Abstract
During the past decade in forest mapping and monitoring applications, the ability to acquire spatially accurate, 3D remote-sensing information by means of laser scanning, digital stereo imagery and radar imagery has been a major turning point. These 3D data sets that use single- or multi-temporal point clouds enable a wide range of applications when combined with other geoinformation and logging machine-measured data. New technologies enable precision forestry, which can be defined as a method to accurately determine characteristics of forests and treatments at stand, sub-stand or individual tree level. In precision forestry, even individual tree-level assessments can be used for simulation and optimization models of the forest management decision support system. At the moment, the forest industry in Finland is looking forward to next generation’s forest inventory techniques to improve the current wood procurement practices. Our vision is that in the future, the data solution for detailed forest management and wood procurement will be to use multi-source and -sensor information. In this communication, we review our recent findings and describe our future vision in precision forestry research in Finland.
Highlights
BackgroundIn Finland, intensive small-scale forestry is practiced mainly in privately owned forests
Precision forestry will be based on more efficient use of 3D information that is acquired from different data sources using various sensors
We review our recent findings and describe our future vision in precision forestry research
Summary
In Finland, intensive small-scale forestry is practiced mainly in privately owned forests. Forest inventory attributes are derived using an area-based approach (ABA) where low-density (~0.5 pulses per m2). ALS data are used to generalize field-measured inventory attributes over an entire inventory area This approach has succeeded in replacing traditional stand-wise field inventories (SWFIs). In Finnish SWFI practices, every stand has been visited by a forest planner, and forest inventory attributes were derived from relascopic measurements and/or visual assessment. Precision forestry will be based on more efficient use of 3D information that is acquired from different data sources using various sensors. In this communication, we review our recent findings and describe our future vision in precision forestry research
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