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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #359198
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Validation of digital terrain models retrieved from UAV point clouds using geometrical information from shadows

Author
item ABOUTALEBI, M. - Utah State University
item TORRES-RUA, A. - Utah State University
item MCKEE, M. - Utah State University
item Kustas, William - Bill
item NIETO, H. - Institute De Recerca I Tecnologia Agroalimentaries (IRTA)
item COOPMANS, C. - Utah State University

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/15/2019
Publication Date: 4/16/2019
Citation: Aboutalebi, M., Torres-Rua, A., Mckee, M., Kustas, W.P., Nieto, H., Coopmans, C. 2019. Validation of digital terrain models retrieved from UAV point clouds using geometrical information from shadows. Meeting Abstract. https://doi.org/10.1117/12.2519694.
DOI: https://doi.org/10.1117/12.2519694

Interpretive Summary:

Technical Abstract: Theoretically, the appearance of shadows in aerial imagery is not desirable for researchers because it leads to errors in object classification, bias in the calculation of indices, such as vegetation indices (VIs), and loss of reflectance information. For example, our recent study revealed that some VIs, such as normalized difference vegetation index (NDVI) and leaf area index (LAI), have higher values in sunlit pixels than in shaded pixels. In contrast, shadows contain useful geometrical information about the objects blocking the light. Several studies have focused on estimation of building heights in urban areas using the position of the sun in the sky, geographical information (latitude and longitude), and the length of shadows. This type of information can be used to predict the population of a region, water demand, water usage, etc., in urban areas. With the emergence of unmanned aerial vehicles (UAVs) and the availability of high- to super-high-resolution imagery, the importance of shadows and improvements in detecting and compensating for them have received more attention. Today, three-dimensional imagery can be generated using UAV-based photogrammetric techniques. This can be very useful, particularly in agricultural applications such as development of an empirical equation between biomass or yield and the geometrical information of canopies or crops. However, evaluating the accuracy of the canopy or crop height requires labor-intensive efforts. In contrast, using the shadow length measured from high-resolution imagery, along with time and location where the imagery was captured, the geometrical relationship between the length of the shadows and the crop or canopy height can be inversely solved. In this study, canopy heights retrieved from UAV point clouds are validated using the geometrical shadow information retrieved from four sets of high-resolution imagery captured by the Utah State University AggieAir unmanned aerial vehicle (UAV) system. These flights were conducted in 2014, 2015, and 2016 over a commercial vineyard located in California for the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program. The results showed that, although this approach could be computationally expensive, it is still faster than fieldwork and does not require an expensive and accurate instrument like real-time kinematic (RTK) GPS.