The Forest Observation System, building a global reference dataset for remote sensing of forest biomass

International audienceForest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (aGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. aGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. all plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities

Prospective carbon balance of the wood sector in a tropical forest territory using a temporally-explicit model

International audienceSelective logging in tropical forests is often perceived as a source of forest degradation and carbon emissions. Improved practices, such as reduced-impact logging (RIL), and alternative timber production strategies (e.g. plantations) can drastically change the overall carbon impact of the wood production sector. Assessing the carbon balance of timber production is crucial but highly dependent on methodological approaches, especially regarding system boundaries and temporality. We developed a temporally-explicit and territory scale model of carbon balance calibrated with long-term local data using Bayesian inference. The model accounts for carbon fluxes from selective logging in natural forest, timber plantation, first transformation and avoided emissions through energy substitution. We used it to compare prospective scenarios of development for the wood sector in French Guiana. Results show that intensification of practices, through increased logging intensity conducted with RIL and establishment of timber plantations, are promising development strategies to reduce the carbon emissions of the French-Guianese wood sector, as well as the area needed for wood production and hence the pressure on natural forests. By reducing logging damage by nearly 50%, RIL allows increasing logging intensity in natural forest from 20 m 3 ha − 1 to 30 m 3 ha − 1 without affecting the carbon balance. The use of logging byproducts as fuelwood also improved the carbon balance of selective logging, when substituted to fossil fuel. Allocating less than 30 000 ha to plantation would allow producing 200 000 m 3 of timber annually, while the same production in natural forest would imply logging more than 400 000 ha over 60 years. Timber plantation should be preferentially established on non-forested lands, as converting natural forests to plantation leads to high carbon emission peak over the first three decades. We recommend a mixed-strategy combining selective logging in natural forests and plantations as a way to improve long-term carbon balance while reducing short-term emissions. This strategy can reduce the pressure on natural forests while mitigating the risks of changing practices and allowing a diversified source of timber for a diversity of uses. It requires adaptation of the wood sector and development of technical guidelines. Research and monitoring efforts are also needed to assess the impacts of changing practices on other ecosystem services, especially biodiversity conservation

Prospective carbon balance of the wood sector in a tropical forest territory using a temporally-explicit model

International audienceSelective logging in tropical forests is often perceived as a source of forest degradation and carbon emissions. Improved practices, such as reduced-impact logging (RIL), and alternative timber production strategies (e.g. plantations) can drastically change the overall carbon impact of the wood production sector. Assessing the carbon balance of timber production is crucial but highly dependent on methodological approaches, especially regarding system boundaries and temporality. We developed a temporally-explicit and territory scale model of carbon balance calibrated with long-term local data using Bayesian inference. The model accounts for carbon fluxes from selective logging in natural forest, timber plantation, first transformation and avoided emissions through energy substitution. We used it to compare prospective scenarios of development for the wood sector in French Guiana. Results show that intensification of practices, through increased logging intensity conducted with RIL and establishment of timber plantations, are promising development strategies to reduce the carbon emissions of the French-Guianese wood sector, as well as the area needed for wood production and hence the pressure on natural forests. By reducing logging damage by nearly 50%, RIL allows increasing logging intensity in natural forest from 20 m 3 ha − 1 to 30 m 3 ha − 1 without affecting the carbon balance. The use of logging byproducts as fuelwood also improved the carbon balance of selective logging, when substituted to fossil fuel. Allocating less than 30 000 ha to plantation would allow producing 200 000 m 3 of timber annually, while the same production in natural forest would imply logging more than 400 000 ha over 60 years. Timber plantation should be preferentially established on non-forested lands, as converting natural forests to plantation leads to high carbon emission peak over the first three decades. We recommend a mixed-strategy combining selective logging in natural forests and plantations as a way to improve long-term carbon balance while reducing short-term emissions. This strategy can reduce the pressure on natural forests while mitigating the risks of changing practices and allowing a diversified source of timber for a diversity of uses. It requires adaptation of the wood sector and development of technical guidelines. Research and monitoring efforts are also needed to assess the impacts of changing practices on other ecosystem services, especially biodiversity conservation

Weak Environmental Controls of Tropical Forest Canopy Height in the Guiana Shield

Canopy height is a key variable in tropical forest functioning and for regional carbon inventories. We investigate the spatial structure of the canopy height of a tropical forest, its relationship with environmental physical covariates, and the implication for tropical forest height variation mapping. Making use of high-resolution maps of LiDAR-derived Digital Canopy Model (DCM) and environmental covariates from a Digital Elevation Model (DEM) acquired over 30,000 ha of tropical forest in French Guiana, we first show that forest canopy height is spatially correlated up to 2500 m. Forest canopy height is significantly associated with environmental variables, but the degree of correlation varies strongly with pixel resolution. On the whole, bottomland forests generally have lower canopy heights than hillslope or hilltop forests. However, this global picture is very noisy at local scale likely because of the endogenous gap-phase forest dynamic processes. Forest canopy height has been predictively mapped across a pixel resolution going from 6 m to 384 m mimicking a low resolution case of 3 points·km − 2 . Results of canopy height mapping indicated that the error for spatial model with environment effects decrease from 8.7 m to 0.91 m, depending of the pixel resolution. Results suggest that, outside the calibration plots, the contribution of environment in shaping the global canopy height distribution is quite limited. This prevents accurate canopy height mapping based only on environmental information, and suggests that precise canopy height maps, for local management purposes, can only be obtained with direct LiDAR monitoring

Detecting Ditched Sites on Lidar-Generated Digital Elevation Models: From technical specifications to interpretation keys

International audienceIn addition to other remote sensing methods, LiDAR has been used for many years in temperate regions or open tropical agricultural landscapes to detect signs of past human activities. The improvement of sensors, and therefore of LiDAR’s ability to penetrate dense vegetation, allows us now to produce digital elevation models (DEMs) that are precise enough to reveal topographic artefacts over large tropical rainforest tracts. We discuss here the requirements for producing such DEMs and detail how they can be used to detect particular landscape-scale pre-Columbian features known as ring-ditched sites in French Guiana

LoggingLab: An R package to simulate reduced-impact selective logging in tropical forests using forest inventory data

International audienceEven where Reduced-Impact Logging (RIL) practices are applied, selective logging causes substantial damage to tropical forests. To further reduce selective logging damage, the practices that cause the most damage need to be identified and alternatives tested. To this end, we developed the R package LoggingLab, a spatially-explicit and individual tree-based selective logging simulator and demonstrated its functions using data from French Guiana. LoggingLab explicitly simulates damage during each stage of the selective logging process taking into account topography and hydrography, which are main constraints on logging. Most LoggingLab parameters can be easily adjusted to a wide range of local contexts. LoggingLab can also be coupled with forest dynamics models to simulate the long- term effects of different selective logging scenarios

From technical specifications to interpretation keys

In addition to other remote sensing methods, LiDAR has been used for many years in temperate regions or open tropical agricultural landscapes to detect signs of past human activities. The improvement of sensors, and therefore of LiDAR’s ability to penetrate dense vegetation, allows us now to produce digital elevation models (DEMs) that are precise enough to reveal topographic artefacts over large tropical rainforest tracts. We discuss here the requirements for producing such DEMs and detail how they can be used to detect particular landscape-scale pre-Columbian features known as ring-ditched sites in French Guiana.CEnter of the study of Biodiversity in Amazoni

Across Date Species Detection Using Airborne Imaging Spectroscopy

International audienceImaging spectroscopy is a promising tool for airborne tree species recognition in hyper-diverse tropical canopies. However, its widespread application is limited by the signal sensitivity to acquisition parameters, which may require new training data in every new area of application. This study explores how various pre-processing steps may improve species discrimination and species recognition under different operational settings. In the first experiment, a classifier was trained and applied on imaging spectroscopy data acquired on a single date, while in a second experiment, the classifier was trained on data from one date and applied to species identification on data from a different date. A radiative transfer model based on atmospheric compensation was applied with special focus on the automatic retrieval of aerosol amounts. The impact of spatial or spectral filtering and normalisation was explored as an alternative to atmospheric correction. A pixel-wise classification was performed with a linear discriminant analysis trained on individual tree crowns identified at the species level. Tree species were then identified at the crown scale based on a majority vote rule. Atmospheric corrections did not outperform simple statistical processing (i.e., filtering and normalisation) when training and testing sets were taken from the same flight date. However, atmospheric corrections became necessary for reliable species recognition when different dates were considered. Shadow masking improved species classification results in all cases. Single date classification rate was 83.9% for 1297 crowns of 20 tropical species. The loss of mean accuracy observed when using training data from one date to identify species at another date in the same area was limited to 10% when atmospheric correction was applied