250 million people who live in the drylands of Africa and Asia face a shortage of water for their entire lives. Hundreds of millions more in less drought-prone regions of the ‘Third World’ have to cope repeatedly with reduced supplies. A rapid and effective assessment of how to alleviate the shortfall of safe water is therefore vital. In arid and semi-arid areas surface water storage is subject to a greater rate of evaporation than precipitation, so groundwater, hidden beneath the land surface, provides a better alternative. Rainwater is also lost by flowing away far more quickly than in areas with substantial vegetation. Harvesting that otherwise lost resource and diverting it to storage secure from evaporation – ideally by using it to recharge groundwater – is an equally important but less-used strategy. Securing a sustainable water supply for all peoples is the most important objective that geoscientists can address.
In practice, to assure good quality water supplies to a community in the form of productive wells, surface water harvesting schemes or planning the recharge of exploited aquifers requires skill, a great deal of work and considerable financial resources. Yet in many parts of sub-Saharan Africa and arid areas of Asia knowing where to focus effort and increase the chances of it being fruitful is one the biggest hurdles to overcome. Such reconnaissance – highlighting the most probable localities on geological and hydrological grounds, and screening out those least likely to yield water for drinking and hygiene – depends on details of the geology and topography of the terrain in which needy communities are situated. For most of the Afro-Asian dryland belt adequate geological and topographic maps are in as short supply as potable water itself. Remote sensing combined with an understanding of groundwater storage and surface-water harvesting is a powerful tool for bridging that knowledge gap, and is routinely used successfully in areas blessed with abundances of experienced geoscientists, money and engineering infrastructure. Again, most of the Afro-Asian dryland belt is poorly endowed in these respects.
Having long ago written a textbook on general remote sensing for geoscientists, now out of print (Image Interpretation in Geology (3rd edition): 2001. Nelson Thorne/Blackwell Science), I decided to re-issue revised parts of it framed in the specific context of water exploration in arid and semi-arid terrains, and to add practical case studies and exercises based on a free version of professional image processing and desktop mapping software. Some of the most geologically revealing remotely sensed image data – those from the Landsat series of satellites and the joint US-Japan ASTER system carried by Terra, one of NASA’a Earth Observing System satellites – are now easily and freely available for the whole of the Earth’s land surface. Given basic familiarity with theory and practicalities, a computer and appropriate software together with a moderately fast internet connection there is nothing to stop any geoscientist, university geology student or engineer working in the water, sanitation and hygiene (WASH) sector from becoming a proficient, self-contained practitioner in water reconnaissance. Water Exploration: Remote Sensing Approaches has that aim. Online access to the theoretical parts is free, and a DVD that combines theory, software, exemplary data and several exercises that teach the use of image processing/desktop mapping software is available at cost of reproduction and postage.
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Posted in Economic and applied geology, Environmental geology and geohazards, GIS and Remote Sensing
Tagged Distance learning, Drinking water, Drylands, Free course, GIS, Hydrogeology, Reconnaissance exploration, Remote sensing, WASH sector, Water supply
With the growing diversity of data from those parts of the electromagnetic spectrum that pass freely though Earth’s atmosphere, mainly acquired from orbit, an increasing number of attributes of the surface can be mapped remotely. The initial impetus to launch remote sensing satellites in the 1960’s and early 70’s had two strands: to monitor weather conditions and assess vegetation cover with the early metsats, such as TIROS-1, and the first Landsat platform that exploited green plants’ propensity for absorbing visible and largely reflecting near-infrared (NIR) radiation. With the incorporation in the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instruments of wavelength regions in which minerals show spectral diversity, in the reflected short-wave infrared (SWIR) and emitted thermal infrared (TIR), remote sensing became a viable and useful tool for geologists. It figures strongly in lithological mapping and also in the detection of minerals related to various kinds of alteration associated with metal mineralisation and the migration of hydrocarbon-related fluids. The more wavebands with narrower coverage of radiation wavelengths, the more likely are the subtle differences in mineral spectra able to be detected and mapped. Yet, apart from one experimental system (Hyperion aboard NASA’s EO-1 orbital platform) our home planet is not as well served by such hyperspectral systems as is Mars, blessed by two which have fuelled the on-going search for past habitable zones on the Red Planet.
The May 2014 issue of Scientific American includes an article on remote sensing that follows what to many might seem an odd direction: how to increase the chance of finding rich fossil deposits (Anemone, R.L. & Emerson, C.W. 2014. Fossil GPS. Scientific American, v. 310(5), p. 34-39). Apart from targeting a particular stratigraphic unit on a geological map, palaeontological collection has generally been a hit-or-miss affair depending on persistence and a keen eye, with quite a lot of luck. Once a productive locality turns up, such as the Cambrian Burgess shale, the dinosaur-rich Cretaceous sandstone of the Red Deer River badlands of southern Alberta in Canada and the hominin sites of Ethiopia’s Afar Depression, palaeontologists often look no further until its potential is exhausted. Robert Anemone and Charles Emerson felt, as may palaeobiologists do, that one fossil ‘hotspot’ is simply not enough, yet balked at the physical effort, time and frustration needed to find more by trekking through their area of interest, the vast Tertiary sedimentary basins of Wyoming, USA. They decided to try an easier tack: using the few known fossil localities as digital ‘training areas’ for a software interrogation of Landsat Enhanced Thematic Mapper data in the hope that fossiliferous spots might be subtly different in their optical properties from those that were barren.
Satellite image of the Wyoming Basin, USA. credit: Wikipedia)
The teeth and bones of early Eocene mammals that had drawn them to Wyoming turn up in sandstone beds of the basins. They are pretty distinctive elements of landscape, forming ridges of outcrop because of their relative resistance to erosion, yet for that very reason present a huge selection of possibilities. Being simple mineralogically they also presented a seemingly daunting uniformity. Anemone and Emerson decided on a purely statistical approach using the six visible, NIR and SWIR bands sensed by Landsat ETM, rather than a spectrally oriented strategy using more sophisticated ASTER data with 14 spectral bands. Their chosen algorithm was that based on an artificial neural network that the fossil rich sandstones would train to recognise patterns present in ETM data recorded over them. This purely empirical approach seems to have worked. Of 31 sites suggested by the algorithm 25 yielded abundant vertebrate fossils. Applied to another of Wyoming’s Tertiary basins it also ‘found’ the three most productive known mammal sites there. So, what is it about the fossil-rich sandstones that sets them apart from those that are more likely to be barren? The authors do not offer an explanation. Perhaps it has something to do with reducing conditions that would help preserve organic material better than would sandstones deposited in an oxidising environment. Iron minerals and thereby colour might be a key factor, oxidised sandstones are generally stained red to orange by Fe-3 oxides and hydroxides, whereas reduced sandstone facies may be grey because of iron in the form of sulfides