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Detecting the presence of natural forests using airborne laser scanning data.

Authors :
Jutras-Perreault, Marie-Claude
Gobakken, Terje
Næsset, Erik
Ørka, Hans Ole
Source :
Forest Ecosystems (KeAi Communications Co.); 2023, Vol. 10, p1-13, 13p
Publication Year :
2023

Abstract

Centuries of forest exploitation have caused significant loss of natural forests in Europe, leading to a decline in populations for many species. To prevent further loss in biodiversity, the Norwegian government has set a target of protecting 10% of the forested area. However, recent data from the National Forest Inventory (NFI) reveals that less than 2% of Norway's forested area consists of natural forests. To identify forests with high conservation value, we used vertical and horizontal variables derived from airborne laser scanning (ALS) data, along with NFI plot measurements. Our study aimed to predict the presence of natural forests across three counties in southeastern Norway, using three different definitions: pristine, near-natural, and semi-natural forests. Natural forests are scarce, and their underrepresentation in field reference data can compromise the accuracy of the predictions. To address this, we assessed the potential gain of including additional field data specifically targeting natural forests to achieve a better balance in the dataset. Additionally, we examined the impact of stratifying the data by dominant tree species on the performance of the models. Our results revealed that semi-natural forests were the most accurately predicted, followed by near-natural and pristine forests, with Matthews correlation coefficient values of 0.32, 0.24, and 0.17, respectively. Including additional field data did not improve the predictions. However, stratification by species improved the accuracy of predictions for near-natural and semi-natural forests, while reducing accuracy for pristine forests. The use of horizontal variables did not improve the predictions. Our study demonstrates the potential of ALS data in identifying forests with high conservation value. It provides a basis for further research on the use of ALS data for the detection and conservation of natural forests, offering valuable insights to guide future forest preservation efforts. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20956355
Volume :
10
Database :
Complementary Index
Journal :
Forest Ecosystems (KeAi Communications Co.)
Publication Type :
Academic Journal
Accession number :
175625902
Full Text :
https://doi.org/10.1016/j.fecs.2023.100146