Cork oak woodland land-cover types classification: a comparison between UAV sensed imagery and field survey
Visualitza/Obre
Autor/a
Heuschmidt, Florence
Gómez-Candón, David
Soares, Cristina
Cerasoli, Sofia
Silva, Joao M.N.
Data de publicació
2020-07-29ISSN
0143-1161
Resum
This work assesses the use of aerial imagery for the vegetation cover characterization in cork oak woodlands. The study was conducted in a cork oak woodland in central Portugal during the summer of 2017. Two supervised classification methods, pixel-based and object-based image analysis (OBIA), were tested using a high spatial resolution image mosaic. Images were captured by an unmanned aerial vehicle (UAV) equipped with a red, green, blue (RGB) camera. Four different vegetation covers were distinguished: cork oak, shrubs, grass and other (bare soil and tree shadow). Results have been compared with field data obtained by the point-intercept (PI) method. Data comparison reveals the reliability of aerial imagery classification methods in cork oak woodlands. Results show that cork oak was accurately classified at a level of 82.7% with pixel-based method and 79.5% with OBIA . 96.7% of shrubs were identified by OBIA, whereas there was an overestimation of 21.7% with pixel approach. Grass presents an overestimation of 22.7% with OBIA and 12.0% with pixel-based method. Limitations rise from using only spectral information in the visible range. Thus, further research with the use of additional bands (vegetation indices or height information) could result in better land-cover type classification.
Tipus de document
Article
Versió del document
Versió acceptada
Llengua
English
Matèries (CDU)
631 - Agricultura. Agronomia. Maquinària agrícola. Sòls. Edafologia agrícola
Pàgines
13
Publicat per
Taylor and Francis
Publicat a
International Journal of Remote Sensing (IJRS)
Citació
Heuschmidta, Florence, David Gómez-Candón, Cristina Soares, Sofia Cerasoli, and João M. N. Silva. 2020. "Cork Oak Woodland Land-Cover Types Classification: A Comparison Between UAV Sensed Imagery And Field Survey". International Journal Of Remote Sensing 41 (19): 7649-7659. doi:10.1080/2150704X.2020.
Programa
Ús Eficient de l'Aigua en Agricultura
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Drets
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