Mapping by spatial predictors exploiting remotely sensed and ground data: a comparative design-based perspective

This study was designed to compare the performance – in terms of bias and accuracy – of four different parametric,semiparametric and nonparametric methods in spatially predicting a forest response variable using auxiliary information from remote sensing. The comparison was carried out in simulated and real populations where the value of response variable was known for each pixel of the study region. Sampling was simulated through a tessellation stratified design. Universal kriging and cokriging were considered among parametric methods based on the spatial autocorrelation of the forest response variable. Locallyweighted regression and k-nearest neighbor predictors were considered among semiparametric and nonparametricmethods based on the information from neighboring sites in the auxiliary variable space. The study was performed from a design-based perspective, taking the populations as fixed and replicating the sampling procedurewith 1000Monte Carlo simulation runs. On the basis of the empirical values of relative bias and relative root mean squared error it was concluded that universal kriging and cokriging were more suitable in the presence of strong spatial autocorrelation of the forest variable, while locally weighted regression and k-nearest neighbors were more suitable when the auxiliary variables were well correlated with the response variable. Results of the study advise that attention should be paid when mapping forest variables characterized by highly heterogeneous structures. The guidelines of this study can be adopted even for mapping environmental attributes beside forestry

Application of neural networks for the retrieval of forest woody volume from SAR multifrequency data at L and C bands.

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. Keywords: ANN; backscattering; Woody Volume; LiDAR; ALOS/PALSAR; ENVISAT/ASAR

Evaluating the effects of environmental changes on the gross primary production of italian forests

A ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which link the inter-year GPP variations of five forest types (white fir, beech, chestnut, deciduous and evergreen oaks) to seasonal values of temperature and precipitation. The five models obtained, which explain from 52% to 88% of the interyear GPP variability, are then applied to predict the effects of expected environmental changes (+2 °C and increased CO2 concentration). The results show a variable response of forest GPP to the simulated climate change, depending on the main ecosystem features. In contrast, the effects of increasing CO2 concentration are always positive and similar to those given by a combination of the two environmental factors. These findings are analyzed with reference to previous studies on the subject, particularly concerning Mediterranean environments. The analysis confirms the plausibility of the scenarios obtained, which can cast light on the important issue of forest carbon pool variations under expected global changes