Geoinformatics in Applied Geomorphology
Edited by Siddan Anbazhagan
S. K. Subramanian
Xiaojun Yang
CRC Press
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1 Geoinformatics: An Overview and Recent Trends............................. 1 C. Jeganathan
2 Airborne Laser Scanning and Very High-Resolution Satellite Data for Geomorphological Mapping in Parts of Elbe River Valley, Germany....................................................................................... 23 Siddan Anbazhagan, Marco Trommler, and Elmar Csaplovics
3 Geoinformatics in Spatial and Temporal Analyses of Wind Erosion in Thar Desert............................................................................ 39 Amal Kar
4 Remote Sensing and GIS for Coastal Zone Management: Indian Experience..................................................................................... 63 Debashis Mitra
5 Kuwait Coastline Evolution during 1989–2007.................................. 87 S. Neelamani, S. Uddin, and Siddan Anbazhagan
6 Detecting Estuarine Bathymetric Changes with Historical Nautical Data and GIS 105 Xiaojun Yang and Tao Zhang
7 High-Resolution Mapping, Modeling, and Evolution of Subsurface Geomorphology Using Ground-Penetrating Radar Techniques 119 Victor J. Loveson and Anup R. Gjuar
8 Remote Sensing in Tectonic Geomorphic Studies: Selected Illustrations from the Northwestern Frontal Himalaya, India......141 G. Philip
9 Strain Accumulation Studies between Antarctica and India by Geodetically Tying the Two Continents with GPS Measurements................................................................................ 163 N. Ravi Kumar, E.C. Malaimani, S.V.R.R. Rao, A. Akilan, and K. Abilash
10 Indian Ocean Basin Deformation Studies by Episodic GPS Campaigns in the Islands Surrounding India 175 E.C. Malaimani, N. Ravi Kumar, A. Akilan, and K. Abilash
11 Remote Sensing and GIS in Groundwater Evaluation in Hilly Terrain of Jammu and Kashmir................................................ 187 G.S. Reddy, S.K. Subramanian, and P.K. Srivastava
12 Remote S ensing i n D elineating D eep F ractured Aquifer Z ones .................................................................................. 205 Siddan Anbazhagan, Balamurugan Guru, and T.K. Biswal
13 Remote Sensing and GIS for Locating Artificial Recharge Structures for Groundwater Sustainability...................................... 231 S.K. Subramanian and G.S. Reddy
14 Fuzzy Arithmetic Approach to Characterize Aquifer Vulnerability Considering Geologic Variability and Decision Makers’ Imprecision.............................................................................. 249 Venkatesh Uddameri and Vivekanand Honnungar
15 Remote Sensing and GIS in Petroleum Exploration....................... 269 D.S. Mitra
16 Geoinformatics in Terrain Analysis and Landslide Susceptibility Mapping in Part of Western Ghats, India.............. 291 Siddan Anbazhagan and K.S. Sajinkumar
17 Impact of Tsunami on Coastal Morphological Changes in Nagapattinam Coast, India................................................................... 317 E. Saranathan, V. Rajesh Kumar, and M. Kannan
18 Remote Sensing for Glacier Morphological and Mass Balance Studies....................................................................................... 335 Pratima Pandey and G. Venkataraman
19 Geomorphology and Development Mechanism of Sinkholes in Arid Regions with Emphasis on West Texas, Qatar Peninsula, and Dead Sea Area............................................................. 349 Fares M. Howari and Abdulali Sadiq
Preface
Geoinformatics is t he science a nd technology dealing w ith t he acquisition, processing, a nalyzing, a nd v isualization of spatial i nformation. It i ncludes remote sensing, photogrammetry, geographic i nformation systems, global positioning systems, a nd cartography. T he technologies i n geoinformatics have been used i n various d isciplines such as geology, geography, urban planning, environmental science, a nd global c hange science. With recent i nnovations i n data, technologies, a nd t heories i n t he w ider arena of remote sensing a nd geographic i nformation systems, t he u se of geoinformatics i n applied geomorphology has received more attention t han ever. Nevertheless, t here i s no book dedicated exclusively to t he u se of geoinformatics i n applied geomorphology, a field t hat examines t he i nteraction between geomorphology a nd human activities.
Given t he above context, a book discussing t he roles of geoinformatics in applied geomorphology is t imely. T his book examines how modern concepts, technologies, a nd methods i n geoinformatics can be u sed to solve a w ide variety of applied geomorphologic problems, such as c haracterization of a rid, coastal, fluvial, eolian, glacial, k arst, a nd tectonic landforms; natural h azard zoning a nd m itigations; petroleum exploration; a nd g roundwater exploration a nd m anagement. I n total, t his book contains 19 c hapters. Chapter 1 provides a n overview of geoinformatics a nd some recent developments i n t his field. Chapter 2 i ntroduces t he airborne laser scanning technique applied to map fluvial landforms. Chapter 3 d iscusses some environmental i ssues i n a rid environments by remote sensing. Chapters 4 t hrough 7 describe coastal zone management, coastal landform evolution, subsurface coastal geomorphology, and estuarine bathymetric c hange a nalysis. Chapters 8 t hrough 10 deal with tectonic geomorphology. Chapters 11 t hrough 14 discuss g roundwater evaluation, a rtificial recharge for g roundwater sustainability, deep fracture aquifer a nalysis, a nd aquifer v ulnerability. Chapter 15 focuses on petroleum exploration. Chapters 16 a nd 17 deal w ith n atural hazard assessment. Chapter 18 focuses on glacial landform mapping. Finally, Chapter 19 describes t he development of sinkhole landforms i n s everal different a reas.
This book i s t he result of extensive research by i nterdisciplinary ex perts and w ill appeal to students, researchers, a nd professionals dealing w ith geomorphology, geological engineering, geography, remote sensing, a nd geographic i nformation systems. T he editors are g rateful to all t hose who contributed c hapters a nd revised t heir c hapters one or more t imes, as well as to t hose who reviewed t heir c hapters according to our requests a nd
timelines. Reviewers who contributed t heir t ime, t alents, a nd energies are as follows: Kwasi Addo Appeaning, Santanu Banerjee, T.K. Biswal, D. Chandrasekharam, Armaroli Clara, Damien Closson, Michael Damen, Daniela D ucci, G.S. D warakish, M ikhail Ezersky, B Gopala K rishna, S. Muralikrishnan, Jeff Paine, Snehmani, S. Sankaran, Sridhar, K.P. Thrivikramji, P. Venkatachalam, a nd Weicheng Wu. T his book would not have been possible w ithout t he help a nd assistance of several staff members at CRC Press, especially I rma Shagla a nd Stephanie Morkert. T hanks are also due to S. Arivazhagan and Ramesh for their invaluable help.
Siddan Anbazhagan Salem, India
S.K. Subramanian Hyderabad, India
Xiaojun Yang Tallahassee, Florida
Editors
Dr. Siddan A nbazhagan is a director of t he Centre for Geoinformatics and Planetary Studies a nd head of t he Department of Geology at Periyar University, Salem, I ndia. He received h is PhD f rom Bharathidasan University (1995) a nd was awarded t he A lexander von Humboldt fellowship for h is postdoctoral research i n Germany. Dr. A nbazhagan’s research interests i nclude remote s ensing a nd GIS for applied geomorphology, hydrogeology, a nd d isaster m itigation. H is c urrent area of i nterest is planetary remote s ensing. H is research has been f unded by ISRO, DST, M HRD, and UGC. He has authored or coauthored more t han 60 publications including a n edited book entitled Exploration Geology and Geoinformatics. Dr. A nbazhagan serves as a reviewer for several remote sensing, environmental, a nd water resource journals. He has recently been m ade syndicate member a nd coordinator of research a nd development i n Periyar University.
Dr. S.K. Subramanian is a senior scientist a nd head of t he Hydrogeology Division at t he National Remote S ensing Centre (NRSC), I ndian Space Research O rganization (ISRO), Hyderabad, I ndia. He completed h is h igher studies at IIT Bombay a nd PhD f rom I ndian School of M ines, Dhanbad. Dr. Subramanian h as more t han 33 years of professional ex perience i n t he field of remote sensing a nd geomorphology. He has coordinated a number of national m ission projects, i ncluding I ntegrated M ission for Sustainable Development (IMSD), National (Natural) Resources I nformation System (NIRS), Rajiv Gandhi National Drinking Water M ission (RGNDWM), a nd National Agricultural Technology Project a nd a n i nternational project i n Dubai. I n addition, he was i nvolved i n several research projects, i ncluding Geomorphologic Evolution of West Coast, for hydrocarbon ex ploration, Mass Movement i n Kosi Catchment, a nd Geomorphology of Nepal, Chambal Ayacut, Chandrapur d istrict, R ajasthan State, a nd Northeastern states of I ndia. D r. Subramanian has authored or coauthored nearly 40 publications.
Dr. X iaojun Yang i s w ith t he Department of Geography at F lorida State University, Tallahassee, F lorida. He received h is BS i n geology f rom t he Chinese University of Geosciences (CUG) MS i n paleontology f rom CUG’s Beijing Graduate School, MS i n applied geomorphology f rom ITC, a nd his PhD i n geography f rom t he University of Georgia. D r. Yang’s research interest i ncludes t he development of remote s ensing a nd geographic information systems w ith applications i n t he environmental a nd u rban
domains. H is research has been f unded by EPA, NSF, a nd NASA. He h as authored or coauthored more t han 80 publications, i ncluding t wo journal theme i ssues a nd one book on coastal remote s ensing. He was a g uest editor for t he Environmental Management; ISPRS Journal of Photogrammetry and Remote Sensing; Photogrammetric Engineering and Remote Sensing; t he International Journal of Remote Sensing; a nd Computers, Environment and Urban Systems. Dr. Yang c urrently serves as c hair of t he Commission on Mapping f rom Satellite I magery of t he I nternational Cartographic Association.
Contributors
K. Abilash
National Geophysical Research Institute
Hyderabad, India
A. Akilan
National Geophysical Research Institute
Hyderabad, India
Siddan Anbazhagan Department of Geology Centre for Geoinformatics and Planetary Studies
Periyar University Salem, India
T.K. Biswal Department of Earth Sciences Indian Institute of Technology Mumbai, India
Elmar Csaplovics
Institute of Photogrammetry and Remote Sensing University of Technology Dresden Dresden, Germany
Anup R. Gjuar
National Institute of Oceanography Dana Paula, India
Balamurugan Guru
Jamsetji Tata Centre for Disaster Management Mumbai, India
Vivekanand Honnungar Department of Environmental Engineering
Texas A&M University-Kingsville Kingsville, Texas
Fares M. Howari College of Arts and Science The University of Texas of the Permian Basin Odessa, Texas
C. Jeganathan School of Geography University of Southampton Southampton, United Kingdom
M. Kannan
School of Civil Engineering
SASTRA University Thanjavur, India
Amal Kar
Central Arid Zone Research Institute Jodhpur, India
V. Rajesh Kumar
School of Civil Engineering
SASTRA University Thanjavur, India
N. Ravi Kumar
National Geophysical Research Institute Hyderabad, India
Victor J. Loveson
Central Institute of Mining and Fuel Research
Dhanbad, India
E.C. Malaimani
National Geophysical Research Institute
Hyderabad, India
Debashis Mitra
Marine Science Division
Indian Institute of Remote Sensing Dehradun, India
D.S. Mitra
Remote Sensing & Geomatics Division
KDM Institute of Petroleum Exploration
Oil & Natural Gas Corporation Limited Dehradun, India
S. Neelamani
Environment and Urban Development Division
Coastal and Air Pollution Department
Kuwait Institute for Scientific Research
Safat, Kuwait
Pratima Pandey Centre of Studies in Resources
Engineering
Indian Institute of Technology
Mumbai, India
G. Philip
Geomorphology and Environmental Geology Group
Wadia Institute of Himalayan Geology
Dehra Dun, India
S.V.R.R. Rao
National Geophysical Research Institute
Hyderabad, India
G.S. Reddy
National Remote Sensing Centre Hyderabad, India
Abdulali Sadiq
Department of Chemistry and Earth Sciences
Qatar University Doha, Qatar
K.S. Sajinkumar
Geological Survey of India Thiruvananthapuram, India
E. Saranathan
School of Civil Engineering SASTRA University Thanjavur, India
P.K. Srivastava
University of Petroleum and Energy Studies Dehradun, India
S.K. Subramanian
National Remote Sensing Centre Hyderabad, India
Marco Trommler
Institute of Photogrammetry and Remote Sensing University of Technology Dresden Dresden, Germany
Venkatesh Uddameri
Department of Environmental Engineering
Texas A&M University-Kingsville Kingsville, Texas
S. Uddin
Environment and Urban Development Division
Environmental Sciences Department
Kuwait Institute for Scientific Research
Safat, Kuwait
G. Venkataraman
Centre of Studies in Resources Engineering
Indian Institute of Technology
Mumbai, India
Xiaojun Yang Department of Geography
Florida State University
Tallahassee, Florida
Tao Zhang Department of Fisheries and Wildlife
Michigan State University East Lansing, Michigan
1 Geoinformatics: An Overview and Recent Trends
C. Jeganathan
1.6.1
1.6.2
1.1 I ntroduction
Archeological evidences have u nearthed t he fact t hat t he h istory of map making ex isted since ages. Humans h ave broadened t heir u nderstanding over t he years, about size, shape, a nd processes associated w ith earth, which i n t urn contributed i n making sophisticated a nd accurate representation of t he globe a nd its phenomena. Advancements i n space technology, digital i nformation, a nd communication technologies h ave stimulated the g rowth of
development of geographical i nformation system (GIS), which helps i n representing a nd modeling earth’s phenomena i n a n efficient way. Many new terminologies a nd terms, l ike geoinformatics, geomatics, geospatial systems, remote sensing (RS), GIS, etc., are often u sed when one deals with GIS. It has been generally felt t hat t he term “GIS” restricts one to t he idea of computer hardware a nd software, but t he term “geoinformatics” was well received a s it conveys a nd covers a broader meaning. T here are many definitions coined for t he term “geoinformatics.” A simple way to understand t his terminology would be to divide it as geo + i nformatics— the u sage of i nformation technology for geographic a nalysis. T his c hapter considers a definition on geoinformatics as “ an i ntegrated s cience a nd technology t hat deals w ith acquisition a nd manipulation of geographical data, t ransforming it i nto useful i nformation using geoscientific, a nalytical, a nd visualization techniques for making better decisions.” I n t his chapter, t he term GIS i s assumed to represent geoinformatics a nd v ice versa. T his c hapter begins w ith a briefing on h istorical background about GIS; ex plains basic terminologies, concepts, a nd spatial database organization; g ives a glimpse of t he variety of spatial a nalytical f unctions a nd applications; a nd, fi nally, leads to a spectrum of i ssues, t rends, a nd c hallenges in the geoinformatics domain.
1.2 Blossoming o f G eoinformatics
Developments during t he 1960s were caught up w ith m any technical problems l ike converting a nalogue map i nto computer-compatible form, format for storage, display techniques, a nd more. T he 1970s saw i nterests and participation of u niversities a nd t he need for topology (spatial relation) was felt. T he 1980s contributed for t he major g rowth of GIS due to advancements i n personal computers, c heaper hardware, a nd efficient software. T his led to new i nitiatives, progress i n spatial modeling, data structure i ssues, a nd R S l inkages. T his period also s aw successful a nd reliable systems a nd government i nterests a nd i nvestments. Further, a m ajor leap was seen during t he 1990s as more a nd more PCs, object-oriented architectures, networks, I nternet, a nd mobile technology started t aking day-to-day applications a nd hence benefited economic g rowth. Recently, the I nternet has become a major medium of communication a nd data dissemination. Over the years, GIS has evolved into a geographical information science a nd at present it i s a billion dollar market because it leads to geographical i nformation s ervices. Many software companies h ave also evolved w ith GIS a nd t hey have been playing a crucial role i n m aking GIS a commercially v iable solution, providing mechanisms for various
domains of human activity. Readers are h ighly recommended to read a book named The History of Geographical Information Systems: Perspectives from the Pioneers, edited by Foresman (1998), to get a fi rst-hand u nderstanding f rom t he words of t he people who were originally i nvolved i n the development of modern GIS since its beginning.
1.3 E lements o f a GIS
The GIS comprises of four elements. T hey a re hardware, software, dataware, a nd humanware. Hardware refers to physical components l ike CPU, hard d isk, monitors, digitizers, a nd printers. Software refers to programs, algorithms, a nd executable codes. Dataware refers to all po ssible i nput databases. Humanware refers to i nteraction of human to control a nd manipulate hardware, software, and dataware.
1.4 G eographic P henomena, Types, a nd I ts Representation
GIS i s a computer-based tool, which helps i n storing, retrieving, manipulating, a nalyzing, a nd producing maps about i nformation related to geographic phenomena w ith t he help of a human ex pert. Geographic phenomenon refers to a process associated w ith t he earth. I n order to represent geographic phenomenon i n GIS, its po sition, its property, a nd time of occurrence must be k nown so t hat one can retrieve i nformation about what h as h appened, where it has occurred, a nd when it h as h appened. I n simple words, geographic phenomenon i s nothing but what we see or observe about our earth, e.g., observation of daily temperature over a c ity, weather pattern, c rop c ycle, human settlement, m apping, etc. Many geographic phenomena are verbally easy to ex plain; some are easy to represent t hrough drawings l ike buildings, but t here a re phenomena that a re d ifficult to d raw l ike temperature or elevation. S o, i n order to represent such d iverse phenomena, some f ramework needs to be followed so t hat everybody represents t he same t hing i n t he same manner a nd, hence, it w ill be easy to u nderstand by all, globally. I n t his regard, t he geographic phenomena were d ivided i nto t wo m ajor g roups: objects and fields (DeBy et a l., 2004). T his c hapter adopts t his f ramework a s it was logically easy to l ink with g round reality. Objects refer to t he phenomena t hat are bounded by c risp boundaries, i.e., discrete ex istence. Fields refer to the phenomena t hat do not have sharp boundary, but a re rather f uzzy i n
their presence a nd occur at a ll places, i.e., continuous ex istence. Field can be again categorized i nto t wo more categories: continuous field and discrete field, according to t heir f uzziness i n representation. Examples of ob jectlike phenomena a re r ivers, buildings, volcanoes, islands, etc. Examples of discrete fields are land u se map, soil map, geology map, etc. S oil a nd geology occur everywhere a nd we cannot exactly see t heir starting a nd ending points on t he g round, u nless a nd u ntil some sharp natural barriers occur. But for representing t hem, we have to consider some probable end point a nd i ntroduce some artificial discreteness—hence, it i s called discrete fields. Examples of continuous fields a re temperature, elevation, humidity, etc. It is generally obs erved t hat man-made t hings are objects and natural t hings a re fields, w ith exceptions l ike r ivers, volcanoes, a nd islands t hat a re natural t hings but a re considered as objects too, as t hey do not occur everywhere. So we can say t hat “all man-made t hings are objects and all objects are not man made.”
Point, l ine, a nd polygon are t he basic building blocks for representing any phenomena i n t he computer. But representation of a ny phenomenon in computer depends upon t he mapping s cale, because at 1:1 m illion scale towns w ill become points, but at 1:10,000 s cale t hey are polygons. Generally, land use, administrative boundary, a nd t hematic m aps are mapped t hrough polygon. Roads, r ivers, pipelines, a nd electricity lines are mapped a s l ines. Village locations, utility locations, a nd field obs ervations are represented as points.
Apart f rom u nderstanding t heir locational a nd conformal property, one must a lso u nderstand t heir attribute properties, as every element, whether it is a point or l ine or polygon, needs to have some description about what it represents. T he domain of attribute data is c lassified i nto four categories as nominal, ordinal, rational, and interval. Q ualitative data are represented t hrough nominal data, which cannot provide a ny quantitative meaning. Nominal refers to t he data t hat a re generally used for identification purposes. For example, t he name of a person, t he name of a road, telephone number, house number, etc. are nominal k inds of data. Ordinal data refer to ordered data i n which we can i nfer order of i mportance, e.g., i f we rank t he people as per t heir exam score, t hen it is a n ordinal data. R atio data a re t he actual fact/data measured on t he g round quantitatively, which h as actual origin at zero; e.g., 0 m m is t he same as 0 k m; hence, d istance or length i s a “ratio” data. T he term “ratio” does not convey a ny meaning about division; however, t he term “rational” m ight have become “ratio.” I nterval data are data i n which actual origin differs at d ifferent places a nd t he value zero conveys different measures, e.g., 0° Kelvin i s d ifferent f rom 0° Celsius. Hence, temperature is a n i nterval data. Also, when we measure earthquake i n R ichter s cale, t he energy difference of t wo earthquakes of magnitude 5.1 a nd 5.2 i s different t han t he energy released by 7.1 a nd 7.2. A lthough t he difference i n magnitude i s 0.1 i n
both t he cases, t he actual quantitative meaning i s completely different. Hence, earthquake measurement is a n i nterval data. T he u ser must be aware about t hese “types of data” while working on spatial operations, as all the operations/a nalyses are not possible with all types of data.
1.4.1 Spatial D ata S tructure
There a re t wo broad t ypes of data structures generally adopted to represent a ll geographic phenomena i n computer, u nder GIS. T he t ypes are vector a nd raster. Vector i n mathematical sense reveals a “quantity a nd direction.” But i n GIS, vector i s u sed for referring to t he basic m athematical elements or building blocks—point, l ine, a nd polygon. A ny map t hat is prepared u sing t hese building blocks is called vector map. It is po ssible to represent roads, buildings, administrative u nits, land u se, geology, a nd more u sing vector concepts, but t here a re many phenomena t hat cannot be represented i n vector. For example, how can we record temperature or elevation or humidity or R S reflectance value? GIS has been molded to represent t hese t ypes of phenomena u sing tessellation concept. Tessellation i s nothing but t he d ivision of t he space/area i nto u niform grid a nd fi nding t he dominant phenomenon o ccurring w ithin each g rid. The space can be d ivided using u niform g rid, also called regular g rids like squares, rectangles, t riangles, pentagons, or hexagons. Normally, a square g rid is adopted i n t he tessellation process due to its simplicity w ith many h idden advantages; for i nstance, it is easy to fi nd t he location of a ny grid i f we k now t he origin of t he g rid’s location a nd size, a nd it is easy to store, retrieve, a nd a nalyze. I f t he size of t he g rid is smaller, t hen it o ccupies more storage space, a nd i f we i ncrease t he size of t he g rid, t hen we may lose some i nformation variability w ithin t hat g rid. T herefore, one has to come to a compromise i n selecting t he size of g rid, which is called resolution.
In general, the tessellated space looks like a 2D matrix of g rids/cells, which is called raster. Raster has g reater advantage over vector, especially for spatial a nalysis a nd modeling, as we can deal with a ny part of t he study area as every pixel is explicitly represented. But i n vector, only t he boundary of t he phenomena is represented. T herefore, we can do only logical operations l ike AND, OR, NOT with vector layers a nd we cannot perform arithmetic operations l ike addition, subtraction, multiplication, or division. However, i n raster, we can perform all k inds of operations l ike comparison, logical operations, a rithmetic a nd t rigonometric operations, a nd dynamic simulations. RS data a re raster data a nd, hence, we can directly adopt R S into our GIS models, if our data are i n raster structure. Vector data are good for printing accurate representation created i n GIS (Figure 1.1).
If we adopt t he regular tessellation for t he phenomenon, which occurs over very large spatial a rea, t hen we w ill end up h aving t he same cell
World continentsEasting and northingWorld lakes
World rivers
FIGURE 1.1
Different layers and their integration in GIS.
value occurring repeatedly a nd hence o ccupy more storage space. I n such redundant storage, a nother approach was adopted, which is called i rregular tessellation. I n i rregular tessellation, t he space i s d ivided only i f it has diverse features. By t his approach, more storage space can be saved. O ne such i rregular tessellation is Q uadtree data storage structure. T he examples of regular and irregular tessellation are shown in Figure 1.2.
There a re many data storage formats t hat adopt various a lgorithms a nd some of t hem a re even proprietary. T he main objectives behind such a lgorithms a re lossless compression, faster retrieval, a nd efficient manipulation. I n order to represent elevation, a new k ind of storage technique using a n approach called t riangulated i rregular network (TIN) has been used. TIN i s a n i rregular tessellation i n which t riangle is a basic building block i n which t hree i nput reference height points a re u sed. From t he network of t riangles, it would be easy to calculate elevation, slope, a nd any aspect for a ny point i n t he study area. Generally, GIS u sers adopt the vector-based approach for creating i nputs, as it is more convenient. In vector mode, d ifferent software u se different formats. Storing t he data in vector format i s a bit complex because t here are many ways to save coordinates, attributes, data structure, spatial relationship (topology), a nd visualizing the stored information. Some of the most used vector formats are given in Table 1.1.
Raster format is not only used to save images obtained t hrough scanning, digital photographs, or satellite i mages (RS data), but also to save
FIGURE 1.2
Regular a nd i rregular tessellation: (a) square, ( b) hexagonal, (c) t riangular, a nd (d) quadtree.
geographic phenomena, which continuously vary i n space—like topography a nd temperature. Table 1.2 g ives t he description about some of the widely used raster formats.
1.4.2 Spatial L ayers o r G eodatabase
The common requirement to access data, on t he basis of t he t ype of phenomena, has led to t he creation of each t ype i nto a separate entity called layers, such as roads, r ivers, or vegetation t ypes, i n which a ll t he features of t he same t ype are g rouped w ithin a so-called layer or map. The concept of layer i s applicable i n both vector a nd raster models. T he layers can be combined w ith each other i n various ways to c reate new layers t hat a re a f unction of t he i ndividual i nput layers. For a g iven study area, t he layers must h ave a positional i nformation a nd it must belong to a specific geographic range, i rrespective of whether it i s a polygon bounded by l ines i n vector system or a g rid cell i n a raster system. Such database bundle i s called geodatabase. T he layers concepts i n GIS are shown i n Figure 1.1.
1.4.3 Planimetric R equirements
All t he maps are used for some k ind of measurement purposes i n reallife applications. Hence, t he measurement m ade on t he maps has to be accurate. Since our earth i s a 3D body a nd layers have to be i n 2D mode,
(a)
(b)
(d) (c)
TABLE 1 .1
Widely Used Vector Formats
File Format Name
DGN MicroStation design files
DLG Digital line graphs
DWG Autodesk drawing files
DXF Autodesk drawing exchange format
E00 ARC/INFO interchange file
GML Geography markup language
MIF/MID MapInfo interchange format
SDTS Spatial data transfer system
SHP ESRI shapefile
Description
DGN is an intern format for MicroStation, a computer-aided design (CAD) software. DGN file contains detailed visualization information (like color for various layers, pattern, thickness, etc.) apart from the spatial, attribute information
DLG is used by the U.S. Geological Survey (USGS) for handling vector information from printed paper maps. It contains very precise coordinate information and sophisticated information about object classification, but no other attributes. DLG does not contain any visualization information (display)
DWG is an intern format for AutoCAD. Because of the lack of standards for linking attributes, problems may occur while converting this format between various systems
DXF is a common transfer format for vector data. It contains visualization information and is supported by nearly all graphic programs. Nearly all programs can successfully import this format because of high standards
E00 is a transfer format available both as ASCII and binary form. It is mainly used to exchange files between different versions of ARC/INFO, but can also be read by many other GIS programs
XML-standard for exchanging and saving geographical vector data. It is used in the Open GIS Consortium
MIF/MID is MapInfo’s standard format, but most other GIS programs can also read it. The format handles three types of information: geometry, attributes, and visualization
SDTS is a transfer format developed in the United States and is designed for handling all types of geographical data. SDTS can be saved as ASCII or binary. In principle, all geographical objects can be saved as SDTS, including coordinates, complex attributes, and visualization information. These advantages nevertheless increase complexity. To simplify it, many standards have been developed as “coprojects” to SDTS. The first of these standards is Topological Vector Profile (TVP), used to save some types of vector data
Shape is ArcView’s internal format for vector data. Associated to the Shape file (*.shp), there is a file to handle attributes (*.dbf) and an index file (*.shx). Nearly all other GIS programs can import this format
TABLE 1 .1 ( continued )
Widely Used Vector Formats
File Format Name Description
SVG Scalable vector graphics
TIGER Topologically integrated geographic encoding and referencing files
VPF Vector product format
VXP Idrisi32 ASCII vector export format
WMF Microsoft Windows metafile
XML-standard for presentation of vector on the Internet. It is approved in the World Wide Web Consortium
TIGER is an ASCII transfer format made by the U.S. Census Bureau to save road maps. It contains complete geographic coordinates and is line-based. The most important attributes include road names and address information. TIGER has its own visualization information
VPF is a binary format made by the U.S. Defense Mapping Agency. It is well documented and can easily be used internally or as a transfer format. It contains geometry and attribute information, but no visualization information. VPF files are also named VMAP product. The Digital Chart of the World (DCW) is published in this form
IDRISI 32’s vector export format (ASCII)
WMF is a vector file format for Microsoft Windows Operation Systems
Source: CGISLU, GIS educational materials, Centre for Geographical Information System, Lund University, Lund, Sweden, 2003.
there i s a need for a mechanism t hat can help i n achieving t his t ransition between 3D a nd 2D (Figure 1.3). T his mechanism is called projection. T he entire geospatial database generated u nder GIS must be i n a planimetric coordinate system, i.e., it must be represented i n a 2D reference f rame so that we can fi nd out t he area a nd length correctly. T here a re m any ways by which a 3D globe can be converted i nto 2D. Cylinder, cone, a nd plane are simple mathematical figures t hat can be utilized for t his conversion. Conversion is done by w rapping t he earth w ith t he paper of t hese shapes and t hen making t he i mprint of global feature on t he paper a nd t hen unwrapping t he paper. Converting f rom 3D to 2D i ntroduces some loss in either a rea or shape or direction measurement. Based on t he property it preserves, t he projection receives its name l ike equal area projection, conformal projection, etc.
Another major hurdle i n t he projection process is t hat t he earth does not have smooth surfaces. It has g ravitational u ndulations, which are visible from mean sea level plots. Because of t he nonlinear complex u ndulations, it is very difficult to replicate t he exact position and height of a location accurately on a map. Therefore, assumptions about t he shape of our earth have to be made as either ellipsoid or spheroid so t hat mathematically it
TABLE 1 .2
Widely Used Raster Formats
File Format Name
ADRG Arc digitized raster graphics
BIL Band interleaved by line
BIP Band interleaved by pixel
BSQ Band sequential
DEM Digital elevation model
GTOPO30 Global 30 arc second elevation data set
GeoTIFF GeoTIFF
GRIB GRid in binary
PCX PC paintbrush exchange
SDTS Spatial data transfer standard
TIFF Tagged image file format
Description
ADRG is a format created by the U.S. military to save paper maps in raster format
BIL is a computer compatible tape (CCT) format that stores all bands of remotely sensed data in one image file. Scanlines are sequenced by interleaving all image bands
When using the BIP image format, each line of an image is stored sequentially, pixel1 all bands, pixel 2 all bands, etc.
BSQ is a CCT format that stores each band of satellite data in one image file for all scanlines in the imagery array
DEM is a raster format created by the U.S.GS (U.S. Geological Survey) for saving elevation data
GTOPO30 is a global, digital elevation model with a horizontal cell size of 30 s (approx. 1 km). GTOPO30 was created from different raster and vector sources
GeoTIFF is a form of tag image file format(TIFF) format for storing georeferenced raster data
GRIB is the World Meteorological Organisation’s (WMO) standard for grid-based meteorological data
PCX is a common raster format found in many scanners and graphic programs
SDTS is a format for transferring geographical information. An SDTS variant is specifically made for transferring raster data
Like PCX, TIFF is a common raster format produced by drawing programs and scanners. TIFF format gives a relatively big data file, but compresses the data without loss of information
Source: CGISLU, GIS educational materials, Centre for Geographical Information System, Lund University, Lund, Sweden, 2003.
FIGURE 1.3 Map projection.
3D curved earth (longitude and latitude)
2D flat earth (easting and northing)
would lead to projection. Every country has adopted its local mean sea level surface reference for height measurement, which is called vertical datum and has adopted a mathematical surface (ellipsoid) t hat fits better for its portion of t he globe, which is called horizontal datum. If we want to convert from one projection to a nother, we need to have seven parameters related to t ranslation effect (dx, dy, d z), rotation effect (rx, ry, rz), a nd a scale factor.
1.4.4 Errors a nd D ata Q uality i n G IS
In GIS, errors may i ntrude at various stages of operations starting f rom data generation to a nalysis. D uring vector-layer c reation, one may encounter u ndershoots a nd overshoots, which refers to u nclosed polygon l ines and overdrawn l ines, respectively. Undershoot i s a major error for a polygon layer, which leads to u nder- or overestimation of polygon area. Errors may a lso a rise i f one m isses a polygon attribute. O vershoot w ill lead to overestimation of length a nd w ill be a serious error for l inear vector layers. Proper definition of tolerances, l ike snap tolerance for avoiding dangles at t he t ime of d igitization, f uzzy tolerance for removing dangles at the t ime of topology building a fter digitization, a nd t unnel tolerance for removing redundant vertices, would help in making quality input layers. Correctness of t he feature codification needs to be c hecked, for obvious error, against laid down standards. W hen dealing w ith R S data, t he error intrudes i nto georeferencing a nd classification. I n multidate i mage registration, one has to make sure t hat all t he i mages are accurately matched within a single-pixel accuracy. D uring classification, t he t raining samples may contain m isrepresentation a nd, hence, result i n s erious omission or commission errors. Proper field validation a nd cross-verification with ex isting toposheets/maps during t raining would help i n reducing errors. By c ross-tabulating field-check points or t raining sites versus c lassified i mages, one can make confusion matrix, a nd can, t herefore, derive omission error, commission error, average accuracy, a nd Kappa measure. Some of t he standard quality measures a nd mapping standards, as recommended by t he National Natural Resources Management System (NNRMS, 2005) i n I ndia, a re positional accuracy—1 mm of scale; coordinate movement tolerance— 0.125 mm of scale; raster/grid resolution— 0.5 mm of scale; weed tolerance— 0.125 mm of scale; etc.
1.5 Spatial A nalysis
Whether it i s for n atural resources or sustainable development, or n atural disaster management, selecting t he best site for waste disposal, optimum
route a lignment, or local problems, it w ill have a geographical component. GIS has power to create maps, i ntegrate i nformation, v isualize scenarios, solve complicated problems, present powerful ideas, a nd develop effective solutions l ike never before. I n brief, it can be said to be a supporting tool for decision-making processes. O nly when all t he maps are projected to a n agreed u niform coordinate system, would it be possible to perform spatial analysis.
GIS i s u sed to perform a variety of spatial a nalysis, using points, l ines, polygons, a nd raster data sets. GIS operational procedure a nd a nalytical tasks t hat a re particularly u seful for spatial a nalysis i nclude t he following: single-layer operations, multilayer operations, measurement operations, neighborhood a nalysis, network a nalysis, 3D surface a nalysis, a nd predictive a nd simulation a nalyses. T here a re huge a nd d iverse a nalytical groups i n geoinformatics l ike point pattern a nalysis for s ampled n atural resources management; facility management i n u rban environment; decision-support system for planning support; population, health, a nd epidemiological modeling; t ime-series modeling i n land–ocean–atmosphere; geomorphologic–geological a nalysis (landslide zonation, snowmelt r unoff modeling, m ineral ex ploration, hazard a nd r isk assessment, etc.); a nd land cover c hange dynamics a nd predictive modeling. O ut of a ll po ssible functionalities of GIS, t wo a nalytical perspectives m ainly dominate t he global GIS users: (a) geostatistics and (b) spatial decision support system.
1.5.1 Geostatistics
Most of t he spatial a nalysis a nd modeling were done i n t he raster domain, where a value at each raster-pixel location plays a role most of t he t ime— e.g., NDVI is derived by a “local” operation using t wo spectral bands; analysis u sing “weighted sum” does depend on i ndividual raster values. However, t he real power of GIS l ies i n its ability to carry out t he a nalysis t hat t akes i nto account t he i nherent spatial location along w ith its attributes, i n a statistical manner, i.e., geostatistics. Geostatistics received more attention after its successful utilization i n t he domain of economic geology by Matheron (1963) a nd Journel (1974), a nd later i mproved its usability by m any more researchers (Mark a nd Webster, 2006). A lthough the spatial coverage of a n a nalysis can be extended t hrough “focal” a nd “zonal” operations, “Kriging” i s considered as one of t he powerful geostatistical operation, which t ruly considers t he locational i nformation to calculate variability of pixel values t hrough a semivariance measure, and it a lso provides t he spatial variation of error i n prediction. K riging is a k ind of i nterpolation technique to fi nd out values at u nknown locations u sing sparse sampled values. It falls u nder t he category of t wopoint statistics (i.e., one single-pixel value i s a nalyzed w ith reference to another single-pixel value) a nd many texture measures also fall u nder
the t wo-point statistics. Fundamentals about K riging can be found i n Cressie (1990). Geostatistics has been utilized extensively i n different scientific domains—e.g., i n m ining ( Journel a nd Huijbriqts, 1978), for natural resources evaluation (Goovaerts, 1997), for reservoir modeling (Deutsch, 2002), to model spatial u ncertainty (Chiles a nd Delfiner, 1999), in downscaling (Atkinson et a l., 2008), a nd for environmental applications (Atkinson a nd Lloyd, 2010). Recently, due to a l imitation of t wo-point statistics i n revealing t he complex earth surface pattern a nd landforms, many researchers attempted multipoint statistics (MPS) (Boucher, 2008). MPS looks i nto multiple values at multiple locations i n a single operation and helps to simulate complex geological/geomorphological phenomena and patterns. It has been successfully attempted i n modeling fluvial reservoir (Wong a nd Shibli, 2001), geological structure (Strebelle, 2002), a nd aquifer c haracterization (Mariethoz, 2009). It has also proven to be a very powerful tool for super-resolution mapping, where a coarse spatial resolution i mage i s t ransformed i nto a fi ne spatial resolution i mage (Boucher, 2008; Mariethoz, 2009).
1.5.2 Spatial D ecision S upport System
In t he past t wo decades, t he concept of spatial decision-support system (SDSS) has taken a n i mportant role where t he main t hrust is placed on scenario building, data a nalysis, a nd decision-making techniques rather than mapping. SDSS is a n i nteractive, flexible, a nd adaptable computerbased i nformation system, especially developed for fi nding solutions for semistructured management problems (Sharifi a nd Marjan, 2002). Decision making i n GIS has t hree perspectives (Malzewski, 1999; Sharifi and Marjan, 2002): multiattribute decision making (MADM) against multiobjective decision m aking (MODM), i ndividual versus g roup decision problem, a nd decision u nder certainty versus decision u nder u ncertainty. Although d ifferent researchers (Sprague a nd Carlson, 1982; Marakas, 1999; Haag et a l., 2000; Power, 2002) proposed different components for SDSS, five-component a rchitecture i s generally accepted comprising t he following: (i) database, (ii) model-base, (iii) k nowledge-base, (iv) control-unit, and (v) u ser i nterface. Database: T he database component is considered of warehouse i n a n SDSS. Model-base: Under t he control of t he model-base management system, it is able to handle models ( both perspective a nd descriptive) t hat a re designed to perform estimations a nd to solve specific optimization problems. Knowledge-base: T he k nowledge-base contains all the necessary i nformation to handle models a nd data. Control-unit: T he control-unit provides t he necessary coordination between t he various components. User interface: Finally, t he u ser i nterface is considered as a separate component, since t he d ialogue w ith t he controlling is personalized to adapt to a particular u ser/organization/project need. A nalytical
hierarchical process (AHP) is one of t he w idely used MADM techniques, and, recently, techniques l ike cellular automata a nd agent-based modeling tools a re getting w ider scope for a nalyzing spatiotemporal i nformation f rom dynamical processes i n t he u rban a nd natural environment i n order to t ake quicker decisions i n emergency situations a nd d isaster relief modeling.
1.6 R ecent Trends a nd F uture C hallenges i n GIS
The number of GIS u sers has g rown f rom mere t housands i n t he 1980s (Goodchild, 1998) to m illions i n 2005, a nd t he a nnual revenue of GIS industry has g rown f rom m illions to billions of dollars i n 2005 (Blake, 2006). D igital technology, considered once as a n alien discipline of study, has now become a major topic of i nterest i n I ndia a nd a ll over t he globe. Every g raduate i n I ndia pursues computer course simultaneously a long with t heir own d iscipline. Every geosciences student studies about GIS and uses it in his or her curriculum and projects. With t he r ise of t he World Wide Web (WWW), new I nternet protocols such as t he Hypertext Transfer Protocol (HTTP), new markup languages like HTML, DHTML, X ML, GML, as well as easy-to-use i nterfaces ( browsers), tools (Flash), a nd languages (.NET, Java, scripts), t he I nternet has become a powerful media for t he f uture. Google Earth a nd Wikimapia are wonderful examples of such I nternet-based geoinformation s ervices. Furthermore, w ith t he advancements i n mobile communication technology, t he world is moving toward having GIS i n mobile phones, by which one can locate t he nearest ATMs, nearest t heatres, nearest hospitals, optimal route between source a nd destination, etc. Such applications are called location-based services (LBS), which w ill help i n better e -governance a nd also during d isaster relief operations (Bennett a nd Capella, 2006). GIS has g rown f rom Desktop GIS to Enterprise GIS, a nd to I nternet GIS, a nd today it i s a Mobile GIS. Readers are recommended to read a n article by Gupta (2006) t itled “GIS i n t he i nternet era: W hat I ndia w ill gain?” for a detailed outlook on t he g rowth of I nternet i n I ndia a nd t he g ift of its wedding with GIS.
Although t he g rowth of t he GIS is enormous, t here a re some v ital c hallenges still to be a nswered or to be achieved by GIS developers. Satellitebased R S i s providing a d igital data at a resolution (in terms of spatial, spectral, a nd temporal) much h igher t han what is needed for many applications. T his has pushed t he GIS to look for more sophisticated a nd efficient a lgorithms, data models to store, retrieve, a nd a nalyze such huge volumes of data. T his multidimensional g rowth i n digital data h as forced
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“At least,” replied the caballero, “I’ll go with you to the rancho at fall of night.”
The Indian showed his delight in his face. Without a word he slipped away down the cave toward the streak of light in the distance, and the caballero stood beside his horse, listening, waiting, trying to pierce the gloom with his eyes.
“Dead or alive, eh?” he muttered. “I would I had slain this pretty gentleman at San Diego de Alcalá when I had the opportunity!”
It was an hour before the gentile returned—an hour during which the caballero often led his horse through the cave to the exit and looked out over the valley, but dared not leave until he received the Indian’s report. And then the native slipped in past the rocks and stood before him.
“I have been over the hill, señor,” he said. “The soldiers burned the camp in the cañon and then went back. They have been scattered over the hills, and two rode away down the valley, probably to spread the alarm and warn rancho owners to watch for you. It will soon be dark, and we can leave this cave.”
“The soldiers will remain in the hills for the night?” the caballero asked.
“They would fear to do that, señor, if they think we contemplate an attack. They will return to the mission, perhaps, and spend the night there, and go into the hills at dawn again. Two men remain at the presidio with the sergeant who brought the warning, I heard them say. If we could take the presidio to-night, señor, and get the arms there——”
“It may be a trap,” the caballero said. “I know the tricks of the soldiers, remember. It would be better to be guided by me in these matters.”
The gentile replied nothing, but the expression of his face told that he was not pleased. For another hour they remained in the cave, scarcely speaking, and then the Indian crept to the entrance,
remained there for a time, and returned to say that it was time to go.
Emerging from the cavern, they made their way slowly, and as silently as possible, down the slope to the floor of the cañon, and along this they hurried, the Indian leading, the caballero walking beside his horse.
Out upon a plain trail that ran to the south the caballero mounted, with the gentile behind him. At a trot they went along the trail, stopping now and then to listen for sounds of other horsemen, the caballero waiting at every likely ambush until the Indian had made an investigation.
For a time they followed another arroyo, finally to come into a broad valley where there were fields of grain and horses and cattle. At the crest of the slope lights glittered in the buildings of the rancho.
But the Indian did not indicate that they were to go toward the lights. He whispered directions in the caballero’s ear, and they circled the buildings, and so came to the bank of a creek flowing from a group of springs. Down this they made their way to a small basin. A voice hailed them; the gentile answered; they went on. And then they turned the base of a hill and came within sight of two score campfires and groups of teepees, where half-naked gentiles danced around the flames, and others squatted on the ground watching.
In an instant they were surrounded and questions hurled at them, menacing at first, better-natured when the caballero’s guide made himself heard and gave the identity of the man with whom he rode. A young chief ordered his followers to one side, and himself took the caballero’s bridle, and led the horse past the fires to a teepee at the end of the row. There the caballero dismounted and sat upon a skin spread on the ground. No word was spoken while a man brought out food and wine and the caballero ate.
One by one other chiefs made their appearance to sit before the fire in a circle. In the distance groups of warriors gathered to look at their leaders and talk in low tones.
“We have had a messenger, señor,” a chief spoke, finally. “He came from the mission at nightfall. All is known to the soldiers, this man says. They have orders to capture you, dead or alive. The Governor is coming south with a large force. Our friends in the north wait for us to act. And we await the word from you.”
“You will be guided by me in this matter?” the caballero asked.
“If you counsel immediate attack, señor. What is to be gained now by delay? The soldiers from the north may arrive within three days. If we strike now, we succeed before they come.”
Grunts of approval came from the others, and the caballero, looking around the circle, read in the faces there that the words of the spokesman expressed the sentiments of all.
“We have considered the matter to-day,” the chief went on. “Our leaders in the north have been seized. Of all white men who aided us in forming this plan, you alone are at liberty. We thank you, señor, for what you have done. We want to follow you, yet. But we cannot unless you give the word now. Our race is strong in itself, señor; often before we have waited on the words of white men and been betrayed.”
“What is it you want me to do?” the caballero asked.
“Give the word, señor! Our friends at San Luis Rey de Francia will be ready to strike the blow two nights from now, and it is proper we strike together. What say you, señor?”
“I counsel longer delay,” the caballero replied.
“Can you give us good reason?” the chief demanded. “Your words are peculiar to our ears, señor. We had expected you would be eager to make the move. Many things have mystified us, and we are suspicious because of what has happened before. As I said, we have considered the matter, and we have reached a decision.”
“What is it, then?”
“Either give us the word now to attack in two nights’ time, or we attack without your word, señor. To be certain there will be no treachery we will hold you prisoner here, but well treated, until the attack is begun. We do this because of the aid you have given our cause. And after it is over you shall be treated with respect, and no man will harm you. Lead us in two nights’ time, señor, or we strike without your leadership and keep you prisoner until the work is done.”
The caballero swept the circle with his eyes; every man there seemed to approve.
“There are many plans to be made yet,” he said. “I must counsel delay for a time.”
“We have made all plans while awaiting you, señor. It is but for you to lead. The plans may be discussed in half a day’s time, and changed if we decide they should be. If there is a rancho you wish spared, or a man or woman saved——”
“Do I not know what is best in this matter?” the caballero demanded. “Do as I instruct, or I will have nothing further in common with you!”
“That is your answer, señor?”
“It is. I counsel more delay!”
“Then we have decided. You will remain in this camp until the blow is struck. I regret, señor, that your ideas are not ours, but we have gone too far to risk failure. Our friendship for you remains the same; it is merely a disagreement between leaders in a council of war. You will remain in the camp as we ask, señor?”
“Suppose I prefer to ride?” said the caballero.
“We cannot take that risk, señor. If you leave, we must consider you an enemy.”
Another series of grunts came from the circle; again the caballero read determination in the faces that confronted him. He got up, and
the others did likewise.
“I suppose I may have a teepee, food, picket my horse?” he asked. “You shall have every courtesy, señor. This teepee is yours, food will be furnished, you may picket your horse behind you.”
“So be it!”
The caballero caught the reins from the Indian who had been holding them and led the animal to the rear of the teepee. The chiefs scattered to their own huts; the men resumed their dancing around the fires. The caballero threw the reins over his horse’s head and started to fumble at the cinch of the saddle.
The spokesman turned his head aside for an instant to look at the dancers, and in that instant, the caballero vaulted to the horse’s back, shrieked a cry in the animal’s ear, gathered up reins and applied spurs, and dashed past the chief and down the arroyo.
Shrieks of surprise and fear rolled from a hundred throats. The group about the first fire scattered; the horse kicked the embers in the faces of the gentiles. Down the line of fires the caballero rode like a madman, hurling Indians right and left, while behind the chiefs, realising what he was doing, yelled orders to take him, screamed for horsemen to go in pursuit, called for weapons.
Pistols exploded, bullets whistled past him as he rode. Unscathed he reached the darkness and dashed down the valley, while the hoofs of hard-ridden ponies pounded in pursuit. He fired his pistol in the face of an Indian sentinel who would have sprung at the horse’s head—and then he rode madly, blindly, trusting to the surefootedness of the steed he bestrode.
The animal took the backward track, half maddened with fear and gunning like the wind. The caballero bent low over the beast’s neck and let reins fall free. He did not fear being overtaken, but he did not know what was ahead. Soldiers sought him—dead or alive. Indians would slay him without hesitation now, fearful he would use treachery against them. El Camino Real was watched. Rancho
owners were on the alert for him. He was an outlaw in truth, and in a strange country.
Mile after mile he rode, until his horse began to stagger in its stride, and then, emerging from the mouth of a cañon he saw lights in the distance and stopped to reconnoitre. The wash of the sea came to his ears. The horse had circled the valley and the lights ahead were in the presidio.
“Now I am cut off,” quoth the caballero, “from the society of all persons, both reputable and disreputable! Riding alone, however, I shall not be hindered by the opinions of followers. And—by all the saints!—I have much work to do!”
AT THE PRESIDIO
Not a light showed in the Indian huts of tide and straw that clustered near the presidio. From behind the walls came no whisperings of conspiracy, no cries of children in their dreams, no mumblings of women. Since nightfall they had been slipping away down the coast and back into the hills—the men to hurry to their camps, women and children to seek refuge in the wilderness until the war should be over—exactly as they had done in every uprising since the coming of Serra and his coadjutors.
Even the mangy curs that generally infested the road were gone, and so there was none to bark and snap at the heels of the caballero as he made his way slowly toward the presidio, walking silently, holding the scabbard of his sword so it would not strike against boot or spur, stopping every few yards to peer into the night, to listen for some sound above the wash of the sea that would apprise him of the nearness of danger.
Standing beside the bole of a palm he looked up at the structure atop the knoll. The gate was closed, but light came over the wall, and he could hear the sound of voices raised in argument. Then there came to his ears the shrieking of an Indian, a raucous Spanish voice raised in anger and command, the sound of a lash striking into bare skin.
He left the tree and crept through the shadows, avoiding the front, going to the left. Standing against the wall he listened again.
“Tell us, dog!” Sergeant Cassara was shouting. “Tell us, or by the saints we’ll have your hide in strips! Be stubborn before your betters, will you?”
The lash fell again; again the Indian shrieked; coarse laughter smote the air.
“’Tis well we caught one of you!” the sergeant was saying now. “Sneak away like the coyotes you are, will you? Where is that camp —tell us!”
“Señor señor I cannot tell!” the Indian screeched.
“Will not, you mean! Cannot, you hound, when every gentile and neophyte within a score of miles knows of it? Where have the others gone, then? Answer me that!”
“One by one they slipped away, señor.”
“And you do not know where, eh?”
“I I cannot tell, señor.”
“Will not, you mean?”
“Siseñor!I willnot!”
The Indian’s voice changed; the caballero listening by the wall knew what the change meant—stoical resignation to his fate was upon the red man now; he expected to be beaten, perhaps slain, and he was ready.
“Now, by all the saints, this thing passes a jest!” the sergeant cried.
“With the dogs a hundred to one against us, it is proper we should have all information, else soldiers may ride in one direction while gentiles advance from another and sweep all before them. And here is a man admitting he knows where the conspirators’ camp lies, and refusing to tell his betters. For the last time, hound, will you speak?”
“I cannot tell you, señor!”
“You realise what is to happen to you if you do not?”
“It is easy to guess señor.”
The caballero hurried on around the wall until he came to a small rear gate, used generally to take in supplies. It, too, was barred on the inside; but it was studded on the outside with heavy bolts, and the caballero, using these for footholds and handholds, made his way laboriously to the top of the wall.
He raised his head carefully, and peered over. All was darkness in that corner of the enclosure. He pulled himself up and dropped over, and for an instant crouched in the shadows against the wall, listening. But no challenge rang out, and he decided the two soldiers left behind with Cassara were inside the barracks-room.
Silently he walked across to the wall of the building, and silently he followed it until he could peer through a window. He looked into an officers’ room, but through the open door he could see the interior of the barracks-room proper.
An Indian stood in the centre of it, his hands behind his back, his body tall and straight, his face expressionless. Before him was Sergeant Cassara with lash in his hand. The two soldiers sat to one side on stools before a small table with wine cups before them.
Sergeant Cassara swung the whip through the air, and the lash curled around the Indian’s body. There was no shriek this time—the gentile’s eyes closed for a moment, flickered, then opened wide, and his body swayed forward a bit as the sergeant jerked the whip back.
“Speak!” he commanded. “Tell us the whereabouts of the camp, dog!”
Again the lash was raised. The caballero did not wait to see the result. He walked on around the building, and came to the open door. There he took his pistol from his belt, gripped it for action, stepped into the path of light, took a quick step—and had entered the barracks-room and was standing before them.
“Allow me to tell you the location of the camp, Sergeant Cassara!” he said.
The whip dropped to the floor and the sergeant’s hand dropped to the hilt of his sword. The two soldiers had sprung to their feet, but muskets and pistols were on the other side of the room, and the muzzle of the caballero’s weapon menaced them.
“Stand as you are!” the caballero ordered. “At least I will drive the soul of the first man who moves to eternity. As you are!”
“Captain Fly-by-Night, by all the saints!” Cassara cried. “Hah! He walks into a trap!”
“Beater of gentiles, he spares you the instant death you deserved!” answered the caballero. “Move if you like! This pistol of mine gets one, and, as for the others——”
“The others, perhaps, when we do move, will live to see you shot!” Cassara growled.
“Indeed? Careful there, soldier! Your hand may be itching to grasp a pistol, but there is a sure cure here for the itch!”
The Indian slipped like a shadow toward a window.
“Stop!” the caballero commanded. “I have need of you, gentile. You are a brave man to refuse these soldiers information. See if you are brave enough, now, to turn them into helpless slaves. There are thongs in the corner, I perceive. Get them, and fasten the hands of that nearest soldier behind his back!”
“Now by all——” Cassara began.
“Swear not, sergeant! It amuses me to have this done. It was information you desired, I believe, and I am going to give it you, also ask some in return.... Fasten those bonds well, gentile!... I understand, sergeant, it was you came dashing along El Camino Real with word that I was to be taken, dead or alive?”
“I had that pleasure, caballero!”
“Um! And by what right——?”
