Abstract
In this study, airborne laser scanning-based and traditional field-based survey methods for tree heights estimation are assessed by using one hundred felled trees as a reference dataset. Comparisons between remote sensing and field-based methods were applied to four circular permanent plots located in the western Italian Alps and established within the Alpine Space project NewFor. Remote sensing (Airborne Laser Scanning, ALS), traditional field-based (indirect measurement, IND), and direct measurement of felled trees (DIR) methods were compared by using summary statistics, linear regression models, and variation partitioning. Our results show that tree height estimates by Airborne Laser Scanning (ALS) approximated to real heights (DIR) of felled trees. Considering the species separately, Larix decidua was the species that showed the smaller mean absolute difference (0.95 m) between remote sensing (ALS) and direct field (DIR) data, followed by Picea abies and Pinus sylvestris (1.13 m and 1.04 m, respectively). Our results cannot be generalized to ALS surveys with low pulses density (<5/m2) and with view angles far from zero (nadir). We observed that the tree heights estimation by laser scanner is closer to actual tree heights (DIR) than traditional field-based survey, and this was particularly valid for tall trees with conical shape crowns.
Highlights
Sustainable forest management needs a huge amount of tree parameters such as species distribution, timber volume, and average tree height as the basis of broad scale forest inventories [1].Among these, tree height is one of the most important variables in forest inventory, as it is often used in the estimation of forest growth, biomass, carbon stock, and site productivity [2]
We considered as ground truth (Ground Control Points, GCPs) one hundred felled trees to test tree heights measured with two estimation approaches: Airborne Laser Scanning (ALS)
From a comparison between mean tree heights measured by ALS data and indirect” measurement (IND) (Table 3), emerged that ALS10 slightly overestimated tree heights (23.51 m vs. 23.44 m) and the opposite trend was observed for Picea (19.64 m vs. 20.26 m)
Summary
Sustainable forest management needs a huge amount of tree parameters such as species distribution, timber volume, and average tree height as the basis of broad scale forest inventories [1]. Tree height is one of the most important variables in forest inventory, as it is often used in the estimation of forest growth, biomass, carbon stock, and site productivity [2]. Traditional forest inventory field methods for forest height estimation are expensive, time consuming, and almost impossible to perform over large areas [3]. When dealing with large areas, a sampling plot approach is commonly arranged in order to reduce costs, and diameter-height relations are constructed to predict individual tree heights for those areas that were not surveyed. Forests 2017, 8, 7 data over large study areas on a cost efficient basis [4].
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