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  • Agriculture  (2)
  • Geographisches Institut der Universität zu Köln - Kölner Geographische Arbeiten  (2)
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  • Geographisches Institut der Universität zu Köln - Kölner Geographische Arbeiten  (2)
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  • 1
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    Geographisches Institut der Universität zu Köln - Kölner Geographische Arbeiten
    Publication Date: 2022-01-12
    Description: Abstract
    Description: Terrestrial laser scanning provides highly accurate and dense 3D measurements of an object. This technology leads to several applications, for example in topographic surveys, forestry, and as-built documentation. Few developments exist in the area of agriculture and precision farming. In this contribution, multi-temporal 3D terrestrial laser scanning was applied for field crop modelling. The time-of-flight laser scanner Riegl LMS-Z420i was used three to five times per year to estimate plant height distribution of the field crops winter wheat, spring barley, and sugar beet. In 2008 and 2009, the area under investigation was a single field. As a further development, data from plots with different crop varieties of barley and sugar beet were analysed in 2010. As a result, within-field variability was detected by using crop surface models (CSM) and crop volume models (CVM). Single plants were successfully detected. The results will be compared with additional data in the future.
    Description: SeriesInformation
    Description: 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. 25-30
    Keywords: Other ; None ; Agriculture ; Crop/s ; Terrestrial Laser Scanning ; Surface ; Remote Sensing
    Type: Text , Book Section
    Format: 11857 Kilobytes
    Format: 6 Pages
    Format: PDF
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  • 2
    Publication Date: 2022-01-14
    Description: Abstract
    Description: 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.
    Keywords: Nitrogen ; Biomass ; Hyperspectral ; Remote Sensing ; Agriculture ; 550 Earth sciences
    Language: English
    Type: Text , Workshop paper
    Format: 5 Pages
    Format: 1130 Kilobytes
    Format: application/pdf
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