Abstract
Aboveground biomass, volume, and basal area are among the most important structural attributes in forestry. Direct measurements are cost-intensive and time-consuming, especially for old-growth forests exhibiting a complex structure over a rugged topography. We defined a methodology to optimize the plot size and the (total) sampling area, allowing for structural attributes with a tolerable error to be estimated. The plot size was assessed by analyzing the semivariogram of a CHM model derived via UAV laser scanning, while the sampling area was based on the calculation of the absolute relative error as a function of allometric relationships. The allometric relationships allowed the structural attributes from trees’ height to be derived. The validation was based on the positioning of a number of trees via total station and GNSS surveys. Since high trees occlude the GNSS signal transmission, a strategy to facilitate the positioning was to fix the solution using the GLONASS constellation alone (showing the highest visibility during the survey), and then using the GPS constellation to increase the position accuracy (up to PDOP~5−10). The tree heights estimated via UAV laser scanning were strongly correlated (r2 = 0.98, RMSE = 2.80 m) with those measured in situ. Assuming a maximum absolute relative error in the estimation of the structural attribute (20% within this work), the proposed methodology allowed the portion of the forest surface (≤60%) to be sampled to be quantified to obtain a low average error in the calculation of the above mentioned structural attributes (≤13%).
Highlights
Most geomatic techniques have been used, in recent years, to characterize forests’ and trees’ planimetric positions
The most significant progress is due to the introduction of airborne laser scanning (ALS) and recently of unmanned aerial vehicle-borne laser scanning (UAVLS) or unmanned laser scanning (ULS) [6]
We suggested a strategy to fix the initial solution based on the constellation of which the largest number of satellites is visible, regardless of which constellation they belong to
Summary
Most geomatic techniques (conventional traverse surveying and GNSS [1], LiDAR [2], photogrammetry [3], remote sensing [4], geographical information systems [5]) have been used, in recent years, to characterize forests’ and trees’ planimetric positions. Standard practice in establishing ALS-based models for estimating plot- or stand-level structural attributes often employs a two-stage sampling approach with in situ measurements frequently simultaneous with ALS acquisitions [48] This two-stage procedure can be cost-intensive and time-consuming, especially in forests characterized by complex structure and topography. The topography often includes steep slopes, deep valleys, peaks, and rock outcrops These conditions occur in Mediterranean oldgrowth forests located in remote and impervious mountain areas [49–51] characterized by low levels of accessibility. Our specific objectives were to implement a methodology (1) to determine a representative plot size, given the spatial variability of the heights of the trees, and (2) to estimate a sampling area allowing the structural attributes to be characterized with a satisfactory level of accuracy. We suggested a strategy to fix the initial solution based on the constellation of which the largest number of satellites is visible, regardless of which constellation they belong to
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