Pregled bibliografske jedinice broj: 1126967
An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery
An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery // Remote sensing, 13 (2021), 10; 1868, 19 doi:10.3390/rs13101868 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1126967 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
An Evaluation of Pixel- and Object-Based Tree Species
Classification in Mixed Deciduous Forests Using Pansharpened Very
High Spatial Resolution Satellite Imagery
(An Evaluation of Pixel- and Object-Based Tree Species
Classification in Mixed Deciduous Forests Using Pansharpened
Very High Spatial Resolution Satellite Imagery)
Autori
Deur, Martina ; Gašparović, Mateo ; Balenović, Ivan
Izvornik
Remote sensing (2072-4292) 13
(2021), 10;
1868, 19
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
pansharpening ; random forest ; object-based classification (OBIA) ; pixel-based classification ; WorldView-3
Sažetak
Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object- based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object- based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.
Izvorni jezik
Engleski
POVEZANOST RADA
Projekti:
IP-2016-06-7686 - Uporaba podataka daljinskih istraživanja dobivenih različitim 3D optičkim izvorima u izmjeri šuma (3D-FORINVENT) (Balenović, Ivan, HRZZ - 2016-06) ( CroRIS)
EK-H2020-776045 - Operational sustainable forestry with satellite-based remote sensing (MySustainableForest) (EK - H2020-EO-2017) ( CroRIS)
Ustanove:
Geodetski fakultet, Zagreb,
Hrvatski šumarski institut, Jastrebarsko
Poveznice na cjeloviti tekst rada:
Pristup cjelovitom tekstu rada doi www.mdpi.com www.mdpi.com www.mdpi.comCitiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus
