Search

Environmental monitoring networks such as the Integrated Carbon Observation System (ICOS) in Europe, the National Ecological Observatory Network (NEON) in the U.S., or the Terrestrial Ecosystem Research Network (TERN) in Australia deploy different sampling schemes for in situ measurements. We report on the intercomparison of measurements of the canopy gap fraction with different digital hemispherical photography setups adopting ICOS, NEON, and TERN sampling schemes. The test was carried out at the Järvselja Radiation Transfer Model Intercomparison (RAMI) birch stand. Results show that spreading out sampling points which cover more of the plot is important for a good representation of the forest as a whole. The NEON tower plot layout scheme may be more prone to errors in overall canopy properties estimation than ICOS or TERN due to its compact sampling layout and should always be used in conjunction with its distributed plots. Different camera setups involving different camera operators, camera bodies, lenses and settings yield slightly varied results, and it is important to ensure that the images are taken in such a way that they would not be over or underexposed, or out of focus. As a conclusion we recommend always to carry out intercomparison measurements with old and new cameras when devices are upgraded. Our study contributes towards establishing the uncertainty and evaluating potential error budget stemming from collecting in situ measurements using different sampling schemes and camera setups.

2007. aastal rajati Järvseljal kolm ühe hektari suurust katseala, kui kaardistati ja mõõdeti üle kõik puud simulatsioon eksperimentide korraldamiseks (Kuusk, et al., 2013; Widlowski et al., 2015). Need kolm puistut (vt fotod lisas 1) esindavad tüüpilist liitunud võrastikuga kuusikut, kaasikut ja siirdesoo männikut. Kaasikus oli tehtud 2004. aastal harvendusraie, 2018. aastal puhastati katseala alusmetsast, et valmistada ala ette maapealse laser skanneriga mõõtmisteks. Kuusikus ja männikus teadaolevalt viimase 30 aasta jooksul raieid tehtud ei ole. Katsealade (joonised 1, 3, 5) nurkade koordinaadid on mõõdetud diferentsiaal-GPS meetodiga peale mahamärkimist (tabel 1). 2019. aasta kordus mõõtmiste tulemusena ilmnes, et puistute rinnaspindala, keskmine kõrgus ning keskmise puu rinnasläbimõõt olid kasvanud (tabelid 2–4). Puude arv oli kahanenud kõigis puistutes, välja arvatud kaasikus, kus 2018. aasta puhastus raie käigus jäeti alles selleks ajaks 4 cm rinnasdiameetri saavutanud puud. Kuusikus saadi tulemuseks, et võrade raadiused olid võrreldes 2007. aastaga kahanenud. Kas tegemist on võra projektsiooni välispiiri erineva tõlgendamise või tegelikult toimunud muutusega, võiks edaspidi selguda 2013. (Kuusk et al., 2015) ja 2019. aastal tehtud maapealse laserskanneri mõõtmiste andmete analüüsist.

Landsat-8 OLI and Sentinel-2 MSI images from years 2015 and 2016, a 1:10,000 digital soil map and a large number of reference samples were used with a random forest machine learning implementation in GRASS GIS to construct a tree species map for the entire territory of Estonia (42,755 km2). Class probabilities for seven main tree species, an extra class for other species and probability of the forest cover not conforming to the forest definition were assigned for each pixel. Validation of dominant species distribution by area showed very strong correlation at county level both in state forests (R2 = 0.98) and in private forests (R2 = 0.93). Validation of tree species composition using harvester measurement data from 2,045 regeneration felling areas showed also very strong correlation (R2 = 0.75) with the measured values of the proportion of coniferous trees. There was some tendency to underestimate the proportion of more common species and overestimation was found for the species with smaller proportion in the mixture. The accuracy for the proportion of deciduous species that were present in a smaller number of reference observations was substantially smaller. Validation of the results by using data from 659 large sample plots from the database of the Estonian Network of Forest Research Plots and 3,002 small sample plots from the National Forest Inventory (NFI) data base confirmed the findings based on harvester data. The NFI data revealed also a decrease of estimation error with the increase of forest age. Cohen’s kappa index of agreement for main species for NFI sample plots with main species proportion equal to or greater than 75% decreased from 0.69 to 0.66 when observations with forests younger than 20 years were included in the comparison. Overall, the constructed map provides valuable data about tree species composition for the forests where no up to date inventory data are available or for the projects that require continuous cover of tree species data of known quality over the entire Estonia.