Mark James Chopping,  Montclair State University,  choppingm@mail.montclair.edu (Presenter)
Rocio Duchesne-Onoro,  University of Wisconsin - Whitewater,  duchesnr@uww.edu
Zhuosen Wang,  NASA GSFC/University of Maryland,  zhuosen.wang@nasa.gov
Angela Erb,  University of Massachusetts Boston,  angela.erb@umb.edu
Crystal Schaaf,  University of Massachusetts Boston,  crystal.schaaf@umb.edu
Kenneth Tape,  University of Alaska, Fairbanks,  kdtape@alaska.edu
Christopher Chopping,  Virginia Tech,  ctobi@vt.edu

Our goal is to leverage high spatial resolution imagery from commercial Earth observation satellites to assess changes in tall shrub cover and aboveground biomass in sites across the Alaskan and Canadian erect dwarf-shrub and lowshrub Arctic tundra zones over a 10- to 15-year period. This will provide data that can be used to initiate, drive, calibrate and validate ecological models, assess the impact on terrestrial albedo in summer, and validate lower spatial resolution ABoVE remote sensing data products. Two new versions of the Canopy ANalysis with Panchromatic Imagery (CANAPI) code were developed to address imprecision in shrub mapping using the earlier code, following tall shrub mapping tests with WorldView-2 imagery over Alaskan Arctic tundra. The new versions exploit the multispectral image content as well as the panchromatic bands: CANopy Analysis with Panchromatic And NDVI Imagery (CANAPANI) uses the NDVI of shrub crowns detected with the standard CANAPI approach (isolation of the sunlit part) to screen for likely false positives, while CANopy Analysis with Panchromatic And Multispectral Imagery (CANAPAMI) screens the initial detections using multispectral band vectors determined using adaptive filtering (on each iteration, objects whose mean crown pixel values lie outside the mean ±N standard deviations of the previous set are discarded, where N is between 2 and 3). The new codes are expected to reduce dependence on user-determined settings and subjectivity; this was tested by having several users perform multiple runs with different settings and subsequently submitting their "best" result, using QuickBird (QB02) panchromatic, NDVI, and multispectral imagery from June 20, 2003 and WorldView-2 (WV02) panchromatic, NDVI, and multispectral imagery from July 14, 2015. The ability of these new codes to produce realistic shrub heights was tested by comparing the WV-2 results with RH50, RH75, and RH100 values from the ABoVE LVIS L2 Geolocated Surface Elevation Product, Version 1 (ABLVIS2) data set from July 14, 2017.

Associated Project(s): 

• Chopping (TE 2014)

Presentation: ASTM5_Poster_Chopping_2_1_32.pdf 

Jan Eitel,  University of Idaho,  jeitel@uidaho.edu (Presenter)
Kevin Lee Griffin,  Columbia University,  griff@ldeo.columbia.edu
Natalie Boelman,  Lamont-Doherty Earth Observatory, Columbia Univ.,  nboelman@ldeo.columbia.edu
Arjan JH Meddens,  University of Idaho,  ameddens@uidaho.edu
Johanna Jensen,  Columbia University,  jej2141@columbia.edu
Lee A. Vierling,  University of Idaho,  leev@uidaho.edu
Andrew Maguire,  University of Idaho,  magu7563@vandals.uidaho.edu
Stephanie Schmiege,  Columbia University,  sschmiege@gmail.com
Jyoti Jennewein,  University of Idaho,  jjennewein@uidaho.edu

Established linkages between the photochemical reflectance index (PRI) and gross primary productivity (GPP) suggest that remotely sensed time-series of PRI could provide novel insights into climate change effects on carbon cycling dynamics. However, the fate of carbon taken up by photosynthesis is complex, raising an important question – can PRI time-series provide information about wood growth – a key carbon sink of forests? Here, we explore the suitability of PRI time-series to provide insights into intra-annual stem-growth dynamics at one of the world’s largest terrestrial carbon pools – the boreal forest. For this, we collected a unique dataset of tree-level measurements that allowed us to unambiguously link highly temporally resolved PRI observations with information on wood growth dynamics. The results from a mixed- effects model showed that PRI was a statistically significant (p < 0.0001) predictor of intra-seasonal tree growth dynamics and tracked these dynamics in remarkable detail with conditional and marginal coefficients of determination (r2) of 0.48 and 0.96, respectively. Further, via a piecewise regression we show that the onset of the growing season as determined by PRI time-series was significantly earlier (p < 0.05) than the onset of radial growth determined from the point dendrometer data. In contrast, the end of growing season was statistically similar (p > 0.05) when derived from both PRI and dendrometer time-series. Our findings suggest that PRI could provide novel insights into the nuances of carbon cycling dynamics alleviating important uncertainties associated with vegetation response to continued warming.

Associated Project(s): 

• Eitel (TE 2014)

Douglas Morton,  NASA GSFC,  douglas.morton@nasa.gov (Presenter)
Bruce Douglas Cook,  NASA GSFC,  bruce.cook@nasa.gov
Hans Erik Andersen,  USDA Forest Service,  handersen@fs.fed.us
Michael Alonzo,  American University,  alonzo@american.edu
Sean Cahoon,  USDA Forest Service,  sean.cahoon@usda.gov
Caileigh Shoot,  University of Washington,  shootc@uw.edu
Robert Pattison,  USDA Forest Service, Anchorage Forestry Sciences Laboratory,  rrpattison@fs.fed.us
Chad Babcock,  Michigan State University,  chad.babcock3814@gmail.com
Andrew Finley,  Michigan State University,  finleya@msu.edu

Rapid winter and summer warming across interior Alaska have altered growing season length, increased the extent of disturbances from fire and insects, and accelerated permafrost melt and runoff. Given the fine-scale spatial heterogeneity in topography and vegetation structure in interior Alaska, high-resolution, repeat measurements are needed to characterize the drivers of recent changes in boreal forests. Here, we present results from multi-scale analyses of changes in boreal forest cover, structure, and productivity from 1980-2018. Time series of Landsat data offer regional context for the extent and severity of large wildfires and the impact of warming and associated changes in surface hydrology on vegetation productivity. We used Landsat data to stratify historic field and aerial photo plots from the AIRIS Program in the early 1980s based on trends in vegetation greenness and normalized burn ratio (NBR). Selected plots were remeasured and flown with NASA Goddard’s Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager in 2014. Remeasurements were also conducted at a subset of the Forest Inventory & Analysis (FIA) plots in interior Alaska, in combination with G-LiHT (2014, 2018) and UAV flights (2017, 2018) to create extremely dense point clouds (≥2,000 points m-2). Results highlight the potential to assess fine-scale changes in forest and shrub structure, composition, and biomass using repeat, high-resolution remote sensing in concert with tree rings and forest inventory measurements of vegetation and soil carbon stocks.

Associated Project(s): 

• Cook (CMS 2015)
• Cook (TE 2014)

Sergio Armando Vargas Zesati,  University of Texas at El Paso,  savargas@utep.edu (Presenter)
Karl Fred Huemmrich,  NASA GSFC/UMBC,  karl.f.huemmrich@nasa.gov
Mauricio Barba,  University of Texas at El Paso,  mbarba3@utep.edu
Ryan P Cody,  University of Texas at El Paso,  rpcody@utep.edu
Ifeanyi Nwigboji,  University of Texas at El Paso,  ihnwigboji@miners.utep.edu
Sebastian Ruiz,  University of Texas at El Paso,  sruiz4@utep.edu
Marguerite Mauritz-Tozer,  University of Texas at El Paso,  memauritz@utep.edu
Miguel Velez-Reyes,  University of Texas at El Paso,  mvelezreyes@utep.edu
Petya Krasteva Entcheva Campbell,  JCET/UMBC,  petya.campbell@nasa.gov
Elizabeth M. Middleton,  NASA GSFC,  elizabeth.m.middleton@nasa.gov
Craig E. Tweedie,  University of Texas at El Paso,  ctweedie@utep.edu

The Arctic is experiencing among the most dramatic impacts from climate change on the planet. Observed large-scale responses include but are not limited to loss of sea ice and snow cover, sea level rise, enhanced coastal erosion, increases in near-surface air temperature and satellite-derived green biomass, permafrost thaw and degradation, subsidence and geographical shifts in vegetation distribution. NASA’s Terrestrial Ecology Program launched the Arctic-Boreal Vulnerability Experiment (ABoVE) in an effort to better understand the vulnerability and resilience of Arctic and Boreal ecosystems and societies to environmental variability and change. Remote sensing of natural targets, in particular hyperspectral reflectance, from ground, airborne and space borne platforms is a widely used method for monitoring changing arctic landscapes, but remains poorly documented for much of the Arctic. Additionally, there’s a large demand for consistent web based tools that aid the discovery, sharing and visualization of spectral reflectance data for these regions specifically. During the 2017 and 2018 field seasons, our project alone collected over 3,000 field spectra at the leaf and plot level for validation of AVIRIS spectral reflectance and we have sourced more than 5,000 spectra from our archives and those of others that would likely be useful for ABoVE efforts. These include web-based spectral libraries and informational tools such as pre-existing BAID- the Barrow Area Information Database, AOV- the Arctic Observing Viewer, EcoSIS, ASU Spectral Library, JPL HyspIRI Spectral Library, the USGS Digital Spectral Library, and the ASTER Spectral Library. Managing and visualizing large amounts of spectral data originating from a variety of sensors and data collection methods remains a challenge for the majority of the ABoVE projects as discussed at previous meetings. In an effort to alleviate these issues and help bring together our understanding, we have developed a hyperspectral reflectance library fused with a web mapping application that allows for the discovery, visualization, and sharing of spectral data across the ABoVE domain. Here, we present a beta application and welcome constructive suggestions on how to better design the application to be most useful for ABoVE Science Team members and other stakeholders.

Associated Project(s): 

• Huemmrich (TE 2016)

Sergio Armando Vargas Zesati,  University of Texas at El Paso,  savargas@utep.edu (Presenter)
Christian G Andresen,  University of Wisconsin Madison,  candresen@wisc.edu
Mayra Meledez,  University of Texas at El Paso,  mmelendez6@miners.utep.edu
Ryan P Cody,  University of Texas at El Paso,  rpcody@utep.edu
Stephen M Escarzaga,  University of Texas at El Paso,  smescarzaga@miners.utep.edu
Tabatha Fuson,  University of Texas at El Paso,  tlfuson@miners.utep.edu
Steve Oberbauer,  Florida International University,  oberbaue@fiu.edu
Cathy J Wilson,  Los Alamos National Laboratory,  cjw@lanl.gov
Robert Hollister,  Grand Valley State University,  hollistr@gvsu.edu
Craig E. Tweedie,  University of Texas at El Paso,  ctweedie@utep.edu

Microtopography exerts a strong influence on surface hydrology and soil moisture in the Arctic, which are important controls of vegetation distribution and land-atmosphere carbon exchange and energy balance. Mapping microtopographic variability at a spatial resolution suitable for ecosystem studies, to a spatial extent appropriate for landscape level observations and that will allow for spatial extrapolation of smaller-scale processes has proven to be challenging. In recent years, new technological and analytical capacities and approaches have been developed and offer the potential to improve mapping of microtopographic variability at higher spatio-temporal resolutions. This study compares point clouds (PCs), Digital Elevation Models (DEMs), and several hydrological parameters derived from data collected using five remote sensing approaches, over a 0.25 ha study site near Utqiaģvik in northern Alaska characterized by typical polygonised tundra and thermokarst landscape features. Remote sensing approaches spanned a range of technologies differing markedly in cost per survey and technological expertise required; terrestrial light detection and ranging (T-LiDAR), airborne light detection and ranging (A-LiDAR), kite aerial photography (KAP), unmanned aerial system (UAS), and stereo satellite imagery (SSI). Point cloud density and/or resolution of final DEMs was greatest for T-LiDAR (6,317 points/m2), followed by KAP (260 points/m2), UAS (238 points/m2), ALS (15 points/m2), and SSI (0.27 points/m2). This same ranking generally correlated with a capacity of each platform to capture landscape features such as high/low-centered polygons, ridges and troughs, and model hydrological flow and micro-watersheds with some exceptions. Importantly, the photogrammetric approaches used by the airborne sampling platforms (i.e. KAP and UAS) seemed to have a higher capacity in capturing the low-lying areas such as deep troughs and shallow ponds when compared to the T-LiDAR system. This study serves as a technical baseline that may facilitate research-planning, interpolation of results, and help identify the key geomorphic landscape types that are most drastically changing over time across these wetland landscapes. It also serves to show that reasonably cost-effective UAS technologies and non-technical analytical photogrammetric approaches can be utilized to improve the acquisition and representation of microtopographic metrics in arctic ecosystems and landscape studies.

Associated Project(s): 

• Huemmrich (TE 2016)

Karl Fred Huemmrich,  NASA GSFC/UMBC,  karl.f.huemmrich@nasa.gov (Presenter)
Sergio Armando Vargas Zesati,  University of Texas at El Paso,  savargas@utep.edu
Petya Krasteva Entcheva Campbell,  JCET/UMBC,  petya.campbell@nasa.gov
Elizabeth M. Middleton,  NASA GSFC,  elizabeth.m.middleton@nasa.gov
Craig E. Tweedie,  University of Texas at El Paso,  ctweedie@utep.edu

High latitude ecosystems are difficult to access, thus limiting our ability to monitor and describe the nature of their responses to the ongoing climate change, pointing to the value of remotely sensed data to study these critical regions. Numerous studies have used the Normalized Difference Vegetation Index (NDVI) from satellites to describe spatial and temporal variations in vegetation.
In 2017 NASA's The Arctic-Boreal Vulnerability Experiment (ABoVE) collected is a unique set of Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS NG) spectral imagery of high latitude ecosystems providing the opportunity to examine the information content of imaging spectrometer data to study an array of characteristics of tundra landscapes. Algorithms utilizing the AVIRIS spectral information were developed to extend localized ground measurements to the region by applying them to the aircraft imagery. Distributions of plant functional type coverage, chlorophyll content, and gross primary productivity for three study areas along a north-south transect on the Alaskan North Slope are derived from the hyperspectral imagery. These characteristics are compared with NDVI values to assist in the interpretation of this index in tundra landscapes.

Associated Project(s): 

• Huemmrich (TE 2016)

Presentation: ASTM5_Poster_Huemmrich_2_31_30.pdf