Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction

Xuelian Meng,Chunyan Li,Xiaomin Qiu,Kaiguang Zhao,Xukai Zhang,Eddie Weeks,Nan Shang

doi:10.3390/rs9111187

Xuelian Meng, Chunyan Li + Show 5 more

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https://doi.org/10.3390/rs9111187

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Abstract

Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by ~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments.

Highlights

Accurate topographic mapping is critical for various environmental applications in many low-lying coasts including inter-tidal zones as elevation affects hydrodynamics and vegetation distributions [1]
The direct output of digital terrain model (DTM) from the unmanned aerial vehicle (UAV) mapping software showed significant overestimation of topography under dense vegetation with a mean error of 1.305 m, which were most obvious in the red zone on the southwest side of the sand berm dominated by tall spartina alterniflora
The results demonstrated that the direct terrain mapping result from the UAV mapping software has mean errors of 0.302 m for low vegetation (RMSE = 0.342 m) and 1.305 m for tall vegetation (RMSE = 1.399 m) and mean absolute errors of 0.302 m for low vegetation and 1.306 m for tall vegetation

Summary

Introduction

Accurate topographic mapping is critical for various environmental applications in many low-lying coasts including inter-tidal zones as elevation affects hydrodynamics and vegetation distributions [1]. Since many airborne LiDAR missions collect data during winter seasons for better laser penetration when many vegetation species die off or have sparser and flagging conditions, terrain mapping in seasons or wetlands types with fuller vegetation biomass will produce even lower accuracies. These studies prove the existence of severe errors in current coastal topographic mapping, which will have significant impacts on broad applications such as wetland habitat mapping [2], change monitoring [9], modeling of flood [17], inundation [10], storm surge, and sea level rise [17]

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