60 research outputs found

    UAVs for the Environmental Sciences

    Get PDF
    This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

    Remote Sensing Applications in Coastal Environment

    Get PDF
    Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments

    Airborne dual-wavelength waveform LiDAR improves species classification accuracy of boreal broadleaved and coniferous trees

    Get PDF
    Funding Information: This study was conducted on course FOR-254 ‘Advanced Forest Inventory and Management Project’ at the University of Helsinki. Plots IM and OG were measured by students and assistants on course FOR110B with the kind permission of Prof. Pauline Stenberg. Dr. Pekka Kaitaniemi provided phenological observations during LiDAR campaigns, and support by Dr. Antti Uotila was crucial in finding aspen, alder and larch samples in Hyytiälä. The LiDAR and field data in 2013 were collected and processed with funding from the Academy of Finland and Metsämiesten säätiö. Other work by made possible by the University of Helsinki. Publisher Copyright: © 2022, Finnish Society of Forest Science. All rights reserved.Tree species identification constitutes a bottleneck in remote sensing applications. Waveform LiDAR has been shown to offer potential over discrete-return observations, and we assessed if the combination of two-wavelength waveform data can lead to further improvements. A total of 2532 trees representing seven living and dead conifer and deciduous species classes found in Hyytiälä forests in southern Finland were included in the experiments. LiDAR data was acquired by two single-wavelength sensors. The 1064-nm and 1550-nm data were radiometrically corrected to enable range-normalization using the radar equation. Pulses were traced through the canopy, and by applying 3D crown models, the return waveforms were assigned to individual trees. Crown models and a terrain model enabled a further split of the waveforms to strata representing the crown, understory and ground segments. Different geometric and radiometric waveform attributes were extracted per return pulse and aggregated to tree-level mean and standard deviation features. We analyzed the effect of tree size on the features, the correlation between features and the between-species differences of the waveform features. Feature importance for species classification was derived using F-test and the Random Forest algorithm. Classification tests showed significant improvement in overall accuracy (74→83% with 7 classes, 88→91% with 4 classes) when the 1064-nm and 1550-nm features were merged. Most features were not invariant to tree size, and the dependencies differed between species and LiDAR wavelength. The differences were likely driven by factors such as bark reflectance, height growth induced structural changes near the treetop as well as foliage density in old trees.Peer reviewe

    Moniajalliset aaltomuotolaserpiirteet metsäpuissa – fenologian, puulajien ja skannausgeometrian vaikutus

    Get PDF
    Ilmalaserkeilauksella ”airborne LiDAR” (Light Detection and Ranging) tuotetaan korkearesoluutioista 3D-tietoa erittäin kustannustehokkaasti. Tämänhetkiset metsien inventointimenetelmät yhdistävät sekä LiDARin että passiivisen ilmakuvauksen. Mahdollisuus pelkän LiDARin käyttöön on erittäin houkutteleva, koska se johtaisi ainakin osittain kustannusten alenemiseen. Tässä tutkimuksessa keskitytään ns. täyden aaltomuodon havaintoihin, mitkä sisältävät enemmän tietoa lähetetystä ja vastaanotetusta signaalista kuin ’tavanomaiset’ pistepilvet. Tässä tutkimuksessa tarkastellaan metsän latvuston rakenteellisten ominaisuuksien ja LiDAR-signaalien välisiä riippuvuuksia ja pyritään lisäämään ymmärrystämme LiDARin ja kasvillisuuden välisistä vuorovaikutuksista ja tekijöistä, jotka rajoittavat nykyistä kykyä käyttää LiDAR-dataa mm. puulajitulkintaan, ja sitä, kuinka erilaisin prosessointi ja laskentamenetelmin voimme parantaa LiDARin tulkintaa metsässä. Tämän tutkimuksen tarkoituksena on ymmärtää, kuinka erilaisia aaltomuotopiirteitä voidaan tulkita ja kuinka piirteet käyttäytyvät muuttuvan fenologian mukaan. Tutkimusaineisto koostuu kolmesta peräkkäisestä LiDAR- ja ilmakuva kampanjasta, jotka on tehty alueella 38 kuukauden aikana sekä tämän ajanjakson aikana mitatuista maastoreferenssipuista. Käytössä on monen ajankohdan dataa, mikä koostuu kolmesta toistetusta laserkeilauksesta, jotka kaikki käyttivät samaa sensoria, lentoratoja ja keilausasetuksia. Koska LiDAR-havainnot ovat vertailukelpoisia ja samoista puista, voidaan ns. "puutekijää" tutkia ja vaihtelua aaltomuodon ominaisuuksien välillä toistuvissa keilauksissa seurata. Fenologiset muutokset ovat havaittavissa, koska aineistot sisältävät talven (lehdetön aika), alkukesän (alhainen lehtialaindeksi (LAI) havupuilla) ja loppukesän (täyslehti, korkea LAI). Myös skannauszeniittikulman (SZA) vaikutus aaltomuodon ominaisuuksiin ja piirteisiin otettiin huomioon, koska sama puu voitiin nähdä usealta lentolinjalta. Tulokset osoittavat, että huolellisella koeasettelulla on mahdollista havaita lajien sisäisiä ja lajien välisiä fenologisia eroja ja muutoksia moniajallisista aaltomuotopiirteistä. SZA:lla ei ollut merkittävää vaikutusta tuloksiin. Puulajiluokitus onnistui hyvin vaihtelevissa fenologisissa olosuhteissa ja erirakenteellisissa metsiköissä. Fenologiset muutokset olivat hyvin ilmeisiä kausivihannoilla puilla, mutta melko pieniä ainavihannilla havupuilla. Kokonaistarkkuudet puulajiluokituksessa olivat talvella 92 %, alkukesällä 88 % ja loppukesällä 84 % kasvatusmetsässä ja talvella 84 %, alkukesällä 81 % ja loppukesällä 83 % vanhassa puustossa. "puutekijän" osoitettiin olevan merkittävä. Lajien sisäinen varianssi johtuu pääasiassa puutekijästä eli lajinsisäinen ominaisuusvarianssi edustaa luonnollista vaihtelua saman lajin puiden välillä.Airborne LiDAR (Light Detection And Ranging) produces high-resolution and cost-efficient 3D data. Currently, forest inventories combine the use of both LiDAR and passive imaging by cameras, and the possibility of using LiDAR only is very tempting as it would lead to cost reduction. Focus of this study is on the full-waveform observations that extent the information content compared to conventional point clouds and are somewhat rarer to have access to. This study explores basic dependencies between structural canopy features and LiDAR signals over time and aims at augmenting our understanding of LiDAR-vegetation interactions and factors limiting our current ability to use pulsed LiDAR data for species detection, and how possibilities to overcome those limitations. Motivation is to understand how different waveform features can be interpreted and how the features behave over time with changing vegetation phenology. The study material consists of three consecutive LiDAR campaigns and aerial imaging surveys done in the area during a 38-month period and field reference trees that have been measured during this period. I use multi-temporal data that comprise three repeated acquisitions, which all applied same sensor, trajectories, as well as sensor and acquisition settings. As I had repeated LiDAR observations of the same trees where the acquisition settings are comparable, I could study the so-called ‘tree effect’ and overall co-variation between waveform features in the repeated acquisitions. Phenological changes are available as the data comprises winter (leaf-off), early summer (low LAI in conifers) and late summer data (full leaf, high LAI). The influence of scan zenith angle (SZA) on waveform features and attributes is also considered, as the same tree can be seen from multiple strips. The results showed that by using careful experimentation it is possible to detect intra- and interspecies phenological changes from multitemporal full-waveform data, while SZA did not have markable effect on the WF features. I was also able to perform well with the tree species classification task in varying phenological conditions. The phenological changes were very apparent on deciduous trees, but rather small on evergreen conifers. In a 45-year-old stand, the overall accuracies in tree species classification were 92, 87 and 88 % for winter, early summer, and late summer, respectively. These figures were 84, 81, and 83 % for in an old growth forest. The ‘tree effect’ was shown to be significant, i.e., many of the WF features of trees were correlated over time. The intra-species feature variance that is due to the tree effect represents natural variation between trees of the same species

    Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery

    Get PDF
    Large-scale monitoring of vegetation dynamics by remote sensing is key to detecting early signs of vegetation decline. Spectral-based indicators of phys-iological plant traits (PTs) have the potential to quantify variations in pho-tosynthetic pigments, chlorophyll fluorescence emission, and structural changes of vegetation as a function of stress. However, the specific response of PTs to disease-induced decline in heterogeneous canopies remains largely unknown, which is critical for the early detection of irreversible damage at different scales. Four specific objectives were defined in this research: i) to assess the feasibility of modelling the incidence and severity of Phytophthora cinnamomi and Xylella fastidiosa based on PTs and biophysical properties of vegetation; ii) to assess non-visual early indicators, iii) to retrieve PT using radiative transfer models (RTM), high-resolution imagery and satellite observations; and iv) to establish the basis for scaling up PTs at different spatial resolutions using RTM for their retrieval in different vegetation co-vers. This thesis integrates different approaches combining field data, air- and space-borne imagery, and physical and empirical models that allow the retrieval of indicators and the evaluation of each component’s contribution to understanding temporal variations of disease-induced symptoms in heter-ogeneous canopies. Furthermore, the effects associated with the understory are introduced, showing not only their impact but also providing a compre-hensive model to account for it. Consequently, a new methodology has been established to detect vegetation health processes and the influence of biotic and abiotic factors, considering different components of the canopy and their impact on the aggregated signal. It is expected that, using the presented methods, existing remote sensors and future developments, the ability to detect and assess vegetation health globally will have a substantial impact not only on socio-economic factors, but also on the preservation of our eco-system as a whole

    Remote Sensing

    Get PDF
    This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas
    corecore