مختصر البحث:
Abstract.
Recent applications of the Landsat 5/TM, Landsat8/OLI, and (S2/MSI) satellites for land
use and land cover (LULC) data provide a new analytical perspective from remote sensing
data. Mapping and detecting land use/land cover change is ve…
Abstract.
Recent applications of the Landsat 5/TM, Landsat8/OLI, and (S2/MSI) satellites for land
use and land cover (LULC) data provide a new analytical perspective from remote sensing
data. Mapping and detecting land use/land cover change is very important to achieve
sustainability and conservation of natural resources. To achieve this goal, we used the
2015 ERDAS visualization to perform a supervised classification using the maximum-
likelihood technique, the period (2001–2021) as the time frame to study LULCC
dynamics, that is, six images (2001, 2005, 2009), 2013, 2017, and 2021). Analysis of
Satellite Data for Use/Land Cover (LULC) in Karbala Governorate / Iraq. The province is
classified into four categories: bodies of water, urban land, agricultural land, and arid land.
The results showed that the overall accuracy was 92.16%, 91.26%, 90.26%, 92.33%,
94.23% and 92.66 for the years 2001, 2005, 2009, 2013, 2017, and 2021, respectively, and
the kappa coefficients were 0.8583 and 0.8213. . and 0.8147, 0.8210, 0.7064, and 0.8764,
respectively. These figures show that the accuracy of the LULC classes is within
acceptable limits. An equation was reached through mathematical modeling for each
category during the research period, and it was found that the highest value of the
coefficient of determination (R2) was for urban lands where the value of the coefficient of
determination was (R2 = 99%), followed by water bodies where the value of the
coefficient of determination was (R2 = 55%). ). ). Then the agricultural areas where the
value of the coefficient of determination is (R2 = 40%) and the lowest value for the arid
lands where the value of the coefficient of determination is (R2 = 24%). The simulation
results give the future state of the change in each category between (2021-2033). As a
result of the simulation, there is an increase in urban and dry areas of 5.85% and 1.47%,
respectively. It decreased in both water bodies and agricultural lands by 5.56% and 1.79%,
respectively. The results of this study gave useful information for sustainable development
by observing the prosperity and development of the governorate in this research,
especially in 2021, which will benefit the relevant government institutions.
Keywords: Remote Sensing; Multispectral; sustainable development, Karbala, land
use/cover, change detection, image Classification.
