on the rapidly growing Auckland Region [Name of the Writer] [Name of the Institute]

Transcription

1 RUNNING HEAD: PROPOSAL 1 GIS and remote sensing - based approach to assess land cover classification and spatial pattern on the rapidly growing Auckland Region [Name of the Writer] [Name of the Institute]

2 PROPOSAL 2 Abstract A Geographic Information System (GIS) is a smooth integration of hardware, software, and geographic data designed to visualize, analyze and manage geographic information in order to solve planning and management. In recent years, the cartographic science has completely changed as a result of the introduction of geographical information systems, which allow the manipulation of a large number of geo-referenced digital data, that is, with a spatial reference. Geographic information systems allow quick and effectively relate different categories of spatial variables, covering both the physical environment and the human environment, develop qualitative and quantitative indices, regionalize, develop and test hypotheses space and capture the results graphically. Spatial analysis is the examination of the relationships between geographic features and therefore better understand the reality. In a concentrated structure points are close and there are many empty areas and the average distance in a concentrated distribution is smaller than in a random distribution. Changes in land cover and land use have played an important role in the cycle of gas, water, in the increase of pollutants in soil degradation and declining biodiversity. Thus, land use presents a dilemma, is essential for humanity but impairs ecosystems on which they depend for their survival, also presents a challenge to develop sustainable management schemes.

3 PROPOSAL 3 GIS and remote sensing - based approach to assess land cover classification and spatial pattern on the rapidly growing Auckland Region Introduction In recent years, the cartographic science has completely changed as a result of the introduction of geographical information systems, which allow the manipulation of a large number of geo-referenced digital data, that is, with a spatial reference. Geographic information systems allow quick and effectively relate different categories of spatial variables, covering both the physical environment and the human environment, develop qualitative and quantitative indices, regionalize, develop and test hypotheses space and capture the results graphically. This technique has also broadened the traditional field of activity with applied geographer in the field of private enterprise and public facilities, especially on issues related to the best location for business and / or utilities research, identification and market area analysis, and selection of the most efficient transportation routes (Taylor, 2010). Remote sensing might be characterized as the train that permits the obtaining of data from the surface of the earth without reaching the material object of perception. The advancement of apparatuses to dissect and methodology pictures caught by multispectral sensors on board satellites has given the capability to robotize errands that was not possible overall. The primary issue with remote sensing is the expansive volume of information that must be taken care of multidimensional nature. The sorting toward oneself out neural system, specifically the model proposed by Kohonen, has turned out to be a flexible and valuable instrument in exploratory information investigation. The aspects of the created apparatuses have made the proposed philosophy an instrument helpful in different regions of exploration, which they impart

4 PROPOSAL 4 to the Remote Sensing the need to oversee multidimensional data. That is the reason we have included in this exploration tests identified with DNA chain administration and medication of medicinal information related kinematic variables of stride in kids. This would have permitted to accept the created philosophy. Remote sensing is a technique for obtaining information about objects, phenomena or areas located on the earth's surface without physical contact with them by employing electromagnetic methods, such as light, heat and radio wave energy, as a means to detect and measure the characteristics of the object of study. Remote sensing involves two processes. On one hand, the acquisition of information of the land area or the atmosphere capturing electromagnetic radiation emitted or reflected by them. For this normally uses sensors mounted on satellites images obtained at various portions of the electromagnetic spectrum, which are the visible, ultraviolet, infrared or microwave. Thesis Statement Assessing land cover classification and spatial pattern on the rapidly growing Auckland Region using GIS remote sensing Research Objectives The objective of this research is to develop a supervised classifier for land cover images of Auckland region. Analysing the impact of GIS remote sensing on spatial pattern of Auckland region Research Question

5 PROPOSAL 5 1. What is GIS and remote sensing? 2. What is the geography of Auckland? 3. How do you design spatial pattern of Aucland region. Literature Review The Auckland Region is one of the sixteen regions that make up New Zealand and is named after the city of Auckland, the most populous city in the region, located in the geographic center. It is the most populated region and the economic engine of the country. Sensors mounted on satellites recorded energy emitted or reflected by an object or surface in different bands of the electromagnetic spectrum (Trotter et al., 2004). Satellite images provide qualitative data and spatially continuous surface. In this regard, they are much more than a photograph. Ground spread is a geographic component That constitutes the reference base for assorted requisitions related with arranging the utilization of the regular assets, biodiversity and deforestation, as a marker of climatic change and desertification. There are assorted strategies for picture order, yet not every one of them are relevant to the grouping of ground spread. Therefore, the target of this examination was to create and assess an unsupervised order model pertinent to ground spread pictures in color model RGB (Red, Green and Blue), que Automatically orders the distinctive blankets and soil use (Radhakrishnan et.al, 2014). The created technique Corresponds to the gathering of unsupervised order was determined from Procedures and the thresholding measure Suggested by the Otsu strategy, in light of the fluctuation Among and Within classes. For its requisition, ethereal photos Were utilized with space determination of 1:19 500, noticeable phantom determination of three RGB groups, and radiometric determination of 0 to 255 levels. The results Were Compared with Those

6 PROPOSAL 6 Reported by a master with learning of the zone of study. Propels in machine innovation and remote sensing change the best approach to gather, process and use geospatial data. Remote sensing and workstation vision are effective to process this data and encourage information dissection, permitting the observing of progressions in the world's surface devices (Trotter, 2004). The aim of the ranking method is to minimize the deviations between observations belonging to the same group and maximize the distances between the centers of the clusters, which is represented by the objective function of Equation 1. The thresholds obtained for each of the spectral, red, green and blue bands are identified by t r, t g and t b. Each image pixel is represented by: P i, j (r i, j, g i, j, b i, j) T is (i, j) using the spatial coordinate and t r, t g, and t b to represent the preliminary threshold value for each domain of color. Thus a pixel P i, j = (r i, j, g i, j, b i, j) T can be thresholding as.

7 PROPOSAL 7 As a result, each pixel of color aerial photography can be encoded by three bits (c 1, c 2, c 3), where the values of c i (1 <= i <= 3) may be between 0 and n, where n the number of thresholds obtained. The total number of image pixels are grouped into (n +1) 3 classes, labeling each group with a consecutive number obtained by Equation 3, where each label represents a group of classes: Whereas the k-th class as c k to denote the average of each group of class RGB image using the following expression: k = μ (r k, g k, b k) T, whose components are obtained with the Equation 4:

8 PROPOSAL 8 The variance within classes (σ k) provided by the method of Otsu (1979) is calculated for each class by Equation 5: where N k is the number of pixels in the class C k. The variance between two classes (σ k, j), denoted by C k and C j with k different from j: where (r k, g k, b k), (r j, g j, b j) are defined by Equation 4 and the average spectral class C k and C j are the RGB color domain. Based on the variances obtained from the equations (5) and (6) can merge some clusters due to their spectral similarity. Whereas the k classes C and C j for j k, if σ k merge> σ or σ j kj> σ k. This is because the variance between two classes kj σ must be greater than the variance in individual classes and σ j σ k.. This rearrangement process is repeated until all the variance between classes are higher than their corresponding variances within class, in which case the result may be not reclassified. If there was no reclassification and the total

9 PROPOSAL 9 number of classes is equal to (n +1) 3, then the whole process repeats classifier, and increases by one the number of thresholds to be calculated; otherwise grouped as definitive classes are labeled classes (Weeks et.al, 2013).

10 PROPOSAL 10 GIS technology helps in achieving our objective of locating the best wind areas by causing least impact to the environment and the wildlife. Several Geographic information system projects are designed such a way to perform complicated and sophisticated calculations for the prediction and tracking of erosion patterns and storms (Voivontas et. al, 1998, pp ). GIS technology gives people and scientific researchers with geographic advantage that helps in becoming more productive, aware and more responsible citizens of the society (Ye & Wang, 2006, pp ). Remote sensing is also used in renewable energy projects; the use of aerial sensors for the recognition and classification of objects on the earth surface is among these applications. Ecological applications focusing on the classification of different species and habitats also incorporates the use of remote sensing. It also includes habitat mapping of linear resources that may include offshore devices for onshore cable routes (IEA, 2008). Habitat mapping using renewable energy for hydropower is also very common nowadays; it provides imagery of high resolution for river channels. Stereo imaging using 3D mapping is also a blessing of remote sensing, besides this thermal sensing, and automated image analysis is also among these examples (Hammer, 2003, pp ). Geographic information systems and remote sensing both have various applications in renewable energy projects. Applications used in renewable energy projects incorporating these technologies are still under investigation several methods are invented, but there is still the need for GIS and remote sensing procedures (Wang et. al, 2009, pp ). Continuous improvement is observed in sensors technology, besides this, new sensors would create new applications. It is clear from the above study and analysis that renewable energy projects

11 PROPOSAL 11 involving these technologies would develop extraordinary projects (Wakeyamaya & Ehara, 2010). These technologies would be beneficial for both businesses and the society. The technology of Remote Sensing refers to collect visual information or related data regarding a site from the space of air. Remote sensing includes recognizable techniques such as aerial photo analysis, and novel technologies as (InSAR) synthetic aperture radar interferometery. This type of photography is available for major areas, with coverage often extending back to the late 1920, in different wavelength and resolution the data is available for some earth areas (Exponent.com, 2013). (Frelinger & Gabriele, 1999) There are various methods through which remote sensing satellite and the different sensors can be characterized. Some of the classification emphasizes on the spacecrafts as a one entity, focusing mass, power, and orbital characteristics. The rest classifications focus on the sensor suite and emphasize factors as spectral coverage, spatial resolution, and whether the

12 PROPOSAL 12 sensor is passive or active. Radar wavelength is highly relative to visible wavelength. Imaging radar could see through clouds and night (Solano et.al, 2013). The technology of InSAR satellite radar technology produces high accuracy data on non-horizontal ground movements. Achieved data dating back to year of 1992 allow observing of slight to rigorous change in ground surface level caused by earthquakes, landslides, or land subsidence (Nicoll et.al, 2013 pp. 793).

13 PROPOSAL 13 References C.M. Trotter, K.R. Tate, S. Saggar, N.A. Scott, M.A. Sutherland, Trotter et al., (2004) A Multi- Scale Analysis of a National Terrestrial Carbon Budget and the Effects of Land-Use Change Global Environmental Change in the Ocean and on Land (2004), pp Hammer Annette et. al (2003) Solar energy assessment using remote sensing technologies. Remote sensing of environment, vol. 86, pp Data Retrieved from on October 9, 2013 Nicoll, T., Brierley, G., & Yu, G. A. (2013). A broad overview of landscape diversity of the Yellow River source zone. Journal of Geographical Sciences,23(5), Radhakrishnan, N., Eerni, S. K., & Kumar, S. (2014). Analysis of Urban Sprawl Pattern in Tiruchirappalli City Using Applications of Remote Sensing and GIS.Arabian Journal for Science and Engineering, 1-9. Solano, E., & Scavuzzo, M. (2013). Remote Sensing to Assessment Damages Post Earthquakes, focusing in Urban Structures. Taylor (2010), Reconstruction Historical Vegetation Cover in Otago, New Zealand, Using Multiproxy Analysis of Peat Cores V. Vitalis, Vitalis, 2008, Domestic Reform, Trade, Innovation and Growth in New Zealand's Agriculture Sector: Trade and Innovation Project Case Study No. 2 OECD Trade Policy Working Papers74, OECD Publishing (2008) Voivontas D et. al (1998) Evaluation of Renewable Energy potential using a GIS decision support system. Renewable Energy, vol. 13, issue 3, pp Data Retrieved from

14 PROPOSAL 14 on October 9, 2013 Wang Shifeng et. al (2009) A new thinking for renewable energy model: Remote sensing-based renewable energy model. International Journal of Energy Research, vol. 33, issue 8, pp Data Retrieved from on October 9, 2013 Weeks, E. S., Ausseil, A. G. E., Shepherd, J. D., & Dymond, J. R. (2013). Remote sensing methods to detect land use/cover changes in New Zealand's indigenous grasslands. New Zealand Geographer, 69(1), Wirsenius, 2000, Human Use of Land and Organic Materials. Modeling the Turnover of Biomass in the Global Food System. Dept. of Physical Resource Theory Chalmers University of Technology, Goteborg (2000) Ye Chang and Wang Shi (2006) GIS-based evaluation of multifarious local renewable energy sources: a case study of the Chigu area of southwestern Taiwan. Energy Policy, vol. 34, pp Data Retrieved from on October 9, 2013