Urban-Tree-Attribute Update Using Multisource Single-Tree Inventory

Peer reviewe

Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning

Peer reviewe

Metsien kartoitus ja seuranta aktiivisella 3D-kaukokartoituksella

The main aim in forest mapping and monitoring is to produce accurate information for forest managers with the use of efficient methodologies. For example, it is important to locate harvesting sites and stands where forest operations should be carried out as well as to provide updates regarding forest growth, among other changes in forest structure. In recent years, remote sensing (RS) has taken a significant technological leap forward. It has become possible to acquire three-dimensional (3D), spatially accurate information from forest resources using active RS methods. In practical applications, mainly 3D information produced by airborne laser scanning (ALS) has opened up groundbreaking potential in natural resource mapping and monitoring. In addition to ALS, new satellite radars are also capable of acquiring spatially accurate 3D information. The main objectives of the present study were to develop 3D RS methodologies for large-area forest mapping and monitoring applications. In substudy I, we aim to map harvesting sites, while in substudy II, we monitor changes in the forest canopy structure. In studies III-V, efficient mapping and monitoring applications were developed and tested. In substudy I, we predicted plot-level thinning maturity within the next 10-year planning period. Stands requiring immediate thinning were located with an overall accuracy of 83%-86% depending on the prediction method applied. The respective prediction accuracy for stands reaching thinning maturity within the next 10 years was 70%-79%. Substudy II addressed natural disturbance monitoring that could be linked to forest management planning when an ALS time series is available. The accuracy of the damaged canopy cover area estimate varied between -16.4% to 5.4%. Substudy II showed that changes in the forest canopy structure can be monitored with a rather straightforward method by contrasting bi-temporal canopy height models. In substudy III, we developed a RS-based forest inventory method where single-tree RS is used to acquire modelling data needed in area-based predictions. The method uses ALS data and is capable of producing accurate stand variable estimates even at the sub-compartment level. The developed method could be applied in areas with sparse road networks or when the costs of fieldwork must be minimized. The method is especially suitable for large-area biomass or stem volume mapping. Based on substudy IV, the use of stereo synthetic aperture radar (SAR) satellite data in the prediction of plot-level forest variables appears to be promising for large-area applications. In the best case, the plot-level stem volume (VOL) was predicted with a relative error (RMSE%) of 34.9%. Typically, such a high level of prediction accuracy cannot be obtained using spaceborne RS data. Then, in substudy V, we compared the aboveground biomass and VOL estimates derived by radargrammetry to the ALS estimates. The difference between the estimation accuracy of ALS based and TerraSAR X based features was smaller than in any previous study in which ALS and different kinds of SAR materials have been compared. In this thesis, forest mapping and monitoring applications using active 3D RS were developed. Spatially accurate 3D RS enables the mapping of harvesting sites, the monitoring of changes in the canopy structure and even the making of a fully RS-based forest inventory. ALS is carried out at relatively low altitudes, which makes it relatively expensive per area unit, and other RS materials are still needed. Spaceborne stereo radargrammetry proved to be a promising technique to acquire additional 3D RS data efficiently as long as an accurate digital terrain model is available as a ground-surface reference.Metsien kartoitus ja seuranta aktiivisella 3D-kaukokartoituksella. Metsävaroista kerätään mahdollisimman tarkkaa tietoa metsänomistajan päätöksenteon tueksi. Tietoa kerätään puustotunnusten lisäksi toimenpidekohteista ja metsässä tapahtuvista muutoksista, kuten kasvusta ja luonnontuhoista. Laajojen metsäalueiden kartoituksessa käytetään apuna lentokoneesta tai satelliiteista tehtävää kaukokartoitusta. Metsien kaukokartoitus on viime vuosina ottanut merkittävän kehitysaskeleen, kun aktiiviset 3D-kaukokartoitusmenetelmät ovat yleistyneet. Aktiivisessa kaukokartoituksessa, kuten laserkeilauksessa ja tutkakuvauksessa instrumentti vastaanottaa lähettämäänsä säteilyä. Laserkeilaus tuottaa kohteesta 3D-havaintoja, jotka metsäalueilla kuvaavat suoraan puuston pituutta ja metsän tiheyttä. Laserkeilauksella kohteesta saadaan tällä hetkellä tyypillisesti 0,5−20 havaintoa/m2. Laserkeilaus tehdään lentokoneesta 500−3000 m korkeudesta, jolloin aineiston hankinta laajoilta alueilta on kallista verrattuna satelliittikuviin. Myös satelliittitutkakuvilta voidaan tuottaa spatiaalisesti tarkkaa 3D-tietoa, jonka pistetiheys on tosin huomattavasti harvempaa kuin laserkeilauksella. Tutkimuksessa kehitettiin sovelluksia metsien kartoitukseen ja seurantaan hyödyntäen aktiivisia 3D-kaukokartoitusmenetelmiä. Metsiköiden toimenpidetarvetta ennustettiin onnistuneesti laserkeilausaineiston avulla. Harvennettaviksi luokitellut metsiköt pystyttiin kartoittamaan 70%−86% tarkkuudella. Kahden ajankohdan laserkeilausaineistoja käytettiin lumituhojen vuoksi vaurioituneiden puiden kartoittamiseen. Tuhoutuneen latvuspinta-alan kartoitus perustui laserkeilausaineistosta tuotettujen latvusmallien erotuskuviin. Kehitetty menetelmä soveltuu latvusrakenteessa tapahtuneiden muutosten, kuten lumi- ja tuulituhojen, kartoittamiseen ja seurantaan. Laajojen metsäalueiden kartoitus perustuu yleensä kaksivaiheeseen inventointimenetelmään, jossa käytetään maastomittauksia ja tiedon yleistyksessä kaukokartoitusaineistoa. Kartoitusta voidaan tehostaa joko maastomittauksia vähentämällä tai hyödyntämällä mahdollisimman halpaa kaukokartoitusaineistoa. Tutkimuksessa kehitettiin täysin kaukokartoitukseen perustuva kaksivaiheinen metsien inventointimenetelmä. Tarvittava maastotieto mitattiin suoraan laserkeilausaineistosta. Menetelmä soveltuu puuston tilavuuden tai biomassan kartoitukseen erityisesti alueille, joilla maastomittausten kustannukset ovat merkittävät. Satelliittitutkakuvat ovat potentiaalinen aineisto etenkin laajojen alueiden metsävarojen seurannassa. Synteettisen apertuurin tutka (SAR)-stereokuvilta mitattiin automaattisesti 3D-pisteitä, joita käytettiin puustotunnusten ennustamisessa. Keskitilavuus ennustettiin parhaimmillaan lähes samalla tarkkuudella kuin laserkeilauksella. Tutkimus osoitti aktiivisen 3D-kaukokartoitustiedon mahdollistavan entistä yksityiskohtaisemman metsien kartoituksen ja seurannan

Single Tree Detection from Airborne Laser Scanning Data: A Stochastic Approach

Characterizing and monitoring forests are of great scientific and managerial interests, such as understanding the global carbon circle, biodiversity conservation and management of natural resources. As an alternative or compliment to traditional remote sensing techniques, airborne laser scanning (ALS) has been placed in a very advantageous position in forest studies, for its unique ability to directly measure the distribution of vegetation materials in the vertical direction, as well as the terrain beneath the forest canopy. Serving as basis for tree-wise forest biophysical parameter and species information retrieval, single tree detection is a very motivating research topic in forest inventory. The objective of the study is to develop a method from the perspective of computer vision to detect single trees automatically from ALS data. For this purpose, this study explored different aspects of the problem. It starts from an improved pipeline for canopy height model (CHM) generation, which alleviates the distortion of tree crown shapes presented on CHMs resulted from conventional procedures due to the shadow effects of ALS data and produces pit-free CHM. The single tree detection method consists of a hybrid framework which integrates low-level image processing techniques, i.e. local maxima filtering (LM) and marker-controlled watershed segmentation (MCWS), into a high-level probabilistic model. In the proposed approach, tree crowns in the forest plot are modelled as a configuration of circular objects. The configuration containing the best possible set of detected tree objects is estimated by a global optimization solver in a probabilistic framework. The model features an accelerated optimization process compared with classical stochastic models, e.g. marked point processes. The parameter estimation is another issue: the study investigated both a reference-based supervised and an Expectation-Maximization (EM) based unsupervised method to estimate the parameters in the model. The model was tested in a temperate mature coniferous forest in Ontario, Canada, as well as simulated coniferous forest plots with various degrees of crown overlap. The experimental results showed the effectiveness of our proposed method, which was capable of reducing the commission errors produced by local maxima filtering based methods, thus increasing the overall detection accuracy by approximately 10% on all of the datasets

Spatio-temporal and structural analysis of vegetation dynamics of Lowveld Savanna in South Africa

Savanna vegetation structure parameters are important for assessing the biomes status under various disturbance scenarios. Despite free availability remote sensing data, the use of optical remote sensing data for savanna vegetation structure mapping is limited by sparse and heterogeneous distribution of vegetation canopy. Cloud and aerosol contamination lead to inconsistency in the availability of time series data necessary for continuous vegetation monitoring, especially in the tropics. Long- and medium wavelength microwave data such as synthetic aperture radar (SAR), with their low sensitivity to clouds and atmospheric aerosols, and high temporal and spatial resolution solves these problems. Studies utilising remote sensing data for vegetation monitoring on the other hand, lack quality reference data. This study explores the potential of high-resolution TLS-derived vegetation structure variables as reference to multi-temporal SAR datasets in savanna vegetation monitoring. The overall objectives of this study are: (i) to evaluate the potential of high-resolution TLS-data in extraction of savanna vegetation structure variables; (ii) to estimate landscape-wide aboveground biomass (AGB) and assess changes over four years using multi-temporal L-band SAR within a Lowveld savanna in Kruger National Park; and (iii) to assess interactions between C-band SAR with various savanna vegetation structure variables. Field inventories and TLS campaign were carried out in the wet and dry seasons of 2015 respectively, and provided reference data upon which AGB, CC and cover classes were modelled. L-band SAR modelled AGB was used for change analysis over 4 years, while multitemporal C-band SAR data was used to assess backscatter response to seasonal changes in CC and AGB abundant classes and cover classes. From the AGB change analysis, on average 36 ha of the study area (91 ha) experienced a loss in AGB above 5 t/ha over 4 years. A high backscatter intensity is observed on high abundance AGB, CC classes and large trees as opposed to low CC and AGB abundance classes and small trees. There is high response to all structure variables, with C-band VV showing best polarization in savanna vegetation mapping. Moisture availability in the wet season increases backscatter response from both canopy and background classes

Change detection of urban vegetation from terrestrial laser scanning and drone photogrammetry

Urban areas experience continuous transformations, impacting the urban vegetation, particularly urban trees. The expansion of urban landscapes directly impacts green spaces and vegetation within cities. Urban vegetation plays a crucial role in improving the urban environment, benefiting residents' well-being, air quality, and temperature regulation. Monitoring changes in urban vegetation is therefore essential, considering the environmental and well-being aspects. This study focuses on change detection using terrestrial laser scanning (TLS) and drone photogrammetry, utilizing three-dimensional (3D) point cloud data. Change detection compares multi-temporal datasets to analyze variations in a geographic region. TLS and drone photogrammetry techniques have gained popularity for monitoring urban vegetation, as they enable the acquisition of detailed 3D information. Point cloud data captures 3D information, enabling detailed change detection and 3D visualization of urban vegetation. This enhances the level of detail and information provided by the methodologies. The objective is to estimate the growth of urban vegetation in a specific area within Helsinki's Malminkartano region during the spring and fall seasons of 2022 using multi-temporal TLS, UAV photogrammetry, and their integration. The research examines the suitability of different point cloud datasets acquired with different sensors and parameters for change detection analysis, identifying potential differences, challenges, and proposed solutions. Three distinct methods, namely C2C, C2M, and M3C2 are employed for point cloud comparison. The results highlighted that manual processing is required to make the point cloud datasets comparable, with significant issues related to differences in point density and resolution. The sparser UAV photogrammetry datasets pose limitations on detailed analysis for change detection. The visual results reveal that TLS datasets detect changes in urban vegetation up to 2m, while UAV photogrammetry and integrated datasets up to 2.8m. However, applying a threshold at a 95% confidence level, 80-90% of significant changes in TLS datasets are observed up to 0.5m, up to 1m in UAV datasets, and up to 0.5m in integrated datasets. These changes represent the growth of urban vegetation during the leaf-off and leaf-on seasons examined. Overall, the utilized datasets provide valuable insights into changes in urban vegetation within the study area

Mobile Laser Scanning – System development, performance and applications

Osajulkaisut: Publication 1: Antero Kukko, Sanna Kaasalainen, and Paula Litkey. 2008. Effect of incidence angle on laser scanner intensity and surface data. Applied Optics, volume 47, number 7, pages 986-992. doi:10.1364/AO.47.000986 Publication 2: Antero Kukko and Juha Hyyppä. 2009. Small-footprint laser scanning simulator for system validation, error assessment, and algorithm development. Photogrammetric Engineering and Remote Sensing, volume 75, number 9, pages 1177-1189. Publication 3: Antero Kukko, Constantin-Octavian Andrei, Veli-Matti Salminen, Harri Kaartinen, Yuwei Chen, Petri Rönnholm, Hannu Hyyppä, Juha Hyyppä, Ruizhi Chen, Henrik Haggrén, Iisakki Kosonen, and Karel Čapek. 2007. Road environment mapping system of the Finnish Geodetic Institute - FGI ROAMER -. In: Petri Rönnholm, Hannu Hyyppä, and Juha Hyyppä (editors). Proceedings of the ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007. Espoo, Finland. 12-14 September 2007. International Society for Photogrammetry and Remote Sensing. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, volume 36, part 3 / W52, pages 241-247. ISSN 1682-1777. Publication 4: Antero Kukko, Harri Kaartinen, Juha Hyyppä, and Yuwei Chen. 2012. Multiplatform mobile laser scanning: Usability and performance. Sensors, volume 12, number 9, pages 11712-11733. doi:10.3390/s120911712 Publication 5: Harri Kaartinen, Juha Hyyppä, Antero Kukko, Anttoni Jaakkola, and Hannu Hyyppä. 2012. Benchmarking the performance of mobile laser scanning systems using a permanent test field. Sensors, volume 12, number 9, pages 12814-12835. doi:10.3390/s120912814 Publication 6: P. Alho, A. Kukko, H. Hyyppä, H. Kaartinen, J. Hyyppä, and A. Jaakkola. 2009. Application of boat-based laser scanning for river survey. Earth Surface Processes and Landforms, volume 34, number 13, pages 1831-1838. doi:10.1002/esp.1879 Publication 7: Matti Vaaja, Juha Hyyppä, Antero Kukko, Harri Kaartinen, Hannu Hyyppä, and Petteri Alho. 2011. Mapping topography changes and elevation accuracies using a mobile laser scanner. Remote Sensing, volume 3, number 3, pages 587-600. doi:10.3390/rs3030587Laser scanning is a surveying technique used for mapping topography, vegetation, urban areas and infrastructure, ice, and other targets of interest. Its application on a terrestrial mobile platform is a promising method for effectively collecting three-dimensional data for complex environments and for producing model information for location-based services necessitating rapidly collected and up-to-date data. Development of mobile laser scanning (MLS) systems for such purposes is presented in this study. Different aspects of this technology were analyzed in laboratory experiments, simulations and field tests, in order to understand their effects on the ranging, intensity and point cloud data, especially in terms of point distribution and accuracy. In order to validate the performance of the developed ROAMER and AKHKA MLS systems, various three-dimensional mapping tasks were performed during an international benchmarking test, as well as in the field in numerous projects. The results showed that the proposed systems can reliably provide accurate data. It has also been shown that the various modalities of the systems allow data acquisition in numerous application scenarios and environments not previously possible. MLS improves the data output compared to terrestrial laser scanning (TLS) and outperforms airborne laser scanning (ALS) in ranging precision and point density. As a result, MLS is well suited to fill the gap between these two previously dominant 3D data acquisition techniques.Laserkeilaus on mittaustekniikka, jota käytetään maaston topografian kasvillisuuden, rakennettujen alueiden, infrastruktuurin, jään ja muiden kohteiden kartoitukseen. Tekniikan soveltaminen liikkuvalle alustalle on lupaava menetelmä monimuotoisten ympäristöjen tehokkaaseen kolmiulotteiseen mittaamiseen ja mallinnustiedon tuottamiseen paikkatietopalveluihin, jotka edellyttävät tiedon nopeaa hankintaa ja ajantasaisuutta. Tässä tutkimuksessa kehitettiin liikkuvia laserkeilausjärjestelmiä (MLS). Eri tekijöiden vaikutuksia etäisyys- ja intensiteettihavaintoihin, pistejakaumaan ja tarkkuuteen selvitettiin laboratoriokokein, simuloimalla ja koetöin. Tutkimuksessa kehitettyjen ROAMER ja AKHKA MLS-järjestelmien suorituskykyä kolmiulotteisen mittaustiedon tuottamiseen erilaisissa kartoitustehtävissä tutkittiin kansainvälisessä vertailututkimuksessa kaupunkitestikentän avulla, mutta lisäksi käytännön sovelluksissa useassa eri projektissa. Tutkimuksen tulokset osoittavat, että kehitetyt MLS järjestelmät tuottavat tarkkaa tietoa luotettavasti. Järjestelmien monikäyttöisyys mahdollistaa aineistonhankinnan eri sovellustapauksissa ja ympäristöissä tavalla, joka ei ole aikaisemmin ollut mahdollista. Liikkuva laserkeilaus parantaa merkittävästi mittauksen tehokkuutta maalaserkeilaukseen verrattuna, ja ylittää lentolaserkeilauksen suorituskyvyn etäisyysmittauksen tarkkuudessa ja pistetiheydessä. Liikkuva laserkeilaus tarjoaakin näitä kahta aikaisemmin vallitsevaa 3D-mittausteknologiaa hyvin täydentävän kartoitusmenetelmän