Quality Assessment of Worldview-3 Stereo Imagery Derived Models Over Millennial Olive Groves

Šiljeg, Ante; Domazetović, Fran; Marić, Ivan; Panđa, Lovre

doi:10.1007/978-3-030-76374-9_5

Cited by 1 publication

(1 citation statement)

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“…Geospatial technologies and their products have become easily available and widely accessible in everyday life, which has advanced and accelerated their use in various research [16][17][18][19]. The development of advanced geospatial technologies has enabled PA in the olive management sector, especially in the context of precise canopy extraction for management [20][21][22], monitoring [23][24][25], assessment of crop quality [26], disease detection [27], and preventive actions [28]. Therefore, geospatial technologies have a meaningful role in the observation and analysis of various processes located on the Earth's surface.…”

Section: Introductionmentioning

confidence: 99%

GEOBIA and Vegetation Indices in Extracting Olive Tree Canopies Based on Very High-Resolution UAV Multispectral Imagery

et al. 2023

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In recent decades, precision agriculture and geospatial technologies have made it possible to ensure sustainability in an olive-growing sector. The main goal of this study is the extraction of olive tree canopies by comparing two approaches, the first of which is related to geographic object-based analysis (GEOBIA), while the second one is based on the use of vegetation indices (VIs). The research area is a micro-location within the Lun olives garden, on the island of Pag. The unmanned aerial vehicle (UAV) with a multispectral (MS) sensor was used for generating a very high-resolution (VHR) UAVMS model, while another mission was performed to create a VHR digital orthophoto (DOP). When implementing the GEOBIA approach in the extraction of the olive canopy, user-defined parameters and classification algorithms support vector machine (SVM), maximum likelihood classifier (MLC), and random trees classifier (RTC) were evaluated. The RTC algorithm achieved the highest overall accuracy (OA) of 0.7565 and kappa coefficient (KC) of 0.4615. The second approach included five different VIs models (NDVI, NDRE, GNDVI, MCARI2, and RDVI2) which are optimized using the proposed VITO (VI Threshold Optimizer) tool. The NDRE index model was selected as the most accurate one, according to the ROC accuracy measure with a result of 0.888 for the area under curve (AUC).

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