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Mountain Environments – Geography 006d
Application of Remote Sensing to the Study of Mountain Environments
Fall 2005
Background
Spatial digital technologies – such as satellite and aircraft remote sensing, geographic information systems, global positioning systems, data visualizations, and spatial analyses – are a central part of physical geography and important to the study of mountain environments. Used for characterizing the nature of biophysical landscapes and assessing their scale-pattern-process interrelationships, mountain geographers are applying conventional approaches, such as, map and air photo interpretation as well as newly emergent approaches, such as, digital satellite remote sensing, image animation, and computer simulations to study the environment. Empowered by our growing understanding of biophysical and social processes and systems, spatial digital technologies are being used to examine the landscape (a) at specified space-time scales that extend from fine-grained to coarse-grained resolutions, (b) from local, to regional, to global extents, and (c) for historical, contemporary, and future time period.
Spatial patterns of biophysical variables (e.g., vegetation units or snow patches) may be mapped for a discrete time period through field measurements and/or the use of single date air photos; for “snap-shots” in time through an assembled satellite image time-series; or continuously in time through an assortment of electronic devices such as quantum sensors linked to data loggers or meteorological stations transmitting data to orbiting satellites for downloading to ground receiving stations. Geographic Information Systems (GIS) are often used to integrate data characterizing a host of space-time elements of biophysical variables and system parameters so that process relationships can be examined and complex issues assessed. All in all, spatial digital technologies are impacting physical geography in a number of fundamental ways: they enhance and value-add to the richness of our knowledge and understanding of how our environment is organized and how environment functions in a host of ecological and geographic settings.
Goals
The primary goal of this lab is to describe important elements of a selected group of spatial digital technologies, particularly remote sensing and geographic information systems that offer mountain geographers immense potential as well as proven capabilities for studying the biophysical and social landscapes. We will consider how remote sensing is applied to the study of mountains; how data derived from this technology is interpreted; and how the spatial digital technologies are collectively integrated into a data collection, storage, analysis, and display system that offers new insights to mountain geographers about our dynamic earth.
Remote Sensing Basics
Remote Sensing is a surveillance and mapping science that is concerned with the observation and/or measurement of objects and features without having the measuring device in direct contact with the entity of interest. Film and digital sensors borne in aircraft and satellites are the most common types of remote sensing devices. They are engineered to be sensitive to different parts of the electromagnetic spectrum (EMS) (spectral resolution); map different sized objects and features through their spatial resolution; and assess landscape characteristics using a quantitative range of response intensities (radiometric resolution), generally extending from 0 (low intensity of reflectance) to 255 (high intensity of reflectance), which is analogous to qualitatively evaluating the color red by describing it on a range extending from dull (low intensity reflectance) to bright (high intensity reflectance). Remote sensing systems also are capable of rendering views across time (temporal resolution); as a consequence of their historical perspective of operation and their orbital specifications that periodically returns the satellite over the same geographic location for change imaging.
The EMS is a continuum of energy that ranges from the very short wavelengths such as x-rays to the very long wavelengths such as radio waves. Energy travels in a sinusoidal, wave-like pattern much like the pattern generated on a pond when a stone is dropped. In remote sensing, optical sensors are most commonly applied to landscape mapping. Optical sensors typically operate in the visible, near-infrared, and middle-infrared spectral regions of the EMS, because of their capacity to discern important biophysical characteristics of the landscape including special properties of vegetation. For instance, the visible wavelengths of the EMS are best for discerning plant pigmentation or vegetation color; near-infrared wavelengths for discerning the chlorophyll content or structure of the leaf; middle-infrared wavelengths for discerning the moisture content of the leaf; and the thermal-infrared wavelengths for discerning temperature characteristics of the leaf. In film products, information collected about the landscape is generally amalgamated into a single image by compositing the film layers, but in digital data sets, separate images or “channels” are retained for each spectral region so that the user can combine information about the landscape as he/she sees fit. Therefore, remote sensing is a mapping science that takes into account how energy and matter are interrelated at distinct spectral regions, whether collected using our 35-mm camera, our digital camera, or our video camera, or captured on film or by digital sensors placed in trucks, boats, planes, or satellites.
Remote sensing is used to map landscape objects (e.g., a tree or forest), features (e.g., leaf-on or leaf-off), and conditions (e.g., low or high biomass (or plant material) or greenness). Because of the vantage point of Earth observation afforded by aircraft and satellites and the capability of sensors to characterize components of our landscape, we can map landscape patterns for nearly any part of the globe. Sensors providing fine grained views through high spatial resolution sensors are often used to discern relatively small geographic areas, but in great detail, whereas sensors providing coarse grained views through low spatial resolution sensors are often used to discern relatively large geographic areas, but in reduced detail. The extent of the sensor view is also of importance.
Imagine a camera pointed at the ground and placed in a rocket that is about to be launched towards outer space. Before launch, the camera or digital sensor records elements of our landscape in a very restricted area beneath the rocket, called the instantaneous field of view (IFOV), but functionally, it is the camera’s recoding area or what the camera “sees.” The camera or digital sensor may record the presence of flowers, grasses, and limbs of trees, or if the camera or digital sensor has a higher spatial resolution, it might even be capable of picking up individual grains of sand. But upon launch, the camera or digital sensor expands the areal extent that it “sees” and therefore cars, buildings, and forest patches become clearly visible. As the rocket travels higher yet, the camera or digital sensor records landscape objects, conditions, and features for an ever-increasing area by mapping at some predetermined resolution such as at a 30 x 30 meter cell, a 100 x 100 meter cell, or a 1 x 1 kilometer cell. As the satellite orbit is attained or the aircraft flies at a constant or near-constant altitude, the ground resolution of the remote sensing systems begins to function at a regular and preset, spatial resolution. The term “pixel” is given to define the picture element of a digital remote sensing system, meaning for example that each “cell” of information collected about our landscape is contained within a regular matrix of say 30 x 30 meter units or pixels, appearing like tiles of the floor or ceiling in their spatial arrangement. Each cell in the matrix has a number associated with it that indicates something about the character of the objects, features, and conditions of the landscape located and subsequently evaluated within that cell. A host of spatial resolutions and a variety of sensors are available for characterizing our landscape.
Landsat TM is a sensor on-board a family of satellites that operates in the visible, near-infrared, middle-infrared, and thermal-infrared parts of the electromagnetic spectrum. The spatial resolution of the digital sensors of Landsat TM are 30 x 30 meters; 120 x 120 meters for the thermal channel.
Remote Sensing Image Interpretation
To start with, lets consider some basic issues involving the use of remotely sensed satellite data -- how the data are collected, and how best to view the data on a computer display. Landsat satellite data were collected for places around the globe beginning in July 1972. During those early years, the Multispectral Scanner (MSS) and the Return Beam Vidicon (RBV) gathered information about the landscape from an altitude of approximately 570 miles above our planet. MSS quickly became the sensor of choice. The RBV performed much like a TV camera; its data proved less valuable for scientific inquiry, because of limitations in its spatial and spectral resolutions. So the MSS became the biophysical remote sensing “workhorse” of physical geographers, ecologists, and other natural scientists interested in studying the landscape. The MSS and Landsat catalogued thousands of views of important ecological settings around the world and critical environmental issues that included for instance mapping deserts that were experiencing sand dune migration, rivers that were experiencing channel migration, mountains being affected by disturbance regimes, and human settlements that were in the process of relocation through population migration brought about by natural hazards and/or armed conflict. Because the satellite was in an Earth orbit and synchronous with the sun, the Landsat MSS system provided incredibly useful views of our dynamic Earth where landscape patterns and changes in these mapped patterns were studied. But before changes could be assessed, however, baseline mapping was achieved for numerous areas in the USA and around the world. Mapping, for instance, the meander scars of the Mississippi River, the extent of urban places, deforestation in the AmazonBasin, and areas of flood inundation afforded scientists and policy-makers a broad synoptic view seldom seen and even more seldom integrated into the study of physical geography.
With time, newer and more powerful sensors have been developed, and as a consequence more information about our planet can now be distilled. For the Landsat satellite, the Thematic Mapper (TM) sensor came on-line in July 1982. A number of significant improvements were realized over the MSS. First, TM sensed the Earth at a higher spatial resolution: 30 x 30 meters over the 79 x 79 meters of MSS. Second, TM operated in seven spectral regions (spectral resolution) representing the visible, near-infrared, middle-infrared, and thermal-infrared regions of the electromagnetic spectrum, whereas MSS sensed in only 4-spectral regions excluding the middle- and thermal-infrared regions. Third, the orbit of the Landsat satellite that carried the vehicle over nearly all of the Earth (except the poles) improved from 18-days for repeat coverage over the same geographic area for Landsat and the MSS sensor to every 16-days for repeat coverage over the same geographic area for Landsat and the TM sensor (temporal resolution). And fourth, the intensity of the spectral responses captured from the sensors on-board these orbiting satellites increased as well, from 128 intensity levels for MSS to 256 intensity levels for TM, meaning that the radiometric resolution of reflectance intensities was extended for greater precision in characterizing elements of the landscape. For Landsat TM, a spectral response value of 0 indicates the lowest reflectance, whereas a spectral response value of 255 indicates a highest reflectance. An example might be a dark and deep-water body that reflects at the lower end of the radiometer range versus a sandy beach that reflects at the upper end of the radiometric range. These four areas of TM sensor and Landsat vehicle improvements are considered the 4-remote sensing resolutions; they are used to select the most appropriate sensor and satellite system to meet the goals of the mapping mission. Mission goals might be to map phenomena having a high temporal requirement -- that is, it changes very quickly with time, like a forest fire in California, or to map phenomena having a high spatial requirement, like icebergs floating in the North Atlantic shipping lanes.
An easy way to conceptualize how a satellite and its sensors operate is to think of yourself walking across the landscape, traversing space much like a satellite does when it orbits the Earth. As you look down upon the ground that lies beneath you, your vision is constrained; by among other things, your height above the ground, and the spatial resolution of your sensors, that is your eyes. You can certainly see the blades of grass, stems of trees and the associated branches and limbs, as well as sidewalks and so on, but some things are just too small to see, like grains of sand and other tiny features. This ability to see some features but miss other features because of their size and the resolving power of your eyes is analogous to the spatial resolution of sensors – those “artificial eyes” placed on-board satellites and aircraft that can “see” much, but can’t “see” everything, just like us. As you continue to walk and look at the landscape below you, you might be wearing sunglasses that either darken your views or alter the color scheme of light that your eyes are receiving. The lens of the sunglasses might yield a landscape biased towards the reds, greens, or yellows depending upon the type of lens in your sunglasses. Your sunglasses may also be capable of polarizing light and/or filter light in ways that you become more selective or discriminating in the direction and type of wavelengths of energy that are allowed to pass through the lens of your sunglasses to reach your eyes. This is similar in concept to the spectral resolutions of sensors borne in satellites that are capable of “seeing” in only certain parts of the electromagnetic spectrum. Finally, you might be walking across a very highly reflecting concrete road or driveway. Even with your sunglasses on, you can distinguish the visual differences between crossing the bright concrete versus walking across a freshly mowed lawn. This difference in the degree or intensity of reflected light is similar to the radiometric resolution of digital sensors that are placed in satellites (or airplanes). Instead of using a qualitative scale to reference the degree of reflected light coming off a surface, such as dull or bright, the digital sensor uses a numeric scale to represent a low to high reflectance, such as a 0-255 scale with 0 being low reflectance and 255 being high reflectance.
So you can see that a digital sensor in a satellite operates similar to our eyes in many ways. Our eyes and the satellite sensors are involved in information collection about our landscape. While we might construct mental images of what our eyes see, the satellite can output information it collects to a computer for an assortment of visualizations involving the generation of maps, tables, animations, and many more. For viewing satellite images, it is important to realize that a computer graphics card controls how images are presented by using 3-color guns (red, green, and blue) and a computer display screen. Often, the graphics card in the computer differentiates the intensity of the red, green, and blue tones on a 0-255 scale, similar to the way that Landsat Thematic Mapper differentiates the intensity of reflect light coming off the landscape. On the computer screen, it is customary to have available to the operator a color palette of approximately 16.7 million unique color possibilities to differentiate earth features. This color palette is derived by multiplying 256 shades (i.e., 0-255) of red, times 256 shades of green (i.e., 0-255), times 256 shades (i.e., 0-255) of blue. The result is a color-mapping scheme or color model that has more than enough capacity to differentiate and map the compositional diversity of our landscape as viewed from satellites.
One more thing, recall that the Landsat TM sensor operates in 7-parts or channels of the spectrum, simultaneously gathered for different properties or attributes of the same landscape at the time of imaging. Three channels are collected in the blue-visible, green-visible, and red-visible wavelengths; one channel in the near-infrared wavelengths; two channels in the middle-infrared wavelengths; and one channel in the thermal-infrared wavelengths. As a spatial analyst and physical geographer it is up to you to view the satellite channels in a way that gives you the best representation of the region under study and the ecological problem under consideration. Because Landsat TM has 7-spectral channels of information (actually we are using only 6 here; it is common to delete the thermal-infrared channel if temperature profiles are less important to your study) and a computer has 3-color guns for display purposes, you can see that you’ll need to select which channel you wish to display and then to assign it to a specific color gun.