Landscape scale mapping of tundra vegetation structure at ultra-high resolution using UAVs and computer vision

Ilmastomuutoksella on voimakkain vaikutus suurten leveysasteiden ekosysteemeissä, jotka ovat sopeutuneet viileään ilmastoon. Jotta suurella mittakaavalla havaittuja muutoksia tundrakasvillisuudessa ja niiden takaisinkytkentävaikutuksia ilmastoon voidaan ymmärtää ja ennustaa luotettavammin, on syytä tarkastella mitä tapahtuu pienellä mittakaavalla; jopa yksittäisissä kasveissa. Lähivuosikymmenten aikana tapahtunut teknologinen kehitys on mahdollistanut kustannustehokkaiden, kevyiden ja pienikokoisten miehittämättömien ilma-alusten (UAV) yleistymisen. Erittäin korkearesoluutioisten aineistojen (pikselikoko <10cm) lisääntyessä ja tullessa yhä helpommin saataville, ympäristön tarkastelussa käytetyt kaukokartoitusmenetelmät altistuvat paradigmanmuutokselle, kun konenäköön ja -oppimiseen perustuvat algoritmit ja analyysit yleistyvät. Menetelmien käyttöönotto on houkuttelevaa, koska ne mahdollistavat joustavan ja pitkälle automatisoidun aineistonkeruun ja erittäin tarkkojen kaukokartoitustuotteiden tuottamisen vaikeasti tavoitettavilta alueilta, kuten tundralla. Luotettavien tulosten saaminen vaatii kuitenkin huolellista suunnittelua sekä prosessointialgoritmien ja -parametrien pitkäjänteistä testaamista. Tässä tutkimuksessa tarkasteltiin, kuinka tarkasti tavallisella digitaalikameralla kerätyistä ilmakuvista johdetuilla muuttujilla voidaan kartoittaa kasvillisuuden rakennetta maisemamittakaavalla. Kilpisjärvellä Pohjois-Fennoskandiassa kerättiin dronella kolmensadan hehtaarin kokoiselta alueelta yhteensä noin 10 000 ilmakuvasta koostuva aineisto. Lisäksi alueella määritettiin 1183 pisteestä dominantti putkilokasvillisuus, sekä kasvillisuuden korkeus. Ilmakuvat prosessoitiin tiheiksi kolmiulotteisiksi pistepilviksi konenäköön ja fotogrammetriaan perustuvalla SfM (Structure from Motion) menetelmällä. Pistepilvien pohjalta interpoloitiin maastomalli sekä kasvillisuuden korkeusmalli. Lisäksi tuotettiin koko alueen kattava ilmakuvamosaiikki. Näiden aineistojen pohjalta laskettiin muuttujia, joita käytettiin yhdessä maastoreferenssiaineiston kanssa kasvillisuuden objektipohjaisessa analyysissä (GEOBIA, Geographical Object-Based Image Analysis). Suodatetut maanpintapisteet vastasivat luotettavasti todellista maanpinnan korkeutta koko alueella ja tuotetut korkeusmallit korreloivat voimakkaasti maastoreferenssiaineiston kanssa. Maastomallin virhe oli suurin alueilla, joilla oli korkeaa kasvillisuutta. Valaistusolosuhteissa ja kasvillisuudessa tapahtuneet muutokset ilmakuvien keruun aikana aiheuttivat haasteita objektipohjaisen analyysin molemmissa vaiheissa: segmentoinnissa ja luokittelussa. mutta kokonaistarkkuus parani 0,27:stä 0,,54:n kun luokitteluun lisättiin topografiaa, kasvillisuuden korkeutta ja tekstuuria kuvaavia muuttujia ja kohdeluokkien lukumäärää vähennettiin. Konenäköön ja –oppimiseen perustuvat menetelmät pystyvät tuottamaan tärkeää tietoa tundran kasvillisuuden rakenteesta, erityisesti kasvillisuuden korkeudesta, maisemassa. Lisää tutkimusta kuitenkin tarvitaan parhaiden algoritmien ja parametrien määrittämiseksi tundraympäristössä, jossa ympäristöolosuhteet muuttuvat nopeasti ja kasvillisuus on heterogeenistä ja sekoittunutta, mikä aiheuttaa eroja ilmakuvien välillä ja lisää vaikeuksia analyyseissä.Climate change has the strongest impact on high-latitude ecosystems that are adapted to cool climates. In order to better understand and predict the changes in tundra vegetation observed on large scales as well as their feedbacks onto climate, it is necessary to look at what is happening at finer scales; even in individual plants. Technological developments over the past few decades have enabled the spread of cost-effective, light and small unmanned aerial vehicles (UAVs). As very high-resolution data (pixel size <10cm) becomes more and more available, the remote sensing methods used in environmental analysis become subject to a paradigm shift as algorithms and analyzes based on machine vision and learning turn out to be more common. Harnessing new methods is attractive because they allow flexible and highly automated data collection and the production of highly accurate remote sensing products from hard-to-reach areas such as the tundra. However, obtaining reliable results requires careful planning and testing of processing algorithms and parameters. This study looked at how accurately variables derived from aerial images collected with an off-the-shelf digital camera can map the vegetation structure on a landscape scale. In Kilpisjärvi, northern Fennoscandia, a total of ~ 10,000 aerial photographs were collected by drone covering an area of three hundred hectares. In addition, dominant vascular plants were identified from 1183 points in the area, as well as vegetation height. Aerial images were processed into dense three-dimensional point clouds by using SfM (Structure from Motion) method, which is based on computer vision and digital photogrammetry. From the point clouds terrain models and vegetation height models were interpolated. In addition, image mosaic covering the entire area was produced. Based on these data, predictive variables were calculated, which were used together with the terrain reference data in Geographical Object-Based Image Analysis (GEOBIA). The filtered ground points corresponded to observations throughout the region, and the produced elevation models strongly correlated with the ground reference data. The terrain model error was greatest in areas with tall vegetation. Changes in lighting conditions and vegetation during aerial image surveys posed challenges in both phases of object-based analysis: segmentation and classification. but overall accuracy improved from 0.27 to 0.54 when topography, vegetation height and texture variables were added to the classifier and the number of target classes was reduced. Methods based on machine vision and learning can produce important information about vegetation structure, vegetation height, in a landscape. However, more research is needed to determine the best algorithms and parameters in a tundra environment where environmental conditions change rapidly and vegetation is heterogeneous and mixed, causing differences between aerial images and difficulties in analyses

GEOSPATIAL-BASED ENVIRONMENTAL MODELLING FOR COASTAL DUNE ZONE MANAGEMENT

Tomaintain biodiversity and ecological functionof coastal dune areas, itis important that practical and effective environmentalmanagemental strategies are developed. Advances in geospatial technologies offer a potentially very useful source of data for studies in this environment. This research project aimto developgeospatialdata-basedenvironmentalmodellingforcoastaldunecomplexestocontributetoeffectiveconservationstrategieswithparticularreferencetotheBuckroneydunecomplexinCo.Wicklow,Ireland.Theprojectconducteda general comparison ofdifferent geospatial data collection methodsfor topographic modelling of the Buckroney dune complex. These data collection methodsincludedsmall-scale survey data from aerial photogrammetry, optical satellite imagery, radar and LiDAR data, and ground-based, large-scale survey data from Total Station(TS), Real Time Kinematic (RTK) Global Positioning System(GPS), terrestrial laser scanners (TLS) and Unmanned Aircraft Systems (UAS).The results identifiedthe advantages and disadvantages of the respective technologies and demonstrated thatspatial data from high-end methods based on LiDAR, TLS and UAS technologiesenabled high-resolution and high-accuracy 3D datasetto be gathered quickly and relatively easily for the Buckroney dune complex. Analysis of the 3D topographic modelling based on LiDAR, TLS and UAS technologieshighlighted the efficacy of UAS technology, in particular,for 3D topographicmodellingof the study site.Theproject then exploredthe application of a UAS-mounted multispectral sensor for 3D vegetation mappingof the site. The Sequoia multispectral sensorused in this researchhas green, red, red-edge and near-infrared(NIR)wavebands, and a normal RGB sensor. The outcomesincludedan orthomosiac model, a 3D surface model and multispectral imageryof the study site. Nineclassification strategies were usedto examine the efficacyof UAS-IVmounted multispectral data for vegetation mapping. These strategies involved different band combinations based on the three multispectral bands from the RGB sensor, the four multispectral bands from the multispectral sensor and sixwidely used vegetation indices. There were 235 sample areas (1 m × 1 m) used for anaccuracy assessment of the classification of thevegetation mapping. The results showed vegetation type classification accuracies ranging from 52% to 75%. The resultdemonstrated that the addition of UAS-mounted multispectral data improvedthe classification accuracy of coastal vegetation mapping of the Buckroney dune complex

An analysis of multispectral unmanned aerial systems for saltmarsh foreshore land cover classification and digital elevation model generation

1 online resource (viii, 84 p.) : illustrations (chiefly colored)Includes abstract in English and French.Includes appendix.Includes bibliographical references (p. 60-66).Recent advances in Unmanned Aerial Systems (UAS), and increased affordability, have proliferated their use in the scientific community. Despite these innovations, UAS attempts to map a site’s true elevation using Structure from Motion Multi-View Stereo (SFM-MVS) software are obstructed by vegetative canopies, resulting in the production of a Digital Surface Model (DSM), rather than the desired Digital Elevation Model (DEM). This project seeks to account for the varying heights of vegetation communities within the Masstown East saltmarsh, producing DEMs for mudflat/saltmarsh landscapes with an accuracy comparable to that the DSM. DEM generation has been completed in two separate stages. The first stage consists of land cover classifications using UAS derived, radiometrically corrected data. Respective land cover classifications are assessed using confusion matrices. Secondly, surveyed canopy heights and function derived heights are subtracted from their respective classes, generating the DEMs. DEM validation has been performed by comparing topographic survey point values to those modeled, using the Root Square Mean Error (RMSE) measure. The project then compares the various parameters implemented for land cover classifications, and DEM accuracy. DEM generation methods were then coupled to produce a final DEM with a RMSE of 6cm. The results suggest consumer grade Multispectral UAS can produce DEMs with accuracies comparable to the initial DSMs generated, and thus merit further studies investigating their scientific capacities

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

Arctic tundra plant phenology and greenness across space and time

The Arctic is warming at twice the rate of the rest of the planet with dramatic consequences for Northern ecosystems. The rapid warming is predicted to cause shifts in plant phenology and increases in tundra vegetation productivity. Changes in phenology and productivity can have knock-on effects on key ecosystem functions. They directly influence plant-herbivore and plant-pollinator interactions creating the potential for mismatches and changes in food web structure, and they alter carbon and nutrient cycling, which in turn influence feedback mechanisms that couple the tundra biome with the global climate system. Improving our understanding of changes in tundra phenology and productivity is therefore critical to projecting not only the future state of Arctic ecosystems, but also the magnitude of potential feedbacks to global climate change. In this thesis, I combine observations from ground-based ecological monitoring, satellites and drones (also known as unmanned aerial vehicles or remotely piloted aircraft systems) to investigate how tundra plant phenology and productivity are changing across space and time, and to test how observational scales influences our ability to detect these changes. Spring plant phenology is tightly linked to temperatures, and advances in spring phenology are one of the most well documented effects of climate change on global biological systems. With rapid and near-ubiquitous Arctic warming, the absence of consistent trends in tundra spring phenology among sites suggests that additional environmental factors may exert important controls on tundra plant phenology. Indeed, further to temperature, snowmelt and sea-ice have been reported to strongly influence tundra phenology. Yet, the relative influence of these three factors has yet to be evaluated in a single cross-site analysis. In Chapter 2, I tested the importance of local average spring temperatures, local snowmelt and the timing of the drop in regional spring sea-ice extent as controls on variation in spring leaf out and flowering of 14 plant species from long-term records at four coastal sites in Arctic Alaska, Canada and Greenland. I found that spring phenology was best explained by snowmelt and spring temperature. In contrast to previous studies, sea-ice did not predict spring plant phenology at these study sites. This contrasting finding is likely explained by differences in the scale of the sea-ice measures employed. While many previous studies used descriptors of circum-polar sea-ice conditions that serve as aggregate measures for global weather conditions, I tested for the indirect effects of sea-ice conditions at a regional scale. My findings (re)emphasize the importance of snowmelt timing for tundra spring plant phenology and therefore highlight the localised nature of some of the key drivers of tundra vegetation change. Discrepancies between conventional scales of observation and underlying ecological processes could limit our ability to explain variation in tundra plant phenology and vegetation productivity. In the remote biome, ground-based monitoring is logistically challenging and restricted to comparably few sites and small plot sizes. Multispectral satellite observations cover the whole biome but are coarse in scale (tens of meters to kilometres) and uncertainties persist in how trends in vegetation indices like the Normalised Differential Vegetation Index (NDVI) relate to in situ ecological processes. Recent advances in drone technologies allow for the collection of multispectral fine-grain imagery at landscape level and have the potential to bridge the gap in observational scales. However, collecting high-quality multispectral drone imagery that is comparable across sensors, space and time remains challenging particularly when operating in extreme environments such as the tundra. In Chapter 3 of this thesis, I discuss the key error sources associated with solar angle, weather conditions, geolocation and radiometric calibration and estimate their relative contributions to the uncertainty of landscape level NDVI measurements at Qikiqtaruk in the Yukon Territory of Canada. My findings show that these errors can lead to uncertainties of greater than ± 10% in peak season NDVI, but also demonstrate they can be accounted for by improved flight planning, meta-data collection, ground control point deployment, use of reflectance targets and quality control. Satellite data suggest that vegetation productivity in the Arctic tundra has been increasing in recent decades: the tundra is greening. However, the observed trends show a lot of variation: although many parts of the tundra are greening, others show reductions in vegetation productivity (sometimes known as browning), and the satellite-based trends do not always match in situ records of change. Our ability to explain this variation has been limited by the coarse grain sizes of the satellite observations. In Chapter 4, I combined time-series of multispectral drone and satellite imagery (Sentinel 2 and MODIS) of coastal tundra plots at my focal study site Qikiqtaruk to quantify the correspondence among satellite and drone observations of vegetation productivity change across spatial scales. My findings show that NDVI estimates of tundra productivity collected with both platform types correspond well at landscape scales (10 m – 100 m) but demonstrate that the majority of spatial variation in NDVI at the study sites occurs at distances below 10 m and is therefore not captured by the latest generation of publicly available satellite products, like those of the Sentinel 2 satellites. I observed strong differences in mean estimates and variation of vegetation productivity between the dominant vegetation types at the field site. When comparing greening observations over two years, I detected differences in the amount of variation amongst years and a within-season decline in variation towards peak growing season for both years. These results suggest that not only the timing, but also the heterogeneity of tundra landscape phenology can vary within and among years, and if lowered by warming could alter trophic interactions between species. The findings presented in this thesis highlight the importance of the localised processes that influence large-scale patterns and trends in tundra vegetation phenology and productivity. Localised snowmelt timing best explained variation in tundra plant phenology and drone imagery revealed meter-scale heterogeneity in tundra productivity. Research that identifies the most relevant scales at which key biological processes occur is therefore critical to improving our forecasts of ecosystem change in the tundra and resulting feedbacks on the global climate system

Applications of unmanned aerial vehicles in cryosphere : Latest advances and prospects

Peer reviewedPublisher PD

Development and Evaluation of Unmanned Aerial Vehicles for High Throughput Phenotyping of Field-based Wheat Trials.

Growing demands for increased global yields are driving researchers to develop improved crops, capable of securing higher yields in the face of significant challenges including climate change and competition for resources. However, abilities to measure favourable physical characteristics (phenotypes) of key crops in response to these challenges is limited. For crop breeders and researchers, current abilities to phenotype field-based experiments with sufficient precision, resolution and throughput is restricting any meaningful advances in crop development. This PhD thesis presents work focused on the development and evaluation of Unmanned Aerial Vehicles (UAVs) in combination with remote sensing technologies as a solution for improved phenotyping of field-based crop experiments. Chapter 2 presents first, a review of specific target phenotypic traits within the categories of crop morphology and spectral reflectance, together with critical review of current standard measurement protocols. After reviewing phenotypic traits, focus turns to UAVs and UAV specific technologies suitable for the application of crop phenotyping, including critical evaluation of both the strengths and current limitations associated with UAV methods and technologies, highlighting specific areas for improvement. Chapter 3 presents a published paper successfully developing and evaluating Structure from Motion photogrammetry for accurate (R2 ≥ 0.93, RMSE ≤ 0.077m, and Bias ≤ -0.064m) and temporally consistent 3D reconstructions of wheat plot heights. The superior throughput achieved further facilitated measures of crop growth rate through the season; whilst very high spatial resolutions highlighted both the inter- and intra-plot variability in crop heights, something unachievable with the traditional manual ruler methods. Chapter 4 presents published work developing and evaluating modified Commercial ‘Off the Shelf’ (COTS) cameras for obtaining radiometrically calibrated imagery of canopy spectral reflectance. Specifically, development focussed on improving application of these cameras under variable illumination conditions, via application of camera exposure, vignetting, and irradiance corrections. Validation of UAV derived Normalised Difference Vegetation Index (NDVI) against a ground spectrometer from the COTS cameras (0.94 ≤ R2 ≥ 0.88) indicated successful calibration and correction of the cameras. The higher spatial resolution obtained from the COTS cameras, facilitated the assessment of the impact of background soil reflectance on derived mean Normalised Difference Vegetation Index (NDVI) measures of experimental plots, highlighting the impact of incomplete canopy on derived indices. Chapter 5 utilises the developed methods and cameras from Chapter 4 to assess the impact of nitrogen fertiliser application on the formation and senescence dynamics of canopy traits over multiple growing seasons. Quantification of changes in canopy reflectance, via NDVI, through three select trends in the wheat growth cycle were used to assess any impact of nitrogen on these periods of growth. Results showed consistent impact of zero nitrogen application on crop canopies within all three development phases. Additional results found statistically significant positive correlations between quantified phases and harvest metrics (e.g. final yield), with greatest correlations occurring within the second (Full Canopy) and third (Senescence) phases. Chapter 6 focuses on evaluation of the financial costs and throughput associated with UAVs; with specific focus on comparison to conventional methods in a real-world phenotyping scenario. A ‘cost throughput’ analysis based on real-world experiments at Rothamsted Research, provided quantitative assessment demonstrating both the financial savings (£4.11 per plot savings) and superior throughput obtained (229% faster) from implementing a UAV based phenotyping strategy to long term phenotyping of field-based experiments. Overall the methods and tools developed in this PhD thesis demonstrate UAVs combined with appropriate remote sensing tools can replicate and even surpass the precision, accuracy, cost and throughput of current strategies

Drone-based thermal remote sensing provides an effective new tool for monitoring the abundance of roosting fruit bats

Accurate and precise monitoring of species abundance is essential for determining population trends and responses to environmental change. However, traditional population survey methods can be unreliable and labour-intensive, which complicates the effective conservation and management of many threatened species. We developed a method of using drone-acquired thermal orthomosaics to monitor the abundance of grey-headed flying-foxes (Pteropus poliocephalus) within tree roosts, an IUCN Red Listed species of bat. We assessed the accuracy and precision of this new method and evaluated the performance of four semiautomated methods for counting flying-foxes in thermal orthomosaics, including machine learning and Computer Vision (CV) methods. We found a high concordance between the number of flying-foxes manually counted in drone-acquired thermal imagery and the true abundance of flying-foxes in single roost trees, as obtained from direct on-ground observation. This indicated that the number of flying-foxes observed in thermal imagery accurately reflected the true abundance of flying-foxes. In addition, for thermal orthomosaics of whole roost sites, the number of flying-foxes manually counted was highly repeatable between the same-day drone surveys and human counters, indicating that this method produced highly precise abundance estimates independent of the identity/experience of human counters. Finally, the number of flying-foxes manually counted in drone-acquired thermal orthomosaics was highly concordant with the counts derived from CV and machine learning-enabled classification techniques. This indicated that accurate and precise measures of colony abundance can be obtained semi-automatically, thus greatly reducing the amount of human effort involved for obtaining abundance estimates. Our method is thus valuable for reliably monitoring the abundance of individuals in flying-fox roosts and will aid in the conservation and management of this globally threatened group of flying-mammals, as well as other homeothermic arboreal-roosting species

Geomatics support to the metric documentation of the archaeological heritage. Tests and validations on the use of low-cost, rapid, image-based sensors and systems.

L'abstract è presente nell'allegato / the abstract is in the attachmen