Browsing by Subject "Remote sensing"

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  • Alekseev, Alexander; Tomppo, Erkki; McRoberts, Ronald E.; von Gadow, Klaus (2019)
    The State Forest Inventory (SFI) in the Russian Federation is a relatively new project that is little known in the English-language scientific literature. Following the stipulations of the Forest Act of 2006, the first SFI sample plots in this vast territory were established in 2007. The 34 Russian forest regions were the basic geographical units for all statistical estimates and served as a first-level stratification, while a second level was based on old inventory data and remotely sensed data. The sampling design was to consist of a simple random sample of 84,700 circular 500m(2) sample plots over forest land. Each sample plot consists of three nested concentric circular subplots with radii of 12.62, 5.64 and 2.82m and additional subplots for assessing and describing undergrowth, regeneration and ground vegetation. In total, 117 variables were to be measured or assessed on each plot.Although field work has begun, the methodology has elicited some criticism. The simple random sampling design is less efficient than a systematic design featuring sample plot clusters and a mix of temporary and permanent plots. The second-level stratification is mostly ineffective for increasing precision. Qualitative variables, which are not always essential, are dominant, while important quantitative variables are under-represented. Because of very slow progress, in 2018 the original plan was adjusted by reducing the number of permanent sample plots from 84,700 to 68,287 so that the first SFI cycle could be completed by 2020.
  • Alekseev, Alexander; Tomppo, Erkki; McRoberts, Ronald E.; von Gadow, Klaus (Springer Singapore, 2019)
    Abstract The State Forest Inventory (SFI) in the Russian Federation is a relatively new project that is little known in the English-language scientific literature. Following the stipulations of the Forest Act of 2006, the first SFI sample plots in this vast territory were established in 2007. The 34 Russian forest regions were the basic geographical units for all statistical estimates and served as a first-level stratification, while a second level was based on old inventory data and remotely sensed data. The sampling design was to consist of a simple random sample of 84,700 circular 500 m2 sample plots over forest land. Each sample plot consists of three nested concentric circular subplots with radii of 12.62, 5.64 and 2.82 m and additional subplots for assessing and describing undergrowth, regeneration and ground vegetation. In total, 117 variables were to be measured or assessed on each plot. Although field work has begun, the methodology has elicited some criticism. The simple random sampling design is less efficient than a systematic design featuring sample plot clusters and a mix of temporary and permanent plots. The second-level stratification is mostly ineffective for increasing precision. Qualitative variables, which are not always essential, are dominant, while important quantitative variables are under-represented. Because of very slow progress, in 2018 the original plan was adjusted by reducing the number of permanent sample plots from 84,700 to 68,287 so that the first SFI cycle could be completed by 2020.
  • Kivinen, Sonja; Koivisto, Elina; Keski-Saari, Sarita; Poikolainen, Laura; Tanhuanpää, Topi; Kuzmin, Anton; Viinikka, Arto; Heikkinen, Risto K; Virkkala, Raimo; Vihervaara, Petteri; Kumpula, Timo (2020)
    European aspen (Populus tremula L.) is a keystone species in boreal forests that are dominated by coniferous tree species. Both living and dead aspen trees contribute significantly to the species diversity of forest landscapes. Thus, spatial and temporal continuity of aspen is a prerequisite for the long-term persistence of viable populations of numerous aspen-associated species. In this review, we collate existing knowledge on the ecological role of European aspen, assess the knowledge needs for aspen occurrence patterns and dynamics in boreal forests and discuss the potential of different remote sensing techniques in mapping aspen at various spatiotemporal scales. The role of aspen as a key ecological feature has received significant attention, and studies have recognised the negative effects of modern forest management methods and heavy browsing on aspen occurrence and regeneration. However, the spatial knowledge of occurrence, abundance and temporal dynamics of aspen is scarce and incomprehensive. The remote sensing studies reviewed here highlight particularly the potential of three-dimensional data derived from airborne laser scanning or photogrammetric point clouds and airborne imaging spectroscopy in mapping European aspen, quaking aspen (Populus tremuloides Michx.) and other Populus species. In addition to tree species discrimination, these methods can provide information on biophysical, biochemical properties and even genetic diversity of aspen trees. Major obstacles in aspen detection using remote sensing are the low proportion and scattered occurrence of European aspen in boreal forests and the overlap of spectral and/or structural properties of European aspen and quaking aspen with some other tree species. Furthermore, the suitability of remote sensing data for aspen mapping and monitoring depends on the geographical coverage of data, the availability of multitemporal data and the costs of data acquisition. Our review highlights that integration of ecological knowledge with spatiotemporal information acquired by remote sensing is key to understanding the current and future distribution patterns of aspen-related biodiversity.
  • Tang, Zhipeng; Adhikari, Hari; Pellikka, Petri; Heiskanen, Janne (2021)
    Combined with increasing computing ability, the free and open access to Landsat archive has enabled the changes on the Earth’s surface to be monitored for almost 50 years. However, due to missing observations that result from clouds, cloud shadows, and scan line corrector failure, the Landsat data record is neither a continuous nor consistent time series. We present a new gap-filling method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), which uses spectral-temporal metrics computed from Landsat one-year time series and the k-Nearest Neighbor (k-NN) regression. Herein, we demonstrate the performance of MOPSTM by using five, nearly cloud-free, full scene Landsat images from Kenya, Finland, Germany, the USA, and China. Cloud masks from the images with extensive cloud cover were used to simulate large-area gaps, with the highest value we tested being 92% of missing data. The gap-filling accuracy was assessed quantitatively considering all five sites and different land use/land cover types, and the MOPSTM algorithm performed better than the spectral angle-mapper based spatiotemporal similarity (SAMSTS) gap-filling algorithm. The mean RMSE values of MOPSTM were 0.010, 0.012, 0.025, 0.012, and 0.018 for the five sites, while those of SAMSTS were 0.011, 0.017, 0.038, 0.014, and 0.023, respectively. Furthermore, MOPSTM had mean coefficient of determination (R2) values of 0.90, 0.86, 0.78, 0.92, and 0.89, which were higher than those for SAMSTS (0.84, 0.75, 0.55, 0.89, and 0.83). The performance of MOPSTM was not considerably affected by image gap sizes as images ranging from gap sizes of 51% of the image all the way to 92% of the image yielded similar gap-filling accuracy. Also, MOPSTM does not require local parametertuning except for the k values in the k-NN regression, and it can make a gap-free image from any acquisition date. MOPSTM provides a new spectral-temporal approach to generate the gap-free imagery for typical Landsat applications, such as land use, land cover, and forest monitoring.
  • Mingfeng, Wang; Jie, Su; Landy, Jack; Leppäranta, Matti; Lei, Guan (2020)
    Abstract Melt ponds occupy a large fraction of the Arctic sea ice surface during spring and summer. The fraction and distribution of melt ponds have considerable impacts on Arctic climate and ecosystem by reducing the albedo. There is an urgency to obtain improved accuracy and a wider coverage of melt pond fraction (MPF) data for studying these processes. MPF information has generally been acquired from optical imagery. Conventional MPF algorithms based on high-resolution optical sensors have treated melt ponds as features with constant reflectance; however, the spectral reflectance of ponds can vary greatly, even at a local scale. Here we use Sentinel-2 imagery to demonstrate those previous algorithms assuming fixed melt pond-reflectance greatly underestimate MPF. We propose a new algorithm (?LinearPolar?) based on the polar coordinate transformation that treats melt ponds as variable-reflectance features and calculates MPF across the vector between melt pond and bare ice axes. The angular coordinate ? of the polar coordinate system, which is only associated with pond fraction rather than reflectance, is used to determinate MPF. By comparing the new algorithm and previous methods with IceBridge optical imagery data, across a variety of Sentinel-2 images with melt ponds at various stages of development, we show that the RMSE value of the LinearPolar algorithm is about 30% lower than for the previous algorithms. Moreover, based on a sensitivity test, the new algorithm is also less sensitive to the subjective threshold for melt pond reflectance than previous algorithms.
  • Saponaro, Giulia (2020)
    Finnish Meteorological Institute Contributions 156
    Clouds play a vital role in Earth’s energy balance by modulating atmospheric processes, thus it is crucial to have accurate information on their spatial and temporal variability. Furthermore, clouds are relevant in those processes involved in aerosol-cloud-radiation interactions. The work conducted and presented herein concentrates on the retrievals of cloud properties, as well as their application for climate studies. While remote sensing observation systems have been used to analyze the atmosphere and observe its changes for the last decades, climate models predict how climate will change in the future. Altogether, these sources of observations are needed to better understand cloud processes and their impact on climate. In this thesis aerosol and cloud properties from the three above mentioned sources are applied to evaluate their potential in representing cloud properties and applicability in climate studies on local, regional and global scales. One aim of this thesis focuses on evaluating cloud parameters from ground-based remote-sensing sensors and from climate models using the MODerate Imaging Spectroradiometer (MODIS) data as a reference dataset. It is found that ground-based measurements of liquid clouds are in good agreement with MODIS cloud droplet size while poor correlation is found in the amount of cloud liquid water due to the management of drizzle. The comparison of the cloud diagnostic from three climate models with MODIS data, enabled through the application of a satellite simulator, helped to understand discrepancies among models, as well as discover deficiencies in their simulation processes. These findings are important to further improve the parametrization of atmospheric constituents in climate models, therefore enhancing the accuracy of climate projections. In this thesis it is also assessed the impact of aerosol particles on clouds. Satellite data can be used to derive climatically crucial quantities that are otherwise not directly retrieved (such as aerosol index and cloud droplet number concentration) which can be used to infer the sensitivity of clouds to aerosols changes. Results on the local and regional scales show that contrasting aerosol backgrounds indicate a higher sensitivity of clouds to aerosol changes in cleaner ambient air and a lower sensitivity in polluted areas, further corroborating the notion that anthropogenic emission modify clouds. On the global scale, the estimates of the aerosol-cloud interaction present, overall, a good agreement between the satellite- and model-based values which are in line with the results from other models.
  • Tanhuanpää, Topi; Kankare, Ville; Setälä, Heikki; Yli-Pelkonen, Vesa; Vastaranta, Mikko; Niemi, Mikko T.; Raisio, Juha; Holopainen, Markus (2017)
    Assessment of the amount of carbon sequestered and the value of ecosystem services provided by urban trees requires reliable data. Predicting the proportions and allometric relationships of individual urban trees with models developed for trees in rural forests may result in significant errors in biomass calculations. To better understand the differences in biomass accumulation and allocation between urban and rural trees, two existing biomass models for silver birch (Betula pendula Roth) were tested for their performance in assessing the above-ground biomass (AGB) of 12 urban trees. In addition, the performance of a volume-based method utilizing accurate terrestrial laser scanning (TLS) data and stem density was evaluated in assessing urban tree AGB. Both tested models underestimated the total AGB of single trees, which was mainly due to a substantial underestimation of branch biomass. The volume-based method produced the most accurate estimates of stem biomass. The results suggest that biomass models originally based on sample trees from rural forests should not be used for urban, open-grown trees, and that volume-based methods utilizing TLS data are a promising alternative for non-destructive assessment of urban tree AGB. (C) 2017 Elsevier GmbH. All rights reserved.
  • Vauhkonen, Jari; Ruotsalainen, Roope (2017)
    Determining optimal forest management to provide multiple goods and services, also referred to as Ecosystem Services (ESs), requires operational-scale information on the suitability of the forest for the provisioning of various ESs. Remote sensing allows wall-to-wall assessments and provides pixel data for a flexible composition of the management units. The purpose of this study was to incorporate models of ES provisioning potential in a spatial prioritization framework and to assess the pixel-level allocation of the land use. We tessellated the forested area in a landscape of altogether 7500 ha to 27,595 pixels of 48 x 48 m(2) and modeled the potential of each pixel to provide biodiversity, timber, carbon storage, and recreational amenities as indicators of supporting, provisioning, regulating, and cultural ESs, respectively. We analyzed spatial overlaps between the individual ESs, the potential to provide multiple ESs, and tradeoffs due to production constraints in a fraction of the landscape. The pixels considered most important for the individual ESs overlapped as much as 78% between carbon storage and timber production and up to 52.5% between the other ESs. The potential for multiple ESs could be largely explained in terms of forest structure as being emphasized to sparsely populated, spruce-dominated old forests with large average tree size. Constraining the production of the ESs in the landscape based on the priority maps, however, resulted in sub-optimal choices compared to an optimized production. Even though the land-use planning cannot be completed without involving the stakeholders' preferences, we conclude that the workflow described in this paper produced valuable information on the overlaps and tradeoffs of the ESs for the related decision support. (C) 2016 Elsevier B.V. All rights reserved.
  • Marbouti, Marjan; Antropov, Oleg; Eriksson, Patrick; Praks, Jaan; Arabzadeh, Vahid; Rinne, Eero; Leppäranta, Matti (IEEE, 2018)
    IEEE International Symposium on Geoscience and Remote Sensing IGARSS
    In this study, bistatic interferometric Synthetic Aperture Radar (InSAR) data acquired by the TanDEM-X mission were used for automated classification of sea ice over the Baltic Sea, in the Bothnic Bay. A scene acquired in March of 2012 was used in the study. Backscatter-intensity, coherence-magnitude and InSAR-phase, as well as their different combinations, were used as informative features in several classification approaches. In order to achieve the best discrimination between open water and several sea ice types (new ice, thin smooth ice, close ice, very close ice, ridged ice, heavily ridged ice and ship-track), Random Forests (RF) and Maximum likelihood (ML) classifiers were employed. The best overall accuracies were achieved using combination of backscatter-intensity & InSAR-phase and backscatter-intensity & coherence-magnitude, and were 76.86% and 75.81% with RF and ML classifiers, respectively. Overall, the combination of backscatter-intensity & InSAR-phase with RF classifier was suggested due to the highest overall accuracy (OA) and smaller computing time in comparison to ML. In contrast to several earlier studies, we were able to discriminate water and the thin smooth ice.
  • Heikkinen, J.E.P; Virtanen, T.; Huttunen, J.T.; Elsakov, V.; Martikainen, P.J. (American Geophysical Union, 2004)
    [1] We studied the carbon dioxide and methane fluxes from early June to mid-September 2001 in the Russian tundra of northeast Europe. Gas fluxes were measured with chamber techniques to determine the seasonal (100 days) carbon gas balance for terrestrial ecosystems representing various vegetation types. Also, the gas balance for aquatic ecosystems in the region was measured. The 2001 fluxes were compared to colder and wetter season fluxes from 1999. The Sphagnum sp. dominated peat plateau fen and Carex sp. and Sphagnum sp. dominated intermediate flarks were carbon sinks of 106 and 110 g C m2, respectively. In addition, methane emissions were highest from these sites. Other terrestrial surfaces lost carbon to the atmosphere (28-118 g C m2). The thermokarst lake and the river had seasonal carbon losses of 15 and 34 g C m2, respectively. For areal integration, the distributions of the various functional surfaces were classified based on Landsat TM satellite image and on-site validation. This data was used to integrate the carbon fluxes for the entire Lek Vorkuta catchment. The upscaling indicated that the catchment (114 km2) lost 4 (±3.5) Gg C to the atmosphere in summer 2001. The results suggest that predicted warming in the tundra region would induce a substantial loss of carbon. In the warm summer of 2001, the carbon gas released from the whole northeast European tundra (area 205,000 km2) was 8 Tg C when calculated from the Lek Vorkuta data.
  • Bowles, N. E.; Snodgrass, C.; Gibbings, A.; Sanchez, J. P.; Arnold, J. A.; Eccleston, P.; Andert, T.; Probst, A.; Naletto, G.; Vandaele, A. C.; de Leon, J.; Nathues, A.; Thomas, I. R.; Thomas, N.; Jorda, L.; Da Deppo, V.; Haack, H.; Green, S. F.; Carry, B.; Hanna, K. L. Donaldson; Jorgensen, J. Leif; Kereszturi, A.; DeMeo, F. E.; Patel, M. R.; Davies, J. K.; Clarke, F.; Kinch, K.; Guilbert-Lepoutre, A.; Agarwal, J.; Rivkin, A. S.; Pravec, P.; Fornasier, S.; Granvik, M.; Jones, R. H.; Murdoch, N.; Joy, K. H.; Pascale, E.; Tecza, M.; Barnes, J. M.; Licandro, J.; Greenhagen, B. T.; Calcutt, S. B.; Marriner, C. M.; Warren, T.; Tosh, I. (2018)
    CASTAway is a mission concept to explore our Solar System's main asteroid belt. Asteroids and comets provide a window into the formation and evolution of our Solar System and the composition of these objects can be inferred from space-based remote sensing using spectroscopic techniques. Variations in composition across the asteroid populations provide a tracer for the dynamical evolution of the Solar System. The mission combines a long-range (point source) telescopic survey of over 10,000 objects, targeted close encounters with 10-20 asteroids and serendipitous searches to constrain the distribution of smaller (e.g. 10 m) size objects into a single concept. With a carefully targeted trajectory that loops through the asteroid belt, CASTAway would provide a comprehensive survey of the main belt at multiple scales. The scientific payload comprises a 50 cm diameter telescope that includes an integrated low-resolution (R = 30-100) spectrometer and visible context imager, a thermal (e.g. 6-16 mu m) imager for use during the flybys, and modified star tracker cameras to detect small (similar to 10 m) asteroids. The CASTAway spacecraft and payload have high levels of technology readiness and are designed to fit within the programmatic and cost caps for a European Space Agency medium class mission, while delivering a significant increase in knowledge of our Solar System. (C) 2017 COSPAR. Published by Elsevier Ltd. All rights reserved.
  • Lämsä, Suvi (Helsingin yliopisto, 2021)
    Urban environments are constantly changing and expanding. They grow, evolve, and adapt to society and residents’ needs. Environmental changes have an impact also on urban green such as trees. This is because the increase of building stock and expanding cityscape will target these green spaces. However, the significance of those green spaces is understood as they have a positive impact on the residents’ well-being and health. For example, urban trees are known to improve the air quality and to provide mentally relaxing environments for residents. As this importance is emphasized, changes in the areas must be monitored, which increases the importance of the change detection studies. Change detection is a comparison of two or more datasets from the same area but at different times. Principally, changes have been detected with various remote sensing methods, such as aerial- and satellite images, but as airborne laser scanning technology and multi-temporal laser scanning datasets have become more common, the use of laser scanning data has also increased. The advantage of the laser scanning method is especially in its ability to produce three-dimensional information of the area. Therefore, also vertical properties can be studied. The method’s advantage is its ability to detect changes in urban tree cover as well as in tree height. The aim of this study was to investigate how tree cover and especially canopy height have changed in the Kuninkaantammi area in Helsinki during 2008‒2015, 2015‒2017, 2017‒2020, and 2008‒2020 from multi-temporal laser scanning data. One of the starting points of this study was to find out how airborne laser scanning datasets with different sensors and survey parameters are suitable for change detection. Also, what kind of problems the differences between datasets will raise and how to reduce those problems. The study used laser scanning data from the National Land Survey of Finland and from the city of Helsinki for four different years. The canopy height models were produced of each dataset and changes were calculated as the difference of each canopy height model. The results show that multi-temporal laser scanning data require a lot of manual processing to create datasets comparable. The greatest problems were differences in point density and in classification of the data. The sparse data from the National Land Survey of Finland affected how changes were managed to be studied. Therefore, changes were detected only in general level. In addition, each dataset was classified differently which affected the usability of the classes in the datasets. The problems encountered were reduced by manual work like digitizing or by masking non-vegetation objects. The results showed that the change in the Kuninkaantammi area has been relatively large at the time of the study. Between 2008 and 2015, 12.1% of the tree cover was lost, 9.9% between 2015 and 2017, and 13.2% between 2017 and 2020. In addition, an increase in canopy height was detected. Between 2008 and 2015, 44.2% of the area had greater than 2 m increase in canopy height. Similarly, increase occurred in 11.1% and 3.5% of the area in 2015‒2017 and in 2017‒2020, respectively. Although the changes were observed at a general level, it can be concluded that the used datasets can provide valuable information about the changes in urban green that have taken place in the area.
  • Junttila, Samuli; Hölttä, Tiina; Saarinen, Ninni; Kankare, Ville; Yrttimaa, Tuomas; Hyyppa, J.; Vastaranta, Mikko (2022)
    Water plays a crucial role in maintaining plant functionality and drives many ecophysiological processes. The distribution of water resources is in a continuous change due to global warming affecting the productivity of ecosystems around the globe, but there is a lack of non-destructive methods capable of continuous monitoring of plant and leaf water content that would help us in understanding the consequences of the redistribution of water. We studied the utilization of novel small hyperspectral sensors in the 1350-1650 nm and 2000-2450 nm spectral ranges in non-destructive estimation of leaf water content in laboratory and field conditions. We found that the sensors captured up to 96% of the variation in equivalent water thickness (EWT, g/m(2)) and up to 90% of the variation in relative water content (RWC). Further tests were done with an indoor plant (Dracaena marginate Lem.) by continuously measuring leaf spectra while drought conditions developed, which revealed detailed diurnal dynamics of leaf water content. The laboratory findings were supported by field measurements, where repeated leaf spectra measurements were in fair agreement (R-2 = 0.70) with RWC and showed similar diurnal dynamics. The estimation of leaf mass per area (LMA) using leaf spectra was investigated as a pathway to improved RWC estimation, but no significant improvement was found. We conclude that close-range hyper spectral spectroscopy can provide a novel tool for continuous measurement of leaf water content at the single leaf level and help us to better understand plant responses to varying environmental conditions.
  • Forsius, Martin; Kujala, Heini; Minunno, Francesco; Holmberg, Maria; Leikola, Niko; Mikkonen, Ninni; Autio, Iida; Paunu, Ville-Veikko; Tanhuanpää, Topi; Hurskainen, Pekka; Mäyrä, Janne; Kivinen, Sonja; Keski-Saari, Sarita; Kosenius, Anna-Kaisa; Kuusela, Saija; Virkkala, Raimo; Viinikka, Arto; Vihervaara, Petteri; Akujarvi, Anu; Bäck, Jaana; Karvosenoja, Niko; Kumpula, Timo; Kuzmin, Anton; Mäkelä, Annikki; Moilanen, Atte; Ollikainen, Markku; Pekkonen, Minna; Peltoniemi, Mikko; Poikolainen, Laura; Rankinen, Katri; Rasilo, Terhi; Tuominen, Sakari; Valkama, Jari; Vanhala, Pekka; Heikkinen, Risto K (2021)
    The challenges posed by climate change and biodiversity loss are deeply interconnected. Successful co-managing of these tangled drivers requires innovative methods that can prioritize and target management actions against multiple criteria, while also enabling cost-effective land use planning and impact scenario assessment. This paper synthesises the development and application of an integrated multidisciplinary modelling and evaluation framework for carbon and biodiversity in forest systems. By analysing and spatio-temporally modelling carbon processes and biodiversity elements, we determine an optimal solution for their co-management in the study landscape. We also describe how advanced Earth Observation measurements can be used to enhance mapping and monitoring of biodiversity and ecosystem processes. The scenarios used for the dynamic models were based on official Finnish policy goals for forest management and climate change mitigation. The development and testing of the system were executed in a large region in southern Finland (Kokemäenjoki basin, 27,024 km2) containing highly instrumented LTER (Long-Term Ecosystem Research) stations; these LTER data sources were complemented by fieldwork, remote sensing and national data bases. In the study area, estimated total net emissions were currently 4.2 TgCO2eq a−1, but modelling of forestry measures and anthropogenic emission reductions demonstrated that it would be possible to achieve the stated policy goal of carbon neutrality by low forest harvest intensity. We show how this policy-relevant information can be further utilized for optimal allocation of set-aside forest areas for nature conservation, which would significantly contribute to preserving both biodiversity and carbon values in the region. Biodiversity gain in the area could be increased without a loss of carbon-related benefits.
  • Lamminpää, Otto (2020)
    Finnish Meteorological Institute Contributions 172
    Carbon dioxide (CO2) and methane (CH4) are two most significant anthropogenic greenhouse gases contributing to climate change and global warming. Indirect remote sensing measurements of atmospheric concentrations of these gases are crucial for monitoring manmade emissions and understanding their effects and related atmospheric processes. The reliability of these studies depends largely on robust uncertainty quantification of the measurements, which provides rigorous error estimates and confidence intervals for all results. The main goal of this work is to develop and implement rigorous, robust and computationally efficient means of uncertainty quantification for atmospheric remote sensing of greenhouse gases. We consider both CO2 measurements by NASA’s Orbiting Carbon Observatory 2 (OCO-2) and CH4 measurements by Sodankylä Arctic Space Center’s Fourier Transform Spectrometer (FTS), the latter being studied from the perspectives of both individual measurements, and the entire time series from 2009-2018. Our approach leverages recent mathematical results on dimension reduction to produce novel algorithms that are a step towards thorough and efficient operational uncertainty quantification in the field of atmospheric remote sensing. Mathematically, the process of inferring gas concentrations from indirect measurements is an ill-posed inverse problem, meaning that a well-defined solution doesn't exist without proper regularization. Bayesian approach utilizes probability theory to provide a regularized solution to the inverse problem as a posterior probability distribution. The posterior distribution is conventionally approximated using a Gaussian distribution, and results are reported as the mean of the distribution as a point estimate, and the corresponding variance as a measure of uncertainty. In reality, due to non-linear physics models used in the computations, the posterior is not well approximated by a Gaussian distribution, and ignoring its actual shape can lead to unpredictable errors and inaccuracies in the retrieval. Markov chain Monte Carlo (MCMC) methods offer a robust way to explore the actual properties of posterior distributions, but they tend to be computationally infeasible as the dimension of the state vector increases. In this work, the low intrinsic information content of remote sensing measurements is exploited to implement the Likelihood-Informed Subspace (LIS) dimension reduction method, which increases the computational efficiency of MCMC. Novel algorithms using LIS are implemented to abovementioned atmospheric CH4 profile and column-averaged CO2 concentration inverse problems. *** Hiilidioksidi (CO2) ja metaani (CH4) ovat merkittävimmät ihmisperäiset kasvihuonekaasut, joilla molemmilla on huomattava vaikutus ilmastonmuutokseen ja ilmakehän lämpenemiseen. Näiden kaasujen pitoisuuksien epäsuorat kaukokartoitusmittaukset ovat oleellinen osa ihmisperäisten päästöjen kehityksen seurannassa. Näitä mittauksia tarvitaan myös arvioitaessa kasvihuonekaasujen vaikutusta ilmakehän prosesseihin. Edellämainitun tutkimuksen luotettavuus perustuu suurilta osin mittausten epävarmuuden arvioinnin paikkansapitävyyteen, minkä takaamiseksi käytetään korkeatasoista epävarmuusanalyysiä. Tämän väitöskirjatyön tavoitteena on kehittää ja ottaa käyttöön luotettavia ja laskennallisesti tehokkaita epävarmuusanalyysimenetelmiä sovellettuna kasvihuonekaasujen kaukokartoitukseen. Kehitetyt menetelmät perustuvat matemaattisesti käänteisongelmien teoriaan ja todennäköisyysteorian sovelluksiin. Käytämme erityisesti informaatioteoreettisia työkaluja pienentääksemme käänteisongelman ulottuvuutta. Tämä johtaa laskennalliseen ongelmaan, joka on huomattavasti nopeampi ratkaista. Työn sovelluskohteita ovat Nasan Orbiting Carbon Observatory 2 -satelliitin hiilidioksidipitoisuusmittaukset sekä Sodankylän Arktisessa Avaruuskeskuksessa sijaitsevan spektrometrin mittaamat metaanipitoisuudet. Jälkimmäisessä keskitymme sekä yksittäisiin mittauksiin että koko aikasarjan tutkimiseen ajalta 2009–2018. Kehitetyt menetelmät toimivat erittäin hyvin käsitellyissä sovelluksissa luoden pohjaa uusille operatiivisille epävarmuusanalyysialgoritmeille.
  • Suomalainen, Juha; Oliveira, Raquel A.; Hakala, Teemu; Koivumäki, Niko; Markelin, Lauri; Näsi, Roope; Honkavaara, Eija (Elsevier, 2021)
    Remote Sensing of Environment
    Multi- and hyperspectral cameras on drones can be valuable tools in environmental monitoring. A significant shortcoming complicating their usage in quantitative remote sensing applications is insufficient robust radiometric calibration methods. In a direct reflectance transformation method, the drone is equipped with a camera and an irradiance sensor, allowing transformation of image pixel values to reflectance factors without ground reference data. This method requires the sensors to be calibrated with higher accuracy than what is usually required by the empirical line method (ELM), but consequently it offers benefits in robustness, ease of operation, and ability to be used on Beyond-Visual Line of Sight flights. The objective of this study was to develop and assess a drone-based workflow for direct reflectance transformation and implement it on our hyperspectral remote sensing system. A novel atmospheric correction method is also introduced, using two reference panels, but, unlike in the ELM, the correction is not directly affected by changes in the illumination. The sensor system consists of a hyperspectral camera (Rikola HSI, by Senop) and an onboard irradiance spectrometer (FGI AIRS), which were both given thorough radiometric calibrations. In laboratory tests and in a flight experiment, the FGI AIRS tilt-corrected irradiances had accuracy better than 1.9% at solar zenith angles up to 70◦. The system’s lowaltitude reflectance factor accuracy was assessed in a flight experiment using reflectance reference panels, where the normalized root mean square errors (NRMSE) were less than ±2% for the light panels (25% and 50%) and less than ±4% for the dark panels (5% and 10%). In the high-altitude images, taken at 100–150 m altitude, the NRMSEs without atmospheric correction were within 1.4%–8.7% for VIS bands and 2.0%–18.5% for NIR bands. Significant atmospheric effects appeared already at 50 m flight altitude. The proposed atmospheric correction was found to be practical and it decreased the high-altitude NRMSEs to 1.3%–2.6% for VIS bands and to 2.3%– 5.3% for NIR bands. Overall, the workflow was found to be efficient and to provide similar accuracies as the ELM, but providing operational advantages in such challenging scenarios as in forest monitoring, large-scale autonomous mapping tasks, and real-time applications. Tests in varying illumination conditions showed that the reflectance factors of the gravel and vegetation targets varied up to 8% between sunny and cloudy conditions due to reflectance anisotropy effects, while the direct reflectance workflow had better accuracy. This suggests that the varying illumination conditions have to be further accounted for in drone-based in quantitative remote sensing applications.
  • Blanchet, Clarisse C.; Arzel, Celine; Davranche, Aurelie; Kahilainen, Kimmo K.; Secondi, Jean; Taipale, Sami; Lindberg, Henrik; Loehr, John; Manninen-Johansen, Sanni; Sundell, Janne; Maanan, Mohamed; Nummi, Petri (2022)
    Water browning or brownification refers to increasing water color, often related to increasing dissolved organic matter (DOM) and carbon (DOC) content in freshwaters. Browning has been recognized as a significant physicochemical phe-nomenon altering boreal lakes, but our understanding of its ecological consequences in different freshwater habitats and regions is limited. Here, we review the consequences of browning on different freshwater habitats, food webs and aquatic-terrestrial habitat coupling. We examine global trends of browning and DOM/DOC, and the use of remote sensing as a tool to investigate browning from local to global scales. Studies have focused on lakes and rivers while sel-dom addressing effects at the catchment scale. Other freshwater habitats such as small and temporary waterbodies have been overlooked, making the study of the entire network of the catchment incomplete. While past research inves-tigated the response of primary producers, aquatic invertebrates and fishes, the effects of browning on macrophytes, invasive species, and food webs have been understudied. Research has focused on freshwater habitats without consid-ering the fluxes between aquatic and terrestrial habitats. We highlight the importance of understanding how the changes in one habitat may cascade to another. Browning is a broader phenomenon than the heretofore concentration on the boreal region. Overall, we propose that future studies improve the ecological understanding of browning through the following research actions: 1) increasing our knowledge of ecological processes of browning in other wetland types than lakes and rivers, 2) assessing the impact of browning on aquatic food webs at multiple scales, 3) examining the effects of browning on aquatic-terrestrial habitat coupling, 4) expanding our knowledge of browning from the local to global scale, and 5) using remote sensing to examine browning and its ecological consequences.
  • Junttila, Samuli (Helsingin yliopisto, 2014)
    The effect of forest health and structure to the relative surface temperature captured by airborne thermal imagery was investigated in Norway Spruce-dominated stands in Southern Finland. Canopy surface temperature has long been recognized useful in monitoring vegetation water status. Recent studies have shown also its potential in monitoring vegetation health. Airborne thermal imagery, Airborne Light Detection and Ranging (LiDAR) and field measurements were acquired from the area of interest (AOI). The relative surface temperature correlated most negatively with the logarithm of stem volume, Lorey’s height and logarithm of basal area at resolution of 254m2 (9-m radius). In other words, taller and older stands had colder surface temperatures. In addition, LiDAR metrics, such as height percentiles and canopy cover percentage, were compared with surface temperature. Standard deviation of canopy height model, height features (H90, CHM_max) and canopy cover percentage were most strongly negatively correlated with the surface temperature. On average, higher surface temperatures were detected in defoliated canopies indicating that thermal images may provide some additional information for classifying forests health status. However, the surface temperature of defoliated plots varied considerably. It was also found that surface temperature differences between canopy and ground responses were higher in defoliated plots. Based on the results, forest health and structure affect to the surface temperature captured by airborne thermal imagery and these effects should be taken into account when developing forest health mapping applications using thermal imagery.
  • Saarinen, Ninni; Kankare, Ville; Vastaranta, Mikko; Luoma, Ville; Pyörälä, Jiri; Tanhuanpää, Topi; Liang, Xinlian; Kaartinen, Harri; Kukko, Antero; Jaakkola, Anttoni; Yu, Xiaowei; Holopainen, Markus; Hyyppä, Juha (2017)
    Interest in measuring forest biomass and carbon stock has increased as a result of the United Nations Framework Convention on Climate Change, and sustainable planning of forest resources is therefore essential. Biomass and carbon stock estimates are based on the large area estimates of growing stock volume provided by national forest inventories (NFIs). The estimates for growing stock volume based on the NFIs depend on stem volume estimates of individual trees. Data collection for formulating stem volume and biomass models is challenging, because the amount of data required is considerable, and the fact that the detailed destructive measurements required to provide these data are laborious. Due to natural diversity, sample size for developing allometric models should be rather large. Terrestrial laser scanning (TLS) has proved to be an efficient tool for collecting information on tree stems. Therefore, we investigated how TLS data for deriving stem volume information from single trees should be collected. The broader context of the study was to determine the feasibility of replacing destructive and laborious field measurements, which have been needed for development of empirical stem volume models, with TLS. The aim of the study was to investigate the effect of the TLS data captured at various distance (i.e. corresponding 25%, 50%, 75% and 100% of tree height) on the accuracy of the stem volume derived. In addition, we examined how multiple TLS point cloud data acquired at various distances improved the results. Analysis was carried out with two ways when multiple point clouds were used: individual tree attributes were derived from separate point clouds and the volume was estimated based on these separate values (multiple scan A), and point clouds were georeferenced as a combined point cloud from which the stem volume was estimated (multiple-scan B). This permitted us to deal with the practical aspects of TLS data collection and data processing for development of stem volume equations in boreal forests. The results indicated that a scanning distance of approximately 25% of tree height would be optimal for stem volume estimation with TLS if a single scan was utilized in boreal forest conditions studied here and scanning resolution employed. Larger distances increased the uncertainty, especially when the scanning distance was greater than approximately 50% of tree height, because the number of successfully measured diameters from the TLS point cloud was not sufficient for estimating the stem volume. When two TLS point clouds were utilized, the accuracy of stem volume estimates was improved: RMSE decreased from 12.4% to 6.8%. When two point clouds were processed separately (i.e. tree attributes were derived from separate point clouds and then combined) more accurate results were obtained; smaller RMSE and relative error were achieved compared to processing point clouds together (i.e. tree attributes were derived from a combined point cloud). TLS data collection and processing for the optimal setup in this study required only one sixth of time that was necessary to obtain the field reference. These results helped to further our knowledge on TLS in estimating stem volume in boreal forests studied here and brought us one step closer in providing best practices how a phase-shift TLS can be utilized in collecting data when developing stem volume models. (C) 2016 The Authors. Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).