SATELLITE IMAGERY AND WEB TOOLS TO SUPPORT DROUGHT ... · Web • Near real time • Daily revisit • Manageable data volumes Moderate Res (e.g. SPOT, Landsat, Sentinel) 5 to 30m

SATELLITE IMAGERY AND WEB TOOLS

TO SUPPORT DROUGHT DECISION-MAKING

Elsenburg Dr Mike Wallace

Drought Dialogue 2016

Satellite imagery for drought decision support

2 Remote Sensing & Web Tools

© Western Cape Government 2016 |

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

Remote Sensing & Web Tools 3

Farm scale Regional scale


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



Vegetation: Pigment in Plant Leaves (Chlorophyll) strongly absorbs visible light (400 to 700 nm) Cell Structure however strongly reflects Near-IR (700 – 1100 nm)

(Thanks to Robin Weeks) Remote Sensing & Web Tools 5

© Western Cape Government 2016 | (courtesy http://earthobservatory.nasa.gov)

NDVI – Also a function of plant

density and leaf area


http://earthobservatory.nasa.gov/

© Western Cape Government 2016 | Remote Sensing & Web Tools 7

Drought assessment using NDVI-based imagery and maps


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



Interpreting time-series NDVI


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)


Drought assessment using NDVI-based imagery and maps

Remote Sensing & Web Tools

The NDVI images are used together with fieldwork to support veld condition & drought assessments

Local knowledge and experience of local farming systems in crucial

10


Field assessments



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










The same principles are used in crop monitoring

20


In-season wheat monitoring using pixel values of NDVI

Measuring deviations from phenology curves


2012 Season

vs 12 year mean at

Langgewens


2003 drought season

vs 12 year mean at

Eendekuil


In-season wheat monitoring using pixel values of NDVI

Web tools for NDVI-based decision support



Web tools


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)


Registration & passwords can be tedious!


© Western Cape Government 2016 | 26


Note: NDVI vs NDVI Departure from normal



USDA MODIS Time-series: Swartland



USDA MODIS Time-series: Ruens



USDA MODIS Time-series: Central Karoo



Comparing Rooi-Karoo drought – 2003, (2015), 2016


As at 24-May to 8-June


The same period during 2003


As at 24-May to 8-June


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



A MODIS NDVI Anomaly image imported into GIS

– courtesy of the USDA



Adding overlays for context/location



Zoom in to your area of interest





Masked out everything except dryland cultivation





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



Cape Farm Mapper – spatial data viewing tool






Interrogation of other layers



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



Lesson learned

(Since commencing NDVI-based assessments circa 2008)


• 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


Documents

SATELLITE IMAGERY AND WEB TOOLS TO SUPPORT DROUGHT ... · Web • Near real time • Daily revisit • Manageable data volumes Moderate Res (e.g. SPOT, Landsat, Sentinel) 5 to 30m