assessing vegetation condition changes using all available ... · assessing vegetation condition...
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Assessing vegetation condition changes using all available Landsat data
Jim Vogelmann, Qiang Zhou, Alisa Gallant and Zhe Zhu
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Vegetation Condition Changes: What are we interested in measuring?
• Here we are concerned with changes that relate to changes in health of vegetation • Mostly not related to land cover conversion events
• Mostly “intra-state” changes
• Tend to be observable along a spectral continuum
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A few considerations:
• Vegetation condition is constantly changing • Vegetation growth
• Greening/senescence
• Declines caused by climate or insects/disease or overgrazing….
• One of the best ways to make inferences about vegetation condition and condition changes is through time series observations
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A number of ways to assess vegetation condition using multi-temporal data
• Slope of the trend lines
• Number of statistical breaks in the time series
• Timing of the statistical breaks
• Magnitude of the spectral change between breaks points
• Duration of each segment between breaks
• Phenology metrics (most appropriate for high temporal frequency data)
• Variability measures (including statistical significance of the trends)
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Conifer forest growth; NDVI is green trend; NBR is orange trend
NDVI Slope = .01/yr
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Beetle damage; green = NDVI, orange=NBR Note that there was just one break from 1985 until 2014. This is rather typical for the beetle infestation areas for the region
NDVI Slope (segment 1) = 0.00 NDVI Slope (segment 2) = -0.025/yr
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Red = CCDC Statistical Breaks in 2003
What do we get if we simply map when and where the statistical breaks occur?
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Red = CCDC Statistical Breaks in 2003, 2004, or 2005
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Red = CCDC Statistical Breaks in 2003, 2004, 2005, or 2006
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Red = CCDC Statistical Breaks in 2003, 2004, 2005, 2006, or 2007
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Red = CCDC Statistical Breaks in 2003, 2004, 2005, 2006, 2007, or 2008
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Red = CCDC Staistical Breaks in 2003, 2004, 2005, 2006, 2007, 2008, or 2009
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Red = CCDC Statistical Breaks in 2003, 2004, 2005, 2006, 2007, 2008, 2009, or 2010
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Red = CCDC Statistical Breaks in 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, or 2011
This is really easy to do if you have lots of data and a system that enables easy access and analysis!
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Washington State Image with “Flight” Path for Upcoming Animation
No trend
Legend for upcoming animation
Increasing Trend (Increasing Greenness)
Decreasing Trend (Decreasing Greenness)
Mt. Rainier
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We are using just summer observations to calculate slope
Example of NBR pixel Illustrating 30-Year Trend Pattern
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Some Key Lessons
• Our landscapes are very dynamic. Changes are related to: • Natural and anthropogenic events • Land history plays an important role
• Use of many Landsat data sets is important for providing us with a comprehensive understanding of changes taking place
• Understanding vegetation condition and changes in condition is critical to understanding the various processes taking place across our landscapes. Conversion events only tell us part of the story.
• Continuity and many multi-temporal observations are key to understanding these processes
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Landsat band 5 (SWIR): Are the CCDC-defined breaks the best way of portraying changes in rangelands?
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Observation
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Observation
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Observation
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Palmer Drought Severity Index* for southwestern South Dakota with overlain inverted short-wave infrared reflectance data overlain
*PDSI; uses temperature and precipitation data to estimate relative dryness.
The story here is really one of climate/weather impacts and broad trends occurring over multiple years. Also of note: We could not derive these patterns without lots of data.