Dr Ganesh Prusty

Defence Terrain Research LaboratoryDRDO, Metcalfe House, Delhi-110 054

Geospatial Intelligence for Defence Preparedness

----GeoINT is integral part of Intelligence Preparation of Battlefield (IPB)

----play a key role in Military Operations: base for strategic planning & tactical decisions

----GEOINT provides innovative, versatile solutions for meeting today’s demanding intelligence requirements and predicting tomorrow’s future threat environment.

Terrain analysis consists of interpreting natural and man-made features of a geographic area, together with the influences of weather and climate, to determine their effects on military operations.

GeoINT refers to exploitation and analysis of Satellite imagery, AP and geospatial information to describe, assess and visually depict physical feature and geographically referenced activities on the Earth.

Basic input to Terrain Analysis

MILITARY REQUIREMENTS: DRDO Endeavour•Topographic/Terrain Mapping•Visualization- Strategic Planning•Terrain (scene) Matching for cruise missile guidance•War Gaming- Tactical operations, inter-visibility for optimal positioning•X-country trafficability Assessment•Training Simulators for mission planning & rehearsal•Cover & Concealment planning•Natural (Geological) hazards result in changed surface topography

Need of the hour : Rapid-Response – conventional field survey untenableAvailable data is archaicGeoINT of unexplored virgin trans-border terrainFast changing terrain : frequent updatesData in Digital format: Input to contemporary warfareDemand for high resolution TerraINT Interoperable Data for Network centric warfare

Image Intelligence from space platforms is the solution

Geospatial Technology for Military Application

GeoINT Level-1Parameters

GeoINT Level-2Terrain Intelligence

Contour / micro-reliefSlopeSlope aspectPattern -featuresTexture - featuresState-of-the-groundLoad bearing capacityShear strengthforest cover/agriculturegeologygeomorphologyHydrologyHazard Potential Mapping

GeoINT Level-3Military Requirements

Terrain ReliefLand use/land coverSurface waterSoil characteristicsLandformsVegetationSoil Moisture

Geo-visualization: Strategic PlanningTrn Scene MatchingWar GamingTraining SimulatorGoing maps: Mission Plnground water potentialHazard MitigationCover & Conceal. planningTarget IdentificationLine of sightHazard Susceptibility MappingLanding zonesField of fireAmphibious crossing

Oblique Ariel Photography

Advantages of vertical over oblique aerial photographs•present approximately uniform scale throughout the photo and making measurements (e.g., distances and directions) easier and more accurate.

•constant scale throughout a vertical photograph, the determination of directions (i.e., bearing or azimuth) can be performed in the same manner as a map.

•easier to interpret, tall objects (e.g., buildings, trees, hills, etc.) will not mask other objects.

•simple to use photogrammetrically as a minimum of mathematical correction is required.

•To some extent and under certain conditions (e.g., flat terrain), may be used as a map if a coordinate grid system and legend information are added.

•Stereoscopic study is also more effective

Advantages of oblique over vertical aerial photographs•covers much more ground area than a vertical photo taken from the same altitude and with the same focal length.

•If an area is frequently covered by cloud layer, there may be enough clearance for oblique coverage.

•more natural view because we are accustomed to seeing the ground features obliquely, will be more recognizable because the silhouettes of these objects are visible.

•Objects that are under trees or under other tall objects such as ridges, cliffs, caves, etc., may not show on a vertical photograph if they are directly beneath the camera.

•Determination of feature elevations is more accurate

•Because oblique aerial photos are not used for photogrammetric and precision purposes, they may use inexpensive cameras.

Terrain Analysis Applications in tactical operationsArea analysis study

Line of Communication (LOC)Cover & ConcealmentCross Country Movement (CCM)

•Line of Site and Zone of Entry

•Geo-Visualization

•Flood modeling for strategic perception

•Mission planning & execution

•Geo-environmental analysis

•Decision Support System for hazard mitigation

Worldview (2m)Cartosat (5m)DTED 30m

Input Data Resolution Dictates the Feature Scale

Improvement in Elevation Contour Resolution

Contours cannot be generated

Cartosat1

Worldview

Input Data Resolution Dictates the Feature Density

For mapping 1:25K

For mapping 1:5K

INPUTS FOR GIS

Geographical Information System

Digital Elevation Model (DEM)DEM are the terrain elevations at regularly spaced horizontal intervals, i.e., a grid of regularly spaced elevations.

DEM reconstruction from Satellite ImageryInSAR Radargrammetry Stereogrammetry Clinometry

BandF

BandA Reconstructed DEM of Ladakh test Site

Cartosat 1 stereo pair

DEM is the key to all scientific research related to earth surface.

Elevation modeling has become an important part of geo-spatial intelligence required for Military Operations & Planning. However, the data need to be contiguous.

Optical stereo mapping depends on appropriate weather & illumination conditions. Sometime it excludes certain region of earth temporarily.

Radar-grammetry present an alternative due to its cloud penetration capability.

Optical data is available with higher resolution, whereas, Radar data mostly available with lower resolution.

Synergistic use of optical & SAR data for DEM reconstruction can facilitate contiguous mapping and enhance the accuracy.

Comparison & Fusion of DEMs Derived FromComparison & Fusion of DEMs Derived FromMulti-date and Multi-sensor Satellite Data SourcesMulti-date and Multi-sensor Satellite Data Sources

Scientific Rationale

1. DEM reconstruction

2. Co-registration & DEM normalization

3. Void filling

4. Accuracy assessment

5. DEM fusion based on weighted average (correlation image)• Expt-1 with multi-date same sensor data with different cloud

localization• Expt-2 with multi-polarization data• Expt-3 with multi-sensor data having different resolution and

characteristics

6. Validation

Model Development

Ortho Img-O1DEM-1Corr Img-C1 Co-registration

MaskedDEM-1(D3)

MaskedDEM-2(D4)

Demarcation of cloud/shadow regions

•If C1=C2=0 then D1else[(D1*C1)+(D2*C2)] / (C1+C2)

•If D3<0 then D2

•If D4<0 then D1

Fused DEM

Ortho Img-O2 DEM-2 Corr Img-C2

Normalisation Normalisation

D1 D2

Validation

Reconstruction of DEM & Ortho-rectification of PAN image

2008

2009

PAN-OrthoCorrelation ImgDEM

PAN-OrthoDEM Correlation Img

Fusion

DEM Minimum Maximum Mean Standard Deviation

2008 256 953 587.28 114.66

2009 242 921 587.95 112.524

Fused DEM 388 950 588.52 112.67

Statistical observations

Fusion of Multi-date DEMs

2008

2009

Elevation Histogram

Accuracy Assessment

Elevation

No

of

Pix

els

Results

Cartosat

Radarsat

DEM Correlation Image

Multi-sensor DEM Fusion: Reconstruction

Model Developed

Cartosat Radarsat

EITHER $n7_norm_radarsat IF ( $n12_masked==0. ) OR float (($n12_cartosat * $n13_c_score) + ($n7_norm_radarsat * $n5_r_score)) / float ($n13_c_score + $n5_r_score) OTHERWISE

Fusion

Fused DEM

Error Histogram

Relief Category

SD

Accuracy assessment based on Relief categories

Accuracy Assessment

Central Rimo G.

Teram Shehr G.

Siachen

G.

Saltaro Hills

Bila

fond

G.

Teram Shehr Group

Gyongla G.

N

Geo-VisualizationGeo-Visualization

3D Visualization Engine

DTM Generation

Digital database creation

Terrain skin

generation

Feature Extraction+Merging

Enhanced DEM texture Image

Texture based Modeling and Visualization System

Design of process flowDesign of process flow

DEM TIN Modeling DTM

Satellite Imagery Topographical Map

Texture Tile

Geo-visualized model

Trafficability Potential / Going Maps

GM for Tracked Vehicle (L) and GM for Wheeled Vehicle (R)

Texture based 3-D visualization System

Test Sites: Desert, Runn, Coastal Plain, Alluvial plain & Cold desert

Remote Sensing Inputs

TerrainVariables

VIR band data

SAR (MW)data

SignatureExtraction

Model Building

SM estimationand Mapping

Approach Strategies

Multi-polarization strategyMulti-incidence angleChange detection strategy

FieldCampaigns

Data Processing

SynchronizedSame period

SOIL MOISTURE INVERSION MODEL

=1-10%

=11-20%

= 21-30%

Soil Moisture Mapping

Data input

Multi incidence angle SAR

Multi polarized SAR

Surface roughness

Apr_03

Oct_03

Jan_04

Feb_04

Apr_04

Moisture Mapping System

Soil Moisture temporal dynamics: Inversion Model Based on Microwave RS

Data: RADARSAT SAR

Also have Agriculture application

TPMS: Automatic Generation of Terrain TPMS: Automatic Generation of Terrain Parameters- Soil Texture MappingParameters- Soil Texture Mapping

Technique: Rough Set Theory& CBR hybridization

AREA –DIAMOND HARBOUR

CLAY LOAM

SIL.C. LOAM

SILTY CLAY

SILTY LOAM

SANDY LOAM

SAN.C.LOAM

SANDY CLAY

LOAM

CLAY

SANDY

Automatic Terrain Feature Extraction-Landuse Feature

• Features Extraction using Chip Mining approach

• Technique Used-Rough Set theory

• Better Accuracy achieved than statistical techniques of standard I/P software

Automated extraction of Landforms from Multi-spectral imagery

Area details• Physiographic Setting• Geological Set-up• Terrain Characteristics • Trafficability Analysis with Maps

Military Potential • Camouflage and concealment• Movement of Men and Animal• Camping Sites and Dropping Zones• Areas of Artificial Triggering• Areas susceptible to inundation

AREA ANALYSIS STUDY

Going MapIMAGE

Cross country Movement (CCM)

•Trafficability potential of the denied or otherwise inaccessible areas.

•An assessment of trafficability requires knowledge of soil types (which are in turn controlled by the underlying bedrock type); the physical, chemical, and biological soil forming processes at work; and meteorological conditions.

•Creation of computer expert systems that will be able to combine map layers showing roads, soil types, topography, rivers, vegetation, and land use to produce probabilistic estimates of trafficability for specific vehicle types and weather conditions.

•Terrain is classified into three categories based upon trafficability: go, slow-go, and no-go.

Natural Habitat Characterization: Turtle rookery dynamics using multi-temporal & multi-spectral RS data

Video Clip

MAGIC: Progressive change of landforms’ configuration, evident from historical satellite datasets

Nov.’1988 Mar.’1991

Ekakula spit

Nasi sandbar

Satabhaya gap

Jan’1997

Feb.’1998 Mar.’2001Apr.’2003

Mar.’2004

LISS 3PAN

Data fusion Land-water Delineation

Elevation Leveling & Contouring

Shoreline Extraction

Classification (20 classes)

Surfacing for DEM

Acquiring Timely Tidal measurement for each Images from Indian Tidal Tables

Target Shoreline(1.68m tide level)

Reference Shoreline (1.69m ref. level)

Process flow of Tide Elevation Normalized Change Detection: Nasi sandbars

Set of 7 multi-sensor reference data

PAN

01-01-97

Change detection

0 0 0

4.50

2.302.98

2.90

6.206.50

6.80

3.40

6.956.72

3.84

6.59

-30

-20

-10

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190

% s

urfa

ce a

rea

chan

ge w

ith

resp

ect

to r

efer

ence

yea

r

Bar turtle emergence in lakhs

Turtle nesting emergence in relation to surface area dynamics of Nasi barrier bar

Time period in number of months since Nov.1988

Turtle rookery dynamics characterization using multi-temporal & multi-spectral RS data

% S

urf

ace

are

a c

ha

ng

e w

rt r

efe

ren

ce y

ear 70%

-30%

Digital Elevation Model (DEM) of Nasi I and Nasi II barrier bars indicatingeffective nesting surface area above highest high tide line of the season

1999Nasi I

1999Nasi II

2000Nasi I

2000Nasi II

2001Nasi I

2001Nasi II

Estimation of dune celerity and sand flux using Cartosat-1 images: A case study of Gadra,

South Rajasthan, India

Study of dune migration in the dynamic desertic environment is of immense importance for military planning and operation. The Western sector of India shares a strategic international boundary. Since it consists of the dunes which are dynamic, dune celerity and sand flux studies are vital Geospatial Intelligence.

2010 2011

Total no. of tie points 43 31

RMSE 0.665 0.87

Residual Report of DEM reconstruction and orthorectification

Residual Report of 2010 and 2011 registration

2010 & 2011

Total no. of Ground Control Points 23

RMSE 0.43

DEM Reconstruction and Co-registrationDEM Reconstruction and Co-registration

Correlation and Dune Migration• Horizontal ground displacements are retrieved from the sub-pixel correlation of the pre and post-orthorectified images.

• Image correlation is achieved with an iterative, unbiased processor that estimates the phase plane.

• This process leads to two displacement images, each representing one of the horizontal ground displacement components (East-West and North-South)

Vector Field showing directional trend of displacement (left)

Euclidean Displacement Map

2010 2011

Ortho Image

DEM

Euclidean Displacement Map

5151

Accuracy Assessment

SNRMeasure of quality of correlation performedFor our study pixels < 0.9 SNR were excluded because of noisePrecision of correlation

To validate the correlation results observations are taken in the inter-dunal region where the displacement is supposed to be minimum.

Dune elevation map accuracyCalculated by seeing the variation of the reconstructed DEM with a reference

The standard deviation was found to be 2.5m.

Results

IndicesArea1 Area2

Mean SD Mean SD

EDM (m) 1.2959 0.8813 1.2079 0.8878

Sand Flux(m3/m/day) 0.0141 0.0384 0.0171 0.03409

Celerity (m/day) 0.0035 0.0024 0.0033 0.0024

Decision Support System for Landslide Decision Support System for Landslide mitigationmitigation

WATER : INDIA’S NEW BATTLEGROUNDWATER : INDIA’S NEW BATTLEGROUND (TerraINT as Force Multiplier) (TerraINT as Force Multiplier)

• Geo- politics on main river systems- Indus, Ganga and Brahmaputra

• AGRICULTURE• Per-capita demand

• HOSTILE NEIGHBOURS

WATER

Demographic factors

Energy(hydro-power)

Climate change

Industrialization

UrbanizationIrrigation

Trans-border Rivers: Indo-Pakistan borderTrans-border Rivers: Indo-Pakistan border

• Control of the River Jhelum by India • Strategic edge to India and serious threat to Pakistan

Trans- Border Rivers: Indo- China BorderTrans- Border Rivers: Indo- China BorderTibetan Plateau source of Indus, Brahmaputra and Satluj rivers.

China Hydro-Hegemony in South AsiaProposed Projects in TAR

•Tibet water tower- supply 25%world•Diversion of trans-border river water: N-S Link•Using water as weapon: deny (drought)/ oversupply (flood)•Planned/executed 3 Major Dam Systems near IB•Indus, Sutlej & Bramaputra: Indo-China trans-border rivers•Present study: Flooding due to artificial triggering

Shanuan (Cascade)

Zangmu (3 gorges)

Motuo (U Bend)

FMSP: Inundation mapping (in case of artificially triggered flood event)

TEST SITE: Trans- boundary River Siang

• ‘River characterization: Field input & RS

• Scene Modeling using Remote sensing data

• Geometric data input: Flood plain, channel c/s, discharge, channel roughness

•Modeling Envn: HEC-RAS, ArcGIS, CCH2D, River2D

Boundary Initial

Conditions

Flood plain DEM & Channel cross

section

Scene Modeling (RS data)

Hydrodynamic Modeling

(Numerical and Physical)

Depth, Velocity, Discharge

Inundation Area

3D visualized model

Flood Inundation Map

GIS Integration

MSS Imagery(23.5 m) Cartosat-1 DEM (20m)

Field Photograph: 14th- - 25th Mar10•Lead hr with dam burst

•Max flow depth for peak discharge

Collaborators: IIT-G & IIT-K

Steady/unsteady flow: Physical Model (Hydraulics Laboratory at IIT, Kanpur)

Great ‘U’ Bend being exploited by China

Longitudinal Profile of the river

Simulation Results of 1D- Steady flow conditions

Maximum flow depth for peak discharge

Lead Hour at different locations

Simulation Results of 1D- Unsteady flow conditions

Initial and boundary condition for simulation : Normal depth as 0.004 m

Channel velocity at 80,000 cumecs

Hydraulic depth variation:

MappingIMINT

Electro-Optical

DTMNight/Day

RADAR/IFSAR

Terrain classification

Camouflage detection

Multi/Hyper Spectral

GeoINT: supporting military operations

HELICOPTERLANDING ZONES

OBJ

DEM Generation

MissionMission Planning & ExecutionPlanning & Execution

GeoINT typical scenarios

• Problem: Where can I land my humanitarian assistance team?

– Must consider slope, vegetation, obstacles, and proximity to Lines of Communications

Geospatial Intelligence Mission Digital Elevation Models (DEM):

Reconstruction & fusion

Ortho image for Thematic Mapping: Natural & man-made feature extraction

DEM derivatives – contours, spot ht, slope and slope aspect

Flood modeling for strategic perception

Situational Awareness & Analysis System (SAAS) S/W : GeoINT analysis and 3D Geo-visualization

GeoINT products for Tibet & Myanmar (400km depth, 12.7lac Sq Km)

200km

400km

200km

400km

COK

T I B E T

I N D I A

6464