Articles containing the keyword 'aerial images'

Reliable forest inventory methods are important for informed management. The current study compared the quality of forest attribute models based on metrics from image matching point clouds, generated using various software packages, with those based on metrics from airborne laser scanning. The field- and remotely sensed data used in the analyses were collected as part of an operational forest management inventory in Norway. Results indicate that models based on point cloud data from airborne laser scanning (ALS) consistently produced smaller root mean square error values, demonstrating superior accuracy in capturing complex forest structures compared to models using image matching point clouds. While image matching offers advantages such as lower costs and broader area coverage, this data source primarily represents canopy surfaces, which complicate its use in inventories requiring detailed canopy information. Statistical analyses revealed no significant differences in model performance among various image matching software, but all being inferior to ALS. The study emphasizes the importance of selecting the appropriate source of remotely sensed data based on specific inventory needs.

• Bollandsås, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Box 5003, 1430 Ås, Norway https://orcid.org/0000-0002-1231-7692 E-mail: ole.martin.bollandsas@nmbu.no
• Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Box 5003, 1430 Ås, Norway https://orcid.org/0000-0001-5534-049X E-mail: terje.gobakken@nmbu.no
• Næsset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Box 5003, 1430 Ås, Norway E-mail: erik.naesset@nmbu.no
• Roald, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Box 5003, 1430 Ås, Norway E-mail: bjorn-eirik.roald@nmbu.no
• Ørka, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Box 5003, 1430 Ås, Norway https://orcid.org/0000-0002-7492-8608 E-mail: hans-ole.orka@nmbu.no

Exploring the possibility to produce nation-wide forest attribute maps using stereophotogrammetry of aerial images, the national terrain model and data from the National Forest Inventory (NFI). The study areas are four image acquisition blocks in mid- and south Sweden. Regression models were developed and applied to 12.5 m × 12.5 m raster cells for each block and validation was done with an independent dataset of forest stands. Model performance was compared for eight different forest types separately and the accuracies between forest types clearly differs for both image- and LiDAR methods, but between methods the difference in accuracy is small at plot level. At stand level, the root mean square error in percent of the mean (RMSE%) were ranging: from 7.7% to 10.5% for mean height; from 12.0% to 17.8% for mean diameter; from 21.8% to 22.8% for stem volume; and from 17.7% to 21.1% for basal area. This study clearly shows that aerial images from the national image program together with field sample plots from the NFI can be used for large area forest attribute mapping.

• Bohlin, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0002-3318-5967 E-mail: jonas.bohlin@slu.se
• Bohlin, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0003-1224-6684 E-mail: inka.bohlin@slu.se
• Jonzén, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden E-mail: jonas.jonzen@slu.se
• Nilsson, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0001-7394-6305 E-mail: mats.nilsson@slu.se