Forest data services of the Finnish Forest Centre. Juho Heikkilä, Chief Forest Data Specialist, Lic.Sc. (For.) Lahti, Finland, May 31, 2017

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1 Forest data services of the Finnish Forest Centre Juho Heikkilä, Chief Forest Data Specialist, Lic.Sc. (For.) Lahti, Finland, May 31, 2017

2 Finland - the most forested land in Europe The population of Finland is 5.5 mill. and the total area is almost 34 mill. ha of which 23 mill. ha is forest. About 14 mill. ha of forests are owned by private forest owners. Boreal forests including low number of tree species, but often slightly mixed stands (pine or spruce dominated). Public actors, e.g. Finnish Forest Centre - private forestry, Metsähallitus - state forests. Forest companies, e.g. UPM, Stora Enso, Metsä Group. 2

3 The Forest Centre in brief The Finnish Forest Centre promotes management and utilisation of forests and their biodiversity as well as related livelihoods Services Metsään.fi eservices for forest owners and operators Forest data collection and forest information products Forestry legislation enforcement Support under the Sustainable Forest Financing Act (Kemera) Advice and training for forest owners, companies and organisations Forest livelihoods and sustainable forest economy advocacy The Forest Centre has a wide network of regional offices with 590 employees The Forest Centre is part of indirect state administration Operations are guided, funded and monitored by the Ministry of Agriculture and Forestry 3

4 Forest inventory goals and areas in Since 2010 forest inventory has been based mostly on remote sensing (laser scanning and aerial photography). The main goal is to maintain forest resource database that 1. covers the private forests of the whole country, 2. is up-to-date, and 3. is of good quality and thus makes operative planning possible. Due to remote sensing the forest inventory costs have decreased 75% and the Forest Centre use more of its resources for customer service. 4

5 Coverage and content of the forest data At the moment, coverage in our database is about 80 percent of the whole forest area owned by private forest owners and will reach 100 percent by the year Data is collected for every forest compartment (spatial unit which needs homogeneous silvicultural or cutting treatment). Forest data contains information about Soil and forest site Growing stock; pine, spruce, decidious and total (dgm, hgm, ba, n, v, t) Treatment proposals Environmental values (habitats of special importance) Coverage map: 5

6 Aerial photography, laser scanning and sample plot measurements Forest interpretation, stand delineation and field check data ready to use Aerial photography, laser scanning and sample plot measurements Forest interpretation, stand delineation and field check data ready to use Forest data acquisition and maintenance Continuous updating of cuttings or other operations and growth modeling n n + 1 n + 5 n + 9 n + 10 years The aim is an inventorying cycle of 10 years (about 1.5 mill.ha/year) and continuous updating. Updating data sources: 1) official notifications (e.g. forest use announcements or funding applications), 2) forestry actors cutting and silvicultural operations, 3) information directly from forest owners 6

7 Field sample plot points are selected and located beforehand according to the existing forest data 7

8 Sample plot types 1. young/advanced thinning or mature forest 2. Advanced seedlings 3. Young seedlings 0 o (pohjoinen) 6 m 9 m (harvat 12,62 m) 9 m 9 m 5,64 m 2,82 m m sama taimikko, ei siemen- tai jättöpuita 225 o (lounas) 135 o (kaakko) 8

9 Field sample plot measurement Tree heights and diameters, distance control (circle plots of 9 m radius). Exact GNSS location of the sample plot (mid-point). Altogether field sample plots are located and measured for each about ha forest inventory area (approx ha laser scanning area). Pictures Masser Oy 9

10 Field sample plot measurements (GNSS on the left) 10

11 Airborne laser scanning (ALS) Lidar (light detection and ranging) system sends pulses of laser light and measures the distance from the ground or vegetation according to the returning pulses. In forest inventory observations (pulses)/m 2 is needed (areal based method). This means flying altitude of 2 km and about 1 km scanning line width. Because laser scanning does not need sun light, it can be done also during the night. However, rain drops scatter the laser beams. First Second Third Last All Returns 11

12 Airborne laser scanning (ALS) It is important that the ALS data is homogeneous throughout the inventory area and the laser scanning is made during the same phase of the growing season. The scanning parameters may not be altered during the flight, and each inventory area must be scanned using the same scanning unit in order to produce uniform data. 12

13 Forest inventory Inventory is based on laser scanning, but aerial photos are also useful especially in tree species recognition. The remote sensing data is combined with the field data so that each field sample plot gets corresponding characteristics of laser point data and pixel values of aerial photos. After that, modeling between the forest measurements and remote sensing data can be done and utilised in the estimation of the whole inventory area. Lidar does not provide accurate estimates of seedling stands and they must be measured in the field if other sufficient information is not available. Field sample plot, aerial photo and laser points. Picture Blom Kartta Ltd. 13

14 Stand delineation and forest compartment data Lidar Canopy Height Model (CHM) can be used e.g. in automatic stand delineation (microstands, ha). In addition, semi-automatic process has been developed to combine microstands to larger treatment stands (0.5-5 ha). Pictures Blom Kartta Ltd.

15 Forest inventory unit is 16x16 m 2 grid cell. In the next phase the systematic grid data is generalized and calculated to the forest compartments. In addition, silvicultural treatments and cutting proposals are simulated according to the forest management recommendations. Pictures Arbonaut Ltd. 15

16 Laser inventory quality Sample plot level accuracy requirements (leave-one-out cross validation, RMSE%) Total: mean height (hgm) 10%, mean diameter (dgm) 15%, basal area (ba) 20% and volume (v) 20%. Main tree species: hgm 10%, dgm 20%, ba 30%, v 30%. Forest stand level accuracy requirements (quality control is based on systematic field sample plot measurements of each control stand). Total: hgm +/- 2 m, dgm +/- 3 cm, ba +/- 3 m 2, v +/- 20% in 80% of the stands. Tree species: at least right main tree species. Seedling stands: at least right silvicultural treatment proposal. 16

17 Forest Information System Data and Architecture - Forest resource data, forest management planning data, key biotopes (Forest Act). - Cuttings and silvicultural works (announcements from forest owners and forestry actors) - Forest Authority data. - External GIS-data: aerial photos, lidar data, topographic maps, cadastral data, protected areas, ground water areas, prehistoric monuments etc. - 3-level architecture: client application, terminal servers and database server. - Client application: ArcGIS Desktop tools and tailored functionality based on ArcObjects component library. - Oracle database connected directly by ArcGIS Server/ArcSDE 17 - Connections to map servers over the Internet (eg. WMS)

18 Metsään.fi Service State-funded eservices for Finnish forest owners and companies. lucoocfi7cu 18

19 Metsään.fi Shows the possibilities of each forest estate and encourages the users to carry out silvicultural works. The information is as secure as in banking services. The forest owner decides which external parties may access the data. Forest owners can easily share the information with companies and inform the needs for forest work. Forest owners, companies and Forest Centre use the same information. Forest owners and companies can inform Forest Centre about work done and submit forest usage notifications. 19

20 Forest owner s example page Forest estate is divided into compartments on the map or aerial photo. Properties of trees, forest, soil and nature. Options of silvicultural treatments and cuttings (what to plant, cut or protect, estimated costs and earnings). Sustainability of the nature, e.g. important habitats. Metsään.fi is always available on a computer with an internet connection. Easy to use, does not require forest expertise. 20

21 A one-stop principle An impartial meeting point for forest owners and operators Metsään.fi is integrated with internal operative systems customer data and forest data systems the information we use at work is now in use of our customers too Also integrated with external services Public service for identification Identification for companies and organizations Land information, background maps and aerial photography from National Land Survey We try to make the most out of Finnish public sector data sources 21

22 The future In general, the remote sensing (RS) inventory system has been highly successful. The bottleneck, for example for the direct timber trade, is still the tree species separation (especially minor tree species). Also dense tree stocks of young stands are usually underestimated (first thinning). Other problems include local variability of the forests weather problems in optical data (aerial photos) acquisition co-operation between organisations (especially proper cutting and silvicultural treatment information is needed for updating process) 22

23 The future In addition, inventory developments needed Empirical diameter distribution (instead of sum and mean values and theoretical distributions) Sapling stands (expensive field work, % of the inventory area) Multilayered stands (uneven aged silviculture) Biodiversity aspects (based mainly on field work) Site classes (based on the existing field data) Forest health (e.g. insects) Bioenergy 23

24 The future Development of the new systems (2020 ->) Possibility of ITD (individual tree detection) or semi-itd? ITD has developed a lot although basic problems still remain. When we now have RS system, it is also easier to adopt new RS system. Clear improvements in accuracy, especially in tree species detection would be needed. Usability in young stands? Usability in a very large scale (dense laser point data, scanning costs)? Multispectral lidar, full waveform laser, hyperspectral imagery? 24

25 Research - Improved tree species discrimination with multispectral airborne lidar sensor Is it possible to improve the performance of species-specific remote sensing inventory with multispectral ALS data? The use of multispectral ALS intensities instead of aerial images? Optech Titan multispectral lidar. 25

26 Titan RGB_hillshade_050, Terratec Ltd 26

27 Research - High density airborne lidar data improves performance of area based inventory Do we get any benefit from high density ALS point cloud? Comparison of ITD, ABA and edge-tree corrected ABA (semi-itd). Packalen, P., Strunk, J., Pitkänen, J., Temesgen, H. and Maltamo, M Edge-tree Correction for Predicting Forest Inventory Attributes Using Area-based Approach With Airborne Laser Scanning. IEEE J- STARS 8(3):

28 Research - Comparative test to predict species specific diameter distributions and distributions in forest information systems Current diameter prediction system is not optimal. The system relies on species discrimination and prediction of species specific stand attributes which are then used to calculate distribution, i.e. approach is more species recognition than diameter distribution prediction. How much can be obtained if the current system is fine tuned? New approaches include, e.g., different data combinations and statistical methods. 28

29 Forest data services towards 2020 s Forest Centre s goals in practice 1. Wall-to-wall laser inventory (also other than private forests, e.g. municipalities). 2. Ensure the current forest data quality. Further development of forest characteristics and especially tree species interpretation. 3. Systematic grid data also into the forest data download service (interface utilises forest data standard in xml-format). 4. Empirical diameter distributions in laser inventory, updating process, forest calculation and database management. 5. Development of new variables, for example better information of harvesting conditions, forest sites and soil, forest roads etc. 6. Maintain forest data of sufficient quality from all development classes (also seedling stands and young forests). 7. Clear up the requirements and rules for utilizing carried out forest cutting and silvicultural data in continuous updating, and make two-way data flow possible widely in the forest sector (needs a common will, forest standardization and proper ITsystems). 8. Forest resource data and services are in wide use among forest owners and forestry sector in general. 29

30 Thank you!