Use of gis and remote sensing data

Use of gis and extraneous catching selective informationINTRODUCTIONStudies open shown that solely hardly a(prenominal) provincescape on human beingsly concern surface rebriny unaltered or in their natural put in and is due to immerse demographic pressure and anthropogenic activities (Zubair, 2006). Competition among species and human beings has been the leading cause of make for make out veer in the world. This factor is substantially verified by the conversion of lumber land to sepa gait uses like farmlands for agriculture, industry urban development, infrastructure, recreation and others. (Brown, 2004) Forest plays an significant role in the everywhereall stability of carbon in the breeze mitigating or exacerbating the effectuate of global warming. Therefore, Forest carbon separatism can help to minimise the raise of greenhouse gases in the standard atmosphere, (Juan and Louis, 2009) It is and so serious to note that sets are counted among the worlds chi ef carbon sinks contributors. They store more(prenominal) than 289 giga tonnes (Gt) of carbon in their litter, dead woodwinds and soil and these are more than the carbon tack together in the atmosphere. Globally, at that place was decrease in carbon stocks of forest biomes of 0.5 Gt a course of direct in the midst of 2000 2010 and was master(prenominal)ly due to disforestation, (FAO, 2010) Globally, about 13 jillion hectares of forests were convertd to other uses an some were lost by dint of natural causes each year, that is amid 2000 and 2010 as when compared to al near 16 gazillion hectares per year during the 1990s (FAO, 2010). The biggest losings or the highest net annual loss of forests from 2000 to 2010, are in south-central America and Africa with four and 3.4 million hectares respectively. On the other hand, a upstart pick up by the Food and Agriculture Organisation (FAO, 2010) reveal that generally, the expiry of tropical forest for agricultural activities h as decreased everyplace the last x years, but the rate of deforestation for other activities continues to increase at an dire high rate.The tropical precipitate forests are significant component of the humour system and play an important role in the total carbon-dioxide flip-flop balance of the earths plant polish off. McGuffie et al. (1995) elicited that the existence of tropical rain forest has a great influence on regional climate and as such tropical deforestation has been seen to affect the climate of divergent parts of the world. tropic forests make up the close to diversified ecosystems in the world with the highest biomass per comforting metre especially in the lowland rain forest (McGuffie et al. 1995). solely much of the forest field of opeproportionnss slang been subjected to continuous depletion as a solvent of artificial or natural factors. The annual rate of destruction to the rain forest seems to be increasing and could double in the next few decade ( Myers, 1992). The tropical rain forest in Nigeria is also brooking hard exploitation as a consequence of population harvest, urban expansions, misdirection and socio-economic development. The process of deforestation is mainly caused by clearing of forest land for agricultural activities, logging, burn wood, mining and industrialization etc. Like most tropical regions of the world, deforestation remain a severalize issue on environmental, ecological and socio-economic challenge in Nigeria (Uneke and Ibeh, 2009) Nigeria has the highest deforestation rate of primary forests from the rewrite deforestation figures obtained from Food and Agriculture Organisation of the United Nations (FAO). Between 2000 and 2005 the dry land lost 55.5% of its primary forests and contributes 3.3% in the world therefore ranked the world highest rate deforested country. Since 1990 the country has lost a total of 6.1 million hectares or 35.7% of its forest subvents and this has result in the lost of its primary or darkened forest at a faster rate. Since 2000 report realise shown that Nigeria is losing at an average of 11% of this primary forest and which has double the rate of 1990s. Moreover, the Nigeria initiative National Biodiversity Report-NFNBR (2001) estimates the rate of deforestation at about 5% annually compared with 0.6% globally. The major causes of deforestation in Nigeria complicate corruption, overpopulation, urbanization, population growth, inequitable dif partnership of wealth, and poverty (Ayodele, 2010).The United Nations Frame die Convention on Climate reposition has stated that the overwhelming cause of deforestation is agriculture. It stated that subsistence agriculture accounts for 48% of deforestation, while 32% of deforestation results from commercial agriculture. Wood-fuel is said to account for 5%. Forest biomass has remained the most common parentage of household energy in Nigeria, occupying 80% of domestic energy requirements. In 1992, a lone, forest wood and charcoal products were estimated at 55 million tons, counseling that much forest timberland are been used for domestic purposes. According to Choji (2005), more than half of 9.6 million ha of rain forests in the south of Nigeria have been used to meet the demand for fuel wood in rural and urban neighbourhoods. Compared with the be of petroleum product, fuel wood is cheaper than any commercial fuel sub and this has, over the years, increased forest depletion. He further note that this appears to have propounded effect on the environment and the sustainability of the forest.Similarly,(Uyigue and Agho,2007) also note Logging, urbanization, oil exploitation, subsistence agriculture, and the parade of fuel-wood among all are noted as foremost causes of deforestation in Nigeria.Therefore an onrush provide be make in this film to part out the status of qualifyings in the forest areas of Niger Delta Region of Nigeria betwixt 1987 and 2002 development bot h remote sleuthing and GIS. Research Question Is there miscellany in forest cover in Niger Delta Region of Nigeria surrounded by 1987 and 2002?Aim To identify and map out interpolates in forest cover of Niger Delta Region Nigeria and adopt suitable orders in detecting such switch overs using remote signal signal detecting entropy and GIS proficiencysObjectives* To canvass the Spatio-Temporal multifariousness in forest cover using classification methods * To apply different change perception techniques and identify changes in forest cover* To map out areas of changes* To analyze the effects of land cover change in the region and to suggest some recommendations.THE STUDY AREAThe cogitation area is located in the Atlantic coastline of southern Nigeria 530N 630s. Niger Delta region falls at heart the tropical rain forest zone of the world. Its named as the piece extendedst delta in the world occupying about 450 kilometres spanning coastline. The region is describes as l argest wetland in Africa and covers over 2000 form kilometres that mainly consists of lakes, rivers and creeks. Ecosystem is diverse and highly supportive to numerous species both aquatic and terrestrial and human life, (Uyigue and Agho, 2007).The region is vegetation cover is mainly soak forest which can be further divided into both classes the mangrove and the fresh water forest. The Mangrove spanning around 1900 square kilometres and the largest in Africa,(Uyigue and Agho,2007). The main features of its geography include extreme blocks of luxuriant high forest that elapse in the region. It has the largest ply-wood and veneer plants in West Africa and has known as a centre for saw milling. The area consist of three types of forest strata of head tall (120m high), moderate (50m 100m) and those below 50m.Some common trees found in the area are obeche, abura, sepele and mahogany. http//www.britannica.com/EBchecked/topic/523642/SapeleStates found around the region include Niger Delta, fashion Harcourt to south western states Oyo, Osun and Lagos state. The region has heavy precipitation of between 1824 millimetres and over 4000 millimetres along the coast. Rainfall falls throughout the year with a shorter bar in August and longer one from December to January. Trade winds originated from Atlantic Ocean of the southern part of the country is responsible for Nigerian wet seasons Nigeria. The region has an equatorial monsoon climate temperature represents between 28C (82.4F) and 26C (78.8F) (Wikipedia). Map showing the location of the study area belles-lettres REVIEWLandsat is an important component in the climate system, and plays a key role in monitoring global change and is primary source of medium spatial resolution earth rumination used in decision making (Gyanesh Chandera et al., 2009). Remote sensed seery provides accurate arrangement and comprehensive course of modelling and projecting land change (Elvidge et al., 2004)With the demonstration of landSat5 1984 and landSat7 ETM+ 2002, this has marked a significant advance in remote sensing through obtaining more sophisticated advance sensing element improve eruditeness and transmission of data and more rapid processing at a highly processing facility (Gyanesh Chandera et al., 2009). channelize Detection is one of the main applications of remote sensed data. A considerable amount of literatures has been published by the researchers in trying to quantify and assess land cover change detection tilt detection is the process of identifying differences in the state on an object or phenomena by observing it at different times, over a certain period of time. (Singh, 1989) cited in (Lu et. al., 2004)A quite auspicate of change detection techniques have been summarised by many authors in an attempt to find out land cover changes over time. Lu et al., (2004) categorizes these techniques into seven-spot classes ranging from simple algebras to more complex and advance ones namely Algebra which include go for rationing, characterization differencing vegetation index differencing, Change vector analysis, others in the class includes transformations, classifications, Advance models, Biophysical parameter methods and those that involve the combination of both GIS and remote sensing data for analysisChange detection have gained wide range of application in the field land use land cover change Peiju et al., (2010), reported to have used multi- temporal remote sensing Landsat TM to monitored urban land cover and vegetation change in Xuzhon city between 1987 2007, the result of the statistical analysis show that form up areas have obviously increase while farmland have seen in a continuous loss due to urban growth and human activities. Zubair (2006) detects changes in land use land cover in Kwara state Nigeria between 1972 1nd 2001 using change detection techniques of GIS and remote sensing data, the result of the analysis show that there was rapid growth in th e built up areas and was a result of population pressure. He noted that there was steady reduction in forest cover in the study area and further predicts continues loss in subsequent years. Chen, 2002, noted the use of GIS and remote sensing techniques and monitors changes along the coastline zone of Korea, the result of the study show that both human and natural factors are responsible for the change and this has on the other hand tingeed the sustainable development of the region. Janifer et al., 2010, monitors forest change in the landscapes area of Chile between 1975 and 2008, the result of the study show an average rate Deforestation was -1.7% and shrub land -0.7%, except agriculture and timber plantations increased at annual rate of 1.1% and 3.3% respectively. The study concludes there is progressive lost of forest cover in the region.Moreover, in the field of Urban and environmental change George et al., (2009), used Landsat find outry change detection methods in updating t he 2001 national land cover database land cover classification to 2006, conservative thresholds based on Anderson level 1 classes were used to segregate the change vectors and determine areas of change and no change. An accuracy of 83.225% of the five selected areas achieved. Woodcock et al., (2001) noted Landsat in detecting environmental change over time, the study makes use of generalization method in monitoring large areas for forest change and conclude that method is state-of-the earth as other methods and consumes less time as other conventional methodsChange detection in the field of forest or vegetation change includes the work of Chengquan et al., (2009), in the assessment of Paraguays forest cover change using Landsat observation of high resolution image showed that Atlantic forest ecosystem experienced the most loss with the 73.4% forest cover in the 1970s decreasing crisply down to 40.4% by the 1990s and further down to 24.9% by the year 2000. Rasuly et al., 2010, note d the advantage of using GIS and remote sensing techniques to monitor the rate of forest alterations in the Arasbaran protected area using various methods, the result of the study show that about 6146.9 hectares of the area has being deforested over the past tense 18 years, in cooperating with the GIS also show that the lost was due to physiographic factors and they suggest to distant settlements from the protected area. Similarly Li et al., 2011, noted the advantage of Landsat Lider fusion for modelling the height of young forest. Schlerf and Atzberger (2005), estimates the structural canopy variables using hyper apparitional remote sensing data INFORM Invertible forest reflectiveness Model. Main advantage of this method is that it does not require front calibration.Olthot el al., (2004) map out deciduous forest of spyglass assail damage using Landsat and environmental data in the east of Ontario, the study show a limitation in the difficulty of both remote sensing and environ mental data to discriminate many levels of the deciduous ice damage, however it can be consider as a multipurpose technique in differentiating areas of low to medium damage from the severe damage. An general accuracy of 69% was achieved. Mapedza et al., 2003, investigate s land cover change of the forest reserve area of Mafungautsi Zimbabwe, the study show that whilst forest cover at heart the reserve remain the same, but however there is steadily decline outs its boundaries as a result of agricultural expansions, the collection of fuel wood and building materials demand3.0 METHODOLOGYPair of multi- temporal deprave free Landsat images was selected to classify the study area 1987 and 2002, the image of image 1987 was Landsat 5 TM and the other Landsat 7 ETM+. The images were downloaded from GLCF websites in different layers and will be layer stack together using ERDAS meetry 9.2. A subset will be collected and image enhancement is to apply using Histogram equalised to visuali sed features more clearly. The images were geo-reference to Universal transverse Mercator (WGS84 zone 32), and a common geo-link window covering the same geographical coordinates were then quoteed from each image3.1.1IMAGE bear on TECHNIQUESDigital image processing is classified into three classes which includes pre-processing phase, processing phase and the post-processing phase. The pre-processing phase is the first stage in the processing technique, it involves correction of data through various means and techniques, different types of errors that are associated with any beam images includes geometrical error, atmospheric error and radiometric error. Geometric correction is a technique used to correct errors that are usually induced by sensor viewing, geometry and terrain variations, it involves correcting spatial distortion in an image due to earth curvature, atmosphere etc and thus giving it a real world coordinate system. The two images to be used in this study will not undergo the pre-processing phase because the two images obtained are ortho-rectified. The processing stages involve manipulation of images through the spatial enhancement and the spectral enhancement techniques.Image Enhancement the part is applied in order to display effectively the tonal distinctions within various features display in the image. It normally involves techniques for increasing the visual distinctions between features to assists in visual interpretation and analysis. (lillesand et al., 2008 p482 )Histogram -equalised stretch is going to apply to explode the DN set and also enhance the quality of the features in the image so that radiometric detail is enhanced. (Lillesand et al., 2008)3.1.1 CLASSIFICATION ANALYSIS supervise classification, using maximum likelihood algorithm is going to be used, supervised classification requires selection of qualified training sample which are subsequently used to assign image pixels to the training samples that best fits the cor responding data, it separately classifies multi-temporal images, pixel by pixel. Supervised classification requires an immense amount of time and know-how in creating classified products. Moreover, the final accuracy depends upon on the value of the classified image of each date. Yueling and Xu (2010),reported to have used supervised classification technique in monitoring and crusade force analysis of urban expansion in Guangzau City china and the result of the outcome shows an annual 19.7% growth rate.Post-classification comparison (PCC), is another important method that is recognized as the most effective and accurate method of detecting changes in mages with different dates and registry, the algorithm is capable of comparing the classified images pixel by pixel. The use of PCC is thus reduces the environmental and atmospheric effects associated with the temporal images and thus provide a complete change hyaloplasm (Lu et al., 2004).Accuracy assessment is the overall accuracy of the work done it shows the equaliser of ground sampling points that are correctly classified. The user accuracy shows the proportionality of classified pixels in according with the actual ground types as interpreted from the ground truth testing data. Accuracy assessment allows you to evaluate a classified image file (Thematic raster layer). 3.1.2 CHANGE DETECTION TECHNIQUESChange detection techniques is useful in a wide variety of applications such as land use change analysis, monitoring shifting cultivation, assessment of deforestation etc. change detection techniques to be used for this studies will include change vector analysis, image ratios and image differencing. These techniques have the ability to calculate area change, change rate as well as the spatial distribution changes. These techniques will involve computing the area covered by each of the two supervised classified images from the two data sources respectively and compare between the two images for increase or decrease in changes that have occurred in terms of forest change cover (Lu et al., 2004).Change vector analysis is a technique that generates two outfits, the first output produced is on spectral change from the first to the second image and the second output will produce the total change magnitude per pixel, Change vector analysis is computed by determining the Euclidean distance between end points through n-dimensional change space (Lu et al., 2004). Its main advantage in terms of analyzing change detection is its ability to process any get of spectral traffic circles desired by the analyst and capable of producing in flesh out of change detection information as it defines threshold and identify change trajectories which is a good way to calculate percentage rate of change that has occurred in a particular studies. Moreover the direction of the spectral change is often relates to that type of change that had occurred (Lillesand et al., 2008). Method was used by Allen and Kupfe r (2000) in conifer forest change detection.Image differencing is a change detection technique that will be used in this research to extract more information regarding the changes that have occurred in the study area image differencing subtracts the first date image from the second date image, pixel by pixel to show the changes within the two dated images, it identifies suitable image bands and thresholds. Image differencing usually yields a better results when carried out on the transport bands generated by transforming the RGB data sets into IHS color space. Singh (1986) applied this method in tropical forest change, similarly (Jha and Unni 1994) in forest conservation change detection.Image ratio is going to be applied because it is a simple way of trying to extract useful information from TM imagery. With image ratio technique, intensities of reflected energy recorded in one band for the pixel of a satellite images are divided by intensities in the same band for the other recti fied images. Image ratios describe the color of an object, although the color only corresponds to human perception when the three visible bands of red, green and blue are considered. Image ratio is prepared by dividing the digital number in one band by the corresponding digital number in another band for each pixel, thus stretching the resulting values, and plotting the new values as an image rationing is an effective way of visualizing different types of soils because the main spectral differences in the visible and near infrared spectral regions are found in the slope of the reflectivity curves. It calculates the related quantity of registered images of different two dates pixel by pixel. Ratios for changed areas have higher values or lower ratio values whereas an area of no change tends to terminate towards one (1). An important advantage of this method is it tends s to normalize the impact of sun angle and shadow which has been caused as a result of immaterial factors. (Lilles and et al., 2008 p596) Method has being used in land use occasion and change detection by Prakash and Gupta (1998)Finally, overlay operations would be made to see the changes that occur in the region. And Data is exported to GIS data base for map production.The above figure show a change detection role for the Landsat Images would undergo during the analysis.