Luke Wallace

Development of a UAV-LiDAR System with Application to Forest Inventory

We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m2) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points perm2, to 0.15mwhen the higher density data was used. Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.

Direct Georeferencing of Ultrahigh-Resolution UAV Imagery

Micro-unmanned aerial vehicles often collect a large amount of images when mapping an area at an ultrahigh resolution. A direct georeferencing technique potentially eliminates the need for ground control points. In this paper, we developed a camera-global positioning system (GPS) module to allow the synchronization of camera exposure with the airframes position as recorded by a GPS with 10-20-cm accuracy. Lever arm corrections were applied to the camera positions to account for the positional difference between the GPS antenna and the camera center. Image selection algorithms were implemented to eliminate blurry images and images with excessive overlap. This study compared three different software methods (Photoscan, Pix4D web service, and an in-house Bundler method). We evaluated each based on processing time, ease of use, and the spatial accuracy of the final mosaic produced. Photoscan showed the best performance as it was the fastest and the easiest to use and had the best spatial accuracy (average error of 0.11 m with a standard deviation of 0.02 m). This accuracy is limited by the accuracy of the differential GPS unit (10-20 cm) used to record camera position. Pix4D achieved a mean spatial error of 0.24 m with a standard deviation of 0.03 m, while the Bundler method had the worst mean spatial accuracy of 0.76 m with a standard deviation of 0.15 m. The lower performance of the Bundler method was due to its poor performance in estimating camera focal length, which, in turn, introduced large errors in the Z-axis for the translation equations.

HyperUAS-Imaging Spectroscopy from a Multirotor Unmanned Aircraft System

One of the key advantages of a low-flying unmanned aircraft system UAS is its ability to acquire digital images at an ultrahigh spatial resolution of a few centimeters. Remote sensing of quantitative biochemical and biophysical characteristics of small-sized spatially fragmented vegetation canopies requires, however, not only high spatial, but also high spectral i.e., hyperspectral resolution. In this paper, we describe the design, development, airborne operations, calibration, processing, and interpretation of image data collected with a new hyperspectral unmanned aircraft system HyperUAS. HyperUAS is a remotely controlled multirotor prototype carrying onboard a lightweight pushbroom spectroradiometer coupled with a dual frequency GPS and an inertial movement unit. The prototype was built to remotely acquire imaging spectroscopy data of 324 spectral bands 162 bands in a spectrally binned mode with bandwidths between 4 and 5i¾?nm at an ultrahigh spatial resolution of 2-5i¾?cm. Three field airborne experiments, conducted over agricultural crops and over natural ecosystems of Antarctic mosses, proved operability of the system in standard field conditions, but also in a remote and harsh, low-temperature environment of East Antarctica. Experimental results demonstrate that HyperUAS is capable of delivering georeferenced maps of quantitative biochemical and biophysical variables of vegetation and of actual vegetation health state at an unprecedented spatial resolution of 5i¾?cm.

Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data

Light detection and Ranging (LiDAR) is becoming an increasingly used tool to support decision-making processes within forest operations. Area-based methods that derive information on the condition of a forest based on the distribution of points within the canopy have been proven to produce reliable and consistent results. Individual tree-based methods, however, are not yet used operationally in the industry. This is due to problems in detecting and delineating individual trees under varying forest conditions resulting in an underestimation of the stem count and biases toward larger trees. The aim of this paper is to use high-resolution LiDAR data captured from a small multirotor unmanned aerial vehicle platform to determine the influence of the detection algorithm and point density on the accuracy of tree detection and delineation. The study was conducted in a four-year-old Eucalyptus globulus stand representing an important stage of growth for forest management decision-making process. Five different tree detection routines were implemented, which delineate trees directly from the point cloud, voxel space, and the canopy height model (CHM). The results suggest that both algorithm and point density are important considerations in the accuracy of the detection and delineation of individual trees. The best performing method that utilized both the CHM and the original point cloud was able to correctly detect 98% of the trees in the study area. Increases in point density (from 5 to 50 points/m2) lead to significant improvements (of up to 8%) in the rate of omission for algorithms that made use of the high density of the data.

An Assessment of the Repeatability of Automatic Forest Inventory Metrics Derived From UAV-Borne Laser Scanning Data

We assessed the reproducibility of forest inventory metrics derived from an unmanned aerial vehicle (UAV) laser scanning (UAVLS) system. A total of 82 merged point clouds were captured over six 500-m2 plots within a Eucalyptus globulus plantation forest in Tasmania, Australia. Terrain and understory height, together with plot- and tree-level metrics, were extracted from the UAVLS point clouds using automated methods and compared across the multiple point clouds. The results show that measurements of terrain and understory height and plot-level metrics can be reproduced with adequate repeatability for change detection purposes. At the tree level, the high-density data collected by the UAV provided estimates of tree location (mean deviation (MD) of less than 0.48 m) and tree height (MD of 0.35 m) with high precision. This precision is comparable to that of ground-based field measurement techniques. The estimates of crown area and crown volume were found to be dependent on the segmentation routine and, as such, were measured with lower repeatability. The precision of the metrics found within this paper demonstrates the applicability of UAVs as a platform for performing sample-based forest inventories.

Forestry applications of UAVs in Europe: a review

ABSTRACT Unmanned aerial vehicles (UAVs) or remotely piloted aircraft systems are new platforms that have been increasingly used over the last decade in Europe to collect data for forest research, thanks to the miniaturization and cost reduction of GPS receivers, inertial navigation system, computers, and, most of all, sensors for remote sensing. In this review, after describing the regulatory framework for the operation of UAVs in the European Union (EU), an overview of applications in forest research is presented, followed by a discussion of the results obtained from the analysis of different case studies. Rotary-wing and fixed-wing UAVs are equally distributed among the case studies, while ready-to-fly solutions are preferred over self-designed and developed UAVs. Most adopted technologies are visible-red, green, and blue, multispectral in visible and near-infrared, middle-infrared, thermal infrared imagery, and lidar. The majority of current UAV-based applications for forest research aim to inventory resources, map diseases, classify species, monitor fire and its effects, quantify spatial gaps, and estimate post-harvest soil displacement. Successful implementation of UAVs in forestry depends on UAV features, such as flexibility of use in flight planning, low cost, reliability and autonomy, and capability of timely provision of high-resolution data. Unfortunately, the fragmented regulations among EU countries, a result of the lack of common rules for operating UAVs in Europe, limit the chance to operate within Europe’s boundaries and prevent research mobility and exchange opportunities. Nevertheless, the applications of UAVs are expanding in different domains, and the use of UAVs in forestry will increase, possibly leading to a regular utilization for small-scale monitoring purposes in Europe when recent technologies (i.e. hyperspectral imagery and lidar) and methodological approaches will be consolidated.

Detecting pruning of individual stems using Airborne Laser Scanning data captured from an Unmanned Aerial Vehicle

Modern forest management involves implementing optimal pruning regimes. These regimes aim to achieve the highest quality timber in the shortest possible rotation period. Although a valuable addition to forest management activities, tracking the application of these treatments in the field to ensure best practice management is not economically viable. This paper describes the use of Airborne Laser Scanner (ALS) data to track the rate of pruning in a Eucalyptus globulus stand. Data is obtained from an Unmanned Aerial Vehicle (UAV) and we describe automated processing routines that provide a cost-effective alternative to field sampling. We manually prune a 500 m2 plot to 2.5 m above the ground at rates of between 160 and 660 stems/ha. Utilising the high density ALS data, we first derived crown base height (CBH) with an RMSE of 0.60 m at each stage of pruning. Variability in the measurement of CBH resulted in both false positive (mean rate of 11%) and false negative detection (3.5%), however, detected rates of pruning of between 96% and 125% of the actual rate of pruning were achieved. The successful automated detection of pruning within this study highlights the suitability of UAV laser scanning as a cost-effective tool for monitoring forest management activities.

Development of a Multi-Spatial Resolution Approach to the Surveillance of Active Fire Lines Using Himawari-8

Satellite remote sensing is regularly used for wildfire detection, fire severity mapping and burnt area mapping. Applications in the surveillance of wildfire using geostationary-based sensors have been limited by low spatial resolutions. With the launch in 2015 of the AHI (Advanced Himawari Imaginer) sensor on board Himawari-8, ten-minute interval imagery is available covering an entire earth hemisphere across East Asia and Australasia. Existing active fire detection algorithms depend on middle infrared (MIR) and thermal infrared (TIR) channels to detect fire. Even though sub-pixel fire detection algorithms can detect much smaller fires, the location of the fire within the AHI 2 × 2 km (400 ha) MIR/TIR pixel is unknown. This limits the application of AHI as a wildfire surveillance and tracking sensor. A new multi-spatial resolution approach is presented in this paper that utilizes the available medium resolution channels in AHI. The proposed algorithm is able to map firelines at a 500 m resolution. This is achieved using near infrared (NIR) (1 km) and RED (500 m) data to detect burnt area and smoke within the flagged MIR (2 km) pixel. Initial results based on three case studies carried out in Western Australia shows that the algorithm was able to continuously track fires during the day at 500 m resolution. The results also demonstrate the utility for wildfire management activities.

Assessing the stability of canopy maps produced from UAV-LiDAR data

The use of Unmanned Aerial Vehicles (UAVs) as a remote sensing platform offers a unique combination of high resolution data collected within relatively low cost targeted missions. This paper investigates the use of UAV-borne Light Detecting and Ranging (LIDAR) systems (UAVL) as a platform to gain knowledge of the canopy structure within forested environments. Repeat datasets were collected with a UAVL system over six Eucalyptus Globulus plots with varying levels of canopy cover. The relative stability of four metrics for estimating canopy structure, First Cover Index (FCI), Last Cover Index (LCI), a Grid based method (GCI) and an Alpha shape based method (ACI) were assessed using these repeat datasets. It is shown that the repeatability of the GCI metric is subject to variations in plot level point density (standard deviation of 4.06 %). Instabilities in the FCI (1.91 %) and LCI (2.28 %) metrics were found to be related to the properties of the sensor and the lasers interaction with the canopy. The ACI metric (1.86 %) was found to be the most stable.

Assessing metrics for estimating fire induced change in the forest understorey structure using terrestrial laser scanning

Quantifying post-fire effects in a forested landscape is important to ascertain burn severity, ecosystem recovery and post-fire hazard assessments and mitigation planning. Reporting of such post-fire effects assumes significance in fire-prone countries such as USA, Australia, Spain, Greece and Portugal where prescribed burns are routinely carried out. This paper describes the use of Terrestrial Laser Scanning (TLS) to estimate and map change in the forest understorey following a prescribed burn. Eighteen descriptive metrics are derived from bi-temporal TLS which are used to analyse and visualise change in a control and fire-altered plot. Metrics derived are Above Ground Height-based (AGH) percentiles and heights, point count and mean intensity. Metrics such as AGH50change, mean AGHchange and point countchange are sensitive enough to detect subtle fire-induced change (28%–52%) whilst observing little or no change in the control plot (0–4%). A qualitative examination with field measurements of the spatial distribution of burnt areas and percentage area burnt also show similar patterns. This study is novel in that it examines the behaviour of TLS metrics for estimating and mapping fire induced change in understorey structure in a single-scan mode with a minimal fixed reference system. Further, the TLS-derived metrics can be used to produce high resolution maps of change in the understorey landscape.