REMOTE SENSING GEOLOGY Agung Setianto
Geological Engineering Department, Faculty of Engineering
University of Gadjah Mada (UGM)
Visual Interpretation Skills How to read a image
What do you need to interpret remotely sensed imagery? Familiarity with the specific area or similar areas Basic interpretation skills Image prints that are of sufficient quality
Projection grid marks on the image are helpful to locate oneself on the image using a GPS Tools to transcribe information onto the image
Basic Elements of Visual Interpretation Tone (color) Size and shape Texture and pattern Relative and absolute location Shadows
Tone and Color A band of EMR recorded by a remote sensing
instrument can be displayed on an image in shades of gray ranging from black to white. These shades are called “tones”, and can be
qualitatively referred to as dark, light, or intermediate (humans can see 40-50 tones). Tone is related to the amount of light reflected from
the scene in a specific wavelength interval (band).
Tone and Color Variations in tone and
color results in all of the other visual elements When looking at a image photo we associate specific tones to particular features Tones change when we enhance an image or when we change the band combination of a color image
Tone and Color
Jensen (2000) ND GIS Users Workshop Bismarck, ND October 24-26, 2005
Tone and Color
Tone and Color
Tone and Color
Tone and Color
Tone and Color
Tone and Color
Size and Shape Many natural and human-made features have
unique shapes. Often used are adjectives like linear, curvilinear,
circular, elliptical, radial, square, rectangular, triangular, hexagonal, star, elongated, and amorphous.
ND GIS Users Workshop Bismarck, ND October 24-26, 2005
Size and Shape Rectangular features often
indicate human influence such as agriculture Size and shape information greatly influenced by image resolution Knowing the scale of the image helps to convert feature dimensions on the image to actual dimensions
Shape
Jensen (2000)
ND GIS Users Workshop Bismarck, ND October 24-26, 2005
Texture and Pattern Texture: Texture refers to the arrangement of tone or color in
an image. Useful because Earth features that exhibit similar
tones often exhibit different textures. Adjectives include smooth (uniform, homogeneous),
intermediate, and rough (coarse, heterogeneous).
Pattern: Pattern is the spatial arrangement of objects on the
landscape. General descriptions include random and systematic;
natural and human-made. More specific descriptions include circular, oval,
curvilinear, linear, radiating, rectangular, etc.
Texture and Pattern Varies with image
resolution Often noted by roughness or smoothness Influenced by shadows
Relative and Absolute Location Association: This is very important when trying to interpret an
object or activity. Association refers to the fact that certain features and activities are almost always related to the presence of certain other features and activities.
ND GIS Users Workshop Bismarck, ND October 24-26, 2005
Relative and Absolute Location The location of a
feature narrows the list of possible cover types Relative location particularly useful to determine land use
Association
Jensen (2000)
Flood plains Cutbank
Flood plains are the landform adjacent to the river channel that is influenced by modern river processes. Flood plains are constructive, depositional landforms created by stream flow and sediment deposition. Flood plain environments are composed of a mosaic of different landform features including cutbanks, pointbars, natural levees, crevasse channels and crevasse splays, infilled channels and oxbow lakes, backswamps, and occasionally yazoo tributaries and other flood plain channels.
Pointbar Infilled Channel
Oxbow Lakes
This aerial view of the Mississippi River Valley contains many typical floodplain features. The darker, green areas are floodplain forest and they likely flood the most frequently and thus are not developed with agriculture or housing. The surrounding patchwork represents agricultural fields and other developed lands that are probably at a higher elevation formed by natural or artificial levees. Copyright ©2008 Google
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Table of Contents
Shadows Often considered a
contaminant but can be very useful to identify features on an image Helpful to accentuate relief Shadow effects change throughout the day and throughout the year Shadows can give an indication to the size of a particular feature
Shadow
Jensen (2000)
ND GIS Users Workshop Bismarck, ND October 24-26, 2005
Pantulan Spektral dari Objek Karakteristik Spektral dari Beberapa Objek
Reflectance (%)
Limestone
Vegetasi Hijau
Tanah Kering
Air 0.4
0.5
0.6
0.7
0.8
Wavelength (mm)
0.9
Image Ratios It is possible to divide the digital numbers of one image
band by those of another image band to create a third image. Ratio images may be used to remove the influence of light and shadow on a ridge due to the sun angle. It is also possible to calculate certain indices which can enhance vegetation or geology Sensor
Image Ratio
EM Spectrum
Application
Landsat TM Bands 3/2
red/green
Soils
Landsat TM Bands 4/3
PhotoIR/red
Biomass
Landsat TM Bands 7/5
SWIR/NIR
Clay Minerals/Rock Alteration
Instrument Characteristics
60 km swath; <16 day repeat cycle; stereo
Visible-NIR
Visible-NIR
Short Wave IR
Thermal IR
Mining Life Cycle In the mine life cycle, geologic mapping falls under
Exploration, but it effects all of the life cycles Closure Ongoing Operations Temporary Closure
Post-Closure Exploration
Future Land Use Mine Development
Operations (McLemore, 2008)
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SATELLITE LANDSAT
Remote sensing is the science of remotely acquiring, processing and interpreting spectral information about the earth’s surface and recording interactions between matter and electromagnetic energy.
AIRBORNE
HYPERSPECTRAL
GROUND Field Spectrometer
Alumbrera, Ar Data is collected from satellite and airborne sensors. It is then calibrated and verified using a field spectrometer.
CUPRITE, NV
Goldfield, NV
METODOLOGI
CITRA RADAR
CITRA LANDSAT ETM+7
DSM
ORI
ORTHORECTIFIED
SHADDED RELIEF
LAYER INTENSITY
KOMPOSIT RGB : 457
Resampling (Posting 50 m)
DSM (Posting 50 m)
-Peta Geologi 1:250.000
OVERLAY LANDSAT - IFSAR
INTERPRETASI : - Drainage Patern - Infrastruktur
INTERPRETASI : - Kelurusan, Strike/Dip - Litologi/Batas Batuan
Generate Kontur (Interval 25m)
OVERLAY KEGIATAN LAPANGAN
PETA TOPOGRAFI BAKOSURTANAL SKALA 1:50.000
PENAMBAHAN DATA DAN EDITING
KARTOGRAFI
BADAN GEOLOGI PUSAT SURVEI GEOLOGI
PETA GEOLOGI HASIL INTERPRETASI CITRA INDERAAN JAUH
PENGOLAHAN DATA INDERAAN JAUH Citra optik (resolusi sedang) dan citra radar (ORI dan DEM) yang mempunyai resolusi tinggi diintegrasikan menjadi satu tampilan dengan kenampakan tiga demensi, sehingga kenampakan geologi mudah ditafsir. INTEGRASI CITRA OPTIK DAN RADAR CITRA OPTIK
CITRA RADAR
BADAN GEOLOGI PUSAT SURVEI GEOLOGI
IFSAR (DSM)
IFSAR (ORRI)
Overlay IFSAR & LANDSAT
BADAN GEOLOGI PUSAT SURVEI GEOLOGI
LANDSAT
The use of directional filters in determining fault systems
Why faults? Faults are extremely important in analysing both
localised and regional geology Recent economic boom means that areas of previous
exploration activity are been re-visited A good way of quickly checking out the geology of an
area is by taking a quick overview of the faults Implications towards economic geology (i.e. Oil traps,
mining)
Information obtained: Surface and subsurface lithologies Structural framework – stress and strain
relationships Alteration mineralogy Localize possible permeable zones Techniques and tools: Geologic mapping Thin-section alteration mineral recognition Exploration core well
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Current methods of fault detection using DEMs
So what are the popular methods of analysing faults?
DEMs!! The majority are based primarily on data (pixel)
transform processes Examples include:
a) Sun-shading b) Histogram manipulation
Sun-shading DEMs
For sun-shading to work, variables of ANGLE and
AZIMUTH must be constant. i.e. Both must be 45° Why? Primarily in-order to minimise the effects of shadows /
and brighter areas
Sun-shading the SRTM 90m DEM Raw
50km
Traced Faults
Problems with this method? Only areas of a certain elevation will stand out. i.e. Cant
be used in lower terrains (basins) – where oil is most prevalent. The influence of shadows is minimised but still present Higher resolution DEMs are required
Histogram manipulation of 90m DEM Stretching the data according to various representations
such as: LINEAR and HISTOGRAM EQUALISE This gives us;
More control over data range Can work in any terrain Variables are minimised
Linear Transform
Histogram Equalise
Problems with this method? Time consuming Linear features seldom are presented in all transforms Tracing a fault becomes increasingly ambiguous if you
have to revert to different transform types
Directional filters: possible solution
What are they? Directional filters are first derivative edge enhancement
filters that selectively enhance image features according to specific directional components So they are often considered to be a form of edge
detection and have been used often in medical situations i.e. Brain scans
Directional filters: possible solution
How do they work?
Example of an northward directional filter according to a boxed matrix. If in this image, an edge north/south, then an optimal response is detected. Normally this would be represented by an increase in brightness.
Comparatively, if this edge faces west, then a minimal response is detected since the filter has missed it.
For a directional filter to be correctly applied, all major compass directions must be applied to the entire dataset.
Lets put it to the test! Original SAR Image – Same area of interest as before
North – South 0⁰ to 180⁰
North East – South West 45⁰ to 225⁰
South West – North East 135⁰ to 315⁰
East – West 90⁰ to 270⁰
The result
Key North – South 0 to 180 North East – South West 45 to 225 South West – North East 135 to 315 East – West 90 to 270
KONTRAS STRUKTUR GEOLOGI MENGGUNAKAN DIGITAL ELEVATION MODEL (DEM) DENGAN METODE DIGITAL EXTRACTION
Pembuatan data DEM Data xyz Interpolasi dengan metode kriging Citra DEM
Pengutaraan Data Surface
Analyst Slope Analyst Tujuan Analisis : mengetahui kelerengan. Median Slope : 35°
Pengutaraan Data Hillshade Analyst Altitude : 35 ° Azimuth : 8 arah 0, 45, 90, 135, 180, 225, 270, 315 Metode weight overlay pada ArcGIS. Tiap citra memiliki equal influence dalam pembuatan citra gabungan.
Altitude : kemiringan pencahayaan semu Azimuth : arah datang pencahayaan semu
Pengutaraan Data • Citra Shaded Relief
Pengutaraan Data Peta kelurusan digital Parameter penarikan kelurusan digital
RADI = 5 piksel Pendeteksian edge gradient GTHR = 10 piksel Thresholding edge gradient LTHR = 9 piksel Penipisan edge FTHR = 3 piksel Pemangkasan edge (Raster) garis kelurusan (Vektor) ATHR = 10 derajat Penggabungan Garis Berdasarkan Sudut DTHR = 3 piksel Penggabungan Garis Berdasarkan Jarak
Kelurusan terpendek yang akan dihasilkan sepanjang 270 m.
Pengutaraan Data
Peta kelurusan digital
Dikelompokan antara satuan batuan vulkanik dan karbonat.
Pengutaraan Data Data Lapangan Prioritas : nilai densitas panjang kelurusan digital yang tinggi Jumlah STA : 48 titik
Pengutaraan Data Analisis diagram rose Dimensi panjang kelurusan
Vulkanik
Karbonat 1
Karbonat 2
Kontrol Struktur Geologi terhadap Kelurusan Digital
TG
U
BD
Kontrol Struktur Geologi terhadap Kelurusan Digital
S
BL
TG
Kontrol Litologi terhadap Kelurusan digital U
TG
BD
Kontrol Erosi terhadap Kelurusan Digital
T
BL
Kontras Struktur Geologi Batuan Vulkanik dan Karbonat Batuan vulkanik : NW-SE, E-W, NE-SW dan N-S. Reaktifasi Sesar Opak selama pengangkatan Pegunungan Selatan. Batuan karbonat : E-W dan NW-SE. kekar release pada sayap Geantiklin Jawa bagian selatan yang menjadi zona lemah untuk melarutkan batuan karbonat.
Kalimerah Anticline
Latih Fault
Pegah Fault
Latih Syncline Latih Fault
Birang Anticline
Digital Elevation Model Based Watershed and Stream Network Delineation Conceptual Basis Eight direction pour point model (D8) Flow accumulation Pit removal and DEM reconditioning Stream delineation Catchment and watershed delineation Geomorphology, topographic texture and
drainage density Generalized and objective stream network delineation Reading – Arc Hydro Chapter 4
Hydrologic functions
Derive streams, watersheds, and other hydrologic features based on analysis of a DEM.
REMOTE SENSING DATA FOR VOLCANOLOGY
Topographic Change – Pyroclastic Deposit (Before and After the eruption)
Space-borne SAR were used to gather DEM of the mountain terrain (COSMO SkyMed were used)
Data Source : Series of COSMO SAR data in 2010
Mount St Helens Resurgent Dome Temperature Changes MASTER temperature data overlain onto LIDAR DEM hill shade image
09-24-2004 – 4:30pm 09-30-2004 – 1:30pm 10-12-2004 – 8:00am 10-14-2004 – 8:00am 10-14-2004 – 12:45pm
Boundary of Old Dome
High = 51 oC
60-75 oC 45-60 oC 30-45 oC
Boundary of Old Dome
Boundary of New Dome
High = 54 oC
High = 181 oC
105-150 oC 90-105 oC
Boundary of New Dome
Boundary of New Dome
High = 250 oC
High = 330 oC
> 300 oC
200-300 oC 150-200 oC
75-90 oC Courtesy of Greg Vaughan, JPL
Comparison of NW Summit from SPOT data obtained between 1991 and 1998 Red shows vegetation, the ash deposits are light blue
Summit is at lower right
Changes in Mt. Pinatubo lahar deposits. Lower Pasig-Potrero River 1991 - 1996
Lidar Application
Visualization Shaded relief & DEM
illumination can be used as a simple visualization technique These methods are subjective Sensitive to hardware parameters
Lidar VS USGS DEM
Lidar VS USGS DEM
Lidar data
The Even ifhigh only vendor one point uses ainvariety one hundred of software is aeven ground filters point, choose the the huge points number out of ofispoints means cloud that ato For comparison, the best previously available ground model shown on that the Verylidar point density means that into heavily forested areas, itthe is point still possible measure smooth seamless the10-m ground ground surface. model In the can image be made. on The right, image vegetation on Left the left points is aabare are green earth digital and ground elevation left. USGS Digital Elevation Model shows only crude get a The large number of measurements ofthe the ground. image is orthophoto of the points model, yellow. with 3 ft Even pixels, in thick and reveals forest there incredible are numerous detail of the ground terrain points. beneath the trees, including a representation of the realissurface. Tualatin River, right image lidar point cloud with hidden landslide. red points high, blue points low.
Bare earth lidar can show features that you cannot even see on the ground. Perspective view of lidar (Dec. 2007) on left matches photograph (July 2008) on right. The lidar was flown before clear cut logging of the reddishbrown slope, yet clearly shows an old logging road that is barely visible in the photograph.
Arrows connect matching locations.
Additional standard lidar products include a “highest hit” or “first returns” model, which shows the tops of trees and buildings, and an intensity image. True color orthophoto with 0.5 ft pixels
Lidar highest intensityhit image model with with 1 ft 3 pixels ft pixels
Transmission lines
Nursery stock
Residence
Quarry Auto
The highly detailed bare earth model allows for accurate location of roads and provides easy access to unprecedented levels of detail about slopes and shapes Yellow lines are best current digital road map. Roadcut not too steep Drainage ditch on uphill side
Properly crowned for drainage
The lidar image can show where existing maps are inaccurate
or locate roads that are not on existing maps
or show where mapped roads do not exist
Because the bare earth model contains detailed information about the shape of the land surface, it is easy to construct a profile across a road to examine its construction and condition
Comparing the lidar-derived streams with the current stream map shows the current Stream are readily apparent on lidar bare earth GISchannels software can automatically find streamdigital channels from lidar data thatimages data are often wildly inaccurate Blue lines are streams generated by ArcGIS
Dark blue lines are best current digital stream map, light blue are lidar-derived.
Crosses divide, mouth wildly off
misses sinuous channel, climbs ridges
cross divides
In addition to accurately locating streams, lidar easily produces accurate and detailed profiles and sections Light blue line is lidar derived stream location, dark blue are section lines.
A detailed elevation profile down the stream shows areas of steep or gentle grade, waterfalls and pools. Culverts at road crossing Section shows shape of show up as upward blips“v”on the profile.rapidly downcutting stream
Stream section shows distinct floodplain and channel
Continental Shelf and Slope
The continental shelf is a submerged extension of the continental crust that slopes gently outward from the modern shoreline to the deep ocean basin.
The continental shelf varies in width from being almost non-existent along some continental margins to extending outward for nearly 1500 kilometers (930 miles) in other places. On average it extends outward for about 80 kilometers (50 miles) and has an average slope of about 1 degree (2 meters/kilometer or 10 feet/mile).
Ocean floor features including continental shelf and slope. This diagram provides a good illustration of how the shelf is a shallow extension of the continental crust.
Source: NASA, Visible Earth
A digital elevation model (DEM) of the continental shelf and slope near Los Angeles, California. 91 Table of Contents
Lidar: Geomorphologic Applications Volume change in open pit mines
Landslide Detection
Utilities map power lines for signs of damage:
Lidar: Mapping Fault Lines
Image Source: Puget Sound LiDAR Consortium
DEMs
Elephant Butte, New Mexico - Shaded Relief (Aerometric Inc) Hoover Dam:
http://www.youtube.com/watch?v=JvauCmPAjuI&feature=related
TRIM versus LiDAR
Landslides – Turtle Mountain
http://www.ags.gov.ab.ca/geohazards/turtle_mountain/lidar.html
DEMs: Bare Earth and Canopy
San Andreas Fault zone
Landslide example: LiDAR derived hillshade DEM with 2ft contours
~450 ft down axis of slide
Landslide example: LiDAR derived hillshade DEM with 2ft contours
~750 ft down axis of slide
Landslide example: LiDAR derived hillshade DEM with 2ft contours
~280 ft down axis of slide
Results
230 potential landslide extents digitized (polys)
Approximately 10% were initially attributed with high confidence, rest were questionable
LiDAR hillshade geomorphology,
geology, and proximity to urbanized areas dictated classification…subjective
20% (about 45) of these landslides were field checked 20 were confirmed 18 were likely or observed but could not be determined 7 were not accessible
TERIMA KASIH
Offset Validation
Offset Validation
Offset Validation
