Sustainable Development Goal on water (SDG 6) Mapping water quality using high resolution satellite remote sensing data

The United Nations (UN) Sustainable Development Goal 6 (SDG 6) is to ensure clean, accessible water for all. Water quality not only affects human health but it also disturb ecosystems, biodiversity, food production and economic growth. The Earth Observations (EO) data is now an important input to science-based informed decision making management tools. The IIWQ World Water Quality Portal, which was developed in the framework of UNESCO’s International Hydrological Programme (IHP) International Initiative on Water Quality (IIWQ), is a pioneering tool to monitor water quality using Earth Observation. The Portal addresses an urgent need to enhance the knowledge base and access to information to member states in implementing the SDG 6, as well as several other Goals and Targets that are linked directly to water quality and water pollution. The tool also help to understand the impacts of climate- and human-induced change on water security  (Thomas Heege, Chief Executive Officer of  EOMAP).
Sarantuyaa Zandaryaa, Programme Specialist, Division of Water Sciences at IHP, UNESCO says:
“The portal is not only an important contribution to improved global water quality information, but also promotes science- and data-based decision-making on water quality, which will lead to sustainable water resource management towards achieving the SDGs. In view of scarce water quality information – both globally and nationally – the Portal will be a valuable tool to obtain water quality information, especially in remote areas and in developing regions (such as in Africa, Asia, Latin America, and Small Island Developing States) where there is a lack of water quality monitoring networks and laboratory capacity. It is also a decision-making tool and will help countries identify the most pressing water quality problems such as pollution hotpots. Hence, the portal will support national efforts for the implementation of water quality related SDG targets as well as for monitoring progress towards their realisation,”

The online portal will also help developing countries like Pakistan to build capacity and competence in their technical and administrative infrastructures. The maps in following figures shows:

  • Chlorophyll-a CHL an essential pigment included in phytoplankton cells and therefore a measure of phytoplankton. The displayed CHL is calculated from total scattering and total organic absorption of water constituents. Unit is [µg/l].
  • Harmful Algae Blooms (HAB) indicator shows possible areas affected by harmful algae blooms formed by cyanobacteria containing phycocyanin.
  • Total Absorption (ABS) is the absorption of organic and anorganic of water components is provided as absorption unit in [1/m].
  • Turbidity measures the degree to which light is being backscattered by particles in the water.Turbidity caused by scattering of particles is provided in Formazine Turbidity Unit [FTU].

Figure: Karachi a Metropolitan city located on the coastline of Sindh province in southern Pakistan, along a natural harbour on the Arabian Sea.  (1) Turbidity and (2) Chlorophyll-a.

Figure: The Jehlum river near Mandi Bhauddin in Punjab, Pakistan.  (1) Chlorophyll-a , (2) HAB indicator (probability),  (3) Total Absorption, and (4) Turbidity.

Figure: The Mangla Dam is a multipurpose dam located on the Jhelum River in the Mirpur District of Azad Kashmir.  (1) Chlorophyll-a , (2) HAB indicator (probability),  (3) Total Absorption, and (4) Turbidity.

Figure: Gomal Zam Dam is a multi-purpose gravity dam in South Waziristan Agency of Federally Administered Tribal Areas (FATA), Pakistan.  (1) Chlorophyll-a , (2) HAB indicator (probability),  (3) Total Absorption, and (4) Turbidity.

Figure: Ghazi-Barotha Hydropower Project is a 1,450 MW run-of-the-river hydropower connected to the Indus River about 10 km west of Attock in Punjab, Pakistan.  (1) Chlorophyll-a , (2) HAB indicator (probability),  (3) Total Absorption, and (4) Turbidity.

Note: Please read the information booklet for further information on the water quality products and to learn more about the validity range of the products. Products are generated independent on any form of ground truth data, and inter-comparable over the various resolutions provided. The Chlorophyll and HAB indicator may have site-specific limitations e.g. for extremely humid, calcareous, or ferruginous waters, and can be improved with local adaptations. General restrictions are caused by clouds, optical shallow waters, or undetected artefacts from e.g. cloud shadows.

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Monitoring Billion Tree Planation with Remote Sensing Satellite data

Khyber Pakhtunkhaw (KPK) provincial government in Pakistan, govern by the Pakistan Tehreek-e-Insaf (PTI) party launched a reforestation program named “Billion tree Tsunami”, in 2015 (@btap2015). Imran Khan (@ImranKhanPTI), a cricket super star turned politician is the head of PTI party, Prime Minister of Pakistan and main driver behind this massive plantation campaign to turn degraded into forested land.  The important aspect of this project is to monitor and identify the growth of these plantation regions. The remote sensing and Geographic Information Systems (GIS) tools provides this near-real-time (NRT) information at low cost compared to field campaigns.

The well known method to identify and monitor land surface changes using satellite remote sensing data utilizes a combination of band thresholding and optical indices (such as Normalized Difference Vegetation Index – NDVI) to separate land surface features. Applying this approach to two separate images by a given period of time allows changes in the extent of the area of interest to be identified.  The atmospheric correction to the two images  separated over time, extent of land can be compared. allowing for changes to be identified. this approach will provide an excellent alternative to field level change detection methods in challenging environments across Pakistan. We tested this approach for Bannu forest region (as shown in the Figure 2).  The Figure 1 shows the land cover map of Bannu region for the year 2015.

  Figure 1: Land Cover map of Bannu forest region (credit to ESA CCI)

  Figure 2: Map of Bannu forest region (credit to Billion Tree Tsunami website)

Two Landsat 8 images are used for this study area acquired in June 01, 2013 and June 12, 2017. The Landsat 8 images are freely available from the United States Geological Survey (USGS) “EarthExplorer” (https://earthexplorer.usgs.gov/). The images were converted into surface reflectance before NDVI calculations using a standardised approach ( for detail check  http://landsat.usgs.gov/CDR_LSR.php).

 

Figure 3: NDVI map of Bannu forest region derived from Landsat 8 image acquired on June 01, 2013.

Figure 4: NDVI map of Bannu forest region derived from Landsat 8 image acquired on June 12, 2017.

Figure 5: NDVI map in KMZ format of Bannu forest region derived from Landsat 8 image acquired on June 01, 2013 shown in google earth.

Figure 6: NDVI map in KMZ format of Bannu forest region derived from Landsat 8 image acquired on June 12, 2017 shown in google earth.


Please contact me for more detail.  email: kshahidk@gmail.com  twitter: @kshahidkOttawa