Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images
| Autor: | Päivi Lyytikäinen-Saarenmaa, Svein Solberg, Markus Holopainen, Mikko Vastaranta, Mervi Talvitie, Tuula Kantola, Xiaowei Yu, Sanna Kaasalainen, Juha Hyyppä |
|---|---|
| Přispěvatelé: | Department of Forest Sciences, Laboratory of Forest Resources Management and Geo-information Science, Forest Ecology and Management |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Diprionidae
010504 meteorology & atmospheric sciences 411 Agriculture and forestry Science 0211 other engineering and technologies 02 engineering and technology 01 natural sciences Aerial photography forest disturbance MSN 021101 geological & geomatics engineering 0105 earth and related environmental sciences ALS defoliation Diprion pini logistic regression random forest biology Ecology Taiga Scots pine Forestry 15. Life on land biology.organism_classification Random forest Boreal Pinaceae General Earth and Planetary Sciences Environmental science |
| Zdroj: | Remote Sensing, Vol 2, Iss 12, Pp 2665-2679 (2010) Remote Sensing; Volume 2; Issue 12; Pages: 2665-2679 |
| ISSN: | 2072-4292 |
| Popis: | Climate change and rising temperatures have been observed to be related to the increase of forest insect damage in the boreal zone. The common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini can cause severe growth loss and tree mortality in Scots pine (Pinus sylvestris L.) (Pinaceae). In this study, logistic LASSO regression, Random Forest (RF) and Most Similar Neighbor method (MSN) were investigated for predicting the defoliation level of individual Scots pines using the features derived from airborne laser scanning (ALS) data and aerial images. Classification accuracies from 83.7% (kappa 0.67) to 88.1% (kappa 0.76) were obtained depending on the method. The most accurate result was produced using RF with a combination of data from the two sensors, while the accuracies when using ALS and image features separately were 80.7% and 87.4%, respectively. Evidently, the combination of ALS and aerial images in detecting needle losses is capable of providing satisfactory estimates for individual trees. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|