Author Archives: Hammad Gilani

About Hammad Gilani

I earn money by monitoring, mapping and estimating greenery through satellite remote sensing. I explore world by travelling, reading and chatting.

GUEST POST: Time to Map and Monitor Pakistan’s Forests at the National Scale – Transparency and Accuracy

In Pakistan, too often, forested lands are treated as “free wastelands”. Deforestation and forest degradation is occurring primarily due to institutional negligence. An eye-opening example is massive deforestation in just four months observed in National Zoo-cum Park & Botanical Garden, Bani Gala, right in the capital territory of Islamabad. (see Fig. 1).


Figure 1: A massive deforestation in four months (May-Oct, 2016) in National Zoo-cum Park & Botanical Garden, Bani Gala, Islamabad (Source of satellite images: Google Earth)

In Pakistan, many people consider real estate as the best investment, and this gives incentives for encroachers to intrude on state-owned land. Forested lands, due to their natural beauty and as a source of a double benefit, i.e., timber and land, are especially threatened by illegal land grabbers. Another example of forest degradation in Murree, Galliat region can be seen in Fig. 2, where 7.58 km2 of forest land was destroyed by  housing societies.


Figure 2: Illegal encroachments in state-owned forests from 2005 to 2011: Bahria Golf City (Above) and OGDC Housing Society (Below). (Source of satellite images: Google Earth). See more detail in this published research article.

On the bright side, in recent years Pakistan has taken gigantic steps towards tree plantation under national (Green Pakistan Programme) and provincial (Billion Tree Tsunami in Khyber Pakhtunkhwa) initiates. These initiates have been well received and recognised globally. As an example, in 2009, Pakistan received a certificate from Guinness Book of World Records in acknowledgment of planting 541,176 mangrove plants in a single day in Keti Bunder (Indus Delta), Thatta district, Sindh province (see Fig. 3).


Figure 3: Monitoring mangrove plantations: Repeat terrestrial photographs taken on May 2010 and May 2015 (left) and satellite images showing afforestation and conversion of mudflats into new mangroves (right). (Source of photographs: WWF-Pakistan; source of satellite images: Google Earth).

We should not forget that since 2011, Pakistan is part of UN-REDD (United National- Reducing Emission from Deforestation and Forest Degradation) program. Under the REDD program, developing countries receive performance-based incentives (payments) for reducing emissions of greenhouse gasses from forestlands. National Forest Monitoring System (NFMS) and Forest Reference Emission Level (FREL) / Forest Reference Level (FRL) systems are mandatory elements for REDD reporting system to get the financial benefits. Accurate and up-to-date information about the size, distribution, composition, and condition of forests and woodlands is essential for developing and monitoring policies and guidance to support their sustainable management. Although, in Pakistan, many independent researchers and organizations are conducting a number of scattered and local studies (e.g. Mapping Deforestation and Forest Degradation Patterns in Western Himalaya, Pakistan), however, a fundamental question remains:

How can we, in a systematic and transparent manner, map and monitor wall to wall Pakistan land cover and forest areas at the national scale?

Over the years, the use of satellite remote sensing data has become most popular among researchers and policy makers, for both smaller and larger scales. Consistent time series medium resolution freely available remote sensing data (e.g. Landsat, Sentinel-2 etc.) provide frequent, synoptic, and accurate measurements, monitoring, and simulation of earth surface features, especially forests. Unbiased ground information (field surveys, photographs, forest inventory, etc.) are very much necessary for the accuracy and evaluation of any product derived from satellite images. Under the REDD program, for FREL/FRL construction and reporting, Pakistan has to follow the guidance and guidelines of IPCC and the UNFCCC. For reporting to international bodies, Pakistan has to combine remote sensing and ground-based forest carbon inventory approaches for estimating, as appropriate, anthropogenic forest-related greenhouse gas emissions by sources and removals by sinks, forest carbon stocks, and forest area changes.

So, in my view, without further delay, Pakistan needs to take five steps for better forest management and policy formulations on the national scale:

  1. To operationalize satellite-based annual forest monitoring system for spatial quantification of deforestation, forest degradation, and afforestation
  2. To conduct comprehensive forest inventories for accuracy assessment, current forest stock, and greenhouse gas inventory
  3. To assess satellite-based land cover and land use changes at 5 years interval as an activity data for FRL reporting
  4. To map forest type and biomass/carbon stocks through integration of satellite and forest inventory data for spatial identification and quantification of habitats of tree species
  5. To develop a web-based visualization and dissemination tool using geospatial and socio-economic data for transparency and consistency

Conflicts of Interest: The findings reported stand as scientific study and observations of the author and do not necessarily reflect as the views of author’s organizations.

About this post: This is a guest post by Hammad Gilani. Learn more about this blog’s authors here.


GUEST POST: Forest AGB Estimation and Mapping through Hi-Res Satellite Imagery (<5m resolution) – A Global Review

There is no remote sensing method that can give direct measurement of Above Ground Biomass (AGB). In most studies of AGB utilizing remote sensing, field-measured biomass values are used to train methods in predicting AGB values, linking biophysical parameters extracted from remote sensing data. Design (sample extrapolation) or model (empirical and mechanistic) based remote sensing approaches are being commonly adopted for biomass assessment and mapping. Although coarse and medium spatial resolution data, such as MODIS or Landsat TM, provide the potential for AGB estimation at a sub-national to national to regional level, mixed pixels and data saturation are major problems in AGB estimation in sites with complex biophysical environments.

Figure 1: Geographical coverage of peer-reviewed published studies on forest AGB estimation using high-resolution satellite imagery (in the time span 2004-2015).

Figure 1: Geographical coverage of peer-reviewed published studies on forest AGB estimation using high-resolution satellite imagery (in the time span 2004-2015). The legend shows the satellite datasets used for different studies.

In terms of assessment and mapping of deforestation (area, location, type of change), forest degradation (reduction in production capacity i.e. timber volume / biomass) and proxies of forest degradation (canopy closure, canopy morphology, number of mature trees, number of preferred trees, density, species composition, wild fire, and soil surface erosion), high resolution images are capable of predicting accurate results. Apart from other tree parameters, high resolution satellite data are being used for the estimation and mapping of AGB.

For site specific or sub-national level AGB estimation, high resolution satellite data could provide better results. However, due to diversity of data sources, study locations, number of samples, statistical methods and modelling standards, it is difficult to compare studies, and there is still no agreement on best practices to estimate biomass. In research communities, high resolution satellite data is gaining increasing popularity, so this post gives a quick glance of 28 peer reviewed published studies of the last 11 years (2004 – 2015) in this field of study.

Out of 28 peer reviewed published articles, 12 have been on study sites in Asia, 8 in North America, 5 in Africa, 2 in Latin America and only 1 in Europe (Table-1). QuickBird and IKONOS satellite have mostly been used for estimation and mapping forest biomass. Even in some cases, either two different sensors are jointly used (e.g. GeoEye-1 & QuickBird and QuickBird & WorldView-1) or integrated with active and passive medium resolution optical data (e.g. Landsat, ASTER and LiDAR) or microwave remotely sensed data (e.g. SAR / InSAR). Figure-1 provides the geolocations of studied conducted in different parts of world.

Table 1: Continent-level specification of high-resolution imagery used for forest AGB estimation and mapping in the time span 2004-2015.

High resolution satellite sensor* Africa Asia Europe Latin America North America Grand Total
GeoEye-1 4 4
GeoEye-1 & QuickBird 1 1
IKONOS 2 2 1 1 6
QuickBird 1 3 1 5 10
QuickBird & WorldView-1 1 1
SPOT-5 2 1 1 4
WorldView-2 1 1 1
Grand Total 5 12 1 2 8 28

Conflicts of Interest: The findings reported stand as scientific study and observations of the author and do not necessarily reflect as the views of author’s organizations.

GUEST POST: Recent Article – Comparison of Two Forest Regimes in Punjab, Pakistan through Geospatial Techniques

In a recently published paper in Forest Ecosystems, we evaluated forest sustainability of two forests (state and community/private owned) through quantification methods, utilizing SPOT-5 remotely sensed images for the years 2005 and 2011. This study was conducted in a sub-watershed area covering 468 km2, of which 201 km2 is managed by the state and 267 km2 by community/private ownership in the Murree Galliat region of Punjab province of Pakistan.


The results show that between the years 2005 to 2011, a total of 55 km2 (24 km2 in state-owned forest and 31 km2 in community/private forest) was converted from forest to non-forest. The study concludes that state-owned forests are better than the community/private forests in terms of conservation of forests and management. The findings of this paper may help to mobilize community awareness and identify effective initiatives for improved management of community/private forest land for other regions of Pakistan.

Article web link:

Conflicts of Interest: The findings reported stand as scientific study and observations of the author and do not necessarily reflect as the views of author’s organizations.

About this post: This is a guest post by Hammad Gilani. Learn more about this blog’s authors here

GUEST POST: Challenges – Geometric Correction of Optical High Resolution Satellite Imaging

This blog post is in continuation of a previous post i.e. “A Very Brief History of Optical High Resolution Satellite Imaging”. For laymen, high resolution satellite images are fascinating as mere pictures containing earth features. But for the remote sensing analysts or experts, challenges start to come up while trying to quantify earth features through image processing algorithms. To attain accurate quantitative results from high resolution satellite images is subject to positional accuracy. If the images are not observed from exactly the same point in space, then they can have different displacements, which could cause geo-registration errors. Geometric or ortho-rectification (especially in mountain areas) of the satellite images is vital to overcome the distortions related to the sensor (e.g. jitter, view angle effects), satellite (e.g. attitude deviations from nominal), and Earth (e.g. rotation, curvature, relief).

All high resolution optical Earth Observation (EO) satellites are equipped with global navigation satellite systems (such as GPS), star sensors, and gyroscopes. Although most high resolution imaging sensors provide high resolution digital elevation models (DEMs) along with satellite images for accurate ortho-rectification, but due to cost factor of high resolution DEMs, analysts often prefer to rely on publicly freely available DEM data with the integration of Rational Polynomial Coefficient (RPC) files. The sources of distortion can be grouped into two broad categories: the Observer or the acquisition system (platform, imaging sensor and measuring instruments, such as gyroscope, stellar sensors, etc.) and the Observed (atmosphere and Earth). Factors such as sensor view angle, sun elevation and topography have a significant effect on the geometric properties of high resolution image (see table 1).

Table 1: Description of sources of error for the two categories, the Observer and the Observed, with the different sub-categories

Category Sub category Description of error sources
The Observer Platform (spaceborne or airborne) Variation of the movement
Variation in platform attitude (low to high frequencies)
Sensor Variation in sensor mechanics (scan rate, scanning velocity, etc.)
Viewing/look angles
Panoramic effect with field of view
Measuring instruments Time-variations or drift
Clock synchronicity
The Observed Atmosphere Refraction and turbulence
Earth Curvature, rotation, topographic effect
Map Geoid to ellipsoid
Ellipsoid to map


For geo-referencing or ortho-rectification of satellite images, several commercial and non-commercial algorithms are available. ERDAS Imagine, ENVI-IDL, PCI Geomatics, IDRISI, ESRI ArcMap and ArcView, Global Mapper etc. are most common and well known commercial softwares while GRASS, QGIS (Quantum GIS), PostGIS, uDig, gvSIG, etc. are open source softwares. Restore 1.0 software can perform image band operations, mathematical image calculations, bundle adjustment with self-calibration, image transformations, image enhancement, filter operations and rectification of any digital image. AutoGR Toolkit can perform automatic matching (scale, rotation and even color invariant) and geo-referencing in few seconds.. For time-saving and fast output products, batch processes through supercomputing technology are being implemented.


This example is based on GeoEye-1 (0.5 m resolution) satellites images of Dolakha District, Nepal. Two adjacent images were captured on 2nd November, 2009, with 25.4° off-nadir view angle having 40% overlay area. When both images were ortho-rectified using RPC and 20m topographic DEM, a huge displacement with no data between the images was observed (see Figure 1) with irregular shapes of tree crowns (see Figure 2). This distorted area (irregular shape) occurred in small patches and its distribution was not systematic. For example, in some places where there were less steep slopes (up to 40°), distorted parts did not occur. While processing high resolution satellite images, similarly you may find out geometric distortions.

Figure 1: GeoEye-1 image before ortho-rectification (on the left) and after ortho-rectification (on the right) through RPC files and 20m DEM.

Figure 2: Irregular shaped tree crowns due to off-nadir view angle, before (on the left) and after ortho-rectification (on the right).

Conflicts of Interest: The findings reported stand as scientific study and observations of the author and do not necessarily reflect as the views of author’s organizations.

About this post: This is a guest post by Hammad Gilani. Learn more about this blog’s authors here

GUEST POST: A Very Brief History of Optical High Resolution Satellite Imaging

The history of the optical high resolution satellite images starts from classified military satellite systems of the United States of America that captured earth’s surface from 1960 to 1972. All these images were declassified by Executive Order 12951 in 1995 and made publically available (Now freely available through the USGS EarthExplorer data platform under the category of declassified data). From 1999 onward, commercial multispectral and panchromatic datasets have been available for public. Launch of Keyhole Earthviewer in 2001, later renamed as Google Earth in 2005, opened a new avenue for the layman to visualize earth features through optical high resolution satellite images.

A comparison of declassified Corona (1974) vs. GeoEye-1 (2014) image. Image credits: EarthExplorer (Corona) and Google Earth (GeoEye-1).

In the current era, most high resolution satellite images are commercially available, and are being used as a substitute to aerial photographs. The launch of SPOT, IKONOS, QuickBird, OrbView, GeoEye, WorldView, KOMPSAT etc. offer data at fine resolutions in digital format to produce maps in much simpler, cost effective and efficient manner in terms of mathematical modeling. A number of meaningful products are being derived from high resolution datasets, e.g., extraction of high resolution Digital Elevation Models (DEMs) with 3D building models, detailed change assessments of land cover and land use, habitat suitability, biophysical parameters of trees, detailed assessments of pre and post-disaster conditions, among others.

Both aerial photographs and high resolution images are subject to weather conditions but satellites offer the advantage of repeatedly capturing same areas on a reliable basis by considering the user demand without being restricted by considering borders and logistics, as compared to aerial survey.

Pansharpening / resolution merge provides improved visualization and is also used for detecting certain features in a better manner. Pansharpening / resolution merge is a fusion process of co-georegistered panchromatic (high resolution) and multispectral (comparatively lower resolution) satellite data to produce high-resolution color multispectral image. In high resolution satellite data, the spectral resolution is being increased and more such sensors with enhanced spectral sensitivity are being planned in the future.

List of the Spaceborne Sensors with <5 m Spatial Resolution

Sensors Agency/Country Launch Date Platform altitude (km) GSD Pan/MSS (m) Pointing capability (o) Swath width at nadir (km)
IKONOS-2 GeoEye Inc./USA 1999 681 0.82/3.2 Free View 11.3
EROS A1 ImageSat Int./Cyprus (Israel) 2000 480 1.8 Free View 12.6
QuickBird DigitalGlobe/USA 2001 450 0.61/2.44 Pan and MSS alternative Free View 16.5
HRS SPOT Image/France 2002 830 5X10 Forward/left +20/-20 120
HRG SPOT Image/France 2002 830 5(2.5)x10 sideways up to ±27 60
OrbViw-3 GeoEye Inc./USA 2003 470 1/4 Free View 8
FORMOSAT 2 NSPO/China, Taiwan 2004 890 2/8 Free View 24
PAN (Cartosat-1) ISRO/India 2005 613 2.5 Forward/aft 26/5 Free view to side up to 23 27
TopSat Telescope BNSC/UK 2005 686 2.8/5.6 Free View 15/10
PRISM JAXA/Japan 2005 699 2.5 Forward/Nadir/aft -24/0/+24 Free view to side 70 35 (Triplet stereo observations
PAN(BJ-1) NRSCC (CAST)/China 2005 686 4/32 Free View 24/640
EROS B ImageSat Int./Cyprus (Israel) 2006 508 0.7/- Free View 7
Geoton-L1Resurs-DK1 Roscosmos/Russia 2006 330-585 1/3 for h = 330km Free View 30 for h = 330km
KOMPSAT-2 KARI/South Korea 2006 685 1/4 sideways up to ±30 15 km
CBERS-2B CNSA/INPE China/Brazil 2007 778 2.4/20 Free View 27/113
WorldView-1 DigitalGlobe/USA 2007 494 0.45/- Free View 17.6
THEOS GISTDA/Thailand 2008 822 2/15 Free View 22/90
AlSat-2 Algeria 2008 680 2.5 up to 30 cross track Free view 17.5
GeoEye-1 GeoEye Inc./USA 2008 681 0.41/1.65 Free View 15.2
WorldView-2 DigitalGlobe/USA 2009 770 0.45/1.8 Free View 16.4
PAN (Cartosat-2, 2A, 2B) ISRO/India Cartosat 2-2007 Cartosat 2A-2008 Cartosat   2B-2010 631 0.82/- Free View 9.6
KOMPSAT-3 KARI/South Korea 2012 685 0.7/2.8 ±45º into any direction (cross-track or along-track) 15
WorldView-3 DigitalGlobe/USA 2014 617 0.3/1.24/3.7/30 13.1

 Conflicts of Interest: The findings reported stand as scientific study and observations of the author and do not necessarily reflect as the views of author’s organizations.

 About this post: This is a guest post by Hammad Gilani. Learn more about this blog’s authors here

GUEST POST: Land and Forest Cover Mapping and Monitoring – Global Scale Products

This image is taken from a recent paper by Kim et al. (2014), accessible here:

This image is taken from a recent paper by Kim et al. (2014), accessible here:

The availability and accessibility of global land and forest cover data sets plays an important role in many global, regional and national change studies. Recent developments in earth observing satellite technology, information technology, computer hardware and software, and infrastructure development have helped in the development of better quality land cover data sets. As a result, such data sets are increasingly becoming available, the user-base is ever widening, application areas have been expanding, and the potential of many other applications are enormous.

Data Source Year Resolution Available@
Very Coarse Resolution Products Mathews Global Vegetation/Land Use 1983 1° x 1°
Olson Land Cover and Vegetation 1983 0.5° x 0.5°
Willson and Henderson–Sellers Global Land Cover 1985 1° x 1°
Coarse Resolution Products DeFries/Townshend-Global Land Cover 1995 10 km x 10 km
GLCC (IGBP DISCover) 1997 1 km x 1 km
UMD Land Cover 2000 1 km x 1 km
MODIS Land Cover 2003 1 km x 1 km
Vegetation Continuous Fields 2003 1 km x 1 km
GLC-2000 2003 1 km x 1 km
MODIS vegetation continuous fields (VCF) 2011 250 m x 250 m
GLOBCOVER 2009 300 m x 300 m
MODIS Land Cover 2008 500 m x 500 m
Medium Resolution Products JAXA global PALSAR mosaic and forest/non-forest map (2007-2010) 2013 25 m x 25 m JAXA EORC
GeoCover LCTM 2003 30 m x 30 m
China Global Land Cover 2012 30 m x 30 m
Global Forest Watch 2013 30 m x 30 m
GEO US Global Land Cover 2013 30 m x 30 m

How much is the South Asian region benefiting?

I think we can benefit at the regional and/or at the national level. In the South Asian region, as we are well aware, lack of data and information has been one of the major limitations on policy and decision makers in addressing regional environmental issues. These issues include the development of greenhouse gas (GHG) inventories, the evolution of reducing emissions from deforestation and forest degradation (REDD) mechanisms, and the assessment of land degradation, as well as optimal land use planning.

How accurate are these products?

At different platforms debates and collaborations are going on to make global products more accurate and acceptable. Global scale study can’t come up to the demands of the national level scale. But based on our interest (land and/or forest cover change), we can get hold of the above-mentioned products, and after a certain levels of personal validation, these can be used and further analysed, instead of starting from scratch.

Conflicts of Interest: The findings reported stand as scientific study and observations of the author and do not necessarily reflect as the views of author’s organizations.
About this post: This is a guest post by Hammad Gilani. Learn more about this blog’s authors here