27 documents found in 204ms
# 1
Tattaris, Maria • Reynolds, Matthew • Pietragalla, Julian • Molero, Gemma • Cossani, Mariano C. • (et. al.)
Abstract: High resolution remote sensing (RS) of light spectra reflected from plants allows for non-intrusive monitoring of physiological characteristics such as canopy temperature, hydration status, and pigment composition, as well as permitting estimates of agronomic traits such as biomass and yield. While satellite mounted RS platforms have proven efficient at measuring some of these characteristics at a field scale, their spatial resolution is too low for accurate data retrieval at plot level in a plant breeding context. While ground based remote sensing is used for predicting physiological and agronomic traits at a plot scale, temporal variations of environmental variables such as air temperature can introduce confounding factors especially when applied to large trials. Low level airborne remote sensing platform overcomes these restrictions, allowing for fast, non-destructive screening of plant physiological properties over large areas, with enough resolution to obtain information at plot level while being able to measure several hundred plots with one take. Sampling was performed with a helium filled tethered blimp and an 8 rotor unmanned aerial vehicle (UAV). Instruments mounted on the UAV alternate between a 3 channel multispectral imaging spectrometer and a thermal camera. A 12 channel multispectral camera was fixed on the tethered blimp. Flight altitude, between 50-100 m, was a function of the spatial resolution of the camera, wind speed and target plot lengths; ranging from 0.50-8.5 m. Multiple flights were conducted during the 2012 and 2013 cycles over experimental wheat trials. Images were corrected, geo-referenced where possible and processed to determine a data point for each plot within the trial. Aerial images collected were used to calculate a wide range of indices relating to temperature, vegetation, pigments, water status, and biomass. Indices derived from the airborne imagery data were validated by equivalent indices collected at ground level. Correlations between airborne data and yield/biomass at plot level proved to be similar or even better to the equivalent correlations using data collected from instruments on the ground. Results give confidence to the application of such airborne remote sensing techniques for high throughput phenotyping, in particular the ability to evaluate the level of stress and performance of thousands of genetic resources under extreme heat and drought conditions.
# 2
Oppelt, Natascha
Abstract: In coastal aquatic systems, marine macroalgae provide food and habitat for wildlife. Analysis of their occurrence and socialization therefore enables an estimate the state of coastal marine environment and provides evidence for environmental changes. To identify different macroalgae at family or species level, we have to identify their specific pigment composition. Hyperspectral sensors with their narrow band widths enable the detection of local absorption features of pigments and increased the number of possibilities to determine these features. This led to growing research interest to identify and monitor submerged and emerged coastal vegetation using airborne hyperspectral sensors. A precondition for a successful mapping of macroalgal habitats, however, is that their spectral features are spectrally resolvable. Besides the problems of identifying overlapping pigments features in terrestrial plants, the analysis of aquatic plants is difficult due to the dampening effect of water on the spectral signal. Emergent species usually have a higher average reflectance than submerged plants due to the absence of water attenuation. Moreover, the presence of flooding introduces variability in reflectance values due to the mixing of plant and water signals. This mixing usually results in a decrease in total reïflected radiation, especially in the Near to Mid Infrared. This paper discusses the performance of different approaches to determine the distribution of macroalgae communities in the rocky intertidal and sublitoral of Helgoland (Germany) using airborne AISAeagle data. We used standard supervised classification approaches such as the maximum likelihood classifier; to better cope with the varying reflectance levels we also introduced a new approach, which is based on the measurement of the slope between major algae pigments. The slope approach turned out as time effective possibility to identify the dominating macroalgae species via their pigment assemblage in the intertidal and upper sublitoral zone, even in the heterogeneous and patchy coverage of the study area. With increasing water depths (> 2 m), a water column correction is compulsory for macroalgae mapping. In this study, the bio-optical model MIP was applied to identify different types of brown algae in the sublitoral zone of the study area.
# 3
Bendig, Juliane • Bareth, Georg
Abstract: The workshop on UAV-based Remote Sensing Methods for Monitoring Vegetation took place at the University of Cologne in September 2013 and was organized within the activities of the ISPRS Working Group VII/5 “Methods for Change Detection and Process Modelling” of the ISPRS Technical Commission VII “Thematic Processing, Modelling, and Analysis of Remotely Sensed Data”. The Institute of Bio- and Geosciences, Plant Sciences (IBG-2), of the Forschungszentrum Jülich as well as the following research projects supported and co-organized the workshop: The two Collaborative Research Centres, the CRC/TR32 “Patterns in Soil-Vegetation-Atmosphere-Systems: Monitoring, Modelling, and Data Assimilation” and the CRC806 “Our way to Europe: Culture-Environment Interaction and Human Mobility in the Late Quaternary” which are funded by the German Research Foundation (DFG). The CROP.SENSe.net research network which analyses plant phenotypes to enhance efficiency in crop breeding and decision making in crop management. The project is funded by the German Federal Ministry of Education and Research (BMBF) and by the Ziel 2-Programm NRW 2007–2013 “Regionale Wettbewerbsfähigkeit und Beschäftigung (EFRE)” by the Ministry for Innovation, Science and Research (MIWF) of the state North Rhine Westphalia (NRW) and European Union Funds for regional development (EFRE) (005-1103-0018). The International Center for Agro-Informatics and Sustainable Development (ICASD), which is a cooperation of the China Agricultural University and the University of Cologne. The publication of the Special Issue "UAV-Based Remote Sensing Methods for Modeling, Mapping, and Monitoring Vegetation and Agricultural Crops" of Remote Sensing (ISSN 2072-4292) and the Special Issue on "Spatial Data Acquisition, Handling, and Analysis in Agro-Geoinformatics" of the ISPRS International Journal of Geo-Information (ISSN 2220-9964) emerged in the context of this workshop.
# 4
Yu, Kang • Gnyp, Martin Leon • Gao, L. • Miao, Yuxin • Chen, Xinping • (et. al.)
Abstract: Nitrogen (N) is one of the most essential elements in agriculture and ecology due to its direct role in determining crop yield and grain quality, as well as its association with canopy photosynthetic capacity and carbon-nitrogen cycling in the earth ecosystem. Remote sensing provides a useful way to capture canopy nitrogen and biomass with high spatial and temporal resolution. However, seasonal dynamics of plant morphophysiological variation hinder the simultaneous estimation of canopy N concentration (%N) and biomass using a traditional method such as vegetation indices because of the distinct dynamics of canopy biochemical and physical traits. In contrast, multivariate analysis method offers the capability of calibrating a model with multiple dependent variables of interest. Therefore, the main objective of this study was to, simultaneously, estimate canopy %N and biomass of rice using the partial least squares regression (PLSR) model. A field experiment was conducted for paddy rice fertilized with five N rates across five growth stages in 2008, located in the Sanjiang Plain, China. Results showed that the PLS regression model simultaneously explained 84% and 91% of the variation in %N and biomass, respectively, across the five growth stages. Our results also suggest that biomass is the dominant factor that affects the link between canopy dynamical traits and canopy reflectance measures. This study demonstrates that, by incorporating with PLSR for retrieving biophysical and biochemical properties from the full-spectrum analysis, to what extent canopy %N and biomass can be simultaneously estimated from canopy reflectance measurement.
# 5
Drauschke, Martin • Bartelsen, Jan • Reidelstuerz, Patrick
Abstract: In this paper, we describe two experiments regarding the monitoring of a test site in the Bavarian Forest National Park using unmanned aerial vehicles (UAVs) and we show their results. In the first experiment, we show that it is possible to relatively orient the RGB images acquired by a small UAV in power glider configuration without any flight stabilisation and without integrated navigation system (INS) initial values. This enables a 3D scene reconstruction, i.e., we obtain a point cloud showing distinctive 3D points. A much denser point cloud showing trees with branches can be derived from dense image matching. In the second experiment, we demonstrate how multispectral imagery can be interpreted on demand, i.e., without producing an ortho-mosaic, but using reliable features and a powerful classifier. With our algorithm, we follow up the aim to detect bark beetle attack in an early infection stage in Sitka spruce, Picea sitchensis, in the Bavarian Forest National Park.
# 6
Kooistra, Lammert • Suomalainen, Juha • Iqbal, Shahzad • Franke, Jappe • Wenting, Philip • (et. al.)
Abstract: To investigate the opportunities of unmanned aerial vehicles (UAV) in operational crop monitoring, we have developed a light-weight hyperspectral mapping system (<2 kg) suitable to be mounted on small UAVs. It is composed of an octocopter UAV-platform with a pushbroom hyperspectral mapping system consisting of a spectrograph, an industrial camera functioning as frame grabber, storage device, and computer, a separate INS and finally a photogrammetric camera. The system is able to produce georeferenced and georectified hyperspectral data cubes in the 450-950 nm spectral range at 10-100 cm resolution. The system is tested in an agronomic experiment for a potato crop on a 12 ha experimental field in the south of the Netherlands. In the experiment UAV-based hyperspectral images were acquired on a weekly basis together with field data on chlorophyll as indicator for the nitrogen situation of the crop and LAI as indicator for biomass status. Initially, the quality aspects of the developed light-weight hyperspectral mapping system will be presented with regard to its radiometric and geometric quality. Next we would like to present the relations between sensor derived spectral measurements and crop status variables for a time-series of measurements over the growing season. In addition, the spatial variation of crop characteristics within the field can be adopted for variable rate application of fertilizers within the field. The outcome of the experiments should guide the operational use of UAV based systems in precision agriculture systems.
# 7
Eling, Christian • Klingbeil, Lasse • Kuhlmann, Heiner
Abstract: A georeferencing of the collected data is required for many unmanned aerial vehicle (UAV)-based remote sensing applications in the fields of surveying, precision farming, infrastructure inspection or geography. This georeferencing can generally be done indirectly using ground control points, or directly using an on-board sensor system. Since an indirect georeferencing is very time consuming and also not available in real time many users would prefer a direct georeferencing. However, UAVs do mostly only have a C/A code GPS and a low-cost IMU on board. Even if this sensor combination enables a rough navigation of the UAV it does not allow for a precise direct georeferencing. Thus, the development of a precise direct georeferencing system for lightweight unmanned aerial vehicles is currently in great demand. In this contribution a new developed direct georeferencing system for lightweight UAVs is presented, which is designed to (1) enable a precise position and attitude determination (position accuracy σ<5cm, attitude accuracy σ<1deg), (2) to be applicable on lightweight UAVs and (3) to be real-time capable for sampling rates >10 Hz. Generally, the system combines a dual-frequency GPS receiver, a tactical grade IMU, a single-frequency GPS receiver and a real-time processing unit on one board. Both, the RTK-GPS (real-time kinematic) and the attitude determination software are in-house developed and show good performances in first flight tests.
# 8
Caris, Michael • Stanko, Stephan
Abstract: The airborne monitoring of civilian and military scenes (using unmanned aircraft) is becoming increasingly important. Several types of airborne sensors – in the optical, infrared or millimeter wave spectrum - are available for the different platforms. Beside the all-weather suitability of the sensors, the recent deployment scenarios, often in deserts or arid environments, also demand for the ability to look through dust clouds and sandstorms. The only sensor, which is capable to cope with such environmental restrictions and is able to deliver high-resolution images, is the synthetic aperture radar (SAR). In this paper we focus on miniaturized SAR systems which were developed and optimized for utilization in a UAV (unmanned aerial vehicle) with a low loading capacity. This not only requires a compact and light radar sensor but the processing also has to cope with the unstable flight conditions of a small aircraft. Therefore, a high-precision inertial measurement unit (IMU) and motion compensating SAR-algorithms are needed. Thanks to the utilization of a high transmit frequency of either 35 GHz or 94 GHz, the sensors are suitable for the detection of small-scale objects and a very high resolution of 15 cm x 15 cm can be achieved when used in combination with modern FMCW (frequency modulated continuous wave) generation with a high bandwidth (up to 1 GHz) and small antennas.
# 9
Vakalopoulou, Maria • Karantzalos, Konstantinos
Abstract: During the last decade a significant amount of research and development has been focused on imaging spectrometry and a number of lightweight, with low power consumption, frame or pushbroom sensors have been designed to operate onboard Unmanned Aerial Vehicles (UAVs). For frame-type sensors the first essential step in the processing chain is to co-register all the acquired spectral bands. This is not a trivial task when the goal is efficiency and full automation. In this paper, we propose an algorithm for the automated co-registration of frame hyperspectral data. The operated wavelength is divided in an appropriate number of spectral groups. Then the co-registration of all the spectral bands of each group is performed. In particular, for each group the spectral band with the broader spectral variation is used as the reference for all the other ones. In a similar manner, but taking into account both the spectral variation and the proximity of each spectral band, the appropriate bands are selected for the co-registration of each group. We narrow effectively the search space of solutions by selecting with an unsupervised manner the spectral bands which can lead quickly to the solution. The developed algorithm has been evaluated both qualitatively and quantitative over agricultural fields which in contrast to urban areas their smooth terrain structure and texture impede the performance of local descriptors. Experimental results and the overall validation indicate the effectiveness of the developed algorithm.
# 10
Lenz-Wiedemann, Victoria • Bareth, Georg
Abstract: From 18th - 19th of November, 2010, the 'Workshop on Remote Sensing Methods for Change Detection and Process modelling' was held at the University of Cologne, Germany. This workshop was organized by the Working Group 5 'Methods for Change Detection and Process Modelling' within the Commission VII 'Thematic Processing, Modelling and Analysis or Remotely Sensed Data' of the International Society for Photogrammetry and Remote Sensing (ISPRS). Three research projects actively supported the workshop. The CRC/TR32 'Patterns in Soil-Vegetation-Atmosphere-Systems: Monitoring, Modelling, and Data Assimilation' as well as the CRC 806 'Our way to Europe: Culture-Environment Interaction and Human Mobility in the Later Quaternary', both Collaborative Research Centres of the German Research Foundation (DFG). Within the CROP.SENSe.net (funded by the German Federal Ministry of Education and Research, BMBF), sensor methods for monitoring crops are investigated. Finally, the workshop was supported by the International Centre for Agro-Informatics and Sustainable Development (ICASD), which was founded in cooperation with the China Agricultural University and the u CROP.SENSe.net University of Cologne. The goal of the workshop was to bring together scientific disciplines as disparate as geography, soil sciences, plant physiology, hydrology, meteorology, prehistory, archaeology, agronomy, remote sensing, and geoinformatics. The workshop was based on 14 invited talks and unusual long coffee breaks, parallel to poster sessions to encourage and support discussion. The diverse program attracted nearly 40 poster presentations and approximately 90 participants. The papers and abstracts of the workshop are summarized in the workshop proceedings.
Proceedings on the Workshop of Remote Sensing Methods for Change Detection and Process Modelling, 18-19 November 2010, University of Cologne, Germany, Kölner Geographische Arbeiten, 92, pp. III
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