High-resolution Unmanned Aerial Vehicles Imagery Research Articles

Determining tree height and crown diameter from high-resolution UAV imagery

ABSTRACTAdvances in computer vision and the parallel development of unmanned aerial vehicles (UAVs) allow for the extensive use of UAV in forest inventory and in indirect measurements of tree features. We used UAV-sensed high-resolution imagery through photogrammetry and Structure from Motion (SfM) to estimate tree heights and crown diameters. We reconstructed 3D structures from 2D image sequences for two study areas (25 × 25 m). Species composition for Plot 1 included Norway spruce (Picea abies L.) together with European larch (Larix decidua Mill.) and Scots pine (Pinus sylvestris L.), whereas Plot 2 was mainly Norway spruce and Scots pine together with scattered individuals of European larch and Silver birch (Betula pendula Roth.). The involved workflow used canopy height models (CHMs) for the extraction of height, the smoothing of raster images for the determination of the local maxima, and Inverse Watershed Segmentation (IWS) for the estimation of the crown diameters with the help of a geographical information system (GIS). Finally, we validated the accuracies of the two methods by comparing the UAV results with ground measurements. The results showed higher agreement between field and remote-sensed data for heights than for crown diameters based on RMSE%, which were in the range 11.42–12.62 for height and 14.29–18.56 for crown diameter. Overall, the accuracy of the results was acceptable and showed that the methods were feasible for detecting tree heights and crown diameter.

Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery

Sorghum halepense (johnsongrass) is a perennial weed with a vegetative reproductive system and one of the most competitive weeds in maize showing a spatial distribution in compact patches. When maize is irrigated, successive weed emergences occur in the early phenological phases of the crop, which require several herbicide applications. Our aim was to provide an accurate tool for an early detection and mapping of johnsongrass patches and delineate the actual surface area requiring a site-specific herbicide treatment based on the weed coverage. This early detection represents a major challenge in actual field scenarios because both species are in the Poaceae family, and show analogous spectral patterns, an extraordinarily similar appearance and a parallel phenological evolution. To solve this, an automatic OBIA (object-based-image-analysis) procedure was developed to be applied on orthomosaicked images using visible (red-green-blue bands) and multispectral (red-green-blue and near infrared bands) cameras collected by an unmanned aerial vehicle (UAV) that flew at altitudes of 30, 60 and 100 m on two maize fields. One of our first phases was the generation of accurate orthomosaicked images of an herbaceous crop such as maize, which presented a repetitive pattern and nearly no invariant parameters to conduct the aerotriangulation. Here, we show that high-quality orthomosaicks were produced from both cameras and that they were able to be the first step for mapping the johnsongrass patches. The most accurate weed maps were obtained using the multispectral camera at an altitude of 30 m in both fields. These maps were then used to design a site-specific weed management program, and we demonstrated that potential herbicide savings ranged from 85 to 96 %. Our results showed that accurate and timely maps of johnsongrass patches in maize can be a key element in achieving site-specific and sustainable herbicide applications for reducing spraying herbicides and costs.

Short Communication. Using high resolution UAV imagery to estimate tree variables in Pinus pinea plantation in Portugal

Aim of study: The study aims to analyse the potential use of low‑cost unmanned aerial vehicle (UAV) imagery for the estimation of Pinus pinea L. variables at the individual tree level (position, tree height and crown diameter).Area of study: This study was conducted under the PINEA project focused on 16 ha of umbrella pine afforestation (Portugal) subjected to different treatments.Material and methods: The workflow involved: a) image acquisition with consumer‑grade cameras on board an UAV; b) orthomosaic and digital surface model (DSM) generation using structure-from-motion (SfM) image reconstruction; and c) automatic individual tree segmentation by using a mixed pixel‑ and region‑based based algorithm.Main results: The results of individual tree segmentation (position, height and crown diameter) were validated using field measurements from 3 inventory plots in the study area. All the trees of the plots were correctly detected. The RMSE values for the predicted heights and crown widths were 0.45 m and 0.63 m, respectively.Research highlights: The results demonstrate that tree variables can be automatically extracted from high resolution imagery. We highlight the use of UAV systems as a fast, reliable and cost‑effective technique for small scale applications.Keywords: Unmanned aerial systems (UAS); forest inventory; tree crown variables; 3D image modelling; canopy height model (CHM); object‑based image analysis (OBIA), structure‑from‑motion (SfM). ERRATUM PDF

Cultivated land information extraction from high-resolution unmanned aerial vehicle imagery data

The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. Simultaneously, unmanned aerial vehicles (UAVs) have been increasingly used for natural resource applications in recent years as a result of their greater availability, the miniaturization of sensors, and the ability to deploy UAVs relatively quickly and repeatedly at low altitudes. We examine the potential of utilizing a small UAV for the characterization, assessment, and monitoring of cultivated land. Because most UAV images lack spectral information, we propose a novel cultivated land information extraction method based on a triangulation for cultivated land information extraction (TCLE) method. Thus, the information on more spatial properties of a region is incorporated into the classification process. The TCLE comprises three main steps: image segmentation, triangulation construction, and triangulation clustering using AUTOCLUST. Experiments were conducted on three UAV images in Deyang, China, using TCLE and eCognition for cultivated land information extraction (ECLE). Experimental results show that TCLE, which does not require training samples and has a much higher level of automation, can obtain accuracies equivalent to ECLE. Comparing with ECLE, TCLE also extracts coherent cultivated land with much less noise. As such, cultivated land information extraction based on high-resolution UAV images can be effectively and efficiently conducted using the proposed method.