Tag Archives: Optical High Resolution Satellite Images

High-Resolution Population Density Mapping by Facebook and DigitalGlobe

Few months back, we all read the news that Facebook has utilized satellite imagery to generate an estimate of population density over different regions of the Earth. This task was accomplished by Facebook Connectivity Lab, with the goal to identifying possible connectivity options for high population density (urban areas) and low population density (rural areas). These connectivity options can range from Wi-Fi, cellular network, satellite communication, and even laser communication via drones.

Facebook Connectivity Lab found that current population density estimates from censuses are insufficient for this planning purpose, and resolved to make their own high spatial resolution population density estimates from satellite data. What they did was take their computer vision techniques developed for face recognition and photo tagging suggestions in images and applied the same algorithms to analyzing high-resolution satellite imagery (50 cm pixel size) from DigitalGlobe. DigitalGlobe’s Geospatial Big Data platform was made available to Facebook, along with their algorithms for mosaicking and atmospheric correction. The technical methodology employed by DigitalGlobe and Facebook Connectivity Lab, is detailed in this white paper by Facebook. DigitalGlobe’s high resolution satellite data from the past 5 years or so (imagery from high-resolution WorldView and GeoEye satellites), were utilized, and they only used cloud-free visible RGB bands. For cloudy imagery, third party population data was used to fill in the gaps. On this big geospatial dataset from DigitalGlobe, the Facebook team analyzed 20 countries, 21.6 million square km, and 350 TB of imagery using convolutional neural networks. Their final dataset has 5 m resolution, particularly focusing on rural and remote areas, and improves over previous countrywide population density estimates by multiple orders of magnitude.

 

 

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Urthecast Planning Large SAR + Optical Constellation

According to recent news reports, Urthecast is planning to launch a satellite constellation of optical and SAR satellites by the end of the decade. Urthecast started its space business by installing Earth observation cameras on the ISS. If this constellation comes through, it will be the first commercial private SAR + optical constellation. Exciting times ahead for remote sensing, especially for radar imaging. One wonders how the general public will feel about the SAR imagery, because right now they can pretty much understand what optical imagery is showing them, as long as they stick to true color imagery. How they will feel about the “colorless” gray-scale intensity SAR imagery remains to be seen. Of course, with the possibilities of nearly concurrent optical and SAR imagery, the applications are endless, starting with the through-cloud and day-night capability of SAR imagery to support optical imagery.

The proposed constellation is expected to comprise 16 satellites (8 optical and 8 SAR) flying in two orbital planes, with each plane consisting of four satellite pairs, equally-spaced around the orbit plane. Each pair of satellites will consist of a dual-mode, high-resolution optical satellite (both imagery and video) and a dual-frequency (X-band and L-band) SAR satellite (X-band and L-band) flying in tandem.

See Urthecast official announcement about the constellation here, and a good news article about the plan here.

P. S. Here is a non-related but interesting article about the kind of business prospects Urthecast is stepping into.

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: 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

Algorithms

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.

Example

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