Potential of ALOS2 and NDVI to estimate forest above-ground biomass, and comparison with lidar-derived estimates

Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies drive innovations in forest resource assessments and monitoring at varying scales. Data acquired with airborne and spaceborne platforms provide us with higher spatial resolution, more frequent coverage and increased spectral information. Recent developments in ground-based sensors have advanced three dimensional (3D) measurements, low-cost permanent systems and community-based monitoring of forests. The REDD+ mechanism has moved the remote sensing community in advancing and developing forest geospatial products which can be used by countries for the international reporting and national forest monitoring. However, there still is an urgent need to better understand the options and limitations of remote and close-range sensing techniques in the field of degradation and forest change assessment. This Special Issue contains 12 studies that provided insight into new advances in the field of remote sensing for forest management and REDD+. This includes developments into algorithm development using satellite data; synthetic aperture radar (SAR); airborne and terrestrial LiDAR; as well as forest reference emissions level (FREL) frameworks

Combining L- and X-Band SAR interferometry to assess ground displacements in heterogeneous coastal environments: The Po River Delta and Venice Lagoon, Italy

From leveling to SAR-based interferometry, the monitoring of land subsidence in coastal transitional environments significantly improved. However, the simultaneous assessment of the ground movements in these peculiar environments is still challenging. This is due to the presence of relatively small built-up zones and infrastructures, e.g., coastal infrastructures, bridges, and river embankments, within large natural or rural lands, e.g., river deltas, lagoons, and farmland. In this paper we present a multi-band SAR methodology to integrate COSMO-SkyMed and ALOS-PALSAR images. The method consists of a proper combination of the very high-resolution X-band Persistent Scatterer Interferometry (PSI), which achieves high-density and precise measurements on single structures and constructed areas, with L-band Short-Baseline SAR Interferometry (SBAS), properly implemented to raise its effectiveness in retrieving information in vegetated and wet zones. The combined methodology is applied on the Po River Delta and Venice coastland, Northern Italy, using 16 ALOS-PALSAR and 31 COSMO-SkyMed images covering the period between 2007 and 2011. After a proper calibration of the single PSI and SBAS solution using available GPS records, the datasets have been combined at both the regional and local scales. The measured displacements range from ~0 mm/yr down to -35 mm/yr. The results reveal the variable pattern of the subsidence characterizing the more natural and rural environments without losing the accuracy in quantifying the sinking of urban areas and infrastructures. Moreover, they allow improving the interpretation of the natural and anthropogenic processes responsible for the ongoing subsidence