Matteo Mura

Creating a regional MODIS satellite-driven net primary production dataset for european forests

Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp://palantir.boku.ac.at/Public/MODIS_EURO.

Prediction of forest NPP in Italy by the combination of ground and remote sensing data

Our research group has recently proposed a strategy to simulate net forest carbon fluxes based on the coupling of a NDVI-driven parametric model, Modified C-Fix, and of a biogeochemical model, BIOME-BGC. The outputs of the two models are combined through the use of a proxy of ecosystem distance from equilibrium condition which accounts for the occurred disturbances. This modeling strategy is currently applied to all Italian forest areas using an available set of NDVI images and ancillary data descriptive of an 8-year period (1999–2006). The obtained estimates of forest net primary production (NPP) are first analyzed in order to assess the importance of the main model drivers on relevant spatial variability. This analysis indicates that growing stock is the most influential model driver, followed by forest type and meteorological variables. In particular, the positive influence of growing stock on NPP can be constrained by thermal and water limitations, which are most evident in the upper mountain and most southern zones, respectively. Next, the NPP estimates, aggregated over seven main forest types and twenty administrative regions in Italy, are converted into current annual increment of standing volume (CAI) by specific coefficients. The accuracy of these CAI estimates is finally assessed by comparison with the ground data collected during a recent national forest inventory. The results obtained indicate that the modeling approach tends to overestimate the ground CAI for most forest types. In particular, the overestimation is notable for forest types which are mostly managed as coppice, while it is negligible for high forests. The possible origins of these phenomena are investigated by examining the main model drivers together with the results of previous studies and of older forest inventories. The implications of using different NPP estimation methods are finally discussed in view of assessing the forest carbon budget on a national basis.

Performance evaluation of lightweight LiDAR for UAV applications

In this work a new lightweight LiDAR solution designed for UAV application will be investigated. In particular, we show that using this multi-echo LiDAR it is possible to obtain DTM reconstruction of the densely forested area surveyed in good agreement with the local technical regional map (CTR). We have also estimated the mean height of the trees from the estimated CHM with relative error equal to 5%.

Application of Neural Networks for the retrieval of forest woody volume from SAR multifrequency data at L and C bands

Abstract This work aims at investigating the potential of L (ALOS/PALSAR) and C (ENVISAT/ASAR) band SAR images in forest biomass monitoring and setting up a retrieval algorithm, based on Artificial Neural Networks (ANN), for estimating the Woody Volume (WV, in m3/ha) from combined satellite acquisitions. The investigation was carried out on two test areas in central Italy, where ground WV measurements were available. An innovative retrieval algorithm based on ANN was developed for estimating WV from L and C bands SAR data. The novelty consists of an accurate training of the ANN with several thousands of data, which allowed the implementation of a very robust algorithm. The RMSE values found on San Rossore area were ≅ 40 m3/ha (L band data only), and 25–30 m3/ha (L with C band). On Molise, by using combined data at L and C bands, RMSE<30m3/ha was obtained.