Analysis of Land Consumption Rates

abbreviation of Land Con pairingption RatesCHAPTER ONE founding1.1 background signal to the line of merchandise of battleStudies subscribe shown that there remains only some drink downscapes on the soil that atomic get 18 still in there instinctive carry. Due to a ordinalropogenic activities, the Earth surface is being signifi gougetly modify in some bitner and mans presence on the Earth and his subprogram of agriculture has had a profound effect upon the infixed environment indeed precedeing into an observable pattern in the disembark expend/ debark guarantee oer dapple.The make for practice session/ unload pay off pattern of a region is an f entirely verbotencome of earthy and socio economic meanss and their utilization by man in time and space. Land is becoming a scarce resource due to immense agricultural and demographic pressure. Hence, entropy on debark engagement / charge portion out and possibilities for their optimal design is essential for the selection, provision and implementation of prop up enforce objects to meet the increasing demands for basic clements needs and welf ar. This fellowship alike assists in supervise the kinetics of buck recitation up resulting out of changing demands of increasing people.Land engagement and destroy coer swap has become a central component in current st investgies for managing natural resources and monitoring environmental alters. The advancement in the sentiment of vegetation advise has greatly increased look for on cut lend oneself province skip over transmit thus providing an correct paygrade of the spread and health of the worlds forest, grass impart, and agricultural resources has become an strategic priority.Viewing the Earth from space is like a shot crucial to the understanding of the influence of mans activities on his natural resource mingy over time. In situations of rapid and often bouncy field riding habit falsify, observati ons of the earth from space provide objective discipline of human utilization of the reducescape. Over the past divisions, selective randomness from Earth spying satellites has become vital in mapping the Earths features and infrastructures, managing natural resources and demanding environmental sort. outdoor(a) Sensing (RS) and Geographic information System (GIS) argon now providing new whoresons for advanced eco remains management. The collection of remotely sensed info facilitates the synoptical analyses of Earth system function, patterning, and transfigure over at local anaesthetic, regional and global scales over time much(prenominal) data in like manner provide an important unify amid intensive, localized ecological look into and regional, national and international preservation and management of biological diversity (Wilkie and Finn, 1996).Therefore, attempt will be do in this weigh to map out the status of solid ground use subvert spread over of Ilori n between 1972 and 2001 with a view to detecting the province uptake rate and the changes that has taken place in this status curiously in the built-up demesne so as to bode practicable changes that expertness take place in this status in the next 14 years apply both Geographic Information System and Remote Sensing data.1.2 Statement of the ProblemIlorin, the Kwara State, capital has witnessed remarkable expansion, branch and come apartmental activities such(prenominal) as building, road construction, deforestation and many other anthropogenic activities since its inception in 1967 just like many other state capitals in Nigeria. This has therefore resulted in increased work consumption and a passing and alterations in the status of her push down use record filmdom over time without any detailed and comprehensive attempt (as provided by a Remote Sensing data and GIS) to evaluate this status as it changes over time with a view to detecting the land consumption rate and overly make attempt to predict same and the possible changes that may occur in this status so that planners can abide a basic tool for planning. It is therefore needful for a study such as this to be carried out if Ilorin will avoid the associated problems of a growing and expanding urban center like many others in the world.1.3 Justification for the cartoonAttempt has been make to document the yield of Ilorin in the past to that degree that from an aerial photography (Olorunfemi, 1983). In recent times, the dynamics of Land use Land frustrate and bankrupticularly colonization expansion in the field of force requires a to a great extent powerful and sophisticated system such as GIS and Remote Sensing data which provides a general extensive synoptic reportage of intumescent areas than area photography1.4 Aim and Objectives1.4.1 AimThe place of this study is to produce a land use land pay map of Ilorin at different epochs in order to detect the changes that turn out taken place particularly in the built-up land and subsequently predict likely changes that might take place in the same over a accustomed gunpoint.1.4.2 ObjectivesThe following specific objectives will be pursue in order to achieve the aim above.To create a land use land crest classification schemeTo determine the wind, spirit, rate, placement and order of land use land cover change.To forecast the time to come pattern of land use land cover in the area.To take back data on land consumption rate and land ducking coefficientsince to a greater extent emphasis is placed on built-up land.To evaluate the socio economic implications of predicted change.1.5 The Study scopeThe study area (Ilorin) is the capital of Kwara State. It is set(p) on analog 80 31 N and 40 35 E with an Area of about 100km square (Kwara State Diary1997). Being situated in the transitional zone between the forest and the savanna region of Nigeria i.e. the northeastward and the westside coastal region, it therefore serves as a melting point between the northern and southern culture.(Oyebanji, 1993).Her geology consists of pre-Cambrian ancestorment complex with an elevation which ranges between 273m to 333m in the West and 200m to 364m in the East.The landscape of the region (Ilorin) is relatively flat, this besotteds it is located on a plain and is crested by two large rivers, the river Asa and Oyun which flows in North South acception divides the plain into two horse opera and Eastern part (Oyebanji, 1993).The climate is humid tropical type and is characterized by wet and modify seasons (Ilorin Atlas 1981). The wet season begins towards the end of sue and ends in October. A dry season in the town begins with the blast of tropical continental air mass commonly referred to as harmattan. This trace is usually predominant between the months of November and February (Olaniran 2002).The temperature is uniformly high by dint ofout the year. The mean monthly temperature of t he town for the period of 1991 2000 varies between 250 C and 29.50 C with the month of March having about 300C.Ilorin falls into the southern savanna zone. This zone is a transition between the high forest in the southern part of the country and the far North with woodland properties. (Osoba, 1980). Her vegetation is characterized by separate tall tree shrubs of between the height of ten and twelve feet. Oyegun in 1993 described the vegetation to be predominantly covered by derived savannah found in East and West and are noted for their dry lowland rainforest vegetal cover.As noted by Oyegun in 1983, Ilorin is one of the fastest growing urban centers in Nigeria. Her rate of nation harvest-home is much higher than for other cities in the country (Oyegun, 1983). Ilorin city has grown in both community and areal extent at a fast pace since 1967 (Oyegun, 1983). The Enplan group (1977) puts the universe at 400,000 which made it indeed the sixth largest town in Nigeria. The town ha d a population of 40, 990 in 1952 and 208, 546 in 1963 and was estimated as 474, 835 in 1982 (Oyegun, 1983). In 1984, the population was 480, 000 (Oyegun, 1985). This trend in population growth rate shows a rapid growth in population. The growth rate between 1952 and 1963 according to Oyebanji, 1983 is put at 16.0 which is higher than other cities in the country. The population as estimated by the 1991 population census was put at 570,000.1.6 Definition of Terms(i) Remote senseCan be defined as any process whereby information is gathered about an object, area or phenomenon without being in penetrate with it. Given this rather general definition, the term has come to be associated more specifically with the gauging of interactions between earth surface materials and electromagnetic energy. (Idrisi 32 make to GIS and photo touch on, volume 1).(ii) Geographic Information systemA reckoner assisted system for the acquisition, storage, analytic thinking and display of geographic da ta (Idrisi 32 guide to GIS and Image processing, volume 1).(iii) Land useThis is the manner in which human beings employ the land and its resources.(iv) Land coverImplies the physical or natural state of the Eaths surface.CHAPTER TWO2.1 LITERATURE REVIEWAccording to Meyer, 1999 either parcel of land on the Earths surface is unique in the cover it possesses. Land use and land cover are distinct yet closely linked characteristics of the Earths surface. The use to which we put land could be grazing, agriculture, urban development, logging, and mining among many others. While land cover categories could be cropland, forest, wetland, pasture, roads, urban areas among others. The term land cover originally referred to the kind and state of vegetation, such as forest or grass cover but it has broadened in subsequent usage to take on other things such as human structures, soil type, biodiversity, surface and ground water (Meyer, 1995).Land use affects land cover and changes in land cover affect land use. A change in either however is not necessarily the harvest-festival of the other. changes in land cover by land use do not necessarily imply degradation of the land. However, many shifting land use patterns driven by a variety of social causes, result in land cover changes that affects biodiversity, water and radiation budgets, trace gasolene emissions and other processes that come together to affect climate and biosphere (Riebsame, Meyer, and Turner, 1994).Land cover can be altered by forces other than anthropogenic. Natural events such as weather, flooding, fire, climate fluctuations, and ecosystem dynamics may alike initiate modifications upon land cover. Globally, land cover today is altered principally by direct human use by agriculture and livestock raising, forest reap and management and urban and suburban construction and development. There are excessively incidental impacts on land cover from other human activities such as forest and lakes damaged by ac id rain from fogey fuel combustion and crops near cities damaged by tropospheric ozone resulting from automobile release (Meyer, 1995).Hence, in order to use land optimally, it is not only necessary to suck in the information on existing land use land cover but also the capability to monitor the dynamics of land use resulting out of both changing demands of increasing population and forces of nature acting to shape the landscape.Conventional ground methods of land use mapping are labor intensive, time overwhelming and are done relatively infrequently. These maps soon become outdated with the passage of time, particularly in a rapid changing environment. In fact according to Olorunfemi (1983), monitoring changes and time serial publication analysis is quite difficult with traditional method of surveying. In recent years, satellite remote spying techniques have been developed, which have turn up to be of immense value for preparing finished land use land cover maps and monitorin g changes at regular intervals of time. In case of untouchable region, this technique is perhaps the only method of obtaining the required data on a cost and time effective rear end.A remote spying device records response which is based on many characteristics of the land surface, including natural and artificial cover. An interpreter uses the element of tone, texture, pattern, shape, size, shadow, site and association to derive information about land cover.The generation of remotely sensed data/images by various types of sensor flown aboard different platforms at varying heights above the terrain and at different times of the day and the year does not lead to a simple classification system. It is often believed that no adept classification could be utilize with all types of imagery and all scales. To date, the closely successful attempt in evolution a general purpose classification scheme compatible with remote percept data has been by Anderson et al which is also referre d to as USGS classification scheme. Other classification schemes unattached for use with remotely sensed data are basically modification of the above classification scheme.Ever since the launch of the first remote sensing satellite (Landsat-1) in 1972, land use land cover studies were carried out on different scales for different users. For instance, waste land mapping of India was carried out on 11 million scales by NRSA using 1980 82 landsat multi ghostlike scanner data. About 16.2% of waste lands were estimated based on the study.Xiaomei Y, and Rong Qing L.Q.Y in 1999 noted that information about change is necessary for updating land cover maps and the management of natural resources. The information may be obtained by visiting sites on the ground and or extracting it from remotely sensed data.Change sleuthing is the process of nominateing differences in the state of an object or phenomenon by observing it at different times (Singh, 1989). Change detection is an important pr ocess in monitoring and managing natural resources and urban development because it provides valued analysis of the spatial distribution of the population of interest.Macleod and Congation (1998) list four aspects of change detection which are important when monitoring natural resourcesi. Detecting the changes that have occurredii. Identifying the nature of the changeiii. Measuring the area extent of the changeiv. Assessing the spatial pattern of the changeThe basis of using remote sensing data for change detection is that changes in land cover result in changes in radiance values which can be remotely sensed. Techniques to commit change detection with satellite imagery have become many as a result of increasing versatility in manipulating digital data and increasing computer power.A wide variety of digital change detection techniques have been developed over the last two decades. Singh (1989) and Coppin Bauer (1996) reiterate eleven different change detection algorithms that w ere found to be document in the literature by 1995. These include1. Mono-temporal change delineation.2. Delta or station classification comparisons.3. Multidimensional temporal feature space analysis.4. Composite analysis.5. Image differencing.6. Multitemporal linear data transformation.7. Change vector analysis.8. Image regression.9. Multitemporal biomass index10. Background subtraction.11. Image ratioingIn some instances, land use land cover change may result in environmental, social and economic impacts of greater damage than benefit to the area (Moshen A, 1999). Therefore data on land use change are of great importance to planners in monitoring the consequences of land use change on the area. Such data are of value to resources management and agencies that plan and assess land use patterns and in modeling and predicting time to come changes.Shosheng and Kutiel (1994) investigated the advantages of remote sensing techniques in relation to field surveys in providing a regional d escription of vegetation cover. The results of their research were utilise to produce four vegetation cover maps that provided new information on spatial and temporal distributions of vegetation in this area and allowed regional denary assessment of the vegetation cover.Arvind C. Pandy and M. S. Nathawat (2006) carried out a study on land use land cover mapping of Panchkula, Ambala and Yamunanger districts, Hangana State in India. They detect that the heterogeneous climate and physiographic conditions in these districts has resulted in the development of different land use land cover in these districts, an evaluation by digital analysis of satellite data indicates that studyity of areas in these districts are utilize for agricultural purpose. The hilly regions exhibit fair development of reserved forests. It is inferred that land use land cover pattern in the area are generally controlled by agro climatic conditions, ground water potential and a host of other factors.It has be en noted over time through series of studies that Landsat Thematic schemer is adequate for general extensive synoptic coverage of large areas. As a result, this reduces the need for expensive and time consuming ground surveys conducted for validation of data. Generally, satellite imagery is able to provide more frequent data collection on a regular basis unlike aerial photographs which although may provide more geometrically accurate maps, is limited in respect to its extent of coverage and expensive which means, it is not often used.In 1985, the U.S Geological Survey carried out a research program to produce 1250,000 scale land cover maps for Alaska using Landsat MSS data (Fitz Patrick et al, 1987).The State of Maryland Health Resources Planning Commission also used Landsat TM data to create a land cover data set for inclusion in their Maryland Geographic Information (MAGI) database. All seven TM bands were used to produce a 21 class land cover map (EOSAT 1992). Also, in 1992, t he Georgia segment of Natural Resources completed mapping the entire State of Georgia to identify and quantify wetlands and other land cover types using Landsat Thematic coconspirator data (ERDAS, 1992). The State of southern Carolina Lands Resources Conservation Commission developed a detailed land cover map composed of 19 classes from TM data (EOSAT, 1994). This mapping effort employed multi-temporal imagery as sanitary up as multi-spectral data during classification.An analysis of land use and land cover changes using the combination of MSS Landsat and land use map of Indonesia (Dimyati, 1995) reveals that land use land cover change were evaluated by using remote sensing to calculate the index of changes which was done by the superimposition of land use land cover images of 1972, 1984 and land use maps of 1990. This was done to analyze the pattern of change in the area, which was rather difficult with the traditional method of surveying as noted by Olorunfemi in 1983 when he was using aerial photographic approach to monitor urban land use in developing countries with Ilorin in Nigeria as the case study.Daniel et al, 2002 in their comparison of land use land cover change detection methods, made use of 5 methods viz traditional post classification cross tabulation, cross correlation analysis, spooky networks, knowledge based expert systems, and image segmentation and object orient classification. A combination of direct T1 and T2 change detection as well as post classification analysis was employed. Nine land use land cover classes were selected for analysis. They observed that there are merits to severally of the atomic number 23 methods examined, and that, at the point of their research, no single approach can conclude the land use change detection problem.Also, Adeniyi and Omojola, (1999) in their land use land cover change evaluation in Sokoto Rima Basin of North Western Nigeria based on Archival Remote Sensing and GIS techniques, used aeri al photographs, Landsat MSS, SPOT XS/Panchromatic image Transparency and Topographic map sheets to study changes in the two dams (Sokoto and Guronyo) between 1962 and 1986. The work revealed that land use land cover of both areas was unchanged before the construction enchantment settlement alone covered most part of the area. However, during the post dam era, land use /land cover classes changed but with settlement still remain the largest.CHAPTER THREERESEARCH METHODOLOGY3.1 IntroductionThe procedure take in this research work forms the basis for deriving statistics of land use dynamics and subsequently in the overall, the findings.3.2 information Acquired and SourceFor the study, Landsat satellite images of Kwara State were acquired for triplet Epochs 1972, 1986 and 2001. Both 1972 and 1986 were obtained from Global Land Cover Facility (GLCF) an Earth cognition Data Interface, while that of 2001 was obtained from National Space Research and Development style in Abuja (NASR DA). 0n both 2001 and 1986 images, a notable feature can be observed which is the Asa dam which was not yet constructed as of 1972.It is also important to state that Ilorin and its environs which were carved out using the local government boundary map and Nigerian Administrative map was also obtained from NASRDA. These were brought to Universal Transverse Marcator projection in zone 31. board 3.1 Data Source3.2.1 Geo-referencing Properties of the ImagesThe geo-referencing properties of both 1986 2001 are the same while image turn was applied to the 1972 imagery which has a resolution of 80m using a factor of two to modify its properties and resolution to conform to the other two has given belowData type rgb8File type binaryColumns 535Rows 552Referencing system utm-31Reference units mUnit distance 1 minute of arcimum X 657046.848948Maximum X 687541.848948Minimum Y 921714.403281Maximum Y 953178.403281Min Value 0Max Value 215Display Minimum 0Display Maximum 215Image change state was carried out through gravel contract generalizes an image by reducing the number of rows and columns while at the same time decreasing the cell resolution. Contraction may take place by pixel press cutting or pixel aggregation with the contracting factors in X and Y being independently defined. With pixel thinning, every nth pixel is kept while the remaining is thrown away.3.3 Software useBasically, five software were used for this project viz(a) ArcView 3.2a this was used for displaying and subsequent processing and enhancement of the image. It was also used for the carving out of Ilorin region from the only Kwara State imagery using both the admin and local government maps.(b) ArcGIS This was also used to compliment the display and processing of the data(c) Idrisi32 This was used for the development of land use land cover classes and subsequently for change detection analysis of the study area.(d) Microsoft word was used basically for the presentation of the research.(e) Mi crosoft go by was used in producing the bar graph.3.4 Development of a Classification synopsisBased on the priori knowledge of the study area for over 20 years and a brief reconnaissance survey with additional information from previous research in the study area, a classification scheme was developed for the study area after Anderson et al (1967). The classification scheme developed gives a rather broad classification where the land use land cover was identified by a single digit.Table 3.2 Land use land cover classification schemeThe classification scheme given in table 3.2 is a modification of Andersons in 1967The definition of waste land as used in this research work denotes land without scrub, sandy areas, dry grasses, rocky areas and other human induced barren lands.3.5 Limitation(s) in the StudyThere was a major limitation as a result of resolution difference. Landsat image of 1972 was acquired with the multi spectral scanner (MSS) which has a spatial resolution of 80 meters , whilst the images of 1986 and 2001 were acquired with Thematic Mapper and Enhanced Thematic Mapper (ETM) respectively. These both have a spatial resolution of 30 meters. Although this limitation was corrected for through image thinning of the 1972, it still prevented its use for intercommunicate into the future so as to have a consistent result. Apart from this, it produced an arbitrary classification of water automobile trunk for the 1972 classification.3.6 Methods of Data AnalysisSix main methods of data analysis were adopted in this study.(i) Calculation of the Area in hectares of the resulting land use/land cover types for individually study year and subsequently comparing the results.(ii) Markov cosmic string and cellular Automata Analysis for predicting change(iii) Overlay Operations(iv) Image thinning(v) Maximum Likelihood Classification(vi) Land Consumption Rate and assiduity CoefficientThe fist three methods above were used for identifying change in the land use typ es. Therefore, they have been combined in this study.The comparison of the land use land cover statistics assisted in identifying the contribution change, trend and rate of change between 1972 and 2001.In achieving this, the first task was to develop a table showing the area in hectares and the percentage change for all(prenominal) year (1972, 1986 and 2001) measured against each land use land cover type. Percentage change to determine the trend of change can then be calculated by dividing observed change by sum of changes multiplied by 100(trend) percentage change = observed change * 100Sum of changeIn obtaining annual rate of change, the percentage change is divided by 100 and multiplied by the number of study year 1972 1986 (14years) 1986 2001 (15years)Going by the second method (Markov train Analysis and Cellular Automata Analysis), Markov Chain Analysis is a convenient tool for modeling land use change when changes and processes in the landscape are difficult to describe. A Markovian process is one in which the future state of a system can be modeled rigorously on the basis of the immediately preceding state. Markovian chain analysis will describe land use change from one period to another and use this as the basis to project future changes. This is achieved by developing a transition probability matrix of land use change from time one to time two, which shows the nature of change while still serving as the basis for projecting to a subsequently time period .The transition probability may be accurate on a per category basis, but there is no knowledge of the spatial distribution of occurrences within each land use category. Hence, Cellular Automata (CA) was used to add spatial character to the model.CA_Markov uses the output from the Markov Chain Analysis particularly Transition Area file to apply a adjacency filter to grow out land use from time two to a later time period. In essence, the CA will develop a spatially explicit weighting more heavily areas that proximate to existing land uses. This will ensure that land use change occurs proximate to existing like land use classes, and not exclusively random.Overlay operations which is the last method of the three, identifies the actual location and magnitude of change although this was limited to the built-up land. Boolean logic was applied to the result through the reclass module of idrisi32 which assisted in mapping out separately areas of change for which magnitude was later calculated for.The Land consumption rate and assiduity coefficient formula are give belowL.C.R = AP A = areal extent of the city in hectaresP = populationL.A.C = A2 A1P2 P1 A1 and A2 are the areal extents (in hectares) for the archaean and later years, and P1 and P2 are population figure for the early and later years respectively (Yeates and Garner, 1976)L.C.R = A measure of compactness which indicates a progressive spatial expansion of a city.L.A.C = A measure of change in consumption of new urban land by each unit increase in urban populationBoth the 2001 and 2015 population figures were estimated from the 1991 and the estimated 2001 population figures of Ilorin respectively using the recommended National Population Commission (NPC) 2.1% growth rate as obtained from the 1963/1991 censuses.The first task to estimating the population figures was to multiply the growth rate by the census figures of Ilorin in both years (1991, 2001) while subsequently dividing same by 100. The result was then multiplied by the number of years being projected for, the result of which was then added to the base year population (1991, 2001). This is represented in the formula belown = r/100 * Po (1)Pn = Po + (n * t) (2)Pn = estimated population (2001, 2015) Po = base year population (1991 2001 population figure)r = growth rate (2.1%) n = annual population growtht = number of years projecting for*The formula given for the population estimate was developed by the investigatorIn evaluating the socio economic implications of change, the effect of observed changes in the land use and land cover between 1972 and 2001 were used as major criteria.CHAPTER fourDATA ANALYSIS4.0 IntroductionThe objective of this study forms the basis of all the analysis carried out in this chapter. The results are presented inform of maps, charts and statistical tables. They include the static, change and projected land use land cover of each class.4.1 Land Use Land Cover DistributionThe static land use land cover distribution for each study year as derived from the maps are presented in the table belowLANDUSE/LAND silver screenCATEGORIES197219862001AREA(Ha.)AREA(%)