Aboveground Biomass Retrieval Research Articles

Estimating aboveground biomass of tropical urban forests with UAV-borne hyperspectral and LiDAR data

Urban trees and forests can contribute to climate change mitigation by sequestering carbon in their living tissues, with aboveground biomass (AGB) playing a pivotal role. This study explores the capability of UAV-borne hyperspectral and LiDAR data for estimating AGB in tropical urban forests. Structural attributes of trees, such as diameter at breast height (DBH), total height, and wood density, were collected from over 5600 individuals, forming a comprehensive AGB dataset. Our methodology included two primary AGB estimation strategies: an area-based strategy that correlated AGB with hyperspectral and canopy height data across various grid sizes and an individual tree crown (ITC)-based method that integrated canopy height, spectral signatures of individual trees and crown area. The findings indicate that increasing the grid size from 10 m to 50 m improved the R2 from 0.24 ± 0.04–0.61 ± 0.13, mainly due to reduced border effects. Furthermore, integrating canopy height and hyperspectral data enhanced the R2 of AGB estimates from 0.61 ± 0.13–0.70 ± 0.09 for a 50 × 50 m grid. Crucially, wavelengths centered at the green peak and red-edge were identified as key bands in AGB retrieval. Integrating hyperspectral and LiDAR data did not significantly enhance results for individual trees, where AGB was closely linked to tree height and crown area. This study underscores the potential of utilizing integrated UAV-borne sensors for biomass assessment in urban forest settings.

Exploring optimal combinations of multi-frequency polarimetric SAR observations to estimate forest above-ground biomass

ABSTRACT The penetration capability of electromagnetic wave signal into forest increases with increasing wavelength. SAR data at each frequency senses different components of forest structure. Therefore the biomass distributed at various tree components could be estimated using different radar frequencies. Additionally, the synthesis of multiple SAR frequencies could improve the accuracy in retrieving forest above-ground biomass (AGB). Taking advantage of available X-, C-, L-, and P-band quad-polarimetric SAR images of airborne or spaceborne for the test site located at Genhe national forest scientific field station, we used a Genetic Algorithm and Support Vector Regression optimization algorithm (GA-SVR) to explore the sensitivity of polarimetric observations at various frequencies to forest AGB and effectiveness of AGB retrievals using single-frequency, dual-frequency, triple-frequency, and quad-frequency SAR observation combinations. We found that: (i) Most of the polarimetric observations are sensitive to forest AGB, (ii) GA-SVR performed well in forest AGB retrieval using the single frequency SAR observations or combinations of multi-frequency observations; the highest Acc. value for single-frequency-retrieved results is 75.13% acquired at P-band, with multi-frequency, the highest Acc. values is 77.34% acquired by combining C- and P-band. (iii) For forest AGB retrievals, the single-frequency P-band accuracy is comparable to the combined C- and P-band retrieval accuracy, indicating that the long-wavelength single-frequency P-band is sufficient for an accurate forest AGB retrieval. The findings reconfirmed potential of P-band for forest AGB retrievals, they also demonstrated that the optimal combination of multi-frequency SAR for AGB retrievals is by using a short-wavelength (X/C-) and a long-wavelength (L/P-).

Examination of the amount of GEDI data required to characterize central Africa tropical forest aboveground biomass at REDD+ project scale in Mai Ndombe province

The Global Ecosystem Dynamics Investigation (GEDI) is the first spaceborne LiDAR designed to improve quantification of vegetation structure and forest aboveground biomass (AGB) including in the tropics where forest AGB inventory data are limited. GEDI is a sampling instrument on the International Space Station (ISS) and does not provide data on a regular, systematic basis. Reducing Emissions from Deforestation and Degradation and enhancement of carbon stocks (REDD+) projects require forest AGB inventories to quantify avoided carbon emissions achieved by conserving forest biomass. Although there is high confidence that GEDI can retrieve measurements that allow estimation of AGB at scale, less is known about how well its operational deployment performs for measurement of AGB to support REDD+ projects. This includes an understanding of the appropriate time period required to collect sufficient GEDI observations for reliable forest AGB assessment. This paper describes the first study to examine the amount of GEDI data needed to characterize tropical forest AGB at REDD+ project scale. In tropical Africa, the average REDD+ project size documented by the Center for International Forestry Research is equivalent to a square area of approximately 50 × 50 km (250,000 ha). Recently available good quality GEDI footprint-level AGB product data acquired over a 31 month period over Mai Ndombe province in the west of the Democratic Republic of the Congo were considered. A global 30 m percent tree cover product, updated with contemporary mapped forest cover loss, was used to map the intact forest across the province. Fifteen 50 × 50 km test sites, representing example REDD+ project areas with >80% forest cover and good quality AGB forest footprint data distributed across each site, were selected. The sites were selected from five AGB stratum defined from the GEDI data, and with three sites selected per stratum that had low, medium and high semivariogram sill values that reflect increasing within-site AGB spatial variation. The overall mean GEDI AGB (OMGA) was derived from all the good quality forest GEDI footprint AGB values acquired over the 31 months of GEDI operation at each site. The expected minimum number of GEDI orbits (norbitsp) required to characterize the OMGA to within p = ±5%, ±10%, and ±20% was derived by considering different combinations of GEDI orbits randomly selected from the 31 months of GEDI data. The expected minimum number of days (ndaysp) required to characterize the AGB over each site was derived by multiplying the site norbitsp values with a scalar coefficient of 13.03 days. The scalar coefficient was found by counting the temporal intervals between successive GEDI orbits containing good quality forest AGB data and is equivalent to the average number of days required to obtain a GEDI orbit containing good quality forest AGB data at 50 × 50 km scale. Among the 15 sites, observation periods ranging from 65 to 221 days (0.18 – 0.61 years), 143 – 534 days (0.39 – 1.46 years), and 390 – 742 days (1.07 – 2.03 years) were required to characterize the AGB to within ±20%, ±10%, and ±5% of the site OMGA, respectively. The Intergovernmental Panel on Climate Change (IPCC) recommended accuracy requirement for forest AGB estimates is 10%. Thus, to meet this accuracy requirement the findings of this study indicate that at least 534 days (1.46 years) would be required for REDD+ site monitoring using GEDI in Mai Ndombe province. In other central African tropical forest localities these observations periods may be different depending on the forest AGB and spatial variation, cloud cover, ephemeral surface water presence, and GEDI AGB retrieval sensitivity to the forest conditions.

Exploring the Relationship between Forest Canopy Height and Canopy Density from Spaceborne LiDAR Observations

Forest structure is a useful proxy for carbon stocks, ecosystem function and species diversity, but it is not well characterised globally. However, Earth observing sensors, operating in various modes, can provide information on different components of forests enabling improved understanding of their structure and variations thereof. The Ice, Cloud and Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS), providing LiDAR footprints from 2003 to 2009 with close to global coverage, can be used to capture elements of forest structure. Here, we evaluate a simple allometric model that relates global forest canopy height (RH100) and canopy density measurements to explain spatial patterns of forest structural properties. The GLA14 data product (version 34) was applied across subdivisions of the World Wildlife Federation ecoregions and their statistical properties were investigated. The allometric model was found to correspond to the ICESat GLAS metrics (median mean squared error, MSE: 0.028; inter-quartile range of MSE: 0.022–0.035). The relationship between canopy height and density was found to vary across biomes, realms and ecoregions, with denser forest regions displaying a greater increase in canopy density values with canopy height, compared to sparser or temperate forests. Furthermore, the single parameter of the allometric model corresponded with the maximum canopy density and maximum height values across the globe. The combination of the single parameter of the allometric model, maximum canopy density and maximum canopy height values have potential application in frameworks that target the retrieval of above-ground biomass and can inform on both species and niche diversity, highlighting areas for conservation, and potentially enabling the characterisation of biophysical drivers of forest structure.

The Role of Time-Series L-Band SAR and GEDI in Mapping Sub-Tropical Above-Ground Biomass

Physics-based algorithms estimating large-scale forest above-ground biomass (AGB) from synthetic aperture radar (SAR) data generally use airborne laser scanning (ALS) or grid of national forest inventory (NFI) to reduce uncertainties in the model calibration. This study assesses the potential of multitemporal L-band ALOS-2/PALSAR-2 data to improve forest AGB estimation using the three-parameter water cloud model (WCM) trained with field data from relatively small (0.1 ha) plots. The major objective is to assess the impact of the high uncertainties in field inventory data due to relatively smaller plot size and temporal gap between acquisitions and ground truth on the AGB estimation. This study analyzes a time series of twenty-three ALOS-2 dual-polarized images spanning 5 years acquired under different weather and soil moisture conditions over a subtropical forest test site in India. The WCM model is trained and validated on individual acquisitions to retrieve forest AGB. The accuracy of the generated AGB products is quantified using the root mean square error (RMSE). Further, we use a multitemporal AGB retrieval approach to improve the accuracy of the estimated AGB. Changes in precipitation and soil moisture affect the AGB retrieval accuracy from individual acquisitions; however, using multitemporal data, these effects are mitigated. Using a multitemporal AGB retrieval strategy, the accuracy improves by 15% (55 Mg/ha RMSE) for all field plots and by 21% (39 Mg/ha RMSE) for forests with AGB less than 100 Mg/ha. The analysis shows that any ten multitemporal acquisitions spanning 5 years are sufficient for improving AGB retrieval accuracy over the considered test site. Furthermore, we use allometry from colocated field plots and Global Ecosystem Dynamics Investigation (GEDI) L2A height metrics to produce GEDI-derived AGB estimates. Despite the limited co-location of GEDI and field data over our study area, within the period of interest, the preliminary analysis shows the potential of jointly using the GEDI-derived AGB and multi-temporal ALOS-2 data for large-scale AGB retrieval.

New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar.

A large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly owing to the lack of measurements. To date, accessible peer-reviewed data are available for just 10 large tropical trees in the Amazon that have been harvested and directly measured entirely via weighing. Here, we harvested four large tropical rainforest trees (stem diameter: 0.6–1.2 m, height: 30–46 m, AGB: 3960–18 584 kg) in intact old-growth forest in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We first present rare ecological insights provided by these data, including unsystematic intra-tree variations in density, with both height and radius. We also found the majority of AGB was usually found in the crown, but varied from 42 to 62%. We then compare non-destructive approaches for estimating the AGB of these trees, using both classical allometry and new lidar-based methods. Terrestrial lidar point clouds were collected pre-harvest, on which we fitted cylinders to model woody structure, enabling retrieval of volume-derived AGB. Estimates from this approach were more accurate than allometric counterparts (mean tree-scale relative error: 3% versus 15%), and error decreased when up-scaling to the cumulative AGB of the four trees (1% versus 15%). Furthermore, while allometric error increased fourfold with tree size over the diameter range, lidar error remained constant. This suggests error in these lidar-derived estimates is random and additive. Were these results transferable across forest scenes, terrestrial lidar methods would reduce uncertainty in stand-scale AGB estimates, and therefore advance our understanding of the role of tropical forests in the global carbon cycle.

The Performance of Relative Height Metrics for Estimation of Forest Above-Ground Biomass Using L- and X-Bands TomoSAR Data

Both synthetic aperture radar tomography (TomoSAR) profiles and light detection and ranging (LiDAR) waveforms are the responses of a 3-D canopy structure. Relative height (RH) metrics are extensively applied for forest biophysical parameters [i.e., the forest height and above-ground biomass (AGB)] estimation in full-waveform LiDAR studies. However, the use of RH metrics to forest biophysical estimation with TomoSAR profiles is limited due to the estimation error in the ground peak. To overcome this problem, RH metrics were redefined to avoid directly estimating the ground peak in this article. The redefined RH metrics were examined based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> - and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> -bands multibaseline (MB) SAR data simulated by LandSAR, which was a coherent backscattering model of 3-D forest canopies with the capability of MB data simulation at a landscape scale. First, the performance of LandSAR in modeling the tomographic features was verified over mountainous areas using the reference DSM and LiDAR digital terrain model acquired in 2012 in the frame of the Daxinganling campaign. Subsequently, the tomographic profiles were retrieved from the simulated MB data by using the Capon method. Additionally, redefined RH metrics were derived and then applied for retrieving the forest height. The highest performance corresponded to the combination of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> -band redefined RH metrics, with a correlation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.863. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> -band redefined RH metrics performed worst due to limited penetration. Finally, the estimated forest height was used for the AGB retrieval with a height-to-biomass allometry. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and root-mean-square error of the forest AGB estimated using the combined model were 0.814 and 28.566 t/ha, respectively, compared with the reference forest AGB. All findings demonstrated that the redefined RH metrics had the potential of forest height and AGB retrieval using low frequencies TomoSAR data.

Evaluating P-Band TomoSAR for Biomass Retrieval in Boreal Forest

P-band synthetic aperture radar (SAR) is sensitive to above-ground biomass (AGB) but retrieval accuracy has been shown to deteriorate in topographic areas. In boreal forest, the signal penetrates through the canopy to interact with the ground producing variations in backscatter depending on ground topography, forest structure, and soil moisture. Tomographic processing of multiple SAR images Tomographic SAR (TomoSAR) provides information about the vertical backscatter distribution. This article evaluates the use of P-band TomoSAR data to improve AGB retrievals from backscattered intensity by suppressing the backscattered signal from the ground. This approach can be used even when the tomographic resolution is insufficient to resolve the vertical backscatter profile. The analysis is based on P-band data from two campaigns: BioSAR-1 (2007) in Remingstorp, southern Sweden, and BioSAR-2 (2008) in Krycklan (KR), northern Sweden. BioSAR airborne data were also processed to correspond as closely as possible to future BIOMASS TomoSAR acquisitions, with BioSAR-2-based results shown. A power law AGB model using volumetric HV polarized backscatter performs best in KR, with training residual root mean-squared error (RMSE) of 30%–36% (27–33 t/ha) for airborne data and 38%–39% for simulated BIOMASS data. Airborne TomoSAR data suggest that both vertical and horizontal tomographic resolution are of importance and that it is possible to greatly reduce AGB retrieval bias when compared with airborne P-band SAR backscatter intensity-based retrievals. A lack of significant ground slopes in Remningstorp reduces the benefit of using TomoSAR data which performs similar to retrievals based solely on P-band SAR backscatter intensity.

Integration of synthetic aperture radar and multispectral data for aboveground biomass retrieval in Zagros oak forests, Iran: an attempt on Sentinel imagery

ABSTRACT The use of freely accessible Sentinel-1 synthetic aperture radar (S-1 SAR) and Sentinel-2 multispectral instrument (S-2 MSI) data are currently a feasible way of mapping forest aboveground biomass (AGB) over large areas. Despite the extensive mapping of forest AGB by remote sensing, how to effectively combine different sensors data, selecting the proper statistical modelling method, and variable screening are still poorly understood. This paper presents a framework for Sentinel-based AGB estimation through the use of four variable screening techniques, namely, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), boruta, and removal-based; and three statistical modelling methods including multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF). These methods were examined across two forested sites with different levels of human activity (highly-degraded: HD and minorly-degraded: MD) in Zagros coppice oak forests. We used backscatter coefficients and texture metrics of S-1 SAR, in addition to raw bands, simple band ratios, vegetation indices and texture metrics of S-2 MSI to extract 53 explanatory variables. Our findings showed that GA and removal-based variable screening techniques outperformed the others and RF and MLR statistical modelling methods obtained the best results. The use of different variable screening techniques and modelling methods demonstrated higher accuracies of AGB estimates for the MD site compared to the HD site. S-2 MSI (Coefficient of determination (R 2) = 0.76 and Root Mean Square Error (RMSE) = 35.08% of the mean) provided a higher accuracy than S-1 SAR (R 2 = 0.42 and RMSE = 52.94% of the mean). Overall, the study demonstrates encouraging results in the retrieval and predictive mapping of the AGB of Zagros forests using the freely accessible Sentinel imagery.

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land- and water-related applications in coastal zones. Compared to optical satellites, cloud-cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all-weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud-prone tropical and sub-tropical climates. The canopy penetration capability with long radar wavelength enables L-band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change-induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L-band SAR data for geoscientific analyses that are relevant for coastal land applications.

Above-Ground Biomass Retrieval over Tropical Forests: A Novel GNSS-R Approach with CyGNSS

An assessment of the National Aeronautics and Space Administration NASA’s Cyclone Global Navigation Satellite System (CyGNSS) mission for biomass studies is presented in this work on rain, coniferous, dry, and moist tropical forests. The main objective is to investigate the capability of Global Navigation Satellite Systems Reflectometry (GNSS-R) for biomass retrieval over dense forest canopies from a space-borne platform. The potential advantage of CyGNSS, as compared to monostatic Synthetic Aperture Radar (SAR) missions, relies on the increasing signal attenuation by the vegetation cover, which gradually reduces the coherent scattering component σ coh , 0 . This term can only be collected in a bistatic radar geometry. This point motivates the study of the relationship between several observables derived from Delay Doppler Maps (DDMs) with Above-Ground Biomass (AGB). This assessment is performed at different elevation angles θ e as a function of Canopy Height (CH). The selected biomass products are obtained from data collected by the Geoscience Laser Altimeter System (GLAS) instrument on-board the Ice, Cloud, and land Elevation Satellite (ICESat-1). An analysis based on the first derivative of the experimentally derived polynomial fitting functions shows that the sensitivity requirements of the Trailing Edge TE and the reflectivity Γ reduce with increasing biomass up to ~ 350 and ~ 250 ton/ha over the Congo and Amazon rainforests, respectively. The empirical relationship between TE and Γ with AGB is further evaluated at optimum angular ranges using Soil Moisture Active Passive (SMAP)-derived Vegetation Optical Depth ( VOD ), and the Polarization Index ( PI ). Additionally, the potential influence of Soil Moisture Content (SMC) is investigated over forests with low AGB.

The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation

BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements.

Ground and Volume Decomposition as a Proxy for AGB from P-Band SAR Data

In this work, the role of volume scattering obtained from ground and volume decomposition of P-band synthetic aperture radar (SAR) data as a proxy for biomass is investigated. The analysis here presented originates from the BIOMASS L2 activities, part of which were focused on strengthening the physical foundations of the SAR-based retrieval of forest above-ground biomass (AGB). A critical analysis of the observed strong correlation between tomographic intensity and AGB is done in order to propose simplified AGB proxies to be used during the interferometric phase of BIOMASS. In particular, the aim is to discuss whether, and to what extent, volume scattering obtained from ground/volume decomposition can provide a reasonable alternative to tomography. To do this, both are tested on P-band data collected at Paracou during the TropiSAR campaign and cross-validated against in-situ AGB measurements. Results indicate that volume backscattered power as obtained by ground/volume decomposition is weakly correlated to AGB, notwithstanding different solutions for volume scattering are tested, and support the conclusion that forest structure actually plays a non-negligible role in AGB retrieval in dense tropical forests.

PolSAR-Decomposition-Based Extended Water Cloud Modeling for Forest Aboveground Biomass Estimation

Polarimetric synthetic aperture radar (PolSAR) remote sensing has been widely used for forest mapping and monitoring. PolSAR data has the capability to provide scattering information that is contributed by different scatterers within a single SAR resolution cell. A methodology for a PolSAR-based extended water cloud model (EWCM) has been proposed and evaluated in this study. Fully polarimetric phased array type L-band synthetic aperture radar (PALSAR) data of advanced land observing satellite (ALOS) was used in this study for forest aboveground biomass (AGB) retrieval of Dudhwa National Park, India. The shift in the polarization orientation angle (POA) is a major problem that affects the PolSAR-based scattering information. The two sources of POA shift are Faraday rotation angle (FRA) and structural properties of the scatterer. Analysis was carried out to explore the effect of FRA in the SAR data. Deorientation of PolSAR data was implemented to minimize any ambiguity in the scattering retrieval of model-based decomposition. After POA compensation of the coherency matrix, a decrease in the power of volume scattering elements was observed for the forest patches. This study proposed a framework to extend the water cloud model for AGB retrieval. The proposed PolSAR-based EWCM showed less dependency on field data for model parameters retrieval. The PolSAR-based scattering was used as input model parameters to derive AGB for the forest area. Regression between PolSAR-decomposition-based volume scattering and AGB was performed. Without deorientation of the PolSAR coherency matrix, EWCM showed a modeled AGB of 92.90 t ha−1, and a 0.36 R2 was recorded through linear regression between the field-measured AGB and the modeled output. After deorientation of the PolSAR data, an increased R2 (0.78) with lower RMSE (59.77 t ha−1) was obtained from EWCM. The study proves the potential of a PolSAR-based semiempirical model for forest AGB retrieval. This study strongly recommends the POA compensation of the coherency matrix for PolSAR-scattering-based semiempirical modeling for forest AGB retrieval.

Canopy Height and Above-Ground Biomass Retrieval in Tropical Forests Using Multi-Pass X- and C-Band Pol-InSAR Data

Globally available high-resolution information about canopy height and AGB is important for carbon accounting. The present study showed that Pol-InSAR data from TS-X and RS-2 could be used together with field inventories and high-resolution data such as drone or LiDAR data to support the carbon accounting in the context of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) projects.

Exploring combinations of multi-temporal and multi-frequency radar backscatter observations to estimate above-ground biomass of tropical forest

Approaches exploiting the complementary information on forest above-ground biomass (AGB) contained in multi-temporal and multi-frequency radar backscatter have hardly been explored in the tropics. Having available a multi-seasonal stack of air- and spaceborne X-, C-, L-, and P-band imagery for forest sites in Lope and Mondah, Gabon, we analyzed the sensitivity of backscatter at different frequencies to AGB under varying environmental imaging conditions, and the performance of an AGB retrieval when combining multi-temporal and multi-frequency backscatter observations. For the tropical forest sites in Gabon, which differ significantly with respect to forest composition and climatic conditions, we find that i) P-band allows for estimating AGB with the highest accuracy, ii) multi-temporal L-band backscatter may achieve accuracies close to what can be achieved with few P-band observations, iii) the use of multi-temporal observations is beneficial/crucial at all frequencies, in particular when the imaging conditions are persistently moist and the sensitivity of individual images to AGB reduced, and iv) combining P- and L-band backscatter allows for improving the AGB retrieval primarily when few P-band observations are complemented by a larger number of L-band observations. While the results of this study reemphasize that P-band is the most suitable frequency for mapping AGB in the tropics, they also advise consideration of the growing archives of spaceborne backscatter observations acquired at higher frequencies when mapping AGB in the tropics.

Comparison of Aboveground Biomass Estimation From InSAR and LiDAR Canopy Height Models in Tropical Forests

The potential of interferometric synthetic aperture radar (InSAR) heights from TanDEM-X for vegetation canopy height and aboveground biomass (AGB) estimation has long been recognized. Penetration of X-band into the canopy affects these estimations. Thus, the canopy height and AGB retrieval from InSAR are typically biased and cannot be compared directly to estimates from other data sources. The objective of this letter was to apply a penetration depth model to compensate for height biases in TanDEM-X InSAR heights. The resulting canopy height estimates are subsequently converted to AGB estimates using regression models. The uncorrected InSAR heights of the forest canopy are biased due to the penetration of the signal into the canopy and differ substantially to light detection and ranging (LiDAR) canopy height estimates. The application of the penetration depth compensation results in unbiased forest canopy height estimates and AGB regression models that are comparable between InSAR and LiDAR. These results indicate that TanDEM-X InSAR and LiDAR technologies can be used to estimate AGB in complex tropical forests suggesting a synergistic use of these fundamentally different observation concepts.

Benchmarking the Retrieval of Biomass in Boreal Forests Using P-Band SAR Backscatter with Multi-Temporal C- and L-Band Observations

The planned launch of a spaceborne P-band radar mission and the availability of C- and L-band data from several spaceborne missions suggest investigating the complementarity of C-, L-, and P-band backscatter with respect to the retrieval of forest above-ground biomass. Existing studies on the retrieval of biomass with multi-frequency backscatter relied on single observations of the backscatter and were thus not able to demonstrate the potential of multi-temporal C- and L-band data that are now available from spaceborne missions. Based on spaceborne C- and L-band and airborne P-band images acquired over a forest site in southern Sweden, we investigated whether C- and L-band backscatter may complement retrievals of above-ground biomass from P-band. To this end, a retrieval framework was adopted that utilizes a semi-empirical model for C- and L-bands and an empirical parametric model for P-band. Estimates of above-ground biomass were validated with the aid of 20 m-diameter plots and a LiDAR-derived biomass map with 100 m × 100 m pixel size. The highest retrieval accuracy when not combining frequencies was obtained for P-band with a relative root mean square error (RMSE) of 30% at the hectare scale. The retrieval with multi-temporal L- and C-bands produced errors of the order of 40% and 50%, respectively. The P-band retrieval could be improved for 4% when using P-, L-, and C-bands jointly. The combination of C- and L-bands allowed for retrieval accuracies close to those achieved with P-band. A crucial requirement for achieving an error of 30% with C- and L-bands was the use of multi-temporal observations, which was highlighted by the fact that the retrieval with the best individual L-band image was associated with an error of 61%. The results of this study reconfirmed that P-band is the most suited frequency for the retrieval of above-ground biomass of boreal forests based on backscatter, but also highlight the potential of multi-temporal C- and L-band imagery for mapping above-ground biomass, for instance in areas where the planned ESA BIOMASS P-band mission will not be allowed to acquire data.

Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery

The estimation of aboveground biomass (AGB), an important indicator of grassland production, is crucial for evaluating livestock carrying capacity, understanding the response and feedback to climate change, and achieving sustainable development. Most existing grassland AGB estimation studies were based on empirical methods, in which field measurements are indispensable, hindering their operational use. This study proposed a novel physically-based grassland AGB retrieval method through the inversion of PROSAIL model against MCD43A4 imagery. This method relies on the basic understanding that grassland is herbaceous, and therefore AGB can be represented as the product of leaf dry matter content (Cm) and leaf area index (LAI), i.e., AGB = Cm × LAI. First, the PROSAIL model was parameterized according to the literature regarding grassland parameters retrieval, then Cm and LAI were retrieved using a lookup table (LUT) algorithm, finally, the retrieved Cm and LAI were multiplied to obtain the AGB. The method was assessed in Zoige Plateau, China. Results show that it could reproduce the reference AGB map, which is generated by upscaling the field measurements, in terms of magnitude (with RMSE and R-RMSE of 60.06 g·m−2 and 18.1%, respectively) and spatial distribution. The estimated AGB time series also agreed reasonably well with the expected temporal dynamic trends of the grassland in our study area. The greatest advantage of our method is its fully physical nature, i.e., no field measurement is needed. Our method has the potential for operational monitoring of grassland AGB at regional and even larger scales.

Forest biomass retrieval approaches from earth observation in different biomes

The amount and spatial distribution of forest aboveground biomass (AGB) were estimated using a range of regionally developed methods using Earth Observation data for Poland, Sweden and regions in Indonesia (Kalimantan), Mexico (Central Mexico and Yucatan peninsula), and South Africa (Eastern provinces) for the year 2010. These regions are representative of numerous forest biomes and biomass levels globally, from South African woodlands and savannas to the humid tropical forest of Kalimantan. AGB retrieval in each region relied on different sources of reference data, including forest inventory plot data and airborne LiDAR observations, and used a range of retrieval algorithms. This is the widest inter-comparison of regional-to-national AGB maps to date in terms of area, forest types, input datasets, and retrieval methods. The accuracy assessment of all regional maps using independent field data or LiDAR AGB maps resulted in an overall root mean square error (RMSE) ranging from 10 t ha−1 to 55 t ha−1 (37% to 67% relative RMSE), and an overall bias ranging from −1 t ha−1 to +5 t ha−1 at pixel level. The regional maps showed better agreement with field data than previously developed and widely used pan-tropical or northern hemisphere datasets. The comparison of accuracy assessments showed commonalities in error structures despite the variety of methods, input data, and forest biomes. All regional retrievals resulted in overestimation (up to 63 t ha−1) in the lower AGB classes, and underestimation (up to 85 t ha−1) in the higher AGB classes. Parametric model-based algorithms present advantages due to their low demand on in situ data compared to non-parametric algorithms, but there is a need for datasets and retrieval methods that can overcome the biases at both ends of the AGB range. The outcomes of this study should be considered when developing algorithms to estimate forest biomass at continental to global scale level.