5 research outputs found
Application of UAS for Monitoring of Forest Ecosystems – A Review of Experience and Knowledge
In the last couple of years, there have been a great number of articles that cover and emphasize
the advantages and possibilities that UAS (Unmanned Air System) offers in forest ecosystem
research. In the available research, alongside UAS, the importance of developing sensors that
are designed to be used with UAV (Unamnned Air Vehicle), a flight programming software
and UAS collected data processing software have been pointed out. With the widespread use
of high-precision sensors and accompanying software in forestry, it is possible to obtain accurate
data in a short time that replaces long-term manpower in the field with equal or in some
cases, such as windthrow calculation or wildlife counting, greater accuracy. The former practice
of manual imagery processing is being partly replaced with automated approaches. The
paper analyses studies that deal with some form of application of UAS in forestry, e.g. forest
inventory, forest operations, ecological monitoring, forest pests and forest fires, and wildlife
monitoring. In the forest inventory, a large number of studies deal with the possibilities of
applying UAS in mapping vegetation and individual trees, morphological research of individual
parts of trees, surface analysis, etc. The use of remote and proximal sensing technologies
in forest engineering has mainly been focused on defining surface roughness and topology,
road geometry, planning and maintenance, ground-based and cable-based harvesting and soil
characteristics and displacement. Wildfire monitoring already relies heavily on the use of UAS
and thermal cameras in operations, and it is similar to the mapping of windthrow or directions
of the spread of certain insects important for forestry. In wildlife research, numerous studies
deal with abundance research of individual terrestrial birds and mammals using UAS thermal
imagery. With some drawbacks such as wildlife disturbance or limited UAV range, common
to most of the processed studies are positive attitudes regarding the application of UAS in
forestry sensing and monitoring, which is slowly becoming a common operative practice, with
the scientists’ focus being on developing automated approaches in UAS imagery processing.
Reducing the error by improving the technological characteristics of the sensors will in the
long run reduce the number of people required to collect data important for forestry, reduce
risks and in some cases increase accuracy
The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data
This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with LiDAR data. A total of 120 sites in the Sierra Nevada Forest (California) and Shavers Creek Watershed (Pennsylvania) of the United States, covering various vegetation types and characteristics, were used to analyze the performance of the four selected individual tree segmentation algorithms. The results showed that the PFCHM performed best in all forest types, especially in conifer forests. The main forest characteristics influencing segmentation methods were LAI and CC, LAI and TD, and CVTH in conifer, broadleaf, and mixed forests, respectively. Most of the vegetation characteristics (i.e., LAI, CC, and TD) negatively correlated with all segmentation methods, while the effect of CVTH varied with forest type. These results can help guide the selection of individual tree segmentation method given the influence of vegetation characteristics
The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data
This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with LiDAR data. A total of 120 sites in the Sierra Nevada Forest (California) and Shavers Creek Watershed (Pennsylvania) of the United States, covering various vegetation types and characteristics, were used to analyze the performance of the four selected individual tree segmentation algorithms. The results showed that the PFCHM performed best in all forest types, especially in conifer forests. The main forest characteristics influencing segmentation methods were LAI and CC, LAI and TD, and CVTH in conifer, broadleaf, and mixed forests, respectively. Most of the vegetation characteristics (i.e., LAI, CC, and TD) negatively correlated with all segmentation methods, while the effect of CVTH varied with forest type. These results can help guide the selection of individual tree segmentation method given the influence of vegetation characteristics