- Tytuł:
-
Określenie składu gatunkowego lasów Góry Chojnik (Karkonoski Park Narodowy) z wykorzystaniem lotniczych danych hiperspektralnych APEX
Identification of tree species in Mt Chojnik (Karkonoski National Park) forest using airborne hyperspectal APEX data - Autorzy:
-
Raczko, E.
Zagajewski, B.
Ochtyra, A.
Jarocińska, A.
Marcinkowska-Ochtyra, A.
Dobrowolski, M. - Powiązania:
- https://bibliotekanauki.pl/articles/989774.pdf
- Data publikacji:
- 2015
- Wydawca:
- Polskie Towarzystwo Leśne
- Tematy:
-
lesnictwo
Karkonoski Park Narodowy
gory
Chojnik
lasy
sklad gatunkowy
metody badan
teledetekcja
pomiary hiperspektralne
skaner APEX
svm classification
apex hyperspectral data
species structure - Opis:
- We used hyperspectral data from APEX scanner (288 spectral bands in 380−2500 nm spectral range; 3,5 m spatial resolution) to classify five tree species occurring in the area of Mt. Chojnik in the Karkonoski National Park (south−western Poland). Data used to delimit learning and verification polygons were acquired during field research in August 2013, when ground truth polygons were acquired using device equipped with GPS receiver. Raw APEX data went through radiometric and geometric correction at VITO office. To reduce processing time, 40 most informative bands were selected using information content analysis. The Support Vector Machines (SVM) algorithm was used for classification of the following tree species: Fagus sylvatica L., Betula pendula Roth, Pinus sylvestris L., Picea alba L. Karst and Larix decidua Mill. Final classification had 78.66% overall accuracy with Kappa coefficient equal to 0.71. The best classified species included beech (87.09%) and pine (83.96%), while the worst results were obtained for larch (60.29%). Low accuracy for larch could be caused by the fact that most of larch trees in the research area grow in small patches, which made it hard to specify large enough sample of training data. All classified tree species had producer's accuracy of at least 60%, with the highest value reaching 87%. User's accuracies were from 53% for pine to 85% for beech. It is possible to classify tree species using hyperspectral data with moderate to high accuracy even if the data used lacked atmospheric correction. Further work will focus on improving the classification accuracy and use of neural networks based classification methods. Results from this paper will serve as basis for tree species map of the Karkonoski National Park.
- Źródło:
-
Sylwan; 2015, 159, 07; 593-599
0039-7660 - Pojawia się w:
- Sylwan
- Dostawca treści:
- Biblioteka Nauki
Artykuł