Open AccessExamining Forest Structure With Terrestrial Lidar: Suggestions and Novel Techniques Based on Comparisons Between Scanners and Forest Treatments
Author(s) -
Donager Jonathon J.,
Sankey Temuulen Ts.,
Sankey Joel B.,
Sanchez Meador Andrew J.,
Springer Abraham E.,
Bailey John D.
Publication year - 2018
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2018ea000417
Subject(s) - lidar , canopy , remote sensing , forest plot , tree canopy , proxy (statistics) , tree (set theory) , scale (ratio) , percentage point , environmental science , forest inventory , statistics , mathematics , computer science , forest management , geology , geography , cartography , agroforestry , meta analysis , medicine , mathematical analysis , archaeology
Terrestrial laser scanners (TLSs) provide a tool to assess and monitor forest structure across forest landscapes. We present TLS methods, suggestions, and mapped guidelines for planning TLS acquisitions at varying scales and forest densities. We examined rates of point‐density decline with distance from two TLS that acquire data at relatively high and low point density and found that the rates were nearly identical between scanners ( p value <0.01), suggesting that our findings are applicable to a range of TLS types. Using unique, TLS‐adapted processing methods, we determined the relative accuracy of TLS‐derived plot‐scale estimates of tree height, diameter‐at‐breast‐height, height‐to‐canopy, tree counts, as well as treatment‐scale tree density and patch metrics, using both high point density and low point density TLS among thinned and nonthinned forest treatments. The high‐density TLS consistently provides more accurate estimates of plot‐level metrics ( R 2 = 0.46 to 0.87) than the low‐density TLS (R 2 = −0.14 to 0.53). At treatment scales, tree density estimates are similar among scanners ( R 2 = 0.95 vs. 0.71), as are canopy cover and patch metrics. We develop and present the normalized density‐distance index (NDDI), which can account for up to 59% of the variance in estimate error and can be used to guide TLS‐data acquisition plans. This index indicates whether a given location has generally higher point density (higher NDDI) relative to the distance from the scanner and can be used as a proxy for uncertainty. Using NDDI as a guide for fair comparison between scanners, both plot‐ and treatment‐scale estimates improved.