Seminary on Bengladesh_heat such as Methodological courses in GIS at SahelTech

Amadou Bina Prof Coulibaly

Description

This article is a summary of “Remote Sensing of land cover’s effect on surface temperatures: A case study of the Urban Heat Island in Bangalore, India” wrote by Shrinidhi Ambinakudige. In fact, we try disengaging its importance for our country “Mali in Africa” especially all scientific methods used by its experiment authors. We have been not trying to confiscate this article’s properties but we are instead showing Remote Sensing’s utilities to people who are interested about this subject in the other hand we try to make political decision maker paying attention about the high quality of this scientific methods for assessing surface temperature in Cities. Please to cite the original note such as: Ambinakudige, S. (2011) – Remote sensing of land cover’s effect on surface temperatures: a case study of the urban heat island in Bangalore, India, Applied GIS, 7(1), 1-12
COULIBALY A.B, Student in Geography Information Systems and Management of Natural Resources at SahelTech from 2010 to 2012 under the late Professor KONATE D. responsibility, President of SahelTech in Mali.

Sujets

• SL

Informations

• Publié par: Prof_Amadou_Bina_COULIBALY_Directeur_Technique_de_PEDGES_International
• Publié le: 17 mai 2013
• Nombre de lectures: 46
• Langue: English

Extrait

 REPUBLIC OF MALI A People-A Purpose-A Faith Scientia, Virtus, Labore www.stech.edu.ml

Applied GIS Theme of seminary Remote Sensing of land cover’s effect on surface temperatures:

A case study of the Urban Heat Island in Bangalore, India Wrote by: Shrinidhi Ambinakudige Department of Geosciences, Mississippi State University, USA [e-mail]

Summarized and presented by: Amadou Bina Coulibaly [e-mail] Managed by: Pr Dialla Konate [e-mail]

th th From May, 6 to June 6 2012

Bamako/Mali

Contents

I)Abstract, Objective and Methodology of the survey II)Problems expressed in this study: what’s advice for our country Mali? III)Scientific methods and variables used in this survey: Do all of these variables have a sense in Mali? IV)Plan of the survey V)Role of images in the survey VI)How this study can be used to improve the management of the environment, of the human and natural resources? VII)Conclusions of this survey VIII)Can we lead the same survey in Mali? IX)ofArguments from this survey permitting to convince a Malian’s authority the interest of GIS X)Comment about Remote Sensing’sand GIS’ utilities Conclusion  Reference

1)Abstract, Objective and Methodology of the survey Abstract: The author of this article tries demonstrating how to evaluate the temperature changes on earth’s surface in general and especially the caseof the urban heat island in Bangalore, Indialocated at 12°59′ north latitude, 77°57′ east longitudedue to urban development and to rapid growth of its information technology, biotechnology and manufacturing sectors. For that, he extractedtemperatures values from the Landsat satellite’s Enhanced Thematic Mapper Plus (ETM+) thermal bands and calculated“Normalized Difference Vegetation Index” (NDVI)to establish relationship between vegetation cover and temperature. The result that he found shows us the city core has a lower temperature from 1° to 7° because of the presence water bodies and vegetation thanthe city’s outgrowth zonesor city’s outskirts where the higher temperatures explain substantially by the continued expansion of urban infrastructure and new, residential neighborhoods which lack vegetation. Objective: The purpose of this study is especially to analyze the urban heat island (UHI) effect in Bangalore, India by using appropriate data from Landsat satellite thermal imagery. Methodology: General and specially researches about similar or precedent themes were done at the beginning of this study. Among these themes one can notice: High resolution surface temperature pattern in a complex urban terrain, treated by Balling, R. C. & Brazell, S. W. in Photogrammetric Engineering and Remote Sensing1988; Deep soil temperature trends and urban effects in Paris viewed by Detwiller, J. and published Journal of Applied Meteorology, 9, 178-180 in 1970; the recent rise of temperature in Japan, Japanese Progress in Climatology, wrote by Fukui, E in 1970 at Tokyo University of Education46-65; Katsoulis, B. D. & Theoharatos, G.A. about Indications of the urban heat island in Athens, Greece, published in Journal of Climate and Applied Meteorology, 24, 1296-1302 in 1985; the Urban Heat Island analyzed by Kim, H. H. and presented in International Journal of Remote Sensing, 13, 2319- 2336 in 1992. This stage was the basic step because it allows the author of this article to know exactly what has been already done about his theme on somewhere and on his study area. after doing this first methodology research, he was also able to assign a particular objective to this article and attained it by usingRemote sensing’s and GIS’ methods such asCalculating Normalized Difference Vegetation Index and temperatures values with Landsat data on the study area.

2)Problems expressed in this study: what’s advice for our countryMali? Bangalore in India is one of the fastest growing cities in developing world and in recent years it has witnessed tremendous in-migration due to rapid growth of its information technology (IT), biotechnology and manufacturing sectors. About 30% of India’s IT workforce currently lives in Bangalore (Sudhira et al, 2007). This fact contributes to the disturbance of land cover and provokes the augmentation of temperature rate too in this area. It was also noticed over the last century in international scale. Indeed, urbanization throughout the world has substantially altered the earth’s surface (Owen et al., 1998). Impervious surfaces such as concrete and asphalt have changed the surface energy balance (Stull, 1988) and decreased evapotranspiration. The result has been elevated temperatures (Sailor, 1995; Myrup, 1996). Moreover, anthropogenic sources of heating, such as automobiles and power plants, have further increased surface temperatures (Myrup, 1969; Sailor, 1995). Heated surfaces reduce cooling in the late afternoon and evening hours (Oke, 1987). Also, due to the slower wind speeds near buildings, urban geometry changes net radiation and convection (Voogt & Oke, 2003). These influences all culminate in the urban heat island effect a disparity in ambient air temperature between urban and surrounding areas (Oke, 1987). Clearly, land use changes alter the land cover, and this ultimately affects the ambient air temperature in urban areas (Balling & Brazell, 1988; Roth et al., 1989). Almost of these studies have indicated to us that areas with significant tree cover are up to 5ºC cooler compared to open areas, and suburban areas with trees are 2 to 3ºC cooler than suburban areas without trees. Viewed the ampler of the phenomenon expressed above, we have to notice also studies of the UHI effect within the developing world’s cities are very few, and so thisarticle analyzes it in Bangalore. This fact is very capital and worthwhile for us. To the difference of the previous survey onBangalore’s landcover and temperatures, this latter examines temperature anomalies within Bangalore’s different neighborhoods and outgrowth zones. By this study light, we see that it is very important and significant for our country Mali. So viewed the ampler of anarchical occupations of land in city’s area,viewed also the presence of industry in the center of city. We have to cite this survey as an example even a

lesson which allows us evaluating temperature values, getting an idea about our land cover and vegetation degradation and making a real solution for resolving this fundamentally problem by using satellite image on Mali with remote sensing’s method. It is also permits us to prevent the future obstacle even to avoid the climatic changes.

3)Scientific methods and variables used in this survey: Do all of these variables have a sense in Mali? After doing methodological research and for completing the data collecting, this author used the following Scientifics methods: soLandsat’s ETM+ satellite images, captured on March 16, 2000 and on March 09, 2003 (Row/ Path: 144/051), were used in this research. They were acquired from the USGS Earth Resources Observation Systems (EROS) Data Center, and they are of 1G level quality. Clear and cloud free atmospheric conditions pertained. The Band 6 of Landsat images is a thermal-infrared (TIR) band (10.44–12.42 μm) which is commonly used for mapping temperature patterns was also used in this study because it allows us extracting temperature value of land surface. Thus, two images from different dates were used in this study to check all hypotheses assigned to this survey in order to attain the objective. For that he followed also the following steps: 3.1 Extracting temperatures:The spatial resolution of the ETM+ TIR band is 60 meters and although the temperatures it records are known to be accurately calibrated with ground temperature and the thermal band of Landsat ETM+ images was used to extract surface temperature values by mathematical methods in appropriate software conceived for that such as ENVI 4.3. This result is very sufficient but in order to validate the accuracy of the extracted temperatures weather data or the Bangalore weather station was downloaded from the NOAA Satellite and Information Service web sitehttp://lwf.ncdc.noaa.gov/oa/ncdc.html for comparative purposes. This weather station is located near the Bangalore airport. 3.2 Classifying land cover:To analyze the relationship between temperature and vegetation cover, the Normalized Difference Vegetation Index (NDVI) was calculated from Landsat imagesunder ENVI’s software. NDVI is a spectral transformation applied to red, and to near infrared bands in order to assess the health and vigor of vegetated surfaces. This NDVI values ranges from -1 to +1. Thus negative NDVI values representing a non-vegetated area while positive NDVI values signifying the presence of vegetation. Finally, a combination of NDVI values, unsupervised classification and supervised classifications was used in this survey to classify different land-cover classes in Bangalore. With the help of field data, ten sites in five land-use and land-cover (LULC) types were digitized. These LULC types included: 1. Water:NDVI values between -1 and -0.2, 2. Bare land:NDVI values between -0.2 and -0.1, 3. Built-up area:NDVI values between -0.1 and +0.1, 4. Low density vegetation:NDVI values between +0.1 and +0.3, and

5. Medium density vegetation:NDVI values between +0.3 and +1. Based on these values, every pixel (picture element or smallest part of image) within the entire study area was classified into a similar LULC class to one of those identified by Weng et al. (2004). An unsupervised classification of the Landsat images was also conducted, and a supervised classification was conducted with input from the LULC classification of NDVI values, from the unsupervised classification’s results and from field knowledgecalled under ENVI the Region of Interest (ROI). At the end, temperature values and NDVI values were then extracted from 10.000 randomly generated points throughout the study area, and an ANOVA test was carried out to examine the significance of differences in mean temperature for the three administrative zones defined by the city administration as above. Student’s t-tests

were also conducted to compare the differences between mean temperature values amongst different land-cover classes. Pearson correlation coefficients were also calculated to examine the relationship between NDVI values and temperatures in land-cover classes. To analyze variations in temperature between different geographical locations, temperature profiles were created in the north and south directions. Finally, the study identified major landmarks in the city, such as well known residential areas, commercial areas and parks, to examine temperature variations there. All of these variables above constitute an interest thing to evaluate type of natural resources in the world in general and specially the rate of each natural resource in the world even in a given area. Mali, our country is not of this reality. Indeed, this country located between the tropic of cancer and the tropic of Capricorn. That means it has the type of tropical forest and the several types of its soils tremendous the high rate of farmer and rural population about 80 percent. Besides this high of farmer we assist everyday to the phenomenon of rural exodus because of dryness, drought caused by its land cover degradation. This fact consequently increases the temperature value in its cities where the population is growing and growing. The variables or methods used in this study allowMalian’s peopleto evaluate very quickly their land cover and to pay attention about their manner of land use. Brief, with this survey Malian’s authority can record satellitesimages, and then treat them by applying remote sensing’s methods such as NDVIin order to evaluate the rate of vegetation, water, bare land etc.

4)Plan of the survey It is structured as following: Abstract: 1.Introduction 2.Study area 3.Methods 3.1Extracting temperatures 3.2Classifying land cover 4.Results 4.1Temperature differences for land-cover classes 4.2Temperature differences between the city core and outgrowth zones 5.Conclusions References 5)Role of images in the survey The images or pictures even figures in this study constitute the supports as we said in previous cases. They show and explain exactly what the survey is about. Here, we have three images and each of them has its own role. For example: The first one teaches us about the study area location in India:

The second reflects to us the evolution of Urban Heat Island (NDVI and Temperature) on different dates (2000 and 2003):

And the last one show to us land surface temperature at various landmarks within Bangalore on March, 2003:

6)How this study can be used to improve the management of the environment, of the human and natural resources? We have to repeat again this study is about Remote Sensing ofland cover’seffect on surface temperatures especially a case study of the urban heat island in Bangalore, India. Indeed, the Author used some Scientifics methods such as Extracting temperatures, classifying land cover by calculating Normalized Difference Vegetation Index (NDVI), supervised and unsupervised classification etc. on the satellites image on different years 2000 and 2003. With these results, he was able to disengage the relationship between temperature values and vegetation cover and to know the reason of their increasing such as the population growth, industrial technology in the outskirts and the reason of their decreasing in the city core because of water bodies existing. These Scientifics methods allow us to pay attention on our natural resources degradations due to human pressures link to their activities furthermore

to avoid the climatic changes and to improve the management of natural and human resources.That’s very economic and permits also durable development. 7)Conclusions of this survey This survey ended by making a comparison between results of temperature values and NDVI obtained for temperature differences for land cover classes on both dates (2000 & 2003). Thus, temperatures extracted from the Landsat TIR band revealed differences in the temperatures of all land cover classes in the city on both dates. In the 2000 image the mean temperature was 28°C, and it was 29°C in the 2003 image. So according to the weather data for Bangalore airport as provided by the NOAA Satellite and Information Service, the recorded average temperature on March 16, 2000 was 27°C and on March 9, 2003 the average temperature was 25°C but for both dates the maximum was 32°C. For land cover types in Bangalore, in both images (2000; 2003) water bodies had the lowest mean temperatures (respectively 22°07C; 20°89C) and built up areas had the highest (29°65; 29°86) whereas Medium-density vegetation areas, including gardens, had a lower mean temperature (25°67; 24°41) than all other land cover classes except water. Also, low- density vegetation areas, such as streets with side trees, had a lower mean temperature (28°03; 27°19) than built-up areas with no vegetation cover. In order to improve these results, Student’st-test between all possible pairs of land-cover classes was done and showed a significant difference (p < 0.01) between the mean temperatures in each pair. So the Pearson correlation coefficients found significant negative correlations between NDVI and temperature values for all land cover classes except water bodies. This study shows again the mean temperature in the city core which was lower (27°42C in 2000 and 27°51C in 2003) than temperatures recorded in both the outgrowth zone (28°52C in 2000 and 29°20C in 2003) and the potential outgrowth zone (29°34C in 2000 and 30°01C in 2003). That is, a rise of 1° to 2°C is experienced when we move from the core to the outgrowth zone and gain as we move from the outgrowth zone to the potential outgrowth zone. The reason would seem to be the fact that the city core had more areas under vegetation, including parks and trees, whereas outgrowth areas had several open lands as well as lands once used for agricultural purposes. NOVA testing indicated that the mean temperatures in three zones in the city differed significantly (p < 0.01) from each other, as well as the fact that mean temperature values and NDVI values varied significantly amongst the five land-cover classes on both dates (p <0.01). However, mean NDVI values in the city zones were not significantly different from each other. Within the city core zone, NDVI and temperature values were negatively correlated (-0.33 [p< 0.01] in 2000; and -0.44 [p <0.01] in 2003), but no significant correlations were

found either in the outgrowth zone or the potential outgrowth zone. Again, the presence of gardens and water bodies probably helped to keep the temperature at lower levels in the city core compared to the other two zones. This study also compared temperature in various landmark locations around Bangalore including Lalbhag Garden, Cubbon Park, the racecourse, the golf course and well known residential neighborhoods plus historic market areas in the city core. It was found that the racecourse, the airport and traditional market places had the highest mean temperatures; these locations are generally low in vegetation cover. Temperatures in residential areas in the southeastern parts of the city were about 1 degree lower compared to the residential areas in the western side of the city. That is, the presence of more parks, street trees and lakes made the southeastern part cooler than the western parts of the city. Cf. figure below:

Viewed these results we can say that Bangalore is one of the fastest growing cities in India and a technological hotspot of the world. The city has had a tremendous inflow of people in the last decade. It has been nicknamed as a garden city of India; Bangalore is growing in all directions. New road networks are being constructed and crop lands are being converted to