Management Case study : Zambezi Delta Amit Singh Suva, 30 November - - PowerPoint PPT Presentation

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Management Case study : Zambezi Delta Amit Singh Suva, 30 November - - PowerPoint PPT Presentation

GEE as tool for Water Resource Management Case study : Zambezi Delta Amit Singh Suva, 30 November 2017 1 Presentation Outline Motivation Google Earth Engine and analysis Land surface change analysis with Aqua monitor Flood


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GEE as tool for Water Resource Management

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Case study : Zambezi Delta

Suva, 30 November 2017 Amit Singh

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Motivation Google Earth Engine and analysis Land surface change analysis with Aqua monitor Flood analysis with LandSat Water Mask Summary

Presentation Outline

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To assess the vulnerability of delta and coastal areas to CC and natural disasters To better understand the impact of upstream economic and human activity Planetry scale change analysis platform for hazard identification Contribute to achieve SDG’s - about 7 goals relate to water, wetland and delta

Motivation

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It provides planetary scale

analysis

Powered by Google's cloud

infrastructure including computing ability

Aqua Monitor Tool and

LandSat water mask tool (Deltares)

Open source tool that

analyses satellite data and visualises land and water changes around the globe

shows at a 30-meter resolution

where water is converted into land and vice versa.

Why Google Earth Engine??

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The Earth Engine Data Catalog

> 200 public datasets MODIS 250m daily Terra Bella <1m daily– weekly Weather & Climate NOAA NCEP, OMI, ... Terrain & Land Cover > 4000 new images every day > 5 million images > 5 petabytes of data Landsat & Sentinel 10-30m, 14-day ... and many more, updating daily!

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Get an image

Pick your: projection, resolution, bands, bounding-box, visualization

What can Earth Engine do?

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Get an image Apply an algorithm to an image

Use library functions or script your own

What can Earth Engine do?

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Get an image Apply an algorithm to an image Filter a collection

Time, Space & Metadata Search

What can Earth Engine do?

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Get an image Apply an algorithm to an image Filter a collection Map an algorithm over a collection

N → N

What can Earth Engine do?

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Get an image Apply an algorithm to an image Filter a collection Map an algorithm over a collection Reduce a collection

N → 1 or N → M

What can Earth Engine do?

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Get an image Apply an algorithm to an image Filter a collection Map an algorithm over a collection Reduce a collection Compute aggregate statistics What can Earth Engine do?

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Study Area

Zambezi river

 4th largest river in the

Africa

 Total basin area of

1,390,000 km2 with av. discharge of 3400 m3/s.

 Greatly regulated since

1960’s

 Distinct dry (May-Oct)

and wet (Nov – April) season. Study area

 Lower Zambezi  Marromeu of great

ecological importance – RAMSAR site

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GEE and Remote Sensing Analysis

 Sensistivity analysis indicate max error of 11 % ,

associated with selection of water index threshold

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Image collation from different LandSat missions

  • Apply Normalized

Difference Vegetation Index (NDVI)

  • The Normalized

Difference Water Index (NDWI)

GEE and Earth Engine Code editor

  • Apply HAND
  • Export image to

drive

Surface water change algorithm

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Changes in river morphology and surface 1995-2016

  • Green and blue colors

represent areas where surface water changes occurred during the last 20 years.

  • Green pixels show

where surface water has been turned into land (accretion, land reclamation, droughts).

  • Blue pixels show where

land has been changed into surface water (erosion, reservoir construction).

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River morphology and River Dynamics

Changes analysis using images

  • Fluvial process

post river regulation

  • Meandering

Sinuosity index

  • Determining the

sinuosity of the river

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Changes in River Morphology – Disconnection of Secondary Channels

  • Illustrates disconnection of

Marromeu from Zambezi river

  • Disconnection occured

around 2003.

  • Disconnected channels

covered with vegetation

  • Disconnection associated with

reduction peak flows post regulation

  • Shows distinct wet and dry

season

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LandSat Meta Data Analysis

  • 5

5 15 25 35 45 55 1982-02-18 1987-08-11 1993-01-31 1998-07-24 2004-01-14 2009-07-06 2014-12-27

% Cloud Cover

LandSat Images and Percentage Cloud Cover

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Land and water surface change

10 20 30 40 50 2000 2001 2003 2005 2006 2008 2009 2010 2013 2014 2015 Area (km2)

Annual land and water surface changes in study area subset

Land to water Water to land 20 40 60 80 100 2001 2005 2010 2016 Area (km2) Selected years

Annual land and water surface change in the Zambezi delta

Land to water Water to land

Annual changes study area subset

  • surface changes from

land to water tend to dominate except floods

  • 2001 is highest with an

approximately area 40 km2

  • Occurs mainly along

channel banks and islands

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Flood Analysis using LandSat Data

Script of change input parameters Meta data information Meta data information

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Summary

Big data at anyone’s fingertips strong implications on monitoring and resource management

capacities.

Application for monitoring coastal erosion. Monitoring trans boundary impoundments Monitoring hazards and preparation

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VINAKA VAKALEVU !!!

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