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Open access

Tiit Nilson, Mattias Rennel and Mait Lang

Abstract.

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.

Open access

Alo Eenmäe, Tiit Nilson and Mait Lang

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.

Open access

Tiit Nilson, Mattias Rennel, Andres Luhamaa, Maris Hordo, Aire Olesk and Mait Lang

Abstract.

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.

Open access

Mait Lang, Raimo Kõlli, Maris Nikopensius, Tiit Nilson, Mathias Neumann and Adam Moreno

Abstract

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.

Open access

Ave Kodar, Mait Lang, Tauri Arumäe, Alo Eenmäe, Jan Pisek and Tiit Nilson

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.