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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 #350452

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: Assessment of different methods for shadow detection in high-resolution imagery and evaluation of shadows impact on calculation of NDVI, LAI, and evapotranspiration

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

Submitted to: Irrigation Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/19/2018
Publication Date: 12/3/2018
Citation: Aboutalebi, M., Torres-Rua, A., Kustas, W.P., Nieto, H., Coopmans, C., McKee, M. 2018. Assessment of different methods for shadow detection in high-resolution imagery and evaluation of shadows impact on calculation of NDVI, LAI, and evapotranspiration. Irrigation Science. 37(3):407-429.

Interpretive Summary: Unmanned aerial vehicles (UAVs) used for remote sensing purposes have become a rapidly developing technology for acquiring high-resolution imagery for precision agriculture applications. However, as image resolution increases, new challenges emerge such as data transfer and storage, image processing, and detection and characterization of finer-scale features such as plant canopy glint, blurriness due to wind, and shadows. Although in some cases shadows might not be a significant issue, shadows affect reflectance and thermal emission not accounted for in remote sensing energy balance models, which in turn are likely to cause bias in determining plant water use (evapotransporation) and stress. Therefore, this study was conducted to evaluate shadow detection algorithms using high-resolution imagery captured by UAVs over a complex canopy, a vineyard, and to consider the impacts of shaded pixels on remotely sensed vegetation indices, modeled derived leaf area, and evapotranspiration (ET). The impacts of shadows on vegetation indices, leaf area and ET estimation are shown to be significant in specific areas of the vineyard. This implies that models using high-resolution UAV imagery for estimating ET, plant stress, and biophysical parameters should consider the impact of shadowed areas for precision agriculture applications in complex canopies.

Technical Abstract: There have been significant efforts recently in the application of high-resolution remote sensing imagery (i.e., sub-meter) captured by unmanned aerial vehicles (UAVs) for precision agricultural applications for high valued crops such as wine grapes. However, with such high resolution data shadows will appear in the imagery effectively reducing the reflectance and emission signal received by imaging sensors. To date, research that evaluates procedures to identify the occurrence of shadows at this geographic scale in imagery produced by UAVs is limited. In this study, the performance of four different shadow detection methods that have been used in satellite imagery were evaluated for high-resolution UAV imagery collected over a California vineyard during the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) field campaigns. The shadow detection methods were compared and the impacts of shadowed areas on vegetation indices such as the normalized difference vegetation index (NDVI) and leaf area index (LAI) are presented, as well as the impact on estimated evapotranspiration (ET) using a remote sensing-based energy balance model.The results obtained for shadow detection indicated that the supervised classification and index-based methods had better performance than two other methods.