Leónia Nunes

Data fusion for high spatial resolution LAI estimation

Leaf Area Index (LAI) is a critical variable for forest management. It is difficult to obtain accurate LAI estimations of high spatial resolution over large areas. Local estimations can be obtained from in situ field measurements. Extrapolation of local measurements is prone to error. Remote sensing LAI estimation products, such as the one provided by MODIS are of very low resolution and subject to criticism in recent validation works. Forest management requires increasingly high resolution estimations of LAI. We propose a data fusion process for high spatial resolution estimation of the LAI over a large area, combining several heterogeneous information sources: field sampled data, elevation data and remote sensing data. The process makes use of spatial interpolation techniques. We follow a hybrid validation approach that combines the conventional prediction error measures with a spatial validation based on image segmentation. We obtain encouraging results of this information fusion process on data from a forest area in the north of Portugal.

Evaluation of the Climate Change Impact on the Productivity of Portuguese Pine Ecosystems Using the Forest-BGC Model

The growth, survival and geographical distribution of plants are determined by climate, particularly temperature and precipitation. The actual tendency indicates a global climate change with direct negative impact on terrestrial ecosystems. Estimation of net primary production (NPP) allows a prediction of the impact of climate change on ecosystems. Studies on climate and dynamic modelling of stand development are being carried by the scientific community all over the world in order to assess the effects of global climate change. There is the question if that spatial scale of analysis is not overlooking local or regional information that can be important for smaller communities. The object of this study is to identify if the local scale is appropriate to analyse the impact of the climate change on the productivity of the local forest ecosystems. The Forest-BGC model was used to estimate the NPP in pine (Pinus pinaster Aiton) pure and mixed forest based on climate evolution scenarios derived from the analysis of past available climate series and its evolutionary trends for the Tras-os-Montes region, north of Portugal, which has three distinct climatic zones. We concluded that for the study areas temperature is the key variable influencing NPP under climate change scenarios.