Climate change 2014 - Synthesis Report

Remotely Piloted Aircraft (RPA) is presently in continuous development at a rapid pace. Unmanned Aerial Vehicles (UAVs) or more extensively Unmanned Aerial Systems (UAS) are platforms considered under the RPAs paradigm. Simultaneously, the development of sensors and instruments to be installed onboard such platforms is growing exponentially. These two factors together have led to the increasing use of these platforms and sensors for remote sensing applications with new potential. Thus, the overall goal of this paper is to provide a panoramic overview about the current status of remote sensing applications based on unmanned aerial platforms equipped with a set of specific sensors and instruments. First, some examples of typical platforms used in remote sensing are provided. Second, a description of sensors and technologies is explored which are onboard instruments specifically intended to capture data for remote sensing applications. Third, multi-UAVs in collaboration, coordination, and cooperation in remote sensing are considered. Finally, a collection of applications in several areas are proposed, where the combination of unmanned platforms and sensors, together with methods, algorithms, and procedures provide the overview in very different remote sensing applications. This paper presents an overview of different areas, each independent from the others, so that the reader does not need to read the full paper when a specific application is of interest

Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs)

The superior effect of nature based solutions in land management for enhancing ecosystem services.

Accurate correction of the corrupting effects of the atmosphere and the water’s surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi- and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio–Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.

Sensor capability and atmospheric correction in ocean colour remote sensing

Large earthquakes initiate chains of surface processes that last much longer than the brief moments of strong shaking. Most moderate‐ and large‐magnitude earthquakes trigger landslides, ranging from small failures in the soil cover to massive, devastating rock avalanches. Some landslides dam rivers and impound lakes, which can collapse days to centuries later, and flood mountain valleys for hundreds of kilometers downstream. Landslide deposits on slopes can remobilize during heavy rainfall and evolve into debris flows. Cracks and fractures can form and widen on mountain crests and flanks, promoting increased frequency of landslides that lasts for decades. More gradual impacts involve the flushing of excess debris downstream by rivers, which can generate bank erosion and floodplain accretion as well as channel avulsions that affect flooding frequency, settlements, ecosystems, and infrastructure. Ultimately, earthquake sequences and their geomorphic consequences alter mountain landscapes over both human and geologic time scales. Two recent events have attracted intense research into earthquake‐induced landslides and their consequences: the magnitude M 7.6 Chi‐Chi, Taiwan earthquake of 1999, and the M 7.9 Wenchuan, China earthquake of 2008. Using data and insights from these and several other earthquakes, we analyze how such events initiate processes that change mountain landscapes, highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth's surface.

Earthquake-Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping

During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of rather large UAVs. In this article we present a lightweight hyperspectral mapping system (HYMSY) for rotor-based UAVs, the novel processing chain for the system, and its potential for agricultural mapping and monitoring applications. The HYMSY consists of a custom-made pushbroom spectrometer (400–950 nm, 9 nm FWHM, 25 lines/s, 328 px/line), a photogrammetric camera, and a miniature GPS-Inertial Navigation System. The weight of HYMSY in ready-to-fly configuration is only 2.0 kg and it has been constructed mostly from off-the-shelf components. The processing chain uses a photogrammetric algorithm to produce a Digital Surface Model (DSM) and provides high accuracy orientation of the system over the DSM. The pushbroom data is georectified by projecting it onto the DSM with the support of photogrammetric orientations and the GPS-INS data. Since an up-to-date DSM is produced internally, no external data are required and the processing chain is capable to georectify pushbroom data fully automatically. The system has been adopted for several experimental flights related to agricultural and habitat monitoring applications. For a typical flight, an area of 2–10 ha was mapped, producing a RGB orthomosaic at 1–5 cm resolution, a DSM at 5–10 cm resolution, and a hyperspectral datacube at 10–50 cm resolution.

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A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles

Hydrological connectivity describes the physical coupling (linkages) of different elements within a landscape regarding (sub-) surface flows. A firm understanding of hydrological connectivity is important for catchment management applications, for e.g. habitat and species protection, and for flood resistance and resilience improvement. Thinking about (geomorphological) systems as networks can lead to new insights, which has also been recognised within the scientific community, seeing the recent increase in the use of network (graph) theory within the geosciences. Network theory supports the analysis and understanding of complex systems by providing data structures for modelling objects and their linkages, and a versatile toolbox to quantitatively appraise network structure and properties. The objective of this study was to characterise and quantify overland flow connectivity dynamics on hillslopes in a humid sub-Mediterranean environment by using a combination of high-resolution digital-terrain models, overland flow sensors and a network approach. Results showed that there are significant differences between overland flow connectivity on agricultural areas and semi-natural shrubs areas. Significant positive correlations between connectivity and precipitation characteristics were found. Significant negative correlations between connectivity and soil moisture were found, most likely due to soil water repellency and/or soil surface crusting. The combination of structural networks and dynamic networks for determining potential connectivity and actual connectivity proved a powerful tool for analysing overland flow connectivity.

A network theory approach for a better understanding of overland flow connectivity

Hydrological connectivity describes the physical coupling (linkages) of different elements within a landscape regarding (sub-) surface flows. A firm understanding of hydrological connectivity is important for catchment management applications, for example, habitat and species protection, and for flood resistance and resilience improvement. Thinking about (geomorphological) systems as networks can lead to new insights, which has also been recognized within the scientific community, seeing the recent increase in the use of network (graph) theory within the geosciences. Network theory supports the analysis and understanding of complex systems by providing data structures for modelling objects and their linkages, and a versatile toolbox to quantitatively appraise network structure and properties. The objective of this study was to characterize and quantify overland flow connectivity dynamics on hillslopes in a humid sub-Mediterranean environment by using a combination of high-resolution digital-terrain models, overland flow sensors and a network approach. Results showed that there are significant differences between overland flow connectivity on agricultural areas and semi-natural shrubs areas. Significant positive correlations between connectivity and precipitation characteristics were found. Significant negative correlations between connectivity and soil moisture were found, most likely because of soil water repellency and/or soil surface crusting. The combination of structural networks and dynamic networks for determining potential connectivity and actual connectivity proved a powerful tool for analysing overland flow connectivity. Copyright © 2016 John Wiley & Sons, Ltd.

Geomorphological maps that are automatically extracted from digital elevation data are gradually replacing classical geomorphological maps. Commonly, digital mapping projects are based upon statistical techniques, object-based protocols or both. In addition to digital elevation data, expert knowledge can still be used to calibrate feature extraction algorithms. Such hybrid expert/statistical-based methods translate land surface parameters into areal extents of geomorphological features in an automated, reproducible manner, which increases spatial detail of final products and speeds up map production. The development of efficient statistical methods for the extraction of geomorphological features is today promoted by high-resolution digital elevation data from light detection and ranging (LiDAR) technology. In this chapter, case studies from the Netherlands (very low relief) and Austrian Alps (high relief) are presented to illustrate how statistical-based and object-based supervised classification can be used for the semi-automated identification and extraction of geomorphological features using high-resolution LiDAR digital elevation models (DEMs). In the first case study, multinomial logistic regression is used to increase the detail of a classic geomorphological map. Medial axes of the manually delineated polygons are used to locate the training pixels and to build the statistical model, which is then implemented over the whole area of interest. In the second case study, object-based segmentation of slope and topographic openness extracted from LiDAR data are used for rule-based mapping of geomorphological features. Both studies confirm that LiDAR DEMs can be used to increase detail of existing geomorphological maps. In addition, semi-automated techniques provide a more objective framework for geomorphological mapping.

Niels Anders

Papers

Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping

A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles

A network theory approach for a better understanding of overland flow connectivity

A network theory approach for a better understanding of overland flow connectivity

Semi-automated identification and extraction of geomorphological features using digital elevation data