Spaceborne Lidar Missions Research Articles

Continuous mapping of forest canopy height using ICESat-2 data and a weighted kernel integration of multi-temporal multi-source remote sensing data aided by Google Earth Engine.

Forest Canopy Height (FCH) is a crucial parameter that offers valuable insights into forest structure. Spaceborne LiDAR missions provide accurate FCH measurements, but a significant challenge is their point-based measurements lacking spatial continuity. This study integrated ICESat-2's ATL08-derived FCH values with multi-temporal and multi-source remote sensing (RS) datasets to generate continuous FCH maps for northern forests in Iran. Sentinel-1/2, ALOS-2 PALSAR-2, and FABDEM datasets were prepared in Google Earth Engine (GEE) for FCH mapping, each possessing unique spatial and geometrical characteristics that differ from those of the ATL08 product. Given the importance of accurately representing the geometrical characteristics of the ATL08 segments in modeling FCH, a novel Weighted Kernel (WK) approach was proposed in this paper. The WK approach could better represent the RS datasets within the ATL08 ground segments compared to other commonly used resampling approaches. The correlation between all RS data features improved by approximately 6% compared to previously employed approaches, indicating that the RS data features derived after convolving the WK approach are more predictive of FCH values. Furthermore, the WK approach demonstrated superior performance among machine learning models, with random forests outperforming other models, achieving a coefficient of determination (R2) of 0.71, root mean square error (RMSE) of 4.92m, and mean absolute percentage error (MAPE) of 29.95%. Furthermore, in contrast to previous studies using only summer datasets, this study included spring and autumn data from Sentinel-1/2, resulting in a 6% increase in R2 and a 0.5-m decrease in RMSE. The proposed methodology filled the research gaps and improved the accuracy of FCH estimations.

Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling.

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.

Measuring forest height from space. Opportunities and limitations observed in natural forests

The goal of the study is to test a methodology that outputs continuous sets of data by integrating forest canopy height satellite measurements in a Random Forest (RF) algorithm. We assess the performance of the two spaceborne LiDAR missions by comparing them to field measurements taken with a mobile LiDAR scanner (MLS), then we test the compatibility of the sensors as an input for an RF model. The Spearman analysis showed a significant correlation between the MLS height and the GEDI height (p = 0.00015), but not for the ICEsat-2 height measurements (p = 0.3). The same analysis proved to be inconclusive for the RF output, where, although the p-value was 0.00025, the R value was only 0.38. Although the method studied in this paper proved to be only fit for estimation purposes, the integration of spaceborne LiDAR sensors in the RF shows high potential for obtaining homogenous, continuous sets of data.

Consistency analysis of forest height retrievals between GEDI and ICESat-2

Two space-borne LiDAR missions (Global Ecosystem Dynamics Investigation, GEDI; Ice, Cloud, and land Elevation Satellite-2, ICESat-2) have unique advantages in retrieving forest heights. Fusing these two missions will greatly increase the number of forest height samples and realize their geographical complementarity, providing unprecedented opportunities for global forest height mapping. However, ICESat-2 and GEDI use different LiDAR technologies, which may lead to inconsistencies in the forest height estimation between the two missions. This study aims to explore the potential of obtaining consistent forest heights from ICESat-2 and GEDI data in support of global forest height mapping. First, the accuracies of GEDI and ICESat-2 forest heights in four different scenarios (nigh/daytime and strong/weak beam) were validated and compared utilizing airborne LiDAR data. Second, we quantitatively evaluated the differences in the forest heights derived from the GEDI and ICESat-2 data within their overlapping footprints and analyzed the effects of the terrain slope, forest coverage, forest type and study site on these differences. Third, the forest height consistency models were built based on GEDI-derived forest heights and ICESat-2 feature parameters using stepwise regression (SR) and random forest (RF) algorithms as well as a combination of both SR and RF algorithms. Finally, we evaluated the transferability of forest height consistency models to different forest types and study sites, and further analyzed the potential of building a universally consistent model. The results showed that the accuracies of both GEDI and ICESat-2 derived forest heights differ among four different scenarios, and the accuracy of forest heights extracted from GEDI data (R2 = 0.93, RMSE = 2.99 m for power beams at night) is higher than that extracted from ICESat-2 data (R2 = 0.78, RMSE = 4.62 m for strong beams at night) regardless of scenarios. The forest height differences exist between these two missions, and show an apparent change trend with increasing forest coverage. By establishing the consistency models, the forest height difference between GEDI and ICESat-2 can be reduced. Compared to the forest height consistency models built by SR or RF algorithms, the consistency models established by combining the RF and SR algorithms have higher accuracy (average R2 = 0.86, RMSE = 2.56 m for ATL08). Additionally, the consistency models developed specifically for one forest type or study site are less transferable, while a universal consistency model is applicable for various vegetation types and study sites. After building a universal consistency model based on both SR and RF algorithms, the consistent forest heights were obtained from GEDI and ICESat-2 data. Overall, this study demonstrates the possibility of combining different modes of space-borne LiDAR data to obtain consistent forest height datasets.

This is FAST: multivariate Full-permutAtion based Stochastic foresT method—improving the retrieval of fine-mode aerosol microphysical properties with multi-wavelength lidar

Despite the small size and ubiquitous presence, fine-mode aerosols play a vital role in climate change and human health, especially in highly populated regions. Hitherto, some studies have been carried out to retrieve fine-mode aerosol microphysical properties from multi-wavelength lidar measurements. However, there has been a dearth of lidar-based methods with quality retrieval and high time efficiency to adequately quantify the impacts of fine-mode aerosols on Earth's energy budget. The reasons for the gap involve the limitations in current approaches and the nature of the under-determined problem with insufficient information of three backscattering coefficients (β) and two extinction coefficients (α), typically known as the 3β + 2α configuration. Furthermore, the latest lidars, especially for spaceborne and airborne applications, are inherently difficult to perform routine diurnal 3β + 2α observations with high resolution and require high processing speed for massive data. To overcome these conundrums, we developed a novel unsupervised machine learning method—multivariate Full-permutAtion based Stochastic foresT method (dubbed the FAST method)—to improve the retrieval of fine-mode aerosol microphysical properties. To the best of knowledge, this work is the first time that machine learning algorithms are employed in attempts to retrieve the aerosol microphysical properties with stand-alone multi-wavelength lidar data. The major merits of the FAST method include 1) high accuracy of fine-mode aerosol products with the typical 3β + 2α configuration, 2) acceptable performances for fewer input optical channels, and 3) high processing speed for large volume data. Comprehensive simulations have been conducted to investigate the error characteristic of the FAST method under different conditions. We also applied the FAST method to the airborne lidar data acquired during the NASA DISCOVER-AQ field campaign. The retrievals of the FAST method provide high resolution time-height distributions of fine-mode aerosol microphysical properties at 20-s temporal resolution and 45-m vertical resolution. In situ measurements are compared with multi-wavelength lidar retrievals showing good agreements. We achieved 0.010, 0.014 and 0.016 in terms of mean absolute difference for retrieved 532-nm single-scattering albedo with 3β + 2α, 3β + 1α, and 2β + 1α configurations, respectively. The proposed method is expected to represent an important step toward improving microphysical retrievals from multi-wavelength lidar data, especially for airborne and spaceborne lidar missions.

Performance of GEDI Space-Borne LiDAR for Quantifying Structural Variation in the Temperate Forests of South-Eastern Australia

Monitoring forest structural properties is critical for a range of applications because structure is key to understanding and quantifying forest biophysical functioning, including stand dynamics, evapotranspiration, habitat, and recovery from disturbances. Monitoring of forest structural properties at desirable frequencies and cost globally is enabled by space-borne LiDAR missions such as the global ecosystem dynamics investigation (GEDI) mission. This study assessed the accuracy of GEDI estimates for canopy height, total plant area index (PAI), and vertical profile of plant area volume density (PAVD) and elevation over a gradient of canopy height and terrain slope, compared to estimates derived from airborne laser scanning (ALS) across two forest age-classes in the Central Highlands region of south-eastern Australia. ALS was used as a reference dataset for validation of GEDI (Version 2) dataset. Canopy height and total PAI analyses were carried out at the landscape level to understand the influence of beam-type, height of the canopy, and terrain slope. An assessment of GEDI’s terrain elevation accuracy was also carried out at the landscape level. The PAVD profile evaluation was carried out using footprints grouped into two forest age-classes, based on the areas of mountain ash (Eucalyptus regnans) forest burnt in the Central Highlands during the 1939 and 2009 wildfires. The results indicate that although GEDI is found to significantly under-estimate the total PAI and slightly over-estimate the canopy height, the GEDI estimates of canopy height and the vertical PAVD profile (above 25 m) show a good level of accuracy. Both beam-types had comparable accuracies, with increasing slope having a slightly detrimental effect on accuracy. The elevation accuracy of GEDI found the RMSE to be 10.58 m and bias to be 1.28 m, with an R2 of 1.00. The results showed GEDI is suitable for canopy densities and height in complex forests of south-eastern Australia.

High-resolution forest carbon mapping for climate mitigation baselines over the RGGI region, USA

Large-scale airborne lidar data collections can be used to generate high-resolution forest aboveground biomass maps at the state level and beyond as demonstrated in early phases of NASA’s Carbon Monitoring System program. While products like aboveground biomass maps derived from these leaf-off lidar datasets each can meet state- or substate-level measurement requirements individually, combining them over multiple jurisdictions does not guarantee the consistency required in forest carbon planning, trading and reporting schemes. In this study, we refine a multi-state level forest carbon monitoring framework that addresses these spatial inconsistencies caused by variability in data quality and modeling techniques. This work is built upon our long term efforts to link airborne lidar, National Agricultural Imagery Program imagery and USDA Forest Service Forest Inventory and Analysis plot measurements for high-resolution forest aboveground biomass mapping. Compared with machine learning algorithms (r 2 = 0.38, bias = −2.3, RMSE = 45.2 Mg ha−1), the use of a linear model is not only able to maintain a good prediction accuracy of aboveground biomass density (r 2 = 0.32, bias = 4.0, RMSE = 49.4 Mg ha−1) but largely mitigates problems related to variability in data quality. Our latest effort has led to the generation of a consistent 30 m pixel forest aboveground carbon map covering 11 states in the Regional Greenhouse Gas Initiative region of the USA. Such an approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA’s spaceborne lidar missions.

Leveraging TLS as a Calibration and Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass at Landscape-Scales

Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R2 = 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha−1 vs 41.08 Mg ha−1). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R2 = 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions.

Wavelength dependence of ice cloud backscatter properties for space-borne polarization lidar applications.

We investigated the use of backscatter properties of atmospheric ice particles for space-borne lidar applications. We estimated the average backscattering coefficient (β), backscatter color ratio (χ), and depolarization ratio (δ) for ice particles with a wide range of effective radii for five randomly oriented three-dimensional (3D) and three quasi-horizontally oriented two-dimensional (2D) types of ice particle using physical optics and geometrical integral equation methods. This is the first study to estimate the lidar backscattering properties of quasi-horizontally oriented non-pristine ice crystals. We found that the χ-δ relationship was useful for discriminating particle types using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. The lidar ratio (S)-δ relationship, which is determined using space-borne high-spectral-resolution lidar products such as EarthCARE ATLID or future space-borne lidar missions, may also produce robust classification of ice particle types because it is complementary to the χ-δ relationship.

Forest Height Estimation Using Multibaseline PolInSAR and Sparse Lidar Data Fusion

We demonstrate a method using lidar data fusion to improve the forest height estimation accuracy of multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR). Compared to single-baseline PolInSAR, multibaseline PolInSAR allows forest canopy height to be estimated more accurately across a wider range of height values. However, to arrive at a single forest height estimate, the estimates from the multiple baselines must be selected or weighted. A number of approaches to selecting between baselines have been proposed in the literature, but they are generally based on simple metrics of the PolInSAR data and do not necessarily capture the full range of characteristics that make one baseline produce more accurate forest height estimates than another. We solve this problem by treating baseline selection as a supervised classification problem that can be trained using a small amount of sparse lidar data located within the PolInSAR coverage area. We train a support vector machine classifier using a variety of coarse lidar sample spacings of 250 m and greater, to demonstrate that data from future spaceborne lidar missions will be sufficient for this purpose. We demonstrate results for multiple study areas in the country of Gabon using data collected by NASA's uninhabited aerial vehicle synthetic aperture radar and land, vegetation, and ice sensor lidar. The use of lidar fusion for PolInSAR baseline selection yields improved results compared to standard baseline selection methods, and further demonstrates the strong potential of PolInSAR and lidar fusion for remote sensing of forests.

An Assessment of Temporal Decorrelation Compensation Methods for Forest Canopy Height Estimation Using Airborne L-Band Same-Day Repeat-Pass Polarimetric SAR Interferometry

We assess and compare several algorithms to compensate for temporal decorrelation observed in repeat-pass L-band polarimetric interferometric synthetic aperture radar (PolInSAR) measurements of forest canopy height. The analysis is performed on data acquired with an approximately 45-min temporal baseline using the uninhabited aerial vehicle synthetic aperture radar collected in August 2009 over temperate and boreal forests of the U.S. state of Maine and the Canadian province of Quebec. This investigation presents several compensation methods based on the classical random volume over ground model, which include fixing the value of the extinction parameter, fixing the temporal decorrelation magnitude, or varying temporal decorrelation estimates with height. We also compare results with the random motion over ground model. While these methods have been presented in the literature previously, a comparison of the different methods and an assessment of their height estimation accuracy applied to the same datasets have not yet been performed. In addition, we introduce the use of ancillary reference forest height data from airborne large footprint lidar to estimate model parameters and to mitigate solution ambiguities. We finally demonstrate that this mitigation strategy is robust and suitable for use with future spaceborne lidar missions such as the Global Ecosystems Dynamics Investigation. The resulting PolInSAR canopy height estimates correspond well with those obtained from coincident field and airborne lidar data. Height estimation differences of 3.4 m (RMSE) were observed between the PolInSAR- and lidar-derived canopy height maps when using the fixed extinction method. These can be partially attributed to inherent differences in the sensor spatial resolutions and geolocation accuracy. The RMS error between the PolInSAR height estimates and the field collected Lorey's heights was 2.4 m.

The global distribution of cloud gaps in CALIPSO data

Future space-borne lidar missions are foreseen to measure global concentrations of methane, carbon dioxide and aerosols with high sensitivity and to relate the concentrations to their surface sources and sinks. Therefore, full visibility down to the surface is required. We use Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) level-2 total atmosphere cloud optical depths for the full year 2007 to assess the global and seasonal variability of such cloud-free regions with high accuracy and spatial resolution (5km), both to contribute to an improved scientific understanding of their distribution and to identify clear regions where the above missions are expected to significantly add to the current global observation system. The global length distribution of cloudy and of cloud-free regions is strongly skewed towards a high probability of occurrence of small lengths and roughly follows a power law with exponent −5/3 up to scales of about 1000km. Belts with extended cloud-free regions span along the subtropics, seasonally interrupted by monsoon systems. In winter large parts of the Arctic are less cloudy than in summer. Over regions with intense anthropogenic or biogenic aerosol and greenhouse gas emissions, low cloud cover is found in India and Northeast China in winter and in Amazonia, the USA, and Central Asia in summer. Here, favorable conditions for key contributions by the next generation of remote sensing missions are encountered.

Effects of solar activity on noise in CALIOP profiles above the South Atlantic Anomaly

Abstract. We show that nighttime dark noise measurements from the spaceborne lidar CALIOP contain valuable information about the evolution of upwelling high-energy radiation levels. Above the South Atlantic Anomaly (SAA), CALIOP dark noise levels fluctuate by ±6% between 2006 and 2013, and follow the known anticorrelation of local particle flux with the 11-year cycle of solar activity (with a 1-year lag). By analyzing the geographic distribution of noisy profiles, we are able to reproduce known findings about the SAA region. Over the considered period, it shifts westward by 0.3° year−1, and changes in size by 6° meridionally and 2° zonally, becoming larger with weaker solar activity. All results are in strong agreement with previous works. We predict SAA noise levels will increase anew after 2014, and will affect future spaceborne lidar missions most near 2020.

A comparison of foliage profiles in the Sierra National Forest obtained with a full-waveform under-canopy EVI lidar system with the foliage profiles obtained with an airborne full-waveform LVIS lidar system

Foliage profiles retrieved from a scanning, terrestrial, near-infrared (1064nm), full-waveform lidar, the Echidna Validation Instrument (EVI), agree well with those obtained from an airborne, near-infrared, full-waveform, large footprint lidar, the Lidar Vegetation Imaging Sensor (LVIS). We conducted trials at 5 plots within a conifer stand at Sierra National Forest in August, 2008. Foliage profiles retrieved from these two lidar systems are closely correlated (e.g., r=0.987 at 100m horizontal distances) at large spatial coverage while they differ significantly at small spatial coverage, indicating the apparent scanning perspective effect on foliage profile retrievals. Also we noted the obvious effects of local topography on foliage profile retrievals, particularly on the topmost height retrievals. With a fine spatial resolution and a small beam size, terrestrial lidar systems complement the strengths of the airborne lidars by making a detailed characterization of the crowns from a small field site, and thereby serving as a validation tool and providing localized tuning information for future airborne and spaceborne lidar missions.

Quantifying Surface Reflectivity for Spaceborne Lidar via Two Independent Methods

Spaceborne differential absorption lidar has been proposed for accurate measurements of atmospheric CO2 (and surface properties). Lidar instruments typically observe the highest possible surface reflectance due to observing in the retroreflection direction (the so-called ldquohotspotrdquo) where viewed shadow is minimized. The range of observed reflectance will determine instrument dimensions and signal-to-noise ratio, but it is difficult to predict this range globally a priori. Two complementary methods are presented for estimating lidar reflectivity over a range of vegetated surface types. The first method simulates the expected response of a lidar instrument from multiangle multispectral reflectance data. The second method uses detailed 3-D vegetation structural models and Monte Carlo ray tracing to simulate the lidar signal. The simulations are used to validate the first method and assess the impact of possible instrument configurations. Both methods agree well and are robust to error in observations, with predicted lidar reflectivity (at 1570 and 2050 nm here) typically between 10% and 33% higher relative to off-nadir reflectance and ranging from 0.02 to ~ 0.7. We use the 3-D simulations to show that the impact of shifted on-off lidar pulses is not likely to be significant for accuracy of retrieved CO2, and we demonstrate that the 3-D simulation method is a flexible and powerful way of prototyping future spaceborne lidar missions.

Holographic optical elements as scanning lidar telescopes

We have developed and investigated the use of holographic optical elements (HOEs) and holographic transmission gratings for scanning lidar telescopes. Rotating a flat HOE in its own plane with the focal spot on the rotation axis makes a very simple and compact conical scanning telescope. We developed transmission and reflection HOEs for use at the first three harmonic wavelengths of Nd:YAG lasers. The diffraction efficiency, diffraction angle, focal length, focal spot size and optical losses were measured for several HOEs and holographic gratings, and found to be suitable for use as lidar receiver telescopes, and in many cases could also serve as the final collimating and beam steering optic for the laser transmitter. Two lidar systems based on this technology have been designed, built, and successfully tested in atmospheric science applications. This technology will enable future spaceborne lidar missions by significantly lowering the size, weight, power requirement and cost of a large aperture, narrow field of view scanning telescope.

Determination by spaceborne backscatter lidar of the structural parameters of atmospheric scattering layers

Spaceborne active lidar systems are under development to give new insight into the vertical distribution of clouds and aerosols in the atmosphere and to provide new information on variables required for improvement of forecast models and for understanding the radiative and dynamic processes that are linked to the dynamics of climate change. However, when they are operated from space, lidar systems are limited by atmospheric backscattered signals that have low signal-to-noise ratios (SNRs) on optically thin targets. Therefore specific methods of analysis have to be developed to ensure accurate determination of the geometric and optical properties of scattering layers in the atmosphere. A first approach to retrieving the geometric properties of semitransparent cloud and aerosol layers is presented as a function of false-alarm and no-detection probabilities for a given SNR. Simulations show that the geometric properties of thin cirrus clouds and the altitude of the top of the unstable atmospheric boundary layer can be retrieved with standard deviations smaller than 150 m for a vertical resolution of the lidar system in the 50-100-m range and a SNR of 3. The altitudes of the top of dense clouds are retrieved with a precision in altitude of better than 50 m, as this retrieval corresponds to a higher SNR value. Such methods have an important potential application to future spaceborne lidar missions.