The objective of this research is to test Sentinel-1 SAR multitemporal data, supported by multispectral and SAR data at other wavelengths, for fine-scale mapping of above-ground biomass (AGB) at the provincial level in a Mediterranean forested landscape. The regression results indicate good accuracy of prediction (R2=0.7) using integrated sensors when an upper bound of 400Mg ha-1 is used in modeling. Multitemporal SAR information was relevant, allowing the selection of optimal Sentinel-1 data, as broadleaf forests showed a different response in backscatter throughout the year. Similar accuracy in predictions was obtained when using SAR multifrequency data or joint SAR and optical data. Predictions based on SAR data were more conservative, and in line with those from an independent sample from the National Forest Inventory, than those based on joint data types. The potential of S1 data in predicting AGB can possibly be improved if models are developed per specific groups (deciduous or evergreen species) or forest types and using a larger range of ground data. Overall, this research shows the usefulness of Sentinel-1 data to map biomass at very high resolution for local study and at considerable carbon density. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

To answer new scientific and ecological questions and monitor multiple forest changes, a fine scale characterization of these ecosystems is needed, and could imply the mapping of specific species, of detailed forest types, and of functional composition. This characterization can be now provided by the novel Earth Observation tools. This study aims to contribute to understanding the innovation in forest and ecological research that can be brought in by advanced remote sensing instruments, and proposes the guild mapping approach as a tool to efficiently monitor the varied tropical forest resources. We evaluated, in tropical Ghanaian forests, the ability of airborne hyperspectral and simulated multispectral Sentinel-2 data, and derived vegetation indices and textures, to: distinguish between two different forest types; to discriminate among selected dominant species; and to separate trees species grouped according to their functional guilds: Pioneer, Non Pioneer Light Demanding, and Shade Bearer. We then produced guild classification maps for each area using hyperspectral data. Our results showed that with both hyperspectral and simulated Sentinel-2 data these discrimination tasks can be successfully accomplished. Results also stressed the importance of texture features, especially if using the lower spectral and spatial Sentinel-2 resolution data, and highlighted the important role of the new Sentinel-2 data for ecological monitoring. Classification results showed a statistically significant improvement in overall accuracy using Support Vector Machine, over Maximum Likelihood approach. We proposed the functional guilds mapping as an innovative approach to: (i) monitor compositional changes, especially with respect to the effects of global climate change on forests, and particularly in the tropical biome where the occurrence of hundreds of species prevents mapping activities at species level; (ii) support large-scale forest inventories. The imminent Sentinel-2 data could serve to open the road for the development of new concepts and methods in forestry and ecological research. © 2016 Elsevier Inc.

This paper describes the development and testing of a procedure which combines remotely sensed and ancillary data to monitor forest productivity in Italy. The procedure is based on a straightforward parametric model (C-Fix) that uses the relationship between the fraction of photosynthetically active radiation absorbed by plant canopies (fAPAR) and relevant gross primary productivity (GPP). Estimates of forest fAPAR are derived from Spot-VGT NDVI images and are combined with spatially consistent data layers obtained by the elaboration of ground meteorological measurements. The original version of C-Fix is first applied to estimate monthly GPP of Italian forests during eight years (1999-2006). Next, a modification of the model is proposed in order to simulate the short-term effect of summer water stress more efficiently. The accuracy of the original and modified C-Fix versions is evaluated by comparison with GPP data taken at eight Italian eddy covariance flux tower sites. The experimental results confirm the capacity of C-Fix to monitor national forest GPP patterns and indicate the utility of considering the short-term effect of water stress during Mediterranean dry months. © 2008 Elsevier Inc. All rights reserved.