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. Imran Khan, a cricket super star turned politician is the head of PTI party 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” ( The images were converted into surface reflectance before NDVI calculations using a standardised approach ( for detail check



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:  twitter: @kshahidkOttawa


Catalogue of Climate Data Sources

A catalogue of climate data sources with links. Please feel free to add new links or report for broken links.

Data Type Data Source Link
Climate data (raw) (Global Historical Climate Network: weather station records from around the world, temperature and precipitation)
USHCN US. Historical Climate Network
World Monthly Surface Station Climatology
Antarctic weather stations
European weather stations
Italian Meterological Society
Satellite feeds
Tide Gauges
World Glacier Monitoring Service (WGMS)
International Argo Project
International Comphensive Ocean – Atomospheric Dataset
Aerosol Robotic Network
Climate data (processed) Surface temperature anomalies
Satellite temperatures (MSU)
Sea surface temperatures
Stratospheric Temperature
Sea Ice
Cloud and radiation products
Sea Level
Greenhouse Gases
Snow Cover
GLIMS Glacier Database
Ocean Heat Content
Ocean CO2
GCOS Essential Climate Variables
NOAA Climate Indicators
Data Visualization & Analysis GIOVANNI
Climate Explorer
IRI/LDEO Climate Data Library
Wood fro trees
International Panel on Climate Change (IPCC)
Pacific Climate Impacts Consortium
Global Change Master Directory
Canadian Weather RADAR

Synthetic Aperture RADAR (SAR) Remote Sensing Basics and Applications

This post will provide an overview of the basics of Synthetic Aperture RADAR (SAR) and applications. The main topics discussed in the listed documents include: SAR basics, backscatter, geometry, interferometry, polarimetry, SAR data, data acquisition, available data sets/access to data, data analysis tools, future missions and SAR applications.

What is RADAR? – RAdio Detection And Ranging
What is SAR? – Synthetic Aperture Radar – Synthetic Aperture Radar (SAR) is an active remote sensing technology that uses microwave energy to illuminate the surface. The system records the elapsed time an d energy of the return pulse received by the antenna.


Synthetic Aperature Radar (SAR) Tutorials

  1. A Tutorial on Synthetic Aperture RADAR – ESA (PDF )  (PDF) (Radiometric Calibration of SAR Image)
  2. The Canada Centre for Mapping and Earth Observation (CCMEO) is considered an international leader in the development and use of synthetic aperture radar or SAR sensors.  From space, SAR can image the Earth’s surface through clouds and in total darkness.  This makes it a tremendously useful sensor for monitoring Canada’s changing landmass and coastal zones. CCMEO scientists have worked with the Canadian Space Agency in the development of both RADARSAT 1 and RADARSAT 2  satellite missions.  Their research has led to improved data quality through enhanced sensor design and post-launch calibration and validation activities.
  3. This training manual introduces and explains Interferometric Synthetic Aperture Radar (InSAR), including applications for data from the Envisat ASAR sensor and how to combine Envisat and ERS images to produce interferograms and differential interferograms.
  4. Synthetic Aperture RADARs Imaging Basics (PDF)
  5. NOAA SAR Manual (PDF)
  6. Synthetic-aperture imaging from high-Doppler-resolution measurements (PDF)
  7. A Mathematical Tutorial on Synthetic Aperture RADAR (PDF)
  8. Remote sensed ground control points with TerraSAR-X and TanDEM-X (PDF)

Synthetic Aperature Radar (SAR) Applications

  1. Infrastructure Monitoring with Spaceborne SAR Sensors (Link)
  2. Soil Moisture Measurements by SAR (PDF)
  3. Marine applications: Sea Ice (Link), Marine Winds (PDF), Oil Pollution (PDF)
  4. Land deformation (Link)
  5. Flood Mapping (PDF) (PDF)

Detecting rainfall from the bottom up: SM2RAIN

SM2RAIN is a simple algorithm for estimating rainfall from soil moisture data.

The SM2RAIN code and the soil moisture derived rainfall data sets are freely available and can be downloaded here. The description of SM2RAIN and of its performance can be found in Brocca et al. (2013) and Brocca et al., 2014. (also here at IRPI-CNR website)


Here you can find two recent TALKS (IPWG 2016SMAP 2016) showing the latest results with the applications of SM2RAIN to multiple satellite soil moisture products (ASCAT, QUIKSCAT, RAPIDSCAT, AMSR-E, AMSR2, SMOS, and SMAP). In the paper by Massari et al. (2014) it is shown that SM2RAIN-derived rainfall from in-situ soil moisture observations even improves flood modelling (see also here, Italian media). In this paper by Brocca et al. (2015) the application of SM2RAIN to synthetic and in situ observations at several sites in Europe further underlines the robustness of the method. In Ciabatta et al. (2015) the integration of SM2RAIN with state-of-the-art products has provided significant improvements for rainfall estimation over the whole Italian territory. The latest application of SM2RAIN is for irrigation assessment, see here the project and a first overview paper.
Other scientist are using SM2RAIN in Mexico, in the Tibet Pleateau, and on a global scale. Click on the image below for the current list (PDF) of SM2RAIN papers with link to full text.