Eye Classes of Photosensors

Where do the properties of the eye get involved?

It's know that the eye does not see all wavelengths equally. The eye has two general classes of photosensors, cones and rods.

Cones:
The cones are responsible for light-adapted vision; they respond to color and have high resolution in the central foveal region. The light-adapted relative spectral response of the eye is called the spectral luminous efficiency function for photopic vision, V(l) or V(wavelength). This empirical curve, first adopted by the International Commission on Illumination (CIE) in 1924, has a peak of unity at 555 nm, and decreases to levels below 10^–5 at about 370 and 785 nm. The 50% points are near 510 nm and 610 nm, indicating that the curve is slightly skewed. The V(l) curve looks very much like a Gaussian function; in fact a Gaussian curve can easily be fit and is a good representation under some circumstances. I used a non-linear regression technique to obtain the following equation:

More recent measurements have shown that the 1924 curve may not best represent typical human vision. It appears to underestimate the response at wavelengths shorter than 460 nm. Judd (1951), Vos (1978) and Stockman and Sharpe (1999) have made incremental advances in our knowledge of the photopic response.

Rods:
The rods are responsible for dark-adapted vision, with no color information and poor resolution when compared to the foveal cones. The dark-adapted relative spectral response of the eye is called the spectral luminous efficiency function for scotopic vision, V’(l). This is another empirical curve, adopted by the CIE in 1951. It is defined between 380 nm and 780 nm. The V’(l) curve has a peak of unity at 507 nm, and decreases to levels below 10–3 at about 380 and 645 nm. The 50% points are near 455 nm and 550 nm. This scotopic curve can also be fit with a Gaussian, although the fit is not quite as good as the photopic curve. My best fit is

Photopic (light adapted cone) vision is active for luminances greater than 3 cd/m². Scotopic (dark-adapted rod) vision is active for luminances lower than 0.01 cd/m². In between, both rods and cones contribute in varying amounts, and in this range the vision is called mesopic. There are currently efforts under way to characterize the composite spectral response in the mesopic range for vision research at intermediate luminance levels.

The Color Vision Lab at UCSD has an impressive collection of the data files, including V(l), V’(l), and some of the newer ones that you need to do this kind of work.

Difference of lambertian and isotropic

What is the difference between lambertian and isotropic?

Both terms mean "the same in all directions" and are unfortunately sometimes used interchangeably.

Isotropic implies a spherical source that radiates the same in all directions, i.e., the intensity (W/sr) is the same in all directions. We often hear about an "isotropic point source." There can be no such thing; because the energy density would have to be infinite. But a small, uniform sphere comes very close. The best example is a globular tungsten lamp with a milky white diffuse envelope, as used in dressing room lighting. From our vantage point, a distant star can be considered an isotropic point source.

Lambertian refers to a flat radiating surface. It can be an active surface or a passive, reflective surface. Here the intensity falls off as the cosine of the observation angle with respect to the surface normal (Lambert's law). The radiance (W/m2-sr) is independent of direction. A good example is a surface painted with a good "matte" or "flat" white paint. If it is uniformly illuminated, like from the sun, it appears equally bright from whatever direction you view it. Note that the flat radiating surface can be an elemental area of a curved surface.

The ratio of the radiant exitance (W/m²) to the radiance (W/m²-sr) of a lambertian surface is a factor of p or (pi) and not 2p or 2pi . We integrate radiance over a hemisphere, and find that the presence of the factor of cos(q) or (cos teta) in the definition of radiance gives us this interesting result. It is not intuitive, as we know that there are 2p steradians in a hemisphere.

A lambertian sphere illuminated by a distant point source will display a radiance which is maximum at the surface where the local normal coincides with the incoming beam. The radiance will fall off with a cosine dependence to zero at the terminator. If the intensity (integrated radiance over area) is unity when viewing from the source, then the intensity when viewing from the side is 1/p . Think about this and consider whether or not our Moon is lambertian. I'll have more to say about this at a later date in another place!

Quantities and units used in photometry

They are basically the same as the radiometric units except that they are weighted for the spectral response of the human eye and have funny names. A few additional units have been introduced to deal with the amount of light reflected from diffuse (matte) surfaces. The symbols used are identical to those radiometric units, except that a subscript "v" is added to denote "visual". The following chart compares them.

QUANTITY	RADIOMETRIC	PHOTOMETRIC
power	watt (W)	lumen (lm)
power per unit area	W/m2	lm/m2 = lux (lx)
power per unit solid angle	W/sr	lm/sr = candela (cd)
power per area per solid angle	W/m2-sr	lm/m2-sr = cd/m2 = nit

Now we can get more specific about the details.

The candela is one of the seven base units of the SI system. It is defined as follows:

The candela is the luminous intensity, in a given direction, of a source that emits monochromatic radiation of frequency 540 x 10¹² hertz and that has a radiant intensity in that direction of 1/683 watt per steradian.

The candela is abbreviated as cd and its symbol is Iv. The above definition was adopted by the 16th CGPM in 1979.

The candela was formerly defined as the luminous intensity, in the perpendicular direction, of a surface of 1/600 000 square metre of a black body at the temperature of freezing platinum under a pressure of 101 325 newtons per square metre. This earlier definition was initially adopted in 1946 and later modified by the 13th CGPM (1967). It was abrogated in 1979 and replaced by the current definition.

The current definition was adopted because of several reasons. First, the freezing point of platinum (» 2042K) was tied to another base unit, the kelvin. If the best estimate of this point were changed, it would then impact the candela. The uncertainty of the thermodynamic temperature of this fixed point created an unacceptable uncertainty in the value of the candela. Second, the realization of the Pt blackbody was extraordinarily difficult; only a few were ever built. Third, if the temperature were slightly off, possibly because of temperature gradients or contamination, the freezing point might change or the temperature of the cavity might differ. The sensitivity of the candela to a slight change in temperature is significant. At a wavelength 555 nm, a change in temperature of only 1K results in a luminance change approaching 1%. Fourth, the relative spectral radiance of blackbody radiation changes drastically (some three orders of magnitude) over the visible range. Finally, recent advances in radiometry offered a host of new possibilities for the realization of the candela.

The value 683 lm/W was selected based upon the best measurements with existing platinum freezing point blackbodies. It has varied over time from 620 to nearly 700 lm/W, depending largely upon the assigned value of the freezing point of platinum.   The value of 1/600 000 square metre was chosen to maintain consistency with prior standards. Note that neither the old nor the new definition say anything about the spectral response of the human eye. There are additional definitions that include the characteristics of the eye, but the base unit (candela) and those SI units derived from it are "eyeless."

Also note that in the definition there is no specification for the spatial distribution of intensity. Luminous intensity, while often associated with an isotropic point source, is a valid specification for characterizing highly directional light sources such as spotlights and LEDs.

One other issue before we press on. Since the candela is now defined in terms of other SI derived quantities, there is really no need to retain it as an SI base quantity. It remains so for reasons of history and continuity.

The lumen is an SI derived unit for luminous flux. The abbreviation is lm and the symbol is Fv. The lumen is derived from the candela and is the luminous flux emitted into unit solid angle (1 sr) by an isotropic point source having a luminous intensity of 1 candela.   The lumen is the product of luminous intensity and solid angle, cd-sr. It is analogous to the unit of radiant flux (watt), differing only in the eye response weighting. If a light source is isotropic, the relationship between lumens and candelas is 1 cd = 4p lm. In other words, an isotropic source having a luminous intensity of 1 candela emits 4p lumens into space, which just happens to be 4p steradians. We can also state that 1 cd = 1 lm/sr, analogous to the equivalent radiometric definition.

If a source is not isotropic, the relationship between candelas and lumens is empirical. A fundamental method used to determine the total flux (lumens) is to measure Later on, we can use this "calibrated" lamp as a reference in an integrating sphere for routine measurements of luminous flux.

Lumens are what we get from the hardware store when we purchase a light bulb. We want a high number of lumens with a minimum of power consumption and a reasonable lifetime. Projection devices are also characterized by lumens to indicate how much luminous flux they can deliver to a screen.

Illuminance is another SI derived quantity which denotes luminous flux density . It has a special name, lux, and is lumens per square metre, or lm/m2. The symbol is Ev. Most light meters measure this quantity, as it is of great importance in illuminating engineering. The IESNA Lighting Handbook has some sixteen pages of recommended illuminances for various activities and locales, ranging from morgues to museums. Typical values range from 100 000 lx for direct sunlight to 20-50 lx for hospital corridors at night. Luminance should probably be included on the official list of derived SI quantities, but is not. It is analogous to radiance, differentiating the lumen with respect to both area and direction. It also has a special name, nit, and is cd/m2 or lm/m2-sr if you prefer. The symbol is Lv.  It is most often used to characterize the "brightness" of flat emitting or reflecting surfaces.  A typical use would be the luminance of your laptop computer screen.  They have between 100 and 250 nits, and the sunlight readable ones have more than 1000 nits. Typical CRT monitors have between 50 and 125 nits.

Other photometric units

We have other photometric units (boy, do we have some strange ones). Photometric quantities should be reported in SI units as given above. However, the literature is filled with now obsolete terminology and we must be able to interpret it. So here are a few terms that have been used in the past.

Illuminance :

1 metre-candle = 1 lux

1 phot = 1 lm/cm2 = 104 lux

1 foot-candle = 1 lumen/ft2 = 10.76 lux

1 milliphot = 10 lux

Luminance : Here we have two classes of units. The first is conventional, easily related to the SI unit, the cd/m2 (nit).

1 stilb = 1 cd/cm2 = 104 cd/m2 = 104 nit

1 cd/ft2 = 10.76 cd/m2 = 10.76 nit

The second class was designed to "simplify" characterization of light reflected from diffuse surfaces by including in the definitions the concept of a perfect diffuse reflector (lambertian, reflectance r = 1). If one unit of illuminance falls upon this hypothetical reflector, then 1 unit of luminance is reflected. The perfect diffuse reflector emits 1/p units of luminance per unit illuminance. If the reflectance is r, then the luminance is r times the illuminance. Consequently, these units all have a factor of (1/p) built in.

1 lambert = (1/p) cd/cm2 = (104/p) cd/m2

1 apostilb = (1/p) cd/m2

1 foot-lambert = (1/p) cd/ft2 = 3.426 cd/m2

1 millilambert = (10/p) cd/m2

1 skot = 1 milliblondel = (10-3/p) cd/m2

Photometric quantities are already the result of an integration over wavelength. It therefore makes no sense to speak of spectral luminance or the like.

Quantities and Units in Radiometry

What are the quantities and units used in radiometry?

Radiometric units can be divided into two conceptual areas: those having to do with power or energy, and those that are geometric in nature. The first two are:

Energy is an SI derived unit, measured in joules (J). The recommended symbol for energy is Q. An acceptable alternate is W.

Power (a.k.a. radiant flux) is another SI derived unit. It is the derivative of energy with respect to time, dQ/dt, and the unit is the watt (W). The recommended symbol for power is F (the uppercase Greek letter phi). An acceptable alternate is P.

Energy is the integral over time of power, and is used for integrating detectors and pulsed sources. Power is used for non-integrating detectors and continuous sources. Even though we patronize the power utility, what we are actually buying is energy in watt-hours.

Now we become more specific and incorporate power with the geometric quantities area and solid angle

Irradiance (a.k.a. flux density) is another SI derived unit and is measured in W/m2. Irradiance is power per unit area incident from all directions in a hemisphere onto a surface that coincides with the base of that hemisphere. A similar quantity is radiant exitance, which is power per unit area leaving a surface into a hemisphere whose base is that surface. The symbol for irradiance is E and the symbol for radiant exitance is M. Irradiance (or radiant exitance) is the derivative of power with respect to area, dF /dA. The integral of irradiance or radiant exitance over area is power.

Radiant intensity is another SI derived unit and is measured in W/sr. Intensity is power per unit solid angle. The symbol is I. Intensity is the derivative of power with respect to solid angle, dF /dw . The integral of radiant intensity over solid angle is power.

Radiance is the last SI derived unit we need and is measured in W/m2-sr. Radiance is power per unit projected area per unit solid angle. The symbol is L. Radiance is the derivative of power with respect to solid angle and projected area, dF /dw dA cos(q) where q is the angle between the surface normal and the specified direction. The integral of radiance over area and solid angle is power.

A great deal of confusion concerns the use and misuse of the term intensity. Some folks use it for W/sr, some use it for W/m² and others use it for W/m²-sr. It is quite clearly defined in the SI system, in the definition of the base unit of luminous intensity, the candela. Some attempt to justify alternate uses by adding adjectives like field or optical (used for W/m²) or specific (used for W/m²-sr), but this practice only adds to the confusion. The underlying concept is (quantity per unit solid angle). For an extended discussion, I wrote a paper entitled "Getting Intense on Intensity" for Metrologia (official journal of the BIPM) and a letter to OSA's "Optics and Photonics News". A modified version is available on the web.

Photon quantities are also common. They are related to the radiometric quantities by the relationship Q_p = hc/l where Q_p is the energy of a photon at wavelength l , h is Planck's constant and c is the velocity of light. At a wavelength of 1 mm, there are approximately 5×10¹⁸ photons per second in a watt. Conversely, also at 1 mm, 1 photon has an energy of 2×10^–19 joules (watt-sec). Common units include sec^–1-m^–2-sr^–1 for photon radiance.

Projected area and solid angle

What is projected area?

Projected area is defined as the rectilinear projection of a surface of any shape onto a plane normal to the unit vector. The differential form is dAproj = cos(b) dA where b is the angle between the local surface normal and the line of sight. We can integrate over the (perceptible) surface area to get

Some common examples are shown in the table below:

SHAPE	AREA	PROJECTED AREA
Flat rectangle	A = L×W	Aproj= L×W cos b
Circular disc	A = p r2     = p d2 / 4	Aproj = p r2 cos b          = p d2 cos b / 4
Sphere	A = 4 p r2 = p d2	Aproj = A/4 = p r2

What is solid angle?

Plane angle and solid angle are two derived units in the SI system. The following definitions are taken from NIST SP811.

"The radian is the plane angle between two radii of a circle that cuts off on the circumference an arc equal in length to the radius."

The abbreviation for the radian is rad. Since there are 2p radians in a circle, the conversion between degrees and radians is 1 rad = (180/p) degrees.

A solid angle extends the concept to three dimensions.

"One steradian (sr) is the solid angle that, having its vertex in the center of a sphere, cuts off an area on the surface of the sphere equal to that of a square with sides of length equal to the radius of the sphere."

The solid angle is thus ratio of the spherical area to the square of the radius. The spherical area is a projection of the object of interest onto a unit sphere, and the solid angle is the surface area of that projection. If we divide the surface area of a sphere by the square of its radius, we find that there are 4p steradians of solid angle in a sphere. One hemisphere has 2p steradians.

The symbol for solid angle is either w , the lowercase Greek letter omega, or W , the uppercase omega. I use w exclusively for solid angle, reserving W for the advanced concept of projected solid angle (w cosq ).

Both plane angles and solid angles are dimensionless quantities, and they can lead to confusion when attempting dimensional analysis.

Radiometry and photometry

What is radiometry ? 

Radiometry is the measurement of optical radiation, which is electromagnetic radiation within the frequency range between 3×1011 and 3×1016 Hz. This range corresponds to wavelengths between 0.01 and 1000 micrometres (m m), and includes the regions commonly called the ultraviolet, the visible and the infrared. Two out of many typical units encountered are watts/m2 and photons/sec-steradian.

What is photometry ?

Photometry is the measurement of light, which is defined as electromagnetic radiation which is detectable by the human eye. It is thus restricted to the wavelength range from about 360 to 830 nanometers (nm; 1000 nm = 1 mm). Photometry is just like radiometry except that everything is weighted by the spectral response of the eye. Visual photometry uses the eye as a comparison detector, while physical photometry uses either optical radiation detectors constructed to mimic the spectral response of the eye, or spectroradiometry coupled with appropriate calculations to do the eye response weighting. Typical photometric units include lumens, lux, candelas, and a host of other bizarre ones.

How do Radiometry and photometry differ

The only real difference between radiometry and photometry is that radiometry includes the entire optical radiation spectrum, while photometry is limited to the visible spectrum as defined by the response of the eye. In my forty years of experience, photometry is more difficult to understand, primarily because of the arcane terminology, but is fairly easy to do, because of the limited wavelength range. Radiometry, on the other hand, is conceptually somewhat simpler, but is far more difficult to actually do.

Remote Sensing and GIS Use in Coral Reef Management

Remote Sensing and GIS Use in Coral Reef Management

Mochamad Putrawidjaja

Remote Sensing (RS) and Geographic Information System (GIS) have been using in assessing coral reefs for decades by research and academic institutions as well as in managing the resources by the governments. Designed earlier as land-based mapping application, remote sensing and GIS have been applied in coral reef mapping twenty years ago, addressing the requirement of remotely sensed monitoring of extremely wide coral reef area. However, many constraints occurred in its development in marine sector. In this paper, I will describe technical, institutional, data availability and other constraints in developing remote sensing and GIS in marine field, particularly in Indonesia, the country that has the widest coral reef region and the most diverse in marine biodiversity.

Initially, technical constraint in applying remote sensing and GIS in the sea is its capacity to scan the seafloor through certain depth where coral reef lives. Seawater properties, such as salinity and temperature, is potentially scattered or dispersed the electromagnetic wave transmitted by the satellite (Winarso and Budhiman, 2000). Besides, it is difficult to interpret and distinguish a wide array of underwater features from satellite images. Reef flat may be seen identical in several places but appear with distinguished spectrum in satellite image because of different depth and opacities.

Eventually, many researches have been conducted to address that constraint. For instance, CASI (compact airborne spectrographic imager) method that can reach 15 meters depth (Mumby 1998 and Minghelli-Roman et. al. 2000). Moreover, other application of Landsat 7-ETM+, SPOT HRV, SeaWiFS LAC, space shuttle photography and HDTV were able to determine the reef slope and up to 7 classes simple habitat characteristic (Andréfouët et. al. 2000). In contrast, reef biologists have not yet agreed in defining the term “reef” as well as standardizing the classification scheme of the variety reef habitat type.

Vast needs in monitoring the reef health motivate the development of the system that can identify and determine the type seafloor substrate. Australia initiated the effort since they started to protect the Great Barrier Reef from natural and anthropogenic threats. CSIRO (Commonwealth Scientific, Industrial and Research Organization) and AIMS (Australian Institute of Marine Science) intensively observed the health of the Great Barrier Reef using many techniques, such as spectral measurement of radiometric reflectance (Skirving et. al. 2000). Coral bleaching events in the Indo-Pacific region, both as a result of El Ninõ events (1982-83 and 1997-98) and crown-of-thorn starfish grazing accelerates the development. Later, the United States developed higher technology into the system. NASA and USGS developed a cost-effective instrument for investigating high diversity and species rich reefs, the Experimental Advanced Airborne Research LIDAR/EAARL (Brock et. al. 2000). Furthermore, USGS also applied digitized aerial photographs and airborne digital SHOALS (Scanning Hydrographic Operational Airborne LIDAR Survey) laser bathymetry data to locate features on the reef, define the local geomorphology, and as a geographic base to plot results.

Although studying coral reefs was more likely discussing its spatial intactness and competition among its inhabitant, remote sensing and GIS were mostly used to estimate area coverage. Many researches focused more on how to get the finest resolution to map coral reefs, instead of how to define the reef itself. In addition, since most of remote sensing and GIS applications were previously designed to map land-based features, such as Landsat TM satellite images, they can clearly define the building beneath 2-m tree canopy but they could not determine the seabed substrate type below 20 m depth. In fact, observing coral reefs below sea level was not as easy as observing land-based features.

Another technical constraint is how to distinguish the type of seafloor substrate. CASI (compact airborne spectrographic imager), which can reach below 15 meters depth, was unable to determine whether the shelf features shown on the image was coral, sand or any other substrate. Minghelli-Roman et. al. (2000) suggested to combine CASI with ground-level spectra and photographic records to obtain spectral reflectance images of a species rich coral reef. Other data produce by Landsat 7-ETM+, SPOT HRV, SeaWiFS LAC, space shuttle photography and HDTV, which can determine the reef slope and simple habitat characteristic, were unable to determine the coral species (Andréfouët et. al. 2000).

Remote sensing and GIS were more applied as mapping tool instead of management tool in developing countries, particularly in this paper is Indonesia. Intensive use of remote sensing and GIS in Indonesia initiated in the World Bank-funded Land Resource Evaluation and Planning project (LREP) conducted by the Indonesia’s National Land Agency (BPN) in early 1980s. Later on, BPN and the Indonesian Institute of Science (LIPI) attempted to apply such system to marine sector through the Marine Resource Evaluation and Planning project (MREP), which seems to be persisted since they were using the similar software of previous LREP project. In the last ten years, LIPI is conducting the World Bank-loaned Coral Reef Mapping and Management Project (Coremap). In the last five years, more government bodies are also mapping Indonesian coral reefs, including the Indonesia’s National Aeronautics and Space Agency (LAPAN), National Coordination Body of Survey and Mapping (Bakosurtanal) and National Agency of Assessment and Application of Technology (BPPT). Apparently, those organizations performed more coral reefs mapping, instead of gathering related information to be inserted into the system. Most of them focused on introducing and assessing various technologies to map the reefs, instead of developing certain appropriate technique to manage the data.

On the other hand, human resource that can operate the system was not developed very well because lack of expertise and specialties. Many experts and specialists preferred to work in private sector, rather than teaching or doing the researches.

Data availability is another major constraint in developing remote sensing and GIS in coral reef management. Most of basic marine maps in Indonesia were developing upon the more-than-fifty-year-old Dutch’s navigation maps. Most of those maps did not update for years and also did not cover most of remote regions, where coral reefs are still relatively undisturbed and abundant. On the other hand, the satellite image price is very expensive.

Consequently, many coral reef management projects in developing countries attempt to combine the remote sensing and GIS with participatory community mapping. Such combination makes the coral reef mapping is easier to perform and the result can accommodate all stakeholders’ interest, like how the URI’s Coastal Resource Management Project (CRMP) performs in several locations in Indonesia.

That vast development of various techniques and methods to assess coral reefs, both on its coverage and its species richness, seems promising to address the requirements of cost-effective tools to manage the reefs. However, it does not comparable with the deployment cost. Since most of coral reefs are mostly located in economically weak developing and underdeveloped countries, cost of deploying such high technology, which is mostly cost in U.S. dollar, is still a big problem to be solved.

The above conditions made the remote sensing and GIS deployment was slower than its development, and put them either in experimental or conceptual format. Finally, many coral reef management projects in developing countries still preferred to put the community-based mapping as a basis of coral reefs management and put GIS only as presentation enhancement device.

Annotate Bibliography:

Andréfouët, S., J.A. Robinson, G.C. Feldman, F.E. Muller-Karger, C.M. Hu, B. Salvat. 2000. Comparison of space sensors for estimation of coral reef areas in South Pacific atolls. Dept. of Marine Science, University of South Florida. Saint Petersburg, Florida. Proc. 9th International Coral Reef Symp.: 233.
The authors describe variety of remote sensing data, including Landsat 7-ETM+, SPOT-HRV, SeaWiFS Local Area Coverage (LAC), Space Shuttle photography and High Definition Television (HDTV), collected over various atolls of the Tuamotu archipelago (French Polynesia). They found that SeaWIFS data were useful to estimate the atoll area, but cannot distinct between rim and lagoon because lack of sufficient spatial resolution to provide better than 80% accuracy. Space Shuttle HDTV images and photographs were useful for simple characterization of the rims (4 classes) and lagoon features, but could not accurately classify at more detailed levels. SPOT-HRV or LANDSAT/ETM+ were useful to classify the rim structure and simple habitat zones, but did not provide information on the steep outer or inner slopes.

Baxter, K. 2000. Assessing the extent of coral bleaching using aerial photography and image processing techniques, Great Barrier Reef, Australia. School Of Tropical Environment Studies and Geography, James Cook University, Townsville, Australia.

The author found that the management authorities difficult to monitor large-scale disturbances, primarily due to the extent and isolation of their jurisdictions. Remote sensing provides a potential means of cost effectively monitoring coral reefs across a variety of scales. In this paper, he applied remote sensing techniques to high-resolution aerial photographs of two Great Barrier Reef sites to detect coral reef bottom types and in particular, coral reef bleaching. He also applied a supervised remote sensing technique and unsupervised techniques to provide an efficient and accurate means of distinguishing more than 50% bleached corals at couple scales of observation. However, the result accuracy still required to be improved. Determining at which scale reef types are best classified may improve accuracy and ensure the overall health of the reef system is not misinterpreted.

Brock, J.C. and C.W. Wright. 2000. Preliminary results from a NASA Experimental Advanced Airborne Research LIDAR (EAARL) survey of Pacific Reef in Biscayne National Park, Florida. USGS Center For Coastal Geology, Florida. Proc. 9th International Coral Reef Symp.: 233

This paper describe the success of NASA and USGS to develop low-cost instrument to investigate high density and species rich coral reef, the Experimental Advanced Airborne Research LIDAR (EAARL), which couples a small field-of-view receiver with a high repetition rate (5000 Hz), low power, short-pulse laser. This airborne LIDAR remote sensing technique can acquire highly detailed bathymetry and bottom texture, and water depth for the correction of passive imagery, as well as stimulate and detect the fluorescence of coral heads; algae and other benthic cover types. This method was specifically designed for low cost coral reef investigation that require extremely high density bathymetry and hyperspectral scanning for useful benthic reef classification.

Bryant, D., L. Burke, J. McManus and M. Spalding. 1998. Reef at Risk: A Map-Based Indicator of Threat to the World’s Coral Reefs. Box: Tools and Techniques for Monitoring and Mapping Coral Reefs. World Resource Institute, International Center for Living Aquatic Resource Management, World Conservation Monitoring Centre and United Nations Environment Program. p: 40.

This book describes the use of geographic information on coral reefs around the world as a tool to indicate the threat. In one of the report’s boxes, the authors discuss that it is not necessary to deploy high technology or satellite-based method to monitor and map. A low-tech but user friendly community-based reef mapping is one of the most popular and widely consumed in managing coral reefs resources, particularly in less accessible and low infrastructure available region in developing countries. Although they provided an overview on the advantage and disadvantage of some techniques in obtaining coral reef map, the authors did not recommend any specific method.

Chavez P.S., Jr. and M. Field. 2000. Use of digitized aerial photographs and airborne laser bathymetry to map and monitor coral reefs. The United States Geological Survey. Proc. 9th International Coral Reef Symp.: 234.

This paper report that U. S. Geological Survey is using digitized aerial photographs and airborne digital SHOALS (Scanning Hydrographic Operational Airborne LIDAR Survey) laser bathymetry data to help map and study coral reef environments. Main advantage of this remotely sensed data is it is capable to locate features on the reef, define the local geomorphology, and as a geographic base to plot results. A promising application deals with temporal monitoring of change.

Ledrew, E.F.; M. Wulder and H. Holden. Change detection of satellite imagery for mapping and monitoring stressed corals. Department of Geography, University of Waterloo, Ontario, Canada. Proc. 9th International Coral Reefs Symp.: 236

This paper determines a procedure for change detection from the multidate SPOT data that is independent of spatial variations in water depth over the features of interest. The Getis statistic, which is based solely on image characteristics, is evaluated as a tool for change detection. Preliminary examination suggests that it meet the requirements for rapid assessment for environmental change without the need for individual image calibration based upon in situ information.

Minghelli-Roman, A., J.R. M. Chisholm, M. Marchioretti, H. Ripley & J.M. Jaubert. 2000. How good is CASI for Red Sea coral reef survey? Observatoire Océanologique Européen, Centre Scientifique de Monaco, Monaco. Proc. 9th International Coral Reef Symp.: 237

This paper describes the deployment of CASI (compact airborne spectrographic imager) system combined with ground-level spectra and photographic records to obtained spectral reflectance images of a species rich coral reef near Gübal Island, Red Sea. Comparison of CASI-derived thematic maps (by decomposing the pixel reflectance signatures) with photographed reef areas indicated that CASI has the potential to discern and map diverse reef communities to a depth of at least 15 m with good precision.

Mumby, P.J., E.P. Green, A.J. Edwards and C.D. Clark. 1997. Coral reef habitat mapping: how much detail can remote sensing provide? Marine Biology 130: 193-202

The authors studied the accuracy obtained by different imaging techniques and discusses its effectiveness and efficiency based upon cost per image. They found that Landsat TM was the most accurate and cost effective sensor to produce satellite imagery, but it was also less accurate, only 37 %. A 1:10,000 aerial photograph could provide the same detail as the satellite. On the other hand, CASI (compact airborne spectrographic imager) was the most consistently accurate sensor, about 89% in its accuracy. This article should consider to be reviewed by limited budgets institutions which willing to study coral reefs.

Mumby, P.J. E.P. Green, C.D. Clark and A.J. Edwards. 1998. Digital Analysis of multispectral airborne imagery of coral reefs. Coral Reefs 17: 69-69

In this article, authors discuss in detail the use of CASI (compact airborne spectrographic imager) to view 1-m pixels in 8 spectral bands. Reef images taken with CASI was compared to other satellite images produced by Landsat MSS, Landsat TM, SPOT XS, SPOT Pan and merged Landsat TM/SPOT Pan over Turks and Caicos Islands (British West Indies). Overall accuracy of CASI-derived habitat maps were 89% and 81% for coarse and fine levels of habitat discrimination, respectively. Accuracy was greatest once CASI data had been processed to compensate for variations in depth and edited to take account of generic patterns of reef distribution. These overall accuracy were significantly (P<0.001) better than those obtained from satellite imagery of the same site. In addition, this article was more likely a detail technical description of previous article.

Spalding, M.D. 1997. Mapping global reef distribution. Proc. 8th Coral Reef Symposium 2: 1555-1560.

World Conservation Monitoring Centre (WCMC) prepared a new estimation of global coral reef distribution by mapping emergent reef crest and very shallow reef systems. Data had been raster, using 1-km grid squares, as a means of reducing errors arising from variation in scale. Then, global and regional reef coverage was calculated from the resultant grid. They found difficult to create map since data was available in great different quality and scales, ranging from 1:250,000 to 1:1,000,000. However, the resulting map seems not much improved from previous maps.

Skirving, W., T. Kutser, J. Parslow, T. Done, M. Wakeford, I. Miller and L. Clementson. Remote sensing of coral reef health. Australian Institute of Marine Science, Townsville, Australia. Proc. 9th International Coral Reefs Symp.: 240

A joint project of CSIRO and AIMS tried to answer the question of the usefulness of remote sensing for mapping and monitoring the health of coral reefs. Unlike most previous projects, this project has taken a fresh approach to this problem. Instead of using any air- or space-borne data, the project used spectral measurements of radiometric reflectance to allow the development of models, which will help answer the main question of “how useful is remote sensing”, as well as help determine the form of the most suitable instrument. This paper described the techniques used to collect spectral information from the water column and benthic habitat in and around three Great Barrier Reef’s coral reefs. The reefs were chosen to represent a range of water quality. Along with the description of the instrumentation and techniques, some preliminary results were presented.

Winarso, G. and S. Budhiman. 2000. Application of remote sensing data for coral reef mapping in Indonesia. Indonesian Institute of Aeronautics and Space (LAPAN). Bogor, Indonesia

This paper examines the research result of the remote sensing application for coral reef mapping, the problems that occurred, the suggested method to solve the problem, the ability of remote sensing and the quality of the result. Landsat-TM data mainly used as primary data and SPOT multispectral data as comparison because of the availability of data in Indonesia. The author also describes their observation on Indonesia’s experience in implementing remote sensing data to assess coral reef, because it is the easiest and the cheapest. The problem occurred when the electromagnetic wave utilized to identify the coral either scattered or dispersed by the water mass, that made the techniques did not work well.


Recommended websites:

http://www.uncwil.edu/isrs International Society of Reef Studies

http://www.iclarm.org International Center for Living Aquatic Resource Management

http://www.reefcheck.org Reef Check International

http://www.wri.org World Resource Institute who published Reef at risk: a map-based indicator of threats to world coral reefs

http://www.wcmc.org World Conservation Monitoring Centre

http://www.ima-indo.org International Marinelife Alliance – Indonesia

http://www.wwf.or.id Worldwide Fund for Nature – Indonesia

http://www.tnc.or.id The Nature Conservancy - Indonesia

http://www.ci-ip.org Conservation International - Indonesia

http://www.crc.uri.edu URI Coastal Research Center

http://www.aims.edu.au, Australian Institute of Marine Science

http://www.csiro.com.au, Australian Commonwealth Scientific and Industrial Research Organization

http://www.gbrmpa.gov.au, Great Barrier Reef Management Authority

http://www.noaa.gov U.S. National Ocean and Atmosphere Administration

http://www.usgs.gov U.S. Geological Service

http://www.lipi.go.id, Indonesian Institute of Science

http://www.coremap.go.id, Indonesian Coastal Resource Management Project

http://www.bakosurtanal.go.id, Indonesian National Coordination Body of Survey and Mapping

http://www.nature.nl/~edcolijn/ Digital Map of Indonesia by Peter Loud

TAPESTRY OF TIME AND TERRAIN NOT JUST ANOTHER MAP

By combining techniques developed by Leonardo da Vinci with today's computer applications, an artist and two scientists at the U.S. Geological Survey in Menlo Park, Calif., have produced one of the most dramatic and beautiful maps of the United States, ever published.
 
Fittingly titled, "A Tapestry of Time and Terrain," the map weaves together, in vivid colors and shadings, the topographical and geological components of the lower 48 states, as well as the geologic age of those components. This union of topographic texture with the patterns defined by units of geologic time creates a visual synthesis that has escaped most prior attempts to combine shaded relief with a second characteristic shown by color.

The colorful map is an excellent teaching tool, and comes with an interpretive booklet that explains how the map was made, and describes in brief narrative, 48 of the physical features portrayed on the map.

"A Tapestry of Time and Terrain," by Jose Vigil, Richard Pike and David Howell, is available over the counter at USGS Earth Science Information Centers in Menlo Park, Calif.; Spokane, Wash.; Denver, Colo.; and Reston, Va., for $7. It can be ordered by calling 1-888-ASK-USGS (275-8747).

The map can be previewed at tapestry.usgs.gov, which is an interactive website featuring various ways to learn more about the map and the "Rocks of Ages" depicted on it.

The geologic map used is: King, P.B., and Beikman, H.M., compilers, 1974, Geologic map of the United States (exclusive of Alaska and Hawaii): Reston, Va., U.S. Geological Survey,three sheets, scale 1:2,500,000.

The map is available online in two places:

As ARC/INFO 7 and ArcView files
In EPS format

Thanks to Joseph J. Kerski, Ph.D., Geographer - Outreach, USGS
jjkerski@usgs.gov

Orion Partners with Sky-Shine Corporation in Malaysia

Source : http://spatialnews.geocomm.com

RICHMOND HILL, ONTARIO, CANADA and KUALA LUMPUR, MALAYSIA - April 18, 2007 – Orion Technology Inc. is pleased to announce a partnership with Sky-Shine Corporation (M) Sdn. Bhd., a firm specializing in GIS Development and Mapping Services, Surveying & Mapping System, and Environmental & Laboratory Instrumentation in Malaysia.

Sky-Shine offers a full range of Geospatial Information Technology services from data conversion to application development and implementation. They serve both public and private sector agencies and provide services in GIS system development & implementation, digital mapping & data acquisition, data conversion, and remote sensing.

Sky-Shine is the distributor of Digital Globe’s Quick Bird High Resolution Satellite Imagery product and provides value added services to the remote sensing industry in the region. In addition, Sky-Shine is also a distributor for GeoExpress from LizardTech, a powerful geospatial software package for managing, distributing and accessing complex geospatial imagery. As an ESRI business partner in Malaysia, Sky-Shine serves clients in various sectors including government, private, and educational institutions.

“We are committed to exceeding customer expectations for quality and prompt delivery. Being a partner of Orion, our vision becomes more global, and more focused on system and data integration. Our ‘GeoWeb’ initiative, powered by OnPoint, will be the platform of geo services within Malaysia” noted Zalizan Mohd Salleh, Technical Manager of Sky-Shine.

By using Orion’s industry leading OnPoint™ web-GIS solution, Sky-Shine will enhance their services pertaining to spatial data access and solution integration, for both their existing clients, and for new clients in the region. Orion’s out-of-the-box OnPoint solution comes with an Administration Tool, providing a simple user interface to create Configuration Files that define views. The user can change the appearance, functionality, data content and security of OnPoint with a simple point and click. OnPoint allows users to publish their GIS data quickly and securely over the web and connect to any spatial and non spatial data throughout their organization, turning their web-GIS into a true enterprise solution.

“OnPoint continues to gain further acceptance throughout the world as the standard for web-GIS. Sky-Shine is a well established firm that shares our commitment to delivering quality, leading edge solutions to clients. Sky-Shine has significant opportunities to leverage OnPoint in the Malaysian market, and we look forward to working with them in this regard.” commented Faizal Hasham, Director of Sales and Marketing at Orion. About Sky-Shine Corporation (M) Sdn. Bhd. Sky-Shine Corporation (M) Sdn. Bhd. was formally incorporated in 1995. The company provides Geospatial Services and offers scalable GIS and mapping solutions in the Government, Natural Resources, Environmental, Agriculture and Utilities sectors. Founded by experts in the areas of GIS, GPS and Remote Sensing technology, Sky-Shine understand the needs of its diverse clients, and understands that the true power of most geospatial applications is the fusion of varied sources of data together with information technology. To find out more about Sky-Shine Corporation, visit www.skyshine.com.my About Orion Technology Inc. Orion Technology Inc., based in Richmond Hill, Ontario, Canada, is a product development and integration company, specializing in web-GIS. Orion's focus is on helping organizations incorporate spatial technology into all facets of their businesses. Orion’s wealth of knowledge in the GIS domain and top-notch GIS software-development expertise has been showcased with its award-winning flagship product OnPoint™. OnPoint™ is enjoying growing support in the GIS community, both domestically and internationally, as a standard for providing geographic data through intuitive Internet and Intranet web-based user interfaces. The newest release of OnPoint™ provides out-of-the-box implementation capabilities, advanced security, powerful administration tools for customizing and managing web portals, and unparalleled data and application integration features. OnPoint can now connect to more spatial data sources including ESRI ArcIMS, ArcSDE, ArcGIS Server, OGC (WMS & WFS), Microsoft MapPoint, Pictometry and more. It also connects to all non spatial data in Oracle, SQL Server, and any other ODBC complaint database to allow user interaction. In addition, OnPoint™ requires no programming because it is fully configurable through the powerful administration tool and it allows for complete extendibility by using OnPoint’s SDK extension. Orion's easy-to-use Web-GIS solutions have helped Orion achieve phenomenal success in securing and delivering projects for reputable clients worldwide - from small organizations to entire countries. To find out more about Orion and its products, visit www.oriongis.com

Data Structure of GIS And The Basic Steps Of GIS Project

Data Structure of GIS And The Basic Steps Of GIS Project

Essay 1:

ArcGIS did well in organization of the complex and gigantic data in GIS. First the data models are divided into vector model, raster model and triangulated irregular network model to present data in different condition of real world and requirement of analysis. The formats of feature data include shapefiles, coverages and geodatabases.

Metadata consists of properties and documentation. It has 3 parts: description, spatial, and attributes. “Description” contains the information of keywords, status of the data, publication information, data storage and access information, and details about the document. “Spatial” let us know the coordinate system used in the document, the bounding coordinates, and the spatial data description. “Attributes” includes the type of object, number of records, and attribute types in the table.

The steps for a GIS project include: identifying the objectives, creating a project database, data analysis, and results presentation. The project database creation is a critical and time-consuming part of the project, which is a three-step process: designing the database, automating and gathering data for the database, and managing the database. Designing includes identifying the spatial data required by the analysis, determining the required feature attributes, setting the study area boundary, and choosing the coordinate system. Automating the data involves digitizing or converting data form other systems and formats into a usable format as well as verifying the data and correcting errors. Managing the database involves verifying coordinate systems and joining adjacent layers. The completeness and accuracy of the data in the analysis determines the accuracy of the results.


Essay 2:

The most significant things I learned in this Lab were how to define and manipulate various data formats in the new ArcGIS data model. I also learned how different GIS formats are constructed, used and projected within an ARC 8x geodatabase. I gained knowledge about the new ESRI geodatabase model and how it differs from previous GIS data models.


Previously my GIS experience has largely been in an Arcview environment. Although I’ve made several attempts to work in the geodatabase world, job demands and data limited to shapefile format made the attempt to learn this new format somewhat difficult. This lab’s structured approach to defining shapefile, coverage and geodatabase data models clarified differences in file structures of the three types, and how they might be better managed on screen. The lab also was quite helpful in defining how ‘layers’ are merely on-screen pointers or placeholders for data files stored elsewhere.


I am particularly impressed with how the new geodatabase relational model allows much greater interaction with underlying data files than does the older, hierarchical or ‘flat file’ shapefile storage model. Clearly this allows data stored and in today’s relational databases to be more effectively joined, queried and managed in geodatabase feature classes. Similarities in object modeling and behavior between contemporary relational databases such as Oracle, Sybase and others and the ESRI geodatabase became quite apparent. I also was impressed with ArcGIS improved capability for managing and displaying raster and vector data together. In arcview I found that working with raster data was somewhat limited, particularly with the ability to view and modify the behavior and properties of images. This process seems to be greatly enhanced in ArcGIS. I look forward to learning more about raster manipulation, particularly in regards to GIS applications in remote sensing.

Regarding the manipulation of data, I finally learned how tics and topology are used to relate and delineate feature data contained in adjacent tiles. Probably more significantly for effective manipulation of ArcGIS was the description of how annotation is different from labeling, and how annotation is stored with geographic coordinates to maintain position and scale relative to other features displayed in the GIS.

Mapping Coral Reefs from Space

Mapping Coral Reefs from Space

A. Mapping Coral Reefs From Space

To further its mission, PCRF has assembled an interdisciplinary team of advisors and scientists from a variety of institutions including: the College of Charleston, Linnaean Society (U.K.), MIT, Scripps Institution of Oceanography, the Stevens Institute of Technology and USC. Over the past decade, at a series of workshops and conferences, this team has explored the question of whether it is possible to map coral reefs from space, detect change, and then monitor changes in their health and vitality conditions over time.

Using radiative transfer theory, PCRF's team has investigated the upwelling optical properties of reef organisms, and results indicate coral reef signals can indeed be observed from space. (Lubin et al, 2001). In 1991, Dr. Phil Dustan, PCRF's Principal Investigator for the study of coral reefs, was the first to map coral reefs using satellite imagery, thereby proving it possible. Dr. Dustan's studies were conducted off the Florida coast, and he used Spot satellite imagery (Dustan et al, 2000). Since then, the team on board PCRF's research vessel has conducted ground-truthing operations on coral reef communities in Southeast Asia comparing actual surveys to Spot imagery. Further research is now underway which demonstrates how satellite imagery can be used to detect change in coral reef health through remote sensing (Dustan et al, 2002).

Although existing satellite technology, such as Spot, Landsat and Ikonos, has provided imagery proving the possibility of mapping and monitoring coral reefs from space, these images are far too gross to provide the level of quantitative and qualitative data necessary for creating a comprehensive map of living coral reefs or detecting and monitoring changes in their health on a global basis. These existing satellite systems do not possess the spatial, spectral or orbital specifications required for this purpose. Therefore, a specially designed coral reef sensor needs to be placed on a dedicated satellite to achieve this objective.

Additionally, for remote locations, satellites must be specifically programmed to acquire imagery, and until recently, reefs have not been considered important targets. For this reason, while single, gross images of coral reefs exist, these images are not acquired routinely but rather selectively and by chance and largely at the discretion of government agencies who happen to deem a tropical country or coastline of political or military significance. Thus, it is rare to find multiple useable images of individual reef systems ñ which are essential for mapping and monitoring them over time. Finally, even if it were possible to acquire all the existing satellite images necessary to cover the coral reef global area, these images would still be very imprecise for coral reef mapping and monitoring, and the cost to purchase these images ñ not including analysis and presentation ñ is estimated to exceed $50 million.

B. The Case for Remote Sensing of Coral Reefs

Remote sensing technology is the only means to supply the data necessary to map and monitor reefs on a global scale in a cost and time effective manner. Traditionally, reef health has been estimated using expensive and tedious underwater survey techniques that by definition cannot cover large areas. Remote sensing by satellite offers the potential to survey coral reef ecosystem health on a geographic scale not previously possible. This becomes even more important when one considers the remoteness of most reefs and the expense of expeditionary travel. However, it is not a simple task because coral reef environments are optically, spatially, and temporally complex. To extract meaningful information from satellite imagery, techniques must be developed to relate the electronic signals received by a spacecraft to the optical properties of the reef community and its associated biological processes.

Specifically, the myriad of beautiful colors on a coral reef are a mixture of the optical properties of plant and animal pigments, including the symbiotic zooxanthellae of corals, substrate characteristics, and the overlying water column (Dustan et al, 2000). Individual colors blend together with increasing scale, generating a larger scale collage that can be identified by spacecraft imagery. This signal becomes degraded as it passes through the atmosphere and water due to the wavelength-specific selective absorption of light. This degradation can be partially mitigated if the properties of the air and water column are known, and accurate depth measurements can be correlated to the precise geographic coordinates of a geo-registered satellite image.

It is important to note that remote sensing in tropical environments is further complicated by the high probability of cloud cover, and clear scenes can be difficult to obtain. For this reason, multiple orbital passes by a satellite dedicated to the study of coral reefs are critical and an imperative for obtaining the data necessary to map and monitor them over time.
Time-series analysis ñ comparing multiple images of a single reef over time ñ shows the variability of the upwelling signal of the reef. While a single image provides only a snapshot of the reef, a time series analysis can provide the data needed to validate the mapping of the reef and its communities, track changes in coral community structure and health and make predictions about the future health and composition of the reef. Such an analysis can distinguish daily and seasonal variability from larger community scale ecological degradation. In order to accurately interpret the images, we need to understand the effects of both natural and human-induced change on the upwelling signal from a reef.

Presently, the most complete coral reef time series exists for the Florida Keys. Using twenty-two Landsat images of the northern Keys from 1982 to 1996, a team led by Dr. Dustan has performed an analysis of pixel-scale variation through time, termed temporal texture. Using both Landsat satellite images and in situ observations, this team has shown that the process of reef degradation has altered both the spatial patterning and variability of pixel brightness, which can be identified in unclassified Landsat imagery (Dustan et al, 2002).

References:

Dustan, P., S. Chakrabarti, and A. Alling. 2000. Mapping and Monitoring the Health and Vitality of Coral Reefs from Satellite: A Biospheric Approach. Life Support and Biosphere Science, Vol 7: 149-159.

Dustan, P., E. Dobson, and G. Nelson. 2002. Remote Sensing of Coral Reefs: Detection of Shifts in Community Composition of Coral Reefs Using the Landsat Thematic Mapper. (In revision, Conservation Biology)

Lubin, D., W. Li, P. Dustan, C. H. Mazel, and K. Stamnes. 2001. Spectral Signatures of Coral Reefs: Features from Space. Remote Sensing of Environment, 75, 127-137.

source : Planetary Coral Reef Foundation