Hrvoje Marjanović

Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013

Remote sensing provides invaluable insight into the dynamics of vegetation with global coverage and reasonable temporal resolution. Normalized Difference Vegetation Index (NDVI) is widely used to study vegetation greenness, production, phenology and the responses of ecosystems to climate fluctuations. The extended global NDVI3g dataset created by Global Inventory Modeling and Mapping Studies (GIMMS) has an exceptional 32 years temporal coverage. Due to the methodology that was used to create NDVI3g inherent noise and uncertainty is present in the dataset. To evaluate the accuracy and uncertainty of application of NDVI3g at regional scale we used Collection-6 data from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on board satellite Terra as a reference. After noise filtering, statistical harmonization of the NDVI3g dataset was performed for Central Europe based on MOD13 NDVI. Mean seasonal NDVI profiles, start, end and length of the growing season, magnitude and timing of peak NDVI were calculated from NDVI3g (original, noise filtered and harmonized) and MODIS NDVI and compared with each other. NDVI anomalies were also compared and evaluated using simple climate sensitivity metrics. The results showed that (1) the original NDVI3g has limited applicability in Central Europe, which was also implied by the significant disagreement between the NDVI3g and MODIS NDVI datasets; (2) the harmonization of NDVI3g with MODIS NDVI is promising since the newly created dataset showed improved quality for diverse vegetation metrics. For NDVI anomaly detection NDVI3g showed limited applicability, even after harmonization. Climate–NDVI relationships are not represented well by NDVI3g. The presented results can help researchers to assess the expected quality of the NDVI3g-based studies in Central Europe.

A Comparison of Stand-Level Volume Estimates from Image-Based Canopy Height Models of Different Spatial Resolutions

Digital aerial photogrammetry has recently attracted great attention in forest inventory studies, particularly in countries where airborne laser scanning (ALS) technology is not available. Further research, however, is required to prove its practical applicability in deriving three-dimensional (3D) point clouds and canopy surface and height models (CSMs and CHMs, respectively) over different forest types. The primary aim of this study is to investigate the applicability of image-based CHMs at different spatial resolutions (1 m, 2 m, 5 m) for use in stand-level forest inventory, with a special focus on estimation of stand-level merchantable volume of even-aged pedunculate oak (Quercus robur L.) forests. CHMs are generated by subtracting digital terrain models (DTMs), derived from the national digital terrain database, from corresponding digital surface models (DSMs), derived by the process of image matching of digital aerial images. Two types of stand-level volume regression models are developed for each CHM resolution. The first model is based solely on stand-level CHM metrics, whereas in the second model, easily obtainable variables from forest management databases are included in addition to CHM metrics. The estimation accuracies of the stand volume estimates based on stand-level metrics (relative root mean square error RMSE% = 12.53%–13.28%) are similar or slightly higher than those obtained from previous studies in which stand volume estimates were based on plot-level metrics. The inclusion of stand age as an independent variable in addition to CHM metrics improves the accuracy of the stand volume estimates. Improvements are notable for young and middle-aged stands, and negligible for mature and old stands. Results show that CHMs at the three different resolutions are capable of providing reasonably accurate volume estimates at the stand level.

Decomposition of Fine Woody Debris from Main Tree Species in Lowland Oak Forests

ABSTRACT Decomposition is an important carbon flux that must be accounted for in estimates of forest ecosystem carbon balance. Aim of this research is to provide estimate of fine woody debris decomposition rates for different tree species and sample sizes also taking into account the influence of specific microsite meteorological conditions on decomposition rates. In this paper we present results of the first two years of the experiment designed to last six years. Study was conducted in managed lowland oak forest in central Croatia. Decomposition rates (k) of fine woody debris (diameter 0.5–7 cm) for four species (Querus robur L., Carpinut betulus L., Alnus glutinosa Gaernt., Fraxinus angustifolia L.) in four size classes were estimated using litter bag method and mass loss equation of Olson (1963). Overall average k in our study was 0.182 ± 0.011 year-1. Results indicate that decomposition rate is affected by the size of the debris, with the smallest diameter branches (<1 cm) decomposing is significantly faster (k = 0.260 ± 0.018, P <0.05) than the larger one. Tree species from which debris had originated also affected decomposition, although to a lesser extent, with hornbeam samples having significantly (P <0.05) higher average decomposition rate (0.229 ± 0.028), compared to that of ash samples (0.141 ± 0.022). Proportion of variability in k explained by variables ‘species’ and ‘size class’ was assessed with general linear model (R2 = 0.644) also taking into account variables like soil temperature and soil water content. Sample size class explained 22.2%; species explained only 9.4%, while soil water content and temperature combined explained 32.8% of the variance of k. Rate constants obtained within this study might be useful in modelling ecosystem carbon balance for similar lowland forest ecosystems in Europe.