Abstract
Methods for accurately measuring biophysical parameters are a key component for quantitative evaluation regarding to various forest applications. Conventional in situ measurements of these parameters take time and expense, encountering difficultness at locations with heterogeneous microtopography. To obtain precise biophysical data in such situations, we deployed an unmanned aerial system (UAS) multirotor drone in a cypress forest in a mountainous area of Japan. The structure from motion (SfM) method was used to construct a three-dimensional (3D) model of the forest (tree) structures from aerial photos. Tree height was estimated from the 3D model and compared to in situ ground data. We also analyzed the relationships between a biophysical parameter, diameter at breast height (DBH), of individual trees with canopy width and area measured from orthorectified images. Despite the constraints of ground exposure in a highly dense forest area, tree height was estimated at an accuracy of root mean square error = 1.712 m for observed tree heights ranging from 16 to 24 m. DBH was highly correlated with canopy width (R2 = 0.7786) and canopy area (R2 = 0.7923), where DBH ranged from 11 to 58 cm. The results of estimating forest parameters indicate that drone-based remote-sensing methods can be utilized to accurately analyze the spatial extent of forest structures.
Highlights
Collecting accurate forest inventory information faster and more efficiently is one of the concerns and challenges of forest management [1]
Wallace et al [29] used the Lasground tool in Lastools to compute digital terrain model (DTM) data; this tool uses a similar procedure to that provided in Photoscan, in which initial points are selected to generate a triangular irregular network (TIN) surface, and adjustments are made for additional points with set parameters
The computed 3D model of the forest structure and the terrain information led to the development of a satisfactory canopy height model (CHM) of the area indicating tree height information
Summary
Collecting accurate forest inventory information faster and more efficiently is one of the concerns and challenges of forest management [1]. There is a high demand for improved measurement methods for use in such programs as but not limited to Reducing Emissions from Deforestation and Forest Degradation (REDD+; [2]), forests as carbon sinks for national CO2 reduction plan [3] and for meeting the challenges of modeling future climate scenarios by integrating terrestrial biogeochemical feedback [4]. By collecting accurate forest data, we can better estimate and interpret forest dimensions to evaluate various forest ecosystem services, such as biodiversity [5], carbon stocks and CO2 uptake [6,7], and aboveground biomass (AGB; [8])
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