The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space

The primary objective of the European Space Agency's 7th Earth Explorer mission, BIOMASS, is to determine the worldwide distribution of forest above-ground biomass (AGB) in order to reduce the major uncertainties in calculations of carbon stocks and fluxes associated with the terrestrial biosphere, including carbon fluxes associated with Land Use Change, forest degradation and forest regrowth. To meet this objective it will carry, for the first time in space, a fully polarimetric P-band synthetic aperture radar (SAR). Three main products will be provided: global maps of both AGB and forest height, with a spatial resolution of 200 m, and maps of severe forest disturbance at 50 m resolution (where “global” is to be understood as subject to Space Object tracking radar restrictions). After launch in 2022, there will be a 3-month commissioning phase, followed by a 14-month phase during which there will be global coverage by SAR tomography. In the succeeding interferometric phase, global polarimetric interferometry Pol-InSAR coverage will be achieved every 7 months up to the end of the 5-year mission. Both Pol-InSAR and TomoSAR will be used to eliminate scattering from the ground (both direct and double bounce backscatter) in forests. In dense tropical forests AGB can then be estimated from the remaining volume scattering using non-linear inversion of a backscattering model. Airborne campaigns in the tropics also indicate that AGB is highly correlated with the backscatter from around 30 m above the ground, as measured by tomography. In contrast, double bounce scattering appears to carry important information about the AGB of boreal forests, so ground cancellation may not be appropriate and the best approach for such forests remains to be finalized. Several methods to exploit these new data in carbon cycle calculations have already been demonstrated. In addition, major mutual gains will be made by combining BIOMASS data with data from other missions that will measure forest biomass, structure, height and change, including the NASA Global Ecosystem Dynamics Investigation lidar deployed on the International Space Station after its launch in December 2018, and the NASA-ISRO NISAR L- and S-band SAR, due for launch in 2022. More generally, space-based measurements of biomass are a core component of a carbon cycle observation and modelling strategy developed by the Group on Earth Observations. Secondary objectives of the mission include imaging of sub-surface geological structures in arid environments, generation of a true Digital Terrain Model without biases caused by forest cover, and measurement of glacier and icesheet velocities. In addition, the operations needed for ionospheric correction of the data will allow very sensitive estimates of ionospheric Total Electron Content and its changes along the dawn-dusk orbit of the mission

Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

Accurate mapping of forest aboveground biomass (AGB) is critical for better understanding the role of forests in the global carbon cycle. NASA's current GEDI and ICESat-2 missions as well as the upcoming NISAR mission will collect synergistic data with different coverage and sensitivity to AGB. In this study, we present a multi-sensor data fusion approach leveraging the strength of each mission to produce wall-to-wall AGB maps that are more accurate and spatially comprehensive than what is achievable with any one sensor alone. Specifically, we calibrate a regional L-band radar AGB model using the sparse, simulated spaceborne lidar AGB estimates. We assess our data fusion framework using simulations of GEDI, ICESat-2 and NISAR data from airborne laser scanning (ALS) and UAVSAR data acquired over the temperate high AGB forest and complex terrain in Sonoma County, California, USA. For ICESat-2 and GEDI missions, we simulate two years of data coverage and AGB at footprint level are estimated using realistic AGB models. We compare the performance of our fusion framework when different combinations of the sparse simulated GEDI and ICEsat-2 AGB estimates are used to calibrate our regional L-band AGB models. In addition, we test our framework at Sonoma using (a) 1-ha square grid cells and (b) similarly sized irregularly shaped objects. We demonstrate that the estimated mean AGB across Sonoma is more accurately estimated using our fusion framework than using GEDI or ICESat-2 mission data alone, either with a regular grid or with irregular segments as mapping units. This research highlights methodological opportunities for fusing new and upcoming active remote sensing data streams toward improved AGB mapping through data fusion.</p

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

Airborne S-Band SAR for forest biophysical retrieval in temperate mixed forests of the UK

Radar backscatter from forest canopies is related to forest cover, canopy structure and aboveground biomass (AGB). The S-band frequency (3.1–3.3 GHz) lies between the longer L-band (1–2 GHz) and the shorter C-band (5–6 GHz) and has been insufficiently studied for forest applications due to limited data availability. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest biophysical properties. To understand the scattering mechanisms in forest canopies at S-band the Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model was used. S-band backscatter was found to have high sensitivity to the forest canopy characteristics across all polarisations and incidence angles. This sensitivity originates from ground/trunk interaction as the dominant scattering mechanism related to broadleaved species for co-polarised mode and specific incidence angles. The study was carried out in the temperate mixed forest at Savernake Forest and Wytham Woods in southern England, where airborne S-band SAR imagery and field data are available from the recent AirSAR campaign. Field data from the test sites revealed wide ranges of forest parameters, including average canopy height (6–23 m), diameter at breast-height (7–42 cm), basal area (0.2–56 m2/ha), stem density (20–350 trees/ha) and woody biomass density (31–520 t/ha). S-band backscatter-biomass relationships suggest increasing backscatter sensitivity to forest AGB with least error between 90.63 and 99.39 t/ha and coefficient of determination (r2) between 0.42 and 0.47 for the co-polarised channel at 0.25 ha resolution. The conclusion is that S-band SAR data such as from NovaSAR-S is suitable for monitoring forest aboveground biomass less than 100 t/ha at 25 m resolution in low to medium incidence angle rang

Estimation of change in forest variables using synthetic aperture radar

Large scale mapping of changes in forest variables is needed for both environmental monitoring, planning of climate actions and sustainable forest management. Remote sensing can be used in conjunction with field data to produce wall-to-wall estimates that are practically impossible to produce using traditional field surveys. Synthetic aperture radar (SAR) can observe the forest independent of sunlight, clouds, snow, or rain, providing reliable high frequency coverage. Its wavelength determines the interaction with the forest, where longer wavelengths interact with larger structures of the trees, and shorter wavelengths interact mainly with the top part of the canopy, meaning that it can be chosen to fit specific applications. This thesis contains five studies conducted on the Remningstorp test site in southern Sweden. Studies I – III predicted above ground biomass (AGB) change using long wavelength polarimetric P- (in I) and L-band (in I – III) SAR data. The differences between the bands were small in terms of prediction quality, and the HV polarization, just as for AGB state prediction, was the polarization channel most correlated with AGB change. A moisture correction for L-band data was proposed and evaluated, and it was found that certain polarimetric measures were better for predicting AGB change than all of the polarization channels together. Study IV assessed the detectability of silvicultural treatments in short wavelength TanDEM-X interferometric phase heights. In line with earlier studies, only clear cuts were unambiguously distinguishable. Study V predicted site index and stand age by fitting height development curves to time series of TanDEM-X data. Site index and age were unbiasedly predicted for untreated plots, and the RMSE would likely decrease with longer time series. When stand age was known, SI was predicted with an RMSE comparable to that of the field based measurements. In conclusion, this thesis underscores SAR data's potential for generalizable methods for estimation of forest variable changes

Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California

Estimates of the magnitude and distribution of aboveground carbon in Earth's forests remain uncertain, yet knowledge of forest carbon content at a global scale is critical for forest management in support of climate mitigation. In light of this knowledge gap, several upcoming spaceborne missions aim to map forest aboveground biomass, and many new biomass products are expected from these datasets. As these new missions host different technologies, each with relative strengths and weaknesses for biomass retrieval, as well as different spatial resolutions, consistently comparing or combining biomass estimates from these new datasets will be challenging. This paper presents a demonstration of an inter-comparison of biomass estimates from simulations of three NASA missions (GEDI, ICESat-2 and NISAR) over Sonoma county in California, USA. We use a high resolution, locally calibrated airborne lidar map as our reference dataset, and emphasize the importance of considering uncertainties in both reference maps and spaceborne estimates when conducting biomass product validation. GEDI and ICESat-2 were simulated from airborne lidar point clouds, while UAVSAR's L-band backscatter was used as a proxy for NISAR. To estimate biomass for the lidar missions we used GEDI's footprint-level biomass algorithms, and also adapted these for application to ICESat-2. For UAVSAR, we developed a locally trained biomass model, calibrated against the ALS reference map. Each mission simulation was evaluated in comparison to the local reference map at its native product resolution (25 m, 100 m transect, and 1 ha) yielding RMSEs of 57%, 75%, and 89% for GEDI, NISAR, and ICESat-2 respectively. RMSE values increased for GEDI's power beam during simulated daytime conditions (64%), coverage beam during nighttime conditions (72%), and coverage beam daytime conditions (87%). We also test the application of GEDI's biomass modeling framework for estimation of biomass from ICESat-2, and find that ICESat-2 yields reasonable biomass estimates, particularly in relatively short, open canopies. Results suggest that while all three missions will produce datasets useful for biomass mapping, tall, dense canopies such as those found in Sonoma County present the greatest challenges for all three missions, while steep slopes also prove challenging for single-date SAR-based biomass retrievals. Our methods provide guidance for the inter-comparison and validation of spaceborne biomass estimates through the use of airborne lidar reference maps, and could be repeated with on-orbit estimates in any area with high quality field plot and ALS data. These methods allow for regional interpretations and filtering of multi-mission biomass estimates toward improved wall-to-wall biomass maps through data fusion.</p

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

Mapping forest cover and forest cover change with airborne S-band radar

Assessments of forest cover, forest carbon stocks and carbon emissions from deforestation and degradation are increasingly important components of sustainable resource management, for combating biodiversity loss and in climate mitigation policies. Satellite remote sensing provides the only means for mapping global forest cover regularly. However, forest classification with optical data is limited by its insensitivity to three-dimensional canopy structure and cloud cover obscuring many forest regions. Synthetic Aperture Radar (SAR) sensors are increasingly being used to mitigate these problems, mainly in the L-, C- and X-band domains of the electromagnetic spectrum. S-band has not been systematically studied for this purpose. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest characterisation. The Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model is utilised to understand the scattering mechanisms in forest canopies at S-band. The MIMICS-I model reveals strong S-band backscatter sensitivity to the forest canopy in comparison to soil characteristics across all polarisations and incidence angles. Airborne S-band SAR imagery over the temperate mixed forest of Savernake Forest in southern England is analysed for its information content. Based on the modelling results, S-band HH- and VV-polarisation radar backscatter and the Radar Forest Degradation Index (RFDI) are used in a forest/non-forest Maximum Likelihood classification at a spatial resolution of 6 m (70% overall accuracy, κ = 0.41) and 20 m (63% overall accuracy, κ = 0.27). The conclusion is that S-band SAR such as from NovaSAR-S is likely to be suitable for monitoring forest cover and its change

A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement