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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.

Habitats are important indicators of biodiversity in their own right, as well as being linked to species, hence their widespread use in reporting on nature conservation planning and policy. For reporting consistent mapping and monitoring habitat extent and change is important. Remote Sensing techniques are becoming an important tool for this. In this paper we describe four examples of methods of semi-automated mapping using Remote Sensing. Because the most effective way of improving the accuracy of the estimation of habitat area is by increasing the sample number, it is important to develop methods for reducing in situ surveys which are expensive. Remote Sensing has the major advantage of comprehensive coverage and the four examples illustrate the potential of extrapolation from semi-automated habitat classifications. The potential for using these methods at national scales is likely to be limited by the need for validation of the automated images and the subsequent calculation of error terms. Existing major national monitoring programs are described, which still use mainly traditional in situ methods. The selection of relatively small numbers of representative samples from environmental classifications to obtain regional estimates reduces the need for large numbers of in situ survey sites and is therefore discussed. The recent development of the use of drones to acquire detailed imagery to support in situ habitat surveys is also covered. Finally, practical problems linked to the methods described in the paper are considered, as in some cases these will override the theoretical benefits of a particular approach. It is concluded that automated methods can enhance existing monitoring systems and should be considered in any biodiversity monitoring system as they represent an opportunity for reducing costs, if integrated with an in situ program.

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.

Advances in unmanned aerial systems (UASs) have increased the potential of remote sensing to overcome scale issues for soil moisture (SM) quantification. Regardless, optical imagery is acquired using various sensors and platforms, resulting in simpler operations for management purposes. In this respect, we predicted SM at 10 cm depth using partial least squares regression (PLSR) models based on optical UAS data and assessed the potential of this framework to provide accurate predictions across dates and sites. For this, we evaluated models' performance using several datasets and the contribution of spectral and photogrammetric predictors on the explanation of SM. The results indicated that our models predicted SM at comparable accuracies as other methods relying on more expensive and complex sensors; the best R2 was 0.73, and the root-mean-squared error (RMSE) was 13.1%. Environmental conditions affected the predictive importance of different metrics; photogrammetric-based metrics were relevant over exposed surfaces, while spectral predictors were proxies of water stress status over homogeneous vegetation. However, the models demonstrated limited applicability across times and locations, particularly in highly heterogeneous conditions. Overall, our findings indicated that integrating UAS imagery and PLSR modelling is suitable for retrieving SM measures, offering an improved method for short-term monitoring tasks. [ABSTRACT FROM AUTHOR]

In 2007, three mature hemi-boreal stands were selected from Järvselja forest district, South-East Estonia to establish one-hectare-large test plots for the international inter-comparison experiment of radiation models (RAMI). All trees with a stem diameter at breast height greater than 4 cm were mapped and measured in the field. In summer 2019, the forests were inventoried again. Here we present a summary of changes that occurred in the forest structure – mainly growth and mortality. In the birch stand basal area G has increased from 23.3 m2 ha-1 to 28.2 m2 ha-1 in the upper layer and the number of trees N has decreased from 654 to 565 ha-1. In the upper layer of spruce stand G has increased from 30.9 m2 ha-1 to 35.4 m2 ha-1 and N has decreased from 774 to 724 ha-1 and N substantially decreased in the lower layers from 912 to 577 ha-1. In the pine stand G has increased from 28.3 m2 ha-1 to 29.1 m2 ha-1 and N decreased from 1116 to 971 ha-1. The three test stands can be used now for validating remote sensing data-based estimates of forest inventory variables at single tree level. 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. [ABSTRACT FROM AUTHOR]

The contribution of understory vegetation especially to the reflectance of boreal forest landscapes cannot be ignored. In this letter, we identify the most suitable angular configuration for accurate retrieval of the understory signal. We use a new high angular resolution bidirectional reflectance factor data set with accompanying in situ measurements of understory reflectance in two RAdiation transfer Model Intercomparison (RAMI) mature stands in Järvselja, Estonia. Our results are pertinent towards the ultimate goal of achieving more reliable estimates of biophysical properties of the tree layer through separating the effect of understory on forest reflectance. [ABSTRACT FROM AUTHOR]

In this article, we introduce an alternative solution for forest regeneration based on unmanned ground vehicles (UGV) and describe requirements for external data, which could significantly increase the level of automation. Over the past few decades, the global forested area has decreased, and there is a great need to restore and regenerate forests. Challenges such as the lack of labor and high costs demand innovative approaches for forest regeneration. Mechanization has shown satisfactory results in terms of time-efficient planting, although its usage is limited by high operational costs. Innovative technologies must be cost-efficient and profitable for large scale usage. Automation could make mechanized forest regeneration feasible. Forest regeneration operations can be automated using a purpose built unmanned platform. We developed a concept to automate forest planting operations based on mobility platform. The system requires external data for efficient mobility in clear-cut areas. We developed requirements for external data, analyzed available solutions, and experimented with the most promising option, the SfM (structure from motion) technique. Earth observation data are useful in the planning phase. A DEM (digital terrain model) for UGV planter operations can be constructed using ALS (airborne laser scanning), although it may be restricted by the cost. Low-altitude flights by drones equipped with digital cameras or lightweight laser scanners provided a usable model of the terrain. This model was precise (3–20 cm) enough for manually planning of the trajectory for the planting operation. This technique fulfilled the system requirements, although it requires further development and will have to be automated for operational use. [ABSTRACT FROM AUTHOR]

Forest reflectance models are able to contribute to the interpretation of satellite images over forested areas. A multipurpose forest reflectance model, its basic ideas, model input and output options are briefly described. The possibility to apply the model in forest remote sensing is demonstrated through the following simulation examples: the age dependence of forest reflectance, the seasonal course of forest reflectance, defoliation effects on forest reflectance, sensitivity of chlorophyll and water indices with respect to leaf chlorophyll and water content, respectively, and uncertainties in these indices introduced by the variation in stand parameters. When used together with the atmospheric radiative transfer and sensor calibration models, the forest reflectance model is applicable in satelliteimagery-aided forest ecology and health assessment, forest inventory and management. [ABSTRACT FROM AUTHOR]

Abstract: The directional multispectral hybrid type forest radiative transfer model FRT by Kuusk and Nilson (2000) was compared using directional reflectance factor of high angular resolution measured over three mature hemiboreal forest stands in Estonia. The model systematically underestimated backward scattering even in the case of the best available input data, especially in coniferous stands. The measured BRF distribution could be reproduced if the spherical leaf angle distribution was replaced by erectophile in order to better model the angular dependence of gap fraction, and the Ross–Nilson area scattering phase function was replaced by significantly more asymmetric Henyey–Greenstein phase function. The limited amount of available data does not allow deciding how universal are the applied asymmetry parameter values. The asymmetry of the phase function may depend on the species composition, stand age, and site type. [Copyright &y& Elsevier]

Three mature stands at the forest test site Järvselja, Estonia were extensively measured for using as a validation dataset for heterogeneous canopy reflectance models. In order to enable the reconstruction of the 3-D architecture of these 100×100 m2 test plots, individual tree positions and crown dimensions were inventoried. In addition, leaf, needle, stem bark and branch bark visible and near-infrared (VNIR) reflectance spectra, and VNIR reflectance spectra of ground vegetation were measured. This in situ dataset is supported by atmospherically and radiometrically corrected Mode 3 CHRIS reflectance spectra for three view directions, and top of canopy VNIR nadir spectra from airborne measurements. Details of measurements, instruments in use, data processing, and access to data are described in a technical report which is available on-line. [Copyright &y& Elsevier]

Digital cover photography (DCP) has emerged as an indirect method to measure gap fraction of vegetation canopies. However, as with other photographic methods, determining camera relative exposure value (REV) and threshold for pixel classification, cause substantial uncertainties in gap fraction estimates. Here we propose a new method to improve the measurement of gap fraction under various solar zenith angles (SZAs), sky conditions, and canopy structures. This method computes gap fractions of ambiguous vegetation or sky pixels using an unsaturated raw image from DCP and a reconstructed sky image from the raw image, thus taking full advantage of the potential of raw image processing. This is combined with pre-classification of pixels that are unambiguously canopy and sky to greatly reduce light scattering effects that are likely to be present within the canopy. To test the sensitivity of the new method, we acquired images at one-hour intervals between 20 and 85° of SZAs under closed, half-closed, and open canopies with REV settings from 0 to −5. The new method showed little variation in gap fractions across the diverse SZAs in closed, half-closed, and open canopies. A perforated panel experiment, which was used to test the accuracy of the estimated gap fractions, confirmed that the new method accurately estimated gap fractions across a range of hole size, gap fractions and SZAs. We conclude that the new method opens new opportunities to estimate gap fractions accurately regardless of solar positions from open to closed canopies, and is a significant advance for accurate and precise monitoring of canopy cover and leaf area index (LAI), and for calibrating and evaluating satellite remote sensing LAI products. [ABSTRACT FROM AUTHOR]

The components of the forest radiative transfer model FRT developed at Tartu Observatory, Estonia are compared to the results of measurements of forest canopy downward radiance under forest canopy. Measurements were performed with a hemispheric-view imaging CCD-radiometer which was specially designed for this task. A thorough study of metrological properties of the radiometer was carried out and the respective preprocessing algorithms were created. The angular distribution of view probabilities and NIR radiances for tree crowns, trunks and canopy gaps are measured and compared to the model simulations for two coniferous and a broadleaf forests. Generally, the model reproduced the angular courses of component probabilities and NIR radiances rather well, however, in some cases problems with both the absolute levels of radiance and the angular course arose. [Copyright &y& Elsevier]