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The overall objective of the Forest Flux (forestflux.eu) project was to foster the development of the Copernicus Earth Observation (EO) market and improve the profitability of forest management by implementing a world-first service of high-resolution maps of forest carbon fluxes, storage, and their development over time, using satellite imagery and associated data. The services were offered from the same process of data refinement. In this process, the outputs of the previous phase were inputs to the next phase. The earlier phase outputs were also products that were delivered to the users. Forest Flux services were implemented on the scalable Forestry TEP cloud platform that enables integration of the Forest Flux products with the business processes of end users. The services are available via an Internet connection. Computed products were mostly digital maps with supporting quantitative figures, such as statistical data on uncertainty. They were provided for nine user organizations and sites in Europe, South America, and Africa. In total, approximately 1,200 map products were delivered. Forest Flux project received funding from the European Union's Horizon 2020 Research and Innovation program under grant agreement No 821860.

Forest Flux https://www.forestflux.eu/ will renew forestry value-added services in Earth Observation (EO) by creating and piloting cloud-based services for committed users on forest carbon assimilation and structural variable prediction. Forest Flux exploits the explosive increase of high-resolution EO data from the Copernicus program and developments of cloud computing technology. It implements a world-first service platform for high-resolution maps of traditional forestry variables together with forest carbon fluxes. Forest Flux will allow the users to improve the profitability of forest management while taking care of ecological sustainability. The Forest Flux services are implemented on the Forestry Thematic Exploitation cloud platform https://f-tep.com/. In 2020, nearly 700 thematic maps on forest stand and carbon flux variables were delivered to nine specific users in a form that was applicable to their operational forest management systems. The last project year 2021 focuses on map product refinement and improving user services, which eventually lead to operational service concepts.

The paper discusses the recent developments of the landscape in the Earth observation domain and in data analysis. The main drivers for the rapid changes in this field include the European Copernicus program with operational satellite missions, the platform economy and the advancement of machine learning methods.

Finland has excellent potential for becoming an international pioneer of the bioeconomy. Plentiful renewable resources and profound competence in their exploitation create a foundation for the development of new solutions that enable the transition to a low-carbon and resource-efficient economy. The realisation of this goal is promoted by the ongoing digitalisation process, which will make natural resource data available for the use of a wider group of operators and enable new, networked operating models. VTT Technical Research Centre of Finland Ltd and Natural Resources Institute Finland have drawn up this roadmap, which points the way towards a knowledge-intensive, competitive and collaborative bioeconomy making use of the possibilities of digitalisation.We identified three transition paths to achieve a digitalised bioeconomy and presented sample cases connected to those paths. The transition paths are: 1) Smart biomass flow management, aimed at creating more value from raw materials through the implementation of digital solutions for the optimal use of raw materials, smart logistics and flexible production control systems. 2) Data-driven bioeconomy, compiling development needs related to the generation, analysis and refinement of natural resource data that promotes the bioeconomy. Digital solutions can increase transparency and support predictive decision-making. 3) Networked and collaborative natural resource economy, comprising the development of business models and digital platforms to enable the networked operations of bioeconomy actors.Suggested measures for the various key parties were formulated in the course of roadmap work. Digitalisation requires fast action, agility and adaptability. In this operating environment, it is vital to adopt a culture of experimentation. Research should be steered towards the development of new concepts and anticipating the consequences of business models related to them. It is vital to identify customer needs and see services as an integral part of products. Authorities must adopt a predictive approach that supports the creation of new solutions. The promotion of innovative procurement is one way of promoting the change

Suomella on erinomaiset mahdollisuudet nousta biotalouden kansainväliseksi edelläkävijäksi. Runsaat uusiutuvat luonnonvarat ja pitkälle kehittynyt osaaminen niiden kestävässä hyödyntämisessä tarjoavat perustan kehittää uusia ratkaisuja, joiden avulla voidaan siirtyä vähähiiliseen ja resurssitehokkaaseen talouteen. Tavoitteen toteuttamista edistää käynnissä oleva digitalisaatio, joka tuo luonnonvaroja koskevan tiedon entistä laajemman toimijajoukon hyödynnettäväksi ja mahdollistaa uudenlaiset verkottuneet toimintamallit. Teknologian tutkimuskeskus VTT Oy ja Luonnonvarakeskus ovat yhteistyössä laatineet tiekartan, joka viitoittaa polkuja kohti digitalisaation mahdollisuuksia hyödyntävää, tietointensiivistä, kilpailukykyistä ja yhteistoiminnallista biotaloutta. Työssä tunnistettiin kolme muutospolkua digitalisoidun biotalouden saavuttamiseksi ja kuvattiin niihin kytkeytyviä case-esimerkkejä. Muutospolut ovat: 1) Älykäs biomassavirtojen hallinta, jonka tavoitteena on tuottaa enemmän arvoa raaka-aineesta toteuttamalla digitaalisia ratkaisuja raaka-aineen optimaaliseen käyttöön, älykkääseen logistiikkaan ja joustaviin tuotannonohjausjärjestelmiin. 2) Datavetoinen biotalous, joka kokoaa biotaloutta edistävän luonnonvaratiedon tuottamiseen, analysointiin ja muokkaamiseen liittyviä kehitystarpeita. Digitaalisilla ratkaisuilla voidaan lisätä läpinäkyvyyttä ja tukea ennakoivaa päätöksentekoa. 3) Verkottunut ja yhteistoiminnallinen luonnonvaratalous, joka käsittää digitaalisten alustojen ja liiketoimintamallien kehittämisen biotalouden toimijoiden verkostoituneen toiminnan mahdollistamiseksi. Tiekarttatyön tuloksena muotoiltiin toimintasuosituksia eri avaintoimijoille. Digitalisaatio vaatii nopeutta, ketteryyttä ja sopeutumiskykyä. Tällaisessa toimintaympäristössä kokeilukulttuurin omaksuminen on tärkeää. Tutkimusta tulee suunnata uusien konseptien kehittämiseen ja niihin liittyvien liiketoimintamallien seurausten ennakointiin. Keskeistä on tunnistaa asiakastarpeita ja ymmärtää palvelut olennaiseksi osaksi tuotteita. Viranomaistoiminnassa on omaksuttava ennakoiva asenne, joka tukee uusien ratkaisujen syntymistä. Innovatiivisten hankintojen edistäminen on yksi keino toteuttaa muutosta.

This is a selection of results of the North State project, that demonstrate how innovative methods applied to the new Sentinel data streams can be combined with models to monitor carbon and water fluxes for pan-boreal Europe.

The availability of ground reference forest data can be a bottleneck in remote sensing studies. Data may be available in only limited areas because of the cost and lengthy process of traditional forest inventory data collection by professionals. In certain cases, forest inventory data may not be easy to obtain, if not impossible. We propose a new concept that reduces costs of forest inventory data collection compared to traditional methods and is more portable than ground lidar approaches. This participatory sensing approach relies on volunteers to collect forest inventory data. For collecting in-situ data from the forests, a forest measurement tool called Relasphone has been developed. Measured variables include stem biomass, tree species, tree height and age. The collected forest measurements can be combined with remote sensing data and put to a computation engine that produces forest biomass maps. The GPS-based location information embedded with measured forest data makes it possible to easily introduce measurements as a in-situ reference data into any GIS.

Two semi-empirical approaches suitable for forest tree height retrieval from interferometric SAR images were developed in this study. Methods developed are mainly meant for cases when reference elevation model is missing. Spaceborne interferometric SAR data from TanDEM-X mission was used in the study. The study site was located in the south-eastern part of the state of Chiapas, Mexico. The TanDEM-X images were acquired during spring and summer 2012. The height estimates obtained in the study varied between 15 and 35 meters in the area of interest covered by the TanDEM-X images. The Pearson's correlation coefficient between estimated tree heights and reference ground plots form National Forest Inventory were 0.25 and 0.32 for maximum and average tree height measures, respectively. This work on tropical forest tree height retrieval from interferometric TanDEM-X images was performed within the EU FP7 project ReCover

A technique was developed for the mapping of selective logging in tropical forest using PALSAR imagery acquired before and after the logging operations. The technique is based on automatic detection of new forest roads and unsupervised classification of textural features, which were computed from a backscatter ratio image. The technique was adapted for TerraSAR-X and ENVISAT/ASAR data. The accuracy of a PALSAR-based map of selective logging was assessed against reference plot data interpreted from GeoEye-1 imagery. The user's accuracy of selective logging was 95 %. The overall accuracy was 70.4 %. This is affected by the limited size of the area, which was chosen to include a large proportion of selectively logged forest. The accuracy of a TerraSAR-X-based map of selective logging was 53.6 %, but the user's accuracy of selective logging was 100 %. ALOS-type L-band radar data could be applied in an operational system over country-wide datasets to map newly constructed roads. The 40 % underestimation does not make direct reliable mapping of the area of selective logging possible. Instead, L-band derived maps of selectively logged area and new roads can be used in a wider system to pin-point areas of recent logging activity (as a proxy for forest degradation). The detected areas can then be covered by satellite or airborne optical data or ground surveys. The L-band map of areas with logging activity can also be used for stratification for sampling in a statistical area assessment

Project ReCover, funded by the 7th Framework Program of the European Union, developed beyond state-of-theart service capabilities to support fighting deforestation and forest degradation in the tropical region in the context of the REDD process (Reducing Emissions from Deforestation and forest Degradation). A monitoring system for forest cover mapping by combining wall-to-wall mapping and a sample of VHR imagery was introduced. Also biomass and changes of forest cover changes were estimated. ReCover provided close to one hundred products for the study sites in Mexico, Guyana, Democratic Republic of Congo, Colombia and Fiji using optical and SAR data and their combinations. The accuracy in forest and nonforest classification varied from 85 % to 97 %

A system that combines forest variable information from cellular phone images with remote sensing data was developed. The system utilizes the central projection geometry of the cell phone images by applying the so called relascope principle to derive information on stem basal area, that is closely related to forest biomass. Information on tree species and forest types is also collected with the cellular phones. The forest variable information with GPS coordinates from the cell phone images are geo-located in satellite images and used as training data. Another independent image set is used for the accuracy assessment. The predictions from the satellite data are input to the "Planner Engine" that outputs forest resources information for every forest stand, and feeds them into a Geographical Information System (GIS).

The overall objective of the Social Forest Planning project is to develop and demonstrate a novel system for forest management planning and updating of present plans. The system to be developed combines images from cellular phones with remote sensing data to predict forest variables. The variables are input to a Planner Engine that outputs forest resources information for every forest stand and feeds them into a Geographical Information System (GIS). The GPS location tag in the images makes it possible to use them easily as in-situ reference data. The main technical development goal of Social Forest is to develop automatic and interactive methods for the cell phone image analysis. The automatic methods were presented in Remote Sensing Days 2010 [2]. This paper focuses on interactive delineation of tree stems, that enables automatic computation of tree stem basal area [m2/ha], the key variable in the estimation of the growing stock volume.

The overall objective of the Social Forest Planning project is to develop and demonstrate a novel system for forest management planning and updating of present plans. The system combines images from cellular phones with remote sensing data to predict forest variables. The variables are input to a Planner Engine that outputs forest resources information for every forest stand and feeds them into a Geographical Information System (GIS). The GPS location tag in the images makes it possible to use them easily as in-situ reference data. The main technical development goal of Social Forest is to develop automatic and interactive methods for the cell phone image analysis. The automatic methods were presented in Remote Sensing Days 2010 [2], and interactive delineation of tree stems for computing basal area in Remote Sensing Days 2011 [3]. This paper gives an overview of results of the whole processing chain. Latest results include stand-wise predictions of forest variables from satellite and cell-phone images and analyzed cell-phone images as a reference data.

A new concept SilvaSat, is proposed to support sustainable use of forest resources at global scope. The key element of the concept is acquisition of accurate satellite images of 0.5 meter ground resolution using a statistical sampling principle. Such images make it possible to collect reliable statistical information on forests at any global location for areas of 20 000 km2 and larger. A global forest inventory and monitoring system can be created by combining the accurate images with field data and coarser resolution satellite data that cover the whole area of interest. With the help of the new system the accuracy of global forest information can greatly be improved. This paper summarizes results of several studies, in which the concept has been developed and evaluated. The concept can be implemented by launching a specific satellite for the acquisition of the accurate images or by ensuring decadal-term image supply from existing commercial and national satellites. The present estimate on the costs of a new satellite ranges from US$ 70 to 160 million (€50-100 million).

Self-Organising Maps (SOMs) have been successfully applied to content-based image retrieval (CBIR). In this study, we investigate the potential of PicSOM, an image database browsing system, applied to remote sensing images. Databases of small images were artificially created, either from a single satellite image for object detection, or two satellite images when considering change detection. By visually querying those databases, it was possible to detect targets like houses, roads or man-made structures, as well as changes between two QuickBird images. Results open a full range of applications, from structure detection to change detection, to be embedded in a same operative system. The framework may be particularly suitable for long-term monitoring of strategic sites.

Self-Organizing Maps (SOMs) have been successfully applied to content-based image retrieval (CBIR). In this study, we investigate the potential of PicSOM, an image database browsing system, applied to remote sensing images. Databases of small images were artificially created, either from a single satellite image for object detection, or two satellite images when considering change detection. By visually querying those databases, it was possible to detect targets like houses, roads or man-made structures, as well as changes between two QuickBird images. Preliminary results were encouraging, and open a full range of applications, from structure detection to change detection, to be embedded in a same operative system.

This publication presents the results of a feasibility study on a proposed superhigh resolution satellite mission Kioto+. The study was conducted by an international consortium in response to the 2005 call for ideas for Earth Explorer missions of the European Space Agency (ESA). Kioto+ offers reliable and global data to near in-situ measurement accuracy on land cover and forest cover. It also gives information about their development over time. A super-high resolution optical instrument is proposed to achieve statistically representative and precise measurements. The information will greatly improve our understanding of the global carbon and water cycles, and the credibility of estimates of terrestrial carbon storage. The imagery will also give globally accurate training and validation data for wall-to-wall imaging instruments. The mission is named Kioto+ because the projected timescale of the mission (post-2011) means that it will primarily have relevance to successor treaties of the Kyoto Protocol to the FCCC of the United Nations.

The aim of the study was to develop a tool for the estimation of forest variables using high-resolution satellite data. The tool included modular operative software. The image analysis methodology focused on the reduction of the known problems of the previous satellite image based methods, i.e. the saturation of the estimates at higher biomass levels and uncertainty in tree species estimation. Modern contextual image analysis methods were combined with the spectral information of the imagery. In the test application the tool used images from the Ikonos satellite with a ground resolution of one and four meters. The developed Forestime software estimated the forest variables by segmenting the imagery to 'micro-stands', by computing stand-wise image feature vectors for the stands from the input satellite image, and by combining ground reference data with clusters from an unsupervised clustering stage. The estimates are produced as weighted sums of the input sample class probabilities. The target variables in the study were stem volume, average stem diameter, stem number and tree species proportions. The RMSE% for total stem volume was 37.4 % (% of mean), for average stem diameter 23.4 %, for stem number 87 %, for pine percentage 111 %, for spruce percentage 47 %, and for broad-leaved tree percentage 137 %.

Earth observation techniques using optical and SAR data were developed to help the implementation of the Kyoto Protocol. Forest area and major tree species were mapped in study sites in Italy and Finland. Forest biomass was estimated in a plantation in Borneo using very high-resolution data. Results showed that instruments similar to Landsat Thematic Mapper are effective to provide required forest area information. C-band SAR can also be used in pure forest and non-forest mapping. A major challenge is to obtain reliable ground training and validation data as well as adequate and high-quality imagery for the reference year 1990 of the Protocol.

The potential of space-borne earth observation techniques to monitor possible undeclared activities on a nuclear waste repository site was surveyed. The highest potential of earth observation is in the detection of undeclared construction of buildings and roads as well as detection of undeclared quarrying. Real-time applications such as continuous traffic monitoring are not feasible in earth observation applications that require high spatial accuracy. The monitoring system consists of baseline data acquisition that should be donebefore excavation activities start, of routine monitoring, and of an optional alarm survey. Space-borne synthetic aperture radar (SAR) is suggested as the principal instrument tocollect the monitoring data because of its all-weather capability.

Keskiresoluution kuviin voidaan laskea kuvat, joiden erotuskyky on niin karkea, että tulkinta voidaan perustaa erillisten kuvanalkioiden käsittelyyn. Tulkinta kattaa laajahkoja alueita ja on suhteellisen yleispiirteistä. Menetelmät ovat kehittymässä entistä automaattisemmiksi. Mitä karkeampi erotuskyky sitä useampia maastotyyppejä mahtuu saman kuvanalkion sisään. Uusimmat tulkintamenetelmät pystyvät ottamaan tämän huomioon.

Semi-operative or even operative methods are already available for the estimation of the stem volume of forests. However, a drawback of most of the methods is that they need extensive ground data. This paper presents a new method for the estimation of the forest stem volume. The method is based on fitting a stem volume model function to Landsat ETM+ reflectance data and ground data. Separate models were estimated for coniferous and broad-leaved trees. The models were validated using Landsat ETM+ data and ground data from another study site. The root mean square errors in the two study sites for coniferous trees were 50.5 m3/ha and 62.2 m3/ha. The errors for the broad-leaved models were 12.7 m3/ha and 78.4 m3/ha, respectively. The best models were applied to a Modis image mosaic of Finland and Sweden. The resulting estimates were compared to the Finnish and Swedish forestry statistics. The results were better in Finland than in Sweden. For the whole Finland the total bias of the average stem volume was -2.3 m3/ha. In total stem volume the bias was -34.4 mill. m3. In Sweden the total bias of the average stem volume was -15.6 m3/ha, which caused a bias in the total stem volume of -598.0 mill. m3. A major reason for the large underestimation in Sweden was the remaining clouds in the mosaic.