Assessment of Agricultural Land Use Land Cover, Land Surface Temperature and Population Changes Using Remote Sensing and GIS: Southwest Part of Marmara Sea, Turkey
Land Use Land Cover (LULC) changes due to human
activities and natural causes have become a major environmental
concern. Assessment of temporal remote sensing data provides
information about LULC impacts on environment. Land Surface
Temperature (LST) is one of the important components for modeling
environmental changes in climatological, hydrological, and
agricultural studies. In this study, LULC changes (September 7, 1984
and July 8, 2014) especially in agricultural lands together with
population changes (1985-2014) and LST status were investigated
using remotely sensed and census data in South Marmara Watershed,
Turkey. LULC changes were determined using Landsat TM and
Landsat OLI data acquired in 1984 and 2014 summers. Six-band TM
and OLI images were classified using supervised classification
method to prepare LULC map including five classes including Forest
(F), Grazing Land (G), Agricultural Land (A), Water Surface (W),
Residential Area-Bare Soil (R-B) classes. The LST image was also
derived from thermal bands of the same dates.
LULC classification results showed that forest areas, agricultural
lands, water surfaces and residential area-bare soils were increased as
65751 ha, 20163 ha, 1924 ha and 20462 ha respectively. In
comparison, a dramatic decrement occurred in grazing land (107985
ha) within three decades. The population increased 29% between
years 1984-2014 in whole study area. Along with the natural causes,
migration also caused this increase since the study area has an
important employment potential. LULC was transformed among the
classes due to the expansion in residential, commercial and industrial
areas as well as political decisions. In the study, results showed that
agricultural lands around the settlement areas transformed to
residential areas in 30 years.
The LST images showed that mean temperatures were ranged
between 26-32°C in 1984 and 27-33°C in 2014. Minimum
temperature of agricultural lands was increased 3°C and reached to
23°C. In contrast, maximum temperature of A class decreased to
41°C from 44°C. Considering temperatures of the 2014 R-B class and
1984 status of same areas, it was seen that mean, min and max
temperatures increased by 2°C.
As a result, the dynamism of population, LULC and LST resulted
in increasing mean and maximum surface temperatures, living
spaces/industrial areas and agricultural lands.
[1] M. S. Hassan, and S. Mahmud-ul-islam, “Urban area change Analysis in
the Rangpur Sadar Upazila, Bangladesh using Landsat imageries”,
International Journal of Science and Research, vol. 4, no. 1, pp. 469-
474, 2015.
[2] K. M. Kafi, H. Z. M. Shafri, and A. B. M. Shariff, “An analysis of lulc
change detection using remotely sensed data; a case study of Bauchi
city”, 7th International Remote Sensing and GIS Conference and
Exhibition Earth and Environmental Science, vol. 20, pp. 1-9, 2014.
[3] K. Seto, C. E. Woodcock, C. Song, X. Huang, J. Lu, and, R. K.
Kaufmann, “Monitoring land-use change in the Pearl River Delta using
Landsat TM”, International Journal of Remote Sensing, vol. 23, no. 10,
1985-2004, 2002.
[4] K. S. Kumar, P. U. Bhaskar, and K. Padmakumari, “Estimation of land
surface temperature to study urban heat islamd effect using Landsat
ETM+ image”, International Journal Engineering Science and
Technology, vol. 4, no. 2, pp. 771-778, 2012.
[5] TUIK, Turkish Statistical Institute, 2015 http://tuik.gov.tr.
[6] G. Chander, B. L. Markham, and D. L. Helder, “Summary of current
radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO-1 ALI sensors”, Remote Sensing of Environment, vol. 113, no. 5, pp.
893-903, 2009.
[7] A. Fornaciai, M. Bisson, F. Mazzarini, P. D. Carlo, G. Pasquare,
“Landsat 5 TM images and DEM in lithologic mapping of Payen
Volcanic Field (Mendonazo Province, Argentina)”, Rivista Italiano Di
Telerilevamento, vol. 41, no. 1, pp. 11-24.
[8] C. Coll, J. M. Galve, J. M. Sanches, and V. Caselles, “Validation of
Landsat-7/ETM+ thermal-band calibration and atmospheric correction
with ground-based measurements”, IEEE Transactions on Geoscience
and Remote Sensing, vol. 48, no. 1, pp. 547-555, 2010.
[9] YCEO, Yale University Center for Earth Observation,
http://www.yale.edu/ceo/Documentation/Landsat_DN_to_Kelvin.pdf
[10] NASA, National Aeronautics and Space Administration, 2011.
http://landsathandbook.gsfc.nasa.gov/data_prod/prog_sect11_3.
[11] E. Tarantino, “Monitoring spatial and temporal distribution of sea
surface temperature with TIR sensor data”, Italian Journal of Remote
Sensing, vol. 44, no. 1, pp. 97-107, 2012.
[12] E. Ozelkan, S. Bagis, E. C. Ozelkan, B. B. Ustundag, and C. Ormeci,
“Land surface temperature retrieval for climate analysis and association
with climate data”, European Journal of Remote Sensing, vol. 47, pp.
655-669, 2014.
[13] O. Orhan, S. Ekercin, and F. Dadaser-Celik, “Use of Landsat land
surface temperature and vegetation indices for monitoring drought in the
salt lake basin area, Turkey”, Scientific World Journal, vol. 2014, pp. 1-
11, 2014.
[1] M. S. Hassan, and S. Mahmud-ul-islam, “Urban area change Analysis in
the Rangpur Sadar Upazila, Bangladesh using Landsat imageries”,
International Journal of Science and Research, vol. 4, no. 1, pp. 469-
474, 2015.
[2] K. M. Kafi, H. Z. M. Shafri, and A. B. M. Shariff, “An analysis of lulc
change detection using remotely sensed data; a case study of Bauchi
city”, 7th International Remote Sensing and GIS Conference and
Exhibition Earth and Environmental Science, vol. 20, pp. 1-9, 2014.
[3] K. Seto, C. E. Woodcock, C. Song, X. Huang, J. Lu, and, R. K.
Kaufmann, “Monitoring land-use change in the Pearl River Delta using
Landsat TM”, International Journal of Remote Sensing, vol. 23, no. 10,
1985-2004, 2002.
[4] K. S. Kumar, P. U. Bhaskar, and K. Padmakumari, “Estimation of land
surface temperature to study urban heat islamd effect using Landsat
ETM+ image”, International Journal Engineering Science and
Technology, vol. 4, no. 2, pp. 771-778, 2012.
[5] TUIK, Turkish Statistical Institute, 2015 http://tuik.gov.tr.
[6] G. Chander, B. L. Markham, and D. L. Helder, “Summary of current
radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO-1 ALI sensors”, Remote Sensing of Environment, vol. 113, no. 5, pp.
893-903, 2009.
[7] A. Fornaciai, M. Bisson, F. Mazzarini, P. D. Carlo, G. Pasquare,
“Landsat 5 TM images and DEM in lithologic mapping of Payen
Volcanic Field (Mendonazo Province, Argentina)”, Rivista Italiano Di
Telerilevamento, vol. 41, no. 1, pp. 11-24.
[8] C. Coll, J. M. Galve, J. M. Sanches, and V. Caselles, “Validation of
Landsat-7/ETM+ thermal-band calibration and atmospheric correction
with ground-based measurements”, IEEE Transactions on Geoscience
and Remote Sensing, vol. 48, no. 1, pp. 547-555, 2010.
[9] YCEO, Yale University Center for Earth Observation,
http://www.yale.edu/ceo/Documentation/Landsat_DN_to_Kelvin.pdf
[10] NASA, National Aeronautics and Space Administration, 2011.
http://landsathandbook.gsfc.nasa.gov/data_prod/prog_sect11_3.
[11] E. Tarantino, “Monitoring spatial and temporal distribution of sea
surface temperature with TIR sensor data”, Italian Journal of Remote
Sensing, vol. 44, no. 1, pp. 97-107, 2012.
[12] E. Ozelkan, S. Bagis, E. C. Ozelkan, B. B. Ustundag, and C. Ormeci,
“Land surface temperature retrieval for climate analysis and association
with climate data”, European Journal of Remote Sensing, vol. 47, pp.
655-669, 2014.
[13] O. Orhan, S. Ekercin, and F. Dadaser-Celik, “Use of Landsat land
surface temperature and vegetation indices for monitoring drought in the
salt lake basin area, Turkey”, Scientific World Journal, vol. 2014, pp. 1-
11, 2014.
@article{"International Journal of Earth, Energy and Environmental Sciences:70397", author = "Melis Inalpulat and Levent Genc", title = "Assessment of Agricultural Land Use Land Cover, Land Surface Temperature and Population Changes Using Remote Sensing and GIS: Southwest Part of Marmara Sea, Turkey", abstract = "Land Use Land Cover (LULC) changes due to human
activities and natural causes have become a major environmental
concern. Assessment of temporal remote sensing data provides
information about LULC impacts on environment. Land Surface
Temperature (LST) is one of the important components for modeling
environmental changes in climatological, hydrological, and
agricultural studies. In this study, LULC changes (September 7, 1984
and July 8, 2014) especially in agricultural lands together with
population changes (1985-2014) and LST status were investigated
using remotely sensed and census data in South Marmara Watershed,
Turkey. LULC changes were determined using Landsat TM and
Landsat OLI data acquired in 1984 and 2014 summers. Six-band TM
and OLI images were classified using supervised classification
method to prepare LULC map including five classes including Forest
(F), Grazing Land (G), Agricultural Land (A), Water Surface (W),
Residential Area-Bare Soil (R-B) classes. The LST image was also
derived from thermal bands of the same dates.
LULC classification results showed that forest areas, agricultural
lands, water surfaces and residential area-bare soils were increased as
65751 ha, 20163 ha, 1924 ha and 20462 ha respectively. In
comparison, a dramatic decrement occurred in grazing land (107985
ha) within three decades. The population increased 29% between
years 1984-2014 in whole study area. Along with the natural causes,
migration also caused this increase since the study area has an
important employment potential. LULC was transformed among the
classes due to the expansion in residential, commercial and industrial
areas as well as political decisions. In the study, results showed that
agricultural lands around the settlement areas transformed to
residential areas in 30 years.
The LST images showed that mean temperatures were ranged
between 26-32°C in 1984 and 27-33°C in 2014. Minimum
temperature of agricultural lands was increased 3°C and reached to
23°C. In contrast, maximum temperature of A class decreased to
41°C from 44°C. Considering temperatures of the 2014 R-B class and
1984 status of same areas, it was seen that mean, min and max
temperatures increased by 2°C.
As a result, the dynamism of population, LULC and LST resulted
in increasing mean and maximum surface temperatures, living
spaces/industrial areas and agricultural lands.", keywords = "Census data, landsat, land surface temperature
(LST), land use land cover (LULC).", volume = "9", number = "8", pages = "922-6", }