wednesday show and tell

Wednesday Show and Tell

Theresa CrimminsChristina WrightCuyler SmithDave PressGareth RowellJohn BoetschLisa NelsonJohn Gross

Theresa Crimmins USA National Phenology

Network

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Theresa CrimminsNetwork Liaison, USA-NPN

(520) [email protected]

www.usanpn.org

mailto:[email protected]

http://www.science.psu.edu/alert/photos/miscphotos/PostCaribou1.jpg

USA-NPN Progress To-Date

• Plant protocols available online (200+ species)• Phenology data/information system functional• Data entry available online• Data visualization tools in development• Animal protocols in development (available

2010)– Mammals, birds, amphibians, reptiles, fish, insects

(>150 species)

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Example USA-NPN Plant Phenology Protocol (≈ I&M ‘how to observe’ SOP)

NPS-NPN Partnership Strategy

• Monitoring and research: Establish wide-spread monitoring of phenology in NPS units (NPS scientists, staff, visitors…) – develop training materials, encourage research in parks

• Information management: Develop databases, data transfer mechanism, metadata requirements, “one-stop shopping” site for NPS

• Communications: Widely disseminate information and knowledge of the importance and management implications of phenology – develop interp & education messages, fact sheets…

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Pilot in NETN Parks – Summer 2009• Goal: Find the appropriate fit for phenology monitoring and

use of phenology data within NETN parks. • Status:

– Currently identifying target audiences, locations, methods– Identifying major ecological, methodological questions

• Products: Draft field methods standard operating procedures for phenology protocols following Oakley et al. (2003) guidelines

• Evaluation of pilot program – end of 2009• Participants: NETN, Appalachian Trail, Acadia NP,

Appalachian Mtn Club, Appalachian Trail Conservancy, The Wildlife Society, USA-NPN

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CA/PWR Cooperative Efforts• Recent call to engage interested parties• I&M, park biologists and resource managers,

interpreters represented (as well as NPN, UCSB, Project BudBurst)

• Working group established to pilot plant phenology monitoring

• Next steps:– Select plant species & develop protocol for pilot

monitoring– Collaborate with UCSB on two phenology

monitoring training sessions – fall 2009

Christina WrightSoutheast Coast Network

E X P E R I E N C E Y O U R A M E R I C A

SECN SharePoint

2009 Data Management ConferenceShow and Tell

National Park ServiceU.S. Department of the Interior

News and Updates

Project Tracking

Completed Projects Repository

Data Management

Data Entry using

InfoPath Forms

Generate Reports

Links to Monitoring Data

Resource Briefs

Cuyler Smith Southwest Alaska Network

Bear-A-Where…Aerial Bear Survey Software Tools at the Southwest Alaska Network

Cuyler SmithData Manager - SWAN

Data Manager’s Conference 2009

“Beaerial” survey events…• Generate random transects

(initial flight paths)• Fly random transects with 1/2

observers and 1 pilot• Spot bear!• Deviate from flight path to

count/identify attributes• Return to transect• Continue survey


Software Involved

• Existing– ArcMap 9.3– ArcPad 7.1

• Custom (GeoNorth)– ArcMap Random Transect

Generator (Pre-survey transects to be flown)

– ArcPad Bear Tracking Applet (During survey to catalog attributes)



Random Transect Generator (ArcMap)…

Aerial Survey Tracking (ArcPad)… Data Manager’s Conference 2009

For more info…

• Contact Bill Thompson, PI– [email protected]


“No matter how much you push the envelope, it’ll still be stationery!”

Dave Press San Francisco Area Network

Streamflow Monitoring Database: A FAB Example

David PressEcologist / Data Manager

San Francisco Area Network

SFAN Streamflow MonitoringThe specific monitoring objectives are to:

1. Monitor the variability and long-term trends in streamflow using fixed, continuous, water stage recording stations by producing annual mean daily and monthly discharge estimates for core streamflow monitoring stations in GOGA, PRES, PINN, and PORE.

2. Monitor the frequency, magnitude and duration of peak flow events at fixed, surface water level monitoring stations by producing peak and daily summaries of stage height and discharge for core streamflow monitoring stations in GOGA, PORE and PINN.

3. Monitor the timing, frequency, magnitude and duration of unnatural or extreme low water/low flow events in stream reaches known to support threatened and endangered aquatic species in the dry season in GOGA, PORE, and PINN watersheds.

Simplified Data Work Flow

Download raw datalogger files in field

Transfer data to recording gaging station

spreadsheet template Perform QA/QC measures

Identify data gaps, erroneous data

Correct recorded stage

(instantaneous)

Develop Stage-Discharge Rating

CalculateDischarge (instantaneous)

Import Mean Gage Height &

Measured Discharge

Streamflow Measurements

Once a month at each sampling siteHigh stream flow eventsOnce a month at water quality sampling sites also

Gareth Rowell Heartland Network

Monitoring Databases, ODBC and R

Access Bird MonitoringDatabase

qry_BirdSppRich ODBC R

>library(RODBC)>channel <- odbcDriverConnect()>BRich <- sqlFetch (channel, "qry_BirdSppRich")>attach(BRich)>names(BRich)>summary(SppRich_Yr+1)>hist(main="Bird Species Richness, All Parks x Years", SppRich_Yr+1)

Some R code…

• Data displayed in R • Data from ODBC Connection to Access

Why use ODBC and R

• Easy to “re-select” database for data exploration• Data are clean (not corrupt) going from database to R• R provides extensive collection of statistical tests and graphics• R and ODBC are widely used -- excellent documentation• R and ODBC are free

John BoetschNorth Coast and Cascades

Network (Olympic NP)

Reusable Project Application Components

John Boetsch - NCCN / Olympic NP

2009 I&M Data Management ConferenceTucson, Arizona – April 2009


What and Why?1. Multiple project database applications2. Faster development & more consistent look & feel

for project staff3. Why not?

Quality Assurance Tool• Uses pre-built queries to check the completeness, structural

integrity and logical consistency of the data set


Quality Assurance ToolWhat you don’t know CAN hurt you(r analysis)!

• Missing records– List of scheduled sites that weren't visited … were they simply not

entered? • Missing values

– Field coordinate source = 'GPS' but missing GPS model or GPS file name

• Duplicate records– Locations with more than one coordinate record per sampling date -

verify that these are intended • Illogical data combinations

– Marker status = 'removed' but no removal date, or with a removal date and status <> 'removed'


Quality Assurance Tool• We talk a lot about how relational databases

are useful for this type of quality assurance ...• This is a tool for rigorously checking data

against a set of project-specific rules


Quality Assurance ToolThree types of QA queries:

1. Critical– structural issues, missing critical fields, etc … must fix to

complete data certification 2. Warning

– may affect summary output and analysis routines … missing values, etc. (fixes not always possible)

3. Information– list of taxa observed, list of locations visited, range of

dates for sampling events, etc.


Quality Assurance ToolFunctionality:

1. Filter the data being validated – uncertified data, certified data, or both

2. Each query result set may be documented - resolved issues and lingering problems

3. Open the appropriate forms for fixing or exploring the problem for an individual record (may run an update query to fix the problem after the user has reviewed the records)

4. Export results to Excel5. Opens a formatted report object for inclusion in the year's

"certification report"


Quality Assurance Tool1. Filter the data being validated – uncertified data,

certified data, or both


Quality Assurance Tool2. Each query result set may be documented -

resolved issues and lingering problems


Quality Assurance Tool3. Open the appropriate forms for fixing or exploring

the problem for an individual record (may run an update query to fix the problem after the user has reviewed the records)

4. Export results to Excel


Quality Assurance Tool5. Opens a formatted report object for inclusion in the

year's "certification report"


Data BrowserData entry forms weren’t providing sufficient access to

data:Mirror field form structure - often more complex than

they would otherwise be for simply displaying data Goal: Total data access through front end• Simple data browser for more hierarchical data viewing and

editing by a “power user”• Filter by park, location, location status, location type, etc.• Subforms and tabs• Most users satisfied with this, advanced users can still build

queries as they wish


Data Browser

Managing Data Access PrivilegesGoal: Avoid unnecessary risk to data (more valuable

as data accumulate)• Application mode based on user roles (application

side, NOT SQL Server roles)• Different application components hidden/exposed• User login tied to a user role in a system table

– Admin – may create new application versions– Power user – may edit certified data, view the back-end, add new

user roles– Data entry – may enter new (uncertified) data for current season– Read only – view data only


Notification for Application VersionsHelps with far-flung field crews and outdated front-end

versions sitting on hard drives • On opening ...

– Application log of user and version number– User is notified if a new release is available

• User downloads new version from our SharePoint site

– Current – no notification– Supported – notifies user on opening, but they can continue to use

the application– Not supported – application closes after notification; MAJOR

changes, previous versions may endanger data quality


Lisa Nelson I&M Program - Fort Collins

NPS Natural Resource & GIS ProgramsInventory and Monitoring Program

DM Meeting - 2009

Vital Signs Internet Map Services (VSIMS)

• FY2009 Activities:– NRDT upload and map display functions– Integration with IRMA frameworks for web

version– Development staff:

• Position closes April 27


DM Meeting - 2009

Upload from NRDT


DM Meeting - 2009

Display Uploaded Data


DM Meeting - 2009

Comments/Questions• NRDT User Board Meeting:

Wednesday, 4:30 – 5:30pm• Are We Building 32 Silos?

Thursday, 2:45 – 4:00pm

• See Lisa Nelson or Peter Budde

John Gross I&M Program - Fort Collins

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PALMS:A NASA-NPS partnership to monitor

landscape dynamics

John GrossOffice of Inventory, Monitoring, and Evaluation

Ft Collins, Colorado

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TOPS: Common Modeling Framework

Monitoring, modeling,

& forecasting at multiple scales

Nemani et al., 2003 and 2007

TOPS Products forNPS:• Ecosystem productivity /

carbon flux• Trends and anomalies in

vegetation condition and phenological indicators

• Soil moisture / vegetation water stress

• Climate and weather surfaces and forecasted impacts of climate change

TOPS Products for Sierra Greater Park Ecosystems

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Snow Cover

Soil Moisture

EVI

Leaf Area Index

Maximum Temp.

MODIS DirectBroadcast

TOPS GPP

Vapor Pressure Def.

FPAR Anomaly

Satellite Climate Ecosystem Model

Trends &Anomalies

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TOPS Data Gateway

• Using the query interface, users can access, graph, and download timeseries of data for locations of interest.

NASA Project (in 3rd & final year)• Focus on four prototype parks• Uses park-specific data and local knowledge• Sophisticated measurement • State-of-the art processing and modeling• TOPS: http://ecocast.arc.nasa.gov/ & RSE issue

I&M Project:• All I&M parks• Limited to analyses of large data sets• Processing capability is limited • No image classification• http://www1.nrintra.nps.gov/im/monitor/npscape/

wednesday show and tell

Documents

monitoring of phenology

phenology protocols

data transfer mechanism

usanpn progress

npn cooperating

parksinformation management

resource managers

plant species