情報地質 第27巻 第2号 096-097頁 2016年
Geoinformatics, vol.27, no.2, pp.096-097, 2016

Preliminary assessment of groundwater in crystalline basement inMozambique through integration of remotely sensed data
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Luís A. Magaia*, Katsuaki Koike* and Alaa A. Masoud**
空白*Graduate School of Engineering, Kyoto University, Katsura C1-2-215, Kyoto 615-8540, Japan.
E-mail: magaia.andre.27z@st.kyoto-u.ac.jp**Geology Department, Faculty of Science, Tanta University, 31527 Tanta, Egypt.

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Key words: Groundwater, Lineament, Fault system, NDVI, Mozambique

１．IntroductionOne of the major geological challenges in thePrecambrian basements of Mozambique is the developmentof water supply for the growing communities. Generally,
the groundwater occurrence in crystalline rocks islimited within shallow weathered zones of limited extent
typically with low yields. Their productivity is mainly
dependent on the thickness of the weathered bedrock.
Fracture zones are lines of weakness and are foci forweathering, they may induce formation of vegetation of
the same type and produce various soil types by strong
weathering processes (Wang et al., 2013), as thepresence and passage of water through fractures favors
the lateral and vertical expansion of weathering.
Based on that background, this study aims to develop
an integration technique from remotely sensed data andgeophysics for regional groundwater exploration inareas over crystalline basement in Mozambique. The study
area covers the entire districts of Angonia and Tsangano
located NE of Tete province in Mozambique. Accordingly,
the technique will contribute to accessing areas withhighly fractured and deeply weathered overburdens.

２．Methods
2.1 Remotely Sensed Data Processing
The use of remote sensing images for groundwater
investigation has the advantage of providing a spatiallydistributed measurements on a temporal basis over alarge areas and sometimes inaccessible for ground survey
or where the coverage of detailed geological maps andfield data is insufficient. From remotely sensed images,
physical features of the landscape such as lineaments
providevaluable
informationforgroundwater
investigations (Sikakwe et al., 2015; Shaban et al.,2006).
To track the vegetation changes during different
seasons, four scenes of Landsat-8 operational land
imager (OLI) data sets (rows 70 and 71/path 168 of L1T)
acquired on 27 April and 18 September 2015 were used.
The processing of the images was performed using ENVIsoftware. First, the digital numbers were converted into
radiance data, and then atmospheric and radiometriccorrections of the original images were considered using
the fast line-of-sight atmospheric analysis of
hypercubes (FLAASH), which transforms the radiance ofeach pixel into reflectance value. The parameter modelin the FLAASH module was based on a tropical atmosphereand rural aerosol conditions with 2-band (K-T) aerosol
retrieval. Next, the topographic normalization tocorrect the brightness distortions due to the irregularshape of terrain was performed using the C Correction
method (Teillet et al., 1982).
Available water borehole data, supplied by the local
Directorate of Water and Sanitation, comprising depth
and flow rate were used as ground-truth data after
digitalization and conversion of coordinate system.
2.2 Lineament Extraction
Extraction of lineaments were performed using theautomatic and non-filtering approach, Segment Tracing
Algorithm (STA) (Koike et al. 1995). The technique has
been widely used for auto-detection of lineaments
combined with the estimation of fracture planes (Koikeet al., 1998; Wang et al., 2013; Masoud and Koike, 2011a)
for geology, hydrogeology and geo-environmental studies.
Masoud and Koike (2011a) refined the method of shading
the gridded data to maximize the detection of maximum
shading intensity with a technique called adaptive-tilt
multi-direction shading (ATMDS). Connection of
extracted segments to form long lineaments is performed
by applying the B-spline. For more detailed explanationof the algorithm and estimation of fracture planes,please refer to Masoud and Koike (2011a and 2011b).
Flowchart of the overall methodology applied in thisstudy is presented in Fig. 1.

Fig. 1. Flowchart showing the overall methodology.

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3．Results and Discussion

4．Concluding remarks

The derived lineaments indicate that the study area
seats in highly faulted zone, with lineaments forming
three main predominant trends along NW, NNE and NE (Fig.
2). Modelled faults are greater in number compared tothose inferred from geological map scale 1: 250,000. The
fault system is dominated by strike-slip (mixed)faults,
over 58%, followed by reverse and last by normal faults.
These trends have been formed by different tectonic
cycles in the study area. The river regime, flowing along
NE-SW, NW-SE is also controlled by the fault system.
Although some borehole setting was done with no
preliminary geophysical studies, they indicatedproximity to faults or geological contacts. Deeper
boreholes with low flow rate are likely to occur far fromthe fault zone. Conversely, shallow boreholes locatedalong the faults indicate moderate to high flow rate.
However, it can be seen that some boreholes located along
the faults have low yield. These could be related to lackof interconnectivity of the lineaments and therefore,shallow weathered bedrock.

STA was confirmed in this study to be able to extract
more segments that are candidates to form long
lineaments. Most of the modeled faults from the STA are
not present in the current geological map of the country.
The depth and flow rate of the water boreholes is more
likely to correlate with extracted lineaments from STA.
Accordingly, health vegetation inferred from NDVI
occurs along the lineaments and fault zones. Therefore,
we conclude that, the lineaments might be controlling
the presence of groundwater in this hard environment ofcrystalline rocks. However, more research is stillrequired to infer the groundwater potential in thisareas. Areas with stable vegetation between the two
seasons and greater concentration of lineaments are
selected for future geophysical survey in situ to
confirm the potential using the time domain
electromagnetic method.

Fig. 2. (a) Shaded relief map overlaid by modeled faults,(b) fault type frequency, (c) rose diagram showing strike ofthe lineaments and (d) Schmidt net.

By integration of lineaments, vegetation covers andanalysis of physical properties we can extract potential
groundwater zones. For NDVI classification, a thresholdof 2.5 was used to charge if the pixel value represents
vegetation or other land cover, without taking into
account different types of vegetation. Althoughdifferent soil types influence the reflectance values,
this effect was not considered. Health vegetation ismore likely to occur along lineament zones (Fig. 3),
which suggests shallow presence of groundwater.

Fig. 3. Grouped lineaments overlaid by water borehole data(left)and by NDVI (right).

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