Influence of Drone Altitude, Image Overlap, and Optical Sensor Resolution on Multi-View Reconstruction of Forest Images

Abstract

Recent technical advances in drones make them increasingly relevant and important tools for forest measurements. However, information on how to optimally set flight parameters and choose sensor resolution is lagging behind the technical developments. Our study aims to address this gap, exploring the effects of drone flight parameters (altitude, image overlap, and sensor resolution) on image reconstruction and successful 3D point extraction. This study was conducted using video footage obtained from flights at several altitudes, sampled for images at varying frequencies to obtain forward overlap ratios ranging between 91 and 99%. Artificial reduction of image resolution was used to simulate sensor resolutions between 0.3 and 8.3 Megapixels (Mpx). The resulting data matrix was analysed using commercial multi-view reconstruction (MVG) software to understand the effects of drone variables on (1) reconstruction detail and precision, (2) flight times of the drone, and (3) reconstruction times during data processing. The correlations between variables were statistically analysed with a multivariate generalised additive model (GAM), based on a tensor spline smoother to construct response surfaces. Flight time was linearly related to altitude, while processing time was mainly influenced by altitude and forward overlap, which in turn changed the number of images processed. Low flight altitudes yielded the highest reconstruction details and best precision, particularly in combination with high image overlaps. Interestingly, this effect was nonlinear and not directly related to increased sensor resolution at higher altitudes. We suggest that image geometry and high image frequency enable the MVG algorithm to identify more points on the silhouettes of tree crowns. Our results are some of the first estimates of reasonable value ranges for flight parameter selection for forestry applications.

Current Practices in UAS-based Environmental Monitoring

“…The same applies to the dependency of optimal overlap configuration from the UAS sensor type. Similarly, [60] examined the connection between image overlap, the processing time, and low altitude flights, but the images were extracted from compressed MPEG-4 video as JPEG or PNG image files. This approach enables extremely high forward overlap (up to 98.8%) to be achieved without an increase in flying time, and proved to be beneficial in low altitude flights (15-30 m above the canopy).…”

“…For instance, thermal surveys (which generally contain much less feature information than optical images), often require overlaps at least of 80% [51]. Therefore, this upper end of the image overlap range is adopted in cases where a high level of accuracy is required [56,57,59], while the lowest overlaps, for rapid observation with low accuracy requirements, should not be less than 55% [60]. Some studies have also found that interactions between flight collection variables, including flight altitude, image overlap, flying direction, speed and solar elevation affect UAS data quality and the subsequent processing results, emphasizing the need for flight planning optimization [61].…”

Assessing the Influence of UAV Altitude on Extracted Biophysical Parameters of Young Oil Palm

“…Usually, before conducting aerial surveys, several parameters need to be optimized. These include flight altitude, image overlap, speed, resolution, and area of coverage [22]. Logically, higher flight altitude captures a smaller number of images with lower ground sample distance (GSD) because of the broad field of view of the camera sensors onboard the UAV.…”

Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes

“…To date, a number of recent studies have confirmed the great potential of UAS photogrammetry in forest inventory, at both individual tree- [16,18] and plot-levels [14,[40][41][42][43][44][45]. In general, the quality of the obtained forest inventory data depends on the quality and accuracy of the UAS products (e.g., PCs, DSMs) and DTM, while the quality of the UAS products depends on the image block orientation method applied to UAV data, among other factors such as flight characteristics and target properties [62].…”