Emerging and Contemporary Technologies in Remote Sensing for Ecosystem Assessment and Change Detecti

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Emerging and Contemporary Technologies in Remote
Sensing for Ecosystem Assessment and Change
Detection on Military Reservations(CS-1098)

• Randall S. Karalus (Project Coordinator)
• USACE Topographic Engineering Center
• Paul T. Tueller, Ph.D. (Principal Investigator)
• University of Nevada Reno (UNR)

In-Progress Review 26 APRIL 1999
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PERFORMERS

• Randall S. Karalus (Proj. Coordinator) USACE
  TEC
• Paul T. Tueller (PI), Ph.D. U. of
  Nevada, Reno
• R. Douglas Ramsey, Ph.D. Utah State
  U., Logan
• Thomas D. Frank, Ph.D. U. of
  Illinois, Urbana
• Scott A. Tweddale
  USACE CERL
• Robert Washington-Allen Oak Ridge
  National Lab
• Carolyn Hunsaker, Ph.D. USDA Forest
  Service
• Laura McCarthy, Ph.D. Utah State U.,
  Logan
• Thomas G. Van Niel Utah State U.,
  Logan
• Others
• UNR Adrienne Breland (Graduate Assistant)
• UIUC Tari Weicherding (Research Ecologist),
  Michelle Suarez (Undergrad. Assistant)
• USU Eli Rodemaker (Graduate Assistant), Nick
  Zimmerman (Doctoral)
• ORNL Brian Beach, Tameka Ivory
• CERL Alana Anderson, Dr. David Price, William
  Jackson
• TEC Rob Fischer, Mike Campbell, Jeffrey Ruby,
  Gery Wakefield

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PERFORMERS (continued)
Camp Williams (ANG) Directorate of the
Environment Dr. John Crane (Environmental
Director) Maj. Bob Dunton (Environmental
Officer) Mr. Doug Johnson (LCTA
Coordinator) Mr. Joel Godfrey (Fire
Ecologist/Fuels Management) Fort Bliss (Army)
Directorate of the Environment Dr. Keith
Landreth (Chief, Conservation Division) Kevin
von Finger (Senior NEPA Ecologist) Dallas Bash
(GIS Manager) Brett Russell (Senior NEPA
Ecologist) Twentynine Palms (MCAGCC -
USMC) Paul Kip Otis-Diehl Sharon
Jones Patrick Klemens Rhys Evans
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THE PROBLEM
DoD alone has military bases with over 25 million
acres of land, more than 11 million of which are
training lands under Integrated Training Area
Management (ITAM).
Todays training/testing needs lead to changes in
the ecosystem that may exceed the current
estimated annual cost of 56M in land repair and
maintenance.
Hence, DoD has a need for efficient tools,
models, and techniques to better characterize
and quantify the carrying capacity of DoD land
resources to support military training and
testing.
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PROBLEM STATEMENTS
From a management point of view, it is important
to understand how human activities compare to
natural disturbances in effect, extent, and
frequency.
...it is important to respond in a way that
promotes or detracts from desired conditions
through time and space.
Improved understanding leads to more appropriate
ecosystem or management response, and,
Knowledge gained should provide concrete
guidelines for future management options.
Adaptive Management requires that the results of
management actions be measured and used to guide
future actions.
Such perspective should assist DoD and other
federal agency land managers in compliance with
Federal laws and regulations and with efforts to
maintain sustainable conditions for military and
non-military land uses.
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Whats the Problem?
Where do we get the knowledge, the understanding,
for present and future management options? How
do we get there from here?
Improve your land cover characterization.
Acquire better data!
Understand environmental change. Know your
history!
Use new and old sensor technology. It is
extremely capable!
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PROBLEM
MODELING
DATA
RESULTS
Vegetation Mapping Guidelines (Air Photo Interp.)
Current Practice
Training Land Capacity for Mission and Multi-Use
Research
Land Management Decisions
Land/Water Restoration
Vegetation Mapping/Monitoring
Protected Habitat Analysis/Mgt.
Emerging Sensor Technology
Historical Analysis
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RESEARCH STATEMENT
DATA
HISTORY
Imagery
GIS
Temporal Imagery



Orbits, Trajectories and Thresholds

Coarse
Landscape
Fine
Local
Understanding of Temporal Scales
Understanding of Spatial Scales
Increased Knowledge about casual relationships
better understanding better models better
decisions adaptive management
Increased Resolution and Accuracy better data
better modeling better resource
characterization
MODELING
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TECHNICAL OBJECTIVES
Stratify the landscape of individual military
ranges using contemporary and emerging remote
sensing technologies. Identify the fundamental
vegetation and soil attributes of military ranges
as they relate to plant succession, carrying
capacity, habitat management, and land
resources. Identify the spatial, spectral and
temporal attributes of remote sensing systems
necessary to identify ecotones. Establish
ecosystem response and recovery in relation to
disturbance (land use) through retrospective
studies with spatially-explicit spectral-based
indices. Develop methods for scaling through
multi-resolution imagery.
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TECHNICAL APPROACH

• Ecotone and Disturbance Gradient Analysis
• Investigators
• UNR, UIUC, CERL, TEC
• Goals Spatial/Spectral
• Assess high resolution systems to identify the
  sensor attributes necessary to monitor changes in
  plant species composition along disturbance
  gradients and plant successional stages.
• Calibration of scales to allow extrapolation over
  larger geographic regions.

• Retrospective Analysis
• Investigators
• ORNL, USU, TEC
• Goals Temporal/Spectral
• Establish ecological history in relation to land
  use to describe how activities affect ecosystem
  and landscape response and recovery, i.e.,
  resilience.
• Identify the range of variation in the
  characteristics of disturbances associated with a
  landscape.
• Determine the existence of thresholds in response
  and recovery to natural and DoD activities.

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TECHNICAL APPROACH (continued)

• Ecotone and Disturbance Gradient Analysis
• Localized detailed maps will be derived from high
  resolution remote sensing systems based upon
  botanical composition and related land cover
  characteristics, and defined by carrying capacity
  models.
• Contemporary and emerging image data will be
  utilized where,
• Contemporary satellite imagery will identify and
  map landscape level communities associations, and
• Emerging imagery with progressively higher
  spatial and spectral will stratify the landscape
  into progressively more detailed, hierarchical
  levels.
• Fundamental attributes of disturbance will be
  studied as they relate to plant succession.
• Multi-scale/Multi-spectral/Multi-temporal remote
  sensing systems will be used to relate remotely
  sensed data to ecological parameters, i.e.,
  correlate imagery to measures of seral stages.

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TECHNICAL APPROACH
Ecotone and Disturbance Gradient Analysis
Imagery
Image Processing
Predictive Modeling
Coarse
Landscape
DATA

Local
Fine
Improved Understanding of Spatial Scales
Increased Image Resolution and Accuracy better
data better resource characterization
better model results
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TECHNICAL APPROACH (continued)
Ecotone and Disturbance Gradient Analysis
IMAGE PROCEESING
IMAGERY
Predictive Modeling
Veg. from TM
IRS 5-m
DATA
CAMIS 1-m
Veg. From CAMIS
Species List
Photo
Species List
Veg. from Kodak
Kodak 0.2-m
Photo
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TECHNICAL APPROACH (continued)

• Retrospective Analysis
• Dry/Wet season anniversary Landsat images, from
  1972 to the present, will provide historical
  context.
• RFMSS and other land management data will
  characterize the land disturbance.
• Spectral Indices of land degradation will relate
  traditional field measurements to their
  respective imagery counterparts.
• Spectral Indices will then be related to a sites
  ecological resilience and critical thresholds,
  thus representing the point where significant
  change in plant species composition occurred.
• Topographic/hydrologic models will relate
  topographic features (e.g., sinks, sources) to
  soil and vegetation indices.

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TECHNICAL APPROACH (continued)
Retrospective Analysis
Temporal Imagery - seasonal and yearly



Predictive Modeling

Orbits, Trajectories and Thresholds
KNOWLEDGE
Improved Understanding of Temporal Scales
Knowledge gained about casual relationships
better understanding better models better
decisions/management
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TECHNICAL APPROACH (continued)
Retrospective Analysis
Understanding the Ecosystem

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Landsat Series
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State Space Manifold
Vegetation Cover Comparisons
Yearly/Seasonal Vegetation Cover
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