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.

Validation is mandatory to quantify the reliability of satellite biophysical products that are now routinely generated by a range of sensors. This paper presents the VALERI project dedicated to the validation of the products derived from medium resolution satellite sensors (www. avignon.inra.fr/valeri/). It describes the sites used, and the methodology developed to get the high spatial resolution map of the biophysical variables considered, i.e. LAI , fAPAR and fCover that can be estimated from ground level gap fraction measurements. Sites were selected to represent , with the other validation projects, the large variation of biomes and conditions observed over the Earth’s surface. Each site is about 3 × 3 km² in size and should be flat and relatively homogeneous at the medium resolution scale. For each site, the methodology used to generate the high spatial resolution biophysical variable maps is described. It is mainly based on concurent use of local gound measurements and a high spatial resolution satellite image, generally SPOT-HRV. Local ground measurements should be representative of an elementary sampling unit (ESU) that has approximately the same size as a SPOT-HRV pixel. The ground measurements mainly consist of gap fraction measurements achieved with LAI-2000 or hemispherical photographs. The ESUs are selected over the whole 3 × 3 km² site in order to sample the range of vegetation types observed. A transfer function is subsequently established over the ESUs to relate the ground measurements of the biophysical variables considered to the correspodonding high spatial resolution satellite image data. Finally, co-kriging is applied to generate the high spatial resolution map of the biophysical variables over the 3 × 3 km² area. The methodology presented in this paper can serve as a basis for validating medium resolution satellite products. These methodological aspects are discussed and conclusions drawn on the limitations and prospects of beforementioned validation activity.

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.

Gap fraction measurements with digital a hemispherical camera were carried out in 2007-2016 in a Scots pine (Pima sylvestris L.) stand growing on transitional Sphagnum bog in Jarvselja, Estonia. The measurements were done at the time when (1) the pines had maximum foliage, or (2) after autumn needle fall or before bud burst. Data for gap fraction calculation were extracted from camera raw files. For data recording, the sensor output signal maximum was kept according to the image brightness histogram within the interquartile range of the sensor dynamic range. A linear conversion method (LinearRatio) was used for image processing. The relative needle loss in autumn (20-30%) increased gap fraction by 0.02-0.05 at view zenith angles from 10 to 60 and decreased the mean plant area index from 1.86 to 1.68 after autumn litter fall. The measurement results were in good agreement with a theoretical gap fraction model simulation and with gap fraction estimates made from cover photos. The measurement and data processing protocol is appropriate for long-term monitoring in permanent sample plots.

The Photochemical Reflectance Index (PRI) of green leaves is an indicator of photosynthetic downregulation: when the photosynthetic apparatus is close to the saturation limit, PRI becomes dependent on light conditions. Therefore, by measuring the PRI of leaves under different local irradiance conditions, it should be possible to determine the saturation level of the leaves and obtain information on the light use efficiency (LUE) of a vegetation canopy. The dependence of PRI on the ratio of sunlit to shaded foliage (quantified by the canopy shadow fraction) in the field of view of an instrument has been used to remotely measure canopy LUE on clear days. However, besides photosynthetic downregulation, the dependence of canopy PRI on shadow fraction is affected by the blue sky radiation caused by scattering in the atmosphere. To quantify this effect on remotely sensed PRI, we present the underlying definitions relating leaf and canopy PRI and perform the required calculations for typical midsummer conditions in Central Finland. We demonstrate that the effect of blue sky radiation on the variation of PRI with canopy shadow fraction is similar in shape and magnitude to that of LUE variations reported in literature.

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 accuracy of spaceborne/airborne sensor measurements in the solar domain keeps increasing over time. High resolution, multi-directional and hyperspectral image acquisitions start to be abundant. With regard to the multi-angular remote sensing data, the hot spot, i.e. the exact backscattering direction of direct sunlight together with its neighboring directions, is of special interest. Accurate hot spot models have to be used to adequately simulate the hot spot signature and to allow reliable inversion of multi-angular data. In this paper, we propose a physical hot spot model (Leaf Spatial Distribution based Model, LSDM) assuming that for a given point inside the vegetation to be sunlit (respectively, observed) it should be located within a cylinder free from leaf centers. The cylinder is oriented to the sun (respectively, sensor) direction. Assuming a leaf random, regular or clumped spatial distributions, the gap probabilities in the sun and sensor directions are expressed as a function of these cylinder volumes. Based on the same hypothesis, the bidirectional gap probability is estimated as a function of the total volume of the two cylinders. The evaluation of the needed common volume of two cylinders having different radii is reduced to calculation of some elliptic integrals. Finally, the hot spot signature is estimated based on the bidirectional gap probability distribution. Different model versions with different leaf spatial distribution functions are compared. Particularly, it is shown that compared to the random distribution, the regular (the clumped, respectively) distribution increases (decreases, respectively) the reflectance due to single scattering contribution from foliage. The proposed model is validated using the ROMC web-based tool and its better performance compared to the Semi-Discrete Model and Kuusk's model is confirmed.

Studies on the seasonal changes in plant morphology and productivity, also known as phenology, have applied a multitude of methods for detecting changes in the start of spring or length of growing period. Global-scale plant phenology observation is needed, for example, to understand the seasonality of biosphere-atmosphere interactions (e.g. Moulin et al., 1997). Traditional species-specific in situ records of leafing and fruiting (e.g. Menzel et al., 2006), monitoring of atmospheric carbon dioxide as an indicator of photosynthesis (e.g. Keeling et al., 1996) and remote sensing of vegetation reflectance (e.g. Zhou et al., 2001) have all been used in attempts to understand the consequences of the changing climate on primary production and global vegetation cover.

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.

The foliage clumping index quantifies the degree of the deviation of leaf spatial distribution in the canopy from the random case. It is of comparable importance for ecological models as the leaf area index for quantifying radiation interception and distribution in plant canopies. Previously, an improved angular index named normalized difference between hotspot and darkspot was proposed for retrieving the clumping index using multi-angle remote sensing data. Global maps of clumping index have been derived successfully from multi-angular Polarization and Directionality of Earth Reflectance (POLDER) data at ∼6 km resolution. In this article, we investigate whether it is feasible to derive the clumping index at 500 m resolution with the 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function model parameters product. The results are compared with an assembled set of field measurements from 63 different sites, covering five continents and diverse biomes.

Abstract 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.

An estimate of the required amount of LAI-2000 or digital hemispheric image measurements to ensure the desired accuracy of the leaf area index value derived from optical analyser measurements by inversion of gap fraction data is given. The estimate is based on a theoretical model of the second order statistics of gap occurrence in forest canopies. The main attention is paid to gap fractions averaged over the angular rings similar to the LAI-2000 instrument. Nilson's (1999) forest gap fraction model is first extended to simulate the bidirectional gap fraction and next the approximate equations for the standard deviation of gap fraction, correlation matrix of gap readings in different LAI-2000 angular rings, standard deviation of the LAI value as determined by the inversion of gap fraction data are derived. The results of simulation are compared with those obtained by LAI-2000 instrument and digital hemispheric photographs in three mature forests from Jarvselja, Estonia. The comparison shows an acceptable agreement between the simulated and measured second order statistical characteristics. The distance from the observation height to the crowns and crown dimensions appear to be important in determining the magnitude of variance. When the LAI-2000 instrument is used together with a view-cap, the angular size of the view-cap has an effect on the second order statistics and on the value of apparent clumping index. A computer program has been written to calculate the second-order gap statistics. (C) 2011 Elsevier B.V. All rights reserved.

Abstract 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.

Methods for analyzing foliage nonrandomness by means of the TRAC instrument, digital hemispheric photography, and a gap fraction model are assessed at two RAMI (RAdiation transfer Model Intercomparison) mature stands in Jarvselja. Estonia. The six different methods involve calculation of the canopy element clumping index, at scales coarser than that of a shoot. The major aim was to define the merits and limitations of the various methods. We conclude the gap size distribution and beyond-shoot clumping is very stable across the stands for the solar zenith angle range from 30 degrees to 60 degrees. Estimates based on the gap size distribution and the combination of gap size and logarithm methods performed the best while compared with an independent gap fraction model. We clarify the effect of the assumed leaf inclination angle distribution on gap size distribution and differences between estimates of beyond-shoot clumping. We show that the modified, gap-size distribution based method of Chen and Cihlar can provide reliable beyond-shoot clumping estimates without any a priori assumptions about the total gap fraction, segment length or the leaf inclination angle distribution. We also illustrate the changes in element clumping with measurement height. The compiled data extend the original parameter dataset to be used in the next phase of RAMI for different benchmark tests and reflectance modeling experiments, and contribute toward systematic validation efforts of radiative transfer models, operational algorithms, and field instruments, as promoted by the Committee on Earth Observation Satellites (CEOS). (C) 2010 Elsevier B.V. All rights reserved.

Simulated reflectance spectra of three mature hemiboreal forests are compared to top-of-canopy reflectance from helicopter measurements in the spectral range 400-1050 nm. Most of input parameters of the forest reflectance model FRT used in simulations have been measured in situ. The same data sets were used in the Radiation Transfer Model Intercomparison (RAMI). The reasons of the discrepancies between simulated and measured spectra are analyzed.

Thresholding methods for estimating canopy indices from gamma corrected digital hemispherical images are influenced both by the image interpreter and applicability of the automatic interpretation algorithm. The matrix output data of camera sensors (i.e., raw data) can be used to avoid gamma correction. Nevertheless, the impact of an interpreter or a thresholding algorithm is still present. In this paper, we present a new methodology which applies a previously published linear conversion method of raw data to a single below-canopy image to obtain unbiased estimates of canopy gap fraction. The new methodology derives an above-canopy reference image from a below-canopy image using sky radiance information available in gaps. A sky radiance model and interpolation are used to create the above-canopy image which could alternatively be obtained with a second sensor. To evaluate the new method, we conducted test measurements in three mature forest stands. The comparison of image processing output to LAI-2000 Plant Canopy Analyzer data showed high agreement and repeatability. The proposed method can be applied in versatile future versions of hemispherical image analysis programs to create unbiased comparison data for other manual or automatic image processing methods designed for plant canopies.

The aim of this study was to evaluate the use of ground-based canopy reflectance measurements to detect changes in physiology and structure of vegetation in response to experimental warming and drought treatment at six European shrublands located along a North–South climatic gradient. We measured canopy reflectance, effective green leaf area index (green LAIe) and chlorophyll fluorescence of dominant species. The treatment effects on green LAIe varied among sites. We calculated three reflectance indices: photochemical reflectance index PRI [531nm; 570nm], normalized difference vegetation index NDVI680 [780nm; 680nm] using red spectral region, and NDVI570 [780nm; 570nm] using the same green spectral region as PRI. All three reflectance indices were significantly related to green LAIe and were able to detect changes in shrubland vegetation among treatments. In general warming treatment increased PRI and drought treatment reduced NDVI values. The significant treatment effect on photochemical efficiency of plants detected with PRI could not be detected by fluorescence measurements. However, we found canopy level measured PRI to be very sensitive to soil reflectance properties especially in vegetation areas with low green LAIe. As both soil reflectance and LAI varied between northern and southern sites it is problematic to draw universal conclusions of climate-derived changes in all vegetation types based merely on PRI measurements. We propose that canopy level PRI measurements can be more useful in areas of dense vegetation and dark soils.