The NASA AfriSAR campaign: Airborne SAR and lidar measurements of tropical forest structure and biomass in support of current and future space missions

Fatoyinbo, Temilola; Armston, John; Simard, Marc; Saatchi, Sassan; Denbina, Michael; Lavalle, Marco; Hofton, M. A.; Tang, Hao; Marselis, S.; Pinto, N.; Hancock, Steven; Hawkins, Brian; Duncanson, Laura; Blair, Bryan; Hansen, Christy; Lou, Yunling; Dubayah, Ralph; Hensley, Scott; Silva, Carlos Alberto; Poulsen, John R.; Labrière, Nicolas; Barbier, Nicolas; Jeffery, Kathryn J.; Kenfack, David; Memiaghe, Herve R.; Bissiengou, Pulchérie; Alonso, Alfonso; Moussavou, Ghislain; White, Lee; Lewis, Simon L.; Hibbard, K. A.

doi:10.1016/j.rse.2021.112533

Cited by 53 publications

(18 citation statements)

References 39 publications

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“…The main objectives of the BIOMASS mission are producing 200 m spatial resolution maps of both forest AGB and height and a 50 m spatial resolution map of forest disturbance. These maps are expected to be more accurate in complex ecosystems than those produced by SAR with shorter wavelengths, such as L, C, or X-band [35,36].…”

Section: Discussionmentioning

confidence: 99%

Canopy Height Mapping by Sentinel 1 and 2 Satellite Images, Airborne LiDAR Data, and Machine Learning

Almeida

Gerente²,

Campos³

et al. 2022

Remote Sensing

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Continuous mapping of vegetation height is critical for many forestry applications, such as planning vegetation management in power transmission line right-of-way. Satellite images from different sensors, including SAR (Synthetic Aperture Radar) from Sentinel 1 (S1) and multispectral from Sentinel 2 (S2), can be used for producing high-resolution vegetation height maps at a broad scale. The main objective of this study is to assess the potential of S1 and S2 satellite data, both in a single and a multisensor approach, for modeling canopy height in a transmission line right-of-way located in the Atlantic Forest of Paraná, Brazil. For integrating S1 and S2 data, we used three machine learning algorithms (LR: Linear Regression, CART: Classification and Regression Trees, and RF: Random Forest) and airborne LiDAR (Light Detection and Ranging) measurements as the reference height. The best models were obtained using the RF algorithm and 20 m resolution features from only S2 data (cross-validated RMSE of 4.92 m and R2 of 0.58) or multisensor data (cross-validated RMSE of 4.86 m and R2 of 0.60). Although the multisensor model presented the best performance, it was not statistically different from the single-S2 model. Thus, the use of only S2 to estimate canopy height has practical advantages, as it reduces the need to process SAR images and the uncertainties due to S1 noise or differences between the acquisition dates of S2 and S1.

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Section: Discussionmentioning

confidence: 99%

Canopy Height Mapping by Sentinel 1 and 2 Satellite Images, Airborne LiDAR Data, and Machine Learning

Almeida

Gerente²,

Campos³

et al. 2022

Remote Sensing

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“…The European Space Agency's BIOMASS mission [210] will operate a P-band (f = 435 MHz, λ = 0:68 m) SAR capable of penetrating the canopy of denser savannas, such as parts of the Miombo, and provide estimates of biomass at a spatial resolution of 200 m. The NISAR mission is a collaboration between NASA and the Indian Space Research Organization and will operate an L-band (f = 1:3 GHz, λ = 0:24 m) and an S-band (f = 3:2 GHz, λ = 0:12 m) SAR focused on areas with AGB of <100 Mg ha -1 , which encompasses most global savannas. These missions will provide more enhanced opportunities to synergize data not only across SAR platforms but also including lidar and optical sensors, for improved mapping of savanna vegetation structure [211]. For example, because longwave SAR can penetrate through to the understory, it can be fused with observations that capture woody biomass such as GEDI (or Sentinel-1 SAR), which will open up an opportunity to spatially separate herbaceous biomass from woody biomass.…”

Section: Incorporating Higher Resolutions and Multisourcementioning

confidence: 99%

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

et al. 2022

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Savannas cover a wide climatic gradient across large portions of the Earth’s land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such as the encroachment of woody vegetation and increasing land-use intensity. Monitoring the spatial and temporal dynamics of savanna ecosystem structure (e.g., partitioning woody and herbaceous vegetation) and function (e.g., aboveground biomass) is of high importance. Major challenges include misclassification of savannas as forests at the mesic end of their range, disentangling the contribution of woody and herbaceous vegetation to aboveground biomass, and quantifying and mapping fuel loads. Here, we review current (2010–present) research in the application of satellite remote sensing in savannas at regional and global scales. We identify emerging opportunities in satellite remote sensing that can help overcome existing challenges. We provide recommendations on how these opportunities can be leveraged, specifically (1) the development of a conceptual framework that leads to a consistent definition of savannas in remote sensing; (2) improving mapping of savannas to include ecologically relevant information such as soil properties and fire activity; (3) exploiting high-resolution imagery provided by nanosatellites to better understand the role of landscape structure in ecosystem functioning; and (4) using novel approaches from artificial intelligence and machine learning in combination with multisource satellite observations, e.g., multi-/hyperspectral, synthetic aperture radar (SAR), and light detection and ranging (lidar), and data on plant traits to infer potentially new relationships between biotic and abiotic components of savannas that can be either proven or disproven with targeted field experiments.

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“…The existing wall‐to‐wall estimations of vegetation canopy height on regional to global scale were generally associated with high uncertainties and coarse spatial resolution (Dubayah et al, 2020; Lang et al, 2022; Quegan et al, 2019). The potential of spaceborne LiDAR and SAR sensors resulted in the development and launch of different spaceborne missions dedicated to estimate ecosystem structure (Dubayah et al, 2020; Fatoyinbo et al, 2021; Quegan et al, 2019). The Global Ecosystem Dynamics Investigation (GEDI) mission is a spaceborne LiDAR, which was launched in late 2018 and is specifically designed to estimate vegetation structure with a sampling grid resulting in a total coverage of the global land surface of 4% (Dubayah et al, 2020; Lang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…The Global Ecosystem Dynamics Investigation (GEDI) mission is a spaceborne LiDAR, which was launched in late 2018 and is specifically designed to estimate vegetation structure with a sampling grid resulting in a total coverage of the global land surface of 4% (Dubayah et al, 2020; Lang et al, 2022). The exploitation and fusion of GEDI with current and future missions can result in an accurate wall‐to‐wall estimation of canopy height and aboveground biomass on potentially global scale (Dubayah et al, 2020; Fatoyinbo et al, 2021; Qi et al, 2019; Quegan et al, 2019; Silva et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM‐X supported by GEDI data

et al. 2022

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1. Vegetation canopy height is a relevant proxy for aboveground biomass, carbon stock, and biodiversity. Wall-to-wall information of canopy height with high spatial resolution and accuracy is not yet available on large scales. For the globally consistent TanDEM-X data, simplifications are necessary to estimate canopy height with semi-empirical models based on polarimetric synthetic aperture radar interferometry (PolInSAR).2. We trained the semi-empirical models with sampled GEDI data, because the assumptions behind the application of such simplifications are not always valid for TanDEM-X. General linear as well as sinc models and empirical parameterizations of these models were applied to estimate the canopy height in tropical landscapes of Sumatra, Indonesia. Airborne laser scanning (ALS) data were consistently used as an independent reference. The general simplified models were compared with the trained empirical versions to assess the potential improvement of the empirical parameterization of the models. The residuals of the different canopy height models were further evaluated in relation to land use and structural information of the vegetation. 3. Our results indicated that the empirical parameters substantially improved the estimation from a root-mean-square-error (RMSE) of 10.3 m (55.8%) to 8.8 m (47.7%), when using the linear model. In contrast, the improvement of the sinc model with empirical parameters was not substantial compared to the general sinc model (7.4 m [40.4%] vs. 6.9 m [37.5%]). A consistent improvement was observed in the linear model, whereas the improvement of the sinc model was dependent on the land-use type. Structural attributes like the canopy height itself and vegetation cover had a significant effect on the accuracies, with higher and denser vegetation generally resulting in higher residuals.4. We demonstrate the potential of the combined exploitation of the TanDEM-X and GEDI missions for a wall-to-wall canopy height estimation in a tropical region. This study provides relevant findings for a consistent mapping of vegetation

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The NASA AfriSAR campaign: Airborne SAR and lidar measurements of tropical forest structure and biomass in support of current and future space missions

Cited by 53 publications

References 39 publications

Canopy Height Mapping by Sentinel 1 and 2 Satellite Images, Airborne LiDAR Data, and Machine Learning

Canopy Height Mapping by Sentinel 1 and 2 Satellite Images, Airborne LiDAR Data, and Machine Learning

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM‐X supported by GEDI data

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