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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.

The view angle range of Sentinel-1 SAR in the Interferometric Wide swath (IW) measurement mode is 29.1° – 46.0°. The dependence of backscatter on the arbitrary local incidence angle is usually corrected using a linear regression model where the incidence angle is a predictor variable. We analysed the whole time series of Sentinel-1 SAR VV-polarised backscatter over the flat 15×15 km test site in Laeva, Estonia (26° 26′ 43″ E; 58° 31′ 56″ N). Time series containing measurements from three different orbits were constructed for 3,159 stands from nighttime data and for 1,105 stands from daytime data. We can confirm that daytime backscatter is systematically greater than nighttime backscatter. We found a significant deviation from linearity in the backscatter dependence on local incidence angle. The empirical finding may be caused by the microwave scattering dependence on local incidence angle or by the influence of Sentinel-1 SAR sub-swath configuration in the Terrain Observation with Progressive Scans SAR (TOPSAR) method that is used for the measurements.

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.

Horizontal visibility v in hemiboreal forest transects was measured in the field and then predicted, both from forest inventory (FI) data and from airborne laser scanning (ALS) data. Stand density N and mean diameter at breast height D were used as arguments in an FI predictive model assuming Poisson distribution of trees on a horizontal plane. It was found that a lack of FI data on forest regrowth and understorey trees caused v to be overestimated. Point cloud metrics of sparse ALS data from summer 2017 and spring 2019 were used as predictive variables for v in regression models. The best models were based on three variables: the 10th percentile of the point cloud height distribution, relative density of returns in a horizontal layer ranging 0.7–2.2 m above the ground, and canopy cover. The models had a coefficient of determination of up to 67% and a residual standard error of less than 25 m. In forests in which fertile soil produces rapid height growth of understorey woody vegetation after recent thinning, visibility was found to be substantially overestimated because the understorey was not detected by the lidar measurements.

Global change-type droughts will become more frequent in the future and threaten forest ecosystems around the globe. A large proportion of the Estonian forest sector is currently subject to artificial drainage, which could probably lead to negative feedbacks when water supply falls short because of high temperatures and low precipitation during future drought periods. In this short article, we propose a novel research perspective that could make use of already gathered data resources, such as remote sensing, climate data, tree-ring research, soil information and hydrological modelling. We conclude that, when applied in concert, such an assembled dataset has the potential to contribute to mitigation of negative climate change consequences for the Estonian forest sector. In particular, smart-drainage systems are currently a rare phenomenon in forestry, although their implementation into existing drainage systems could help maintain the critical soil water content during periods of drought, while properly fulfilling their main task of removing excess water during wet phases. We discuss this new research perspective in light of the current frame conditions of the Estonian forest sector and resolve some current lacks in knowledge and data resources which could help improve the concept in the future.

Since 1999, Estonia has conducted the National Forest Inventory (NFI) on the basis of sample plots. This paper presents a new module, incorporating remote-sensing feature variables from airborne laser scanning (ALS) and from multispectral satellite images, for the construction of maps of forest height, standing-wood volume, and tree species composition for the entire country. The models for sparse ALS point clouds yield coefficients of determination of 89.5–94.8% for stand height and 84.2–91.7% for wood volume. For the tree species prediction, the models yield Cohen's kappa values (taking 95% confidence intervals) of 0.69–0.72 upon comparing model results against a previous map, and values of 0.51–0.54 upon comparing model results against NFI sample plots. This paper additionally examines the influence of foliage phenology on the predictions and discusses options for further enhancement of the system.

In this summary, we give an overview of the application of airborne laser scanning (ALS) data for predicting the main forest inventory variables in Estonia. When Estonia being one of the few countries with wall-to-wall ALS availability, the need for applicable models for Estonian forests was imminent. Over the past decade, different studies have been carried out to develop models for standing wood volume, forest height, canopy cover, canopy base height, and methods for monitoring height growth and detect small-scale harvests. The main findings showed strong correlations for all the studied parameters and different methods utilizing low-density lidar data for practical forest inventory purposes. Options for using repea ted ALS measurements for continuous forest inventory are discussed.

Hemispherical photography provides permanent records of forest canopy structure. We analysed digital hemispherical images taken during the period of 2007–2018 in a mature silver birch stand located in Järvselja, Estonia. The stand was thinned in 2004. Understory trees were removed in the spring of 2018. Images were processed using the LinearRatioSC method. Effective plant area index Leff during the leafless phenophase increased as a result of tree growth from 0.92 to 1.24 and understory cutting was not detectable. During the full foliage condition Leff increased from 3.6 in 2008 to 5.8 in 2017. After removal of understory trees from the stand Leff decreased, and repeated measurements in the summer of 2018 estimated the plant area index range 4.5 < Leff < 4.8. The results are in agreement with the expected changes following forest growth and demonstrate that LinearRatioSC is a suitable method for the estimation and long-term monitoring of forest canopy properties from digital hemispherical images.

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.

Thinning cuttings create moderate disturbances in forest stands. Thinning intensity indicates the amount of felled wood relative to the initial standing volume. We used sparse point clouds from airborne lidar measurements carried out in 2008 and 2012 at Aegviidu test site, Estonia, to study stand level relationships of thinning intensity to the changes in canopy cover and ALS-based wood volume estimates. Thinning intensity (Kr, HRV) was estimated from forest inventory data and harvester measurements of removed wood volume. The thinning intensity ranged from 17% to 56%. By raising threshold from 1.3 m to 8.0 m over ground surface we observed less canopy cover change, but stronger correlation with thinning intensity. Correlation between ALS-based and harvester-based thinning intensity was moderate. The ALS-based thinning intensity estimate was systematically smaller than Kr, HRV. Forest height growth compensates for a small decrease in canopy cover and intensity estimates for weak thinnings are not reliable using sparse point clouds and a four-year measurement interval.

Forest height increment rate is related to the forest growth conditions. Data bases of previous forest inventories contain information about forest heightage relationship on large number of forest stands while repeated measurements of permanent sample plots provide an excellent reference for comparison. Repeated airborne laser scanning of forest stands is an additional source for the estimation of change in forest structure. In this study, height growth of middle-aged and older forest stands for about 10 year period was compared to an algebraic difference model on permanent sample plots (66) and for a sample of forest stands with repeated airborne laser scanning data (61). The model was based on a large dataset of forest inventory records from the period of 1984–1993. Statistically significant increased forest height growth was found in permanent sample plots based on tree height measurements (9 cm yr−1) as well in stands with repeated laser scanning data (4.5 cm yr−1) in South-East Estonia compared to the algebraic difference model. The difference between the two data sets was explained by their mean age and site class, but the increased forest height growth compared to the old forest inventory data indicates improved growth conditions of forests in the test area. The results hint also that empirical data-based forest growth models need to be updated to avoid biased growth estimates.

A new paradigm in forest management using a streaming input of public participation needs effective online solutions. The process should be real-time, secure, effective and efficient. People are expected to share their data and thoughts on forest management with forest owners for improving forest management and planning. The participatory approach supports communication within society and can be designed as an interactive web-based solution. Many pre-requisites have already been met and society is ready for a successful start of an interactive participatory forest planning system in Estonia. People use digital identification for various purposes and the state already maintains an online public forest register. Motivating people to participate in the planning process is always challenging yet important for the successful implementation of the system. The system should allow simulating the development and management of forest stands following the participatory input and using ecosystem models and economic calculations. The outputs from the system include management alternatives, risk assessments and financial reports. The system requires a reliable financial compensation scheme to ensure overall long-term stability of the system and agreements between interested persons or groups and forest owners.

Optical remote sensing data-based estimates of terrestrial net primary production (NPP) are released by different projects using light use efficiency-type models. Although spatial resolution of the NPP data sets is still too coarse (500–1000 m) for single forest stands, regional monitoring of forest management and growth with 25–100 ha sampling units is feasible if the NPPSAT estimates are sensitive to forest growth differences depending on soil fertility in the area of interest. In this study, NPP estimates for 2,914 mixed forest class pixels (according to the MODIS land cover map) located in Estonia were (1) obtained from three different NPPSAT products, (2) calculated using an empirical soil potential phytoproductivity (SPP) model applied to a 1:10,000 soil map (NPPSPP), and (3) calculated using stem volume increment estimates given in a forest management inventory data base (NPPFIDB). A linear multiple regression model was then used to explore the relationships of NPPSAT with the proportion of coniferous forests, the NPPSPP and distance of the pixels from the Baltic Sea coast – the variables that have been found informative in previous studies. We found a positive moderate correlation (0.57, p < 0.001) between NPPSPP and NPPFIDB. The local or downscaled meteorological data-based NPPSAT estimates were more consistent with the NPPSPP and NPPFIDB, but the correlation with NPPSAT was weak and sometimes even negative. The range of NPP estimates in NPPSAT data sets was much narrower than the range of NPPSPP or NPPFIDB. Errors in land cover maps and in estimates of absorbed photosynthetically active radiation were identified as the main reasons for NPPSAT inconsistencies.

A generic regression model for above-ground biomass of forest stands was constructed based on published data (R2 = 0.88, RSE = 32.8 t/ha). The model was used 1) to verify two allometric regression models of trees from Scandinavia applied to repeated measurements of 275 sample plots from database of Estonian Network of Forest Research (FGN) in Estonia, 2) to analyse impact of between-tree competition on biomass, and 3) compare biomass estimates made with different European biomass models applied on standardized forest structures. The model was verified with biomass measurements from hemiboreal and tropical forests. The analysis of two Scandinavian models showed that older allometric regression models may give biased estimates due to changed growth conditions. More biomass can be stored in forest stands where competition between trees is stronger. The tree biomass calculation methods used in different countries have also substantial influence on the estimates at stand-level. A common database of forest biomass measurements from Europe in similar to pan-tropical tree measurement data may be helpful to harmonise carbon accounting methods.

Airborne laser scanning (ALS) based standing wood volume models were analysed in two contrasting test sites with different forest types in Estonia. In Aegviidu test site main tree species are Scots pine and Norway spruce and Laeva test site is mainly dominated by deciduous species. ALS data measurements were carried out for Aegviidu in 2008 and for Laeva in 2013. Approximately 450 sample plots were established additionally to the forest inventory dataset in both test sites. Exclusive to the sample plots, 46 stands were measured in 2012 in Aegviidu for stand level model. The sample plot-based model standard error in Aegviidu was Se = 59.8 m3/ha (22%) and in Laeva Se = 69.2 m3/ha (29%). The stand-level model based on 46 measured stands from Aegviidu had Se = 38.4 m3/ha. Based on the models a cross-validation between the two test sites was carried out and systematic differences between the two test sites were found. The reasons are related to differences in optical properties of trees, crown shapes, flight configuration and canopy cover even though the sample plot based models included ALS-based canopy cover variable. The ALS-based wood volume estimate was also compared to forest inventory (FI) data and systematically larger estimates compared to FI dataset in both test sites were found. This average systematic error increased substantially (by 100 m3/ha) for stands with volume over 250 m3/ha. It was also detected that a model developed on small point clouds drawn for sample plots may produce systematic errors when applied to stand-level point clouds.

Leaf area index (LAI) characterizes the amount of photosynthetically active tissue in plant canopies. LAI is one of the key factors determining ecosystem net primary production and gas and energy exchange between the canopy and the atmosphere. The aim of the present study was to test different methods for LAI and effective plant area index (PAIe) estimation in mixed hemiboreal forests in Järvselja SMEAR Estonia (Station for Measuring Ecosystem-Atmosphere Relations) flux tower footprint. We used digital hemispherical images from sample plots, forest management inventory data, allometric foliage mass models, airborne discrete-return recording laser scanner (ALS) data and multispectral satellite images. The free ware program HemiSpherical Project Manager (HSP) was used to calculate canopy gap fraction from digital hemispherical photographs taken in 25 sample plots. PAIe was calculated from the gap fraction for up-scaling based on ALS point cloud metrics. The all ALS pulse returns-based canopy transmission was found to be the most suitable lidar metric to estimate PAIe in Järvselja forests. The 95-percentile (H95) of lidar point cloud height distribution correlates very well with allometric regression models based LAI and in birch stands the relationship was fitted with 0.7 m2 m−2 residual error. However, the relationship was specific to each allometric foliage mass model and systematic discrepancies were detected at large LAI values between the models. Relationships between the spectral reflectance and allometric LAI were not good enough to be used for LAI mapping. Therefore, airborne laser scanning data-based PAIe map was created for areas near SMEAR tower. We recommend to establish a network of permanent sample plots for forest growth and gap fraction measurements into the flux footprint of SMEAR Estonia flux tower in Järvselja to provide consistent up to date data for interpretation of the flux measurements.

Terrestrial laser scanner (TLS) measurements were carried out in mature birch, pine, and spruce stands in Järvselja, Estonia. The structure of stands has been previously studied. A simple special clustering procedure is developed for automatic detection of stem positions and estimation of tree stem dimensions from the TLS point cloud. Tree stem diameter at breast height (DBH), vertical profile of stem and size distribution of DBH is estimated with high precision in the pine stand where the second growth tree layer is almost missing. The presence of second growth limits estimating stem dimensions accurately in the birch stand, and the procedure fails in the dense spruce stand of rich undergrowth and low dead branches. In such stands the TLS measurements should be done in dense grid with short-range sounding setup only. The developed procedure is undemanding for computers and can be applied on simple PC-s.

Nearest neighbour techniques are useful for constructing maps of forest inventory variables based on sample plot and auxiliary data from remote sensing. The most problematic issue of the nearest neighbour technique is possible systematic bias in the estimated values. In this study a 15 by 15 km test site in nemoral multilayer mixed forests was established in Laeva, Estonia. A set of 444 circular sample plots was used as reference set. Airborne lidar data and Landsat-8 Operational Land Imager image were used to construct five different feature variable sets consisting of original variables and alternatively principal components. The response variables were wood volume of first tree layer, wood volume of the second tree layer and main species code. A special test was carried out where a substantial amount of Silver birch dominated plots were removed from the reference set. The wood volume prediction validation was carried out on 89 forest growth sample plots and on 2290 forest stands. Species composition prediction was validated on 986 forest stands. As in many previous studies the results confirmed superiority of airborne lidar variables over spectral variables for wood volume estimation. The first three principal components of airborne lidar variables and first five principal components of all possible original feature variables contained over 99% of the information and performed well in imputations. The imputed wood volume at small values was overestimated and underestimated at large values regardless of used reference set. The feature variable sets containing spectral data performed better for species composition imputation. There was a forest age dependent discrepancy in predicted species proportions: birch and spruce proportions were underestimated in young stands and overestimated in older stands while proportion of aspen had exactly the opposite errors. The lack of fit depended slightly on the feature variable sets. The birch dominated plot partial removal from the reference set changed the predicted proportion of species but did not remove the forest age dependent lack of fit. The result can be important for the studies in which bootstrap samples are used to estimate error statistics for nearest neighbour technique based forest inventory variable maps.

The merits and possible problems of the light use efficiency-concept based GPP/NPP models applied together with satellite images and meteorological data to quantitatively understand the role of different meteorological factors in forest productivity are analysed. A concept of the complex meteorological limiting factor for plant productivity is introduced. The factor includes the effects of incoming photosynthetically active radiation as well as the temperature and water limiting factors. Climatologically averaged seasonal courses of the complex meteorological limiting factor derived from the records of two contrasting meteorological stations in Estonia - inland Tartu/Tõravere and coastal Sõrve - are shown. Leaf phenology, here described via the seasonal course of leaf area index (LAI), is interpreted as a possible means to maximise the carbon gain under particular meteorological conditions. The equations for the optimum seasonal course of LAI as derived from the NPP model are presented. As the daily adjustment of plant LAI to sudden changes in meteorological conditions is not possible, several approximate strategies for LAI seasonal course to maximise the yearly NPP of vegetation are analysed. Typical optimal courses of LAI show some seasonal asymmetry resulting in lower values of LAI in the second half of the vegetation period due to higher air temperatures and respiration costs. Knowledge about optimum LAI courses has a cognitive value, but can also be used as the simulated LAI courses in several models when the measured LAI values are not available. As the considered GPP/NPP models fail to adequately describe the local trends in forest and agricultural productivity in Estonia, the ways to improve the model’s performance are shown.

The abandonment of agricultural land is an actual problem in Estonia due to significant impact on landscape ecology and structure. Abandoned agricultural fields are usually converting into forest. Mapping of agricultural land use is a strategic interest of each country. Airborne laser scanning (ALS) is used in many countries for topographical mapping and the laser pulse return positions are promising datasets for mapping the abandonment of agricultural land. We used ALS data based woody plant canopy cover estimates made at certain reference height unachievable for field crops to map abandoned agricultural land in nine test sites in Tartumaa, Estonia. The maximum height of trees in test sites ranged from 6.5 m to 13.4 m. The lidar pulse returns based canopy cover estimate was assessed 1) by using ortophoto based digitized maps of tree canopy, 2) repeated measurements made with plant canopy analyzer LAI-2000 and 3) by using allometric crown radius models and repeated tree measurements from sample plots. The interpretation of canopy boundaries and separation of small spaces between tree crowns from ortophotos is a challenging task for an operator. The relationship between ALS based canopy cover and ortophoto based canopy cover was linear in all test sites except when ALS data from beginning of June were used. It the beginning of June foliage is not fully developed on trees. An increase in the woody canopy cover was detected from repeated LAI-2000 measurements and also from repeated tree measurements-based simulated crowns. The impact of reference height change from 2.0 m to 1.3 m on canopy cover estimations was not significant and much smaller compared to the tree growth induced increase in canopy cover, indicating that similar errors originating from e.g. digital elevation model are not problematic for the proposed method in practical applications.

Canopy gap fraction has been estimated from hemispherical images using a thresholding method to separate sky and canopy pixels. The optimal objective thresholding rule has been searched by many authors without satisfactory results due to long list of reasons. Some recent studies have shown that unprocessed readings of camera CCD or CMOS sensor (raw data) have linear relationship with incident radiation. This allows a pair of cameras used in similar to a pair of plant canopy analyzers and canopy gap fraction can be calculated as the ratio of below canopy image and above canopy image. We tested new freeware program HemiSpherical Project Manager (HSP) for the restoration of the above canopy image from below canopy image which allows making field measurements with single below canopy operated camera. Results of perforated panel image analysis and comparison of plant area index (PAI) estimated independently by three operators from real canopy hemispherical images showed high degree of reliability of the new approach. Determination coefficients of linear regression of the PAI estimations of the three operators were 0.9962, 0.9875 and 0.9825. The canopy gap fraction data obtained from HSP were used to validate Nobis-Hunziker automatic thresholding algorithm. The results indicated that the Nobis-Hunziker algorithm underestimated PAI from out of camera JPEG images and overestimated PAI from raw data.

Information about the status of agricultural land is of strategic interest in every country. In Estonia, different and even contradicting estimates of area of actively used as agricultural land exist. To solve this problem, airborne laser scanning (ALS) data can be used to map the woody plant cover (values range from 0 to 1 corresponding to the no cover and full tree canopy cover) in agricultural land and to provide digital maps for further analysis. Canopy cover was estimated from ALS data by setting reference height to 2 m from ground. Validation dataset was created from ortophotos on 442 rectangular 100 m2 elementary sampling units. The relationship was linear and determination coefficient was high (R2 = 0.795) and the model fitting residual standard error was 0.116 indicating good applicability of ALS for woody plant cover mapping. The method can be easily automated, does not require additional fieldwork and can be applied in all places where ALS data are available

The space-borne Moderate Resolution Imaging Spectroradiometer (MODIS) data based net primary production (NPP) product from Numerical Terradynamic Simulation Group (NTSG) was tested in the Kurzeme region, Latvia using a stand-wise forest inventory database. The NPP product has been validated globally and found to have no overall bias. In this study the NPP product was compared with stem biomass increment and soil fertility in respect to distance from the Baltic Sea coast. For each MODIS NPP product pixel we calculated forest cover, share of coniferous trees, average stem biomass increment and average site fertility (growth potential estimate). Then, 2432 pixels with a forest cover over 75% were selected for analysis. The results indicated that MODIS NPP decreased with distance from Baltic Sea coast but stem biomass increment and site fertility indicated a trend of increase. There was no functional relationship between MODIS NPP and stem biomass increment. Analysis of the landcover map used by NTSG for MODIS NPP product showed that the classes “Evergreen needleleaf” and “Mixed forests” differentiated only 10% by mode value of coniferous proportions in species composition. A non-natural jump was detected in the MODIS NPP values at a longitude of 22.5 degrees east corresponding to the border of the coarse scale meteorological dataset (NCEP Reanalysis (R2)) data representation unit. According to the results the MODIS NPP product is not applicable for regional level planning but can probably provide only rough average estimates of NPP for the Baltic region

Airborne laser scanner (ALS) measurements from two test sites in Estonia were used to estimate forest canopy-base height (HVL). The ALS data was collected by Estonian Land Board using Leica ALS50-II scanner. The HVL was estimated by using mode value and standard deviation of the ALS pulse reflection position height distribution. The pulse reflections which had height less than 0.5 m over the estimated digital terrain model were excluded from the analysis. In situ measurements of canopy base height (HVA) were carried out in 20 mesotrophic Norway spruce and silver birch forest stands in Järvselja and in 45, mostly Scots pine dominant, mesotrophic forest stands in Aegviidu. Determination coefficients of linear regression between HVL and HVA for both test sites were over 0.8 and the residual standard errors of the models were less than two meters. The influence of forest understory vegetation to the estimation of HVL was tested by excluding the near-to-ground vegetation reflections which had height less than 1.5 m. The test results revealed no significant impact of forest understory to the HVL models. The cross validation showed that the HVL models were independent of test sites and tree species composition. The Järvselja data based HVL model had 1.3 m negative bias if applied to Aegviidu forests and the Aegviidu data based HVL model had 1.4 m positive bias if applied to Järvselja forests. In the Aegviidu test site, difference of HVL models of coniferous and deciduous stands was tested and the difference was found not to be significant

A light use efficiency (LUE) type model named EST_PP to simulate the yearly gross primary production (GPP) and net primary production (NPP) of Estonian land on a 1 km2 grid is described. The model is based on MERIS (MEdium Resolution Imaging Spectrometer) satellite images to describe the fraction of photosynthetically active radiation (fAPAR) and leaf area index (LAI) as well as meteorological reanalysis datasets on 11 km2 grid produced by Estonian Meteorological Institute (EMHI) and Tartu University (TU) by means of the HIRLAM (High Resolution Limited Area Model) numerical weather prediction model. The land cover map of Estonia needed for the model was derived using DMCii (Disaster Monitoring Constellation International Imaging) SLIM-6-22 (Surrey Linear Imager - 6 channel - 22 m resolution) images and ancillary information. The EST_PP model was run for the period from years 2003 to 2011. The results of GPP and NPP simulation are compared with the available global MODIS (Moderate Resolution Imaging Spectroradiometer) GPP/NPP product and with the Estonian statistical data on yearly volume increment in forests and on yield of agricultural crops. The NPP simulation results on coniferous and deciduous forests are compared with the data from tree ring analyses from different counties. These comparisons show us that the simulated country average yearly NPP values for Estonian forests agree reasonably well with the indirect estimates from other sources, taking into account the rather high uncertainty of the model predictions, uncertainty of forest inventory-based estimates and limited representativity of existing tree ring data. However, problems arise with the ability of present versions of EST_PP and MODIS NPP models to adequately simulate the regional differences of productivity and of variability of productivity in different years. The model needs some modification and the basic LUE principles to be tested in Estonia. Nevertheless, the MODIS NPP and EST_PP models offer additional possibilities to map yearly productivity and carbon sequestration by Estonian vegetation. There is a perspective to add the model-simulated NPP values into the national inventory datasets.

Field measurements from 450 sample plots, airborne lidar data and spectral images from Aegviidu, Estonia, 15 by 15 km test site were used to analyse options to estimate main forest inventory variables using remote sensing data. Up to 7 m random error in location of 15 m radius sample plots within homogeneous stands causes usually about 0.5 m standard deviation in lidar pulse return height distribution percentiles. Forest mean height can be predicted with linear relationship from 80th percentile of lidar pulse return height distribution. Upper percentiles of pulse return height distribution are not significantly affected by omitting returns from ground and forest understorey vegetation. Total stem volume in forest can be predicted by using 80th percentile, 25th percentile and canopy cover as model arguments with less than 70 m3 ha-1 standard error. Best species specific stem volume models had 10 m3 ha-1 smaller standard error.

The aim of this study was to compile the leaf area index (LAI) map of a 3×3 km VALERI test site in Järvselja, Estonia. Canopy transmittance measurements of LAI by LAI-2000 Plant Canopy Analyzers and digital cameras supplied with fisheye converters were carried out on 42 elementary sampling units at ground level and at breast height level. The vegetation LAI was estimated as a sum of the tree canopy true green LAI obtained from the inversion of canopy gap fraction data and of the ground vegetation effective LAI. Red channel from SPOT-4 HRV-IR image and airborne lidar data based canopy transmittance were used for up-scaling. The LAI map was compared with standard LAI products from Terra MODIS and ENVISAT MERIS. We found that lidar data based LAI estimate on up-scaled map saturated at high values (LAI > 4.5) compared to the LAI estimates based on SPOT-4 HRV-IR red channel. Validation of MODIS LAI product revealed substantial underestimates of LAI compared to the up-scaled field measurements and rather large random noise. ENVISAT MERIS LAI product was more similar to up-scaled field measurements; however, rather large unexpected random variations exist in its time series.

The MODIS (The Moderate Resolution Imaging Spectroradiometer) yearly NPP (Net Primary Production) 1 km resolution products were collected over Estonia for years 2000-2010. The MODIS NPP product for forest pixels showed a clear West-East decreasing trend over the Estonian territory. At the same time the trunk volume increment estimates extracted from the Estonian national statistics averaged over the same period showed the opposite trend. The MODIS NPP algorithm seems to overestimate the contribution of meteorological variables and to ignore the role of soil fertility differences. To improve the predictive power of MODIS algorithm to describe local NPP differences, the local meteorological data with higher spatial resolution should be used as an input in the NPP calculations, whereas the algorithm should be modified by optimizing the input parameters and including parameters of soil fertility into the calculation scheme.

In this summary, we give an overview of the application of airborne laser scanning (ALS) data for predicting the main forest inventory variables in Estonia. When Estonia being one of the few countries with wall-to-wall ALS availability, the need for applicable models for Estonian forests was imminent. Over the past decade, different studies have been carried out to develop models for standing wood volume, forest height, canopy cover, canopy base height, and methods for monitoring height growth and detect small-scale harvests. The main findings showed strong correlations for all the studied parameters and different methods utilizing low-density lidar data for practical forest inventory purposes. Options for using repea ted ALS measurements for continuous forest inventory are discussed. [ABSTRACT FROM AUTHOR]

Forest height increment rate is related to the forest growth conditions. Data bases of previous forest inventories contain information about forest heightage relationship on large number of forest stands while repeated measurements of permanent sample plots provide an excellent reference for comparison. Repeated airborne laser scanning of forest stands is an additional source for the estimation of change in forest structure. In this study, height growth of middle-aged and older forest stands for about 10 year period was compared to an algebraic difference model on permanent sample plots (66) and for a sample of forest stands with repeated airborne laser scanning data (61). The model was based on a large dataset of forest inventory records from the period of 1984-1993. Statistically significant increased forest height growth was found in permanent sample plots based on tree height measurements (9 cm yr-1) as well in stands with repeated laser scanning data (4.5 cm yr-1) in South- East Estonia compared to the algebraic difference model. The difference between the two data sets was explained by their mean age and site class, but the increased forest height growth compared to the old forest inventory data indicates improved growth conditions of forests in the test area. The results hint also that empirical data-based forest growth models need to be updated to avoid biased growth estimates. [ABSTRACT FROM AUTHOR]

Field measurements from 450 sample plots, airborne lidar data and spectral images from Aegviidu, Estonia, 15 by 15 km test site were used to analyse options to estimate main forest inventory variables using remote sensing data. Up to 7 m random error in location of 15 m radius sample plots within homogeneous stands causes usually about 0.5 m standard deviation in lidar pulse return height distribution percentiles. Forest mean height can be predicted with linear relationship from 80th percentile of lidar pulse return height distribution. Upper percentiles of pulse return height distribution are not significantly affected by omitting returns from ground and forest understorey vegetation. Total stem volume in forest can be predicted by using 80th percentile, 25th percentile and canopy cover as model arguments with less than 70 m³ ha-1 standard error. Best species specific stem volume models had 10 m³ ha-1 smaller standard error. [ABSTRACT FROM AUTHOR]