气候变化领域集成服务门户(CCPortal): Forest inventories for small areas using drone imagery without in-situ field measurements

CCPortal

DOI	10.1016/j.rse.2019.111404
	Forest inventories for small areas using drone imagery without in-situ field measurements
	Kotivuori E.; Kukkonen M.; Mehtätalo L.; Maltamo M.; Korhonen L.; Packalen P.
发表日期	2020
ISSN	00344257
卷号	237
英文摘要	Drone applications are becoming increasingly common in the arena of forest management and forest inventories. In particular, the use of photogrammetrically derived drone-based image point clouds (DIPC) in individual tree detection has become popular. Use of an area-based approach (ABA) in small areas has also been considered. However, in-situ field measurements of sample plots substantially increase the cost of small area forest inventories. Therefore, we examined whether small-scale forest management inventories could be carried out without local field measurements. We used nationwide and regional ABA models for stem volumes fitted with airborne laser scanning (ALS) data to predict stem volumes using corresponding metrics calculated from DIPC data. The stem volumes were predicted at the cell level (15 × 15 m) and aggregated to test plots (30 × 30 m). Height metrics for the dominant tree layer from the DIPC data showed strong correlations with similar metrics computed from the ALS data. The ALS-based models applied with DIPC metrics performed well, especially if the ABA model was fitted in the same geographical area (regional model) and the inventory units were disaggregated to coniferous and deciduous dominated stands using auxiliary information from Multi-source National Forest Inventory data (root mean square error at 30 × 30 m level was 13.1%). The corresponding root mean square error associated with the nationwide ABA model was 20.0% with an overestimation (mean difference 9.6%). © 2019 Elsevier Inc.
英文关键词	Airborne laser scanning; Area-based approach; Drone; Forest inventory; Image point cloud; Remote sensing; Remotely piloted aerial system; Unmanned aerial system; Unmanned aerial vehicle
语种	英语
scopus关键词	Aircraft detection; Drones; Laser applications; Mean square error; Remote sensing; Unmanned aerial vehicles (UAV); Aerial systems; Airborne Laser scanning; Area-based; Forest inventory; Image points; Unmanned aerial systems; Forestry
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179495
作者单位	University of Eastern Finland, School of Forest Sciences, P.O. Box 111, Joensuu, FI-80101, Finland; University of Eastern Finland, School of Computing, P.O. Box 111, Joensuu, FI-80101, Finland
推荐引用方式 GB/T 7714	Kotivuori E.,Kukkonen M.,Mehtätalo L.,et al. Forest inventories for small areas using drone imagery without in-situ field measurements[J],2020,237.
APA	Kotivuori E.,Kukkonen M.,Mehtätalo L.,Maltamo M.,Korhonen L.,&Packalen P..(2020).Forest inventories for small areas using drone imagery without in-situ field measurements.Remote Sensing of Environment,237.
MLA	Kotivuori E.,et al."Forest inventories for small areas using drone imagery without in-situ field measurements".Remote Sensing of Environment 237(2020).