Tag Archives: Earth Observation

Imagery Downlinked from Satellite to Ground in 6 Minutes

Some months ago, I had written about the EDRS SpaceDataHighway and real-time provision of Sentinel-1 satellite imagery through laser link and satellite relay. Now, ESA has done an experiment with Sentinel-2B to deliver imagery to the ground moments after it was capture by the satellite. The captured image strip was downlinked in just 6 minutes. This experiment brings us closer to the amazing future when we would be able to access satellite imagery as non-defence / non-strategic users in near-real-time.

See more details here:

http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Sentinel-2/First_Sentinel-2B_images_delivered_by_laser

Aperture Synthesis and Azimuth Resolution in Synthetic Aperture Radar – Lecture Notes

Teaching the fundamentals of Synthetic Aperture Radar (SAR) system design and imaging mechanism to remote sensing students / professionals is always a difficult task. Remote sensing students / professionals generally do not have an in-depth background of signal processing and radar system design, and as an instructor, I always have to think over how much I need to tell them about SAR system design, without diving into the detailed mathematics of signal processing and imaging mechanism. Normally, I go in-depth towards the imaging geometry and an understanding of the Doppler history curve, and briefly go over the signal-processing heavy concepts like pulse compression and matched filtering. A good fundamental understanding of the SAR system design, imaging geometry, and image formation is essential for remote sensing students / professionals to have a background context knowledge when they select SAR data and process / analyze it for different remote sensing applications.

For the past few years, I have been teaching a graduate course in Radar Remote Sensing and also run an annual Summer School on Earth Remote Sensing with SAR at our research group GREL. One of the core issues in understanding the aperture synthesis process is the requirement for enhancement of the azimuth / along-track resolution. It is always interesting to discuss in class how in normal imaging radar the azimuth resolution depends inversely on the antenna along-track length, while in fully-focussed SAR the azimuth resolution becomes half of the antenna along-track length. This is a significant reversal: In normal imaging radar, we need a bigger antenna in along-track dimension to get better azimuth resolution, while in SAR, the smaller the antenna in the along-track dimension, the better the azimuth resolution.

AzimuthResolutionSAR

To explain how aperture synthesis changes the azimuth resolution to half of the along-track antenna length, I have made some detailed notes for my ongoing graduate class on Radar Remote Sensing. These notes require just basic knowledge of geometry, algebra, and sum series in mathematics. I would like to share them with the wider scientific audience, please access the PDF notes here: Aperture Synthesis and Azimuth Resolution.

ApertureSynthesis

The synthetic aperture length is defined in the figure above. The azimuth resolution in fully-focussed SAR becomes half of the antenna along-track dimension.

I have taken the help of two excellent resources on SAR remote sensing in developing these notes:

For more in-depth understanding and analysis of how SAR is used for remote sensing, you can consider attending the next Summer School on Earth Remote Sensing with SAR, which I will be offering this summer. The summer school is coming up in July, 2018, and it will be open for international participants; formal dates will be announced soon. Keep watching the GREL website for updates.

Big Earth Data Documentary

Recently I ran into this wonderful documentary about how scientists are handling the huge amounts of remote sensing and earth science data being collected in the current age.

The documentary spends a lot of time talking about how remote sensing is used for oceanography and marine security monitoring, looking at concerns like monster waves, oil spills, surface ice content, ship routing through polar oceans, etc.

The EarthServer project is mentioned, which establishes “big earth data analytics, rapid ad-hoc processing and filtering on massive geodata.” Satellite images are shown to be useful also for automatic counting of houses or camps, and for disaster damage assessment. The use of GRACE satellite system for Earth gravimetry and water content measurement is mentioned.

For background information regarding the documentary, go here.

 

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).

Figure1

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.

Figure2

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).

Figure3

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.

Ocean Eddies & Slicks in SAR Imagery

In a recent post, I talked about observing an eddy in the Arabian Sea in L-band ALOS PALSAR SAR imagery. In this post, I want to talk briefly about the physical interaction between SAR signals and eddies.

Spiral eddies are often convergence zones and act as accumulators of surface slicks. These surface slicks (could be biogenic / natural oil seeps / mineral oil etc.) make a surface layer over the ocean and actually dampen the surface waves of the ocean through a phenomenon called Marangoni Damping (see this seminal paper by Alpers and Hühnerfuss).

However, sometimes it is also possible that an eddy may appear brighter in SAR imagery than the surrounding ocean, due to wave-current and shear interactions.

In my paper on ocean currents from sequential SAR imagery, I talk about this phenomenon in the introduction, and you can also find some good references therein.

For further interest, here are a few other seminal papers on the science of ocean wave damping by surface slicks:

Looking Back in Time with SAR Satellite Imagery: Tracing the Path of a Dead River

In a recent blog post, I had explained how the low-frequency SAR signal can penetrate dry soil and give us sub-surface imaging capability. Building on that, I want to highlight our recently published paper on an application of the penetration property of SAR images, through which we detected a buried paleochannel in the Cholistan desert area in Eastern Pakistan. A “palaeochannel” is a dried up old river bed or stream bed that has been either filled or buried by younger sediment. Paleochannels either change their courses due to past seismic or flooding activities or cease to exist due to various climatological factors. The Hakra paleochannel in the Cholistan desert is well-renowned in the region, especially with its connection to the old Indian Saraswati river.

studyarea

The Cholistan desert and main network of irrigation canals in Punjab, Pakistan. Figure from Islam et al. (2016).

In our paper published in the SPIE Journal of Applied Remote Sensing, we used both optical and SAR remote sensing imagery to identify and delineate the Hakra river paleochannel. The dried river channel is buried under sand and not visible from the surface in optical / IR wavelengths, but SAR signals can penetrate dry sand (see earlier blog post)! The detailed methodology is given in the paper. To summarise the methodology, we utilized a 3-band false color combination of bands 3, 5, and 7 from Landsat 8 reflectance data and merged it with pre-processed Envisat ASAR imagery through data fusion to generate one image product for analysis. Data fusion was done through the Principal Component (PC) fusion method, in which the 3-band false color composite is transformed into principal components, the first component is replaced with the SAR data, and the resulting new merged 3-band composite in the PC feature space is transformed back into regular feature space.

colors

3-band multisensor fused image generated from Principal Component image fusion of Landsat 8 reflectance data false color composite (bands 357) and Envisat ASAR calibrated sigma-nought image. The Hakra palaeochannel signature is visible as linear green segments extending toward southwestren direction from the visible portion of Hakra. Figure and more details in Islam et al. (2016).

Ideally, we would have liked to use L-band SAR data for this study, as it penetrates more into dry sand, however ALOS PALSAR L-band data was not available for this study. We settled therefore for the next best frequency, i.e. C-band, and utilised data from Envisat ASAR satellite. Sentinel-1 data is also C-band, however we needed a long-term time series to choose the best data for analysis, and Sentinel-1 being a recently launched satellite, does not provide that advantage. Furthermore, the Envisat ASAR datasets selected for this study were acquired in the hottest / driest part of the seasons, so as to capture maximum subsurface signal.

The remote sensing results were validated with in-situ geophysical surveys for groundwater, i.e. electrical resistivity and conductivity. The presence of high apparent electrical resistivity with corresponding low soil water conductivity values intersects well with the paleochannels identified from the remote sensing data. We also utilized ancillary data and historical evidences like locations of old wells and forts for validation.

wells

Point locations of old forts and water sources (at which water conductivity readings were taken) in the regions overlaid on the detected Hakra palaeochannel from the Landsat 8 and Envisat ASAR fused imagery. Figure and more details in Islam et al. (2016).

I had presented the initial results of this work during my TEDxIslamabad 2014 talk. This paper is the result of collaborative research between research groups at GREL-IST and IGIS-NUST. We also thank officials from Pakistan Council for Research in Water Resources (PCRWR) for guidance and support during this research.

See the paper here:

Islam Z., Iqbal J., Khan J., Qazi W. A. (2016). Paleochannel delineation using Landsat 8 OLI and Envisat ASAR image fusion techniques in Cholistan desert, Pakistan. J. Appl. Remote Sens. 0001;10(4):046001.  doi:10.1117/1.JRS.10.046001

Mapping 3 Decades of Global Surface Water Occurrence with Landsat

Recently, I posted an analysis of the Orbital Insight’s Global Water Reserves product, in which they use deep learning to automatically detect global surface water on a weekly to bi-weekly basis using Landsat images. In this post, I want to draw attention to work done by the European Commission’s Joint Research Centre (JRC) in which they used Google Earth Engine‘s extensive Landsat archive to derive global surface water occurrence map, along with probability and seasonality measures. They have used Landsat 5, 7, and 8 for this study.

This work by JRC is of a much more scientific nature than Orbital Insight’s global water mapping, giving the capability of study and analysis of river dynamics and morphology also. The study also reports some validation statistics.

See this amazing talk video on the study from the Google Earth Engine User Summit, Oct., 2015. The slide deck is available here.

Other research groups are also working on similar solutions; see, for example, this news report about Amy Hudson at the University of Maryland trying to use GEE in a similar manner to analyse global surface water dynamics using Landsat.