Tree species identification using LIDAR and optical imagery

Prieditis, G., Latvia Univ. of Agriculture, Jelgava; Smits, I., Latvia Univ. of Agriculture, Jelgava; Dagis, S., Latvia Univ. of Agriculture, Jelgava; Dubrovskis, D., Latvia Univ. of Agriculture, Jelgava

Tree species identification using LIDAR and optical imagery

2013

Prieditis, G., Latvia Univ. of Agriculture, Jelgava (Latvia) | Smits, I., Latvia Univ. of Agriculture, Jelgava (Latvia) | Dagis, S., Latvia Univ. of Agriculture, Jelgava (Latvia) | Dubrovskis, D., Latvia Univ. of Agriculture, Jelgava (Latvia)

Tree species identification is important for a variety of natural resource management and monitoring activities especially in forest inventory. The objective of research is to identify tree species using digital aerial photography and LIDAR data in Latvian forest conditions. The study outlines a number of tree species identification possibilities: the ability to identify conifers and deciduous trees; the ability to identify pine and spruce; the ability to identify birch, aspen and black alder. The study site is a forest in the middle part of Latvia at Jelgava district (56º39’ N, 23º47’ E). Aerial photography camera (ADS 40) and laser scanner (ALS 50 II) were used to capture the data. LIDAR resolution is 9 points m-2 (500 m altitude). The image data is RGB, NIR and PAN spectrum with 20 cm pixel resolution. During the study a modified region growing algorithm was developed to determine tree canopy and tree species identification using threshold segmentation, Fourier transform, frequency filtering and reverse Fourier transform. Tree species classification of coniferous and deciduous trees is possible in 82% of the cases; the first storey of the trees can be classified correctly in 96% of the cases, but the second storey of the trees only in 49% of the cases. Spruce identification is possible in 81.1% of the cases, for first storey trees in 89.6% of the cases and for the second storey trees in 72.9% of cases. Deciduous tree correct classification is possible in 63% of the cases, birch 75%, black alder 60% and aspen only in 41% of the cases.

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Mots clés AGROVOC

aerial photography arbre forestier data analysis data collection data processing determination of species forest inventories forest trees fotografia aerea inventaire forestier inventarios forestales latvia letonia lettonie procesamiento de datos remote sensing

Informations bibliographiques

Publié dans

Research for Rural Development. International Scientific Conference Proceedings (Latvia)

Numéro 19/2013 ISSN 1691-4031

Editeur

Latvia University of Agriculture

Pagination

p. 34-41

D'autres materias

Traitement des donnees; Analisis de datos; Photographie aerienne; Analyse de donnees; Arboles forestales; Coleccion de datos; Teledetection; Collecte de donnees; Determination des especes; Teledeteccion

Langue

anglais

Note

Summary (En)

7 fig., 18 ref.

Type

Conference; Summary; Web Site; Ams

Source

Research for Rural Development 2013. Annual 19th International Scientific Conference Proceedings. Volume 2, Treija, S.Skujeniece, S..- Jelgava (Latvia): Latvia University of Agriculture, 2013. International Scientific Conference: Research for Rural Development, 19, Jelgava (Latvia), 15-17 May 2013.- p. 34-41

Conférence

International Scientific Conference: Research for Rural Development, 19, Jelgava (Latvia), 15-17 May 2013

Sur AGRIS depuis: 2015-05-15

Format: AGRIS AP

Fournisseur de données

Cette notice bibliographique a été fournie par Fundamental Library of Latvia University of Life Sciences and Technologies

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Liens

http://www2.llu.lv/research_conf/Proceedings/19th_volume1.pdf http://www2.llu.lv/research_conf/proceedings.htm

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