Evaluating TIFFS (Toolbox for LiDAR Data Filtering and Forest Studies) in Deriving Forest Measurements from LiDAR Data
Parole chiave:
LiDAR, remote sensing, forestryAbstract
Recent advances in LiDAR (Light Detection and Ranging) technology have allowed for the remote sensing of important forest characteristics to be more reliable and commercially available. Studies have shown that this technology can adequately estimate forest characteristics such as individual tree locations, tree heights, and crown diameters. These values are then used to estimate biophysical properties of forests, such as basal area and timber volume. This study assessed the capability of a commercially available program, Tiffs (Toolbox for Lidar Data Filtering and Forest Studies), to accurately estimate forest characteristics, as compared to data collected at the plot level using traditional timber sampling methods. We found a high, positive correlation coefficient ( r)of 0.8223 for tree heights, between the LiDAR-derived measurements and the field measurements, which is somewhat promising. However, we found low correlations in tree count per plot ( r = 0.1777) and tree crown radius ( r = 0.1517), between the LiDAR-derived measurements and the field measurements, results which are far from satisfactory.
Dowloads
Pubblicato
2010-08-28
Fascicolo
Sezione
Special Section on the Southern Forestry GIS Conference
Licenza
Authors who publish with this journal agree to the following terms:- Authors grant the journal right of first publication, with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) following the publication by the journal, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Come citare
Evaluating TIFFS (Toolbox for LiDAR Data Filtering and Forest Studies) in Deriving Forest Measurements from LiDAR Data. (2010). Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS), 2(2), Pages: 145-152 (8). https://mcfns.com/index.php/Journal/article/view/2.21
