airborne remote sensing

What Happens to Ecology When Humans Can’t Get to the Field?

gentes — Wed, 05 Aug 2020 16:11:46 +0000

What Happens to Ecology When Humans Can’t Get to the Field?

August 5, 2020

gentes Wed, 08/05/2020 - 10:11

The COVID-19 pandemic has impacted nearly every aspect of human life, and ecology is no exception. Travel restrictions and health and safety concerns have limited the ability of NEON scientists to get to the field across much of the country. However, that doesn’t mean that the Observatory has shut down entirely. Sample collection has resumed in some locations, and even where it has not, automated instrument systems—including tower instruments, soil sensors, and aquatic instrument arrays—are still collecting data.

Can technology fill in the missing pieces when humans can’t get to the field? While there will almost certainly be data gaps for NEON and other large-scale ecological programs this year, automated instrument programs can still provide a lot of ecological insights. In the future, emerging technologies such as drones, smart sensors, and robots could help ecologists collect field data even under challenging circumstances. But no matter how much technology we deploy in the field, there will always be activities that require a human touch.

What’s Happening (and What Isn’t) at the NEON Field Sites for 2020

You might think that field ecology would be the ultimate social distancing job, but it’s not easy to get ecologists to the field during a pandemic. Scientists and field technicians work in teams and congregate in field offices. They also may find themselves in close proximity to each other and to local community members while traveling to field locations. For these reasons, on March 23, 2020 the NEON program temporarily suspended all activities across the Observatory that required in-person or on-site work. Limited activities resumed May 18 at selected sites. As of August 2020, and for the foreseeable future, decisions about resuming on-site activities for each location are being made based on local conditions. That means many sites have resumed field sampling activities, some are only conducting high priority activities and maintenance for automated instrument systems, and a small number remain closed. Get the latest updates on Observatory Status during COVID-19.

At the NEON program, data collection falls broadly into three categories:

Automated instrument systems, which include meteorological and eddy flux data from the towers, phenocams, soil sensors, and ground and surface water sensors.
Airborne remote sensing using light aircraft carrying instrument payloads to gather photos, lidar and hyperspectral data.
Observational sampling, which encompasses all of the observations and physical samples typically collected by humans in the field. These include data products resulting from breeding landbird counts; small mammal trapping; tick, mosquito and ground beetle sampling; pathogen sampling; physical collection of water and soil samples; collection of herbaceous clips, leaf litter and root samples; phenological observations; and sampling of fish, macroinvertebrates and zooplankton at aquatic sites.

It is the last two categories that are most heavily impacted by COVID-19 restrictions. While the pandemic has limited the ability of technicians to perform standard maintenance on instrument systems, nearly all of the automated instrument systems in the Observatory are continuing to collect and transmit data. However, the timing of the pandemic meant that we missed spring sampling bouts at most of the field sites. For some sites, ongoing local surges of COVID-19 may result in missing an entire sampling season. The Airborne Observation Platforms (AOP) aircraft, which normally collect remote sensing data at “peak greenness�� at the terrestrial sampling sites, have also been grounded for much of the season, and are only performing flights over a small subset of field sites for 2020.

Airborne instrumentation, left: twin otter plane, middle: imaging spectrometer, right: payload inside of the twin otter

Some NEON sites have been more disrupted than others. In Alaska, field sites for D18 (Tundra) and D19 (Taiga) are taking extreme precautions, despite the relatively small case load in the state. Domain Manager Chris Baird explains, “We are not working at the Utqiagvik site at all this summer. The risk of bringing COVID into a vulnerable rural community with an almost nonexistent healthcare system is just too high. For these communities, they can’t just send people to other hospital systems if theirs is overloaded.�� For the three NEON Tundra field sites that are run out of the Toolik field station on the North Slope of the Brooks Range, all summer workers must spend two weeks in strict quarantine upon arrival in Alaska before going on to the station.

Flux tower in the snow at TOOL

In D14 (Desert Southwest), a delayed start to the sampling season meant missing measurements for the start of the Monsoon season in July. The North American Monsoons, which run from mid-July through late September each year, drive much of the ecology of the deserts in the southwest. Sampling timing is usually tied to the Monsoon season, with sampling bouts before, during and after the Monsoon. This year, the D14 team was not able to collect the “before�� samples in July, though they still hope to be able to sample later in the season.

What Technology Can Do �� and What it Can’t

The data products collected by the automated instruments allow analysis of many long-term ecological trends—such as changes in climate and carbon cycling—to continue without significant interruption. Automated instruments may also pick up interesting data during this time, such as changes in aerosol optical depth (which can be used as a proxy for particulate level) as a result of reduced human activity during the pandemic (this data is stored, processed, and made available by AERONET at NASA).

For 2020, sensor data will provide an largely-uninterrupted record of meteorological readings, eddy flux data, water flow and chemistry and other measurements. Phenocams will allow some remote observations of phenological events such as leaf out and blooming. In places where the AOP aircraft are able to fly, remote sensing data can make up for some losses as well. For example, hyperspectral data provides insights into canopy chemistry in the absence of ground observations and clip harvests. Hyperspectral and lidar data can also provide information on plant community composition and structure. These data sets can be used to extrapolate a lot of information about forest health, growth rates and other key ecological indicators.

Still, the automated data products cannot totally make up for the loss of physical samples and human observations. Dr. Mike SanClements, the Terrestrial Instrument System Lead for the NEON Program, explains, “Sensors collect data that show trends and patterns over time, but observational methods and experiments in the field are where we get an understanding of the mechanisms of ecology. It is the coupling of sensors and observational methods where the true power lies, so you can not only see the trends but start to understand the drivers behind those trends.��

Missing a few sampling bouts—or even the entire 2020 season—won’t be the end of the world for a long-term program like NEON. NEON is working to get ecologists into the field wherever it can be done safely this year to keep data gaps to a minimum. But even for those sites that will not have observational data this year, the loss will not be highly significant in most cases when looked at across the 30-year timeline of the program.

Looking to the Future: Pairing Technology and People

Could technology play a larger role in data collection in the future? Ecologists are already starting to use robot and drone technologies, such as the DeLeaves canopy sampling drone, to speed up physical sampling or collect samples from inaccessible locations. However, most of these technologies still require a nearby human operator to guide sampling efforts. Current robot and drone technologies are not yet capable of making the kinds of judgments that humans make when collecting physical samples in the field. Mike explains, “There is both an art and a science involved in many types of sample collection. For example, think about collecting soil samples. You have to be able to recognize where soil horizons begin and end as well as physical properties like texture and roots, rocks, etc. It’s not just about sticking a shovel in the ground and digging up some soil. These kinds of problems, which require judgment as well as sensing, are still very difficult for robots to solve.��

The DeLeaves drone collects a foliar sample from the forest canopy at WREF, WA. July 2019.

For the near term, emerging automated sensor and remote sensing technologies provide the most promise for expanding our perceptions in the field. For example, Mike believes that we could soon add artificial intelligence (AI) to automated sensors. He says, “Imagine automated technologies that can respond to their environment, such as a water sampler that automatically increases sampling rate in real-time when a sensor detects a probable flood event, or a camera that sends an alert and starts recording when it detects a plume of smoke for early wildfire detection. Real-time or near-real-time automated sensing and data collection could enable early detection of anomalous events and improve iterative forecasting models.��

Drones are another technology that could play a larger role for the NEON program in the future. The NEON program has already done some work to correlate drone-collected data to AOP data so that data can be compared and analyzed together. Drones are not likely to fully replace the sophisticated (and heavy) instrument payloads carried by the AOP aircraft for some time, but they could enable ecologists to collect data when aircraft cannot be flown or expand remote sensing capabilities with a cheaper, more nimble technology. Because they are easier to operate by an individual or a small, socially-distanced team, it might be possible to fly drones even when the AOPs are grounded.

However, technology will never fully replace human presence in the field. Sensors, robots and drones may expand our ability to collect field data, but they still need to be maintained, guided and monitored by people. At the same time, all that new data will create many more opportunities to ask and explore ecological questions—work that will always involve humans.

Mike says, “The coupling of sensors and observational data and physical samples is what makes the NEON program so powerful. We are always going to need hands-on samples, and we are always going to need humans to ask the right questions, conduct experiments and interpret results. When we bring the technology and people together, that is where the truly interesting advances in ecology are being made.��

NEON’s Airborne Remote Sensing Flight Season Announced for 2020

mfaust — Wed, 04 Mar 2020 15:57:22 +0000

NEON’s Airborne Remote Sensing Flight Season Announced for 2020

March 4, 2020

mfaust Wed, 03/04/2020 - 08:57

Interested in planning a ground sampling project in coordination with one of our airborne remote sensing surveys? The 2020 NEON flight schedule is now available. Two aircraft will be deployed June through September to collect data over 20 terrestrial and 11 aquatic sites, covering 9 of the 20 NEON domains.

Each NEON Airborne Observation Platform (AOP) payload consists of an imaging spectrometer, a discrete and waveform light detection and ranging (LIDAR) instrument and a high-resolution digital camera. These instruments are mounted in a DeHavilland DHC-6 Twin Otter aircraft that flies at a nominal altitude of 1000m above ground level (AGL) at a speed of 100 knots over NEON field sites. The flight parameters enable meter-scale spectroscopy, decimeter-scale photography, and ~4 points-per-meter discreet and waveform lidar measurements.

AOP surveys are synchronized with data collected on the ground at each site by NEON field staff. This allows scientists to develop a more comprehensive picture of how different observations scale and how measurements taken from airborne remote sensing instruments correlate with observations made on the ground. Raw data obtained from flights are processed and made available via the NEON data portal ~60 days after collection.

Airborne instrumentation, left: twin otter plane, middle: imaging spectrometer, right: payload inside of the twin otter

Measurements obtained from the AOPs provide a range of physical, biological and biochemical data products available both as flightlines and mosaics, including:

Topography (elevation, slope and aspect)
Canopy chemistry (lignin, nitrogen, water content, xanthophyll cycle)
Ecosystem structure (canopy height and Leaf Area Index (LAI)
Total biomass maps and vegetation indices
High-resolution orthorectified camera imagery

Data products from YELL (D12) site. Left: Discrete lidar point cloud colorized by RGB camera imagery. Right: Digital surface model.

If you would like to plan research activities on the ground to coincide with these surveys, please sign up to join our mailing list. The AOP Flight Operations team will email daily reports on the status of flights over each site which will be more up-to-date than the schedule posted on the website. Although they will attempt adherence to the published schedule, weather and logistical constraints may result in modifications to this plan.

We are likely unable to include additional flight areas to the existing NEON observatory collection plan this year. However, AOP will attempt to accommodate all requests to support Principal Investigator (PI)-led science flights via the NEON Assignable Assets program.

Aerial views of NEON sites

Now, it's time to fly!

Quick Links

2020 Flight Schedule
Overview of an AOP
Tips for Getting Airborne Data
Request an Airborne Remote Sensing Survey
Daily Flight Report Email Sign Up: If you are interested in tracking the 2020 season, you can sign up to receive daily emails by NEON domain. Please note, you will only receive emails during the time period the AOP is in the domain you have signed up to follow
NEON Data Portal: all data products from 2019 flights are now available for download on the data portal

A Remote Sensing Partnership Made in Paradise

lgoldman — Fri, 01 Nov 2019 14:22:30 +0000

A Remote Sensing Partnership Made in Paradise

November 1, 2019

lgoldman Fri, 11/01/2019 - 08:22

Hawaii's Pu`u Maka`ala Natural Area Reserve (PUUM) field site is a beautiful place to work. The only problem? It's just a tad hard to get to—especially with the NEON Airborne Observation Platform (AOP). That's why the NEON program is excited to have a partnership with the Arizona State University Global Airborne Observatory (ASU-GAO) for remote sensing surveys on the Big Island.

Eyes in the Sky Over Hawaii

The NEON AOP conducts remote sensing flyovers each year at peak greenness over most sites. However, making the 2,500-mile journey over the Pacific to the Hawaiian Islands is logistically complicated for the Twin Otter aircrafts that carry the instrument payloads for AOP. Due to the expense of transporting the AOP to the islands, the original remote sensing plan only included flyovers of PUUM every five years.

The partnership with ASU-GAO will make more frequent airborne observations possible at PUUM. The GAO team completed their first flyover of PUUM in January 2019. These data are now available on the NEON Data Portal. GAO and the NEON program have made plans to continue the collaboration in future years.

GAO offers a comparable suite of instruments to NEON’s AOP, including an identical JPL hyperspectral sensor—one of only a handful in the country. This allows NEON to generate a standard suite of remote sensing data products that are consistent with the rest of the U.S. sites.

Hawaii’s remote location in the Pacific Ocean makes it a unique ecological case study. The islands have a large number of endemic and endangered species that have been jeopardized over the past centuries by a variety of factors such as the introduction of invasive species and land use change. Collecting a standard set of remote sensing data over the next several decades will facilitate better monitoring of this unique and fragile ecosystem.

An Experienced Airborne Observation Team

ASU-GAO, formerly the Carnegie Airborne Observatory (CAO), was developed by Dr. Greg Asner, who still directs the program. Greg developed the original platform in 2006 while working at the Carnegie Institute for Science out of Stanford University. Greg and the airborne observatory team moved to Arizona State University in January 2019. In addition to directing GAO, Greg directs the ASU Center for Global Discovery and Conservation Science.

Greg and his team have continued to upgrade the technology over the years, with the most recent iteration of the platform unveiled in 2015. The instrument payload for GAO is housed in a highly customized Dornier 228-202, a versatile twin-engine transport aircraft capable of overseas journeys and takeoffs and landings from small, rough runways. The aircraft carries three integrated remote sensing technologies:

High Fidelity Visible-Shortwave Infrared (VSWIR) Imaging Spectrometer
Dual-laser, waveform Light Detection and Ranging (wLiDAR) Scanner
High-resolution Visible-to-Near Infrared (VNIR) Imaging Spectrometer

The technology is backed by an experienced full-time team that handles scheduling, operations, maintenance, science design, and data collection and processing.

The GAO team stays busy. In 2018, they flew more than 200 missions. Roughly half of each year is spent in Hawaii, where they are mapping rainforests and coral reefs. Other missions have taken them all over the world, including Belize, Borneo, Colombia, Costa Rica, the Dominican Republic, Ecuador, Madagascar, Mexico, Peru, South Africa and the U.S. Virgin Islands. Their work has contributed to policy decisions and conservation efforts around the world.

Ramping Up Remote Sensing for PUUM and Beyond

Remote sensing technology has been transformative for ecology. Airborne observation using sophisticated LiDAR and hyperspectral instruments enables researchers to gather data over large geographic scales in incredible detail. Greg says, "These instruments allow us to see the ecosystem with entirely new eyes��3D chemical eyes! We are looking at structural and chemical information not only at the landscape level but down to the individual tree level."

The instrument payloads carried by the GAO and NEON AOPs produce a number of different data products that provide a detailed look at the structure and biogeochemistry of the landscape below.

LiDAR produces complex 3D maps of terrestrial and marine environments. With LiDAR, researchers can peek beneath the forest canopy to see the structure of the understory or get detailed maps of coral reefs beneath the ocean.
Spectral remote sensing uses special imaging spectrometers that collect reflected light in narrow spectral bands from 380nm to 2500nm (the visible light spectrum for humans ranges from 380nm to 740nm). Hyperspectral imaging can be used to estimate the amount of vegetation in an area (total biomass and leaf area index) and determine the chemical makeup of the vegetation, including water, chlorophyll, lignin, nitrogen or carbon content.

Greg is enthusiastic about the value of the partnership for both GAO and the NEON program. He has been involved with NEON since the beginning and helped to design the NEON AOP. Since the data are comparable between the NEON AOP and GAO, he sees plenty of opportunity for the programs to work together and extend each other's impact.

"There aren't many people collecting these kind of data," he says. "For a long time, it was just me and NASA's JPL. NEON is programmatically scaling the technology to the continental level, with field sites in every U.S. ecoclimatic domain. I can't fly all of these types of environments on my own, but people can use the NEON data along with ours for comparative studies over time."

Since moving to ASU, Greg has also been focused on getting more students involved in using remote sensing data and technologies. Now that more remote sensing data are becoming available, he sees a critical need to get more people trained in how to use and interpret it. He believes there is role for the NEON program here as well. "We have the students, and NEON has the data," he says. "I see us working together to scale up the science and build the user community. ASU is a great place to be doing this work, because we have a huge and eager student body and a focus on mentorship and training. I want to use this work to reach more students and get them excited and involved."

NEON’s Airborne Remote Sensing Flight Season Announced for 2019

lgoldman — Tue, 29 Jan 2019 18:14:30 +0000

NEON’s Airborne Remote Sensing Flight Season Announced for 2019

January 29, 2019

lgoldman Tue, 01/29/2019 - 11:14

Thinking about planning a ground sampling project in coordination with one of our airborne remote sensing surveys? The 2019 NEON flight campaign plans have been announced.

The season will run from March to October, covering fifteen NEON domains and including 35 terrestrial sites and 21 aquatic sites. Data will be collected using a NEON Airborne Observation Platform (AOP). Each AOP consists of an imaging spectrometer, a discrete and waveform light detection and ranging (LIDAR) instrument and a high-resolution digital camera mounted into a DeHavilland DHC-6 Twin Otter aircraft that is flown at a nominal altitude of 1000 m above ground level (AGL) at a speed of 100 knots at NEON field sites. The flight parameters enable meter-scale spectroscopy, decimeter-scale photography, and ~4 points-per-meter discreet and waveform lidar measurements at a sufficient signal-to-noise ratio to retrieve vegetation vertical structure and biogeochemical properties from measured reflectance spectra.

At each terrestrial site a minimum of 100 km² will be surveyed over an area encompassing the NEON flux tower airsheds and distributed long-term observational sampling plots, representative vegetation types, and watershed boundaries for both terrestrial and aquatics sites. To minimize signal uncertainty due to plant phenology and to ensure spatial and temporal consistency in data products across multiple years, all terrestrial sites are scheduled to be flown during mean peak greenness, defined as the range of dates where MODIS NDVI is within 90% of the site maximum. To minimize atmospheric effects, data acquisition occurs at less than 10% cloud cover.

While preliminary data collection started in 2013, 2019 marks the second year of full operations in which Battelle field scientists follow a standard schedule of collecting remote sensing data from each site on a rotating basis for the duration of the NEON project. Under the current plan, data will be collected from sites in the continental U.S. and Alaska three years out of every five, and in Puerto Rico and Hawaii every five years.

Collection of AOP data is synchronized with data collected on the ground at each site. This allows scientists to develop a more comprehensive picture of how different observations scale and how measurements taken from airborne remote sensing instruments correlate with observations made on the ground. The data are then processed and made available via the NEON data portal.

If you are interested in tracking the 2019 season, daily flight reports can be found here. You can also sign up to receive daily email updates by NEON domain. Please note, you will only receive emails during the time period the AOP is in the domain you have signed up to follow. See below for the planned schedules of the two AOPs that will be flying this year.

2019 Flight Schedule

Last updated February 21, 2019

Domain Name	SITE ID	Scheduled date range
17 Pacific Southwest	SJER	Mar 25 - Mar 28
3 Southeast	DSNY	Apr 3 - Apr 23
3 Southeast	OSBS	Apr 3 - Apr 23
3 Southeast	SUGG	Apr 3 - Apr 23
3 Southeast	BARC	Apr 3 - Apr 23
11 Southern Plains	OAES	Apr 19 - May 4
11 Southern Plains	CLBJ	Apr 19 - May 4
11 Southern Plains	OAES	Apr 19 - May 4
11 Southern Plains	BLUE	TBD
11 Southern Plains	PRIN	TBD
8 Ozarks Complex	BLWA	Apr 25 - May 11
8 Ozarks Complex	DELA	Apr 25 - May 11
8 Ozarks Complex	LENO	Apr 25 - May 11
8 Ozarks Complex	TOMB	Apr 25 - May 11
8 Ozarks Complex	MAYF	Apr 25 - May 11
8 Ozarks Complex	TALL	Apr 25 - May 11
15 Great Basin	ONAQ	May 6 - May 13
15 Great Basin	REDB	TBD
2 Mid-Atlantic	BLAN	May 15 - Jun 4
2 Mid-Atlantic	LEWI	May 15 - Jun 4
2 Mid-Atlantic	POSE	May 15 - Jun 4
2 Mid-Atlantic	SCBI	May 15 - Jun 4
2 Mid-Atlantic	SERC	May 15 - Jun 4
6 Prairie Peninsula	KING	May 15 - May 27
6 Prairie Peninsula	KONZ	May 15 - May 27
6 Prairie Peninsula	KONA	May 15 - May 27
6 Prairie Peninsula	UKFS	May 15 - May 27
6 Prairie Peninsula	MCDI	TBD
5 Great Lakes	CHEQ	May 29 - Jun 23
5 Great Lakes	CRAM	May 29 - Jun 23
5 Great Lakes	STEI	May 29 - Jun 23
5 Great Lakes	TREE	May 29 - Jun 23
5 Great Lakes	UNDE	May 29 - Jun 23
5 Great Lakes	LIRO	TBD
17 Pacific Southwest	TEAK	Jun 10 - Jun 17
17 Pacific Southwest	SOAP	Jun 10 - Jun 17
12 Northern Rockies	BLDE	Jun 19 - Jul 3
12 Northern Rockies	YELL	Jun 19 - Jul 3
18 Tundra	BARR	Jul 9 - Jul 17
18 Tundra	TOOL	Jul 9 - Jul 17
18 Tundra	OKSR	TBD - TBD
16 Pacific Northwest	ABBY	Jul 11 - Jul 22
16 Pacific Northwest	WREF	Jul 11 - Jul 22
16 Pacific Northwest	MCRA	TBD
19 Taiga	CARI	Jul 20 - Aug 20
19 Taiga	DEJU	Jul 20 - Aug 20
19 Taiga	HEAL	Jul 20 - Aug 20
9 Northern Plains	DCFS	Jul 25 - Aug 7
9 Northern Plains	NOGP	Jul 25 - Aug 7
9 Northern Plains	PRLA	Jul 25 - Aug 7
9 Northern Plains	PRPO	Jul 25 - Aug 7
9 Northern Plains	WOOD	Jul 25 - Aug 7
13 Southern Rockies & Colorado Plateau	COMO	Aug 9 - Sep 1
13 Southern Rockies & Colorado Plateau	MOAB	Aug 9 - Sep 1
13 Southern Rockies & Colorado Plateau	NIWO	Aug 9 - Sep 1
13 Southern Rockies & Colorado Plateau	WLOU	TBD
14 Desert Southwest	JORN	Aug 26 - Sep 10
14 Desert Southwest	SRER	Aug 26 - Sep 10
3 Southeast	JERC	Sep 6 - Sep 20

Quick Links

Daily Flight Report Email Sign Up: If you are interested in tracking the 2019 season, you can sign up to receive daily emails by NEON domain. Please note, you will only receive emails during the time period the AOP is in the domain you have signed up to follow.
Daily Flight Reports Archive
Tips for Getting Airborne Data
Overview of an AOP
Request an Airborne Remote Sensing Survey

Calibrating Remote Sensing Data with Field Observations in the East River Watershed

lgoldman — Wed, 12 Dec 2018 17:25:04 +0000

Calibrating Remote Sensing Data with Field Observations in the East River Watershed

December 12, 2018

lgoldman Wed, 12/12/2018 - 10:25

The East River watershed encompasses 330 square kilometers of gorgeous mountainous terrain near Crested Butte, Colorado. To build better models of watershed processes and calibrate remote sensing data with observations on the ground, a diverse team of researchers spent two weeks this summer gathering soil and vegetation data from hundreds of individual sites within the watershed.

These detailed ground observations will be correlated with remote sensing data collected by a NEON Airborne Observation Platform (AOP) during the same time period. The correlations will be used to examine relationships between different ecological variables, from subsurface microbial activity to foliar chemistry. Combining ground and remote sensing data will also enable development of new models of watershed functioning and bridge the gaps between data collected at scales ranging from the microscopic to the geographic.

A Massive Collaborative Effort in Field Data Collection

The airborne remote sensing data collection was funded by the Watershed Function SFA, a Department of Energy (DOE) program studying the interactions among different components of watersheds, with contribution from Stanford University. Using NEON to conduct airborne remote sensing surveys was part of a study of the East River watershed led by Lawrence Berkeley National Laboratory (LBNL) and was coordinated through the NEON Assignable Assets program, which provides researchers with access to various aspects of NEON’s infrastructure.

The ground team was led by Dana Chadwick, an NSF postdoctoral fellow co-hosted by Stanford University's Department of Earth System Science and LBNL. In all, more than twenty researchers, from undergraduate students to principal investigators, came together to support the massive data collection effort. The field collection team brought together students and scientists from LBNL, Stanford, Berkeley, Rocky Mountain Biological Lab, Desert Research Institute, Harvard, Fort Lewis College, and the USGS, representing a range of disciplines including geochemical modeling, hydrology, soil science, biochemistry and botany.

Dana says, "This was collaborative group science at its finest. Our team was composed of grad students, undergrads, postdoctoral researchers, technicians and investigators with diverse research backgrounds. It brought together a large group of people who don't often interact in the field. I am hopeful that many ongoing collaborations will be developed as a result of this community effort."

In addition to the collection of physical samples and species information, Nicola Falco, a postdoc at LBNL, led a geophysics team working alongside the sample collection effort. They collected geophysical data which will be processed to understand the distribution of soil moisture through the subsurface.

The teams collected samples of leaves, soils, litter, roots and microbes from 438 designated locations within the watershed research area. The collection locations were organized into 12 sub-sites that represent different ecosystems within four distinct watersheds in the Upper East River Basin. Between June 14 and June 28, 2018, the ground teams collected more than 4,000 physical samples along with geophysical measurements and information on plant species. Soil samples were sent to Corey Lawrence’s lab at USGS for initial processing and to the Maher lab at Stanford to be analyzed for nutrients and bulk chemistry. This analysis is supported by a grant through NSF’s Signals in the Soils program. In addition, Eoin Brodie’s group at LBNL collected soil subsamples for microbial analysis.

Dana acted as the point person coordinating field collection activities with the remote sensing team, led by the NEON project's Matt DeVoe. The flights over the East River watershed were among the first flown through the NEON Assignable Assets program. Ground and airborne data were collected for each location within 72 hours of each other so that observations would be made within the same time frames and under similar weather conditions.

Connecting Ground and Airborne Observations

The ground data collected by the Stanford-LBNL team will be correlated with remote sensing data collected by the AOP. High-resolution GPS measurements were taken at each sampling location by Nicola’s team so that they could be easily found in the remote sensing imagery.

Correlating the datasets will help researchers link data collected through remote sensing with the ecological data gathered on the ground. Airborne remote sensing with the AOP allows researchers to collect surface data across broad geographic areas at scales and speeds not possible to achieve through ground collection. But it does not provide detailed information about soil characteristics, hydrology, or subsurface processes and microbial communities. The team hopes to find correlations in the datasets between the observations made on the ground and data that can be gathered through remote sensing. This would allow future researchers to use remote sensing data to infer what is happening on the ground.

Dana explains, "We are creating datasets that will allow us to scale from local vertical profiles to a broader areas and link what is happening in the subsurface to what we are seeing from above. This will allow us to extrapolate processes using remote sensing data in ways that are not possible now and develop a more nuanced understanding of how subsurface ecosystem properties are distributed across the entire watershed."

The research team hopes that the study will lead to better models that can be used to further our understanding of watershed processes and how watersheds are changing across time. "There are still a lot of questions about watershed processes and how they are linked," says Dana. "For example, how are variations in topography, geography, soil chemicals or moisture levels linked to plant species distribution and foliar characteristics across landscapes? And how are changes in snowmelt and other variables impacting vegetation patterns and watershed function?"

Understanding these issues will help land managers and policy makers make more effective decisions to protect the watersheds that we all depend on. The Crested Butte study is helping to fill critical holes in our understanding.

Case Study

NEON Remote Sensing Flights Over Crested Butte Gather Data for LBNL Watershed Study

lgoldman — Thu, 16 Aug 2018 18:41:04 +0000

NEON Remote Sensing Flights Over Crested Butte Gather Data for LBNL Watershed Study

August 16, 2018

lgoldman Thu, 08/16/2018 - 12:41

A study led by Lawrence Berkley National Laboratory (LBNL) has used the NEON Assignable Assets Program to gather airborne remote sensing data near Crested Butte, Colorado. The Watershed Function Scientific Focus Area (SFA) project will use data from the NEON Airborne Observation Platform (AOP) to study plant community distributions and canopy biochemistry to shed light on watershed systems.

Studying Watershed Health and Function

The Watershed Function SFA is a Department of Energy (DOE) program studying the interactions among vegetation, soil, fluvial (stream and river), and subsurface components of watersheds. Watersheds have critical hydrological and biogeochemical functions that ultimately support all forms of life on Earth. Understanding these processes is essential for effective management of water and land resources.

The Watershed Function SFA looks at watersheds as "systems of systems" to study how water, nitrogen, carbon and other elements move into, out of and through the watershed. Looking at how hydrological and biogeochemical processes are changing over time will also help researchers understand, model and predict the impact of climate change and land use changes on watersheds and their downstream areas.

Vegetation health, distribution and biochemistry are an important part of the watershed study. Plants continually exchange water, nutrients and gases between soil systems and the atmosphere. Studying these exchanges provides insights into the overall health of the watershed and its vegetation, the carbon storage potential of vegetation in the watershed, the net exchange of water in the watershed system, and other key indicators. Looking at how plant distribution and traits are changing over time can also help researchers understand how the watershed is responding to changes in climate (including temperature and precipitation patterns), invasive species and land use.

The Watershed Function SFA is specifically focused on watersheds in mountainous regions. The study seeks to understand how permutations in variables in mountainous watersheds such as precipitation patterns, snowmelt dates and temperature impact the delivery of water and nutrients in downstream areas.

One of the watershed areas studied in the SFA is near Crested Butte, CO. The East River Watershed SFA site encompasses the drainages of the East River, Washington Gulch, Slate River and Coal Creek, which feed into water systems that irrigate 5.5 million acres of land and deliver water to one in ten people in the U.S. Researchers are developing models of the hydrological and biogeochemical processes that will help them understand how the watershed is changing on both a seasonal and decadal scale, and how these changes will impact all of the areas downstream that depend on it.

Aerial images of Crested Butte from a data collection campaign

Using Airborne Remote Sensing Data to Study Vegetation Indices

Gathering information on plant species distribution, traits, health and biochemistry has traditionally been done through on-the-ground observations collected by field ecologists. These methods are both time and labor intensive, which limits the area that can be effectively studied.

Airborne remote sensing can help researchers gather important data over much larger geographic scales than is possible through manual field observation. Light aircraft are equipped with a variety of sensors that collect data as the planes fly over the landscape being studied. The NEON AOPs have sensor payloads that include:

a hyperspectral imaging spectrometer
a full waveform and discrete return LiDAR
a high-resolution Red, Blue Green (RGB) camera

Together, these instruments create a data-rich map of the landscape below, including visual, topographic and biochemical information. The NEON project gathers remote sensing data over each of the NEON field sites on a rotating schedule (see the 2018 schedule). NEON AOPs provide a number of data products related to key vegetation parameters, including:

Vegetation cover and dominant vegetation type
Vegetation structure including height and Leaf Area Index (LAI)
Vegetation condition
Vegetation biochemistry and heterogeneity
Canopy chemistry (Nitrogen index)
Topography, such as elevation, slope and aspect
Vegetation greenness and health (NDVI, EVI)

Researchers at LBNL were interested in using NEON AOPs to gather broader scale vegetation. But first, they needed to see how remote sensing data correlates with field observations. Adding remote sensing data to the field data they are already collecting could help them scale up their foliar chemistry observations from the individual tree level to a regional level.

Collecting Vegetation Data from the East River Watershed

After several months of planning, remote sensing data collection surveys took place over the East River Watershed SFA site during the last two weeks of June 2018. The NEON airborne remote sensing team conducted eight flights during this time to cover the 82,500-acre site. Overall, they ran 72 flight lines, ranging in length from 2 km to 24 km, to build a complete map of the area. The dataset includes all of the standard AOP data products.

Areas observed with the AOP were coordinated with the LBNL field team so that field observation data and remote sensing data for each section of the site would be coincident. This was done so that data on the ground and from the air would both be taken under similar weather conditions for comparative purposes.

The mountainous terrain near Crested Butte added some challenges for the flight team. Rather than maintaining a steady altitude, as the AOPs do over most sites, the flight team adjusted their altitude to account for the topography of the land below, a practice known as "terrain following."

The data products collected over the East River Watershed SFA site are currently being processed and quality checked. Once this is complete, they will be turned over to the LBNL research team for their own analysis.

The First AOP Run for the NEON Assignable Asset Program

The East River Watershed SFA is not part of the regular AOP flight plan for the NEON project. Researchers for the Watershed Function SFA requested the data acquisition over the Crested Butte site through the NEON Assignable Assets Program. This program allows principal investigators to contract with Battelle to collect remote sensing data customized to the needs of their studies at the locations they choose. Researchers can also use the Assignable Assets program to request access and use of other parts of the NEON infrastructure including deployments of mobile sensor arrays, additions of sensors to NEON field sites and even using field staff for additional field sampling activities.

The LBNL project was the first use of the AOP under the Assignable Assets Program. The NEON project has three AOPs. Two of these are used to complete planned data acquisitions over NEON field sites. The third is available to other researchers on a cost reimbursable basis. Requests are evaluated based on feasibility, scientific justification and the potential impact on other NEON research activities. All assignable asset requests require the principal investigators to cover the costs of using NEON infrastructure.

Currently, there are already two additional AOP projects planned for outside researchers in 2018, and others under consideration. Battelle is currently accepting requests for AOP use for the 2018 and 2019 flight seasons.

For more information on requesting access to NEON infrastructure, visit the Assignable Assets Program to learn more.

To explore our open access airborne remote sensing data from NEON field sites, visit the NEON Data Portal.

Case Study

Send in the Drones: Exploring the Future of Airborne Remote Sensing

lgoldman — Fri, 03 Aug 2018 22:12:13 +0000

Send in the Drones: Exploring the Future of Airborne Remote Sensing

August 3, 2018

lgoldman Fri, 08/03/2018 - 16:12

Could drones be part of the NEON program's arsenal of data collection platforms one day?

Drones are gaining popularity in environmental and agricultural applications. Farmers use drones to monitor crop growth and plant health. In the ecological community, drones have been used to gather remote sensing data after floods or forest fires, track wildlife, conduct geospatial mapping and monitor ecosystem change. Compared to traditional light aircraft missions, drones provide a cost-effective and easy alternative for gathering a variety of airborne data products.

Their low cost and versatility could help NEON expand its airborne remote sensing capacities. Before integrating them into NEON's operations, however, more work has to be done to see how they compare to our existing airborne platforms and to develop workflows and data delivery mechanisms. A recent Battelle study took the first step towards evaluating their use for remote sensing for NEON.

Expanding Airborne Remote Sensing Capacity for NEON

NEON already has remote sensing capabilities in the form of Airborne Observation Platform (AOP). The AOPs are light aircraft equipped with a payload of sophisticated sensors, including a hyperspectral imaging spectrometer, a high-resolution camera and a full waveform and discrete return LIDAR system. These sensors make the AOP a powerful remote sensing platform. The AOP offers a full spectrum of high-resolution remote sensing data. These data—which are used to produce a range of data products such as Leaf Area Index, total biomass and other vegetation indices along with topographical data—are part of the NEON remote sensing data product suite available through the NEON Data Portal.

While drones currently lack the payload capacity for sophisticated sensors required to wholly replace the AOP, there is tremendous potential for drones to expand the temporal coverage of remotely sensed data products at greatly reduced costs. Adding drones to the NEON toolbox would allow researchers to collect data outside the regularly scheduled AOPs flights and respond more nimbly to extreme events and other time-sensitive research opportunities.

As an initial step toward incorporating drones into NEON, Battelle scientists David Durden, Tristan Goulden, Josh Roberti, John Adler, Michael Wussow, and Mike SanClements are working to assess the correlations between drone-collected and AOP data. To date they've tested three platforms: the DJI Phantom 4 Quadcopter (popular with serious hobbyists), the DJI Matrice 210 and the DJI Matrice 600. Experimental flights were conducted in concert with AOP calibration flights to collect initial data under the same atmospheric and light conditions. These flights also served to develop data processing workflows, storage methods and delivery mechanisms. Most importantly, these initial data, and data from flights planned later in the summer, will be the foundation for correlating drone and AOP data products.

The sensors currently being tested provide a limited, but valuable, set of vegetation indices, such as the Normalized Difference Vegetative Index (NDVI). This provides an important indicator of vegetation health. The drones could one day be outfitted with a variety of different sensors that could be swapped out depending on the needs of the project. These could include a full hyperspectral imaging spectrometer, high-resolution cameras and LIDAR. Someday, drones could even be equipped with sampling equipment—for example, to collect water from the middle of lakes or acid mine drainage sites.

The Future of Drones at NEON

As drones continue to get more powerful and less expensive, their potential for environmental data collection will only continue to grow. This study puts NEON one step closer to integrating the use of drones with our other data collection efforts.

The incorporation of drone technology into the NEON network also furthers our capacity to serve as a collaborative partner in modern ecological research. Already, additional partnerships are being investigated, including a joint project between NEON, the University of Colorado–Boulder and The High-Latitude Drone Ecology Network at the NIWOT LTER/NEON site. This collaboration will facilitate interoperable remote-sensing data products between the various networks. It serves as a test case to collect project logistics information that will guide the development of drone-based remote-sensing services.

Eventually, drones could be flying regularly over NEON field sites and be available for outside researchers as part of the NEON Assignable Assets program. Ultimately, they will help the National Science Foundation’s goal with NEON, a user facility that provides a diverse suite of comparable and consistent ecological data at multiple spatial and temporal scales. The data collected through these studies are an important step forward towards expanded remote sensing capabilities.

Remote Sensing Data Science Challenge Yields Positive Results

lgoldman — Mon, 02 Jul 2018 15:47:36 +0000

Remote Sensing Data Science Challenge Yields Positive Results

July 2, 2018

lgoldman Mon, 07/02/2018 - 09:47

The NEON project is producing a vast treasure trove of open access remote sensing data. Can computer algorithms help ecologists make sense of it all?

A team of ecologists and data scientists at the University of Florida thought so. To accelerate the process, they initiated a data science challenge. The NIST-DSE Plant Identification with NEON Remote Sensing Data Challenge invited teams to compete to develop algorithms that could correctly classify and delineate trees using NEON remote sensing data. The results of the challenge were recently released in PeerJ: A Data Science Challenge For Converting Airborne Remote Sensing Data Into Ecological Information.

Data Competitions and the Rise of "Big Data" Ecology

The challenge was organized by researchers from the Data Science Research lab (led by Daisy Wang), the WEecology lab (led by Ethan White), and Stephanie Bohlman’s lab at the University of Florida in active collaboration with scientists from the NEON project. It was sponsored by the National Institute of Standards and Technology (NIST) Data Science Evaluation (DSE) Series and the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative.

Data challenges have become a common strategy for advancing data science in many fields, ranging from image classification to finance. The competitions invite individuals or teams of data scientists to work independently to solve a specific problem. The solutions are then evaluated against a common rubric to determine which approach works best for the challenge presented. Often, each solution has its own set of strengths and weaknesses, and the "ideal" solution may combine elements of two or more different submissions. Looking at the insights gained through each solution can significantly accelerate the rate of progress in solving complex "big data" problems.

Until now, this approach has not been used widely to solve ecological problems—in fact, the Remote Sensing Data Science Challenge is believed to be the first of its kind. Sergio Marconi, a PhD student in interdisciplinary ecology at the University of Florida and one of the organizers of the challenge, says, "We are facing a moment in which the use of big data in ecology has become a reality. We need to bring ecology and data science together if we're going to get value from all of the data we now have available."

Putting Remote Sensing Data to Work

The organizers decided to use airborne remote sensing data from the NEON project because the data are freely available, consistent from site to site and year to year, and represent a large and growing data set that provides an ideal testing ground for data science solutions. Sarah Graves, a doctoral candidate in the School of Forest Resources and Conservation at the University of Florida, explains, "Remote sensing data is an important tool for understanding forest composition and structure. Using computer algorithms to analyze the data will speed up this process by orders of magnitude. But before that is a viable solution, we need to test those algorithms to see how accurate they are and how their results compare to data gathered on the ground."

The Remote Sensing Data Challenge asked participants to tackle three problems:

identify the location and size of individual trees (crown segmentation)
match remote sensing data on individual trees to data verified through ground observations; and
classify individual trees by species using the remote sensing data.

Participants were each given identical remote sensing data collected by the NEON AOP over the Ordway-Swisher Biological Station in north-central Florida. The NEON project uses three Airborne Observation Platforms (AOPs) -- each AOP includes a hyperspectral imaging spectrometer, a full waveform and discrete return LiDAR, and a high-resolution Red-Blue-Green (RGB) camera. Once installed into a Twin Otter aircraft, the AOP is flown over NEON field sites annually to collect a variety of data products. The data challenge used four of these data products: LiDAR point cloud data, LiDAR canopy height model (CHM), hyperspectral surface reflectance, and high resolution visible color (RGB) photograph. Teams used these data products to identify individual trees and classify them by species. The results were compared to observational data gathered on the ground to determine the accuracy of each algorithm.

Six teams, comprised of 16 individual participants, entered the data challenge. Several of the algorithms performed well for the species classification part of the challenge, with the best correctly classifying 92% of the trees. The crown segmentation part of the challenge proved to be more challenging, with the highest-performing algorithm generating a 34% overlap with data verified through on-the-ground observations.

Bringing Data Science and Ecology Together

While none of the algorithms performed perfectly, they are a big step forward from out-of-the-box software used for other types of image classification. Organizers noted that the best performing teams combined both data science expertise and an understanding of the subject area.

Ultimately, the organizers see the data challenge process as collaborative rather than competitive. Each of the teams contributes something of value and they all learn from each other's approaches. Sergio says, "We’ve made huge gains in our ability to tackle these kinds of problems in impressively little time, even with a small number of groups. We're able to see how different algorithms work and which work better in different situations. There is never just one right solution. It's about finding which solutions work best for the task at hand."

This year's data challenge may be the first of many. The team hopes to make this an annual event, gradually increasing the complexity and difficulty of the challenge questions. Moving forward, they plan to continue to use NEON data. Sarah says, "The NEON data is a great dataset to use for these types of competitions because it's easily accessible to everyone for free and the documentation for how data are collected is very clear and transparent. And we know these data will continue to be collected in a standardized and open way for 30 years. That means we can use the same type of datasets over time, but ask tougher questions and ask participants to do more with them."

Moving forward, both Sarah and Sergio say they would like to see more collaboration between data scientists and the ecology community. They both reference participation in past NEON Data Institutes as one of the factors that led to their interest in the idea of a data challenge. Sergio says, "The Institute helped me understand how to approach ecological data from a data science perspective and what data scientists would need to make the data usable."

Building tighter connections between ecologists and data scientists will help the research community maximize the value of large data sets from the NEON project and other observatory networks. Ultimately, this approach will accelerate scientific progress. Sarah says, "We've learned so much by working with computer science students. Working across disciplines is challenging, but it is when we are challenged that we are able to start asking better questions. That's where the real advances are made."

Learn more about NEON's airborne data.

Case Study

NEON Airborne Takes Flight for 2018 Season

lgoldman — Mon, 19 Mar 2018 16:42:15 +0000

NEON Airborne Takes Flight for 2018 Season

March 19, 2018

lgoldman Mon, 03/19/2018 - 10:42

If you happen to be at a NEON field site during the peak of the growing season this year, look up. There’s a chance you’ll see one of the NEON Airborne Observation Platforms (AOPs) flying overhead.

Where will we fly this year?

The 2018 flight campaign will run from March to October, covering 13 NEON domains and including 36 terrestrial sites and 14 aquatic sites using two payloads (Payloads 2 and 3 �� see Tables 1-2 and Figures 1-2). The remote sensing instruments �� consisting of an imaging spectrometer, a waveform light detection and ranging (LIDAR) instrument and a high-resolution digital camera �� are installed into a DeHavilland DHC-6 Twin Otter aircraft flying at a nominal altitude of 1000 m above ground level (AGL) at a speed of 100 knots. The flight parameters enable meter-scale spectroscopy, decimeter-scale photography, and ~4 points-per-meter discreet and waveform lidar measurements at a sufficient signal-to-noise ratio to retrieve vegetation vertical structure and biogeochemical properties from measured reflectance spectra. This year’s data collection schedule will in an inaugural survey of the Guanica (GUAN) field site in Puerto Rico. The 2018 data collection season starts in March with the first flight over the San Joaquin Experimental Range (SJER) in California (D17: Pacific Southwest). The planes will follow peak greenness cycles across Alaska, the lower 48 states and Puerto Rico, with the final run taking place in Florida in September. Under the current plan, data will be collected from sites in the continental U.S. and Alaska three years out of every four, and in Puerto Rico and Hawaii every five years.

If you are interested in tracking the 2018 season, daily flight reports can be found here. You can also sign up to receive daily email updates by NEON domain. Please note, you will only receive emails during the time period the AOP is in the domain you have signed up to follow. See below for the planned schedules of the two AOPs that will be flying this year.

Table 1: 2018 Flight Campaign Schedule �� Payload 3

Domain	Site (Terrestrial / Aquatic)	Survey Area (Km²)	Target Survey Dates
D17	San Joaquin Ecological Range	100	Mar 31 �� Apr 4
D11	LBJ Grasslands	196	Apr 17 �� Apr 25
D11	Klemme Range Research Station	100	Apr 17 �� Apr 25
D08	Talladega National Forest / Mayfield Creek	122	Apr 27 �� May 13
D08	Lenoir Landing / Lower Tombigbee River	100	Apr 27 �� May 13
D08	Dead Lake / Black Warrior River	108	Apr 27 �� May 13
D07	Oak Ridge National Laboratory / Walker Branch	181	May 19 �� Jun 23
D07	Great Smoky Mountains National Park / LeConte Creek	374	May 19 �� Jun 23
D07	Mountain Lake Biological Station	100	May 19 �� Jun 23
D12	Yellowstone Northern Range / Blacktail Deer Creek	205	Jun 28 �� Jul 6
D16	Wind River Experimental Forest / Martha Creek	130	Jul 8 �� Jul 24
D16	Abby Road	100	Jul 8 �� Jul 24
D14	Santa Rita Ecological Range	368	Aug 18�� Aug 30
D14	Jornada	245	Aug 18�� Aug 30

Table 2: 2018 Flight Campaign Schedule �� Payload 2

Domain	Site (Terrestrial / Aquatic)	Survey Area (Km2)	Target Survey Dates
D04	Guanica Forest	100	May 8 �� May 24
D04	Lajas Experimental Station (we will be attempting Rio Cupeyes and Rio Guilarte as well)	100	May 8 �� May 24
D06	Konza Prairie Biological Station / Konza Prairie Biological Station (Agricultural) / Kings Creek	149	May 30 �� Jun 7
D06	The University of Kansas Field Station	100	May 30 �� Jun 7
D17	Soaproot Saddle / Upper Big Creek	100	Jun 16 �� Jun 26
D17	Teakettle / Teakettle Creek	160	Jun 16 �� Jun 26
D18	Toolik	581	Jul 3 �� Jul 25
D18	Barrow	160	Jul 3 �� Jul 25
D19	Caribou-Poker Flats / Caribou Creek	192	Jul 27 �� Aug 19
D19	Delta Junction	185	Jul 27 �� Aug 19
D19	Healy	112	Jul 27 �� Aug 19
D01	Harvard Forest	234	Aug 30 �� Sep 12
D01	Bartlett Experimental Forest	100	Aug 30 �� Sep 12
D03	Ordway-Swisher Biological Station / Suggs Lake / Barco Lake	167	Sep 15 �� Oct 4
D03	Disney Wilderness Preserve	138	Sep 15 �� Oct 4
D03	Jones Ecological Research Center	230	Sep 15 �� Oct 4

Frequently Asked Questions

Can the AOP fly additional areas?

Due to the intensive nature of the NEON Flight Campaigns, we are unable to include additional flight areas to the existing NEON observatory collection plan. However, an additional payload is available to support Principal Investigator (PI)-led science flights via the NEON Assignable Assets program.

What remote sensing data are collected?

Measurements taken from the AOPs include a range of physical, biological and biochemical data available both as flightlines and mosaics, including:

Topography (elevation, slope and aspect)
Canopy chemistry (lignin, nitrogen, water content, xanthophyll cycle)
Ecosystem structure (canopy height and Leaf Area Index (LAI)
Total biomass maps and vegetation indices
High-resolution orthorectified camera imagery

Why does the NEON project collect airborne remote sensing data?

The NEON airborne remote sensing system fills a critical hole in ecological data collection. Standardized, regular airborne data collection over the NEON field sites will allow scientists to monitor changes in vegetation patterns and canopy chemistry on a continental scale over an extended time period. These data will provide new insights into how invasive species are spreading over time and how changes in climate and land use impact forest health and their ability to sequester carbon.

NEON remote sensing data, along with tower sensors, soil sensors and observational field sampling, are freely available on the NEON data portal.

Are researchers using AOP data yet?

The data are already being used by scientists for many different projects. For example, a team led by Dr. Phil Townsend, an ecologist out of the University of Wisconsin, is using the data to build a spectral library of vegetation types that links physical and biochemical traits to spectral data. This will enable scientists to classify and map plant species using remote sensing data. At the Smithsonian Environmental Research Center (SERC), Dr. Jess Parker, a forest ecologist, plans to incorporate AOP data into his studies of tree growth and the exchange of carbon, radiation and moisture between forests and the atmosphere. In addition, there have been several educational projects using NEON data including a late-2017 project led by Ethan White.

The NEON remote sensing team is working closely with the science community to optimize data collection for the needs of researchers. Two NEON Technical Working Groups (TWGs) have been formed to guide further data collection efforts, one on airborne sampling design and one on LiDAR. These advisory groups, along with additional discussions with researchers using NEON remote sensing data, will help refine data collection protocols and guide future decisions for expanded data collection or new instrumentation.

Learn more about NEON airborne remote sensing.

NEON's 2016 remote sensing flight schedule now available

lgoldman — Fri, 19 Feb 2016 17:58:08 +0000

NEON's 2016 remote sensing flight schedule now available

February 19, 2016

lgoldman Fri, 02/19/2016 - 10:58

The 2016 flight campaign for the Airborne Observation Platform (AOP) will run from April to October this year and cover nine of 20 NEON domains. Survey flights are scheduled for 25 terrestrial field sites and 17 aquatic field sites. Read on to learn more about what instruments will be used, how to plan your research activities in correlation with NEON’s flight schedule and how to access AOP data.

Find a detailed flight schedule at the bottom of this article.

How NEON conducts airborne surveys

A minimum of 100 km² will be surveyed at all terrestrial sites, optimized to capture the spatial heterogeneity of ecological features in the landscape. Where possible, AOP flights also cover the watershed of each terrestrial and aquatic site.

At terrestrial sites, the surveyed area includes the NEON tower airshed and distributed long-term sampling plots. These sites are scheduled to be flown during peak greenness, defined as the range of dates where MODIS NDVI is within 90% of the site maximum. Surveys conducted during peak greenness will minimize signal uncertainty caused by seasonal variations in plant phenology and ensure spatial and temporal consistency in data products across multiple years. To minimize atmospheric and solar effects, data acquisition will be attempted at less than 10% cloud cover and within solar elevation angles of 40° above the horizon.

What instruments are used for NEON’s AOP?

Each AOP payload consists of an imaging spectrometer, a waveform light detection and ranging (LIDAR) instrument and a high-resolution digital camera. These instruments are mounted in a DeHavilland DHC-6 Twin Otter aircraft that flies at a nominal altitude of 1000 m above ground level (AGL) at a speed of 100 knots. The flight parameters enable meter-scale spectroscopy, decimeter-scale photography, and ~4 points-per-meter discreet and waveform lidar measurements at a sufficient signal-to-noise ratio to retrieve vegetation vertical structure and biogeochemical properties from measured reflectance spectra.

Data collected by the AOP

Data production from the AOP 2016 survey flights is estimated to be 26 Tb of raw L0 data which will yield approximately 100 Tb in L1-L3 data products.

Level 0 data are defined as the raw data records collected directly by the AOP instruments. L0 data are then processed into physical units and placed into a coordinate reference system to produce Level 1 data products. Examples of L1 data include orthorectified directional surface reflectance spectrometer data or LiDAR LAS 1.3 point clouds, both delivered by flight line. Level 2 data are derived data products, such as vegetation indices derived from surface reflectance, and are also distributed by flight line. Level 3 data are mosaicked and separated into 1 km by 1 km tiles and may contain data such as directional reflectance or derived data products such as vegetation indices, high resolution camera mosaics, terrain slope and terrain aspect, and others.

As 2016 airborne data become available, you may request these data online. We also have data from previous survey flights already available.

Planning your research in coordination with AOP flights

If you would like to plan research activities on the ground to coincide with AOP surveys of certain areas, please [email protected]?subject=2016%20AOP%20flight%20campaigns">email us to join our mailing list. The NEON AOP flight operations team will email daily reports on the status of flights over each site which will be more accurate than the schedule posted in this article. Although the NEON AOP flight operations team will attempt adherence to the the published schedule, weather and logistical constraints may result in modifications to this plan.

At this time we are unable to accommodate requests for flights over additional areas due to the constraints in our flight campaign schedule. We do anticipate outfitting a payload in 2018 that that will be dedicated to Principal Investigator (PI)-led science flights under a separate process led by NSF.