from topographic features based on the DEM. The logistic
regression model allowed to connect 69% of the originally erroneous false
disconnections in the network. The number of network fragments was reduced
with 71%, representing the reference ditch network fairly well.
Cross sectional profiles of ditches could be modelled in an automated way
using the accurately detected and connected ditch network centre lines. The
cross sectional profiles were modelled by extracting the LiDAR points making
up the profile and using splines. The presence of water and vegetation affect
the extraction of cross-sectional profiles. Nevertheless, the extracted ditch
widths were highly correlated with the reference observations (R2 = 0.87, ME =
−0.15m). The extraction method proved to be robust with water levels being
successfully determined.
The potential, and limitations, of the developed methods for detecting and
characterising drainage ditches for modelling water runoff and the leaching
of nitrates from agricultural land was analysed for a 285km2 large subbasin
in West-Flanders using the Nutrient Emission Model for agriculture (NEMO).
The developed methods allowed to update hydrographic reference data sets on
the occurrence of drainage ditches and on their actual drainage depth. The
total density of the input watercourse network increased from 1.5 km/km2 to 4
km/km2. The originally fixed drainage depth of 0.9m was replaced by spatially
variable drainage depths. The output of the NEMO model showed that for
the downstream catchments, both the relative contribution of the groundwater
runoff to the total runoff and the nitrate concentrations in the river surface
water were most sensitive to the drainage extent. For the headwater catchment,
the relative groundwater runoff and total nitrate concentrations were most
sensitive to the spatially distributed drainage depths from the river cells, both
for the original and updated drainage extent input.
Where the hydrographic reference data was incomplete – or contained errors –
it could be updated with the proposed methods using LiDAR data. Identifying
areas with excessive runoff of water or nutrients with spatially distributed
hydrologic models can also be improved with the proposed methods using
airborne LiDAR data.
LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future.
The aim of this chapter is to provide a general overview about the main components of a developed UAS mapping system, the survey, and processing procedure. At first (4.1), a brief introduction is given about basic operational elements and accessories of UAS. Then, recent camera/sensor technologies allowing various survey solutions are going to be discussed. Once these hardware components are presented, the detailed workflow of a basic UAV-based mapping procedure is described (4.2). A further discussion focuses not only on the analytical or planning phases but also on providing useful information on the operational and processing parts as well (4.3). Then, there comes image acquisition and project planning (4.4). The photogrammetry-based image processing requires detailed expertise and attention; Sect. 4.5 maybe helpful to avoid potential mistakes. The last section (4.6) summarizes some aspects of the use of LiDAR technologies in UAV-based surveys.
