SATELLITE IMAGERY AND WEB TOOLS TO SUPPORT DROUGHT ... · Web • Near real time • Daily revisit...
Transcript of SATELLITE IMAGERY AND WEB TOOLS TO SUPPORT DROUGHT ... · Web • Near real time • Daily revisit...
SATELLITE IMAGERY AND WEB TOOLS
TO SUPPORT DROUGHT DECISION-MAKING
Elsenburg Dr Mike Wallace
Drought Dialogue 2016
Satellite imagery for drought decision support
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Intro - Satellite Imagery Platforms & Application Scales
Low Res (e.g. MODIS,
SPOT Veg, NOAA)
250m – 1km +
• Free – often on Web
• Near real time
• Daily revisit
• Manageable data volumes
Moderate Res (e.g.
SPOT, Landsat,
Sentinel)
5 to 30m
• Cheaper or free
• Revisit 2 -16 days
High Res (e.g. GeoEye, Quickbird,
Drone)
Sub-meter (similar to aerial
photography
• Costly
• Low revisit frequency
• Can be tasked
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Farm scale Regional scale
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The concept of NDVI
• Normalized Difference Vegetation Index
• The most common of many indices used for vegetation analysis
• Relatively simple concept:
• Healthy vegetation reflect well in the NIR part of the NIR
spectrum/absorb well in the visible spectrum
• Facilitates a passive “unemotional” comparison of current conditions
with past conditions
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Vegetation: Pigment in Plant Leaves (Chlorophyll) strongly absorbs visible light (400 to 700 nm) Cell Structure however strongly reflects Near-IR (700 – 1100 nm)
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NDVI – Also a function of plant
density and leaf area
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Drought assessment using NDVI-based imagery and maps
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Some common sources of NDVI data
• NOAA
• MODIS
• Landsat
• Sentinel 2 (new)
• SPOT Image
• SPOT Vegetation – replaced by Proba-V
• Quickbird
• also various aircraft or drone-borne instruments
There are also various satellite-borne tools and methods other than vegetation
indices that can support drought decision-making:
e.g. Evapotranspiration, SAR, SMAP
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Interpreting time-series NDVI
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Strength is in comparison with previous scenarios either
Dekadal (10 day period – “snapshot”)
Cumulative (PASG) for a specified period – adds some cumulative effects
(Vegetation condition Index) VCI for a specified period
Vegetation Condition Index (VCI) The VCI is an indicator of the vigour of the vegetation cover as a function of the NDVI minimum and maximum encountered for a specific pixel and for a specific period, calculated over many years. The VCI normalizes the NDVI according to its changeability over many years and results in a consistent index for various land cover types. It is an effort to split the short-term weather-related signal from the long-term climatological signal as reflected by the vegetation. The VCI is a better indicator of water stress than the NDVI. (Johan Malherbe, Umlindi Report, ARC ISCW)
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Drought assessment using NDVI-based imagery and maps
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The NDVI images are used together with fieldwork to support veld condition & drought assessments
Local knowledge and experience of local farming systems in crucial
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Field assessments
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Some examples of drought/vegetation monitoring
The following are (were!) near real-time images
from the Proba-V satellite platform.
Because of cloud effect, the images are processed
at 10-day time slices and the maximum NDVI value
during that period is used for each pixel.
Images show the development of dry conditions
experienced during autumn in the Western Cape
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The same principles are used in crop monitoring
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In-season wheat monitoring using pixel values of NDVI
Measuring deviations from phenology curves
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2012 Season
vs 12 year mean at
Langgewens
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2003 drought season
vs 12 year mean at
Eendekuil
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In-season wheat monitoring using pixel values of NDVI
Web tools for NDVI-based decision support
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Web tools
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Many Web tools offer some form of NDVI imagery
In order to be useful they have to be:
• Up-to-date (near real time)
• At a task-appropriate resolution (many of the tools are global)
• Provide supporting information regarding the image
• Provide orientation support/landmarks
• Exportable – where further analysis is required
• Easy to use
• Ideally….free. (There are some sites that re-package and
commercialise free data)
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Registration & passwords can be tedious!
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Note: NDVI vs NDVI Departure from normal
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USDA MODIS Time-series: Swartland
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USDA MODIS Time-series: Ruens
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USDA MODIS Time-series: Central Karoo
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Comparing Rooi-Karoo drought – 2003, (2015), 2016
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As at 24-May to 8-June
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The same period during 2003
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As at 24-May to 8-June
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Orientation and context
• These Web tools are great, but difficult to use for detailed analysis
• Very few orientation landmarks
• NASA/USDA/USGS do make certain satellite data available for use in
GIS
• This allows one to add any number of overlays to provide context &
locational data and any ancillary data
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A MODIS NDVI Anomaly image imported into GIS
– courtesy of the USDA
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Adding overlays for context/location
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Zoom in to your area of interest
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Masked out everything except dryland cultivation
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Orientation and context II
• The preceding examples require GIS
• WCDoA GIS unit is working on including time-series NDVI data in Cape
Farm Mapper
• This will be become available to anyone to assist with drought
decision-making
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Cape Farm Mapper – spatial data viewing tool
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Interrogation of other layers
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Mobile Web Solutions: Access NDVI in the field?
Cape Agricultural Mobile Information System
• location-based mobile web application
• HTML5, jQuery Mobile, OpenLayers 3 API
URL: http://gis.elsenburg.com/mobile/camis
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Lesson learned
(Since commencing NDVI-based assessments circa 2008)
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• Fieldwork is essential, as is local knowledge.
• Ephemerals vs “bossies” in Karoo – NB to determine sustainable grazing.
• Signal to noise ratio in Karoo – bossie vegetation extremely sparse in places!
• Land use & systems knowledge essential to interpretation, e.g. late planting of
crops can paint the NDVI anomaly “red”.
• Water supply? Surface water stores not considered.
• Canopy effects.
• Stocking rate impacts. The playing fields are not level….!
• Timing of drought in relation to fodder reserves/fodder production season.
• Analysis of time-series NDVI is important.
• Use ancillary data where possible. Ideal to have a rainfall time-series (spatially
interpolated) dataset.
Thank you
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