第29回日本情報地質学会講演会 講演要旨集 035-036頁 2018年

How can satellite imagery be used to locate groundwater wells in semiarid
region with crystalline basement?
空白Luís A. Magaia*,** and Katsuaki Koike**

空白*Faculty of Sciences, Eduardo Mondlane University, Ave. Mozambique km 1.5, P.O. Box 257, Maputo,Mozambique.
E-mail: luisandremagaia@gmail.com**Graduate School of Engineering, Kyoto University, Katsura C1-2-225, Kyoto 615-8540, Japan.

空白

Key words: Groundwater, lineaments, clay index, vegetation index, surface backscattering coefficient

1. IntroductionIn crystalline basement areas of the arid andsemiarid regions, exploring for groundwater resources ischallenging due to limited precipitation amount and highrates of evapotranspiration combined with the complex
structurally
controlledhydrogeology.
Effective
explorations in such areas have been achieved bygeophysical surveys to locate thick zones of weatheredbasement. Such explorations require a considerable
number of data, resulting in high cost of exploration,
usually not affordable for many African countries.
In contrast, use of satellite imagery for groundwater
survey has the advantage of providing spatiallydistributed measurements on a temporal basis over large
areas, including areas inaccessible and insufficient in thecoverage of detailed hydrogeological data. Anotheradvantage is the availability of high resolution data
supplied at no cost for research purposes.
This paper introduces a method that integrates
optical and microwave sensor satellite images to support
the drilling programs for water supply in crystalline
basement rocks of arid and semiarid regions with lessdetailed hydrogeological data.

2. Study Area
The selected area is chiefly siting on theMesoproterozoic rocks of Nampula complex, mainlycomposed of leucocratic quartz-feldspar gneiss,
hornblende-bearing granodioritic gneiss, and banded
biotite gneiss and migmatite (Lächelt, 2004; Macey et al.,2006). With 2025 km2, it covers west of the Nampula andeast of the Murrupula districts, Mozambique. Like manyother areas located in the crystalline basement rocks ofthe country, problems in locating suitable groundwater
wells have been reported in this area. Poor groundwateramount and quality are generally found in the zones ofshallow weathered basement.

3．Data and Methodology
Two scenes representing the dry season: one fromSentinel-2A level 1C and another from the Advanced
Land Observing Satellite (ALOS) Phased Array typeL-band Synthetic Aperture Radar (PALSAR), and Digital
Elevation Model (DEM) data from the Shuttle Radar
Topographic Mission (SRTM) were selected for this study.

3.1 Linear Features
Fractures represent zones of rock weakness that canfacilitate the groundwater flows and enhance theweathering of the subsurface rocks. In this study,
extraction of lineaments, which are regarded as
fracture-related topographic features, was performed
using a non-filtering technique, Segment Tracing
Algorithm, combined with a technique termed
adaptive-tilt multi-direction shading (Masoud and Koike,2011) using the DEM data.
3.2 Vegetation Index
Vivid vegetation in arid and semiarid regions during
the dry season indicates richness of shallow groundwaterzones. To access such zones, a vegetation index suitable
for areas with sparse vegetation,a Modified Soil Adjusted
Vegetation Index (MSAVI: Qi et al., 1994), was appliedas:(1)2𝜌𝜌b8 + 1 − �(2𝜌𝜌b8 + 1)2 − 8(𝜌𝜌b8 − 𝜌𝜌b4 )MSAVI =2where 𝜌𝜌b4 and 𝜌𝜌b8 are the red and near infrared band
reflectances, respectively.

3.3 Clay Index
Advance of weathering of the basement rocks induces
development of secondary clay minerals such as kaolinite.
Accessing such zones is implemented with a Modified
Clay Index (MCI), in which a simple shortwave infrared(SWIR) band ratio (𝜌𝜌SWIR1 ⁄𝜌𝜌SWIR2 ) (Segal and Merin, 1989)
is normalized by a simple vegation index ( 𝜌𝜌NIR ⁄𝜌𝜌Red )(Jordan, 1969) as:𝜌𝜌SWIR1 /𝜌𝜌SWIR2 𝜌𝜌b11 ∙ 𝜌𝜌b4(2)MCI ==𝜌𝜌NIR /𝜌𝜌Red𝜌𝜌b12 ∙ 𝜌𝜌b8where 𝜌𝜌b11 and 𝜌𝜌b12 are the reflectances in the SWIR1and SWIR2, respectively.

3.4 Surface Backscattering Coefficient
Abundance of clay minerals results in generation ofsmooth Earth surface. To convert the digital number ofthe microwave amplitude ( 𝐷𝐷𝐷𝐷 ) of the ALOS PALSAR
image into surface backscattering coefficient (𝜎𝜎 0 ), anequation by Shimada et al. (2009) is applied as:
𝜎𝜎 0 = 10 ∙ log10 (𝐷𝐷𝐷𝐷)2 + 𝐾𝐾(3)where 𝐾𝐾 is the calibration factor.

4. Results and Discussion

The results of the study are summarized in maps of
Figure 1. In the maps, the lineaments are distributed
along narrows zones of high vegetation activity. Such36

activity is also denounced by large 𝜎𝜎 0 as the result of
volumetric scattering due to vegetation. These zones are
regarded to indicate shallow groundwater occurrence(Magaia et al., 2018). The mountain area in the southeast
is marked by poor clay content class and large 𝜎𝜎 0 class.
Despite showing active vegetation, its groundwateramount is probably small as the result of poor
weathering of the granites due to slopping conditions
that accelerate the runoff of precipitation. The northareas, dominated by small 𝜎𝜎 0 and rich in clay content
are regarded as areas of thick weathered regolith.

Moderate to active vegetation is also noticeable inthese areas. High amount of groundwater is likely to befound in such zones if high lineament density occurs as
similar to dense fault zones, because they promote
weathering of the subsurface rocks. Conversely, theoccurrence of dykes and sealed faults may pose barriers
to groundwater movement. Our next step will consider
the integration of subsurface structure obtained fromgeophysical explorations.

5. Concluding Remarks
This study represents preliminary results to accesshigh groundwater potential zones in semiarid regions by
an integration of optical and microwave data fromsatellite imagery. Zones along the lineaments in low land
areas were regarded to indicate shallow groundwater
occurrence. Clay-rich areas with low backscatteredcoefficient values were considered as occurrence of thickregolith in which large amount of groundwater is likely
to be stored. Further integration of groundwater wells
data and hydrogeological and geophysical data isexpected to improve the present results and pinpointwith high accuracy potential zones to locate correctly
groundwater wells in arid and semiarid regions.

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Fig. 1. Maps of the study area classified into four classes byquartiles of the following three parameter values distribution
and overlain upon the lineaments: (a) Modified Soil Adjusted
Vegetation Index (MSAVI) map in which red class is bare soil
and vegetation activity increases from orange to dark green
classes, (b) Modified Clay Index (MCI) map in which clay
richness increases from red (poorest) to dark blue (richest)classes, and (c) surface backscattering coefficient map with theincrease of surface roughness from dark brown to yellow classes.

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