gis in natural disaster management geo hazards

RS & GIS Applications in Natural Disaster Management
- Geological Disasters / Geohazards
Dr. S K. Saha
Dept. Petroleum Engineering & Earth Sciences
UPES, Dehradun

Geo- Hazards - Landslide
 Causes of Landslide
 Landslides are downward and outward movement of slope-forming material due to gravity and are
particularly important in projects related to highways, railroads, dam reservoirs and safety of human
habitations in mountainous terrains.

 Remote Sensing in Landslide Inventory
o Landslides are best studied on scale of about 1:10,000−1:25,000 that provide spatial
resolution of about 5–10 m on the ground. The high spatial resolution satellite sensor data are
now quite routinely utilized for such investigations.
o While studying remote sensing data for landslides, the most useful strategy is to identify
situations and phenomena which lead to slope instability, such as –
 (1) presence of weak and unconsolidated rock material,
 (2) bedding and joint planes dipping towards the valley,
 (3) presence of fault planes and shear zones etc.;
 (4) undercutting by streams and steepening of slopes,
 (5) seepage of water and water saturation in the rock material, and
 (6) increase in overburden by human activity such as movement of heavy machinery,
construction etc.
o As remote sensing data provide a regional view, areas likely to be affected by landslide can
be easily delineated for further detailed field investigations

o Landslides are marked by a number of photo characteristics on panchromatic images and
photographs, viz. sharp lines of break in the topography, hummocky topography on the down-slope
side, abrupt changes in tone and vegetation, and drainage anomalies such as a lack of proper drainage
on the slided debris. Figure presents an example of a landslide occurring in the Gola river (Kumaon,
Himalayas).
Fig. IRS-1D PAN image showing the occurrence of
landslide in the Gola river valley (Himalayas). Note that
the Gola river, flowing from east to west in this section,
is blocked by the landslide debris originating from the
southern slope of the valley. The various typical photo-
characteristics of landslides (sharp lines of break, abrupt
change in tone, vegetation, lack of drainage on the
debris) are well depicted

o Another related feature that needs attention in
this context landslide is the debris flow track.
Debris flows derive their source material from
landslides, and move in surges, not in a
continuous manner.
o They may remain almost just inactive and dry
for most part of the year but may carry a
sizeable amount of debris during rainy season,
sufficient to block the transportation network. It
is therefore necessary to identify and map
debris flow tracks during planning of
developmental activities, particularly for
highways, in mountainous terrains.
Fig. : A swarm of landslides and debris flow tracks near
Uttarkashi, Himalayas (IRS-1D PAN image; bar on lower left
corner = 400 m); the light-toned scars-edges in the higher-
elevated areas with fan apex-downward are the source areas, and
the thin linear light-toned features are the debris flow track

o During the last decade, InSAR techniques have
shown distinct promise for satellite based landslide
investigations. For example, time series InSAR
analysis of ALOS/PALSAR image data revealed
deformation of slopes in the range of 30–70 mm
year−1 regularly over a 3-year period, prior to the
major landslide that occurred in 2010 (Zhouqu
landslide, China) killing >1700 people (Sun et al.
2015a). Slow moving landslides, also called creeps,
and can also be monitored by InSAR techniques
(Sun et al. 2015b).
Geo- Hazards - Landslide Remote Sensing in Landslide Inventory
Fig.: Slope deformation rates as determined from
InSAR at Suoertou slope. (a) The average LOS
displacement rates of selected SDFP pixels
superimposed on the Google Earth map for
perspective view; (b) SAR multi-image reflectivity map
of the area showing the distribution and mean
velocities of identified SDFP pixels; (c, d) the
displacement time series for two representative pixels
C and D, respectively.

 RS & GIS in Landslide Hazard Zonation
o Landslides cause widespread damage the world over, every year. Mitigation of disasters
caused by landslides is possible only when knowledge about the expected frequency of
mass movements in the area is available.
o Landslide hazard zonation is a process of ranking different parts of an area according to the
degrees of actual or potential hazard from landslides. The evaluation of landslide hazard is a
complex task as the occurrence of a landslide is dependent on many factors.
o With the advent of remote sensing and GIS technology, it has become possible to efficiently
collect, manipulate and integrate a variety of spatial data, such as lithological map, structural
data (lineaments, faults etc.), land use land cover, surface conditions, and slope
characteristics of an area, which can be used for landslide hazard zonation.
o Several statistical data processing techniques such as ANN, fuzzy, combined neural-fuzzy
(Kanungo et al. 2006) and analytical hierarchy process (Kumar and Anbalagan 2016) have
also been applied for remote sensing—GIS based landslide hazard zonation.

 RS & GIS in Landslide Hazard Zonation (LHZ)
o Case example RS & GIS-based data integration methodology for LHZ in part of the Bhagirathi Valley,
Himalayas
 It utilized different types of data, including topographic maps, DEM, lithological and structural maps,
remote sensing multispectral and PAN sensor data, and field observations. Processing of multi-geodata
sets was carried out in a raster GIS environment to generate the following data layers:
• buffer map of thrust faults
• buffer map of photo lineament
• lithology map
• land-use/land-cover map
• buffer map of drainage
• slope angle map
• relative relief map
• landslide distribution map (training area).
 As landslides are caused by a collective interaction of the above factors, relative importance of these
factors was estimated. A simple approach that involved putting all data on ordinal scale and then
implementing weighting–rating system for integration was adopted (Figure). The Landslide Hazard Index
(LHI) frequency was used to delineate various landslide hazard zones, namely, very low, low, moderate,
high and very high, which were validated from field data

Fig.: Scheme of data integration in
GIS for landslide hazard zonation
 RS & GIS in Landslide
Hazard Zonation (LHZ)

o Case Example: Integrated Use of RS & GIS in Landslide Hazard Zonation in Uttarakhand and HP
 RS & GIS in Landslide Hazard Zonation (LHZ) Geo- Hazards - Landslide

Geo- Hazards – Earthquake / Seismic Hazard
 Introduction
o Earthquakes cause great misery and extensive damage every year. The technology of
earthquake prediction, to enable the sounding of warning alarms beforehand to save people
and resources, is still in its infancy.
o However, the earthquake risk is not the same all over the globe, and therefore seismic risk
analysis is carried out in order to design structures (such as atomic power plants, dams,
bridges, buildings etc.) in a cost-effective manner.
o Seismic risk analysis deals with estimating the likelihood of seismic hazard and damage in a
particular region. It is based mainly on two types of input data: (1) neotectonism, i.e. spatial
and temporal distribution of historical earthquakes, and observation of movements along
faults, and (2) local ground conditions, because the degree of damage is linked to the local
ground and foundation conditions.
o Remote sensing can provide valuable inputs to both these aspects. Further, high-resolution
remote sensing is becoming a powerful tool in damage assessment.

 Neotectonism
o Earthquakes are caused by rupturing and movement accompanied by release of
accumulated strain in parts of the Earth’s crust. Most earthquakes are caused by
reactivation of existing faults, as they provide the easiest channels of release of strain—the
natural lines of least resistance.
o Remote sensing can help in locating such active and neotectonic fault zones, and this
information could be well utilized by earthquake engineers while designing structures.
Neotectonic or active faults are considered to be those along which movements have
occurred in the Holocene (past 11,000 years).
o Seismologists distinguish between neotectonic and active faults, calling neotectonic those
which have been active in geologically Recent times, and active those which exhibit present-
day activity. However, no such distinction exist between neotectonic and active faults.
o Evidence for neotectonic movements may comprise one or more of the following: (1)
structural disruption and displacement in rock units of age less than 11,000 years, (2)
indirect evidence based on geomorphological, stratigraphic or pedological criteria, and (3)
historical record of earthquakes.

 Neotectonism
1. Structural disruption and displacement in the rock units of Holocene age.
o This forms a direct indication of neotectonic activity. Commonly, high spatial resolution remote sensing
data coupled with ground data are useful in locating such displacement zones, e.g. in Holocene terraces,
alluvium etc. A prerequisite in this case is knowledge of the age of the materials in which the
displacement is mapped. For example, in the Cottonball Basin, Death Valley, California, Berlin et al.
(1980), using 3-cm-wavelength radar images, deciphered two neotectonic faults in the evaporite deposits
that are less than 2000 years old. The delineation was made possible as the disturbed zone is
represented by a somewhat more irregular surface than is found in immediately adjacent areas.
o Figure - A is an image of the Aravalli hills, Rajasthan. The rocks are strongly deformed Precambrian
metamorphic and possess a general strike of NNE–SSW. The Landsat image shows the presence of an
extensive lineament (L-L in Fig. A) in the Recent sediments, on the western flank of the Aravalli hill range.
It is marked by numerous headless valleys, off-setting of streams and abrupt changes in gradients of
streams (alignment of knick points), indicating a Recent fault. The aerial photographic interpretation of
Sen and Sen (1983) is in conformity with the above Landsat image interpretation.
o This fault extends for about 300 km in strike length, parallel to the Aravalli range, and can be called the
western Aravalli fault. It is inferred that the fault has a strike-slip displacement with a left-lateral sense of
movement, and a vertical component of movement with the eastern block relatively upthrown.

 Neotectonism
Fig. A The Landsat image shows a prominent lineament L − L extending for >100 km along strike. The lineament is
marked by morphological features such as headless valleys, off-set streams and alignment of knick points, indicating it
to be a neotectonic fault. It is inferred to have a left-lateral strike-slip component and a vertical component of
movement with the eastern block upthrown [Landsat MSS2 (red-band) image of part of the Aravalli hill ranges, India; N
= Nimaj; D = Deogarh]

Figure shows the Kunlun fault, which binds Tibet on
the north. This is a gigantic strike-slip fault running
for a strike length of about 1500 km. The geological
data brought in contact with alluvial fans and
displacement of young streams.
 Neotectonism
Fig.: The Kunlun fault, one of the gigantic strike-slip faults
that bound Tibet on the north. In the image, two splays of the
fault, both running E–W, are distinctly shown; the northern
fault (A-A) brings sedimentary rocks of the mountains
against alluvial fans on the south; the southern fault (B-B)
cuts through the alluvium; off-sets of young streams with
left-lateral displacement is observed (courtesy of
NASA/GSFC/MITI/ERSDAC/JAROS, and US/Japan ASTER
Science Team)

2. Indirect evidence based on geomorphological features.
o Mapping of present-day morphological features can
provide important, though indirect, clues for
delineating neotectonism. Characteristic patterns such
as bending and off-setting of streams ridges, sag-
ponds, springs, scarps, hanging and headless valleys,
river capture etc., and their alignments in certain
directions indicate Recent movements.
o These features may be relatively difficult to decipher in
the field, and more readily observed on remote
sensing images, due to their advantage of plan-like
synoptic overview.
o RS data plays an important role in delineating active
fault signatures (Figure) specially high spatial
resolution data plays significant role.
o DEM generated from stereo pair of spaceborne SAR
sensors / aerial photographs facilitates deriving
geomorphic indices indicative of neotechtonic
signatures.
 Neotectonism
Fig.: Geomorphic indicators of neotechnic signatures

o The Insubric–Tonale Line (Fig. a) provides an example. It is a major tectonic feature in the Alps that
runs for a distance of more than 100 km in nearly straight E–W direction, disregarding all geological–
structural boundaries. On the Landsat image, the Insubric–Tonale Line appears as a well-defined zone,
marked by drag effects, indicating a right-lateral sense of displacement. Based on field data, Gansser
(1968) and Laubscher (1971) also inferred displacement of similar type along this zone. Figure b shows
the Insubric-Tonale Line together with other neotectonic lineaments deciphered on the basis of
Landsat image interpretation; some of these lineaments possess left-lateral and some right-lateral
displacement. However, the sense of movement along these lineaments as interpreted from the
Landsat data, is in conformity with the orientation of the present-day stress field as deduced from in-
situ stress measurements and fault-plane solution studies in the Central Europe
 Neotectonism
Fig. a The Insubric–Tonale Line, Eastern Alps;
the present-day geomorphological features
on either side of the geotectonic boundary are
aligned with drag effects indicating a right-
lateral sense of displacement.
b Neotectonic lineaments in a section of the
eastern Alps interpreted from Landsat
images; these lineament features, with their
sense of movement are in conformity with the
orientation of the present-day stress field
(shown as P1) deduced from fault-plane
solutions and in-situ stress measurements

o Aseismic creep exhibited along the Hayward fault,
California, is another interesting example of neotectonic
movement. Figure is an interferogram generated from the
pair of C-band ERS-SAR data sets acquired in June 1992
and September 1997.
o A gradual displacement of 2−3 cm, with a right-lateral
sense of movement, occurred during the 63-month interval
between the acquisition of the two SAR images. The fault
movement is aseismic because the movement occurred
without being accompanied by earthquake.
 Neotectonism
Fig.: Aseismic creep along the Hayward fault, California. Based
on SAR interferogram generated from images acquired in June
1992 and September 1997, aseismic creep of 2–3 cm with right-
lateral sense of movement has been inferred

3. Historical record of earthquakes
The data record on past (historical) earthquakes is another type of evidence of seismicity. It can be
carefully interpreted in conjunction with data on the structural–tectonic setting in order to derive useful
information (Allen 1975). The neotectonic potential of lineaments can be assessed by co-relating
historical earthquake data with lineaments. Figure a shows the distribution of earthquakes (magnitude >
6.0) in the region of the San Andreas fault, California and Fig. b shows the SRTM-derived perspective view
of the San Andreas fault.
 Neotectonism
Fig. a Relationship of
earthquake (magnitude > 6.0,
1912– 1974) and Quaternary
faulting, southern California .
b Perspective view of the San
Andreas fault generated from
the SRTM (February 2000).
The view looks south-east;
the fault is the distinct linear
feature to the right of the
mountains

o Micro-earthquake (MEQ) data can also be utilized in a similar manner for understanding the neotectonic
potential of lineaments. Figure a is a Landsat MSS image showing the presence of an important
lineament in Shillong plateau (India). Micro-earthquake epicenters appear to preferentially cluster along
the lineament, which points towards the neotectonic activity along this lineament (Fig. b).
 Neotectonism
3. Historical record of earthquakes
Fig. a Landsat MSS image (infrared) of a
part of Shillong plateau (India). Note the
prominent lineament running between
Dalgoma (DA) and Durgapur (DU), for a
distance of more than 60 km. Cross
marks indicate the rocks of carbonatite-
type reported in the region; in the north
is the Brahmaputra river. b Micro-
seismicity map showing alignment of
MEQs along the lineament (mapped as
Dudhnai fault)

o Damage resulting from an earthquake varies spatially. Close to the epicentre, the point
directly above the initiation of rupture, disaster is far more severe, and farther away, it
generally decreases due to reduced intensity of vibration.
o Post-earthquake surveys rely on field observations of damage to different types of
buildings and structures. Within the same zone of vibration or shock intensity, the damage
may vary locally, being a function of both the type of structure and ground conditions.
o Some of the ground materials forming foundations are more susceptible to damage than
others. Remote sensing can aid in delineating different types of foundation materials, such
as soil types etc., which may have a different proneness to earthquake damage.
o Liquefaction is a peculiar problem in soils and occurs due to vibrations in saturated, loose
alluvial material. It is more severe in fine sands and silts than in other materials.
 Liquefaction

o Case Example - Liquefaction during the north Bihar earthquake (1934).
 In the north Bihar (India) earthquake of 1934, extensive damage occurred in the northern plains of Bihar
(Fig. a). Based on the initial analysis, it was postulated that the epicentre was located near Madhubani (in
Bihar), where the intensity of disaster was most severe. However, subsequent detailed seismological
analysis has shown that the 1934 earthquake epicenter was located in Nepal, about >100 km away from
the main damage zone. The widespread and severe damage in Bihar was a result of liquefaction of soil in
the alluvial plains, and the striking feature is that the slump belt — the zone of liquefaction—is located far
from the epicentral estimates.
Geo- Hazards – Earthquake / Seismic Hazard Liquefaction
Fig. a Disaster map of the north Bihar
earthquake, 1934; isoseismals on Mercalli
scale are redrawn after GSI (1939); much
damage occurred due to soil liquefaction in
the slump belt; epicentral estimates of the
earthquake after GSI (R) and Seeber et al.
(1981) (SA) are indicated; note that the
slump belt is located quite a distance from
the recent estimates of the earthquake
epicentre. b Landsat TM image of part of the
above area. The dark zone on the image is a
wet clayey zone, north of which lie fine
sands, a lithology more susceptible to
liquefaction; note that the boundary
passing north of Darbhanga (D), seen on
the image, matches closely with the
southern limit of the slump belt in (a)

o Figure a shows the zone of soil liquefaction mapped soon after the earthquake of 1934 (GSI 1939). Figure
b is the Landsat TM image (25 May 1986) of part of the area. On the image, a gradational boundary can
be marked separating alluvial (fine) sands on the north from a wet clayey zone (dark tone, abundant
backswamps etc.) on the south, and this boundary has a close correspondence with the limit of the
liquefaction zone of the 1934 earthquake. The above is in conformity with the ideas that fine sands are
susceptible to soil liquefaction during vibrations, whereas clayey zones are not.
 Liquefaction
Fig. a Disaster map of the north Bihar
earthquake, 1934; isoseismals on Mercalli
scale are redrawn after GSI (1939); much
damage occurred due to soil liquefaction in
the slump belt; epicentral estimates of the
earthquake after GSI (R) and Seeber et al.
(1981) (SA) are indicated; note that the
slump belt is located quite a distance from
the recent estimates of the earthquake
epicentre. b Landsat TM image of part of the
above area. The dark zone on the image is a
wet clayey zone, north of which lie fine
sands, a lithology more susceptible to
liquefaction; note that the boundary
passing north of Darbhanga (D), seen on
the image, matches closely with the
southern limit of the slump belt in (a)

o Case Example - Liquefaction during the Bhuj (Kutch) earthquake (2001).
 A severe earthquake struck western parts of India on 26 January 2001. It caused extensive damage in the
area around Bhuj (Kutch), where the epicentre was located. The earthquake was also accompanied by
substantial discharge of water from subsurface to surface, due to soil liquefaction. Figure obtained from
IRS-WiFS sensor gives a time series of the phenomenon. Figurea is a pre-earthquake image; Fig.b–d
were acquired sequentially after the earthquake, and show the emergence of some water on the surface
and its gradual drying up (Mohanty et al. 2001).
Geo- Hazards – Earthquake / Seismic Hazard Liquefaction
Fig. Soil liquefaction during the Bhuj earthquake (26 January 2001); the images are from IRS-WiFS, NIR-band. a Image
of 23 January 2001, before the earthquake. b Image of 26 January 2001, about 100 min after the earthquake, shows
some water surges on the surface. c Image of 29 January 2001 shows substantial spread of water (arrows). d Image of
4 February 2001, showing that most of the water channels have dried up (a–d)

 Earthquake and Satellite Derived Thermal Anomalies

o Disaster following an earthquake gets spread across a region. For rescue, relief, and reconstruction
purposes, the management authorities require information about the area, amount, and type of damage
particularly to habitats and buildings. Remote sensing techniques play an important role in this respect
because of their fast response, non-contact, low cost and synoptic view capabilities.
 Earthquake Damage Assessment
o The data used in both cases has included
optical, LiDAR and SAR images. Whereas
optical data has the advantage of easy
interpretability, SAR images have advantage of
all-time all-weather capability. It is generally
considered that a spatial resolution of about 1–
0.5 m is adequate for damage assessment
purposes. Figure shows the damage occurring
during the Bhuj earthquake (26 Jan 2001).
o Remote sensing application to earthquake
induced damage assessment to buildings is
reviewed by Dong and Shan (2013). There are
two basic approaches: (a) those that utilize
multi-temporal strategy, i.e. evaluation of
changes between pre- and post-event images,
and (b) those that interpret post-event data
(mono-temporal strategy) only.
Fig. Damage during the Bhuj earthquake (26 January 2001);
the IKONOS Pan (1-m resolution image acquired on 2 Feb
2001 shows extensive damage to individual buildings
caused by the earthquake; some buildings have collapsed
and some appear to have altered rooflines

o Figure shows an example of pre- and post-earthquake images of the Gorkha earthquake that hit Nepal
on 25th April 2015.
Fig. a, b Damage assessment during the Gorkha earthquake (25 April 2015), Nepal; the figure
shows pre-earthquake (25 Oct 2014) and post-earthquake (27 April 2015) images of the central part
of Kathmandu; note the conspicuous damage to the Tower and the adjoining areas

Earthquake Damage Assessment

Monitoring Land Displacement
due to Earthquake by SAR
Interferometry
L-band ALOS-2 PALSAR DInSAR based deformation map of part of Nepal during 24 November, 2014 – 27 April,
2015 highlighting high relative deformation along Tamakosi, Rolwaling & DudhKosi river valleys and along the
weak structural planes Lambagar town and Gaurishankar peak.
(GS & GHD, IIRS)

Before Earthquake After Earthquake
Amplitude Image Interfermetric
Coherence Image
Raw Interferogram Interferogram
(Topographic Effect
Corrected)
Land Displacement Map due to Earthquake
(Prepared through DInSAR Analysis of
Envisat Data December 2003 & January 2004
Monitoring Land Displacement due to Eathquake by SAR Interferometry
(GS&GHD, IIRS)
Date of Earthquake: 26th December 2003; Magnitude: MW=6.5; Depth: 4-5
km; Epicenter: 28.99N, 58.29E (near Bam city, Iran); Rupture: 20-km long
strike slip fault; Effect: 85% of the buildings were damaged /destroyed;
Death: 43,000. Date of Earthquake: 26th December 2003

Geo- Hazards – Volcano Mapping and Monitoring
 Introduction
o Areas of volcanic and geothermal energy are characterized by higher ground temperatures, which can
be detected on thermal-IR bands from aerial and space-borne sensors. In usual practice, the thermal-IR
data are collected at pre-dawn hours in order to eliminate the direct effect of heating due to solar
illumination, and minimize that of topography.
o However, daytime thermal-IR data can be well utilized for observing volcanic and geothermal energy
areas (Watson 1975). The effect of solar heating can be considered to be uniform across a region of flat
topography. In the forenoon (09.00−10.00 h) and late afternoon (16.00 h), when thermal crossing
pertaining to solar heating occurs, differential effect due to solar heating or ground physical properties
is minimal ; these hours become suitable for picking up geothermal anomalies.
o Therefore, thermal-IR remote sensing surveys can be carried out at 09.30 and 16.00 h to map volcanic
and geothermal energy areas.
o Volcanic eruptions are natural hazards that destroy human property and lives and also affect the
Earth’s environment by emitting large quantities of carbon dioxide and sulfur dioxide into the
atmosphere (Figure – see next slide). Monitoring of volcanoes is important in order to understand their
activity and behaviour and also possibly predict eruptions and related hazards. Satellite remote
sensing offers a means of regularly monitoring the world’s volcanoes, generating data on even
inaccessible or dangerous areas.

 Introduction
Fig.:Chaitén volcano, Chile, in
eruption during May 2008,
releasing plumes of steam and
volcanic ash (Black-and-white
printed from ASTER colour image;
courtesy NASA/METI/AIST/Japan
Space Systems, and U.S./Japan
ASTER Science Team)
o In the Central Andes, for example, using Landsat TM multispectral
data, Francis and De Silva (1989) mapped a number of features
characteristic of active volcanoes, such as the well-preserved
summit crater, lava flow texture and morphology, flank lava flows
with low albedo, and higher radiant temperatures (from SWIR
bands). This led them to identify presence of more than 60 major
potentially active volcanoes in the region, whereas only 16 had
previously been catalogued.
o A convenient criterion for regarding a volcano as ‘active’ or
‘potentially active’ is that it should exhibit evidence of having
erupted during the last 10,000 years. In the absence of isotope
data, morphological criteria have to be used. A volcano may be
taken as potentially active if it possesses such features as an on-
summit crater with pristine morphology or flank lava with pristine
morphology. Surface expression of hot magmatic features
associated with volcanism, particularly at the pre-eruption stage,
is usually of relatively small spatial extent. This implies that the
use of thermal-IR imagery with a high spatial resolution would be
most appropriate to monitor volcanic activity.

 Lava Flow Mapping
The Lascar volcano, Chile, has been one of the most active volcanoes recently, and has been a site of
many investigations. The various products of eruption, viz. volcanic lava, ash, pumice, plume etc. can be
mapped on remote sensing data. Figure shows the extent of pyroclastic flows during the April 1993
eruption at Lascar. Figure a corresponds to pre-eruption where older lava flows can be seen; Fig.b,
acquired one day after the eruption shows the new pyroclastic flows emplaced on the N, NW and SE
flanks of the crater.
Fig. JERS-1-OPS sensor
composite of channels 831
(RGB) of the Luscar volcano,
Chile (images approx. 10
km2). a Pre-eruption; b 1-day
after the eruption, showing
the new pyroclastic flows

o Figure shows a Landsat-8 thermal-IR image of the Holuhraun flow,
Iceland. The image was acquired during a night-time overpass (24
Oct 2014). The lava field is more than 85 km2 in area and 1.4 km3
in volume, one of the largest in Iceland.
 Lava Flow Mapping
Fig. Night-time (local-time: 22.07) image of Holuhraun lava field,
Iceland, acquired by Landsat-8 on 24 Oct 2014; the volcanic
eruption started on 29 Aug. 2014 and ended on 27 Feb. 2015.
o If a hot volcanic lava flow is sensed by a
multispectral sensor with the same pixel size
in different bands (e.g. ASTER having 6
bands in SWIR, all with 30 m spatial
resolution), then the event appears
possessing a wider area on the longer
wavelength image than on the shorter
wavelength image (Fig. a, b; Blackett 2017).
o This is due to the fact that longer wavelength
band detects radiance from relatively cooler
surfaces e.g. on the periphery of the lava
flow, whereas the shorter wavelength image
detects emissions only from the relatively
hot surface in the central zone of the lava
flow
Fig.ASTER-SWIR band images of the lava flow at Mount Etna,
Italy (29 July 2007); a in ASTER band 4; b in ASTER band 9; the
wider area in (b) demonstrates the detection of radiance from
relatively cooler surfaces by B9, on the periphery of the lava
flow, than by B4, which detects the emissions only from the
very hot lava flow itself

 Temperature Assessment of Volcano
o The volcanic vent is found to have a temperature of generally around 1000 °C, and emissivity
would be in the range 0.6–0.8. Features with such high temperatures emit radiation also in the
SWIR region (1.0−3.0 μm), as indicated by the Planck’s law. Therefore, although, the SWIR
region is generally regarded as suitable for studying reflectance properties of vegetation,
soils and rocks, it can also be used for studying high-temperature features.
o Although the vent may have a temperature of around 1000 °C, it need not occupy the whole of
the pixel. For this reason, the temperature integrated over the entire pixel, would be less than
the vent temperature, unless the vent fully covers the pixel.
o The pixel-integrated temperatures for Landsat TM/ASTER resolution are found to be around
200 −400 °C. The thermal-IR band Landsat TM6 gets saturated at 68 °C and the ASTER-TIR
bands have sensitivity up-to 97 °C; therefore, the Landsat TM/ASTER thermal-IR are not
ideally suited for studying high-temperature objects having 200−400 °C.
o On the other hand, the SWIR bands viz. Landsat TM7, TM5, and ASTER-SWIR can be used for
such investigations.

o The procedure of temperature estimation
involves the following main steps:
(1) determination of emitted radiation for each
pixel, including subtraction of radiation from
other sources, such as solar reflected
radiation etc;
(2) conversion of corrected DN values into
emitted radiance; and finally
(3) conversion of emitted spectral radiance
into radiant temperature (pixel-integrated
temperature) values.
Fig. - a Anomaly on Landsat TM7 image (16 March 1985)
within a crater of the Luscar volcano, Chile; b corresponding
TM7-based pixel-integrated temperatures
o Figure- a shows a Landsat TM7 image exhibiting a thermal anomaly within a crater on Lascar, with
computed pixel-integrated temperatures in Fig. - b.

o An active lava flow will consist of hot, incandescent, molten material in cracks or open channels,
surrounded by a chilled crust. Therefore, thermally, the source pixel will be made up of two distinct
surface components: (1) a hot molten component (occupying fraction ‘p’ of the pixel), and (2) a cool crust
component which will occupy the remaining (1 − p) part of the pixel. Using the dual-band method the
temperature and size of these two sub-pixel heat sources can be calculated.
o Rothery et al. (1988), Glaze et al. (1989) and Oppenheimer (1991) adapted this technique to estimate sub-
pixel temperatures at several volcanoes. Thus, satellite remote sensing using SWIR bands can generate
data for understanding the cooling of lava flows— an information that would hardly be available at
erupting volcanoes by any other technique.
o In the case of an active lava flow or surface fire, the source pixel is likely to be made up of two thermally
distinct surface components: (1) a hot component such as molten lava or fire with temperature TL,
occupying a portion ‘p’ of the pixel, and (2) a cool component (background area) with temperature TC,
which will occupy the remaining portion of the pixel (1 − p). If the same thermally radiant pixel is
concurrently sensed in two channels of the sensor, then we have two simultaneous equations:
o where Li and Lj are the at-satellite spectral radiances in channels i and j, p is the portion
of the pixel occupied by the hot source, Li (Th) and Lj (Th) are the spectral radiances for
the hot source in channels i and j respectively, and Li (Tc) and Lj (Tc) are the spectral
radiances for the cool source in channels i and j.
o Using the dual-band method , the temperature and size of sub-pixel heat sources can be calculated if any one
of the three variables Th, Tc or p is known (as this leaves two unknowns and two equations). Commonly, Tc
can be reasonably estimated as the temperature of the background area; then using data from two bands (e.g.
SWIR bands TM7/ETM+7 and TM5/ETM +5),

 Volcano Plume Observations
o Plume columns as high as tens of kilometres may accompany large volcanic explosions. Due to such
heights, adiabatic cooling takes place. Therefore, the plumes are marked by a negative thermal
anomaly and a higher reflectance in the VNIR.
o Jayaweera et al. (1976) were some of the first to observe a direct eruption of a volcano on remote
sensing (NOAA) data and measure the height of the plume from such data as shadow length and sun
elevation. The dimensions of a plume at a particular instant can be calculated using simple
trigonometric relations and data on plume height. Such data are useful in aviation management and
damage assessment.
o The Eyjafjallajokull Volcano, Iceland, erupted in 2010 emitting a huge quantity of smoke and gas. This
eruption was specifically covered by ASTER at every possible overpass, day and night. All data were
processed to either brightness temperature in Celsius, VNIR reflectance, or plume composition.
Composition of the plume was derived from a spectral deconvolution approach using laboratory TIR
spectra.
o Figure - a is day-time plume composition image derived from the thermal-IR data, and Fig. - b is an
example of night-time temperature distribution image derived from the thermal-IR data. The entire set
of data allows for a more precise determination of thermal output, monitoring of potentially new
temperature anomalies, and determination of the products in the plume.

 Volcano Plume Observations
Fig.: ASTER images of Eyjafjallajokull Volcano, Iceland, acquired during the eruption in 2010; a day-time plume
composition image derived from the thermal-IR data (3 May 2010), note the distribution of silicate ash, SO2 and water
vapour at that instant; b night-time temperature distribution image derived from the thermal-IR data (12 May 2010)

 Global Monitoring and Early-Warning of Volcanic Eruption
o Global monitoring of volcanic activity is an issue of utmost concern and priority. Significant advances
have been made in the field of global monitoring and early-warning of volcanic eruptions using satellite
observations. Broadly, there are two main approaches to the problem: (a) detecting thermal anomalies,
and (b) detecting surface deformation (InSAR application).
(a) Methods utilizing thermal anomalies
o Pre-eruption indications of a volcanic activity may be in the form of surface temperature anomalies—
that are small in spatial extent, and have a limited time window. This means that ideally the satellite-
based pre-eruption warning system ought to have a high spatial resolution and a high repetivity.
However, both these requirements are difficult to fulfill concurrently.
o Presently, there are satellite sensors with large swath width providing high repetivity but with low
spatial resolution; for example: AVHRR and MODIS provide twice-daily coverage but with a coarse
spatial resolution of *1 km. On the other hand there are satellites sensors with higher spatial resolution
(Landsat TM, ETM+, OLI, ASTER.) that have good spatial resolution (60–90 m in the TIR), but a low
repetivity (repeat cycle of *16 days). Nevertheless, in spite of the above limitations, significant advances
have been made in thermal remote sensing in the context of volcano monitoring.
o ASTER-TIR sensor is sensitive to temperatures that range from −73 to 97 °C, and has a 1–2 °C detection
threshold with a ±3 K radiometric accuracy (Yamaguchi et al. 1998); this makes it ideal to observe low
temperature as well as lightly hotter thermal features resulting from magmatic activity. Thus, ASTER
has a unique utility in watching world’s volcanoes.

o Using ASTER-TIR image data, Pieri and Abrams (2005)
detected pre-eruption thermal anomaly of Chikurachki
volcano, Russia (Fig - A).
o This observation and ASTER sensor capability was
further corroborated by Carter et al. (2008). Carter and
Ramsay (2010) analysed ASTER-TIR time series data of
Shiveluch volcano (Kamchatka, Russia) pertaining to
the period 2000–2009, during which period six explosive
eruptions occurred at Shiveluch.
o Figure - B presents a multi-temporal FCC of the volcano
showing hot flows that were outpoured during three
different episodes of volcanic activity. Carter and
Ramsay (2010) deduced the pixel-integrated temperature
of the hottest pixels at the volcano summit in this span
of time. They found that the temperature of the hottest
pixel at the volcano summit gradually rose till eruption
occurred, and then the temperature decreased—this
happening in a cyclic manner.
Fig. – A: ASTER image (14 Feb 2003) of Chikurachki
volcano summit, Russia; the image preceded the
eruption by about two months; white pixels correspond
to temperatures of *266 K pixel integrated temperature,
whereas the darkest pixels correspond to *250 K pixel
averaged temperature; this indicates that high spatial
resolution thermal IR data has the potential to detect
subtle thermal anomalies (on the order of 3–5 K) with
typical dimensions of about 100 m, that may precede a
volcanic eruption

Fig. – B: False colour composite generated
from multi-temporal ASTER-TIR temperature
images of Shiveluch volcano, Russia; the
FCC shows images acquired on 19 May 2001
(red), 11 May 2004 (green), and 29 March 2005
(blue), and highlights the extent (area) and
magnitude (intensity of the colour) of the
warm deposits which were generated by the
explosive eruptions on 19 May 2001, 9 May
2004, and 28 February 2005

o MODVOLC
o For pre-eruption warning, the basic strategy using satellite remote sensing is to detect a hotspot at the
volcano summit that could be attributed to fresh magma; this would indicate that the volcano could
erupt in the near future. For this purpose, SWIR and TIR band data are used in conjunction.
o For AVHRR, the method used brightness temperature difference between the SWIR (3.8 μm) and TIR
(10.8 μm) bands; where-ever the brightness temperature difference exceeded a given threshold of 10 K,
the surface was interpreted as possessing a subpixel hotspot that contributed to SWIR band (Harris et
al. 1995). This became the foundation of OKMOK algorithm that was applied to Aleutian Islands volcano
(Dean et al. 1998; Dehn et al. 2000).
o Presently, MODVOLC is the most pervasive and extensively used volcanic detection thermal algorithm.
This also uses the same principle that hotter (lava flow or magmatic) surface will produce higher
spectral radiance at shorter wavelengths than at longer wavelengths. MODVOLC uses MODIS data
which has a rather coarse spatial resolution (500 m in SWIR and 1 km in TIR) but a high repeat cycle
daily of twice daily (with two MODIS satellites in orbit). MODVOLC computes normalized temperature
index (NTI) as (Flynn et al. 2002; Wright et al. 2004):
where R22 and R32 refer to reflectance values in
MODIS B22 (3.9 μm) and B32 (12.00 μm) respectively.

(b) Methods utilizing detection of surface deformation (InSAR technique)
o As is well known, emplacement of dykes and evolution of magma reservoir at shallow depth
are precursors to volcanic eruption, and concurrently with that some ground deformation
including volcanic inflation/deflation or flank deformation around volcanoes may occur,
which could be detected by InSAR. With the above background, numerous investigations
have been carried out for monitoring volcanoes using SAR interferometry, the world over.
o Figure (See next slide) presents an example of Kilauea volcano, Hawaii, the world’s most
active volcano. There is a prominent rift running eastward from the main summit, along
which lava eruption and tectonic movements also occur. On 5 March 2011, a large fissure
eruption began on the east rift zone of Hawaii’s Kilauea volcano.
o The interferometric image (Figure) here depicts the relative deformation of Earth’s surface at
Kilauea. Deflation of the magma source beneath the Kilauea caldera and deformation caused
by the volcanic dyke intrusion and subsequent fissure eruption along the east rift zone are
well documented

Fig.: SAR-interferometry image of Kilauea volcano, Hawaii. The image has been generated from SAR overpasses of 11
Feb 2011 and 7 March 2011(Italian Space Agency—ASI constellation of COSMOSkyMed radar satellites). On 5 March 2011
(two days before the second overpass), a large fissure eruption began on the east rift zone of Hawaii’s Kilauea volcano.
Surface displacements are seen as contours or fringes where each colour cycle represents 1.5 cm of surface motion.
The circular pattern of concentric fringes towards the left represent deflation of the magma source beneath the Kelauea
caldera. The complex pattern towards the right represents the deformation caused by the volcanic dyke intrusion and
subsequent fissure eruption taking place along the east rift zone

o The Three Sisters volcano region in Oregon, USA,
makes another interesting case study. It drew much
attention when initially field surveys indicated that
a phase of uplift had started in 1997 in this area.
Using satellite SAR data of 1996 and 2000, the
USGS detected uplift of the ground surface over an
area of 15–20 km diameter (Figure).
o The uplift was observed to be maximum (10 cm) at
the centre, having risen at an average rate of 2.5
cm per year. Presumably, this could be a result of
the intrusion of a small volume of magma below
the ground surface. However, subsequent
investigations during 2005 showed the uplift to
have slowed down, and therefore the rate of
magma intrusion also apparently declined
Fig.: Differential InSAR from satellite SAR data (passes
in 1996, 2000) of the Three Sisters volcano region,
Oregon. A broad uplift of the ground surface over an
area of about 15–20-km diameter, with maximum uplift of
about 10 cm at its centre, is detected

o As such, the duration, and final culmination of subsurface activity and their intensity and
episodes are quite impossible to forecast, and only continued monitoring can help safeguard
against possible disasters.
o Finally, efforts have been made to deduce interrelationship between earthquakes and
volcanism on global scale. Donne et al. (2010) computed heat flux inventory for volcanism
from satellite data and inter-related this with earthquake activity. With data of the period
2000–2007, they found that earthquake incidence frequently leads to subsequent increase in
heat flux at volcano with-in 1–21 days. Whether a volcano responds or not, depends on
several factors viz., the earthquake magnitude, distance to the epicenter, and orientation of
the earthquake focal mechanism in respect to the volcano.

 Introduction
o Coal fires are a widespread problem in coal mining areas the world over, e.g. in Australia, China, India,
South Africa, USA, Venezuela and various other countries. These coal fires exist as coal seam fires,
underground mine fires, coal refuse fires and coal stack fires. The main cause of such fires is
spontaneous combustion of coal occurring whenever it is exposed to oxygen in the air, which may pass
through cracks, fractures, vents etc. to reach the coal.
o The fires burn out a precious energy resource, hinder mining operations and pose a danger to man and
machinery, besides leading to environmental pollution and problems of land subsidence. There is,
therefore, a need to monitor the distribution and advance of fires in coal fields.
o Various field methods, such as the delineation of smoke-emitting fractures, thermal logging in bore
holes etc., have been adopted with varying degrees of success at different places. However, the study of
coal fires is a difficult problem as fire areas are often inaccessible; therefore, remote sensing
techniques could provide valuable inputs.
o The first documented study of coal fires using thermal-IR remote sensing is that of Slavecki (1964) in
Pennsylvania (USA). Ellyett and Fleming (1974) reported a thermal-IR aerial survey for investigating coal
fires in the Burning Mountain, Australia. Since then, a number of remote sensing studies have been
carried out world-wide.
o To deal with problems of coal fires, information is often sought on a number of aspects, such as:
occurrence, distribution and areal extent of fires, whether the fire is surface or subsurface or both,
depth of coal fire, temperature of ground or surface fire, propagation of fire, subsidence etc. Remote
sensing can provide useful inputs on all the above aspects.
Geo- Hazards – Coal Fires

1. Fires in the Jharia Coal Field, India (Case Example)
o The Jharia coal field (JCF), India, is a fairly large coal
field of high-quality coking coal. It covers area of about
450 km2, where about 70 major fires are reported to be
actively burning (Sinha 1986).
o Coal fires, both surface and subsurface, are
distributed across the entire Jharia coal field (Figs.- a,
b). Detailed investigations have been carried out to
evaluate the utility of remote sensing technology for
the study of coal fires and related problems in the JCF
o Subsurface fires
 Surface temperature of the ground above subsurface
coal fires is usually marked by a mild thermal anomaly
of about 4–8 °C, due to the low thermal conductivity of
rocks such as sandstone, shale, coal etc. As Landsat
TM6 gets saturated at 68 °C, and ASTER TIR at 97 °C,
these sensors are well-suited for sensing thermal
anomalies over subsurface fires.
Fig. Field photographs showing a surface
fire and b subsurface fire in the JCF

 Figure – A shows an IHS-processed Landsat image of a part of the JCF. On the lower-left corner is the
inset Landsat TM6 band sub-scene in psuedocolour, where pixels related to higher ground
temperatures (subsurface fires) are discriminated from non-fire areas, using density slicing. It is
obvious that the thermal anomalies related to various land surface features can be much better located
on the IHS image than on the psuedocolour. Ground surface temperatures corresponding to thermal
anomalies as derived from TM6 data are found to be in the range of 25.6–31.6 °C, the background
temperatures being <24 °C. These temperature observations are corroborated by field measurements
(Fig. – B , see next slide).
o Subsurface fires
Fig. - A: Processed Landsat TM data of JCF (IHS- processed, I = TM4,
H = TM6, S = constant, such that red corresponds to highest DNs);
the sickle-shaped field above is the Jharia coal field; the relief shown
in the background is from TM4; black linears and patches are coal
bands, quarries and dumps; blue is the background (threshold)
surface temperature, anomalous pixels have green, yellow and red
colours with increasing temperature; on the lower left is an inset of
the psuedocolour TM6 band; the Damodar river appears in the south)

o Subsurface fires
Fig. A typical profile of surface
temperatures (field measurements) above
subsurface fire area; the background
temperatures are <24 °C; the anomalous
ground temperatures reach up-to 28 °C in
the profile
o Du et al. (2015) developed a self-adaptive threshold based method for subsurface coal fire detection.
This is based on segmentation and thresholding of image data, which they applied on ASTER TIR data
of Wuda coalfield, China, to auto-detect spatial distribution of thermal features.

 Estimating the depth of subsurface fires. Subsurface fires in the JCF occur at varying depth,
ranging from just a few meters up to tens of metres. Estimating the depth of a fire is
important not only for combating fire but also for various applications, e.g. for hazard
assessment, rehabilitation plans etc. Depth modelling of buried hot features (such as
subsurface fire) from remote sensing data is still in its infancy, as it is quite a difficult
problem requiring repetitive TIR data and estimates of realistic values of various physical
parameters.
 In simple cases, however, a geometric method can be employed for depth estimation,
collectively using information on geological-structural setting and the position of anomalous
thermal pixels. The location of a subsurface fire can be determined from thermal anomalies,
and VNIR images can provide information on the location of the outcrop (Fig. - a, b). With the
field information on orientation of the strata, the depth of a subsurface fire can be computed
using simple planar geometry (Fig. - c).
 Results obtained by the above method are reported to be in reasonable agreement with the
field data in the JCF (Saraf et al. 1995). However, the method may have limitations in areas of
multiple coal seams, particularly if information on the specific coal seam with the fire is
lacking.
o Subsurface fires

o Subsurface fires
Fig. a Landsat TM4 and b Landsat TM6 digital data outputs of the same area showing location of a coal
seam and thermal anomaly respectively; c principle of computing depth of fire from location on thermal
anomaly and outcrop

o Surface fires
 Surface fires in coal fields are features of local high surface temperature but generally small areal
extent. As Landsat (TM/ETM+/OLI) SWIR bands have the capability to measure temperatures in the range
approx. 150−500 °C range, and ASTER-SWIR that in 101–449 °C range , these sensors can be used for
studying surface fires.
 Figure shows a Landsat FCC TM753 (coded in RGB). The
higher DN values in Landsat TM7 and TM5 enable
identification of surface fires. TM7 has sensitivity in the
temperature range of 160–277 °C and that TM5 in the range
of 267–420 °C. Pixels with high temperatures (>267 °C) are
radiant in both TM7 and TM5, and so appear yellow. Pixels
with of temperatures <267 °C are radiant in only TM7, and
hence appear red. In many places, a sort of ‘zoning effect’ is
seen where red pixels (of relatively lower temperature)
enclose or border the yellow (highest temperature) pixels.
Fig.: FCC of Landsat TM753 (RGB); windows I, J, K, L, M and N
depict areas of surface fires; yellow pixels correspond to areas of
highest temperatures being radiant in both TM5 and TM7; red areas
(radiant only in TM7) are relatively lower temperatures (see enlarged
windows K and L)

o Surface fires
 In the JCF, the pixel-integrated temperatures (based on TM7 and TM5) have been found to
range between 217 and 410 °C.
 Further, in many cases, fires do not occupy the whole of the pixel, i.e. only a part of the pixel
is filled with surface fire. The pixel-integrated temperatures are therefore less than the actual
surface temperatures of fires.
 In suitable data conditions it is possible to compute sub-pixel area and temperature using the
dual-band method developed by Matson and Dozier (1981). The sub-pixel temperatures are
found to be in the range of 342−731 °C and sub-pixel areas in the range between 0.2 of a pixel
(= 180 m2) and 0.003 of a pixel (= 27 m2).
 The above demonstrates the utility of Landsat SWIR data for delineation and mapping of
areas affected by surface as well as subsurface fires in coal fields.

2. Coal Fires in Xinjiang, China (Case Example)
o One of the largest deposits of coal in the world
occurs in north China, stretching over a region of
about 5000 km E–W along strike, and 750 km N–
S. Coal fires occur in almost all the fields—in
scattered or clustered forms. Several workers
(e.g. Huang et al. 1991) have reported coal-fire
studies in China using remote sensing.
o Figure (Zhang 1998) shows the airborne thermal-
IR image draped over the DEM. The coal fires
detected from the thermal-IR scanner appear red,
and are distributed generally along the NE–SW
strike of the coal seam.
Fig. Coal fires in the Xinjiang coal field, China;
airborne thermal-IR image data co-registered and
draped over the DEM; the coal fires appear
aligned and distributed generally along the NE–
SW strike of the coal seam

3. Coal Fires in Wuda, China (Case Example)
o This is another large coal field located in north
China where major fires are known to occur. To
study coal fire dynamics in the coal field, Huo
et al. (2015) derived land surface temperatures
(LST) from remote sensing (Landsat
TM/ETM+.) data sets and identified thermal
anomalies related to coal fires.
o Based on the results from long-time (years:
1999, 2000, 2001, 2002, 2003, 2004, 2006) series
of data sets, they deduced the gradual
spreading directions of coal fires in
successive years, and then predicted the likely
coal fire development/dynamics for the entire
coal field in future (Figure).
Fig.: Coal fires in Wuda coal field, China; the figure
shows spreading directions predicted based on the
coal fires that were extracted from Landsat TM/ETM+
thermal-IR long-time series data (1999–2006)

 Coal Mine Subsidence
o Underground mining areas often face problems of land subsidence where it is caused by
volume loss due to mining, underground water, unfilled stopes or their gradual compaction,
and subsurface fires, as in the case of coal fields.
o Generally, subsidence occurs after mining has ceased in an area; however, sometimes it
occurs even when a mine is still in operation, in which case it may lead to loss of lives,
settlements, resources, infrastructure etc. Therefore, it is one of the worst environmental
hazards.
o In the earlier days, conventional surveying techniques were used to generate subsidence
data and maps. However, with the advent of Synthetic Aperture Radar Interferometry (InSAR)
techniques, small-scale surface deformations and elevation changes can be mapped by SAR
data.
o Subsidence studies in coal mining areas using DInSAR technique have been carried out by
Yue et al. (2011) in Fengfeng coal mine area, China, Engelbrecht et al. (2011) in South Africa,
Dong et al. (2013) in Huainan coal field, China, and Chatterjee et al. (2015, 2016) in Jharia coal
field, India, among others.

 Coal Mine Subsidence
o Using Radarsat-2 C-band InSAR data pairs
of 2012, Chatterjee et al. (2016) identified
recently subsiding areas in the JCF. It is
well known that dynamic land cover
changes result in temporal decorrelation
problems for DInSAR processing in
mining areas.
o They innovatively used smaller temporal
baseline data pairs and adopted InSAR
coherence guided incremental filtering
with smaller moving windows to highlight
the deformation fringes over temporal
decorrelation noise.
o The identified deformation fringes were
validated with ground precision levelling
data. This resulted in detection of several
new previously unreported subsidence
areas (Figure).
Fig. Radarsat-2 C-band differential interferogram of the JCF
showing DInSAR fringes at multiple locations corresponding to the
subsiding areas during 2012; solid black line is the outline of JCF

 Environmental Effects of Coal Fires
o High temperature areas related to coal mine fires have a negative effect on vegetation and lead to
reduction in potential of soil to support plant growth. Therefore, it is important to understand the
relationship between vegetation index and ground temperature, both of which can be evaluated from
remote sensing data on spatio-temporal basis.
o For the study of vegetation from remote sensing data, various vegetation indices can be used. Out of
these, NDVI has been most extensively used in general. However, as NDVI has been found to be
sensitive to soil brightness, the soil adjusted vegetation index (SAVI) may be better used when
vegetative cover is rather low (Huete 1988), as happens in many mining areas.
o Inter-relationship between SAVI and temperature was studied by Saini et al. (2016) in a part of the Jharia
coal field using data from Landsat OLI/TIRS for the period year 2013. SAVI image was generated using
the standard algorithm from TOA reflectance data ; brightness temperature image was derived the
thermal-IR data.
o Figures (See next slide) shows the SAVI and temperature images. Profiles drawn at a selected
alignment clearly brings out the inverse relationship. It is observed that SAVI values are low where the
temperature is high and vice-versa (Figure), implying a negative correlation between temperature and
SAVI.

 Environmental Effects of Coal Fires
Fig.: Inter-relationship between SAVI and temperature in a part of the Jharia coal field where numerous
coal fires are known to exist; a1 – a3 are generated from Landsat TM (15 Jan 1991) and b1 – b3 from
Landsat OLI (13 Dec 2013); a1, b1 are SAVI images, a2, b2 are temperature images a3, b3 are the profiles;
note the inverse relationship between SAVI and temperature; also note the change in spatial distribution
of SAVI and temperature over a period of * 20 years (1991–2013)

gis in natural disaster management geo hazards

Recommended

Recommended

More Related Content

What's hot

What's hot (18)

Similar to gis in natural disaster management geo hazards

Similar to gis in natural disaster management geo hazards (20)

More from Soumik Chakraborty

More from Soumik Chakraborty (19)

Recently uploaded

Recently uploaded (20)

gis in natural disaster management geo hazards