17 documents found in 237ms
# 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
Bareth, Georg • Aasen, Helge • Bendig, Juliane • Gnyp, Martin Leon • Bolten, Andreas • (et. al.)
Abstract: The non-destructive monitoring of crop growth status with field-based or tractor-based multi- or hyperspectral sensors is a common practice in precision agriculture. The demand for flexible, easy to use, and field scale systems in super-high resolution (<20 cm) or on single plant scale is given to provide in-field variability of crop growth status for management purposes. Satellite and airborne systems are usually not able to provide the spatial and temporal resolution for such purposes within a low-cost approach. The developments in the area of Unmanned Aerial Vehicles (UAV) seem to fill exactly that niche. In this contribution, we introduce two hyperspectral frame cameras weighing less than 1 kg which can be mounted to low-weight UAVs (<3 kg). The first results of a campaign in June 2013 are presented and the derived spectra from the hyperspectral images are compared to related spectra collected with a portable spectroradiometer. The results are promising.
# 4
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.
# 5
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.
# 6
Calderon, Rocio • Navas Cortes, Juan A. • Lucena, Juan C. • Zarco-Tejada, Pablo J.
Abstract: Verticillium wilt (VW) caused by the soil-borne fungus Verticillium dahliae Kleb, is the most limiting disease in all traditional olive-growing regions worldwide. This pathogen colonizes the vascular system of plants, blocking water flow and eventually inducing water stress. The present study explored the use of high-resolution thermal imagery, chlorophyll fluorescence, structural and physiological indices (xanthophyll, chlorophyll a+b, carotenoids and B/G/R indices) calculated from multispectral and hyperspectral imagery as early indicators of water stress caused by VW infection and severity. The study was conducted in two olive orchards naturally infected with V. dahliae. Time series of airborne thermal, multispectral and hyperspectral imagery were conducted with 2-m and 5-m wingspan electric Unmanned Aerial Vehicles (UAVs) in three consecutive years and related to VW severity at the time of the flights. Concurrently to the airborne campaigns, field measurements conducted at leaf and tree crown levels showed a significant increase in crown temperature (Tc) minus air temperature (Ta) and a decrease in leaf stomatal conductance (G) across VW severity levels, identifying VW-infected trees at early stages of the disease. At leaf level, the reduction in G caused by VW infection was associated with a significant increase in the Photochemical Reflectance Index (PRI570) and a decrease in chlorophyll fluorescence. The airborne flights enabled the early detection of VW by using canopy-level image-derived airborne Tc-Ta, Crop Water Stress Index (CWSI) calculated from the thermal imagery, blue / green / red ratios (B/BG/BR indices) and chlorophyll fluorescence, confirming the results obtained in the field. Airborne Tc-Ta showed rising temperatures with a significant increase of ~2K at low VW severity levels. Early stages of disease development could be differentiated based on CWSI increase as VW developed, obtaining a strong correlation with G (R2=0.83, P<0.001). Likewise, the canopy-level chlorophyll fluorescence dropped at high VW severity levels, showing a significant increase as disease progressed at early VW severity levels. These results demonstrate the viability of early detection of V. dahliae infection and discrimination of VW severity levels using remote sensing. Indicators based on crown temperature, CWSI, and visible ratios B/BG/BR as well as fluorescence were effective in detecting VW at early stages of disease development. In affected plants, the structural indices, PRI, chlorophyll and carotenoid indices, and the R/G ratio were good indicators to assess the damage caused by the disease.
# 7
Gnyp, Martin Leon • Miao, Yuxin • Yuan, Fei • Yu, Kang • Yao, Yinkun • (et. al.)
Abstract: Aboveground biomass (AGB) plays an important role in agriculture for assessing the production of foods, forage and renewable energy. Hyperspectral field measurements are an efficient method for nondestructive monitoring of AGB. Recent studies have confirmed the benefit of using different types of reflectance such as raw reflectance and its derivatives in the non-destructive methods. The objective of this study was to improve the estimation of rice (Oryza sativa L.) AGB with Optimum Multiple Narrow Band Reflectance (OMNBR) models based on raw reflectance (RR) and its first derivative of reflectance (FDR). Experiments with different nitrogen rates were conducted in experimental and farmers fields from 2007 through 2009 in Jiansanjiang, Northeast China. Hyperspectral data and AGB were collected at two growth stages - tillering and stem elongation. OMNBR models with 1-4 bands based on RR and FDR were performed. The results indicated that FDR-based OMNBR models were more accurate than RR-based ones, with the highest improvement found in FDR-based 1-2 band models. At the tillering stage, red and near infrared bands were selected, while the near infrared and shortwave infrared bands were applied at the stem elongation stage. Across both stages, FDR-based OMNBR models performed better than RR-based OMNBR models. These findings imply that derivative analysis may help to reduce the background influence of soil and water as well as the effects of illumination variations at early growth stages. More studies are needed to further explore the potential of derivative analysis.
# 8
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.
# 9
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.
# 10
Li, Fei • Miao, Yuxin • Chen, Xinping
Abstract: Timely and accurate qualification of aerial nitrogen uptake is of special significance to precision N management and recommendation for maize. Recent studies have confirmed the feasibility of retrieval of aerial N uptake of crops from spectral indices composed by the reflectance of 2-3 sensitive wavebands. In the present study, experiments involving different N rates in maize were conducted at Quzhou County of the North China Plain in 2009 and 2010. Several hyperspectral indices obtained from representative ratio- and area-based indices reported in the literature were selected to explore their potentials and stability for the estimation of aerial N uptake of maize across different growth stages, cultivars, sites and years. The results showed the optimum triangle vegetation index (OTVI) is most appropriate for aerial N uptake estimation with high correlation coefficients R2 of 0.84. Compared with triangular vegetation index (TVI), modified triangular vegetation index 1 (MTVI1) and modified triangular vegetation index 2 (MTVI2) with fixed bands, OTVI optimized by bands optimum algorithm increased R2 by 42%, 31% and 25%, respectively. The high correlation between the OTVI and aerial N uptake obtained in the different developmental stages of maize indicated that band optimized algorithms can potentially be implemented in future aerial N uptake monitoring by hyperspectral sensing.
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