The 2016 NASA AfriSAR campaign: Airborne SAR and Lidar measurements of tropical forest structure and biomass in support of future satellite missions

Fatoyinbo, Lola; Pinto, N.; Hofton, M. A.; Simard, Marc; Blair, Bryan; Saatchi, Sassan; Lou, Yunling; Dubayah, Ralph; Hensley, S.; Armston, John; Duncanson, Laura; Lavalle, Marco

doi:10.1109/igarss.2017.8127949

Cited by 18 publications

(15 citation statements)

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“…Global gross and net primary productivity (GPP and NPP) and how these are changing with climate and human land use are critical for understanding the Earth as a biogeophysical system, for understanding carbon cycleÀclimate feedback, and for defining limits to sustainability (Vitousek et al, 1986;Running, 2012;Sellers et al, 2018). New remote sensing methodologies provide new constraints on GPP (Frankenberg et al, 2011) and biomass observations over time from radar and LiDAR provide a direct measure of aboveground biomass accumulation and loss (Fatoyinbo et al, 2017).…”

Section: What Is the Primary Productivity Of The Globe And What Contrmentioning

confidence: 99%

Flux towers in the sky: global ecology from space

2019

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Summary Global ecology – the study of the interactions among the Earth's ecosystems, land, atmosphere and oceans – depends crucially on global observations: this paper focuses on space‐based observations of global terrestrial ecosystems. Early global ecology relied on an extrapolation of detailed site‐level observations, using models of increasing complexity. Modern global ecology has been enabled largely by vegetation indices (greenness) from operational space‐based imagery but current capabilities greatly expand scientific possibilities. New observations from spacecraft in orbit allowed an estimation of gross carbon fluxes, photosynthesis, biomass burning, evapotranspiration and biomass, to create virtual eddy covariance sites in the sky. Planned missions will reveal the dimensions of the diversity of life itself. These observations will improve our understanding of the global productivity and carbon storage, land use, carbon cycle−climate feedback, diversity−productivity relationships and enable improved climate forecasts. Advances in remote sensing challenge ecologists to relate information organised by biome and species to new data arrayed by pixels and develop theory to address previously unobserved scales.

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Section: What Is the Primary Productivity Of The Globe And What Contrmentioning

confidence: 99%

Flux towers in the sky: global ecology from space

2019

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“…The calibrated and geocoded backscattering data from each campaign were delivered by the ESA through the "Testbed", as described in the BRIX documentation [48]. We referred to the support documentation of each campaign for the description of the SAR data processing and of the in situ biomass measurements [38][39][40][41][42][43]. The main dataset was derived from the joint NASA and ESA AfriSAR experiment, which was conducted in Gabon.…”

Section: Experimental Sites and Sar Datamentioning

confidence: 99%

“…The main dataset was derived from the joint NASA and ESA AfriSAR experiment, which was conducted in Gabon. The experiment was composed of two campaigns-the first was carried out in 2015 with the ONERA (Office National d'Études et de Recherches Aérospatiales) SETHI SAR system and the second one in 2016 with the NASA (National Aeronautics and Space Administration) Land Vegetation and Ice Sensor (LVIS) LiDAR, the NASA L-band UAVSAR, and the DLR (German Aerospace Center) F-SAR [38]. The area covered by the flights was composed of four test sites.…”

Section: Experimental Sites and Sar Datamentioning

confidence: 99%

“…The possibility to observe forests from data. SVR was successfully applied to the retrieval of surface parameters by the authors in [37], where this method was also compared to ANN. In this study, two algorithms for the retrieval of forest biomass, based on ANN and SVR, respectively, were implemented and validated using P-band polarimetric airborne SAR data acquired by the ESA during the BioSAR, AfriSAR, and TropiSAR campaigns [38][39][40][41][42][43]. Inputs of both algorithms are the polarimetric SAR backscattering, and output is the biomass.…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning Applications for the Retrieval of Forest Biomass from Airborne P-Band SAR Data

et al. 2020

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This study aimed at evaluating the potential of machine learning (ML) for estimating forest biomass from polarimetric Synthetic Aperture Radar (SAR) data. Retrieval algorithms based on two different machine-learning methods, namely Artificial Neural Networks (ANNs) and Supported Vector Regressions (SVRs), were implemented and validated using the airborne polarimetric SAR data derived from the AfriSAR, BioSAR, and TropiSAR campaigns. These datasets, composed of polarimetric airborne SAR data at P-band and corresponding biomass values from in situ and LiDAR measurements, were made available by the European Space Agency (ESA) in the framework of the Biomass Retrieval Algorithm Inter-Comparison Exercise (BRIX). The sensitivity of the SAR measurements at all polarizations to the target biomass was evaluated on the entire set of data from all the campaigns, and separately on the dataset of each campaign. Based on the results of the sensitivity analysis, the retrieval was attempted by implementing general algorithms, using the entire dataset, and specific algorithms, using data of each campaign. Algorithm inputs are the SAR data and the corresponding local incidence angles, and output is the estimated biomass. To allow the comparison, both ANN and SVR were trained using the same subset of data, composed of 50% of the available dataset, and validated on the remaining part of the dataset. The validation of the algorithms demonstrated that both machine-learning methods were able to estimate the forest biomass with comparable accuracies. In detail, the validation of the general ANN algorithm resulted in a correlation coefficient R = 0.88, RMSE = 60 t/ha, and negligible BIAS, while the specific ANN for data obtained R from 0.78 to 0.94 and RMSE between 15 and 50 t/ha, depending on the dataset. Similarly, the general SVR was able to estimate the target parameter with R = 0.84, RMSE = 69 t/ha, and BIAS negligible, while the specific algorithms obtained 0.22 ≤ R ≤ 0.92 and 19 ≤ RMSE ≤ 70 (t/ha). The study also pointed out that the computational cost is similar for both methods. In this respect, the training is the only time-demanding part, while applying the trained algorithm to the validation set or to any other dataset occurs in near real time. As a final step of the study, the ANN and SVR algorithms were applied to the available SAR images for obtaining biomass maps from the available SAR images.

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“…Gabon was chosen as case-study location because of the large availability of field and remote sensing datasets collected during the AfriSAR campaign (Fatoyinbo et al 2017). The four study sites in Gabon are referred to as Lopé, Mondah, Mabounié and Rabi (figure 1) and each site includes multiple sampling plots.…”

Section: Field Data Processingmentioning

confidence: 99%

Exploring the relation between remotely sensed vertical canopy structure and tree species diversity in Gabon

Marselis

Tang

Armston

et al. 2019

Environ. Res. Lett.

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Mapping tree species diversity is increasingly important in the face of environmental change and biodiversity conservation. We explore a potential way of mapping this diversity by relating forest structure to tree species diversity in Gabon. First, we test the relation between canopy height, as a proxy for niche volume, and tree species diversity. Then, we test the relation between vertical canopy structure, as a proxy for vertical niche occupation, and tree species diversity. We use large footprint full-waveform airborne lidar data collected across four study sites in Gabon (Lopé, Mabounié, Mondah, and Rabi) in combination with in situ estimates of species richness (S) and Shannon diversity (H′). Linear models using canopy height explained 44% and 43% of the variation in S and H′ at the 0.25 ha resolution. Linear models using canopy height and the plant area volume density profile explained 71% of this variation. We demonstrate applications of these models by mapping S and H′ in Mondah using a simulated GEDI-TanDEM-X fusion height product, across the four sites using wallto-wall airborne lidar data products, and across and between the study sites using ICESat lidar waveforms. The modeling results are encouraging in the context of developing pan-tropical structurediversity models applicable to data from current and upcoming spaceborne remote sensing missions.

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The 2016 NASA AfriSAR campaign: Airborne SAR and Lidar measurements of tropical forest structure and biomass in support of future satellite missions

Cited by 18 publications

References 0 publications

Flux towers in the sky: global ecology from space

Flux towers in the sky: global ecology from space

Machine-Learning Applications for the Retrieval of Forest Biomass from Airborne P-Band SAR Data

Exploring the relation between remotely sensed vertical canopy structure and tree species diversity in Gabon

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