Comprehensive Analysis of Remote Sensing Techniques and Applications

This report comprehensively analyzes remote sensing techniques, focusing on image classification and mapping applications. It explores the bands that produce maximum contrast, specifically highlighting the effectiveness of bands 4 and 5, as well as the use of NDVI for vegetation analysis. The report compares and contrasts supervised and unsupervised classification methods, detailing their strengths and weaknesses in producing accurate land-cover maps. It also discusses contrast enhancement techniques, the process of converting image data into maps, and the importance of ancillary data in improving image classification accuracy. The report references several studies and provides a detailed overview of the image-to-map processing steps, including GeoTIFF output generation, resampling, band grouping, and map projection using the UTM system. The report emphasizes the practical applications of remote sensing in environmental monitoring and land management.

Running head: REMOTE SENSING
REMOTE SENSING
Name of Student
Institution Affiliation

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Bands that produce maximum contrast.
Band 4,5, the combination provides the maximum contrast Healthy vegetation looks trendy
shadows of reds, browns, oranges, and yellows. Soil might remain trendy greens besides browns,
city features remain white, cyan and gray, perky blue zones denote freshly clear-cuts zones and
magenta areas show fresh vegetation growing, possibly scarce savannahs. Strong, deep sea will
be very dark in this amalgamation if the sea is shallow or covers residues it would look like as
shadows of brighter blue(King, Kaufman, Menzel, & Tanre, 1992). For undergrowth readings,
the accumulation of the Mid-IR band escalation sensitivity of sensing numerous periods of
vegetation progress or strain; yet attention must be given to understanding if achievement
carefully trails rainfall. Usage of TM 4 and TM 5 demonstrates great reflectance in well-loafed
areas. It is accommodating to associate submerged zones and red loafed areas with the
conforming colors in the 3 2 1 amalgamation to guarantee accurate understanding. This is not a
worthy band amalgamation for reviewing national landscapes such as infrastructures (Jia et al.,
2014).

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Compare and contrast the following two types of image classification operations in terms of their
ability to produce an accurate land-cover map for the area you mapped in part one of this
question: supervised classification and unsupervised classification
The purposes of most satellites remote sensing functionality is their ability to give an
accurate interpretation of the data they observe from the ground earth and group the
features into classes. Two methodologies is broadly used namely, The supervised and the
unsupervised methods of classification, (Waldhoff, Curdt, Hoffmeister, & Bareth, 2012)
Supervised method is where a quantitative detailed information about the remote image
is extracted for classification, Using this approach, the person analyzing the data from the
image has sufficient information to accurately know each parameter based on the known
pixels, a process called training (Richards & Richards, 1999). The widely used method of

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supervised classification is the is the MLC(Maximum Likelihood Classification)., which
does its classification through the assumption that the different spectral classes are able to
be defined by the MND(multivariate normal distribution). Accurate results are often
found when the various classes are distributed in multimode
Unsupervised method of classification doesn’t require much human interaction to know
the knowledge about the different classes and instead deploy the abilities of a complex
algorithm used to put data from various images into classes(Zhu & Woodcock, 2012).
The algorithm can be used to calculate the number and pinpoint the location various
spectral classes of the unimodal nature lies. A widely deployed method of unsupervised
classification is the MMC(Migrating Means Clustering ). This algorithm gives labels to
the pixels to some unknown centers for clustering and the algorithm does the
classification from one cluster to another,
Screen Captures
Greatest General Contrasts

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In True View Image Capture
NDVI and IRON OXIDE
NDVI is used commonly in satellite images for vegetation (Adam & Mutanga, 2009). It is
known fact that there exist a close relationship between the soil and vegetation. For example, soil
directly influences plants and inversely plants to some degree affects the various soil
characteristics. (Asner, 1998). Some extreme activities such as drought could possibly lead to
desertification especially in those arid and semi-arid environments.
NDVI has been the defacto tools for closely monitoring and able to detect the effects of drought
on some aspects of the economy such as agriculture (Artigas & Yang, 2005) Iron oxide to
continue.

REMOTE SENSING 6
Improving Image Classification
Ancillary data usage has been on the rise especially in the classification of sensed images when
trying to resove classes of information(Lu & Weng, 2007) The procedure of changing an image
records into a database data by tagging bands of data into classes. Accuracy of this procedure can
be enhanced by intergrating data other than the imagery ones (Song, Woodcock, Seto, Lenney, &
Macomber, 2001) .Numerous attemps have been employed to increase the general accuracy of
the classification, various attemps are suggested below,
First, Introduction of the logical channel in the image processing system shall increase the
number of data about the image which is helpful in classification, secondly, the process of
classier adjustment which purposes at refining the priori chances conferring to areal
conformation of the anticipated map based on the image statics (Manandhar, Odeh, & Ancev,
2009). From the above suggestions, the logical channel adjustements provides the best approach
to enhancing the image characteristcis due its simplicity and save time relative to the other
methods, The method no doubt have limitation as it cant be used before the traditional method of
make samples of the routine classes, (Eiumnoh & Shrestha, 2000)
Contrast Enhancement
It not uncommon for remote sensing reflectance values which are collected to overlap the display
unit. This is because different materials on the earth surface do radiate different amounts of
energy. The same wavelength can record two materials with different energy levels.
Enhancement of images aims at making sure the image becomes easier for analysts to draw an
interpretation of the image data. Contrast can be defined as the different range of the brightness

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of the image. Therefore contrast enhancement entails making changes to the original image to
make the image outstanding and more importantly clearer.
Bands 4,5 provides the best contracts of the image since it provides the best linear contrast
enhancement which generally is the starting of the contrast of the original digital data of the
remotely sensed image into a totally new image data distribution. (Anderson, 1995)
Image to Map processing
All the image data from the Landstat transponder are processed using the Landstat product
generation system (Fuller, Groom, & Jones, 1994), the process follow the following typical steps
to convert the image data into a map,
i. Generation of GeoTIFF output .
ii. Resample the data using s sampling technique known as the cubic convention.
iii. Grouping of the reflective bands to be used
iv. Use the universal transverse meralator (UTM) for projecting the map. Typically the
stereographic projection ares normally produces scenes with a relatively centered latitude
which is normally larger than or equal to 0.63 degrees (Bernstein, Lotspoech, Myers,
Kolsky, & Lees, 1984).
v. The output is placed in the world transverse system (WGS) datum
vi. And finally the output is taken for map northing orientation.

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References
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Landsat 8 Operational Land Imager data in Beijing, China. Geocarto International,
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Adam, E., & Mutanga, O. (2009). Spectral discrimination of papyrus vegetation (Cyperus
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Anderson, J. (1995). Spectral signature of wetland plants (350–900). USA Army Topographic
Engineering Centre, Alexandria.
Artigas, F., & Yang, J. (2005). Hyperspectral remote sensing of marsh species and plant vigour
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Asner, G. P. (1998). Biophysical and biochemical sources of variability in canopy reflectance.
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Bernstein, R., Lotspoech, J. B., Myers, H. J., Kolsky, H. G., & Lees, R. D. (1984). Analysis and
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