Armillaria root disease causes tree stress and mortality worldwide, including in conifer forests of western North America. Armillaria-induced tree mortality is slower than other disturbances, such as fire, but persistent over time, and therefore difficult to detect across large areas. Hence methods to detect Armillaria-affected trees and identify infested stands are of great value to forest managers. We used high-density light detection and ranging (lidar), high-resolution aerial orthoimagery, and associated field observations to map individual tree health across an Armillaria-affected forest dominated by Abies in south central Oregon. The lidar point cloud was segmented into individual tree objects (polygons representing tree crown extents as viewed from nadir), for which lidar metrics and spectral metrics derived from orthoimagery were computed. Lidar-detected tree objects were paired with 150 field-observed trees with corresponding health measurements, and a random forest classifier was developed that separated trees into: 1) asymptomatic; 2) live, Armillaria-infected; 3) recently killed (>50% of red needles remaining); and 4) dead (<50% of dead needles remaining) classes with 83% accuracy using lidar and spectral metrics. The classifier was applied to map individual tree health status for 290,964 tree objects across the 1257-ha study area. Approximately 20% of trees were classified as unhealthy including live, infected and 4% of trees were classified as recently killed or dead. We created hotspot maps using the Getis-Ord Gi* statistic and analyzed clustering of tree health spatial patterns using Ripley's L statistic. Hotspot maps effectively identified clusters of live unhealthy trees and tree mortality; unhealthy and dead trees were found to be significantly spatially clustered at distances of 100-2500 m. We created a dead tree density grid, which we coupled with a lidar-derived canopy gap grid to identify sites and stands affected by root disease. Canopy openings were mapped using a canopy height model with a minimum opening area of > 202 m2. Clusters of mapped dead trees (excluding recently killed trees) intersecting canopy gaps were used to detect sites with root disease-induced mortality. Twenty-seven stands containing long-term plots from previous studies were evaluated for the presence of root disease-induced mortality. All 27 stands were correctly identified with conifer mortality induced by root disease. This approach for detecting dead trees intersecting canopy openings induced by root disease can aid in: i) prioritizing subsequent field data collection; ii) planning silvicultural prescriptions; and iii) assessing management expectations for snags and wildlife habitat where root disease-induced mortality is altering stand structure.

Hurricane Maria, a Category 4 storm, snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne Lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100–800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥ 1 m in canopy height (mean = −7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13.5% of the study area had canopy heights ≤ 2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss > 1 m (2017–2018) followed by rapid height gain > 1 m (2018–2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no height changes larger than ± 1 m in either period. Canopy layers < 10 m tall accounted for most increases in canopy height and fractional cover between 2018 and 2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage and highlight the diversity of ecosystem impacts from heterogeneous spatial patterns and vertical stratification of forest regrowth following a major disturbance event.

Mangroves buffer inland ecosystems from hurricane winds and storm surge. However, their ability to withstand harsh cyclone conditions depends on plant resilience traits and geomorphology. Using airborne lidar and satellite imagery collected before and after Hurricane Irma, we estimated that 62% of mangroves in southwest Florida suffered canopy damage, with largest impacts in tall forests (>10 m). Mangroves on well-drained sites (83%) resprouted new leaves within one year after the storm. By contrast, in poorly-drained inland sites, we detected one of the largest mangrove diebacks on record (10,760 ha), triggered by Irma. We found evidence that the combination of low elevation (median = 9.4 cm asl), storm surge water levels (>1.4 m above the ground surface), and hydrologic isolation drove coastal forest vulnerability and were independent of tree height or wind exposure. Our results indicated that storm surge and ponding caused dieback, not wind. Tidal restoration and hydrologic management in these vulnerable, low-lying coastal areas can reduce mangrove mortality and improve resilience to future cyclones.

Mangroves buffer inland ecosystems from hurricane winds and storm surge. However, their ability to withstand harsh cyclone conditions depends on plant traits and geomorphology. Using airborne lidar and satellite imagery collected before and after Hurricane Irma, we estimated that 62% of mangroves in southwest Florida suffered canopy damage, with largest impacts in tall forests (>10 m). Mangroves on well-drained sites (83%) flushed new leaves within one year after the storm. In poorly-drained sites, Irma triggered one of the largest mangrove diebacks ever recorded (10,760 ha), primarily affecting low elevation and interior mangrove areas dominated or co-dominated by A. germinans (73%). Our results show storm surge and ponding caused dieback, not wind. Identifying and monitoring vulnerable, low-lying coastal areas is essential to mitigate mangrove dieback from future storms.

Solar induced fluorescence (SIF) is an ecological variable of interest to remote sensing retrievals, as it is directly related to vegetation composition and condition. FIREFLY (fluorescence imaging of red and far-red light yield) is a high performance spectrometer for estimating SIF. FIREFLY was flown in conjunction with NASA Goddard’s lidar, hyperspectral, and thermal (G-LiHT) instrument package in 2017, as a technology demonstration for airborne retrievals of SIF. Attributes of FIREFLY relevant to SIF retrieval, including detector response and linearity; full-width at half maximum (FWHM); stray light; dark current; and shot noise were characterized with a combination of observations from Goddard’s laser for absolute measurement of radiance calibration facility; an integrating sphere; controlled acquisitions of known targets; in-flight acquisitions; and forward modelling. FWHM, stray light, and dark current were found to be of acceptable magnitude, and characterized to within acceptable limits for SIF retrieval. FIREFLY observations were found to represent oxygen absorption features, along with a large number of solar absorption features. Shot noise was acceptable for direct SIF retrievals at native resolution, but indirect SIF retrievals from absorption features would require spatial aggregation, or repeated observations of targets.

FIREFLY (Fluorescence Imaging of REd and Far-red Light Yield) is a compact, fine-resolution imaging spectrometer that was designed and assembled by Headwall Photonics (Fitchburg, MA, USA) in collaboration with NASA scientists for airborne measurements of Solar-Induced Fluorescence (SIF). FIREFLY is integrated into the next generation of NASA Goddard's Lidar, Hyperspectral and Thermal airborne imager (G-LiHT; www.gliht.nasa.gov), providing a complete system for measuring, interpreting and scaling SIF emissions. Characterization of FIREFLY was performed here to evaluate its performance and suitability for retrieving SIF.

Leaf fluorescence can be used to track plant development and stress, and is considered the most direct measurement of photosynthetic activity available from remote sensing techniques. Red and far-red sun-induced chlorophyll fluorescence (SIF) maps were generated from high spatial resolution images collected with the HyPlant airborne spectrometer over even-aged loblolly pine plantations in North Carolina (United States). Canopy fluorescence yield (i.e., the fluorescence flux normalized by the light absorbed) in the red and far-red peaks was computed. This quantifies the fluorescence emission efficiencies that are more directly linked to canopy function compared to SIF radiances. Fluorescence fluxes and yields were investigated in relation to tree age to infer new insights on the potential of those measurements in better describing ecosystem processes. The results showed that red fluorescence yield varies with stand age. Young stands exhibited a nearly twofold higher red fluorescence yield than mature forest plantations, while the far-red fluorescence yield remained constant. We interpreted this finding in a context of photosynthetic stomatal limitation in aging loblolly pine stands. Current and future satellite missions provide global datasets of SIF at coarse spatial resolution, resulting in intrapixel mixture effects, which could be a confounding factor for fluorescence signal interpretation. To mitigate this effect, we propose a surrogate of the fluorescence yield, namely the Canopy Cover Fluorescence Index (CCFI) that accounts for the spatial variability in canopy structure by exploiting the vegetation fractional cover. It was found that spatial aggregation tended to mask the effective relationships, while the CCFI was still able to maintain this link. This study is a first attempt in interpreting the fluorescence variability in aging forest stands and it may open new perspectives in understanding long-term forest dynamics in response to future climatic conditions from remote sensing of SIF.

Ash (Fraxinus L.) species are currently threatened by the emerald ash borer (EAB; Agrilus planipennis Fairmaire) across a growing area in the eastern US. Accurate mapping of ash species is required to monitor the host resource, predict EAB spread and better understand the short- and long-term effects of EAB on the ash resource. Hyperspectral remote sensing technologies have been used to successfully map forest species, although most efforts are focused on healthy canopies for relatively homogeneous forested stands. This study uses imagery collected by the NASA Goddard LiDAR, Hyperspectral and Thermal (GLiHT) airborne imager to map ash species at the tree level in an EAB infested urban setting. The overall goal of the study is to understand how canopy condition impacts species mapping accuracy and identify data collection and image processing techniques to more accurately map the location of ash species in infested regions.Results indicate that while overall independent validation mapping accuracy of ash and non-ash trees was 81%, correct identification of ash canopies dropped from 62% for vigor 1 trees to 22% for vigor 2 trees. To minimize these errors, we developed a multiple endmember, spectral unmixing technique to overcome challenges presented by a spectrally complicated target in a complex urban environment. This hinges on the use of endmember spectra from trees across a range of canopy condition, including the derivation of vegetation indices to inform the spectral unmixing calibration. This approach was more accurate than calibrations performed using traditional unmixing based only on pure endmember spectra.Implications for this work suggest that urban forest managers may attain more accurate maps by conducting remote sensing data collections prior to infestation while the trees are still healthy. Where this is not possible, mapping efforts must reflect a range of canopy conditions and include vegetation indices concurrent with reflectance data. The resulting ash species maps provide urban forest managers spatially explicit products to help estimate the extent of possible impacts in their communities, guide the implementation of management and monitoring efforts and provide the basis for planning as EAB continues to spread.

The first European Space Agency (ESA) and NASA collaboration in an airborne campaign to support ESA’s FLuorescence EXplorer (FLEX) mission was conducted in North Carolina, USA during September–October 2013 (FLEX-US 2013) at the Parker Tract Loblolly Pine (LP) Plantation (Plymouth, NC, USA). This campaign combined two unique airborne instrument packages to obtain simultaneous observations of solar-induced fluorescence (SIF), LiDAR-based canopy structural information, visible through shortwave infrared (VSWIR) reflectance spectra, and surface temperature, to advance vegetation studies of carbon cycle dynamics and ecosystem health. We obtained statistically significant results for fluorescence, canopy temperature, and tower fluxes from data collected at four times of day over two consecutive autumn days across an age class chronosequence. Both the red fluorescence (F685) and far-red fluorescence (F740) radiances had highest values at mid-day, but their fluorescence yields exhibited different diurnal responses across LP age classes. The diurnal trends for F685 varied with forest canopy temperature difference (canopy minus air), having a stronger daily amplitude change for young vs. old canopies. The Photochemical Reflectance Index (PRI) was positively correlated with this temperature variable over the diurnal cycle. Tower measurements from mature loblolly stand showed the red/far-red fluorescence ratio was linearly related to canopy light use efficiency (LUE) over the diurnal cycle, but performed even better for the combined morning/afternoon (without midday) observations. This study demonstrates the importance of diurnal observations for interpretation of fluorescence dynamics, the need for red fluorescence to understand canopy physiological processes, and the benefits of combining fluorescence, reflectance, and structure information to clarify canopy function versus structure characteristics for a coniferous forest.

Remote Sensing of Sun-Induced Chlorophyll Fluorescence (SIF) is a research field of growing interest because it offers the potential to quantify actual photosynthesis and to monitor plant status. New satellite missions from the European Space Agency, such as the Earth Explorer 8 FLuorescence EXplorer (FLEX) missionscheduled to launch in 2022 and aiming at SIF mappingand from the National Aeronautics and Space Administration (NASA) such as the Orbiting Carbon Observatory-2 (OCO-2) sampling mission launched in July 2014, provide the capability to estimate SIF from space. The detection of the SIF signal from airborne and satellite platform is difficult and reliable ground level data are needed for calibration/validation. Several commercially available spectroradiometers are currently used to retrieve SIF in the field. This study presents a comparison exercise for evaluating the capability of four spectroradiometers to retrieve SIF. The results show that an accurate far-red SIF estimation can be achieved using spectroradiometers with an ultrafine resolution (less than 1 nm), while the red SIF estimation requires even higher spectral resolution (less than 0.5 nm). Moreover, it is shown that the Signal to Noise Ratio (SNR) plays a significant role in the precision of the far-red SIF measurements.

The concept of light use efficiency (ε) and the concept of fraction of photosynthetically active ration (PAR) absorbed for vegetation photosynthesis (PSN), i.e., fAPARPSN, have been widely utilized to estimate vegetation gross primary productivity (GPP). It has been demonstrated that the photochemical reflectance index (PRI) is empirically related to ε. An experimental US Department of Agriculture (USDA) cornfield in Maryland was selected as our study field. We explored the potential of integrating fAPARchl (defined as the fraction of PAR absorbed by chlorophyll) and nadir PRI (PRInadir) to predict cornfield daily GPP. We acquired nadir or near-nadir EO-1/Hyperion satellite images that covered the cornfield and took nadir in-situ field spectral measurements. Those data were used to derive the PRInadir and fAPARchl. The fAPARchl is retrieved with the advanced radiative transfer model PROSAIL2 and the Metropolis approach, a type of Markov Chain Monte Carlo (MCMC) estimation procedure. We define chlorophyll light use efficiency (εchl) as the ratio of vegetation GPP as measured by eddy covariance techniques to PAR absorbed by chlorophyll (εchl=GPP/APARchl). Daily εchl retrieved with the EO-1 Hyperion images was regressed with a linear equation of PRInadir (εchl=α×PRInadir+β). The satellite εchl-PRInadir linear relationship for the cornfield was implemented to develop an integrated daily GPP model [GPP=(α×PRInadir+β)×fAPARchl×PAR], which was evaluated with fAPARchl and PRInadir retrieved from field measurements. Daily GPP estimated with this fAPARchl-PRInadir integration model was strongly correlated with the observed tower in-situ daily GPP (R2=0.93); with a root mean square error (RMSE) of 1.71gCmol−1 PPFD and coefficient of variation (CV) of 16.57%. Both seasonal εchl and PRInadir were strongly correlated with fAPARchl retrieved from field measurements, which indicates that chlorophyll content strongly affects seasonal εchl and PRInadir. We demonstrate the potential capacity to monitor GPP with space-based visible through shortwave infrared (VSWIR) imaging spectrometers such as NASA’s soon to be decommissioned EO-1/Hyperion and the future Hyperspectral Infrared Imager (HyspIRI).

This presentation provides information on the development of a small Unmanned Aerial System(UAS) with a low power, high performance Intelligent Payload Module (IPM) and a hyperspectral imager to enable intelligent gathering of science grade vegetation data over agricultural fields at about 150 ft. The IPM performs real time data processing over the image data and then enables the navigation system to move the UAS to locations where measurements are optimal for science. This is important because the small UAS typically has about 30 minutes of battery power and therefore over large agricultural fields, resource utilization efficiency is important. The key innovation is the shrinking of the IPM and the cross communication with the navigation software to allow the data processing to interact with desired way points while using Field Programmable Gate Arrays to enable high performance on large data volumes produced by the hyperspectral imager.

Scientists in the Biospheric Sciences Laboratory at NASA's Goddard Space Flight Center have undertaken a unique instrument fusion effort for an airborne package that integrates commercial off the shelf LiDAR, Hyperspectral, and Thermal components. G-LiHT is a compact, lightweight and portable system that can be used on a wide range of airborne platforms to support a number of NASA Earth Science research projects and space-based missions. G-LiHT permits simultaneous and complementary measurements of surface reflectance, vegetation structure, and temperature, which provide an analytical framework for the development of new algorithms for mapping plant species composition, plant functional types, biodiversity, biomass, carbon stocks, and plant growth. G-LiHT and its supporting database are designed to give scientists open access to the data that are needed to understand the relationship between ecosystem form and function and to stimulate the advancement of synergistic algorithms. This system will enhance our ability to design new missions and produce data products related to biodiversity and climate change. G-LiHT has been operational since 2011 and has been used to collect data for a number of NASA and USFS sponsored studies, including NASA's Carbon Monitoring System (CMS) and the American ICESat/GLAS Assessment of Carbon (AMIGA-Carb). These acquisitions target a broad diversity of forest communities and ecoregions across the United States and Mexico. Here, we will discuss the components of G-LiHT, their calibration and performance characteristics, operational implementation, and data processing workflows. We will also provide examples of higher level data products that are currently available.