This study employs the developed algorithm for retrieving land surface temperature (LST)
from Landsat TM over Saudi Arabia. The algorithm is a mono window algorithm because the
Landsat TM has only one thermal band between wavelengths of 10.44-12.42 μm. The
proposed algorithm included three parameters, brightness temperature, surface emissivity and
incoming solar radiation in the algorithm regression analysis. The LST estimated by the
proposed developed algorithm and the LST values produced using ATCORT2_T in the PCI
Geomatica 9.1 image processing software were compared. The mono window algorithm
produced high accuracy LST values using Landsat TM data.
We attempted to investigate the potential of using satellite image for
acquiring data for remote sensing application. This study investigated the potential of
using digital satellite image for land cover mapping over AlQasim, Saudi Arabia.
Satellite digital imagery has proved to be an effective tool for land cover studies.
Supervised classification technique (Maximum Likelihood, ML, Minimum Distance-to-
Mean, MDM, Parallelepiped, P) techniques were used in the classification analysis to
extract the thematic information from the acquired scenes. Besides that, neutral network
also performed in this study. The accuracy of each classification map produced was
validated using the reference data sets consisting of a large number of samples collected
per category. The study revealed that the ML classifier produced better result. The best
supervised classifier was chosen based on the highest overall accuracy and Kappa
statistic. The results produced by this study indicated that land cover features could be
clearly identified and classified into a land cover map. This study suggested that the land
cover types of AlQasim, Saudi Arabia can be accurately mapped.
A method to retrieve the land surface temperature (LST) over Mecca, Saudi Arabia are developed
using band 6 of the Landsat TM thermal channel. The objective of this study was to focus on the
estimation of the LST from Landsat TM 5 imageries. The data used was captured by Thematic
Mapper (TM) sensor onboard the Landsat 5 satellite. Landsat TM has only one thermal band, and
therefore the spilt-window algorithm cannot be used for the retrieval of LST. In this study, we are
proposed a single channel algorithm for retrieving LST. The land surface emissivity and solar angle
values are needed in order to apply these in the proposed algorithm. The surface emissivity values
were computed based on the NDVI values. The correlation between the LST and the brightness
temperature had increased significantly after the surface emissivity and solar zenith angle were
included in the algorithm. The reference values LST were determined using ATCOR2_T in the PCI Geomatica image 9.1 processing software for algorithm calibration. The results indicate that the
single channel algorithm was suitable for retrieving LST values from remotely sensed data.
Remote sensing data have been widely used for land cover mapping using supervised and unsupervised methods. The produced land cover maps are useful for various applications. This paper examines the use of remote sensing data for land cover mapping over Saudi Arabia. Three supervised classification techniques Maximum Likelihood, ML, Minimum Distance-to-Mean, MDM, and Parallelepiped, P were applied to the imageries to extract the thematic information from the acquired scene by using PCI Geomatica software. Training sites were selected within each scene. This study shows that the ML classifier was the best classifier and produced superior results and achieved a high degree of accuracy. The preliminary analysis gave promising results of land cover mapping over Saudi Arabia by using Landsat TM imageries.