R-package for Accessing NASA Open-Source APIs

The open-source NASA APIs can now be accessed through an R package; see below:

http://enelmargen.org/nasadata/

https://github.com/Eflores89/nasadata

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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.

Summary: ISNET / NARSS Workshop on SAR Remote Sensing, 27th Nov. – 1st Dec., 2016

The Inter-Islamic Network on Space Sciences & Technology (ISNET), in collaboration with National Authority for Remote Sensing & Space Sciences (NARSS), held a 5-day Workshop on “Earth Remote Sensing with Synthetic Aperture Radar (SAR)” from 27 November – 1st Dec 2016 at NARSS premises, Cairo, Egypt. This workshop was supported by the OIC Ministerial Standing Committee for Scientific and Technological Cooperation (COMSTECH) and the Islamic Development Bank (IDB).

Teaching complex numbers NARSS SAR workshop

Reviewing complex numbers, which form the basis of SAR imaging.

The initial part of the workshop comprised of seminar and research presentations on SAR remote sensing applications. This was followed by 2.5 days of extensive tutorial modules on SAR fundamentals, and hands-on training workshop sessions on different softwares and tools that are required for SAR remote sensing applications. The tutorial and workshop sessions were led by me, and I was honoured to be invited by ISNET and NARSS to conduct these sessions.

Group picture NARSS SAR workshop

Participants of the hands-on training workshop sessions.

The hands-on workshop modules were conducted with actual SAR remote sensing imagery to give experience to participants on processing and analysis of SAR data. Open-source software tools specifically made for SAR data processing, such as ESA Sentinel Applications Platform (SNAP) and ASF MapReady, were utilized for this workshop to ensure large no. of participants and to make the hands-on workshop modules accessible to all participants. The hands-on modules covered topics like identifying errors in SAR imagery (topographic, radiometric, geometric), data pre-processing, SAR sub-surface imaging and SAR-optical data fusion, interpreting SAR data over the ocean, understanding complex SAR data, and basics of interferometry.

Overall, more than 60 participants took part in the training workshop. Although I teach a graduate course on Radar Remote Sensing and also conduct a SAR Remote Sensing summer school since the last 2 years at our research group, yet this was a first experience for me to conduct a international SAR workshop. I got great feedback, and more motivation to continue forward on my SAR journey.

 

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:

A comparison of Landsat 7 ETM+, Landsat 8 OLI, and Sentinel 2A MSI over the visible and near-infrared parts of the spectrum

Scientia Plus Conscientia

How do different sensors perform across the electromagnetic spectrum? This question bears practical importance when we want to combine data acquired by different sensors. I therefore thought it would be interesting and fun to do a simulation of how different common sensors see the same feature.

We could in principle do this using subsets of images of the same region captured by different sensors, but it is actually easier to compare them using a given spectral signature, the reflectance (or emittance) of a certain material as a function of wavelength.

I therefore went to the Aster spectral library and downloaded several datasets corresponding to different spectral signatures. In the following example, we use that of common lawn grass:

Spectral signature of lawn grass. Spectral signature of lawn grass. Source: ASTER spectral library.

How do Landsat 7 ETM+, landsat 8 OLI and Sentinel 2A MSI “see” this grass? To answer this question…

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