Long Term Ecological Research (LTER)
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Aerial Imagery for the HJ Andrews and Surrounding Areas
LiDAR (2008 Leaf On)
Photography (Multiple Years)
Digital Orthophotography (Multiple Years)
Projects
Remote Sensing Resources
2008 HJ Andrews LiDAR Data
The US Forest Service has contracted with Watershed Sciences to provide LiDAR data collection for:
1. H.J. Andrews Experimental Forest located within the Willamette National Forest, consisting of approximately 16,700 acres in Lane and Linn Counties, Oregon. The data were collected on August 10th and 11th, 2008. Original plans called for leaf-off, but this was not possible due to the late snow year.
First views of the new LiDAR data
Contact Theresa Valentine for access to the raw data. You will be asked to provide a short proposal of your intended use, to help coordinate efforts.
Click on the image for a larger image.
Color Bare Earth Black and White Bare Earth 1 meter DEM 1 meter DEM download image download image Light detection and Ranging (LiDAR) is a remote sensing system used to collect topographic and land cover data. These data are used for urban planning, hydrology and floodplain analysis, forestry mapping, riparian and wetland mapping and restoration design. LiDAR data points are typically processed to generate digital terrain models (DTMs), vegetation canopy surfaces, and building surfaces. Recent software developments allow direct point processing for forest inventory and fuels analysis.
Light Detection and Ranging (LiDAR) data are collected with a laser system which is mounted to an aircraft that emits laser pulses toward the ground. The laser pulses reflect off of terrestrial surfaces are then received by the sensor that records the time elapsed. Using the speed of light, aircraft position and attitude information, the system then calculates the point from which the laser was reflected. High quality systems emit up to 150,000 laser pulses per second and record at least four returns per laser pulse. This allows for the differentiation of forest canopy from the bare ground, (e.g. understory and overstory). In essence, layers of vegetation become visible and are distinct from the topography (Bare Earth model). High quality LiDAR can accurately measure the elevations of terrestrial surfaces (i.e. ground surfaces, buildings and vegetation) at 0.3-0.5m vertical resolution and at 1.0m2 horizontal resolution and are ideal for high resolution DEM and stream, riparian and forest mapping.
LiDAR is a new technology that will likely prove to be more useful than aerial photography. EFRs are ideal places to explore this new technology due to the abundance of essential, background data at EFRs. Its availability at EFRs would also result in many new research projects at the forests. It would provide consistent and high resolution DEMs for researchers at these sites and allow creation of precise topographic maps for hydrologic modeling, mapping and landscape histories. Vegetation analyses from LiDAR are starting to show utility for numerous parameters including species maps, tree height, density and distributions, LAI and biomass as well as vegetation modeling. (Several USFS PNW pub links for LiDAR vegetation analyses:
FS Pub 7347 FS Pub 24843 Documents
Technical Details of the HJ Andrews and Biscuit Fire Projects(PDF file) USGS web page about LiDAR LiDAR Projects Timeline
HJ Andrews Data: To be flown spring 2008 (leaf off), and available by late summer or fall Contact
Technical/Data: Theresa Valentine Research Questions: Sherri Johnson Photography
Hard copy aerial photographs (9x9 format) are available for the HJ Andrews for the following years:
Year of Flight Flight Line Map 1946 yes 1949 yes 1959 yes 1967 yes 1972 yes 1979 yes 1990 no 1996 no
These photos are in the process of being scanned, and will be available on-line in the near future.
There are additonal photos available within the Forest Science Data Bank (FSDB). These photos are archived and available for checkout on request.
They are not digital. FSDB Aerial Photography Inventory
Contact for aerial photographs: Theresa Valentine
Digital Orthophotography
A digital orthophoto quadrangle (DOQ) is a computer-generated image of an aerial photograph in which image displacement caused by terrain relief and camera tilts has been removed. It combines the image characteristics of a photograph with the geometric qualities of a map.
CD Library: DOQ's are currently stored on CD's in FSL 354, and are available for the following years: 1994, 2000, 2003, and 2004. There is an index available that indicates the extent of the coverage for each year. Contact On-line Resources: Statewide 2005 0.5 meter aerial imagery is available on-line for Oregon: Oregon Imagery Explorer You can stream the imagery into your application or view and/or download the imagery. Follow the instructions at the link. Projects
Digital Forest
A major effort in LTER6 will be the creation of digital forest composition/structure layers (hereafter referred to as the “Digital Forest”) for the Andrews Forest LTER. Current vegetation layers for Andrews are too spatially and taxonomically coarse to study and represent the complexity introduced by topographic-climate interactions or created by past climate-disturbance interactions. These prior interactions have resulted in the juxtaposition of species that could respond with differing behaviors to a warmer or colder climate. Variation in climate in the past has lead to disturbances that created a mixture of species at a single site that are commonly associated with either low or high elevations (Urban et al. 1993). We hypothesize that this fine grained species heterogeneity will allow the forest to quickly respond if temperatures change and optimal climates of dominant species no longer exist.
The Digital Forest will be a set of spatial models of species composition and structure. It will include tree species as well as major shrub and herb species. It will also describe the live and dead wood biomass and physical structure of forests, including tree diameters. The Digital Forest, with a grain size of one meter to about 30 m (forest structure and composition), will be created by combining remotely sensed data from LIDAR and TM imagery and other GIS layers with on the ground measurements from existing vegetation plots and additional vegetation plots that are needed to characterize undersampled portions of the Andrews Forest watershed. Digital Forest layers will be generated using multiple types of statistical models. For species and community models, we will use a multivariate imputation approach (Ohmann and Gregory 2002). For the structure models (e.g., biomass, and likely diameter distributions), we will use both regression models (Lefsky et al. 1999) and imputation approaches. Models of canopy density, which will be important for our microclimate studies, will be generated using multivariate statistical models and LIDAR.
Contact Tom Spies for more information.
LiDAR Projects
Title PI Objectives Products Agency Forest Structure and Composition Tom Spies map forest structure and composition model inputs, maps US Forest Service Zhiqiang Yang examine forest structure OSU Improve estimates of forest carbon fluxes using remote sensing products to constrain disturbance history. Jeffery Masek assess rates of vertical growth following clearing biomass accumulation rates NASA Goddard Space Flight Center Mapping Forests at Plot to Lanscape Scales Van Kane forest structure University of Washington Andrews Book Phil Sollins Describing Andrews Soils Chapter in Andrews book OSU OSU Coursework Ann Nolin Working with data from Watersheds 6 and 7 Class materials for remote sensing class OSU LiDAR Admin Theresa Valentine manage LiDAR products stream layer, shaded relief US Forest Service Solar Radiation Loads Travis Roth quantifying how riparian vegetation affects these solar radiations loadings models, solar loads OSU Remote Sensing Resources
The science of remote sensing has developed to provide spatial information about features on the Earth's surface. Since the advent of the Space Age, a wide variety of airborne and satellite sensing devices have been created to detect characteristics such as surface temperature, atmospheric composition, vegetation structure, and the presence of precious minerals. The various sensors now used by many public and private organizations are generally geared toward specific targets, and typically vary in spatial and spectral resolution to enhance important features.
In most instances, remotely sensed data are stored as matrices, with the X and Y dimensions relating to a geographical projection of the Earth's surface and the pixel value representing some characteristic of that surface, such as reflectance in the near-infrared spectrum. Multiple-band images are expressed as muti-dimensional matrices. For display purposes, however, computer monitors can only display three bands (Red, Green, and Blue), so the user must choose how to display his image, even though all bands can be used in data manipulations. In this regard, remote sensing datasets are quite similar to digital color photographs, and the software we use to process the imagery is comprable to packages such as Adobe Photoshop.
For more information on remote sensing, you may wish to register for one of the courses on campus, particularly, GEO 463/563, or review one of the standard college texts, for example:
Avery, Thomas E. 1992. Fundamentals of Remote Sensing and Airphoto Interpretation. Macmillan. Lillesand, Thomas M. and Ralph W. Kiefer. 1994. Remote Sensing and Image Interpretation. Wiley & Sons.
or one of the primary journals: many are available through OSU Library
Canadian Journal of Remote Sensing International Journal of Remote Sensing Photogrammetric Engineering and Remote Sensing Remote Sensing of Environment Local Remote Sensing Resources
Laboratory for Applications of Remote Sensing in Ecology
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