Depending on the rapid growth in infrastructural developments along with the increasing of human population, quality of cities is being deteriorated globally. Assurance of environmental quality is essential for urban planning and developments. This paper presents the potential application of urban green areas as an indicator of urban environmental quality in Addis Ababa, Ethiopia based on indicators of natural parameters extracted from remotely sensed images, and socio-economic variables derived from census data. Physical environmental variables such as land-use/land-cover data, surface temperature, normalized difference vegetation index, and transformed remote sensing variables derived from three landsat images of 1986, 2000 and 2015 were analyzed for the present study. Socio-economic variables including population density and greenhouse gas emission in 2012 were used. Regression analysis, factor analysis and overlay analysis were performed after the two groups of variables were integrated. Four factors such as greenness, crowd, heat island and greenhouse gas emission were used for interpretation. By assigning different weights to each of these factors and proportion of green areas, land-use/land-cover map, environmental risk map and environmental quality index map were generated. The results show deterioration of environmental quality in the study area. It is recommended that future studies should include more parameters to provide a holistic view of the changes in greenness of the city and to try to mitigate adverse effects of development activities leading to human density and depletion of green area in the city.
Urban environment quality in the developing part of the world is deteriorating day by day. Large cities are reaching human saturation levels, and are unable to cope up with diverse types of human-induced pressures [
Urban greenery is defined as the overall extent of outdoor space with vegetation cover of trees, bushes, ornamental plants, or grass. Examples of such spaces are squares, parks, rows of trees on road sides, groves, and planted spaces in yards of public or private buildings [
The integrated framework of Remote Sensing (RS) and Geographical Information System (GIS) techniques greatly reduces time, effort and expenses in using geographical data. Remote sensing with its advantages of spatial, spectral and temporal availability of data coverage of large and inaccessible areas within a short time has become a handy tool in assessing, monitoring and conserving urban greenery [
In this research, distribution of urban green area and vegetation density were analysed as the major index for air freshening, which generally improves the urban environmental quality. Building density, population density, temperature, humidity, waste deposal, accessibility to major roads and carbon emission were considered as major indices of degradation of city environment. Understanding the causal factors is a prerequisite to assess and maintain sustainable urban environmental quality. The objectives of the present study were to map the urban green space and evaluate environmental quality of the ten sub-sites of Addis Ababa using natural and social parameters, derived from remote sensing satellite imagery and secondary data.
Addis Ababa, the capital city of Ethiopia, is one of the largest urban centers in the Sub-Saharan Africa. It is located between latitudes 8˚49'N - 9˚5'N and longitudes 38˚38'E - 38˚54'E, covering a total area of 51,957.92 ha (
in to 10 sub-cities and 116 woredas (administrative districts). Long-term mean annual maximum and minimum temperatures of the city are 24.4˚C and 7.2˚C, respectively. The total population of Addis Ababa is 3,275,348, which is about 60% of the total urban population in Ethiopia [
The data sources for this research primarily came from Landsat images. Secondary data were from National Meteorology Agency of Ethiopia, Central Statistical Agency and Addis Ababa Environmental Protection Authority. The population census data were collected from Central Statistical Agency. The three Landsat images included TM 1986, Landsat 2000 and ETM+ 2015. All images were georectified to a common UTM coordinate system. For the image, 250 ground control points were selected to generate coefficients for a first-order polynomial, and a nearest-neighbor method was applied to resample the image according to their original theoretical spatial resolution. The distribution and quantity of land-use/land-cover patterns, distribution and density of vegetation cover Normalized Difference Vegetation Index (NDVI), surface temperature, greenhouse gas emission, built up density and population density were measured, evaluated and compared for each of the sub-cities in Addis Ababa.
Normalized Difference Vegetation Index was applied for measurement of vegetation distribution and density. Vegetation has a high reflectance in the Near Infrared Red (NIR) bands of a sensor system because of the internal reflectance by the spongy mesophyll tissue of green leaves [
The thermal bands of Landsat 8 band 10 and 11 were converted to radiance by calculating the satellite temperature. The satellite temperature was corrected using the emissivity values computed from the NDVI and the satellite temperature was changed to the surface temperature. Basically, the following three steps were involved in the procedure: first, converting the digital number (DN) values of the thermal band into spectral radiance; second, converting the spectral radiance to at satellite brightness temperature, viz., blackbody temperature; and third, adjusting the blackbody temperature to land surface temperature by incorporating emissivity biases due to land-cover differences.
1) Conversion of DN Values to Radiance
Operational land imager (OLI) and Thermal Infrared Sensor (TIRS) band data
were converted to Top-Of-Atmosphere (TOA) special radiance rescaling factors provided in the metadata file. The first step was conversion of the DN value to the radiance units using Equation (2).
where, Lλ is the TOA special Radiance(watts/(m2 srad µm)), ML is the Band specific multiplicative rescaling factor from metadata (Radiance_Mult_Band_x where x is band number), AL is the Band specific additive rescaling factor from metadata (Radiance_Add_Band_x where x is band number) and Qcal is the Quantized calibrated pixel value in DNs.
2) Conversion of Radiance to at Satellite Temperature
The next step during quantitative analysis was conversion of radiance to temperature. The satellite temperature was calculated under an assumption of unity emissivity using pre-launch calibration constants K1 and K2 by the following Equation (3):
where, T is the satellite brightness tempreture (K), K2 is the calibration constant (K) and K1 is the calibration constant (W/m2 sr µm).
3) Land Surface Temperature
Land surface temperature was computed from calculated emissivity values and calculated temperature at satellite using the following Equation (4):
where, TLST → Surface temperature (K), TB = at satellite temprature, W is the wavelength of emitted radiance (11.45 µm), ρ = h*c/k (1.438 × 10−2 m・k) with σ = stefan Boltzmann constant (1.38 × 10−23 J/K), h is the plank’s constant (6.26 × 10−34 J・s) and c is the velocity of light (2.998 × 10−8 m/s).
All the five parameters extracted from the physical and secondary datasets were first entered to run ordinary list square correlation analysis. To shed some light on their qualification on the study, Coefficients have the expected sign, statistically significant, no redundancy among explanatory variables, residuals are normally distributed, residuals are not spatially auto-correlated and strong adjusted r-squared value summarizes their correlation with all the variables that have been evaluated. For land-use/land-cover, the following methods of data analyses were adopted for the study using Maximum Likelihood classification, accuracy assessment with 250 reference points calculation of the area in hectares of the land-use/land-cover types obtained for each study year and subsequently comparing the results and finally annual rate of change (ha/year) = (% of recent land-use/land-cover change −% of previous land-use/land-cover change)/100 * # (15 years).
Overlay Analysis tools included in the spatial analyst extension commonly used to solve multi-criteria problems such as optimal site selection or suitability modeling. It is a technique for applying a common scale of values to diverse and dissimilar inputs to create an integrated analysis [
The results of 1986, 2000 and 2015 classified images are presented in
Variable | Coefficient | t-Statistic | Probability | Robust-t | Robust-Pr [b] | VIF [c] |
---|---|---|---|---|---|---|
Intercept | 0.31966 | 4.195808 | 0.005943* | 7.353267 | 0.000393* | - |
Population | −3.038025 | −8.255098 | 0.000132* | −10.294585 | 0.000002* | 1.923088 |
Temperature | −0.006742 | −2.649933 | 0.037747 | −4.367938 | 0.004997 | 1.463664 |
Greenhouse gas emission | 0.000027 | 2.722399 | 0.034257 | 4.62643 | 0.003897 | 2.416063 |
Note: *indicate probability (b) and Robust-pr (b) variable correlation is significant (P < 0.01).
Land-use/land cover category | 1986 | 2000 | 2015 | ||||
---|---|---|---|---|---|---|---|
Area (ha) | Area (%) | Area (ha) | Area (%) | Area (ha) | Area (%) | ||
Thick vegetation/ forest land | 11,040.50 | 21.2 | 5238.90 | 10.08 | 2730.42 | 5.26 | |
Urban crop land/ grassland | 20,705.40 | 40 | 18,835.10 | 36.25 | 10,756.70 | 20.70 | |
Settlement | 7005.60 | 13.4 | 14,037.60 | 27.02 | 25,715 | 49.49 | |
Water body | 4416.84 | 8.5 | 5870.10 | 11.30 | 6838.6 | 13.16 | |
Bare land | 8789.58 | 16.9 | 7976.22 | 15..35 | 5919.20 | 11.39 | |
Total | 51,957.92 | 100 | 51,957.92 | 100 | 51,957.92 | 100 |
was 20,705.40 ha in 1986, which declined to 18, 835.10 ha and 10,756.7 ha in 2000 and 2015, respectively. On the other hand, water body has increased from 4416.84 ha in 1986 to 5870.1 ha in 2000 and to 6836.6 ha in 2015. Settlement area had the highest increase from the initial estimate of 7005.60 ha in 1986. The extent has increased to 14, 037.6 ha in 2000 and to 25,715 ha in 2015. This was the most dynamic land-cover in Addis Ababa, which got extended year after year. The extent of bareland showed slight decline from 8789.58 ha to 7976.22 ha and to 5919.2 ha under classification of the years 1986, 2000 and 2015, respectively. The overall accuracy for land-use/land-cover was 88.80% in 1986, 85% for 2000, and 84.00% for 2015. Overall land-use/land-cover changes are given in
Land-use/ land-cover category | Changes during 1986-2015 | |||||
---|---|---|---|---|---|---|
1986-2000 | 2001-2015 | |||||
Extent | Rate | Extent | Rate | |||
(ha) | (%) | (ha/year) | (ha) | (%) | (ha/year) | |
Thick vegetation/forest land | −5801.6 | −11.17 | −386.77 | −2508.48 | −4.83 | −167.23 |
Urban crop land/grass land | −1870.5 | −3.59 | −124.69 | −8078.4 | −15.55 | −538.56 |
Settlement | 7032 | 13.53 | 468.8 | 11,677.4 | 22.47 | 778.49 |
Bare land | −813.36 | −1.57 | −54.22 | −2057.02 | −3.96 | −137.13 |
Water body | 1453.26 | 2.80 | 96.88 | 966.5 | 1.86 | 64.43 |
Note: Annual Rate of Change (ha/year) = (% of recent land-use/land-cover change-% of Previous land- use/land-cover change)/100 * # of Study years (15 no of years).
white represent man-made surfaces (concrete, roofs and roads) and water bodies. Light green represents grassy fields and dark green represents tree covered areas. The pixel values of 1986 NDVI image ranged from around −0.34 for not very vegetated surfaces to values of 0.63 for vegetated surfaces. For 2000, the pixel values for the NDVI images ranged from −0.14 for not very vegetated surfaces to 0.51 for vegetated surfaces. For 2015, the pixel values for the NDVI image ranged from −0.52 for none and not very vegetated surfaces to 0.57 for vegetated surfaces.
Green area distribution patterns in Addis Ababa city are shown in
Population density in Addis Ababa city in relation to that of the ten sub-cities indicates that the central region is the densest. Addis Ketema with >350 people/ ha is the most populated region in Addis Ababa. Arada and Lideta sub-cites have 300 people/ha, while the Akaki-Kality, Yeka and Bole sub-cities have <17 people/
ha. There is a general increase of surface temperature in the impervious surfaces (building, concreat and asphalt) in comparison with vegetated areas. Maximum average surface temperature recorded was 36.79˚C and minimum was 17.40˚C. Based on zonal mean statistics, the surface temperature in Akaki-Kality and Bole sub-city was 29˚C, followed by Addis-Ketema, Arada, Kirkose, Liedeta and Nifase-Silke (28˚C), Yeka (27˚C) and Kolfe (26˚C), respectively, while Gulele sub-city experienced the lowest surface temperature (24˚C) in Addis Ababa.
Greenhouse gas emission data are shown in
Based on weighted overlay analysis, there was a need to have a universal integration result of the five parameters (land-use/land-cover, population density, surface temperature, greenhouse gas emission and built-up density) to sum up based on the weighting (
Parameters | Criteria | Unit | Urban environmental quality indicator rating | ||||
---|---|---|---|---|---|---|---|
High | Moderate | Marginal | Not suitable | ||||
Land-cover | LU/LC | Class | Forest/ vegetation land | Crop/land/grass land | Water body | Bare land | Settlement |
NDVI | Value | 0.22 - 0.52 | 0.15 - 0.22 | 0.09 - 0.15 | −0.15 | −0.01 | |
Existing LU/LC | Built up area | Density | 0.05 - 0.08 | 0.08 - 0.09 | 0.09 - 0.14 | >0.14 | |
Urban heat | Surface temperature | ˚C | 17 - 24 | 24 - 27 | 27 - 29 | >29 | |
Population | Population size | Density | 0.0018 - 0.003 | 0.003 - 0.005 | 0.005 - 0.006 | >0.006 | |
Green house gas | Carbon emission | Tone | 0.02 - 0.03 | 0.03 - 0.14 | 0.14 - 0.23 | >0.23 |
No | Criteria | Weight % |
---|---|---|
1 | Land-use/land-cover | 24 |
2 | Built up density | 17 |
3 | Surface temperature | 16 |
4 | Population density | 21 |
5 | Green house gas emission | 22 |
Consistency Ratio (CR) is 0.02.
However, in this study the CR calculated as 0.02 is acceptable. Based on the urban environmental quality indicator rating (
During the present study, a comparison was made between the increase in impervious surface against the reduction in greenness in Addis Ababa city areas that might signify deterioration in the environmental quality of this African city. Vegetation, crop land/grassland, bare land, settlement and water bodies were either increased or decreased in extent in different rates during 1986-2015 in
Addis Ababa city area. Greenness of the area serves as a means of urban environmental quality [
Settlement areas increased from the initial estimate of 7005.60 ha in 1986 to 25,715.0 ha in 2015 with the change rate of 468.8 ha/year during 1986-2000 and 778.49 ha/year during 2001-2015. This was the most dynamic land-cover change observed in Addis Ababa city area, the cause of which is none other than urbanization and rapid population growth in the recent past. Unbalanced and uncontrolled urbanization lead to environmental degradation in the quality of urban life [
In the light of the present findings, the practical use of geospatial approach involving natural and census data, GIS and remote sensing in tracking urban environment quality change stands appropriate for sustainable environmental planning, considering the little efforts in the past to assess urban environmental quality. Integrated data analysis using remotely sensed satellite imagery and GIS modeling facilitated the analysis of the spatial distribution of environmental changes involving land-use/land-cover classification changes from time to time. Results of this study can influence policy assessments and assist local governments and environmental agencies in monitoring Urban Environmental Quality (UEQ). The UEQ models established in this paper can be applied to assist urban planners not only to evaluate the city’s current UEQ condition, but also to devise efficient development polices to construct a more desirable future UEQ environment at the national level. Land-use planners and policy makers can thus make decisions towards a more sustainable city featuring green areas, which correspond well as a key factor in determining a city’s UEQ condition.
We are grateful to the School of Earth Sciences, Addis Ababa University for access to the laboratory facilities and support for this research programme. We are also thankful to the Central Statics Agency and Addis Ababa Environmental Protection Agency, for their collaboration in delivering valuable data used in this study. We also thank the anonymous reviewers for their valuable comments to improve this manuscript.
Assaye, R., Suryabhagavan, K.V., Balakrishnan, M. and Hameed, S. (2017) Geo-Spatial Approach for Urban Green Space and Environmental Quality Assessment: A Case Study in Addis Ababa City. Journal of Geographic Information System, 9, 191-206. https://doi.org/10.4236/jgis.2017.92012