RS Applications for Ecosystems,
Biogeography, and Land-use/Landcover Change studies
Michael W. Binford, Prof and Chair
Department of Geography
University of Florida
23 March 2012: Center for Remote Sensing Seminar
Series
Study Sites - Geography
Research Questions in Geography
• Landscape-level Carbon cycling (Binford, Gholz, Barnes)
• Economic-Environmental Interactions in Thailand and
Cambodia
• Land-use/Land-cover change as a consequence of
protected areas in East and southern Africa (Binford,
Goldman, Southworth)
• Roads, Landscape Change, LULCC in the MAPI Region
(Southworth)
• Climate change and livelihoods in southern Africa;
Okavango, Kwando, Zambezi river basins (Southworth,
Child, Qiu, Waylen, Binford, Kiker, Munoz-Carpena)
• Macrosystems Biology (Regional and Continental Scale
ecosystem function): NEON
Basic Principle for RS Applications
• Each study has its own research questions
(DORQ) – always about some aspect of humanenvironment interactions.
• Each research question determines the spatial,
spectral, temporal, and radiometric scale of data
that will be useful to address the question.
• We use everything that flies: NASA, NOAA, USGS,
Private – OTHER PEOPLE’S INSTRUMENTS AND
DATA!
Organization by instruments
• Landsat MSS, TM, ETM+ (none currently
working – dates)
• ASTER
• MODIS
• Hyperion
Landsat MSS, TM, and ETM+ and
landsat-like instruments - Workhorse
• Carbon Studies
• Land-use/land-cover change studies
– E and S Africa
– SE Asia
AVHRR Bands
Troposphere CO2 increase 1958–2010
http://scrippsco2.ucsd.edu/index.php
Atmospheric CO2 content and Carbon Cycle:
Atmospheric and Sedimentary Cycles
Gaseous Cycle
Sedimentary
Cycle
http://scrippsco2.ucsd.edu/index.php
Cycling Time: minutes to
hours to years for gaseous
cycle;
hundreds of millions of years
for sedimentary cycle.
Source: Smith, R.L. 1992. Elements
of Ecology. Harper/Collins
Specific Research Questions
• How do climate variability and land ownership and management
practices influence long-term carbon budgets of managed forested
ecosystems in northern Florida between 1975 and 2000?
• How do both ownership and changes in ownership affect
regional carbon uptake and storage over time in the
southeastern U.S. coastal plain?
• Subquestion: is ownership a useful proxy for management?
• Methods:
– satellite remote sensing (RS) multi-temporal imagery
– in situ measurement of ecosystem processes
– Land-ownership data from county tax assessors
– ecosystem modeling of climate variation and land
management.
Forest Stages
Long-leaf
pine/Wiregrass
Myers, R.L., and J.J. Ewel. 1990
Ecosystems of Florida. University
Presses of Florida, Gainesville, FL.
765 pages.
Recently planted
clear cut
Natural regeneration,
mixed pine stand
Mid-rotation
pine plantation
Using eddy
covariance
methods for
estimating
NEE
Ecosystem studies (biomass,
primary production,
biogeochemistry) since 1979
Industrial
Forestry – Slash
Pine
Mid-rotation pine plantation
Vegetation Dynamics and Carbon
Sequestration in North Florida
Landsat
TM/ETM
5,4,3 = R,G,B
Composite
Alachua
County
Study Site;
all scenes
from
December,
January, or
February.
Urban
Recent Burn
Pine Forest
Recent Clearcut
Pasture - Clear
Cypress Wetland
Riparian Forest
N
5 km
Forest Age Maps
January 1998
January 1999
NDVI Difference
1998-1999
January 2000
NDVI Difference
1999-2000
Clearing
1998-1999
Clearing
1999-2000
Forest Age Maps
Net Ecosystem Carbon Exchange
(NEE) Maps
Cadastral Methods – Field Work
Extracting Cadastral Data from the Alachua County
Property Appraiser’s Office
Individual Owners and Ownership Classes
NEE – T C 30-m-pixel-1
Regional Annual Carbon Budgets 19752000 (T landscape-1) by Ownership Class
All Areas NEE and Total C Exchange by Ownertype
C Exchange (T C landscape-1)
200000
150000
TIMBER
PRIVATE
100000
MINING/OTHER
COM
GOVT
50000
NEE
Total
0
-50000
1970
1975
1980
1985
1990
Year
1995
2000
2005
Biomass (C) by “Trajectory” and Effect
of Timber Prices
Kibale NP & Landscapes
Tea estates
Less intensive farms
Intensive
cropping
Tarangire
Landscapes
Tarangire Biodiversity
Tarangire – Defensive Cultivation
Pastoralists to Agriculturists
Defensive Cultivation
Area measurement
incomplete
U.S. Earth Systems Missions: Earth
Observing System (EOS)
• Series of coordinated polar-orbiting satellites
• Monitor and understand key components of the
climate system and their interactions through longterm global observations.
• Radiation, clouds, water vapor, and precipitation
• the oceans
• greenhouse gases
• land-surface hydrology and ecosystem processes;
• glaciers, sea ice, and ice sheets
• ozone and stratospheric chemistry; and natural and
anthropogenic aerosols
http://eospso.gsfc.nasa.gov/eos_homepage/mission_profiles/index.php
EOS Platforms
• Terra - Launched December
18, 1999, began collecting
data on February 24, 2000.
• Aqua – Launched May 4,
2002, began collecting data
Image sources: NASA
Terra Satellite
Terra is the flagship of NASA's ESE (Earth Science Enterprise).
ASTER is the zoom lens of Terra!
MODIS
ASTER (TIR)
ASTER (SWIR)
ASTER (VNIR)
MISR
MOPITT
CERES
EOS Terra Instruments
• ASTER (Advanced Spaceborne Thermal Emission
and Reflection Radiometer)
• CERES (Clouds and the Earth's Radiant Energy
System)
• MISR (Multi-angle Imaging SpectroRadiometer)
• MODIS (Moderate-resolution Imaging
Spectroradiometer)
• MOPITT (Measurements of Pollution in the
Troposphere)
http://www.scielo.br/scielo.php?pid=s0001-37652009000200008&script=sci_arttext
EOS Terra: ASTER DEM
http://www.terrainmap.com/rm22.html
EOS Terra - MODIS
Black: Atmospheric Transmittance. Red:
VIIRS bands. Green: MODIS bands.
http://cimss.ssec.wisc.edu/dbs/Nor
way2006/Labs/NorwayHandout.htm
MODIS Data from LP-DAAC
The MOD13Q1 images shown are
samples of the MODIS/Terra
Vegetation Indices 16-Day L3
Global 250m SIN Grid. The NDVI
and EVI have been pseudocolored to represent the biomass
health of the western United
States using tile h08v05 from
June 25 July 10, 2000.
https://lpdaac.usgs.gov/content/view/full/6652
LP-DAAC = Land Processes Distributed Active Archive Center
MODIS Data
Products
https://lpdaac.usgs.gov/products/
modis_products_table
• Radiation Budget Variables
• Ecosystem Variables
– Vegetation Indices
– Leaf Area Index - Fraction of
Photosynthetically Active
Radiation 8-Day L4 Global 1km
• Land Cover Characteristics
Gross and Net Primary
Production
MODIS - Hypertemporal
• Part of EOS: Terra and Aqua
• Data major source of inputs to Earth-Systems
Science models
• “Products” vs. reflectance data
• Landscape dynamics studies
• Earth Systems; Macrosystems biology
Research questions
• 1 What are the spatial and temporal patterns of NPP in the
Okavango-Kwando-Zambezi catchment from 2001 through
2011? And How does the NPP over the study region change
over the study region since 2000?
• 2 How do precipitation and land cover interact to impact the
spatial and temporal NPP change?
Study region
From Gaughan & Waylen, 2012 (in press)
Data
• Collection 4 MODIS 1-km land cover (MOD12Q1) (20012009)
• Collection 5 MODIS 1-km yearly net primary production
(MOD17A3) (2000-2010)
• Collection 5 MODIS 1-km monthly net primary production
(MOD17A2) (2000-2011)
• Monthly Tropical Rainfall Measuring Mission data
(TRMM 3B43) (2000-2011)
Results
Inter-annual variability of NPP and precipitation
Temporal patterns of NPP and precipitation over the study region by land cover
from 2001 through 2011
Spatial pattern of NPP changing trends
Spatial pattern of NPP variations
during 2000-2010 based on MannKendall test. The pixel values
combine the Sen’s slope and z value.
If |Z| of the pixel is less than 1.645,
the critical Z value at the significance
level of 0.1, there is no significant
trend for that pixel. If there is a
significant trend for the pixel
(|Z|>1.645), the sign and magnitude of
the pixel value indicate the decreasing
or increasing trend.
Spatial patterns of yearly precipitation and NPP
UF
UNIVERSITY of
FLORIDA
Using normalized spectral entropy to indicate
vegetation dyanmic in the MAP region
Jing Sun & Jane Southworth
Department of Geography
University of Florida
Normalized spectral entropy (Hsn)
Ecological variables are recoded across time and space, they serve as indicators, giving
information concerning the state and changes of ecosystem.
Amplitude
Amplitude
Time domain: Autocorrelation, Cross-correlation, ARMA or ARIMA
Frequency domain: Fourier transform, wavelet transform
0
Time
(1)
∞
a
f ( x) = 0 + ∑ [ak cos(kx) + bk sin( kx)]
2 k =1
0
Frequency
(2)
f ( x) =
∞
α n e inx ,where α n = 1
∑
2π
n = −∞
π
∫π
−
f ( x) ⋅ e −inx dx
Information theory. Entropy is a measure of the uncertainty associated with a random var.
Thermodynamics. Entropy is associated with the amount of order, disorder, and/or chaos
Critical phenological dates
Vegetation index
Piecewise logistic regression (Zhang et al., 2003)
Maturity onset
Senescence onset
Greenup onset
Dormancy onset
Julian Day
Temporal variation in satellite derived VI data for a single growth or senescence cycle can
be modeled using a function of the form:
c
y (t ) =
+d
1 + e a +bt
where t is time in days, y(t) is the VI value at time t, a and b are fitting parameters, c + d is
the maximum VI value, and d is the initial background VI value
Macrosystems Biology: Forest Management and
Carbon, Water Yield, and Biodiversity at
Regional and Continental Scales
• NEON
• Research Question
• Study Areas
• RS Instruments and Data
– Landsat, ASTER; MODIS; Hyperion; Hand-held
Spectroradiometer
– Resolution characteristics of instruments
• Animation
• Different sensors, instruments, resolutions for
different research questions.
• Desires: hyperspectral
Hyperion - hyperspectral
• Part of EO-1 program
– Experimental, no archive
• Landscape Dynamics
– Woody plant encroachment
49 spectral readings, 20° FOV