Airborne Lidar Measurements Research Articles

Evaluation of GEDI footprint level biomass models in Southern African Savannas using airborne LiDAR and field measurements

Savannas cover more than 20% of the Earth and account for the third largest stock of global aboveground biomass yet estimates of their above ground biomass density (AGBD) are very inaccurate. The Global Ecosystem Dynamic Investigation (GEDI) sensor provides near-global full-waveform LiDAR data with 25 m footprints, from which various structural metrics are derived that are used to predict footprint level AGBD. The current GEDI L4A AGBD product uses a comprehensive Forest Structure and Biomass Database (FSBD) to develop models for specific plant functional types and geographic regions, but southern African savannas have been underrepresented in the reference data. The objectives of this study were to (i) validate GEDI L4A AGBD in South African savannas using field measurements and ALS datasets and (ii) develop and evaluate local GEDI footprint-level AGBD estimates from multiple L2A and L2B metrics. The local GEDI AGBD models outperformed GEDI L4A AGBD (R2 = 0.42, RMSE = 12 Mg/ha, %RMSE = 79.5%) with higher R2 and smaller error measures. The local GEDI AGBD using a random forest model (RF) had the highest R2 of 0.71 and lowest %RMSE of 53.3%, while the generalized linear model (GLM) results provided the lowest Relative Mean Systematic Deviation (RMSD) of 9.2%, which was half that of RF model. L4A significantly underestimated AGBD with an RMSD up to −37%. This highlights the importance and benefits of local calibration of biomass models to unlock the full potential of GEDI metrics for estimating AGBD. The field and ALS data have subsequently been contributed to the GEDI FSBD and should be used in calibration of future versions of GEDI L4A AGBD product. This research paves the way for the integration of the local GEDI AGBD estimates with other sensors, notable the eminent NISAR mission, to derive regional to global gridded AGBD products that will enable the monitoring of savanna carbon stocks.

Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties

Abstract. Mixed-phase clouds are not well represented in climate and weather forecasting models, due to a lack of the key processes controlling their life cycle. Developing methods to study these clouds is therefore essential, despite the complexity of mixed-phase cloud processes and the difficulty of observing two cloud phases simultaneously. We propose in this paper a new method to retrieve the microphysical properties of mixed-phase clouds, ice clouds and supercooled water clouds using airborne or satellite radar and lidar measurements, called VarPy-mix. This new approach extends an existing variational method developed for ice cloud retrieval using lidar, radar and passive radiometers. We assume that the lidar attenuated backscatter β at 532 nm is more sensitive to particle concentration and is consequently mainly sensitive to the presence of supercooled water. In addition, radar reflectivity Z at 95 GHz is sensitive to the size of hydrometeors and hence more sensitive to the presence of ice particles. Consequently, in the mixed phase the supercooled droplets are retrieved with the lidar signal and the ice particles with the radar signal, meaning that the retrievals rely strongly on a priori and error values. This method retrieves simultaneously the visible extinction for ice αice and liquid αliq particles, the ice and liquid water contents IWC and LWC, the effective radius of ice re,ice and liquid re,liq particles, and the ice and liquid number concentrations Nice and Nliq. Moreover, total extinction αtot, total water content (TWC) and total number concentration Ntot can also be estimated. As the retrieval of ice and liquid is different, it is necessary to correctly identify each phase of the cloud. To this end, a cloud-phase classification is used as input to the algorithm and has been adapted for mixed-phase retrieval. The data used in this study are from DARDAR-MASK v2.23 products, based on the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) observations from the CALIPSO and CloudSat satellites, respectively, belonging to the A-Train constellation launched in 2006. Airborne in situ measurements performed on 7 April 2007 during the Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) campaign and collected under the track of CloudSat–CALIPSO are compared with the retrievals of the new algorithm to validate its performance. Visible extinctions, water contents, effective radii and number concentrations derived from in situ measurements and the retrievals showed similar trends and are globally in good agreement. The mean percent error between the retrievals and in situ measurements is 39 % for αliq, 398 % for αice, 49 % for LWC and 75 % for IWC. It is also important to note that temporal and spatial collocations are not perfect, with a maximum spatial shift of 1.68 km and a maximum temporal shift of about 10 min between the two platforms. In addition, the sensitivity of remote sensing and that of in situ measurements is not the same, and in situ measurement uncertainties are between 25 % and 60 %.

Airborne lidar measurements of atmospheric CO2 column concentrations to cloud tops made during the 2017 ASCENDS/ABoVE campaign

Abstract. We measured the column-averaged atmospheric CO2 mixing ratio (XCO2) to a variety of cloud tops with an airborne pulsed multi-wavelength integrated path differential absorption (IPDA) lidar during NASA's 2017 ASCENDS/ABoVE airborne campaign. Measurements of height-resolved atmospheric backscatter profiles allow this lidar to retrieve XCO2 to cloud tops, as well as to the ground, with accurate knowledge of the photon path length. We validated these measurements with those from an onboard in situ CO2 sensor during spiral-down maneuvers. These lidar measurements were 2–3 times better than those from previous airborne campaigns due to our using a wavelength step-locked laser transmitter and a high-efficiency detector for this campaign. Precisions of 0.6 parts per million (ppm) were achieved for 10 s average measurements to mid-level clouds and 0.9 ppm to low-level clouds at the top of the planetary boundary layer. This study demonstrates the lidar's capability to fill in XCO2 measurement gaps in cloudy regions and to help resolve the vertical and horizontal distributions of atmospheric CO2. Future airborne campaigns and spaceborne missions with this capability can be used to improve atmospheric transport modeling, flux estimation and carbon data assimilation.

Six Consecutive Seasons of High‐Resolution Mountain Snow Depth Maps From Satellite Stereo Imagery

AbstractFine‐scale seasonal snow depth observations can improve estimates of snow water equivalent at critical times of year. Airborne lidar is the current gold standard for snow depth measurement, but it involves high costs and relatively limited coverage. Using very‐high‐resolution satellite stereo images from WorldView‐2, WorldView‐3, and Pléiades‐HR 1A/1B, we produced a six‐year time series (2017–2022) of spatially continuous digital elevation models for an 874 km2 study area over Grand Mesa, Colorado. We generated high‐resolution stereo snow depth maps that capture intra‐ and interannual variability and span multiple anomalous years (58%–158% of median peak SNOTEL snow depth). Comparisons with near‐contemporaneous airborne lidar snow depth measurements showed good agreement, with median offset of −0.13 m, precision of 0.19 m and accuracy of 0.31 m. Our results suggest that satellite stereo can provide snow depth observations with the spatiotemporal coverage needed to improve operational forecast models and inform adaptive management strategies.

Mapping tropical forest aboveground biomass using airborne SAR tomography

Mapping tropical forest aboveground biomass (AGB) is important for quantifying emissions from land use change and evaluating climate mitigation strategies but remains a challenging problem for remote sensing observations. Here, we evaluate the capability of mapping AGB across a dense tropical forest using tomographic Synthetic Aperture Radar (TomoSAR) measurements at P-band frequency that will be available from the European Space Agency’s BIOMASS mission in 2024. To retrieve AGB, we compare three different TomoSAR reconstruction algorithms, back-projection (BP), Capon, and MUltiple SIgnal Classification (MUSIC), and validate AGB estimation from models using TomoSAR variables: backscattered power at 30 m height, forest height (FH), backscatter power metric (Q), and their combination. TropiSAR airborne campaign data in French Guiana, inventory plots, and airborne LiDAR measurements are used as reference data to develop models and calculate the AGB estimation uncertainty. We used univariate and multivariate regression models to estimate AGB at 4-ha grid cells, the nominal resolution of the BIOMASS mission. Our results show that the BP-based variables produced better AGB estimates compared to their counterparts, suggesting a more straightforward TomoSAR processing for the mission. The tomographic FH and AGB estimation have an average relative uncertainty of less than 10% with negligible systematic error across the entire biomass range (~ 200–500 Mg ha−1). We show that the backscattered power at 30 m height at HV polarization is the best single measurement to estimate AGB with significantly better accuracy than the LiDAR height metrics, and combining it with FH improved the accuracy of AGB estimation to less than 7% of the mean. Our study implies that using multiple information from P-band TomoSAR data from the BIOMASS mission provides a new capability to map tropical forest biomass and its changes accurately.

A Comparative Assessment of Multi-Source Generation of Digital Elevation Models for Fluvial Landscapes Characterization and Monitoring

Imaging and measuring the Earth’s relief with sensors mounted upon unmanned aerial vehicles is an increasingly frequently used and promising method of remote sensing. In the context of fluvial geomorphology and its applications, e.g., landform mapping or flood modelling, the reliable representation of the land surface on digital elevation models is crucial. The main objective of the study was to assess and compare the accuracy of state-of-the-art remote sensing technologies in generating DEMs for riverscape characterization and fluvial monitoring applications. In particular, we were interested in DAP and LiDAR techniques comparison, and UAV applicability. We carried out field surveys, i.e., GNSS-RTK measurements, UAV and aircraft flights, on islands and sandbars within a nature reserve on a braided section of the Vistula River downstream from the city of Warsaw, Poland. We then processed the data into DSMs and DTMs based on four sources: ULS (laser scanning from UAV), UAV-DAP (digital aerial photogrammetry), ALS (airborne laser scanning), and satellite Pléiades imagery processed with DAP. The magnitudes of errors are represented by the cross-reference of values generated on DEMs with GNSS-RTK measurements. Results are presented for exposed sediment bars, riverine islands covered by low vegetation and shrubs, or covered by riparian forest. While the average absolute height error of the laser scanning DTMs oscillates around 8–11 cm for most surfaces, photogrammetric DTMs from UAV and satellite data gave errors averaging more than 30 cm. Airborne and UAV LiDAR measurements brought almost the perfect match. We showed that the UAV-based LiDAR sensors prove to be useful for geomorphological mapping, especially for geomorphic analysis of the river channel at a large scale, because they reach similar accuracies to ALS and better than DAP-based image processing.

Regional Stem Volume Mapping: A Feasibility Assessment of Scaling Tree-Level Estimates

Spatially detailed monitoring of forest resources is important for sustainable management but limited by a lack of field measurements. The increasing availability of multisource datasets offers the potential to characterize forest attributes at finer resolutions with regional coverage. This study aimed to assess the potential of mapping stem volume at a 30 m scale in eastern Texas using multisource datasets: airborne lidar, Landsat and LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) datasets. Gradient-boosted trees regression models relating total volume, estimated from airborne lidar measurements and allometric equations, and multitemporal Landsat and LANDFIRE predictors were developed and evaluated. The fitted models showed moderate to high correlation (R2 = 0.52–0.81) with reference stem volume estimates, with higher correlation in pine forests (R2 = 0.70–0.81) than mixed forests (R2 = 0.52–0.67). The models were also precise, with relative percent mean absolute errors (pMAE) of 13.8–21.2%. The estimated volumes also consistently agreed with volumes estimated in independent sites (R2 = 0.51, pMAE = 34.7%) and with US Forest Service Forest Inventory Analysis county-level volume estimates (R2 = 0.93, pBias = −10.3%, pMAE = 11.7%). This study shows the potential of developing regional stem volume products using airborne lidar and multisource datasets, supporting forest productivity and carbon modeling at spatially detailed scales.

Eucalyptus carbon stock estimation in subtropical regions with the modeling strategy of sample plots – airborne LiDAR – Landsat time series data

Updating eucalyptus carbon stock data in a timely manner is essential for better understanding and quantifying its effects on ecological and hydrological processes. At present, there are no suitable methods to accurately estimate the eucalyptus carbon stock in a large area. This research aimed to explore the transferability of the eucalyptus carbon stock estimation model at temporal and spatial scales and assess modeling performance through the strategy of combining sample plots, airborne LiDAR and Landsat time series data in subtropical regions of China. Specifically, eucalyptus carbon stock estimates in typical sites were obtained by applying the developed models with the combination of airborne LiDAR and field measurement data; the eucalyptus plantation ages were estimated using the random localization segmentation approach from Landsat time series data; and regional models were developed by linking LiDAR-derived eucalyptus carbon stock and vegetation age (e.g., months or years). To examine the models’ robustness, the developed models at the regional scale were transferred to estimate carbon stocks at the spatial and temporal scales, and the modeling results were evaluated using validation samples accordingly. The results showed that carbon stock can be successfully estimated using the age-based models (both age variables in months and years as predictor variables), but the month-based models produced better estimates with a root mean square error (RMSE) of 6.51 ​t·ha−1 for Yunxiao County, Fujian Province, and 6.33 ​t·ha−1 for Gaofeng Forest Farm, Guangxi Zhuang Autonomous Region. Particularly, the month-based models were superior for estimating the carbon stocks of young eucalyptus plantations of less than two years. The model transferability analyses showed that the month-based models had higher transferability than the year-based models at the temporal scale, indicating their possibility for analysis of carbon stock change. However, both the month-based and year-based models expressed relatively poor transferability at a spatial scale. This study provides new insights for cost-effective monitoring of carbon stock change in intensively managed plantation forests.

Biomass Estimation and Uncertainty Quantification From Tree Height

We propose a tree-level biomass estimation model approximating allometric equations by LiDAR data. Since tree crown diameters estimation is challenging from spaceborne LiDAR measurements, we develop a model to correlate tree height with biomass on the individual tree level employing a Gaussian process regressor. In order to validate the proposed model, a set of 8,342 samples on tree height, trunk diameter, and biomass has been assembled. It covers seven biomes globally present. We reference our model to four other models based on both, the Jucker data and our own dataset. Although our approach deviates from standard biomass-height-diameter models, we demonstrate the Gaussian process regression model as a viable alternative. In addition, we decompose the uncertainty of tree biomass estimates into the model- and fitting-based contributions. We verify the Gaussian process regressor has the capacity to reduce the fitting uncertainty down to below 5%. Exploiting airborne LiDAR measurements and a field inventory survey on the ground, a stand-level (or plot-level) study confirms a low relative error of below 1% for our model. The data used in this study are available at https://github.com/zhu-xlab/BiomassUQ .

Evaluating Statewide NAIP Photogrammetric Point Clouds for Operational Improvement of National Forest Inventory Estimates in Mixed Hardwood Forests of the Southeastern U.S.

The U.S. Forest Service, Forest Inventory and Analysis (FIA) program is tasked with making and reporting estimates of various forest attributes using a design-based network of permanent sampling plots. To make its estimates more precise, FIA uses a technique known as post-stratification to group plots into more homogenous classes, which helps lower variance when deriving population means. Currently FIA uses a nationally available map of tree canopy cover for post-stratification, which tends to work well for forest area estimates but less so for structural attributes like volume. Here we explore the use of new statewide digital aerial photogrammetric (DAP) point clouds developed from stereo imagery collected by the National Agricultural Imagery Program (NAIP) to improve these estimates in the southeastern mixed hardwood forests of Tennessee and Virginia, United States (U.S.). Our objectives are to 1. evaluate the relative quality of NAIP DAP point clouds using airborne LiDAR and FIA tree height measurements, and 2. assess the ability of NAIP digital height models (DHMs) to improve operational forest inventory estimates above the gains already achieved from FIA’s current post-stratification approach. Our results show the NAIP point clouds were moderately to strongly correlated with FIA field measured maximum tree heights (average Pearson’s r = 0.74) with a slight negative bias (−1.56 m) and an RMSE error of ~4.0 m. The NAIP point cloud heights were also more accurate for softwoods (R2s = 0.60–0.79) than hardwoods (R2s = 0.33–0.50) with an error structure that was consistent across multiple years of FIA measurements. Several factors served to degrade the relationship between the NAIP point clouds and FIA data, including a lack of 3D points in areas of advanced hardwood senescence, spurious height values in deep shadows and imprecision of FIA plot locations (which were estimated to be off the true locations by +/− 8 m). Using NAIP strata maps for post-stratification yielded forest volume estimates that were 31% more precise on average than estimates stratified with tree canopy cover data. Combining NAIP DHMs with forest type information from national map products helped improve stratification performance, especially for softwoods. The monetary value of using NAIP height maps to post-stratify FIA survey unit total volume estimates was USD 1.8 million vs. the costs of installing more field plots to achieve similar precision gains. Overall, our results show the benefit and growing feasibility of using NAIP point clouds to improve FIA’s operational forest inventory estimates.

Airborne Lidar Measurements of XCO2 in Synoptically Active Environment and Associated Comparisons With Numerical Simulations.

Frontal boundaries have been shown to cause large changes in CO2 mole-fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO2 dry air mole-fraction (XCO2) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO2 frontal contrasts (ΔXCO2, i.e., warm minus cold sector average of XCO2). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO2 during four seasonal field campaigns of the Atmospheric Carbon and Transport-America (ACT-America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting--Chemistry (WRF-Chem) showed that (a) all products had a similar sign of ΔXCO2 though with different levels of agreement in ΔXCO2 magnitudes among seasons; (b) ΔXCO2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO2 frontal contrasts. A linear regression analyses between ΔXCO2 for MFLL versus GMAO, and MFLL versus WRF-Chem for summer-2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO2 variability among four seasons provide guidance to the spatial structures of XCO2 transport errors in models and satellite measurements of XCO2 in synoptically-active weather systems.

The potential of citizen science data to complement satellite and airborne lidar tree height measurements: lessons from The GLOBE Program

The Global Learning and Observations to Benefit the Environment (GLOBE) Program is an international science, citizen science, and education program through which volunteers in participating countries collect environmental data in support of Earth system science. Using the program’s software application, GLOBE Observer (GO), volunteers measure tree height and optional tree circumference, which may support the interpretation of NASA and other space-based satellite data such as tree height data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation instrument. This paper describes tree heights data collected through the GO application and identifies sources of error in data collection. We also illustrate how the ground-based citizen science data collected in the GO application can be used in conjunction with ICESat-2 tree height observations from two locations in the United States: Grand Mesa, Colorado, and Greenbelt, Maryland. Initial analyses indicate that data location accuracy and the scientific relevance of data density should be considered in order to align GLOBE tree height data with satellite-based data collections. These recommendations are intended to inform the improved implementation of citizen science environmental data collection in scientific work and to document a use case of the GLOBE Trees data for the science research community.

Wintertime Saharan dust transport towards the Caribbean: an airborne lidar case study during EUREC<sup>4</sup>A

Abstract. Wintertime Saharan dust plumes in the vicinity of Barbados are investigated by means of airborne lidar measurements. The measurements were conducted in the framework of the EUREC4A (Elucidating the Role of Cloud-Circulation Coupling in Climate) field experiment upstream the Caribbean island in January–February 2020. The combination of the water vapor differential absorption and high spectral resolution lidar techniques together with dropsonde measurements aboard the German HALO (High Altitude and Long-Range) research aircraft enable a detailed vertical and horizontal characterization of the measured dust plumes. In contrast to summertime dust transport, mineral dust aerosols were transported at lower altitudes and were always located below 3.5 km. Calculated backward trajectories affirm that the dust-laden layers have been transported in nearly constant low-level altitude over the North Atlantic Ocean. Only mixtures of dust particles with other aerosol species, i.e., biomass-burning aerosol from fires in West Africa and marine aerosol, were detected by the lidar. No pure mineral dust regimes were observed. Additionally, all the dust-laden air masses that were observed during EUREC4A came along with enhanced water vapor concentrations compared with the free atmosphere above. Such enhancements have already been observed during summertime and were found to have a great impact on radiative transfer and atmospheric stability.

Application of Airborne LiDAR Measurements to the Topographic Survey of the Tidal Flats of the Northern Jiangsu Radial Sand Ridges in the Southern Yellow Sea

The northern Jiangsu radial sand ridges are typical geomorphic deposit units distributed off the Jiangsu coast. A coastal tidal flat typically develops and provides a good habitat for many migratory birds and benthic organisms. However, topographic surveys of tidal flats have always been difficult in marine surveys because of the dense tidal creek, poor accessibility, and difficulty in setting up control points. In this study, we quickly obtained the point cloud data of the tidal flat near Yangkou Port in the southern part of the radial sand ridges based on an airborne LiDAR system, an integrated 3D laser scanner and a positioning and attitude determination system. We analyzed the adaptabilities of multiple filtering algorithms to tidal flats. In addition, a digital elevation model (DEM) of the tidal flat was constructed and the accuracy was verified with synchronized beach GPS-RTK topographic elevation measurements. The results show that the following: (1) Airborne LiDAR can quickly obtain high precision, high resolution, and a large area of ground point cloud information for tidal flats, overcoming the shortcomings of traditional measurement methods. (2) The triangulated irregular network (TIN) filtering effect is better than that of mathematical morphology and the filtering effect of point cloud normal vector clustering is mediocre. (3) The DEM of the LiDAR point cloud is in good agreement with RTK and the average error of the measurement results is 0.108 m. The error accuracy of the DEM satisfies the surveying specification of a 1:500 topographic map in a flat area, which proves that the airborne LiDAR system can be suitable for tidal flat elevation measurement. Nevertheless, it is possible to provide high precision terrain detection and DEM construction of a tidal flat with the development of airborne infrared and blue-green laser detection radar.

Definition of Tomographic SAR Configurations for Forest Structure Applications at L-Band

Synthetic aperture radar tomography (TomoSAR) at lower frequencies allows the reconstruction of the 3-D radar reflectivity of volume scatterers allowing access to their physical 3-D structure by means of multiangular SAR acquisitions. The performance of the reconstruction critically depends on the number and (spatial) distribution of the tomographic acquisitions (tracks). This dependence is addressed in this letter with respect to forest applications (volume scatters) at L-band. The letter discusses the optimum definition of tomographic configurations based on the peak sidelobe level (PSL) of the point spread function (PSF). For demonstration, a tomographic data set consisting of 15 acquisitions acquired by the DLR’s F-SAR system at L-band over the Traunstein test site in Germany is used, complemented by airborne LiDAR measurements. Three different reconstruction algorithms (Fourier beamforming, Capon beamforming, and compressive sensing) are implemented and compared to each other for scenarios with a reduced number of acquisitions. Although the limitation of the specific forest, the results show the potential of using the PSL of the PSF to define tomographic configurations optimized for forest structure applications.

Where the Methane Is-Insights from Novel Airborne LiDAR Measurements Combined with Ground Survey Data.

Airborne LiDAR measurements, parallel controlled releases, and on-site optical gas imaging (OGI) survey and pneumatic device count data from 1 year prior, were combined to derive a new measurement-based methane inventory for oil and gas facilities in British Columbia, Canada. Results reveal a surprising distinction in the higher magnitudes, different types, and smaller number of sources seen by the plane versus OGI. Combined data suggest methane emissions are 1.6-2.2 times current federal inventory estimates. More importantly, analysis of high-resolution geo-located aerial imagery, facility schematics, and equipment counts allowed attribution to major source types revealing key drivers of this difference. More than half of emissions were attributed to three main sources: tanks (24%), reciprocating compressors (15%), and unlit flares (13%). These are the sources driving upstream oil and gas methane emissions, and specifically, where emerging regulations must focus to achieve meaningful reductions. Pneumatics accounted for 20%, but this contribution is lower than recent Canadian and U.S. inventory estimates, possibly reflecting a growing shift toward more low- and zero-emitting devices. The stark difference in the aerial and OGI results indicates key gaps in current inventories and suggests that policy and regulations relying on OGI surveys alone may risk missing a significant portion of emissions.

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

In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies.During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30,000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1-hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community.

The Impact of Offshore Wind Farms on Sea State Demonstrated by Airborne LiDAR Measurements

The increasing number of wind farms installed in the North Sea has an impact on the downstream wind speed. This has been hypothesized as well for sea state properties. Wave effects can be expected in particular in fetch-limited conditions with offshore wind directions. With systematic flights deploying an airborne laser scanner, these impacts are shown directly for the first time. The flights were conducted perpendicular to the main wind direction upstream and downstream of the cluster of the offshore wind parks Amrumbank West, Nordsee Ost, and Meerwind Süd/Ost. The flight legs covered the area potentially influenced by the wind parks and the undisturbed area next to the wind parks. The analysis of the spectral energy distribution shows a re-distribution of the wave energy in the downstream area with enhanced energy at smaller wavelengths. The effect is still clearly visible at a distance of 55 km. As the sea surface constitutes the link between the atmosphere and the ocean, it is very likely that wind parks modify the properties of the water column as well.

High-resolution CubeSat imagery and machine learning for detailed snow-covered area

Snow cover affects a diverse array of physical, ecological, and societal systems. As such, the development of optical remote sensing techniques to measure snow-covered area (SCA) has enabled progress in a wide variety of research domains. However, in many cases, the spatial and temporal resolutions of currently available remotely sensed SCA products are insufficient to capture SCA evolution at spatial and temporal resolutions relevant to the study of fine-scale spatially heterogeneous phenomena. We developed a convolutional neural network-based method to identify snow covered area using the ~3 m, 4-band PlanetScope optical satellite image dataset with ~daily, near-global coverage. By comparing our model performance to snow extent derived from high-resolution airborne lidar differential depth measurements and satellite platforms in two North American sites (Sierra Nevada, CA, USA and Rocky Mountains, CO, USA), we show that these emerging image archives have great potential to accurately observe snow-covered area at high spatial and temporal resolutions despite limited radiometric bandwidth and band placement. We achieve average snow classification F-Scores of 0.73 in our training basin and 0.67 in a climatically-distinct out-of-sample basin, suggesting opportunities for model transferability. We also evaluate the performance of these data in forested regions, suggesting avenues for further research. The unparalleled spatial and temporal coverage of CubeSat imagery offers an excellent opportunity for satellite remote sensing of snow, with real implications for ecological and water resource applications.

Radiative effects of long-range-transported Saharan air layers as determined from airborne lidar measurements

Abstract. The radiative effect of long-range-transported Saharan air layers is investigated on the basis of simultaneous airborne high-spectral-resolution and differential-absorption lidar measurements in the vicinity of Barbados. Within the observed Saharan air layers, increased water vapor concentrations compared to the dry trade wind atmosphere are found. The measured profiles of aerosol optical properties and water vapor mixing ratios are used to characterize the atmospheric composition in radiative transfer calculations, to calculate radiative effects of moist Saharan air layers and to determine radiative heating rate profiles. An analysis based on three case studies reveals that the observed enhanced amounts of water vapor within Saharan air layers have a much stronger impact on heating rate calculations than mineral dust aerosol. Maximum mineral dust short-wave heating and long-wave cooling rates are found at altitudes of highest dust concentration (short wave: +0.5 K d−1; long wave: −0.2 K d−1; net: +0.3 K d−1). However, when considering both aerosol concentrations and measured water vapor mixing ratios in radiative transfer calculations, the maximum heating/cooling rates shift to the top of the dust layer (short wave: +2.2 K d−1; long wave: −6.0 to −7.0 K d−1; net: −4.0 to −5.0 K d−1). Additionally, the net heating rates decrease with height – indicating a destabilizing effect in the dust layers. Long-wave counter-radiation of Saharan air layers is found to reduce cooling at the tops of the subjacent marine boundary layers and might lead to less convective mixing in these layers. The overall short-wave radiative effect of mineral dust particles in Saharan air layers indicates a maximum magnitude of −40 W m−2 at surface level and a maximum of −25 W m−2 at the top of the atmosphere.