GEOGRAPHIC INFORMATION SYSTEM (GIS) AND REMOTE SENSING Lecture 4 Zakaria Khamis

From the spectral reflectance values of an object, the spectral reflectance curve can be drawn. This curve is very important, for it portrays the spectral characteristics of a given feature. Spectral reflectance curve helps to select the band width (wavelength range) which will be used in remote sensing to acquire data for a given feature. The spectral reflectance curve is drawn not using single values which produce single line, rather it is drawn as a ribbon (envelope), for the spectral reflectance values vary somewhat within a given material class (i.e. there is a range of values for a spectral reflectance of a given feature). For example, the spectral reflectance of one orange species and another will not be identical; as well as, the spectral reflectance of the same tree specie will never be exactly the same. It is difficult to differentiate Coniferous trees from Deciduous trees in the forest in the visible band; however, in the IR band, Deciduous trees appears BRIGHT in tone; whereas, Coniferous trees appear DARKER in tone.

Note the range of spectral values

Infrared (IR) Photograph for Trees in a Forest

Spectral Reflectance of Vegetation, Soil, and Water Through remote sensing, these features can be distinctly identified. The spectral reflectance curve of these features can help to identify the bandwidth in which the sensor can sense. Note: the graphs represent the average values for spectral reflectance of these 3 features. Healthy green vegetation, dry bare soil (gray-brown loam) and clear lake water are 3 basic features on the earth’s surface.

Chlorophyll highly absorbs EME in the wavelength bands of about 0.45μm and 0.67μm (often called chlorophyll absorption bands). Thus we see the healthier vegetations green in color, for the plant leaves highly absorb the blue and red waves; whereas, highly reflecting the green wave. When the plant is under stress, the chlorophyll production decreases. This increases the red wave reflection on the leaves. Due to the combination of red and green, we see the plant leaves YELLOW. The reflectance of healthy vegetation increases sharply at NIR (0.7μm to 1.3μm). The plant leaf typically reflects 40% to 50% of the NIR energy incident on it.

Most of the remaining energy is transmitted; since absorption in this spectral band (IR) is minimum – less than 5%. The plant spectral reflectance in IR range (0.7μm to 1.3μm) is primarily the result of the internal structure of the leaves. Because this structure is highly variable between plant species, reflectance measurements in this range often permit us to discriminate between species, even if they look the same in visible band. Beyond 1.3μm, the incident energy is essentially absorbed, with little to no transmission Dips in the reflectance curve of healthy vegetation occur at 1.4μm, 1.9μm and 2.7μm. Because water in the leaves absorb strongly at this bands. These spectral regions are known as Water Absorption Bands. Beyond 1.3μm, leaf reflectance is the function of total water presents in the leaf. The two are inversely related

Soil spectral reflectance curve shows less peaks and valleys variation. Factors that affect the soil reflectance include – soil moisture, soil texture (proportion of sand, silt and clay), surface roughness, iron oxide, and organic matter among others. Presence of soil moisture decreases the soil reflectance at variable bands; especially, in the water absorption bands (1.4μm, 1.9μm and 2.7μm). Clay soil also has Hydroxyl absorption bands – 1.4μm and 2.2μm Soil moisture content is strongly related to the soil texture. Coarse sandy soil is usually well drained; hence low moisture content high reflectance. In the absence of water, dry soil itself exhibit the reverse tendency. Coarse texture soil will appears darker than fine texture soil.

The location and delineation of water bodies in remote sensing are easily done in NIR band, because of this absorption property. The reflectance of water bodies is not only the function of water, but also the suspended materials within the water. Clear water absorbs relatively little energy having the wavelength less than about 0.6μm. As turbidity of water changes, due to the presence of suspended materials (organic and inorganic), the reflectance property change. For the case of water, the most distinctive characteristic is the energy absorption at NIR and beyond.

For example, water containing large quantity of suspended sediments resulting from soil erosion normally have much higher visible band reflectance than clear water. Moreover, the chlorophyll concentration in the water changes the reflectance property of the water. Increasing in chlorophyll concentration tend to decrease water reflectance in blue and increase the reflectance in green wavelength. The characteristics of the presence of chlorophyll in water has been used to monitor the concentration of ALGAE via remote sensing

Spectral Response Patterns In remote sensing, spectral responses measured by the remote sensors are what permit us to differentiate types of features and their conditions. The spectral responses of a given feature are referred as SPECTRAL SIGNATURES of that feature. Earth’s features manifest very distinctive spectral reflectance characteristics (see the previous spectral reflectance curves for vegetation, water and soil), these characteristics result to SPECTRAL RESPONSE PATTERN. In remote sensing, the term spectral response pattern is preferred over spectral signature, because spectral signature tend to imply a pattern which is absolute and unique. Whereas, the spectral response is not a unique pattern, it may change based on spatial, temporal and atmospheric factors.

An Ideal Remote Sensing System There are elements necessary to conceptualize the remote sensing system. These elements are: - 1.A uniform Energy Source – this source would provide energy over all wavelengths, at a constant, known, high level of output, irrespective of time and place. 2.A non-interfering Atmosphere – This would be an atmosphere that would not alter the energy from the source in any manner, weather that energy is in its way to the earth’s surface or coming from it (irrespective of wavelength, place, time and sensing altitude involved). 3.A series of unique energy-matter interactions at the earth’s surface – these interactions will generate reflected signals that are selective with respect to wavelength, known, invariant and unique to each and every earth’s feature type and subtype.

4.A super-sensor – this would be highly sensitive sensor to all wavelengths, yielding spatially detailed data on the absolute brightness throughout the spectrum. This sensor would be simple and reliable, requires virtually no power or space, and be accurate and economical to operate. 5.A real-time data processing and supply system – in this system, the instant the radiance-versus-wavelength response over a terrain element was generated, it would be transmitted to ground, geometrically and radiometric ally corrected as necessary and processed into readily interpretable format. 6.Multiple data Users – these people would have knowledge of great depth, both of their respective disciplines and remote sensing data acquisition and analysis techniques. Unfortunately, and ideal remote sensing system as described above doesn’t exist.