Improved parametrisation of a physically-based forest reflectance model for retrieval of boreal forest structural properties

Physically-based reflectance models offer a robust and transferable method to assess biophysical characteristics of vegetation in remote sensing. Forests exhibit explicit structure at many scales, from shoots and branches to landscape patches, and hence present a specific challenge to vegetation reflectance modellers. To relate forest reflectance with its structure, the complexity must be parametrised leading to an increase in the number of reflectance model inputs. The parametrisations link reflectance simulations to measurable forest variables, but at the same time rely on abstractions (e.g. a geometric surface forming a tree crown) and physically-based simplifications that are difficult to quantify robustly. As high-quality data on basic forest structure (e.g. tree height and stand density) and optical properties (e.g. leaf and forest floor reflectance) are becoming increasingly available, we used the well-validated forest reflectance and transmittance model FRT to investigate the effect of the values of the “uncertain” input parameters on the accuracy of modelled forest reflectance. With the state-of-the-art structural and spectral forest information, and Sentinel-2 Multispectral Instrument imagery, we identified that the input parameters influencing the most the modelled reflectance, given that the basic forestry variables are set to their true values and leaf mass is determined from reliable allometric models, are the regularity of the tree distribution and the amount of woody elements. When these parameters were set to their new adjusted values, the model performance improved considerably, reaching in the near infrared spectral region (740–950 nm) nearly zero bias, a relative RMSE of 13% and a correlation coefficient of 0.81. In the visible part of the spectrum, the model performance was not as consistent indicating room for improvement

Utility of hyperspectral compared to multispectral remote sensing data in estimating forest biomass and structure variables in Finnish boreal forest

Three-quarters of Finland’s land surface area is filled with forests, which compose a great part of the country’s biomass, carbon pools and carbon sinks. In order to acquire up-to-date information on the forests, optical remote sensing techniques are commonly used. Moreover, in the future hyperspectral satellite missions will start providing data to support the needs of natural resource management practices, such as forestry. It is, however, unclear what would be the additional value from using hyperspectral data compared to multispectral in quantifying forest variables of Finnish boreal forest. In this study, we used the remote sensing data by hyperspectral AISA imager (128 bands, 400–1000 nm, resolution 0.7 m) and Sentinel-2 (10 bands, resolution 10 m) to assess the possible benefits of higher spectral resolution. As reference data, we used a new nationwide forest resource dataset (stand-level data), which has a high potential in further remote sensing applications. In addition, we used a set of independent in situ measurements (plot-level data) for validation. We applied two kernel-based machine learning regression algorithms (Gaussian process and support vector regression) to relate boreal forest variables with the remote sensing data. The variables of interest were mean height, basal area, leaf area index (LAI), stem biomass and main tree species. The regression algorithms were trained with stand-level data and estimations were evaluated with stand- and plot-level holdout sets. The estimation accuracies were examined with absolute and relative root-mean-square errors. Successful variable estimations showed that kernel-based regression algorithms are suitable tools for forest structure estimation. Based on the results, the additional value of hyperspectral remote sensing data in forest variable estimation in Finnish boreal forest is mainly related to variables with species-specific information, such as main tree species and LAI. The more interesting variables for forestry industry, such as mean height, basal area and stem biomass, can also be estimated accurately with more traditional multispectral remote sensing data.Peer reviewe

Assessing spatial variability and estimating mean crown diameter in boreal forests using variograms and amplitude spectra of very-high-resolution remote sensing data

Funding Information: This work was supported by the Academy of Finland under Grant [317387]. We would like to acknowledge assistance from the University of Helsinki and Ilkka Korpela for providing us with the field measured tree data from Hyyti?l?. Publisher Copyright: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.The retrieval of forest variables from optical remote sensing data using physically-based models is an ill-posed problem and does not make full use of the high spatial resolution imagery that is becoming available globally. A possible solution to this is to use prior information about the retrieved variables, which constrains the possible solutions and reduces uncertainty in forest variable estimation. Therefore, we tried to quantify physically-based parameters that could be retrieved using the second-order statistics of measured and simulated very-high-resolution (pixel size less than 1 m) images of Finnish boreal forests. These forests have a well-defined structure and are usually not closed, i.e. the reflected signal has a considerable contribution from a green forest floor. We retrieved the second-order statistics using variograms and Fourier amplitude spectra. We found, in line with previous studies, that the range of variograms correlates well (r = 0.83) with the mean crown diameter for spatially homogeneous forest patches, and it can be used to estimate crown diameters with reasonable accuracy (RMSE = 0.42 m). We present a novel approach, which uses the Fourier amplitude spectrum to study the spatial structure of a forest. The approach provided encouraging results with the measured data: despite the lower accuracy (RMSE = 0.67 m) compared with variograms, we found that it could also be used to estimate mean crown diameters for heterogeneous forest areas. The Fourier amplitude spectrum approach did not work with the simulated images. Our results highlight the possibility to obtain further information from very-high-resolution images of forests to solve the ill-posed problem of forest variable estimation from optical remote sensing data using physically-based models.Peer reviewe

Deep neural networks with transfer learning for forest variable estimation using sentinel-2 imagery in boreal forest

Estimation of forest structural variables is essential to provide relevant insights for public and private stakeholders in forestry and environmental sectors. Airborne light detection and ranging (LiDAR) enables accurate forest inventory, but it is expensive for large area analyses. Continuously increasing volume of open Earth Observation (EO) imagery from high-resolution (|BIAS%| = 0.8%). We found 3×3 pixels to be the optimal size for the sampling window, and two to three hidden layer DNNs to produce the best results with relatively small improvement to single hidden layer networks. Including CHM features with S2 data and additional features led to reduced relative RMSE (RMSE% = 28.6–30.7%) but increased the absolute value of relative bias (|BIAS%| = 0.9–4.0%). Transfer learning was found to be beneficial mainly with training data sets containing less than 250 field plots. The performance differences of DNN and random forest models were marginal. Our results contribute to improved structural variable estimation performance in boreal forests with the proposed image sampling and input feature concept

Avaruus arjessamme : Avaruustoiminnan yhteiskunnallinen vaikuttavuus (AVARTAVA) loppuraportti

Selvityksen tavoitteena oli muodostaa ajankohtainen kuva siitä, miten avaruustoiminnan mahdollisuuksia hyödynnetään hallitusohjelman tavoitteiden ja muiden yhteiskunnallisten tavoitteiden toteuttamisessa sekä eri hallinnonalojen päätöksenteossa. Päähavainto on, että avaruustoimintaa hyödynnetään laajasti eri hallinnonalojen päätöksenteossa sekä hallitusohjelman ja muiden yhteiskunnallisten tavoitteiden toteuttamisessa, vaikka avaruustoiminnan roolia aina ei välttämättä tiedosteta. Avaruustoiminnan palveluilla, kuten paikkatiedolla ja aikasignaalilla, kaukokartoituksella ja satelliittitietoliikenteellä on merkittäviä sovelluksia viranomaistoiminnassa, esim. pelastuksessa, ympäristövalvonnassa ja turvallisuusviranomaisten toiminnassa, sekä liike-elämässä, erilaisten tietoliikenne- ja energiaverkkojen, kaupankäynnin, kuljetusten ja palveluiden mahdollistajana. Kaiken kaikkiaan avaruustoiminta on kriittistä yhteiskunnan normaalille toiminnalle. Selvitysryhmä suosittaakin tiedostamaan avaruustoiminnan roolin kriittisyyden. Selvityksessä esitetään toimenpiteitä tarvittavan osaamisen ja resurssien varmistamiseksi, jotta avaruustoiminnan palveluita voidaan hyödyntää jatkossa entistä laajemmin.Tämä julkaisu on toteutettu osana valtioneuvoston selvitys- ja tutkimussuunnitelman toimeenpanoa. (tietokayttoon.fi) Julkaisun sisällöstä vastaavat tiedon tuottajat, eikä tekstisisältö välttämättä edusta valtioneuvoston näkemystä

A methodology for implementing a digital twin of the earth’s forests to match the requirements of different user groups

Publisher Copyright: © 2021 GI_Forum.Europe has acknowledged the need to develop a very high precision digital model of the Earth, a Digital Twin Earth, running on cloud infrastructure to bring data and end-users closer together. We present results of an investigation of a proposed submodel of the digital twin, simulating the worlds’ forests. We focus on the architecture of the system and the key user needs on data content and access. The results are based on a user survey showing that the forest-related communities in Europe require information on contrasting forest variables and processes, with common interest in the status and forecast of forest carbon stock. We discuss the required spatial resolution, accuracies, and modelling tools required to match the needs of the different communities in data availability and simulation of the forest ecosystem. This, together with the knowledge on existing and projected future capabilities, allows us to specify a data architecture to implement the proposed system regionally, with the outlook to expand to continental and global scales. Ultimately, a system simulating the behaviour of forests, a digital twin, would connect the bottom-up and top-down approaches of computing the forest carbon balance: from tree-based accounting of forest growth to atmospheric measurements, respectively.Peer reviewe

Turvavalaistus : Toteutus Hyvinkään ammattioppilaitokselle

Insinöörityö pohjautuu kesätyönä Hyvinkään kaupungin sähköyksikön alaisuudessa tehtyyn turvavalaistusasennukseen Hyvinkään ammattioppilaitoksessa. Kouluun asennettiin koko turvavalaistusjärjestelmän kattava laitteisto. Varsinaiset asennustyöt ammattioppilaitoksella aloitettiin vuoden 2009 kesäkuussa. Työ sisälsi valaisimet, kaapeloinnin, syötönvaihtoyksiköt, turvavalokeskuksen sekä akuston ja niiden asennuksen toimivaksi kokonaisuudeksi. Asennus tehtiin sähkösuunnitelmien mukaisesti pieniä, käytännössä merkityksettömiä muutoksia lukuun ottamatta. Insinöörityön raporttiosan tekeminen aloitettiin loppuvuodesta 2010. Raporttiosassa käytiin läpi vuoden 2009 ja uudemmat turvavalaistusmääräykset ja -ohjeet. Tämän jälkeen on kerrottu, miten asennukset tehtiin käytännössä sekä asennuksien yhteydessä ilmenneistä ongelmista. Tutkittaessa määräyksiä ja standardeja havaittiin, että ammattioppilaitoksen vanha turvavalaistusjärjestelmä oli lähes täysin vanhentunut. Peruskorjauksen yhteydessä Hyvinkään ammattioppilaitoksen turvavalaistus saatiin vastaamaan nykyajan määräyksiä ja standardeja. Päivitetyt määräykset edistävät merkittävästi rakennuksesta poistumista hätätilanteen sattuessa.This thesis is based on a summer placement for Hyvinkää city electric department and its emergency lighting installation project in Hyvinkää vocational school. A complete emergency lighting system was installed in the vocational school. The actual installation work at the vocational school began in June 2009. The work included lights, cables, power supply switches, emergency lighting center and a battery unit making the installation a complete system. The installation was conducted according to the electrical plans except for a few minor, practically meaningless, changes. The written part of this thesis was started at the end of the year 2010. The thesis describes all the security lighting specifications and guidelines from the year 2009 until today. This study also explains how the installations were made and discusses the problems that occurred during the installations. When studying the regulations and standards it was noticed that the old emergency lighting system in the vocational school was almost entirely outdated. During the renovation the Hyvinkää vocational school emergency lighting was made to match the current regulations and standards. The updated regulations contribute a great deal to exiting the building safely in case of an emergency

Etäältä havaitun spektrisen tiedon hyöty metsämuuttujien arvioinnissa

Three-quarters of Finland’s land surface area (22.8 million hectares) is filled with forests. In Finland, forests serve as a resource for the nature conservation as well as for the forestry industry. In addition, forests compose a great part of the country’s biomass and carbon sinks. At present, remote sensing technology is the main instrument in acquiring up-to-date and near real time information on the forests. Moreover, open and free Earth observation (EO) data volume increases constantly. In future also hyperspectral satellite missions (e.g., Sentinel-10) will start providing remote sensing data to support the needs of forestry and other natural resource management practices. However, to this day it is unclear what will be the additional value of hyperspectral remote sensing data compared to multispectral data in forest variable and biomass estimation. Therefore, the present thesis investigated the influence of spectral and spatial resolution of remote sensing data on forest variable estimation in the boreal forests of Finland. The study used the remote sensing data by Sentinel-2 and hyperspectral AISA imager. As reference data, study used forest resource data provided by the Finnish Forest Centre and additional independent in situ measurements. Machine learning based regression models were applied to relate the forest variables of interest with the remotely sensed data. Based on recent studies, Gaussian process regression (GPR) and Support vector regression (SVR) were selected. Both of these have proven to work well with hyperspectral and multispectral remote sensing data. Regression estimations were performed for seven different forest variables: mean height, stem count, stem biomass, leaf area index (LAI), basal area, leaf biomass and main tree species. Estimation accuracies were mainly examined with absolute and relative root-mean-square errors (RMSE). The forest variable estimations with both algorithms and the reference data by the Finnish Forest Centre were successful. Compared with previous stand-wise estimations of forest variables in Finland, the machine learning approach used in this study was more accurate. The estimation accuracies of the algorithms were similar. However, the faster SVR algorithm was found to be more practical. Best estimation accuracies were obtained for the same variables that were given most accurately in the reference data. Respectively, the weakest estimations were for the same variables that had the worst accuracy in the reference data. Results of this study showed that stand-wise forest variable estimations have approximately the same accuracy with multi- and hyperspectral remote sensing imagery, and that the hyperspectral data improves only the estimation accuracies of main tree species and mean height. In stand-wise forest variable estimations, the importance of spatial resolution was minor.Metsät peittävät kolme neljäsosaa Suomen maapinta-alasta (22,8 miljoonaa hehtaaria). Suomessa metsät toimivat resursseina niin metsäteollisuudelle kuin luonnonsuojelullekin. Lisäksi metsät kattavat suuren osan maan biomassasta sekä hiilinieluista. Tällä hetkellä kaukokartoitustekniikka on pääasiallinen menetelmä ajantasaisen ja lähes reaaliaikaisen metsätiedon keräämisessä. Avoimen ja ilmaisen kaukokartoitusaineiston saatavuus kasvaa myös jatkuvasti. Tulevaisuudessa hyperspektriset satelliittimissiot (esim. Sentinel-10) aloittavat myös kaukokartoitusaineiston tuottamisen metsätalouden ja muun luonnonvaratalouden tarpeisiin. On kuitenkin edelleen epäselvää minkälaisen lisäarvon hyperspektrinen kaukokartoitusaineisto tuo multispektriseen aineistoon verrattuna metsämuuttujien ja biomassan arvioinnissa. Tämän vuoksi tässä tutkimuksessa tutkittiin kaukokartoitusaineiston spektrisen ja spatiaalisen resoluution vaikutusta metsämuuttujien arviointiin Suomen boreaalisen vyöhykkeen metsissä. Tutkimuksessa käytettiin kaukokartoitusaineistona Sentinel-2 sekä hyperspektristä AISA kuvaa. Referenssiaineistoina käytettiin Metsäkeskuksen avointa metsävaratietokantaa sekä itsenäisiä maastomittauksia. Koneoppiin pohjautuvia regressiomalleja käytettiin yhdistämään metsämuuttujat sekä kaukokartoitusaineisto. Viimeaikaisten tutkimuksien pohjalta työhön valittiin regressiomalleiksi Gaussin prosessi -regressio sekä tukivektoriregressio. Molemmat regressioalgoritmit ovat todettu hyvin toimiviksi hyper- ja multispektristen kaukokartoitusaineistojen kanssa. Regressioestimointeja tehtiin seitsemälle eri metsämuuttujalle, joita olivat keskipituus, runkoluku, runkobiomassa, lehtialaindeksi, pohjapinta-ala, lehtibiomassa ja pääpuulaji. Estimointitarkkuuksia tutkittiin pääasiassa absoluuttisten sekä suhteellisten keskivirheiden avulla. Metsämuuttujaestimoinnit molemmilla algoritmeilla sekä Metsäkeskuksen referenssiaineistolla olivat onnistuneita. Verrattuna edellisiin Suomessa tehtyihin metsäkuviotason metsämuuttujien arviointeihin, tässä työssä käytetyt koneoppimenetelmät antoivat tarkempia tuloksia. Algoritmien välillä arviointitarkkuudet olivat samanlaiset. Tukivektoriregressio oli kuitenkin nopeampi ja todettiin täten käytännöllisemmäksi. Parhaimmat arviointitarkkuudet saavutettiin samoilla metsämuuttujilla, joilla oli parhaimmat tarkkuudet myös Metsäkeskuksen lähtöaineistossa. Vastaavasti, huonoimmat arviointitarkkuudet saavutettiin metsämuuttujille, joiden tarkkuustaso oli myös heikoin lähtöaineistossa. Tutkimuksen tulokset osoittivat, että metsäkuviotasoisessa metsämuuttujien arvioinnissa saavutetaan suunnilleen sama tarkkuus sekä hyper- että multispektrisellä kaukokartoitusaineistolla. Hyperspektrinen aineisto parantaa ainoastaan pääpuulajin sekä keskipituuden arviointitarkkuuksia. Metsäkuviotasoisessa arvioinnissa spatiaalisen resoluution merkitys oli vähäinen

Socio-economic impact of space activities in Finland (AVARTAVA)

The objective of this study was to provide a picture how space-based services are used in the Finnish Government in fulfilling the goals set in the Government programme, in government activities, and advancing other societal goals.The main findings of the study are that downstream space activities and space-based services are well integrated and utilized in a variety of sectors in the Finnish Government and private sectors, and the applications are used to advance a variety of societal goals, although the contribution of space activities are not always well recognized by the end users and beneficiaries. Mainstream space-based services include satellite navigation and time signal, satellite-based earth observation and satellite communication, which all are utilized in multiple government sectors, including public safety, transport, environmental protection, agriculture, as well as in private sector, including telecom and energy sectors, transport, and logistics and as platforms for a variety of services.Altogether, space capabilities are critical to the normal functioning of Finnish society. The study consortium recommends a set of actions for raising public awareness of the role of space activities and securing the resources and knowledge to fully utilize and secure future functioning of space-based services and solutions