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当前位置: 首页 > 文章 > Detecting the presence of natural forests using airborne laser scanning data 森林生态系统(英文版) 2023,10
Position: Home > Articles > Detecting the presence of natural forests using airborne laser scanning data Forest Ecosystems 2023,10

Detecting the presence of natural forests using airborne laser scanning data

作  者：
Marie-Claude Jutras-Perreault;Terje Gobakken;Erik Næsset;Hans Ole Ørk
单  位：
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box, NO-1432 Ås, Norway;Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box, NO-1432 Ås, Norwa
关键词：
Natural forest;ALS;Naturalness;Vertical variables;Horizontal variables;Biodiversit
摘  要：
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.

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