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cover change on land surface temperatures - The Nile Delta, Egypt
Journal of African Earth Sciences 126 (2017) 75e83
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Effect of land use/cover change on land surface temperatures - The Nile Delta, Egypt Mohamed E. Hereher Department of Environmental Sciences, Faculty of Science, Damietta University, New Damietta, Egypt
a r t i c l e i n f o
a b s t r a c t
Article history: Received 30 April 2016 Received in revised form 28 September 2016 Accepted 22 November 2016 Available online 23 November 2016
In this study remote sensing techniques were employed to investigate the impact of land use/cover change on land surface temperatures (LST) for a highly dynamic landscape, i.e. the Nile Delta. Land use change was determined from analyzing a 15 years of bi-monthly normalized difference vegetation index (NDVI) dataset acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite along with a synchronized 13 years of bi-monthly LST dataset retrieved from MODIS Aqua satellite. Time series analysis for NDVI and LST data was carried out at selected locations experiencing land use change. Mean LST change was determined for each location before and after the land use change. Results indicate that NDVI composite data for 15 years proved sufficient for delineating land use change. Significant spatial changes include the transformation from agriculture to urban land, which increased the LST by 1.7 C during the 13 years and the transformation of bare land to agriculture, which decreased the LST by 0.52 C for the same period. Due to the explosive population growth in the Nile Delta, urban encroachment upon agricultural land could, hence, promote a prolonged regional warming by modifying the micro-climate and other climate-related phenomena. © 2016 Elsevier Ltd. All rights reserved.
Keywords: LST NDVI Urban encroachment Climate change
1. Introduction The land use/cover change is a global environmental issue, particularly in developing countries (Li et al., 2009). Land use change tremendously impacts the social attitude of the changed region and its ecological as well as physical characteristics (Petropoulos et al., 2014). Land use/cover change is one of the main driving forces for global climate change. For example, the change from vegetation to urban land causes not only interruptions of carbon and water cycles but also energy fluxes between the land and the atmosphere (Lejeune et al., 2015). The spatial distribution of the LST varies depending on the land cover type (Voogt and Oke 2003 & Ali and Shalaby (2012)). Vegetation is known to greatly influence the atmosphere and the land temperatures by the process of evapotranspiration in which the green vegetation absorbs heat from the atmosphere for the evaporation of water that eventually reduces the temperature of the land surface (Yuan and Bauer, 2007). In addition, the land use change from agricultural to urban land alters the surface permeability and moisture content by impermeable features, such as pavements and buildings which can
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significantly affect the energy budget on the surface and eventually rises the surface temperature (Guo et al., 2012). Therefore, deforestation and urban encroachment upon green vegetation could severely raise the surrounding LST. Amazonian deforestation is a good example for rising the earth's temperature, where the mass cutting of trees raised the temperature by an annual rate of 0.5 C (Lejeune et al., 2015). LST is, therefore, a proxy to the heat budget over the earth's surface and it is a key parameter for the trend of the climate change (Srivastava et al., 2009; Amiri et al., 2009). The NDVI is the most common spectral transform utilized to map land use/ cover (Fung and Siu, 2000; Muttitanon and Tripathi, 2005; Sahebjalal and Dashtekian, 2013). The NDVI is calculated from two spectral bands in satellite images as follows: NDVI ¼ [(NIRR)/ (NIR þ R)], where the NIR and R refer to the reflection in the near infrared and the red bands, respectively (Tucker, 1979). NDVI values typically range from 1.0 to 1.0 with green vegetation having the greatest values because green vegetation always reflects much of the NIR radiation and significantly absorbs visible light for photosynthesis. Any change from green vegetation to other surface features is detected by applying the NDVI algorithm to multi-temporal satellite data. Time series analysis of NDVI data were successfully employed to map land use change (Fuller, 1998; Waylen et al., 2014; Eckert et al., 2015; Nguyen et al., 2015). The remotely sensed NDVI-
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LST relationship has also been used to delineate the land use/cover dynamics (Lambin and Ehrlich, 1996; Srivastava et al., 2009; Feizizadeh et al., 2013). Because land-based conventional meteorological stations are not distributed along wide areas and therefore they could not provide sufficient land surface temperature data. Advance and progress in remote sensing technology allowed for highlighting land use/cover change and extracting LST data in high temporal and spatial resolutions from multiple sensors and at different scales. MODIS is one of the recent satellites which was launched in the third millennium and proved to be powerful for providing information about the dynamics of the terrestrial system on the earth. MODIS sensor aboard two satellites: Terra orbits the earth from north to south and overpasses the equator in the morning and Aqua orbits the earth in the opposite direction and overpasses the equator afternoon. Both Terra and Aqua satellites acquire their data within 36 spectral bands covering the visible and infrared portions of the spectrum. The data from MODIS Terra were available since Feb. 2000, whereas MODIS Aqua data became available since July 2002 (Williamson et al., 2014). MODIS provides NDVI and LST data for the entire globe in different spatial and temporal resolutions arranged across definite vertical (v) and horizontal (h) paths. In the present study, the main objective is to detect the temporal pattern of land surface temperatures for selected regions experiencing transformation from one land use/cover to another in the Nile Delta of Egypt. Results of this study are expected to provide an understanding of the impacts of land use/cover change on regional warming of the climate. 2. The study area The Nile Delta is one of the most dynamic river deltas in the world. Its recent formation goes back to about 6000 years ago after the last stabilization of the sea water at its current level (Stanley and Warne, 1994). The Nile Delta (20,000 km2) extends from its head at Cairo to the Mediterranean Sea coast for about 200 km and extends from Port Said to Alexandria for about 220 km (Fig. 1). It
forms the break basket of Egypt and hosts more than 66% of its population. The delta slopes gradually northward from some 60 m above the sea level near Cairo to the sea level in the north. The delta soil is formed from a thick column of fine-grained black sediments and it is bordered from the east and west by two sandy plateaus. The climate of the Nile Delta is generally Mediterranean with hot dry summers and mild rainy winters. According the Central Laboratory of Agricultural Climate of the Ministry of Agriculture (www. clac.edu.eg), the mean annual temperatures for Damietta north of the Nile Delta is 19.6 C and 21.5 C at Cairo south of the delta. The precipitation rates are 104.6 mm/y at Damietta and 20 mm/y at Cairo, whereas the evapotranspiration rates are 1450 mm/y at Damietta and 2040 mm/y Cairo. The Nile Delta includes four coastal lakes, namely: Manzala, Burullus, Idku and Maryut. Both Manzala and Burullus are the biggest and witness severe human interference by drying for urban and agricultural purposes. Cultivation and urbanization are the major land use changes in the Nile Delta. Population overgrowth constitutes the primary driving force for such changes. 3. Materials and methods 3.1. Satellite images Two types of MODIS images from the Terra and Aqua platforms, which were launched in 1999 and 2002, respectively were the sources of data for the present study area (the tile h20v5). NDVI data were acquired from the 16-day composite vegetation index product (MOD13A2) of the MODIS Terra platform from its first available image (Feb. 2000) to the most recent (July 2015) with a total of 356 images for the study period. MODIS vegetation index composites are provided in HDF format at a standard Sinusoidal grid projection and each image consists of 12 layers (1 km pixel size) containing the NDVI and covers an area of 1200 1200 km. MODIS vegetation index composites have the advantage of being preprocessed at the origin for cloud occurrence, atmospheric and radiometric disorders. NDVI images are provided in 16 bit and the
Fig. 1. The Nile Delta of Egypt as recorded in Landsat satellite images in 2015. Note also the locations of the selected land use/cover change regions.
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Fig. 2. NDVI and LST values for the land use/cover units in the Nile Delta.
value of each pixel is multiplied by a factor of 10,000. LST data were acquired from the 8-day composite land surface temperature product (MYD11A2) of the MODIS Aqua platform. This product was chosen because the time of image acquisition from the Terra sensor is at 1:30 p.m. every day, which guarantees that the acquired temperature is at the peak. The MODIS LST data (1 km pixel size) are gridded clear sky thermal IR images. Data were acquired each 16 day from July 2002 to July 2015 with a total of 301 images. MODIS LST data are provided in HDF format at the Sinusoidal projection and are calibrated for cloud contamination. Each image consists of 12 bands containing the 1 km LST in 16 bit and the pixel values are multiplied by a factor of 50. MODIS LST data were downloaded for the same days as the MODIS NDVI for the period from July 2002 to July 2015. MODIS data are available free of charge at the United States Geological Survey (USGS) web portal (https:// lpdaac.usgs.gov/). 3.2. Locations of land use/cover change Four types of land use change were selected for time series NDVI and consequent LST change (Fig. 2). These locations were selected on the basis of a real change occurred during the investigation period (2000e2015). Processed MODIS data from the NDVI and LST products have the advantage of being in the same spatial (1 km 1 km) and temporal (16 days) resolutions and were acquired in the same dates. Each land use/cover change was represented by choosing one representative pixel (1 km 1 km) in the NDVI and LST images. Locations of these pixels were carefully selected after many attempts with the aid of Google Earth images to identify the coordinates of each pixel experiencing such change.
Temporal changes in the selected locations for the past 15 years were examined in recent and old Google Earth images and displayed in a scale of 1:100,000. The chosen land use/cover change patterns for this analysis were most available from the following regions in the Nile Delta: 1- a newly reclaimed area west of the Nile Delta representing the land cover change from bare land to agriculture; 2- an extension of urban community in the 6th of October city southwest of the Nile Delta as an example of land use change from bare land to urban; 3- a periphery region to Cairo metropolitan area south of the Nile Delta experiencing land use change from agriculture to urban; and 4- an area in the southern fringes of the Lake Manzala was selected as an example for land cover change from water to bare land where drying of water bodies took place,. 3.3. Data processing and time series analysis MODIS data have the advantages of being preprocessed at the origin for cloud occurrence and for atmospheric and radiometric disorders. ERDAS Imagine was utilized as the processing environment in this study. Raster MODIS HDF data were converted to IMG format before regression and time series analyses and the 1 km NDVI and LST layers in each image were individually extracted to form one separate image for each acquisition date. These dates start from Feb. 2000 for MODIS data and July 2002 for LST data. For MODIS NDVI, the total dataset (356 images) was stacked to form a single image of 356 layers covering 15 years in a chronologic order so that each pixel has the same coordinates in all layers. The new MODIS NDVI image was divided by 10,000 in order to rescale the NDVI values in each pixel. In a same manner, the LST dataset (301 images) was stacked to form one LST image containing 301 layers
Fig. 3. A regression correlation between NDVI and LST for different land use/cover units.
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Fig. 4. Google Earth images for the selected locations for land use change, a) shows the reclamation of a bare desert to agriculture land west of the Nile Delta; b) shows the transformation of a bare land to urban at the 6th of October city; c) shows the transformation farmland to urban land north of Cairo and d) the conversion of water to bare land south of the Lake Manzala.
covering 13 years. The LST image was then multiplied by 0.02 to get the pixel values in Kelvin scale and then each pixel was converted to degree Celsius by subtracting 273.15 from each pixel. The final two NDVI and LST images were geographically linked in ERDAS Imagine so that any pixel in the NDVI image was joined to its corresponding location in the LST image. The trend of NDVI time series was attempted for the pixels of appreciable change from one land use to another with the focus on the change from bare land to
urban; bare land to agriculture; agriculture to urban and water to vegetation. For the selected locations, the date of change as determined in the NDVI time series was determined and considered the “break point”. The corresponding LST time series for the same pixel was also prepared. The mean LST is calculated before and after the break point, where the LST change was then determined. The mean LST of the entire Nile Delta was compared using the total images of 2003 and 2014 in order to delineate the regional trend in LST
M.E. Hereher / Journal of African Earth Sciences 126 (2017) 75e83
Fig. 5. The NDVI (Feb. 2000eJuly 2015) time series curves of the selected locations.
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caused by the land use/cover change occurred between 2003 and 2014.
after that date is 37.54 C, with a decrease of 0.52 C due to this change (Fig. 6b).
4. Results and discussions
4.3. Land use/cover change increasing LST
4.1. Land surface temperatures of land use/cover units
The conversion of farmlands to urban land use is a common practice in the Nile Delta, particularly at the peripheries of the cities and villages (Fig. 4c). The selected location in Cairo at the south end of the Nile Delta reveals a negative tendency of the NDVI time series (Fig. 5c). The NDVI decreased from 0.45 in Feb. 2000 to 0.17 in July 2015. The change date in NDVI is observed in Feb. 2012. The corresponding change in the mean LST for this location is from 34.01 C to 35.71 C (Fig. 6c), with a net LST increase of 1.70 C in 13 years at this location (Table 1). The findings of the this study are consistent with those of Zhou and Wang (2011), who reported that the LST increases as the land use changes from vegetation to urban land. As urban encroachment is the significant land use change in the Nile Delta, this transformation incorporates severe warming as the newly urban areas are being deprived from the cooling effect of vegetation. The transformation of water to bare land or even to farmlands is performed at the fringes of the Lake Manzala and Lake Burullus at the north of the Nile Delta by drying and filling practices. Such locations are generally shallow, isolated and rich in swampy vegetation (Fig. 4d). The studied location, which occurs south of the Lake Manzala has a brilliant inversion in the NDVI time series trend, which started by NDVI of 0.17 in Feb. 2000 and ended by NDVI of 0.08 in July 2015 (Fig. 5d). This change is observed in July 2009, where the mean LST before this date is 37.33 C and after this date is 39.66 C (Fig. 6c), with an increase of 2.33 C for the study period. Water has the maximum heat capacity to restrain heat and by converting water to non-water features the thermal properties change and consequently the heat delivers to the environment, which eventually increases the LST.
In a remote sensing perspective, each land use/cover has a unique interaction with the electromagnetic spectrum; hence such surface features could be recorded in satellite images and could be identified by applying spectral indices, such as the NDVI transform. Green vegetation has the maximum NDVI values and water bodies always have negative NDVI values. Urban and bare lands have comparable NDVI values of about 0.1e0.2 (Tam et al., 2010), nevertheless urban lands have little greater NDVI values than bare lands (Fig. 2) because in urban areas gardens and green belts act to increase the NDVI value. Each land use/cove unit has also its unique thermal properties. LST extracted from stable and pristine land use/ cover units show that water bodies have the least mean LST (21 C) because water has the maximum heat capacity to store a large amount of potential energy (Sharp, 2001), whereas urban and bare lands with lower heat capacities show the highest LST (36 C for urban and 37 C for bare land). These results conform to Feizizadeh et al. (2013) who reported that the LST of urban lands are close to bare soil surfaces and approach about 34 C and 35 C for urban and bare land, respectively. Dense vegetation has low LST (26 C) due to the cooling effect of the evapotranspiration process (Yuan and Bauer, 2007). There is a strong reverse relationship between the vegetation cover and intensity in terms of NDVI and its LST (Fig. 3). Losing vegetation cover by built up areas should raise the LST of the region (Zhou and Wang, 2011). On the other hand, the urban development upon bare desert should lower its LST. 4.2. Land use/cover change decreasing LST The land cover change from bare desert to agricultural land is examined west to the Nile Delta (Fig. 4a), where reclamation projects extended to convert sandy desert to farmlands. The time series analysis reveals a positive NDVI trend for that location (Fig. 5a) with a pronounced change in NDVI from 0.11 in Feb. 2000 to 0.36 in Dec. 2005 (Table 1). There is a corresponding net negative LST trend for that location (Fig. 6a). The mean LST before the change is 38.07 C and it was lowered to 37.85 C after change, with a decrease in the mean LST by 0.22 C in 13 years. The higher mean temperature in this region is attributed to the hot and dry climate of this part of the Western Desert of Egypt. The land use change from bare to urban land is represented by the extensions in the built up area of the 6th of October city west of Cairo (Fig. 4b). This region is experiencing a rapid expansion onto the Western Desert. The NDVI time series trend in this location (Fig. 5b) does not reflect sharp variation in the two land use/cover units because they have comparable index values, however urban land shows little higher NDVI values than bare desert. This increase in the NDVI of urban land may be attributed to the green cover within this location. The break point of change is determined in Oct. 2007. The mean LST before that date is 38.06 C, whereas the LST
4.4. Implications of the LST change on regional climate of the Nile Delta The Nile Delta is witnessing tremendous land use changes due to the exponential population growth upon the limited natural resources of the region (Shalaby et al., 2012). Although new agricultural projects east and west of the Nile Delta were conducted, only about 410,000 acres were added between 2003 and 2012 (Hereher, 2013). On the other hand, there were urban development projects, west and east of the Nile Delta, such as the expansions of the 6th of October and the New Cairo, respectively. Although reclamation of the desert regions west and east of the Nile Delta for agricultural and urban developments has a main trend for lowering the regional LST, the major land use change is the encroachment of urban land upon cultivated areas, which drives for raising the regional LST. The change in the mean LST of the Nile Delta between 2003 and 2014 (Fig. 7) shows significant increase in LST, where the peak of LST shifted from 29 C in 2003 to 30 C in 2014 (Fig. 8). The population of the Nile Delta increased from about 46 million in 2003 to about 57 million in 2014 (the Central Agency for Public Mobilization and Statistics, http://www.capmas.gov.eg). This
Table 1 The LST change matrix of the studied locations within the Nile Delta. Dates of change are extracted from the NDVI time series data for each point. Land use change
Location
Date of change (break point)
Mean LST (before change)
Bare to agriculture Bare to urban Agriculture to urban Water to bare
West Delta 6th of October Cairo South Lake Manzala
Dec. 2005 Oct. 2008 Feb. 2012 July 2009
38.07 38.06 34.01 37.33
C C C C
Mean LST (after change) 37.85 37.54 35.71 39.66
C C C C
LST difference 0.22 0.52 þ1.70 þ2.33
C C C C
M.E. Hereher / Journal of African Earth Sciences 126 (2017) 75e83
Fig. 6. The LST (July 2002eJuly 2015) time series curves of the selected locations.
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Fig. 7. Mean LST images of the Nile Delta in 2003 (top) and 2014 (bottom) showing the spatial variations of LST in the two dates.
Fig. 8. Distribution of the number of pixels (1 km2 each) with different LST in 2003 and 2014. Note that the peak of LST is 29 C in 2003 and 30 C in 2014.
M.E. Hereher / Journal of African Earth Sciences 126 (2017) 75e83
increase in population (11 million) is accompanied by removal of tremendous farmlands on the peripheries of towns and villages. Shalaby (2012) reported that about 2336 km2 (10% of the Delta area) of urbanization was encroached upon agricultural lands in the Nile Delta between 1984 and 2006. In Cairo region about 140 km2 of agricultural lands were lost to urban lands between 1973 and 2006 (Hereher, 2012). The drying activities of the northern lakes are among the reasons for raising the regional LST. The total loss of water body of the Lake Manzala was estimated at about 355 km2 between 2003 and 2012 (Hereher, 2014) and about 34 km2 of the Lake Burullus between 1999 and 2011 (El-Asmar et al., 2013). The overall impact of rising the LST in urban lands is the formation of urban heat islands that could disrupt the thermal balance (Feizizadeh et al., 2013) and disturb the hydrological cycle (Zhou and Wang, 2011). Burning of fossil fuels and agricultural wastes (straw) act to increase greenhouse gasses and aerosols in the atmosphere, which eventually lead to regional warming in the region (Hereher, 2016). The micro-climate should, hence, respond negatively to these changes by a regional warming in the Nile Delta that is recently conceivable. 5. Conclusions The present study demonstrates that the anthropogenic activities in the Nile Delta not only change the land use in the region but also its LST. Time series analysis of 15 years NDVI data is suitable for addressing the land use change, particularly in agricultural area. These changes could not be recorded by the few and not-evenly distributed meteorological stations, particularly east and west of the Nile Delta. Furthermore, data from these local weather stations are discrete points in space, which hardly reflect the LST spatial variations caused by land-use/cover change. Fine spatial resolution images visualized by Google Earth aided for proper mapping of changed land use during the study period. Losing about 62% of vegetation cover along 13 years is accompanied by an increase of 1.7 C of land surface temperatures, whereas converting water to bare soil is associated with severe warming (2.33 C for the same period). The Nile Delta is exposed to a regional increase in the surface temperature due to urbanization, urban encroachment upon green areas and the construction of impermeable concrete surfaces upon porous and moist vegetated ones. Consequently, ramifications on the environmental and ecological factors, such as humidity and soil microbiology are inevitable. Accordingly, long term consequences should be elaborated in detailed studies. Acknowledgements The author deeply acknowledges the revisions of two anonymous reviews. References Ali, R.R., Shalaby, A., 2012. Response of topsoil features to the seasonal changes of land surface temperature in the arid environment. Int. J. Soil Sci. 7 (2), 39e50. Amiri, R., Weng, O., Alimohammadi, A., Alavipanah, A., 2009. Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area. Iran. Remote Sens. Environ. 113, 2606e2617. Eckert, S., Hüsler, F., Liniger, H., Hodel, E., 2015. Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. J. Arid
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Journal of African Earth Sciences journal homepage: www.elsevier.com/locate/jafrearsci
Effect of land use/cover change on land surface temperatures - The Nile Delta, Egypt Mohamed E. Hereher Department of Environmental Sciences, Faculty of Science, Damietta University, New Damietta, Egypt
a r t i c l e i n f o
a b s t r a c t
Article history: Received 30 April 2016 Received in revised form 28 September 2016 Accepted 22 November 2016 Available online 23 November 2016
In this study remote sensing techniques were employed to investigate the impact of land use/cover change on land surface temperatures (LST) for a highly dynamic landscape, i.e. the Nile Delta. Land use change was determined from analyzing a 15 years of bi-monthly normalized difference vegetation index (NDVI) dataset acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite along with a synchronized 13 years of bi-monthly LST dataset retrieved from MODIS Aqua satellite. Time series analysis for NDVI and LST data was carried out at selected locations experiencing land use change. Mean LST change was determined for each location before and after the land use change. Results indicate that NDVI composite data for 15 years proved sufficient for delineating land use change. Significant spatial changes include the transformation from agriculture to urban land, which increased the LST by 1.7 C during the 13 years and the transformation of bare land to agriculture, which decreased the LST by 0.52 C for the same period. Due to the explosive population growth in the Nile Delta, urban encroachment upon agricultural land could, hence, promote a prolonged regional warming by modifying the micro-climate and other climate-related phenomena. © 2016 Elsevier Ltd. All rights reserved.
Keywords: LST NDVI Urban encroachment Climate change
1. Introduction The land use/cover change is a global environmental issue, particularly in developing countries (Li et al., 2009). Land use change tremendously impacts the social attitude of the changed region and its ecological as well as physical characteristics (Petropoulos et al., 2014). Land use/cover change is one of the main driving forces for global climate change. For example, the change from vegetation to urban land causes not only interruptions of carbon and water cycles but also energy fluxes between the land and the atmosphere (Lejeune et al., 2015). The spatial distribution of the LST varies depending on the land cover type (Voogt and Oke 2003 & Ali and Shalaby (2012)). Vegetation is known to greatly influence the atmosphere and the land temperatures by the process of evapotranspiration in which the green vegetation absorbs heat from the atmosphere for the evaporation of water that eventually reduces the temperature of the land surface (Yuan and Bauer, 2007). In addition, the land use change from agricultural to urban land alters the surface permeability and moisture content by impermeable features, such as pavements and buildings which can
E-mail address: [email protected]. http://dx.doi.org/10.1016/j.jafrearsci.2016.11.027 1464-343X/© 2016 Elsevier Ltd. All rights reserved.
significantly affect the energy budget on the surface and eventually rises the surface temperature (Guo et al., 2012). Therefore, deforestation and urban encroachment upon green vegetation could severely raise the surrounding LST. Amazonian deforestation is a good example for rising the earth's temperature, where the mass cutting of trees raised the temperature by an annual rate of 0.5 C (Lejeune et al., 2015). LST is, therefore, a proxy to the heat budget over the earth's surface and it is a key parameter for the trend of the climate change (Srivastava et al., 2009; Amiri et al., 2009). The NDVI is the most common spectral transform utilized to map land use/ cover (Fung and Siu, 2000; Muttitanon and Tripathi, 2005; Sahebjalal and Dashtekian, 2013). The NDVI is calculated from two spectral bands in satellite images as follows: NDVI ¼ [(NIRR)/ (NIR þ R)], where the NIR and R refer to the reflection in the near infrared and the red bands, respectively (Tucker, 1979). NDVI values typically range from 1.0 to 1.0 with green vegetation having the greatest values because green vegetation always reflects much of the NIR radiation and significantly absorbs visible light for photosynthesis. Any change from green vegetation to other surface features is detected by applying the NDVI algorithm to multi-temporal satellite data. Time series analysis of NDVI data were successfully employed to map land use change (Fuller, 1998; Waylen et al., 2014; Eckert et al., 2015; Nguyen et al., 2015). The remotely sensed NDVI-
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LST relationship has also been used to delineate the land use/cover dynamics (Lambin and Ehrlich, 1996; Srivastava et al., 2009; Feizizadeh et al., 2013). Because land-based conventional meteorological stations are not distributed along wide areas and therefore they could not provide sufficient land surface temperature data. Advance and progress in remote sensing technology allowed for highlighting land use/cover change and extracting LST data in high temporal and spatial resolutions from multiple sensors and at different scales. MODIS is one of the recent satellites which was launched in the third millennium and proved to be powerful for providing information about the dynamics of the terrestrial system on the earth. MODIS sensor aboard two satellites: Terra orbits the earth from north to south and overpasses the equator in the morning and Aqua orbits the earth in the opposite direction and overpasses the equator afternoon. Both Terra and Aqua satellites acquire their data within 36 spectral bands covering the visible and infrared portions of the spectrum. The data from MODIS Terra were available since Feb. 2000, whereas MODIS Aqua data became available since July 2002 (Williamson et al., 2014). MODIS provides NDVI and LST data for the entire globe in different spatial and temporal resolutions arranged across definite vertical (v) and horizontal (h) paths. In the present study, the main objective is to detect the temporal pattern of land surface temperatures for selected regions experiencing transformation from one land use/cover to another in the Nile Delta of Egypt. Results of this study are expected to provide an understanding of the impacts of land use/cover change on regional warming of the climate. 2. The study area The Nile Delta is one of the most dynamic river deltas in the world. Its recent formation goes back to about 6000 years ago after the last stabilization of the sea water at its current level (Stanley and Warne, 1994). The Nile Delta (20,000 km2) extends from its head at Cairo to the Mediterranean Sea coast for about 200 km and extends from Port Said to Alexandria for about 220 km (Fig. 1). It
forms the break basket of Egypt and hosts more than 66% of its population. The delta slopes gradually northward from some 60 m above the sea level near Cairo to the sea level in the north. The delta soil is formed from a thick column of fine-grained black sediments and it is bordered from the east and west by two sandy plateaus. The climate of the Nile Delta is generally Mediterranean with hot dry summers and mild rainy winters. According the Central Laboratory of Agricultural Climate of the Ministry of Agriculture (www. clac.edu.eg), the mean annual temperatures for Damietta north of the Nile Delta is 19.6 C and 21.5 C at Cairo south of the delta. The precipitation rates are 104.6 mm/y at Damietta and 20 mm/y at Cairo, whereas the evapotranspiration rates are 1450 mm/y at Damietta and 2040 mm/y Cairo. The Nile Delta includes four coastal lakes, namely: Manzala, Burullus, Idku and Maryut. Both Manzala and Burullus are the biggest and witness severe human interference by drying for urban and agricultural purposes. Cultivation and urbanization are the major land use changes in the Nile Delta. Population overgrowth constitutes the primary driving force for such changes. 3. Materials and methods 3.1. Satellite images Two types of MODIS images from the Terra and Aqua platforms, which were launched in 1999 and 2002, respectively were the sources of data for the present study area (the tile h20v5). NDVI data were acquired from the 16-day composite vegetation index product (MOD13A2) of the MODIS Terra platform from its first available image (Feb. 2000) to the most recent (July 2015) with a total of 356 images for the study period. MODIS vegetation index composites are provided in HDF format at a standard Sinusoidal grid projection and each image consists of 12 layers (1 km pixel size) containing the NDVI and covers an area of 1200 1200 km. MODIS vegetation index composites have the advantage of being preprocessed at the origin for cloud occurrence, atmospheric and radiometric disorders. NDVI images are provided in 16 bit and the
Fig. 1. The Nile Delta of Egypt as recorded in Landsat satellite images in 2015. Note also the locations of the selected land use/cover change regions.
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Fig. 2. NDVI and LST values for the land use/cover units in the Nile Delta.
value of each pixel is multiplied by a factor of 10,000. LST data were acquired from the 8-day composite land surface temperature product (MYD11A2) of the MODIS Aqua platform. This product was chosen because the time of image acquisition from the Terra sensor is at 1:30 p.m. every day, which guarantees that the acquired temperature is at the peak. The MODIS LST data (1 km pixel size) are gridded clear sky thermal IR images. Data were acquired each 16 day from July 2002 to July 2015 with a total of 301 images. MODIS LST data are provided in HDF format at the Sinusoidal projection and are calibrated for cloud contamination. Each image consists of 12 bands containing the 1 km LST in 16 bit and the pixel values are multiplied by a factor of 50. MODIS LST data were downloaded for the same days as the MODIS NDVI for the period from July 2002 to July 2015. MODIS data are available free of charge at the United States Geological Survey (USGS) web portal (https:// lpdaac.usgs.gov/). 3.2. Locations of land use/cover change Four types of land use change were selected for time series NDVI and consequent LST change (Fig. 2). These locations were selected on the basis of a real change occurred during the investigation period (2000e2015). Processed MODIS data from the NDVI and LST products have the advantage of being in the same spatial (1 km 1 km) and temporal (16 days) resolutions and were acquired in the same dates. Each land use/cover change was represented by choosing one representative pixel (1 km 1 km) in the NDVI and LST images. Locations of these pixels were carefully selected after many attempts with the aid of Google Earth images to identify the coordinates of each pixel experiencing such change.
Temporal changes in the selected locations for the past 15 years were examined in recent and old Google Earth images and displayed in a scale of 1:100,000. The chosen land use/cover change patterns for this analysis were most available from the following regions in the Nile Delta: 1- a newly reclaimed area west of the Nile Delta representing the land cover change from bare land to agriculture; 2- an extension of urban community in the 6th of October city southwest of the Nile Delta as an example of land use change from bare land to urban; 3- a periphery region to Cairo metropolitan area south of the Nile Delta experiencing land use change from agriculture to urban; and 4- an area in the southern fringes of the Lake Manzala was selected as an example for land cover change from water to bare land where drying of water bodies took place,. 3.3. Data processing and time series analysis MODIS data have the advantages of being preprocessed at the origin for cloud occurrence and for atmospheric and radiometric disorders. ERDAS Imagine was utilized as the processing environment in this study. Raster MODIS HDF data were converted to IMG format before regression and time series analyses and the 1 km NDVI and LST layers in each image were individually extracted to form one separate image for each acquisition date. These dates start from Feb. 2000 for MODIS data and July 2002 for LST data. For MODIS NDVI, the total dataset (356 images) was stacked to form a single image of 356 layers covering 15 years in a chronologic order so that each pixel has the same coordinates in all layers. The new MODIS NDVI image was divided by 10,000 in order to rescale the NDVI values in each pixel. In a same manner, the LST dataset (301 images) was stacked to form one LST image containing 301 layers
Fig. 3. A regression correlation between NDVI and LST for different land use/cover units.
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Fig. 4. Google Earth images for the selected locations for land use change, a) shows the reclamation of a bare desert to agriculture land west of the Nile Delta; b) shows the transformation of a bare land to urban at the 6th of October city; c) shows the transformation farmland to urban land north of Cairo and d) the conversion of water to bare land south of the Lake Manzala.
covering 13 years. The LST image was then multiplied by 0.02 to get the pixel values in Kelvin scale and then each pixel was converted to degree Celsius by subtracting 273.15 from each pixel. The final two NDVI and LST images were geographically linked in ERDAS Imagine so that any pixel in the NDVI image was joined to its corresponding location in the LST image. The trend of NDVI time series was attempted for the pixels of appreciable change from one land use to another with the focus on the change from bare land to
urban; bare land to agriculture; agriculture to urban and water to vegetation. For the selected locations, the date of change as determined in the NDVI time series was determined and considered the “break point”. The corresponding LST time series for the same pixel was also prepared. The mean LST is calculated before and after the break point, where the LST change was then determined. The mean LST of the entire Nile Delta was compared using the total images of 2003 and 2014 in order to delineate the regional trend in LST
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Fig. 5. The NDVI (Feb. 2000eJuly 2015) time series curves of the selected locations.
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caused by the land use/cover change occurred between 2003 and 2014.
after that date is 37.54 C, with a decrease of 0.52 C due to this change (Fig. 6b).
4. Results and discussions
4.3. Land use/cover change increasing LST
4.1. Land surface temperatures of land use/cover units
The conversion of farmlands to urban land use is a common practice in the Nile Delta, particularly at the peripheries of the cities and villages (Fig. 4c). The selected location in Cairo at the south end of the Nile Delta reveals a negative tendency of the NDVI time series (Fig. 5c). The NDVI decreased from 0.45 in Feb. 2000 to 0.17 in July 2015. The change date in NDVI is observed in Feb. 2012. The corresponding change in the mean LST for this location is from 34.01 C to 35.71 C (Fig. 6c), with a net LST increase of 1.70 C in 13 years at this location (Table 1). The findings of the this study are consistent with those of Zhou and Wang (2011), who reported that the LST increases as the land use changes from vegetation to urban land. As urban encroachment is the significant land use change in the Nile Delta, this transformation incorporates severe warming as the newly urban areas are being deprived from the cooling effect of vegetation. The transformation of water to bare land or even to farmlands is performed at the fringes of the Lake Manzala and Lake Burullus at the north of the Nile Delta by drying and filling practices. Such locations are generally shallow, isolated and rich in swampy vegetation (Fig. 4d). The studied location, which occurs south of the Lake Manzala has a brilliant inversion in the NDVI time series trend, which started by NDVI of 0.17 in Feb. 2000 and ended by NDVI of 0.08 in July 2015 (Fig. 5d). This change is observed in July 2009, where the mean LST before this date is 37.33 C and after this date is 39.66 C (Fig. 6c), with an increase of 2.33 C for the study period. Water has the maximum heat capacity to restrain heat and by converting water to non-water features the thermal properties change and consequently the heat delivers to the environment, which eventually increases the LST.
In a remote sensing perspective, each land use/cover has a unique interaction with the electromagnetic spectrum; hence such surface features could be recorded in satellite images and could be identified by applying spectral indices, such as the NDVI transform. Green vegetation has the maximum NDVI values and water bodies always have negative NDVI values. Urban and bare lands have comparable NDVI values of about 0.1e0.2 (Tam et al., 2010), nevertheless urban lands have little greater NDVI values than bare lands (Fig. 2) because in urban areas gardens and green belts act to increase the NDVI value. Each land use/cove unit has also its unique thermal properties. LST extracted from stable and pristine land use/ cover units show that water bodies have the least mean LST (21 C) because water has the maximum heat capacity to store a large amount of potential energy (Sharp, 2001), whereas urban and bare lands with lower heat capacities show the highest LST (36 C for urban and 37 C for bare land). These results conform to Feizizadeh et al. (2013) who reported that the LST of urban lands are close to bare soil surfaces and approach about 34 C and 35 C for urban and bare land, respectively. Dense vegetation has low LST (26 C) due to the cooling effect of the evapotranspiration process (Yuan and Bauer, 2007). There is a strong reverse relationship between the vegetation cover and intensity in terms of NDVI and its LST (Fig. 3). Losing vegetation cover by built up areas should raise the LST of the region (Zhou and Wang, 2011). On the other hand, the urban development upon bare desert should lower its LST. 4.2. Land use/cover change decreasing LST The land cover change from bare desert to agricultural land is examined west to the Nile Delta (Fig. 4a), where reclamation projects extended to convert sandy desert to farmlands. The time series analysis reveals a positive NDVI trend for that location (Fig. 5a) with a pronounced change in NDVI from 0.11 in Feb. 2000 to 0.36 in Dec. 2005 (Table 1). There is a corresponding net negative LST trend for that location (Fig. 6a). The mean LST before the change is 38.07 C and it was lowered to 37.85 C after change, with a decrease in the mean LST by 0.22 C in 13 years. The higher mean temperature in this region is attributed to the hot and dry climate of this part of the Western Desert of Egypt. The land use change from bare to urban land is represented by the extensions in the built up area of the 6th of October city west of Cairo (Fig. 4b). This region is experiencing a rapid expansion onto the Western Desert. The NDVI time series trend in this location (Fig. 5b) does not reflect sharp variation in the two land use/cover units because they have comparable index values, however urban land shows little higher NDVI values than bare desert. This increase in the NDVI of urban land may be attributed to the green cover within this location. The break point of change is determined in Oct. 2007. The mean LST before that date is 38.06 C, whereas the LST
4.4. Implications of the LST change on regional climate of the Nile Delta The Nile Delta is witnessing tremendous land use changes due to the exponential population growth upon the limited natural resources of the region (Shalaby et al., 2012). Although new agricultural projects east and west of the Nile Delta were conducted, only about 410,000 acres were added between 2003 and 2012 (Hereher, 2013). On the other hand, there were urban development projects, west and east of the Nile Delta, such as the expansions of the 6th of October and the New Cairo, respectively. Although reclamation of the desert regions west and east of the Nile Delta for agricultural and urban developments has a main trend for lowering the regional LST, the major land use change is the encroachment of urban land upon cultivated areas, which drives for raising the regional LST. The change in the mean LST of the Nile Delta between 2003 and 2014 (Fig. 7) shows significant increase in LST, where the peak of LST shifted from 29 C in 2003 to 30 C in 2014 (Fig. 8). The population of the Nile Delta increased from about 46 million in 2003 to about 57 million in 2014 (the Central Agency for Public Mobilization and Statistics, http://www.capmas.gov.eg). This
Table 1 The LST change matrix of the studied locations within the Nile Delta. Dates of change are extracted from the NDVI time series data for each point. Land use change
Location
Date of change (break point)
Mean LST (before change)
Bare to agriculture Bare to urban Agriculture to urban Water to bare
West Delta 6th of October Cairo South Lake Manzala
Dec. 2005 Oct. 2008 Feb. 2012 July 2009
38.07 38.06 34.01 37.33
C C C C
Mean LST (after change) 37.85 37.54 35.71 39.66
C C C C
LST difference 0.22 0.52 þ1.70 þ2.33
C C C C
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Fig. 6. The LST (July 2002eJuly 2015) time series curves of the selected locations.
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Fig. 7. Mean LST images of the Nile Delta in 2003 (top) and 2014 (bottom) showing the spatial variations of LST in the two dates.
Fig. 8. Distribution of the number of pixels (1 km2 each) with different LST in 2003 and 2014. Note that the peak of LST is 29 C in 2003 and 30 C in 2014.
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increase in population (11 million) is accompanied by removal of tremendous farmlands on the peripheries of towns and villages. Shalaby (2012) reported that about 2336 km2 (10% of the Delta area) of urbanization was encroached upon agricultural lands in the Nile Delta between 1984 and 2006. In Cairo region about 140 km2 of agricultural lands were lost to urban lands between 1973 and 2006 (Hereher, 2012). The drying activities of the northern lakes are among the reasons for raising the regional LST. The total loss of water body of the Lake Manzala was estimated at about 355 km2 between 2003 and 2012 (Hereher, 2014) and about 34 km2 of the Lake Burullus between 1999 and 2011 (El-Asmar et al., 2013). The overall impact of rising the LST in urban lands is the formation of urban heat islands that could disrupt the thermal balance (Feizizadeh et al., 2013) and disturb the hydrological cycle (Zhou and Wang, 2011). Burning of fossil fuels and agricultural wastes (straw) act to increase greenhouse gasses and aerosols in the atmosphere, which eventually lead to regional warming in the region (Hereher, 2016). The micro-climate should, hence, respond negatively to these changes by a regional warming in the Nile Delta that is recently conceivable. 5. Conclusions The present study demonstrates that the anthropogenic activities in the Nile Delta not only change the land use in the region but also its LST. Time series analysis of 15 years NDVI data is suitable for addressing the land use change, particularly in agricultural area. These changes could not be recorded by the few and not-evenly distributed meteorological stations, particularly east and west of the Nile Delta. Furthermore, data from these local weather stations are discrete points in space, which hardly reflect the LST spatial variations caused by land-use/cover change. Fine spatial resolution images visualized by Google Earth aided for proper mapping of changed land use during the study period. Losing about 62% of vegetation cover along 13 years is accompanied by an increase of 1.7 C of land surface temperatures, whereas converting water to bare soil is associated with severe warming (2.33 C for the same period). The Nile Delta is exposed to a regional increase in the surface temperature due to urbanization, urban encroachment upon green areas and the construction of impermeable concrete surfaces upon porous and moist vegetated ones. Consequently, ramifications on the environmental and ecological factors, such as humidity and soil microbiology are inevitable. Accordingly, long term consequences should be elaborated in detailed studies. Acknowledgements The author deeply acknowledges the revisions of two anonymous reviews. References Ali, R.R., Shalaby, A., 2012. Response of topsoil features to the seasonal changes of land surface temperature in the arid environment. Int. J. Soil Sci. 7 (2), 39e50. Amiri, R., Weng, O., Alimohammadi, A., Alavipanah, A., 2009. Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area. Iran. Remote Sens. Environ. 113, 2606e2617. Eckert, S., Hüsler, F., Liniger, H., Hodel, E., 2015. Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. J. Arid
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