Browsing by Author "Rautiainen, Miina"

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  • Analyses of Impact of Needle Surface Properties on Estimation of Needle Absorption Spectrum: Case Study with Coniferous Needle and Shoot Samples
    (2016-07) Yang, Bin; Knyazikhin, Yuri; Lin, Yi; Yan, Kai; Chen, Chi; Park, Taejin; Choi, Sungho; Mottus, Matti; Rautiainen, Miina; Myneni, Ranga B.; Yan, Lei
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Leaf scattering spectrum is the key optical variable that conveys information about leaf absorbing constituents from remote sensing. It cannot be directly measured from space because the radiation scattered from leaves is affected by the 3D canopy structure. In addition, some radiation is specularly reflected at the surface of leaves. This portion of reflected radiation is partly polarized, does not interact with pigments inside the leaf and therefore contains no information about its interior. Very little empirical data are available on the spectral and angular scattering properties of leaf surfaces. Whereas canopy-structure effects are well understood, the impact of the leaf surface reflectance on estimation of leaf absorption spectra remains uncertain. This paper presents empirical and theoretical analyses of angular, spectral, and polarimetric measurements of light reflected by needles and shoots of Pinus koraiensis and Picea koraiensis species. Our results suggest that ignoring the leaf surface reflected radiation can result in an inaccurate estimation of the leaf absorption spectrum. Polarization measurements may be useful to account for leaf surface effects because radiation reflected from the leaf surface is partly polarized, whereas that from the leaf interior is not.
  • Applicability of optical moisture data in detecting moisture content in Sphagnum species
    (2023-12-11) Karlqvist, Susanna
     Insinööritieteiden korkeakoulu | Master's thesis
    Despite their limited geographical extent, northern peatlands play a crucial role in the global carbon cycle, serving as significant carbon sinks capable of accumulating substantial carbon reserves. However, anthropogenic activities and climate change threaten these critical environments, jeopardizing their carbon dynamics and risking peatlands’ transition from carbon sinks to sources. Sphagnum mosses, the primary biomass builders of northern peatlands, are integral species for carbon sequestration. Their carbon sequestration capacity is intricately linked to moisture levels, highlighting the importance of monitoring Sphagnum moisture content for comprehensive peatland assessments. However, to realize the full potential of remote sensing datasets, it is crucial to first obtain reference measurements of key peatland species through close-range sensing methods. Remote sensing offers a promising alternative for estimating peatland moisture conditions, with satellite remote sensing facilitating monitoring on large spatial extents. However, to fully realize the potential of remote sensing datasets, it is crucial to obtain reference measurements of key peatland species through close-range sensing methods. This thesis explores the application of optical close-range data to estimate the moisture content of nine distinct Sphagnum moss species found in northern peatlands. The study explores various moisture estimation methods, including the optical trapezoid model (OPTRAM), continuous wavelet transform (CWT), and six spectral moisture indices. Additionally, the data is processed to align with multispectral bands of common satellite sensors, evaluating their applicability in estimating peatland moisture. The results reveal the suitability of different methods for distinct Sphagnum species and habitats. CWT exhibits promise when aggregating all Sphagnum species, while OPTRAM proves effective for drier mesotrophic species, demonstrating robust relationships with moisture content independent of species. Conversely, the modified Moisture Stress Index (MMSI) yields the best results for wetter ombrotrophic species. Finally, this study proposes that multi-spectral data could also be used for comprehensive peatland moisture analysis.
  • Application of L-band radiometry in snow characteristics analysis - L-band snow measurements in the Canadian Arctic
    (2023-10-09) Mäkinen, Kristofer
     Insinööritieteiden korkeakoulu | Master's thesis
    The Arctic is warming up to four times faster than the global average, yet data availability over this area is notoriously scarce. L-band radiometry is a promising cryosphere surface state variable monitoring method, due to its ability to penetrate through snow and pass through clouds. For proper application in the cryosphere, it is necessary to understand the effects that snow has on L-band emissions. The main objective of this thesis was to investigate the potential of L-band in informing on snow state variables utilizing a new L-band radiometer. To achieve this, I conducted ground reference measurements in Cambridge Bay, Nunavut, between the 1st and 14th of April 2023. Snowpack macrostructure and microstructure properties, as well as snow interface interactions, were analyzed in the context of ground L-band emissions. The impact of snow on ground L-band emissions was found to be highly spatially variable, with effects reaching up to ±7%. The effects varied by polarization and measurement angle, with horizontally polarized emissions experiencing strengthening at low measurement angles from nadir, while the impact on vertical polarization was mostly arbitrary. Snowpack microstructure had a noticeable impact on the emissions; increasing prevalence of depth hoar was found to strongly correlate with decreasing polarization ratio. Ground surface roughness also showed negative correlation with the emissions. A surface ice layer exhibited a strong but varying impact on the emissions. Overall, the Canadian Arctic snowpack was found to exhibit unique responsiveness especially to snowpack microstructural properties, which underscores the need for further understanding between the Arctic snowpack and L-band. The findings also emphasize snow's relevance in L-band applications, as properly characterizing these interactions will enhance accurate ground and snow data retrieval in the Arctic.
  • Assessing current biodiversity status (CBS) from remote sensing data in boreal forests
    (2023-10-09) Nuutila, Olli
     Insinööritieteiden korkeakoulu | Master's thesis
    Boreal forests cover a third of Earth's forested land. The variety of species in different ecosystems is massive. The ongoing biodiversity loss due to human acts has accelerated the need for biodiversity monitoring. Nature reserves are needed to preserve the biodiversity of boreal forests. The aim of this master’s thesis was to examine a biodiversity assessment-method, and its suitability for assessing the current status of biodiversity in boreal forests. The method and chosen indicators used in this study are based on Aleksandra Holmlund’s and Martin Pilstjärna’s (2022) biodiversity assessment-method: Current Biodiversity Status (CBS). The exploited data has been obtained from 150 sample plots in Kopparfors, Sweden, using Airborne LiDAR, along with Drone-LiDAR, multispectral Drone imagery, and Sentinel-2 image mosaics. The CBS-method was implemented by using an overlay analysis with vector data and three different spatial units (grid sizes). The different grid sizes gave various results in the same locations. Terrain also had an influence on the analysis’ ability to extract values with certain grid size. Overall, 35mx35m grid resulted the largest amount of the highest CBS-values. The 35mx35m grid was also examined from the inside using smaller spatial units. The 16mx16m grid was the most capable to extract higher CBS-values within the surrounding 35mx35m grid. The study showed that the exploited method and the data were suitable to be used with a spatial overlay analysis and easily modifiable regarding the required parameters. However, I could not validate my findings because the study was lacking ground truth data. Therefore, the improved version of the method could include support from ground truth data, as well more accurate classification considering the indicators. The level of automatization in the analysis could be increased as well to make the analysis faster and more reliable.
  • Assessment of a photon recollision probability based forest reflectance model in European boreal and temperate forests
    (2022-02) Hovi, Aarne; Schraik, Daniel; Hanuš, Jan; Homolová, Lucie; Juola, Jussi; Lang, Mait; Lukeš, Petr; Pisek, Jan; Rautiainen, Miina
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    We report a new version and an empirical evaluation of a forest reflectance model based on photon recollision probability (p). For the first time, a p-based approach to modeling forest reflectance was tested in a wide range of differently structured forests from different biomes. To parameterize the model, we measured forest canopy structure and spectral characteristics for 50 forest plots in four study sites spanning from boreal to temperate biomes in Europe (48°–62°N). We compared modeled forest reflectance spectra against airborne hyperspectral data at wavelengths of 450–2200 nm. Large overestimation occurred, especially in the near-infrared region, when the model was parameterized considering only leaves or needles as plant elements and assuming a Lambertian canopy. The model root mean square error (RMSE) was on average 80%, 80%, 54% for coniferous, broadleaved, and mixed forests, respectively. We suggest a new parameterization that takes into account the nadir to hemispherical reflectance ratio of the canopy and contribution of woody elements to the forest reflectance. We evaluated the new parameterization based on inversion of the model, which resulted in average RMSE of 20%, 15%, and 11% for coniferous, broadleaved, and mixed forests. The model requires only few structural parameters and the spectra of foliage, woody elements, and forest floor as input. It can be used in interpretation of multi- and hyperspectral remote sensing data, as well as in land surface and climate modeling. In general, our results also indicate that even though the foliage spectra are not dramatically different between coniferous and broadleaved forests, they can still explain a large part of reflectance differences between these forest types in the near-infrared, where sensitivity of the reflectance of dense forests to changes in the scattering properties of the foliage is high.
  • Avoimia spektrikirjastoja Suomen metsistä
    (2022) Rautiainen, Miina; Hovi, Aarne; Kuusinen, Nea; Juola, Jussi; Forsström, Petri; Salko, Sini-Selina; Schraik, Daniel; Burdun, Iuliia
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
  • Bayesian estimation of seasonal course of canopy leaf area index from hyperspectral satellite data
    (2018) Varvia, Petri; Rautiainen, Miina; Seppänen, Aku
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    In this paper, Bayesian inversion of a physically-based forest reflectance model is investigated to estimate of boreal forest canopy leaf area index (LAI) from EO-1 Hyperion hyperspectral data. The data consist of multiple forest stands with different species compositions and structures, imaged in three phases of the growing season. The Bayesian estimates of canopy LAI are compared to reference estimates based on a spectral vegetation index. The forest reflectance model contains also other unknown variables in addition to LAI, for example leaf single scattering albedo and understory reflectance. In the Bayesian approach, these variables are estimated simultaneously with LAI. The feasibility and seasonal variation of these estimates is also examined. Credible intervals for the estimates are also calculated and evaluated. The results show that the Bayesian inversion approach is significantly better than using a comparable spectral vegetation index regression.
  • Bayesian inversion of a forest reflectance model using Sentinel-2 and Landsat 8 satellite images
    (2019-08-01) Schraik, Daniel; Varvia, Petri; Korhonen, Lauri; Rautiainen, Miina
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The inversion of reflectance models is a generalizable tool to obtain estimates on forest biophysical parameters, such as leaf area index, with theoretically little information need from a study area, instead relying on the knowledge about physical processes in the forest radiation regime. The use of prior information can greatly improve the reflectance model inversion, however, the literature does not yet provide much information on the selection of priors and their influence on the inversion results. In this study, we used a Bayesian approach to invert the PARAS forest reflectance model and retrieve leaf area index from Sentinel-2 MSI and Landsat 8 OLI multispectral satellite images. The PARAS model is based on the theory of spectral invariants, which describes the influence of wavelength-independent parameters on forest radiative transfer. The Bayesian inversion approach is highly flexible, provides uncertainty quantification, and enables the explicit incorporation of prior knowledge into the inversion process. We found that the choice of prior information is crucial in inverting a forest reflectance model to predict leaf area index. Regularizing and informative priors for leaf area index strongly improved the predictions, relative to an uninformative prior, in that they counteracted the saturation effect of the optical signal occuring at high values for leaf area index. The predictions of leaf area index were more accurate for Landsat 8 than for Sentinel-2, due to potential inconsistencies in the visible bands of Sentinel-2 in our data, and the higher spectral resolution. (C) 2019 The Authors. Published by Elsevier Ltd.
  • A Bibliometric Visualization Review of the MODIS LAI/FPAR Products from 1995 to 2020
    (2021) Yan, Kai; Zou, Dongxiao; Yan, Guangjian; Fang, Hongliang; Weiss, Marie; Rautiainen, Miina; Knyazikhin, Yuri; Myneni, Ranga B.
     A2 Katsausartikkeli tieteellisessä aikakauslehdessä
    The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000. This review intends to summarize the history, development trends, scientific collaborations, disciplines involved, and research hotspots of these products. Its aim is to intrigue researchers and stimulate new research direction. Based on literature data from the Web of Science (WOS) and associated funding information, we conducted a bibliometric visualization review of the MODIS LAI/FPAR products from 1995 to 2020 using bibliometric and social network analysis (SNA) methods. We drew the following conclusions: (1) research based on the MODIS LAI/FPAR shows an upward trend with a multiyear average growth rate of 24.9% in the number of publications. (2) Researchers from China and the USA are the backbone of this research area, among which the Chinese Academy of Sciences (CAS) is the core research institution. (3) Research based on the MODIS LAI/FPAR covers a wide range of disciplines but mainly focus on environmental science and ecology. (4) Ecology, crop production estimation, algorithm improvement, and validation are the hotspots of these studies. (5) Broadening the research field, improving the algorithms, and overcoming existing difficulties in heterogeneous surface, scale effects, and complex terrains will be the trend of future research. Our work provides a clear view of the development of the MODIS LAI/FPAR products and valuable information for scholars to broaden their research fields.
  • Biomassan arviointi satelliittikaukokartoituksella
    (2016-04-24) Halme, Eelis
     Insinööritieteiden korkeakoulu | Bachelor's thesis
  • Boreaalisten metsien albedosta
    (2020) Rautiainen, Miina; Kuusinen, Nea; Hovi, Aarne; Majasalmi, Titta
     A2 Katsausartikkeli tieteellisessä aikakauslehdessä
    Metsien albedon vaihtelun selvittäminen antaa tietoa energiavirroista maan ja ilmakehän välillä sekä tarkennettuja lähtötietoja ilmastomallinnukseen. Siihen, pitäisikö albedon vaihtelu erityyppisten metsien välillä ottaa huomioon ilmastopolitiikassa tai metsänhoidon päätöksenteossa, ei pystytä vastaamaan ennen kuin kaikki metsien ilmastoon vaikuttavat tekijät on arvioitu samanaikaisesti. Boreaalisten metsien albedolla on omat erityispiirteensä verrattuna muiden kasvillisuusvyöhykkeiden albedoon; metsiemme albedo vaihtelee muun muassa lumitilanteen, puulajikoostumuksen, metsän rakenteen sekä aluskasvillisuuden mukaan. Tässä katsausartikkelissa kerromme mikä albedo on, miksi siitä ollaan kiinnostuneita, miten metsän albedoa on mahdollista arvioida ja mitkä tekijät vaikuttavat siihen. Tarkastelumme painottuu suomalaisiin metsiin. Lisäksi tarkastelemme Suomen metsien albedon kehitystä viime vuosikymmenten aikana ja pohdimme mitä seikkoja boreaalisten metsien albedosta ei vielä ymmärretä.
  • Clarifying the role of radiative mechanisms in the spatio-temporal changes of land surface temperature across the Horn of Africa
    (2019-02-01) Abera, Temesgen Alemayehu; Heiskanen, Janne; Pellikka, Petri; Rautiainen, Miina; Maeda, Eduardo Eiji
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Vegetation plays an important role in the climate system. The extent to which vegetation impacts climate through its structure and function varies across space and time, and it is also affected by land cover changes. In areas with both multiple growing periods and significant land cover changes, such as the Horn of Africa, identifying vegetation influence on land surface temperature (LST) through radiative changes needs further investigation. In this study, we used a 13-year time series (2001 - 2013) of remotely sensed environmental data to estimate the contribution of radiative mechanism to LST change due to growing season albedo dynamics and land cover conversion. Our results revealed that in taller woody vegetation (forest and savanna), albedo increases during the growing period by up to 0.04 compared with the non-growing period, while it decreases in shorter vegetation (grassland and shrubland) by up to 0.03. The warming impact due to a decrease in albedo during the growing period in shorter vegetation is counteracted by a considerable increase in evapotranspiration, leading to net cooling. Analysis of land cover change impact on albedo showed a regional annual average instantaneous surface radiative forcing of -0.03 +/- 0.02 W m(-2). The land cover transitions from forest to cropland, and savanna to grassland, displayed the largest mean albedo increase across all seasons, causing an average instantaneous surface radiative forcing of -2.6 W m(-2) and -1.5 W m(-2) and a decrease in mean LST of 0.12 K and 0.09 K, all in dry period (December, January, February), respectively. Despite the albedo cooling effect in these conversions, an average net warming of 1.3 K and 0.23 K was observed under the dominant influence of non-radiative mechanisms. These results show that the impact of radiative mechanism was small, highlighting the importance of non-radiative processes in understanding the climatic impacts of land cover changes, as well as in delineating effective mitigation strategies.
  • Classification of tree species based on hyperspectral reflectance images of stem bark
    (2023) Juola, Jussi; Hovi, Aarne; Rautiainen, Miina
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Automatization of tree species identification in the field is crucial in improving forest-based bioeconomy, supporting forest management, and facilitating in situ data collection for remote sensing applications. However, tree species recognition has never been addressed with hyperspectral reflectance images of stem bark before. We investigated how stem bark texture differs between tree species using a hyperspectral camera set-up and gray level co-occurrence matrices and assessed the potential of using reflectance spectra and texture features of stem bark to identify tree species. The analyses were based on 200 hyperspectral reflectance data cubes (415–925 nm) representing ten tree species. There were subtle interspecific differences in bark texture. Using average spectral features in linear discriminant analysis classifier resulted in classification accuracy of 92–96.5%. Using spectral and texture features together resulted in accuracy of 93–97.5%. With a convolutional neural network, we obtained an accuracy of 94%. Our study showed that the spectral features of stem bark were robust for classifying tree species, but importantly, bark texture is beneficial when combined with spectral data. Our results suggest that in situ imaging spectroscopy is a promising sensor technology for developing accurate tree species identification applications to support remote sensing.
  • Comparative analysis of heat-induced vegetation changes in wetlands
    (2024-08-19) Norola, Miikka
     Insinööritieteiden korkeakoulu | Master's thesis
    Europe, along with the rest of the world has seen an increase in the amount of extreme hot days over the past 30 years. Research has suggested that some types of wetlands have potentially low resilience to climate change, making them exceptionally vulnerable. Optical satellite remote sensing is a versatile option for monitoring wetlands and the changes in their vegetation. Use of MODIS data is justified when researching wetlands daily changes as it provides high temporal resolution. As of now, there are very few studies that have focused on comparing the informativeness of different vegetation indices when monitoring the changes particularly in wetlands. This thesis studied the heat-induced changes in vegetation of two diverse wetlands and compared their responses using the estimations of five vegetation indices. Anomalies in the values of vegetation indices were calculated for both the extreme hot days and for normal days. Statistical analysis was conducted to study whether the anomalies in temperature were associated with the anomalies in vegetation indices and to find the potential spatial patterns for both study areas. Findings of this thesis suggest that the results of monitoring the responses of wetlands are highly dependent on their physical properties, which has a direct effect on their spectral behaviour, highlighting the cruciality of accurate knowledge of the wetland’s physical properties when choosing which vegetation index to use. No single vegetation index used alone was found to accurately provide the necessary information regarding the changes in vegetation condition and moisture levels. The heat-induced changes in the two wetlands varied between the different indices. The vegetation of wetland located in the Southern Europe saw on average higher negative impact during the extreme hot days than the wetland located in Northern Europe.
  • Comparison of contemporaneous Sentinel-2 and EnMAP data for vegetation index-based estimation of leaf area index and canopy closure of a boreal forest
    (2024-11-27) Juola, Jussi; Hovi, Aarne; Rautiainen, Miina
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Data from the new hyperspectral satellite missions such as EnMAP are anticipated to refine leaf area index (LAI) or canopy closure (CC) monitoring in conifer-dominated forest areas. We compared contemporaneous multispectral and hyperspectral satellite images from Sentinel-2 MSI (S2) and EnMAP and assessed whether hyperspectral images offer added value in estimating LAI, effective LAI (LAIeff), and CC in a European boreal forest area. The estimations were performed using univariate and multivariate generalized additive models. The models utilized field measurements of LAI and CC from 38 forest plots and an extensive set of vegetation indices (VIs) derived from the satellite data. The best univariate models for each of the three response variables had small differences between the two sensors, but in general, EnMAP had more well-performing VIs which was reflected in the better multivariate model performances. The best performing multivariate models with the EnMAP data had ~1–6% lower relative RMSEs than S2. Wavelengths near the green, red-edge, and shortwave infrared regions were frequently utilized in estimating LAI, LAIeff, and CC with EnMAP data. Because EnMAP could estimate LAI better, the results suggest that EnMAP may be more useful than multispectral satellite sensors, such as S2, in monitoring biophysical variables of coniferous-dominated forests. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
  • Contribution of woody elements to tree level reflectance in boreal forests
    (2021) Kuusinen, Nea; Hovi, Aarne; Rautiainen, Miina
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Spectral mixture analysis was used to estimate the contribution of woody elements to tree level reflectance from airborne hyperspectral data in boreal forest stands in Finland. Knowledge of the contribution of woody elements to tree or forest reflectance is important in the context of lea area index (LAI) estimation and, e.g., in the estimation of defoliation due to insect outbreaks, from remote sensing data. Field measurements from four Scots pine (Pinus sylvestris L.), five Norway spruce (Picea abies (L.) Karst.) and four birch (Betula pendula Roth and Betula pubescens Ehrh.) dominated plots, spectral measurements of needles, leaves, bark, and forest floor, airborne hyper-spectral as well as airborne laser scanning data were used together with a physically-based forest reflectance model. We compared the results based on simple linear combinations of measured bark and needle/leaf spectra to those obtained by accounting for multiple scattering of radiation within the canopy using a physically-based forest reflectance model. The contribution of forest floor to reflectance was additionally considered. The resulted mean woody element contribution estimates varied from 0.140 to 0.186 for Scots pine, from 0.116 to 0.196 for birches and from 0.090 to 0.095 for Norway spruce, depending on the model used. The contribution of woody elements to tree reflectance had a weak connection to plot level forest variables.
  • Corrigendum to ‘A dataset composed of multiangular spectral libraries and auxiliary data at tree, leaf, needle, and bark level for three common European tree species’ [Data in Brief 35 (2021) 106820] (Data in Brief (2021) 35, (S2352340921001049), (10.1016/j.dib.2021.106820))
    (2021-08) Hovi, Aarne; Forsström, Petri R.; Ghielmetti, Giulia; Schaepman, Michael E.; Rautiainen, Miina
     Comment/debate
    The authors regret that Eq. 6 in the article is incorrect. The term GT should be subtracted from the quotient of DNleaf,T/DNWR_leaf,T (not from DNleaf,T in the numerator). The correct equation is: [Formula presented] The error was only in the article text and therefore did not influence the data published. The authors would like to apologise for any inconvenience caused.
  • Crop identification with Sentinel-2 satellite imagery in Finland
    (2018-08-20) Laine, Joona
     Insinööritieteiden korkeakoulu | Master's thesis
    European Union member countries are obligated to control the validity of Common Agricultural Policy subsidy applications. Each member country performs manual inspection for at least 5% of these subsidy applications. This is both expensive and a considerable administrative burden. According to European Union, the crop type identifcation process in Common Agricultural Policy could be carried out using remote sensing or orthophoto imagery for an alternative to physical inspections by competent authorities. Automated crop type identifcation would reduce the costs signifcantly. This master’s thesis addressed the crop identifcation with optical Sentinel-2 satellite imagery in Finland. The aim was to investigate whether it was possible to reliably identify the crop growing in land parcels by using machine learning classifcation methods. This thesis presented an automated approach of identifying crops. Multiple different machine learning classifcation algorithms were trained and tested to find out the most suitable processing method, time period and classifcation algorithm by utilizing the land parcels obtained from the Finnish Agency for Rural Affairs. The developed processing method and most of the tested classifcation algorithms were able to perform relatively well in crop identifcation in cloudy growth period 2017 of Finland. Therefore, the developed method could be applied to different use cases and cloudy weather conditions. The further development and training of the classifcation algorithms could make it possible to utilize this approach in Finland as well as in other EU countries for the Common Agricultural Policy control and possibly in numerous other tasks.
  • Crown level clumping in Norway spruce from terrestrial laser scanning measurements
    (2021-01-15) Schraik, Daniel; Hovi, Aarne; Rautiainen, Miina
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The clumping of coniferous needles into shoots is widely acknowledged as a structural feature that cannot be ignored in radiation regime models of coniferous forests. However, higher level clumping, i.e. the aggregation of leaves and shoots in tree crowns and forest stands, is still rarely accounted for in the models. Clumping reduces the light interception of and increases the light penetration depth in a plant stand. To improve forest radiation regime models with respect to this forest structural parameter, we propose a method that can quantify clumping at different hierarchical levels by estimating the silhouette to total area ratio from point clouds acquired by laser scanners. Our method is based on estimating attenuation coefficients in a voxel grid, and subsequently computing the total leaf area and spherically averaged silhouette area of a tree crown or forest stand. We tested our method with empirical data in young Norway spruce trees, where we compared leaf area and silhouette area to destructive and photogrammetric reference measurements. The accuracy of leaf area estimates depended strongly on the voxel size, with voxel sizes below 10 cm side length exhibiting up to 100% higher estimates than the reference leaf area, and large voxels with 90 cm side length being closest to the reference measurements due to crown clumping. The silhouette area estimates varied less with voxel size and were slightly higher than the reference estimates. We analyzed possible error sources and point out ways to improve the measurements of leaf and silhouette area for conifer trees using laser scanning data.
  • A data cube framework for earth observation data analysis: Case study on arctic fires
    (2024-11-18) Mattila, Riikka
     School of Engineering | Master's thesis
    The development of satellite technology and the increasing volume of Earth Observation (EO) data have opened new opportunities for studying environmental phenomena. However, the massive volume of satellite data poses challenges for processing. Data may come in different resolutions, spatial grids, coordinate systems, and file formats, making preprocessing time-consuming and requiring programming skills. Data cubes are a relatively new method for organizing EO data into a more user-friendly format, reducing the workload of preprocessing. In this thesis, a Python-based framework was developed to preprocess data from various measurement instruments and store it in a unified data cube format for analysis. The framework does not require extensive programming experience, lowering the barrier for utilizing EO data. Datasets related to fires in the Arctic region were used as test data for the framework due to the specific challenges it presents, such as distortions in scaled data near the north pole. Additionally, the Arctic is an important area of study because of the increasing number of wildfires observed in the region recently, the origins of which are not fully understood. These wildfires release significant amounts of carbon mon-oxide (CO), impacting the global climate. The region's ecosystems, which have previously acted as carbon sinks, are undergoing changes. The framework developed in this work facilitates the processing of EO data and can enhance the analysis process in areas where data characteristics pose specific challenges.