Change detection captures the spatial changes from multi temporal satellite images due to manmade or natural phenomenon. It is of great importance in remote sensing monitoring environmental changes and land use–land cover change detection. Gather information about Change Detection is fundamental for a better understanding the relationships and interactions between humans and the natural environment. Remote sensing (RS) data have been one of the most important data sources for studies of Change Detection spatial and temporal changes. In fact, multi-temporal RS datasets, opportunely processed and elaborated, allow mapping and identifying landscape changes, giving an effective effort to sustainable landscape planning and management [Dewan et al., 2009]. In particular, by means of the integration of RS and GIS techniques, it is possible to analyse and to classify the changing pattern of Change Detection during a long time period and, as a result, to understand the changes within the area of interest. So, a GIS and RS technique is very helpful to analyse Change Detection and to understand the factors that are able to drive the dynamic processes of rural-urban land transformation.
Each composed image was ordered into 5 area classes: built-up areas, vegetation, bare-surface, water body and rocks. Then, to each image has been applied the supervised Maximum Likelihood Classification(MLC) algorithm, more suitable when each class defined has a Gaussian distribution [Bolstad and Lillesand 1991]. Finally, a 3×3 majority filter has been applied to the classified Change Detection data, to reduce the “salt and pepper” effect [Lillesand and Kiefer, 1999]. In order to evaluate the user’s and the producer’s accuracy, a confusion matrix was applied to the classified images [Congalton, 1991; Congalton and Green, 2009]. In each Landsat images, the Change Detection class assigned to 256 pixels was visual compared with the equivalent area in the aerial frames closer to the same period. The outcome of the classifications of land cover is found in the above Figure. The classification and quantification of images of the study zone (which covers an aggregate of land area of 4578.56 square kilometres (km2) were necessary for the detection of changes in various LULC observed within the study area and over the study period. Thus, the fixed LULC dispersal for each study year was imitative above the three study years (2000, and 2020). The table reveals that as of 2000, Vegetation occupies the largest area with 1398.44 km2of the entire study area, while built-up covered 1306.15 km2, bare-surface covered 364.03km2, water body covered 1232.38km2 and rocks occupied the least area of 277.56km2. Measurements for the year 2020 indicate that there is a substantial gain in built-up areas as it increased to 2113.54 km2, while vegetation as a terrestrial cover style experienced significant loss of 1060.59 km2, bare-surface covered 106.51km2, water body covered 364.4km2 and rocks occupied the least area of 933.52km2. It’s well known that the development of the urban areas is able to transform landscapes formed by rural into urban life styles and to make functional changes, from a morphological and structural point of view [Antrop, 2000; 2004]. Historically, urban development and agriculture are competing for the same land: cities expansion has typically take place on former agricultural use.
LULC of the study area for 2000 and 2020
Years
|
|
2000
|
2020
|
LULC
|
|
Area(km2)
|
Area(km2)
|
Vegetation
|
|
1398.44
|
1060.59
|
Built-Up
|
|
1306.15
|
2113.54
|
Bare-Land
|
|
364.03
|
106.51
|
Water-body
|
|
1232.38
|
364.4
|
Rocks
|
|
277.56
|
933.52
|
Total
|
|
4578.56
|
4578.56
|
Net alteration in land-use classes involving 2000 and 2020
Category
|
|
Net change between 2000 and 2020
|
|
|
(Km2)
|
Vegetation
|
|
337.85
|
Built-Up
|
|
807.39
|
Bare-Land
|
|
257.52
|
Water-body
|
|
867.98
|
Rocks
|
|
655.96
|
The spatial variant of NDVI is not merely focused on the impact of vegetation quantity, nevertheless landscape, hill, lunar mission obtainable, and further reasons. NDVI is frequently used as a ration of terrestrial apparent greenness grounded on the theory that the NDVI significance is absolutely relative to the sum of green vegetation in an image pixel region. NDVI ethics are signified as a percentage oscillating in rate from -1 to 1 nevertheless in preparation, great adverse values signify water, values with zero signify bare soil and values near one signify solid green vegetation. The longitudinal dispersal of NDVI over the study area for the period 2000 and 2020 is shown below.
Visual inspection ascertains the variances of each NDVI. The maximum grade of the alteration was perceived in 2020. The ascendancy of adverse NDVI values may be accredited to accumulative expansion leading to new urbanized regions and bare surfaces. The experiential adverse value is for the reflectance assessment in the red band which is advanced than the reflectance value in the near-infrared band. Also, recent decline trendy apparent water employing dehydrated climate was essential to truncated standards of the NDVI in 2020 since the directory declines as vegetation comes under water stress.
Land Surface Temperature (LST) is a fundamental aspect of climate and biology, affecting organisms and ecosystems from local to global scales. Identified as one of the most important Earth System Data Records by NASA and other international organizations (King, 1999), LST measures the emission of thermal radiance from the land surface where the incoming solar energy interacts with and heats the ground, or the surface of the canopy in vegetated areas. LST is nothing but the skin temperature of the land surface. Worldwide urbanization has significantly reshaped the landscape, which has important climatic implications across all scales due to the simultaneous transformation of natural land cover and introduction of urban materials i.e. anthropogenic surfaces. The land surface temperature over the study area shows that the LST ranged from 28.28oC to 55.26oC in 2000 and 27.93oC to 58.09oC in 2020. In 2000, it is evident that the temperature is extremely subsidiary to the LST of 2020. The metropolitan temperature landmass observed accumulative drift above 20 years era as an effect of cumulative population, anthropogenic activities, and alteration of land cover. It is vibrant that in all the years’ 2020 study area exhibits the highest land surface temperature. This is attributed to recent high concentration of buildings and structures, increase in anthropogenic activities, increase in deforestation, lack of adequate drainage facilities for proper channelling of runoff water, increase in concrete pavements along the roads and exploration of minerals might be ascribed to the observed increase of land surface temperature.
This index regulates the thermal comfort or discomfort of the three local government area within the study area namely; Enugu East, Enugu North and Enugu South obtained particular heat and virtual moisture in the state, with reverence to the thermal discomfort index (DI). This index was considered from Thom’s method which is DI = T - (0.55-0.0055*RH) (T-14.5). The limits of airborne heat and virtual moistness were acquired from records of POWER, NASA (Prediction of Worldwide Energy Resource) for the years 2000 and 2019.
Where DI=Discomfort index
T= Temperature
RH=Relative humidity.
The data were acquired during the raining and dry season from January to December 2000 and 2019 for the three local governments of the study area. An annual calculation of temperature and relative humidity was done to get the average mean annual data for our calculation.
Enugu East
The Average Mean Annual data in year 2000 are as follows:
Temperature: 25.08oC
Relative Humidity: 81.24 %
And from the equation DI = T - (0.55-0.0055*RH) (T-14.5)
DI = 25.08 - (0.55-0.0055*81.24) (25.08-14.5) = 23.98oC
From Thom’s’ classification of DI ranges 21≤DI <24 shows that under 50% of Population feel discomfort, and our DI for Enugu East in 2000 is 23.98oC, meaning that under 50% of the population feels discomfort.
While in the year 2019, the Average Mean Annual data are as follows:
Temperature: 26.02oC
Relative Humidity: 81.39%
And from the equation DI = T - (0.55-0.0055*RH) (T-14.5)
DI = 26.02 - (0.55-0.0055* 81.39) (26.02 -14.5) = 24.84oC
From Thom’s’ classification of DI ranges 24≤DI<27 shows that over 50% of Population feel discomfort, and our DI for Enugu East in 2019 is 24.84oC, meaning that over 50% of the population feels discomfort.
Enugu North
The Average Mean Annual data in year 2000 are as follows:
Temperature: 25.08oC
Relative Humidity: 81.24 %
And from the equation DI = T - (0.55-0.0055*RH) (T-14.5)
DI = 25.08 - (0.55-0.0055*81.24) (25.08-14.5) = 23.98oC
From Thom’s’ classification of DI ranges 21≤DI <24 shows that under 50% of Population feel discomfort, and our DI for Enugu North in 2000 is 23.98oC, meaning that under 50% of the population feels discomfort.
While in the year 2019, the Average Mean Annual data are as follows:
Temperature: 26.02oC
Relative Humidity: 81.39%
And from the equation DI = T - (0.55-0.0055*RH) (T-14.5)
DI = 26.02 - (0.55-0.0055* 81.39) (26.02 -14.5) = 24.84oC
From Thom’s’ classification of DI ranges 24≤DI<27 shows that over 50% of Population feel discomfort, and our DI for Enugu North in 2019 is 24.84oC, meaning that over 50% of the population feels discomfort.
Enugu South
The Average Mean Annual data in year 2000 are as follows:
Temperature: 25.08oC
Relative Humidity: 81.24 %
And from the equation DI = T - (0.55-0.0055*RH) (T-14.5)
DI = 25.08 - (0.55-0.0055*81.24) (25.08-14.5) = 23.98oC
From Thom’s’ classification of DI ranges 21≤DI <24 shows that under 50% of Population feel discomfort, and our DI for Enugu South in 2000 is 23.98oC, meaning that under 50% of the population feels discomfort.
While in the year 2019, the Average Mean Annual data are as follows:
Temperature: 26.02oC
Relative Humidity: 81.39%
And from the equation DI = T - (0.55-0.0055*RH) (T-14.5)
DI = 26.02 - (0.55-0.0055* 81.39) (26.02 -14.5) = 24.84oC
From Thom’s’ classification of DI ranges 24≤DI<27 shows that over 50% of Population feel discomfort, and our DI for Enugu South in 2019 is 24.84oC, meaning that over 50% of the population feels discomfort.
From the result of our analysis, it can be said that the thermal index of the study area is on increase and these can be attributed to high rate of deforestation as a result of expansion in urbanization, due to increase in deforestation, there is an increase in discharge runoff in hydrology and thereby leads to high evaporation of water vapour to the atmosphere thereby causing increase in the relative humidity. This result to excessive sweating and making the body feeling hot, increase in rate of blood circulation and also affect the rate of respiration. Also high rate of emission of green house gas and other chemical compositions which are harmful to the ecosystem contributes to the alteration of the temperature variation in the study area. Excessive and indiscriminate anthropogenic activities in Enugu metropolis, also affects the increase in the thermal index of the study area.
Rainfall Analysis: The three local governments that make up the city center of the State had an increase in its mean annual rainfall in year 2000 and a decrease in year 2019. The rainfall data were measured in mm day-1
Mean Annual Rainfall in Enugu Metropolis
|
LGA
|
Yr2000
|
Yr2019
|
Enugu South
|
8.48mm
|
4.15mm
|
Enugu North
|
8.48mm
|
4.15mm
|
Enugu East
|
8.48mm
|
4.15mm
|