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

Erich Seifert,Thomas Seifert,Jan Van Aardt,David Drew,Holger Vogt,Anton Kunneke,Stefan Seifert

doi:10.3390/rs11101252

Erich Seifert, Thomas Seifert + Show 5 more

Open Access

https://doi.org/10.3390/rs11101252

Copy DOI

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.

Highlights

IntroductionThe application of drone-based remote sensing is an emerging technology, increasingly used in environmental and forestry applications where trees are of specific interest [1,2]
The number of points in the dense point cloud regressed over the number of points in the sparse point cloud (Figure 4a) showed a tight positive relation, which approached a horizontal asymptote. This means that higher tie point numbers in the sparse point cloud will lead to higher detail in the dense point cloud reconstruction, which supports our choice of selecting tie numbers as a surrogate for reconstruction detail
The results show that sensor resolution and forward overlap have the strongest influence on the standardised RMSRE (SRMSRE)

Summary

Introduction

The application of drone-based remote sensing is an emerging technology, increasingly used in environmental and forestry applications where trees are of specific interest [1,2]. Precision forestry, or spatially-explicit forest management, is arguably an important driver of technology development for drone-based remote sensing of forests. A large portfolio of technologies for data acquisition are applied in the context of precision forestry. Part of this portfolio are remote sensing systems on terrestrial, airborne, and space-borne platforms [1,2,4,5,6,7]. The application of drones for photogrammetric applications is considered economically viable for smaller areas, generally up to about 100 ha [9]

Objectives

Methods

Results

Discussion

Conclusion