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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

SMAP Detects Soil Moisture under Temperate Forest Canopies

Soil moisture dynamics in the presence of dense vegetation canopies are determinants of ecosystem function and biogeochemical cycles, but the capability of existing spaceborne sensors to support reliable and useful estimates is not known. New results from a recently initiated field experiment in the northeast United States show that the NASA SMAP (Soil Moisture Active Passive) satellite is capable of retrieving soil moisture under temperate forest canopies. We present an analysis demonstrating that a parameterized emission model with the SMAP morning overpass brightness temperature resulted in a RMSD (root mean square difference) range of 0.047-0.057 m(exp 3)/m(exp 3) and a Pearson correlation range of 0.75-0.85 depending on the experiment location and the SMAP polarization. The inversion approach included a minimal amount of ancillary data. This result demonstrates unequivocally that spaceborne L-band radiometry is sensitive to soil moisture under temperate forest canopies, which has been uncertain because of lack of representative reference data.

Andreas Colliander↗
NASA NTRSDate not supplied

FIRMS US/Canada – An Extension of NASA Near Real-Time FIRMS for the Forest Service and Inter-agency Wildfire Management Community

As a part of broader, long time collaboration efforts between the two agencies, the USDA Forest Service has partnered withNASA for nearly 20 years to leverage near real-time MODIS and VIIRS fire products to support wildfire management. Thetimely availability of these data to the Forest Service and interagency partners indicate the location, extent, intensity and impactsof wildfire activity at a regional/national scale as well as informs decisions by fire managers regarding strategic planning andresponse to wildfire incidents.

fire monitoring↗
NASA NTRS2018

Fusion of Multiple Low-Resolution NASA Airborne Snow Observatory (ASO) Lidar Data for Forest Vegetation Structure Characterization

Airborne lidar provides timely updated maps for monitoring forest change at high resolution but it has been little used for that purpose due to the scarcity of long-term time-series over a common area. The NASA Jet Propulsion Laboratory Airborne Snow Observatory (ASO) is a landscape-level monitoring system that provides ongoing multi-year remote sensing measurements over mountainous ecosystems to primary quantify snow volume and dynamics. It collects low-resolution lidar data (~1.5 pt/m2) with a nominal weekly frequency up to 12 times a year with measurements that span 2013-2017 over 12 mountain watersheds across the western US that currently face ecological threads. In this work, we present a method to automatically register ASO weekly low-resolution lidar point clouds in order to calculate spatially consistent datasets (~12 pt/m2) adapted to fine scale forestry studies. We test the method using 12 lidar datasets acquired over the Tuolumne River Basin (Sierra Nevada, California) in the spring and summer of 2014. On average, the ASO lidar system provides accurate measurements in terms of geolocation (0.38m and 0.12m for the horizontal and vertical dimension, respectively) but some datasets are biased up to 1.38m and 0.53m, respectively. Our registration method successfully corrected for systematic bias improving the 3D geometry of forest point clouds.

Painter, Thomas H.↗
NASA NTRS2020

Harnessing Big Data to Support the Conservation and Rehabilitation of Mangrove Forests Globally

Mangrove forests are found on sheltered coastlines in tropical, subtropical, and some warm temperate regions. These forests support unique biodiversity and provide a range of benefits to coastal communities, but as a result of large-scale conversion for aquaculture, agriculture, and urbanization, mangroves are considered increasingly threatened ecosystems. Scientific advances have led to accurate and comprehensive global datasets on mangrove extent, structure, and condition, and these can support evaluation of ecosystem services and stimulate greater conservation and rehabilitation efforts. To increase the utility and uptake of these products, in this Perspective we provide an overview of these recent and forthcoming global datasets and explore the challenges of translating these new analyses into policy action and on-the-ground conservation. We describe a new platform for visualizing and disseminating these datasets to the global science community, non-governmental organizations, government officials, and rehabilitation practitioners and highlight future directions and collaborations to increase the uptake and impact of large-scale mangrove research.

mangrove↗
NASA NTRS2019

Improved assessment of mangrove forests in Sundarbans East Wildlife Sanctuary using WorldView 2 and TanDEM-X high resolution imagery

Recent developments of remote sensing techniques which can capture both the structure and function of the ecosystem provide a more representative view of the landscape. These unique Earth observations were used to help improve traditional forestry surveys by providing species-specific land cover classes for mangrove forests in the Sundarbans East Wildlife Sanctuary. By combining optical data from WorldView2 (WV2; 2 m pixel) and a canopy height model derived using radar data from TanDEM-X (TDX; 12 m pixel), we identified nine mangrove and five non-mangrove classes by following an Iterative Self-Organizing Data Analysis Algorithm. Three dominant mangrove species accounted for nearly 50% of the sanctuary. Heritieria fomes disproportionately covered the largest area at 43%, overturning previous field-based estimates of Excoecaria agallocha dominance. E. agallocha and Sonneratia apetala, covered 3% and 1.47% of the sanctuary, respectively. Four mixed species classes were also identified with clear vegetation zonation patterns that trended toward species homogeneity with increasing distance from shore. The overall land cover accuracy (WV2: 89.33%; WV2-TDX: 89.89%), the Kappa Coefficient (WV2:0.88; WV2-TDX: 0.89) and change statistics between WV2 and WV2-TDX landcover classifications indicate that the WV2 imagery can separate mangrove community types without structural data. The combination of the land cover classifications and the canopy height model indicated that H. fomes were not only the most dominant forest but also, on average, the tallest (12.3 m) among the other eight mangrove types. Our large-scale mapping with high resolution optical and radar platforms can capture subtle changes in mangrove vegetation and canopy structural gradients more accurately and be used to monitor biodiversity changes and Aichi Biodiversity Targets and Indicators, which would contribute to biodiversity policy updating.

Md Mizanur Rahman↗
NASA NTRS2021

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 30000 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.

AfriSAR↗
NASA NTRS2020

SMAP Validation Experiment 2019-2020 (SMAPVEX19/20): Detection of Soil Moisture Under Forest Canopy

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will run through 2020 and they will be augmented with two intensive observation periods (IOP). The first IOP will be conducted in June-July 2020 and the other one in October 2020. The IOPs will see deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

Andreas Colliander↗
NASA NTRS2021

Estimating Bare Earth in Sparse Boreal Forests With WorldView Stereo Imagery

Circumboreal forests are currently experiencing rapid climate warming which is altering their structure, productivity, and status as a carbon sink. Very-high resolution (VHR, <2 m) stereo derived digital surface models are available to monitor these forests, but, a similar resolution digital terrain model (DTM) is required to extract information about tree height, which is often used to estimate carbon content. To our knowledge, no openly available VHR DTM currently exists. To address this need, we developed approaches to extract DTMs by filtering VHR stereo point clouds (PCs) in sparse canopies of Alaska. Our evaluation consisted of two stereo processing methods with three PC search radii, at six different tree canopy cover (TCC) intervals. We found VHR DTMs were robust for estimating bare ground at TCC intervals less than 40% with vertical errors <1.6 m using airborne small footprint light detection and ranging (LiDAR) as reference.

Christopher S R Neigh↗
NASA NTRS2020

Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors

Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.

Jeff W. Atkins↗
NASA NTRS2021

Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

The frequency and severity of spruce bark beetle outbreaks are increasing in boreal forests leading to widespread tree mortality and fuel conditions promoting extreme wildfire. Detection of beetle infestation is a forest health monitoring (FHM) priority but is hampered by the challenges of detecting early stage (“green”) attack from the air. There is indication that green stage might be detected from vertical gradients of spectral data or from shortwave infrared information distributed within a single crown. To evaluate the efficacy of discriminating “non-infested”, “green”, and “dead” health statuses at the landscape scale in Alaska, USA, this study conducted spectral and structural fusion of data from: (1) Unoccupied aerial vehicle (UAV) multispectral (6 cm) + structure from motion point clouds (~700 pts per sq. m); and (2) Goddard Lidar Hyperspectral Thermal (G-LiHT) hyperspectral (400 to 1000 nm, 0.5 m) + SWIR-band lidar (~32 pts per sq.m). We achieved 78% accuracy for all three health statuses using spectral + structural fusion from either UAV or G-LiHT and 97% accuracy for non-infested/dead using G-LiHT. We confirm that UAV 3D spectral (e.g., greenness above versus below median height in crown) and lidar apparent reflectance metrics (e.g., mean reflectance at 99th percentile height in crown), are of high value, perhaps capturing the vertical gradient of needle degradation. In most classification exercises, UAV accuracy was lower than G-LiHT indicating that collecting ultra-high spatial resolution data might be less important than high spectral resolution information. While the value of passive optical spectral information was largely confined to the discrimination of non-infested versus dead crowns, G-LiHT hyperspectral band selection (~400, 675, 755, and 940 nm) could inform future FHM mission planning regarding optimal wavelengths for this task. Interestingly, the selected regions mostly did not align with the band designations for our UAV multispectral data but do correspond to, e.g., Sentinel-2 red edge bands, suggesting a path forward for moderate scale bark beetle detection when paired with suitable structural data.

Janice Cessna↗
NASA NTRS2021

SMAP Validation Experiment 2019-2021 (SMAPVEX19-21): Detection of Soil Moisture Under Temperate Forest Canopy

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will be augmented with two intensive observation periods (IOP). The first IOP is planned for July and the other one for October. The IOPs will see deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

Yueh, Simon H.↗
NASA NTRS2021

Estimating Bare Earth in Sparse Boreal Forests With WorldView Stereo Imagery

Circumboreal forests are currently experiencing rapid climate warming which is altering their structure, productivity, and status as a carbon sink. Very-high resolution (VHR, <2 m) stereo derived digital surface models are available to monitor these forests, but, a similar resolution digital terrain model (DTM) is required to extract information about tree height, which is often used to estimate carbon content. To our knowledge, no openly available VHR DTM currently exists. To address this need, we developed approaches to extract DTMs by filtering VHR stereo point clouds (PCs) in sparse canopies of Alaska. Our evaluation consisted of two stereo processing methods with three PC search radii, at six different tree canopy cover (TCC) intervals. We found VHR DTMs were robust for estimating bare ground at TCC intervals less than 40% with vertical errors <1.6 m using airborne small footprint light detection and ranging (LiDAR) as reference.

Christopher S. R. Neigh↗
NASA NTRSDate not supplied

Detecting Satellite Laser Ranging Station Data and Operational Anomalies with Machine Learning Isolation Forests at NASA's CDDIS

The International Laser Ranging Service (ILRS) is currently composed of 45 active satellite laser ranging (SLR) stations with several more set to join the network over the next several years. Station changes and histories are logged to files, but not always in real time. Sometimes these details are not added until long after changes have been made to the station –on occasion, years later. This in addition to unexpected hardware errors and other system issues that are not immediately detected impact the products generated by analysts. The ILRS Central Bureau (CB) and NASA’s Crustal Dynamics Data Information System (CDDIS) have worked to provide tools for station engineers to use. This includes the creation of station plots which contain temperature and pressure information along with LAser GEOdynamic Satellite (LAGEOS) and LAser RElativity Satellite (LARES) tracking information that enable the monitoring of station performance and todetermine whether the station has undergone any changes. As next steps, the CDDIS is working to enhance these station performance monitoring tools through machine learning. Isolation forest is an unsupervised machine learning algorithm commonly applied to anomaly detection. In this poster, the CDDIS details the steps taken to track anomalies within SLR station performance using isolation forest with LAGEOS and LARES satellite data.

Benjamin P Michael↗
NASA NTRSDate not supplied

Predicting GPS Fidelity in Heavily Forested Areas

There is a compelling need to advance the safety of low altitude flight in forested areas. Signal scattering by trees can interfere with GNSS signal reception and can cause navigation loss within and adjacent to woodlands. An estimate of the signal loss vs. foliage depth is needed to quantify navigational degradation by trees at low altitudes. A previous report described a method which attempts to quantify satellite signal degradation caused by foliage by comparing carrier-to-noise ratio (C/N0) to foliage depth along geometric rays cast from the receiver location to the orbital position of GNSS satellites through a 3D matrix of terrain data. A characteristic curve of attenuation vs. foliage depth was found for both L1 and L2 signals at a single forested site. The current study replicates this result at three additional sites, describes refinements to the method, and explores inherent uncertainties that arise from radiofrequency fading and from receiver limitations for weak signals. For the sites surveyed, 60% and 90% of navigational signal is lost at 10m and 20m of foliage depth, respectively. Since this technique uses low-cost hardware and readily available data collection software, it can allow aviators to estimate GNSS position fidelity in flight ranges near trees.

GPS↗
NASA NTRSDate not supplied

Predicting GPS Fidelity in Heavily Forested Areas

There is a compelling need to advance the safety of low altitude flight in forested areas. Signal scattering by trees can interfere with GNSS signal reception and can cause navigation loss within and adjacent to woodlands. An estimate of the signal loss vs. foliage depth is needed to quantify navigational degradation by trees at low altitudes. A previous report described a method which attempts to quantify satellite signal degradation caused by foliage by comparing carrier-to-noise ratio (C/N0) to foliage depth along geometric rays cast from the receiver location to the orbital position of GNSS satellites through a 3D matrix of terrain data. A characteristic curve of attenuation vs. foliage depth was found for both L1 and L2 signals at a single forested site. The current study replicates this result at three additional sites, describes refinements to the method, and explores inherent uncertainties that arise from radiofrequency fading and from receiver limitations for weak signals. For the sites surveyed, 60% and 90% of navigational signal is lost at 10m and 20m of foliage depth, respectively. Since this technique uses low-cost hardware and readily available data collection software, it can allow aviators to estimate GNSS position fidelity in flight ranges near trees.

GPS↗
DOE OSTI2023

Science Plan for the Deployment of the Third ARM Mobile Facility to the Southeastern United States at the Bankhead National Forest, Alabama (AMF3 BNF)

In 2018, the U.S. Department of Energy (DOE) held a workshop for the Atmospheric Radiation Measurement (ARM) (Mather and Voyles 2013) user facility to discuss critical climate challenges and locations where key ARM Mobile Facility (AMF) observational assets could impact Earth system modeling (ESM). As an outcome, the southeast United States (SE U.S.) was identified as a high-priority region to target climate-process studies that promote a deeper understanding of the climate system and bolster ARM interactions with the community to drive ESM advancement. The DOE ARM user facility is a globally recognized leader in deploying and operating strategically located observation sites around the world for studying the properties of aerosols and clouds and their interaction with radiation, precipitation, and the Earth’s surface. In partnering with the DOE Atmospheric System Research (ASR) program, ARM solicited a multi-agency Site Science Team approach to provide input and close interaction with ARM management towards a successful SE U.S. deployment of the ARM third Mobile Facility (AMF3) (Miller et al. 2016). These efforts included identifying key locations, science drivers and instruments, and measurement strategies to address the wider climate-process needs and ESM improvement. Community input served a vital role in establishing, refining, and informing the relevant drivers and decisions regarding this AMF3 deployment. The team has identified Northern Alabama (N. AL) as regionally representative to unlock the key opportunities that will improve our understanding and model representation of aerosol, cloud, and land surface processes and their couplings in the SE U.S. A defining aspect of the AMF3 deployment is its commitment to long-term (anticipated five-year) observations to mitigate potential seasonal-to-annual variability that often limits appropriate attribution of phenomena to local or larger-scale processes. The proposed location may leverage nearby surface networks and multi-agency and partner assets to enrich this multi-year deployment. One motivation is to understand the role of spatiotemporal variability (thermodynamic, land-surface) across aspects of the climate system, with our AMF3 team anticipating future demands on characterizing the relationships between local-to-regional cloud development and surface processes across a diverse patchwork of natural, managed, and urban landscapes as found throughout the N. AL regions. The main site targets an intact, representative, forested region – the Bankhead National Forest (BNF) – underscoring further team commitment to regionally important land atmosphere two-way interactive studies “from the canopy to the clouds”, with enhanced tower instrumentation augmenting traditional ARM capabilities adjacent to this site. Multiple supplemental sites will also be distributed across this region, prioritizing added needs for biodiversity. Anticipated high-priority cloud science themes will target N. AL as a regional SE U.S. hotbed for high-impact weather, convective cloud onset, and shallow to-deep cloud transitioning. Anticipated aerosol drivers will focus on chemical processes that control the evolution of organic aerosol, the seasonality and spatial distribution of water vapor and particle-phase water, and its role on aerosol optical properties. Anticipated land atmosphere drivers consider the two-way feedbacks between surface influence on aerosols, clouds, and precipitation properties and the associated radiative impacts on plant physiology and canopy-scale fluxes. Emphasis will include the study of the impact of surface processes on aerosols via precursor emission, and on clouds via moisture flux and thermal development.

Doppler lidar, aerosols, convection↗
DOE OSTI2025

DESI DR1 Ly$α$ forest: 3D full-shape analysis and cosmological constraints

We perform an analysis of the full shapes of Lyman-$α$ (Ly$α$) forest correlation functions measured from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). Our analysis focuses on measuring the Alcock-Paczynski (AP) effect and the cosmic growth rate times the amplitude of matter fluctuations in spheres of $8$$h^{-1}\text{Mpc}$, $fσ_8$. We validate our measurements using two different sets of mocks, a series of data splits, and a large set of analysis variations, which were first performed blinded. Our analysis constrains the ratio $D_M/D_H(z_\mathrm{eff})=4.525\pm0.071$, where $D_H=c/H(z)$ is the Hubble distance, $D_M$ is the transverse comoving distance, and the effective redshift is $z_\mathrm{eff}=2.33$. This is a factor of $2.4$ tighter than the Baryon Acoustic Oscillation (BAO) constraint from the same data. When combining with Ly$α$ BAO constraints from DESI DR2, we obtain the ratios $D_H(z_\mathrm{eff})/r_d=8.646\pm0.077$ and $D_M(z_\mathrm{eff})/r_d=38.90\pm0.38$, where $r_d$ is the sound horizon at the drag epoch. We also measure $fσ_8(z_\mathrm{eff}) = 0.37\; ^{+0.055}_{-0.065} \,(\mathrm{stat})\, \pm 0.033 \,(\mathrm{sys})$, but we do not use it for cosmological inference due to difficulties in its validation with mocks. In $Λ$CDM, our measurements are consistent with both cosmic microwave background (CMB) and galaxy clustering constraints. Using a nucleosynthesis prior but no CMB anisotropy information, we measure the Hubble constant to be $H_0 = 68.3\pm 1.6\;\,{\rm km\,s^{-1}\,Mpc^{-1}}$ within $Λ$CDM. Finally, we show that Ly$α$ forest AP measurements can help improve constraints on the dark energy equation of state, and are expected to play an important role in upcoming DESI analyses.

Cuceu, Andrei [LBL, Berkeley; Chicago U., KICP] (O↗
DOE OSTI2026

Dynamical Dark Energy Imprints in the Lyman-Alpha Forest

The nature of dark energy (DE) remains elusive, even though it constitutes the dominant energy-density component of the Universe and drives the late-time acceleration of cosmic expansion. By combining measurements of the expansion history from baryon acoustic oscillations, supernova surveys, and cosmic microwave background data, the Dark Energy Spectroscopic Instrument (DESI) Collaboration has inferred that the DE equation of state may evolve over time. The profound implications of a time-variable, ``dynamical" DE (DDE) that departs from a cosmological constant motivate the need for independent observational tests. In this work, we use cosmological hydrodynamical simulations of structure formation to investigate how DDE affects the properties of the Lyman-Alpha ``forest'' of absorption features produced by neutral hydrogen in the cosmic web. We find that DDE models consistent with the DESI constraints induce a spectral tilt in the forest transmitted flux power spectrum, imprinting a scale- and redshift-dependent signature relative to standard Lambda-CDM cosmologies. These models also yield higher intergalactic medium temperatures and reduced Lyman-Alpha opacity compared to Lambda-CDM. We discuss the observational implications of these trends as potential avenues for independent confirmation of DDE.

Garza, Diego [UC, Santa Cruz, Inst. Part. Phys.] (↗