Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non-forest ecosystems.

Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R 2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1-10 ha-1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.

Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges.

This study evaluated the opportunities and challenges of using drones to obtain multispectral orthomosaics at ultra-high resolution that could be useful for monitoring large and heterogeneous burned areas. We conducted a survey using an octocopter equipped with a Parrot SEQUOIA multispectral camera in a 3000 ha framework located within the perimeter of a megafire in Spain. We assessed the quality of both the camera raw imagery and the multispectral orthomosaic obtained, as well as the required processing capability. Additionally, we compared the spatial information provided by the drone orthomosaic at ultra-high spatial resolution with another image provided by the WorldView-2 satellite at high spatial resolution. The drone raw imagery presented some anomalies, such as horizontal banding noise and non-homogeneous radiometry. Camera locations showed a lack of synchrony of the single frequency GPS receiver. The georeferencing process based on ground control points achieved an error lower than 30 cm in X-Y and lower than 55 cm in Z. The drone orthomosaic provided more information in terms of spatial variability in heterogeneous burned areas in comparison with the WorldView-2 satellite imagery. The drone orthomosaic could constitute a viable alternative for the evaluation of post-fire vegetation regeneration in large and heterogeneous burned areas.

Automatic Detection and Classification of Pole-Like Objects for Urban Cartography Using Mobile Laser Scanning Data.

Mobile laser scanning (MLS) is a modern and powerful technology capable of obtaining massive point clouds of objects in a short period of time. Although this technology is nowadays being widely applied in urban cartography and 3D city modelling, it has some drawbacks that need to be avoided in order to strengthen it. One of the most important shortcomings of MLS data is concerned with the fact that it provides an unstructured dataset whose processing is very time-consuming. Consequently, there is a growing interest in developing algorithms for the automatic extraction of useful information from MLS point clouds. This work is focused on establishing a methodology and developing an algorithm to detect pole-like objects and classify them into several categories using MLS datasets. The developed procedure starts with the discretization of the point cloud by means of a voxelization, in order to simplify and reduce the processing time in the segmentation process. In turn, a heuristic segmentation algorithm was developed to detect pole-like objects in the MLS point cloud. Finally, two supervised classification algorithms, linear discriminant analysis and support vector machines, were used to distinguish between the different types of poles in the point cloud. The predictors are the principal component eigenvalues obtained from the Cartesian coordinates of the laser points, the range of the Z coordinate, and some shape-related indexes. The performance of the method was tested in an urban area with 123 poles of different categories. Very encouraging results were obtained, since the accuracy rate was over 90%.

Geometric stability and lens decentering in compact digital cameras.

A study on the geometric stability and decentering present in sensor-lens systems of six identical compact digital cameras has been conducted. With regard to geometrical stability, the variation of internal geometry parameters (principal distance, principal point position and distortion parameters) was considered. With regard to lens decentering, the amount of radial and tangential displacement resulting from decentering distortion was related with the precision of the camera and with the offset of the principal point from the geometric center of the sensor. The study was conducted with data obtained after 372 calibration processes (62 per camera). The tests were performed for each camera in three situations: during continuous use of the cameras, after camera power off/on and after the full extension and retraction of the zoom-lens. Additionally, 360 new calibrations were performed in order to study the variation of the internal geometry when the camera is rotated. The aim of this study was to relate the level of stability and decentering in a camera with the precision and quality that can be obtained. An additional goal was to provide practical recommendations about photogrammetric use of such cameras.

Metric Potential of a 3D Measurement System Based on Digital Compact Cameras.

This paper presents an optical measuring system based on low cost, high resolution digital cameras. Once the cameras are synchronised, the portable and adjustable system can be used to observe living beings, bodies in motion, or deformations of very different sizes. Each of the cameras has been modelled individually and studied with regard to the photogrammetric potential of the system. We have investigated the photogrammetric precision obtained from the crossing of rays, the repeatability of results, and the accuracy of the coordinates obtained. Systematic and random errors are identified in validity assessment of the definition of the precision of the system from crossing of rays or from marking residuals in images. The results have clearly demonstrated the capability of a low-cost multiple-camera system to measure with sub-millimetre precision.