Detecting Invasive Insects with Unmanned Aerial Vehicles

A key aspect to controlling and reducing the effects invasive insect species have on agriculture is to obtain knowledge about the migration patterns of these species. Current state-of-the-art methods of studying these migration patterns involve a mark-release-recapture technique, in which insects are released after being marked and researchers attempt to recapture them later. However, this approach involves a human researcher manually searching for these insects in large fields and results in very low recapture rates. In this paper, we propose an automated system for detecting released insects using an unmanned aerial vehicle. This system utilizes ultraviolet lighting technology, digital cameras, and lightweight computer vision algorithms to more quickly and accurately detect insects compared to the current state of the art. The efficiency and accuracy that this system provides will allow for a more comprehensive understanding of invasive insect species migration patterns. Our experimental results demonstrate that our system can detect real target insects in field conditions with high precision and recall rates.Comment: IEEE ICRA 2019. 7 page

Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Forest inventory information is a principle driver for forest management decisions. Information gathered through these inventories provides a summary of the condition of forested stands. The method by which remote sensing aids land managers is changing rapidly. Imagery produced from unmanned aerial systems (UAS) offer high temporal and spatial resolutions to small-scale forest management. UAS imagery is less expensive and easier to coordinate to meet project needs compared to traditional manned aerial imagery. This study focused on producing an efficient and approachable work flow for producing forest stand board volume estimates from UAS imagery in mixed hardwood stands of West Virginia. A supplementary aim of this project was to evaluate which season was best to collect imagery for forest inventory. True color imagery was collected with a DJI Phantom 3 Professional UAS and was processed in Agisoft Photoscan Professional. Automated tree crown segmentation was performed with Trimble eCognition Developer’s multi-resolution segmentation function with manual optimization of parameters through an iterative process. Individual tree volume metrics were derived from field data relationships and volume estimates were processed in EZ CRUZ forest inventory software. The software, at best, correctly segmented 43% of the individual tree crowns. No correlation between season of imagery acquisition and quality of segmentation was shown. Volume and other stand characteristics were not accurately estimated and were faulted by poor segmentation. However, the imagery was able to capture gaps consistently and provide a visualization of forest health. Difficulties, successes and time required for these procedures were thoroughly noted

Detecting Invasive Insects Using Unmanned Aerial Vehicles

A key aspect to controlling and reducing the effects invasive insect specieshave on agriculture is to obtain knowledge about the migration patterns of thesespecies. Current state-of-the-art methods of studying these migration patternsinvolve a mark-release-recapture technique, in which insects are released afterbeing marked and researchers attempt to recapture them later. However, thisapproach involves a human researcher manually searching for these insects inlarge fields and results in very low recapture rates. This thesis proposes anautomated system for detecting released insects using an unmanned aerialvehicle. Our system utilizes ultraviolet lighting technology, digital cameras, andlightweight computer vision algorithms to more quickly and accurately detectinsects compared to the state of the art. The efficiency and accuracy that thissystem provides will allow for a more comprehensive understanding of invasiveinsect species migration patterns. Our experimental results demonstrate that oursystem can detect real target insects in field conditions with high precision andrecall rates. Additionally, insect GPS coordinates can be localized using an imagereprojection algorithm, resulting in a generated map of the test field with insectlocations

An Agave Counting Methodology Based on Mathematical Morphology and Images Acquired through Unmanned Aerial Vehicles

Blue agave is an important commercial crop in Mexico, and it is the main source of the traditional mexican beverage known as tequila. The variety of blue agave crop known as Tequilana Weber is a crucial element for tequila agribusiness and the agricultural economy in Mexico. The number of agave plants in the field is one of the main parameters for estimating production of tequila. In this manuscript, we describe a mathematical morphology-based algorithm that addresses the agave automatic counting task. The proposed methodology was applied to a set of real images collected using an Unmanned Aerial Vehicle equipped with a digital Red-Green-Blue (RGB) camera. The number of plants automatically identified in the collected images was compared to the number of plants counted by hand. Accuracy of the proposed algorithm depended on the size heterogeneity of plants in the field and illumination. Accuracy ranged from 0.8309 to 0.9806, and performance of the proposed algorithm was satisfactory.This research was supported by the Spanish Ministerio de Economía y Competitividad, contract TIN2015-64395-R (MINECO/FEDER, UE), as well as by the Basque Government, contract IT900-16. This work was also supported in part by CONACYT (Mexico), grant 258033

Individual Tree Measurements From Three-Dimensional Point Clouds

This study develops and tests novel methodologies for measuring the attributes of individual trees from three-dimensional point clouds generated from an aerial platform. Recently, advancements in technology have allowed for the acquisition of very high resolution three-dimensional point clouds that can be used to map the forest in a virtual environment. These point clouds can be interpreted to produce valuable forest attributes across entire landscapes with minimal field labor, which can then aid forest managers in their planning and decision making. Biometrics derived from point clouds are often generated on a plot level, with estimates spanning many meters (rather than at the scale of individual the individual tree), a process known as area-based estimation. As the resolution of point clouds has increased however, the structural attributes of individual trees can now be distinguished and measured, which allows for tree lists including species and size metrics for individual trees. This information can be of great use to forester managers; thus, it is essential that proper methods be developed for measuring these trees. To this end, an algorithm called layer stacking, was developed to isolate points representing the shapes of individual trees from a Light Detection and Ranging (LiDAR) derived point cloud, a process called segmentation. The validity of this algorithm was assessed in a variety of forest stand types, and comparisons were made to another popular tree segmentation algorithm (i.e., watershed delineation). Results indicated that when compared to watershed delineation, layer stacking produced similar or improved detection rates in almost all forest stands, and excelled in deciduous forests, which have traditionally been challenging to segment. The algorithm was then implemented on a large scale, for individual measurements on over 200,000 trees. The species and diameter of each tree was predicted via modeling from structural and reflectance characteristics, and allometric equations were used to obtain volume and carbon content of each tree. These estimates were then compared to measurements taken in the field, and to area-based estimates. Results indicated improved accuracy of plot level basal area, volume, and carbon estimation over traditional area-based estimation, as well as moderately reliable individual tree estimates, and highly reliable species identification. Finally, because LiDAR point clouds can be expensive to acquire, point clouds generated from aerial photos via structure-from-motion (SfM) reconstruction were evaluated for their accuracy at a tree level. An analysis between tree height measurements obtained by SfM, SfM in conjunction with LiDAR, LiDAR alone, digital stereo-photo interpretation, and field measurements was conducted. Results indicated no difference between SfM in conjunction with LiDAR and LiDAR alone. We concluded that SfM represents a valid low cost means of producing a point cloud dense enough to measure individual trees. Thus, high resolution point clouds can be used to generate forest inventories containing a number of valuable biometrics, such as tree height, species, volume, biomass, and carbon mass. Such estimates may allow for the automatic development of large-scale, detailed, and precise forest inventories without the cost, effort, and safety concerns associated with extensive field inventories

Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019

Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. The main adopted image-based technologies are RGB, multispectral, and thermal infrared. LiDAR sensors are becoming commonly used to improve the estimation of relevant plant traits. In comparison with other permanent ecosystems, forests are particularly affected by climatic changes due to the longevity of the trees, and the primary objective is the conservation and protection of forests. Nevertheless, forestry and agriculture involve the cultivation of renewable raw materials, with the difference that forestry is less tied to economic aspects and this is reflected by the delay in using new monitoring technologies. The main forestry applications are aimed toward inventory of resources, map diseases, species classification, fire monitoring, and spatial gap estimation. This Special Issue focuses on new technologies (UAV and sensors) and innovative data elaboration methodologies (object recognition and machine vision) for applications in forestry

Improving Measurement of Forest Structural Parameters by Co-Registering of High Resolution Aerial Imagery and Low Density LiDAR Data

Forest structural parameters, such as tree height and crown width, are indispensable for evaluating forest biomass or forest volume. LiDAR is a revolutionary technology for measurement of forest structural parameters, however, the accuracy of crown width extraction is not satisfactory when using a low density LiDAR, especially in high canopy cover forest. We used high resolution aerial imagery with a low density LiDAR system to overcome this shortcoming. A morphological filtering was used to generate a DEM (Digital Elevation Model) and a CHM (Canopy Height Model) from LiDAR data. The LiDAR camera image is matched to the aerial image with an automated keypoints search algorithm. As a result, a high registration accuracy of 0.5 pixels was obtained. A local maximum filter, watershed segmentation, and object-oriented image segmentation are used to obtain tree height and crown width. Results indicate that the camera data collected by the integrated LiDAR system plays an important role in registration with aerial imagery. The synthesis with aerial imagery increases the accuracy of forest structural parameter extraction when compared to only using the low density LiDAR data

A multi-plot assessment of vegetation structure using a micro-unmanned aerial system (UAS) in a semi-arid savanna environment.

Unmanned Aerial Systems (UAS) have emerged as a capable platform for measuring vegetation health, structure and productivity. Products derived from UAS imagery typically have much finer spatial resolutions than traditional satellite or aircraft imagery, allowing the spectral and structural heterogeneity of vegetation to be mapped and monitored with more detail. This study uses UAS-captured imagery from the Chobe Enclave of northern Botswana. Flights were conducted across a gradient of savanna sites classified as grass-, shrub-, or tree-dominated. We compare multiple approaches for extracting woody vegetation structure from UAS imagery and assess correlations between in situ field measurements and UAS estimates. Sensor types were also compared, to determine whether multispectral data improves estimates of vegetation structure at the expense of spatial resolution. We found that leveraging multispectral reflectance information aids in crown delineation, areal estimates, and fractional cover of woody and non-woody vegetation within the study area. Comparisons are made between two crown delineation techniques, and the efficacy of each technique within savanna environments is discussed. The methods presented hold potential to inform field sampling protocols and UAS-based techniques for autonomous crown delineation in future dryland systems research. These findings advance research for field and remote sensing analyses assessing degradation in heterogeneous landscapes where varying vegetation structure has implications on land use and land functions