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Pregled bibliografske jedinice broj: 1243915

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


Šiljeg, Ante; Marinović, Rajko; Domazetović, Fran; Jurišić, Mladen; Marić, Ivan; Panđa, Lovre; Radočaj, Dorijan; Milošević, Rina
GEOBIA and Vegetation Indices in Extracting Olive Tree Canopies Based on Very High-Resolution UAV Multispectral Imagery // Applied Sciences, 13 (2023), 2; 1-20 doi:10.3390/app13020739 (međunarodna recenzija, članak, znanstveni)


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

Autori
Šiljeg, Ante ; Marinović, Rajko ; Domazetović, Fran ; Jurišić, Mladen ; Marić, Ivan ; Panđa, Lovre ; Radočaj, Dorijan ; Milošević, Rina

Izvornik
Applied Sciences (2076-3417) 13 (2023), 2; 1-20

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
geospatial technologies ; Lun olive groves ; object-based image analysis ; classification algorithms ; machine learning ; accuracy assessment

Sažetak
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).

Izvorni jezik
Engleski

Znanstvena područja
Geodezija, Interdisciplinarne tehničke znanosti, Poljoprivreda (agronomija), Interdisciplinarne biotehničke znanosti, Geografija



POVEZANOST RADA


Projekti:
UIP-2017-05-2694 - Laboratorij za geoprostorne analize (GAL / GAL) (Šiljeg, Ante, HRZZ - 2017-05) ( CroRIS)
Ostalo-STREAM - STREAM - Strategic Development of Flood Management (STREAM) (Šiljeg, Ante, Ostalo - INTERREG Italija - Hrvatska 2014. - 2020.) ( CroRIS)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi www.mdpi.com

Citiraj ovu publikaciju:

Šiljeg, Ante; Marinović, Rajko; Domazetović, Fran; Jurišić, Mladen; Marić, Ivan; Panđa, Lovre; Radočaj, Dorijan; Milošević, Rina
GEOBIA and Vegetation Indices in Extracting Olive Tree Canopies Based on Very High-Resolution UAV Multispectral Imagery // Applied Sciences, 13 (2023), 2; 1-20 doi:10.3390/app13020739 (međunarodna recenzija, članak, znanstveni)
Šiljeg, A., Marinović, R., Domazetović, F., Jurišić, M., Marić, I., Panđa, L., Radočaj, D. & Milošević, R. (2023) GEOBIA and Vegetation Indices in Extracting Olive Tree Canopies Based on Very High-Resolution UAV Multispectral Imagery. Applied Sciences, 13 (2), 1-20 doi:10.3390/app13020739.
@article{article, author = {\v{S}iljeg, Ante and Marinovi\'{c}, Rajko and Domazetovi\'{c}, Fran and Juri\v{s}i\'{c}, Mladen and Mari\'{c}, Ivan and Pan\dja, Lovre and Rado\v{c}aj, Dorijan and Milo\v{s}evi\'{c}, Rina}, year = {2023}, pages = {1-20}, DOI = {10.3390/app13020739}, keywords = {geospatial technologies, Lun olive groves, object-based image analysis, classification algorithms, machine learning, accuracy assessment}, journal = {Applied Sciences}, doi = {10.3390/app13020739}, volume = {13}, number = {2}, issn = {2076-3417}, title = {GEOBIA and Vegetation Indices in Extracting Olive Tree Canopies Based on Very High-Resolution UAV Multispectral Imagery}, keyword = {geospatial technologies, Lun olive groves, object-based image analysis, classification algorithms, machine learning, accuracy assessment} }
@article{article, author = {\v{S}iljeg, Ante and Marinovi\'{c}, Rajko and Domazetovi\'{c}, Fran and Juri\v{s}i\'{c}, Mladen and Mari\'{c}, Ivan and Pan\dja, Lovre and Rado\v{c}aj, Dorijan and Milo\v{s}evi\'{c}, Rina}, year = {2023}, pages = {1-20}, DOI = {10.3390/app13020739}, keywords = {geospatial technologies, Lun olive groves, object-based image analysis, classification algorithms, machine learning, accuracy assessment}, journal = {Applied Sciences}, doi = {10.3390/app13020739}, volume = {13}, number = {2}, issn = {2076-3417}, title = {GEOBIA and Vegetation Indices in Extracting Olive Tree Canopies Based on Very High-Resolution UAV Multispectral Imagery}, keyword = {geospatial technologies, Lun olive groves, object-based image analysis, classification algorithms, machine learning, accuracy assessment} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • Social Science Citation Index (SSCI)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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