Abstract
The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps on digital aerial photogrammetry (DAP) processing and the ability of its products to provide point clouds for forestry inventory. For this, we used 18 combinations of flight settings for data acquisition, and a nationwide airborne laser scanning (ALS) dataset as reference data. Linear regression was applied for modeling DAP quality indicators and model fitting quality as the function of flight settings; equivalence tests compared DAP- and ALS-products. Most of DAP-Digital Terrain Models (DTM) showed a moderate to high agreement (R2 > 0.70) when fitted to ALS-based models; nine models had a regression slope within the 1% region of equivalence. The best DAP-Canopy Height Model (CHM) was generated using ALS-DTM with an R2 = 0.42 when compared with ALS-CHM, indicating reduced similarity. Altogether, our results suggest that the optimal combination of flight settings should include a 90% lateral overlap, a 70% longitudinal overlap, and a minimum altitude of 120 m above ground level, independent of the availability of an ALS-derived DTM for height normalization. We also provided insights into the effects of flight settings on DAP outputs for future applications in similar forest stands, emphasizing the benefits of overlaps for comprehensive scene reconstruction and altitude for canopy surface detection.
Highlights
Our study aimed to evaluate the effects of flight settings on the quality of digital aerial photogrammetry (DAP) processing, and the ability of resultant processing products to reproduce airborne laser scanning (ALS) information
The disagreement about the effects of texture and the lack of detection of canopy points in flights lower than 100 m suggests that there is an optimal ALT for the adequate reconstruction of this layer, in which textural homogenization is positive for the identification of common features in image dataset and for the addition of texture to canopy surface by matching ground sampling distance (GSD) and the scale of features; our results indicate that this optimal ALT value is around 120 m above ground level
We have assessed the effects of flight settings on processing results and the quality of DAP products, comparing its equivalence to an ALS point cloud
Summary
Remote sensing (RS) includes the use of both active and passive sensing technologies for the measurement of surface characteristics from a distance [1,2]. Given its variable spatial scale, systematic acquisition schedule of some platforms, and diversity of sensors, RS data have been applied in various topics, such as land cover processes and atmospheric, hydrologic, oceanographic, and especially forest studies [1,3,4,5]. In this context, RS applications for forests are complementary to traditional frameworks for data collection, minimizing spatial and cost limitations of the latter approach, and offer opportunities for accurate landscape-scale estimation of forestry inventory variables [6,7].
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