ALBERT

Description: Estimating forest canopy height from large-footprint satellite LiDAR waveforms is challenging given the complex interaction between LiDAR waveforms, terrain, and vegetation, especially in dense tropical and equatorial forests. In this study, canopy height in French Guiana was estimated using multiple linear regression models and the Random Forest technique (RF). This analysis was either based on LiDAR waveform metrics extracted from the GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data and terrain information derived from the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model) or on Principal Component Analysis (PCA) of GLAS waveforms. Results show that the best statistical model for estimating forest height based on waveform metrics and digital elevation data is a linear regression of waveform extent, trailing edge extent, and terrain index (RMSE of 3.7 m). For the PCA based models, better canopy height estimation results were observed using a regression model that incorporated both the first 13 principal components (PCs) and the waveform extent (RMSE = 3.8 m). Random Forest regressions revealed that the best configuration for canopy height estimation used all the following metrics: waveform extent, leading edge, trailing edge, and terrain index (RMSE = 3.4 m). Waveform extent was the variable that best explained canopy height, with an importance factor almost three times higher than those for the other three metrics (leading edge, trailing edge, and terrain index). Furthermore, the Random Forest regression incorporating the first 13 PCs and the waveform extent had a slightly-improved canopy height estimation in comparison to the linear model, with an RMSE of 3.6 m. In conclusion, multiple linear regressions and RF regressions provided canopy height estimations with similar precision using either LiDAR metrics or PCs. However, a regression model (linear regression or RF) based on the PCA of waveform samples with waveform extent information is an interesting alternative for canopy height estimation as it does not require several metrics that are difficult to derive from GLAS waveforms in dense forests, such as those in French Guiana.

Description: A recent policy response to halting global forest deforestation and degradation, and any resulting greenhouse gas emissions is REDD+, which also includes the role of conservation, sustainable management of forests and enhancement of forest carbon stocks. Although still in its infancy, the success of REDD+ will depend significantly on whether it can be economically viable and if any resulting payments are sufficient to cover the opportunity cost plus any transaction cost. Where tenure security over forest is weak, REDD+ can pose a risk for forest communities, who could be dispossessed, excluded and marginalized. This review of existing studies explores how payment for avoided deforestation, and forest tenure impact the success of REDD+ projects in terms of effectiveness, efficiency and equity. Effectiveness refers to the difference between deforestation with and without REDD+, efficiency refers to avoiding deforestation at minimal cost, and equity refers to the implication of REDD+ on benefit sharing. We conclude that the potential success or failure of REDD+ as a means to reduce deforestation and carbon emission on forest commons depends critically on designing projects that work within existing informal tenure institutions to ensure that carbon storage benefits align with livelihood benefits.

Description: This work investigates the possibility of performing target analysis through the Multi-Chromatic Analysis (MCA), a technique that basically explores the information content of sub-band images obtained by processing portions of the range spectrum of a synthetic aperture radar (SAR) image. According to the behavior of the SAR signal at the different sub-bands, MCA allows target classification. Two strategies have been experimented by processing TerraSAR-X images acquired over the Venice Lagoon, Italy: one exploiting the phase of interferometric sub-band pairs, the other using the spectral coherence derived by computing the coherence between sub-band images of a single SAR acquisition. The first approach introduces the concept of frequency-persistent scatterers (FPS), which is complementary to that of the time-persistent scatterers (PS). FPS and PS populations have been derived and analyzed to evaluate the respective characteristics and the physical nature of the targets. Spectral coherence analysis has been applied to vessel detection, according to the property that, in presence of a random distribution of surface scatterers, as for open sea surfaces, spectral coherence is expected to be proportional to sub-band intersection, while in presence of manmade structures it is preserved anyhow. First results show that spectral coherence is well preserved even for very small vessels, and can be used as a complementary information channel to constrain vessel detection in addition to classical Constant False Alarm Rate techniques based on the sole intensity channel.

Description: LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km.

Description: Remote Sensing, Vol. 9, Pages 879: Split-Band Interferometry-Assisted Phase Unwrapping for the Phase Ambiguities Correction Remote Sensing doi: 10.3390/rs9090879 Authors: Ludivine Libert Dominique Derauw Nicolas d’Oreye Christian Barbier Anne Orban Split-Band Interferometry (SBInSAR) exploits the large range bandwidth of the new generation of synthetic aperture radar (SAR) sensors to process images at subrange bandwidth. Its application to an interferometric pair leads to several lower resolution interferograms of the same scene with slightly shifted central frequencies. When SBInSAR is applied to frequency-persistent scatterers, the linear trend of the phase through the stack of interferograms can be used to perform absolute and spatially independent phase unwrapping. While the height computation has been the main concern of studies on SBInSAR so far, we propose instead to use it to assist conventional phase unwrapping. During phase unwrapping, phase ambiguities are introduced when parts of the interferogram are separately unwrapped. The proposed method reduces the phase ambiguities so that the phase can be connected between separately unwrapped regions. The approach is tested on a pair of TerraSAR-X spotlight images of Copahue volcano, Argentina. In this framework, we propose two new criteria for the frequency-persistent scatterers detection, based respectively on the standard deviation of the slope of the linear regression and on the phase variance stability, and we compare them to the multifrequency phase error. Both new criteria appear to be more suited to our approach than the multifrequency phase error. We validate the SBInSAR-assisted phase unwrapping method by artificially splitting a continuous phase region into disconnected subzones. Despite the decorrelation and the steep topography affecting the volcanic test region, the expected phase ambiguities are successfully recovered whatever the chosen criterion to detect the frequency-persistent scatterers. Comparing the aspect ratio of the distributions of the computed phase ambiguities, the analysis shows that the phase variance stability is the most efficient criterion to select stable targets and the slope standard deviation gives satisfactory results.

Description: Sustainability, Vol. 9, Pages 1858: Natural Resource Economics, Planetary Boundaries and Strong Sustainability Sustainability doi: 10.3390/su9101858 Authors: Edward Barbier Joanne Burgess Earth systems science maintains that there are nine “planetary boundaries” that demarcate a sustainable, safe operating space for humankind for essential global sinks and resources. Respecting these planetary boundaries represents the “strong sustainability” perspective in economics, which argues that some natural capital may not be substituted and are inviolate. In addition, the safe operating space defined by these boundaries can be considered a depletable stock. We show that standard tools of natural resource economics for an exhaustible resource can thus be applied, which has implications for optimal use, price paths, technological innovation, and stock externalities. These consequences in turn affect the choice of policies that may be adopted to manage and allocate the safe operating space available for humankind.

Description: Sustainability, Vol. 10, Pages 13: Innovative Corporate Initiatives to Reduce Climate Risk: Lessons from East Asia Sustainability doi: 10.3390/su10010013 Authors: Edward Barbier Joanne Burgess Businesses, investors, and insurers are requiring better quantitative assessments of their exposure to climate risks and their impact on climate change. They are incorporating these assessments in their day-to-day management and long-term investment decisions. Already, there are efforts to develop international guidelines, common policies and legal frameworks for such assessments, as well as the desire to foster climate financing. We examine recent progress in East Asia and the rest of the world in setting targets, pricing policies, and other mechanisms to reduce climate risks. We develop a model that demonstrates how reduced climate risk management may lower the total cost of capital of firms, thus making them more attractive to investors. We discuss the additional policies needed to support improved climate risk management in investment decisions, private investments in climate science, technology and innovation (STI) expansion, and more widespread adoption of climate financing and principles. Central banks, financial authorities, and governments can advance this objective by creating financial incentives to support investment decision-making. This would take into account factors such as improving climate performance, establishing better climate risk management and reporting requirements to foster green STI, and developing international guidelines and common policy and legal frameworks to support better climate risk management, assessments and reporting.

Description: Large scale assessment of aboveground biomass (AGB) in tropical forests is often limited by the saturation of remote sensing signals at high AGB values. Fourier Transform Textural Ordination (FOTO) performs well in quantifying canopy texture from very high-resolution (VHR) imagery, from which stand structure parameters can be retrieved with no saturation effect for AGB values up to 650 Mg·ha−1. The method is robust when tested on wet evergreen forests but is more demanding when applied across different forest types characterized by varying structures and allometries. The present study focuses on a gradient of forest types ranging from dry deciduous to wet evergreen forests in the Western Ghats (WG) of India, where we applied FOTO to Cartosat-1a images with 2.5 m resolution. Based on 21 1-ha ground control forest plots, we calibrated independent texture–AGB models for the dry and wet zone forests in the area, as delineated from the distribution of NDVI values computed from LISS-4 multispectral images. This stratification largely improved the relationship between texture-derived and field-derived AGB estimates, which exhibited a R2 of 0.82 for a mean rRMSE of ca. 17%. By inverting the texture–AGB models, we finally mapped AGB predictions at 1.6-ha resolution over a heterogeneous landscape of ca. 1500 km2 in the WG, with a mean relative per-pixel propagated error 〈20% for wet zone forests, i.e., below the recommended IPCC criteria for Monitoring, Reporting and Verification (MRV) methods. The method proved to perform well in predicting high-resolution AGB values over heterogeneous tropical landscape encompassing diversified forest types, and thus presents a promising option for affordable regional monitoring systems of greenhouse gas (GhG) emissions related to forest degradation.