A comparison of machine learning models for the mapping of groundwater spring potential

Groundwater resources are vitally important in arid and semi-arid areas meaning that spatial planning tools are required for their exploration and mapping. Accordingly, this research compared the predictive powers of five machine learning models for groundwater potential spatial mapping in Wadi az-Zarqa watershed in Jordan. The five models were random forest (RF), boosted regression tree (BRT), support vector machine (SVM), mixture discriminant analysis (MDA), and multivariate adaptive regression spline (MARS). These algorithms explored spatial distributions of 12 hydrological-geological-physiographical (HGP) conditioning factors (slope, altitude, profile curvature, plan curvature, slope aspect, slope length (SL), lithology, soil texture, average annual rainfall, topographic wetness index (TWI), distance to drainage network, and distance to faults) that determine where groundwater springs are located. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to evaluate the prediction accuracies of the five individual models. Here the results were ranked in descending order as MDA (83.2%), RF (80.6%), SVM (80.2%), BRT (78.0%), and MARS (75.5%).The results show good potential for further use of machine learning techniques for mapping groundwater spring potential in other places where the use and management of groundwater resources is essential for sustaining rural or urban life

RELATIONSHIP BETWEEN URBANIZATION AND DENGUE HAEMORRHAGIC FEVER INCIDENCE IN SEMARANG CITY

Unplanned urbanization can cause unhealthy urban environment, which in turn increases the population of mosquitoes carrying the dengue vector. Consequently, this would reduce the urban life quality because public health is an important aspect of it. The increasing incidence of Dengue Haemorrhagic Fever (DHF) in Semarang City has been alarming. In 2013, the incidence was 2,364 cases, which increased up to 89.11% from the 1,250 cases of 2012. So, it is necessary to study about what relationship is there between the level of urbanization and the incidence of DHF in Semarang. This study used quantitative and spatial approach. The unit of analysis is sub-district with time series data from 2006 to 2013. The analysis technique is spatial analysis through image interpretation, regression, and descriptive analysis. The level of urbanization has been measured through the variables of population growth, population density, land use change, and building density. The results have shown that there is no significant correlation between the level of urbanization and the incidence of dengue fever. The urbanization is acknowledged as influencing only about 28% of the DHF incidence in the city, while the other 72% has been influenced by other factors

Remote sensing applications in vegetation mapping with special reference to the Langebaan area, South Africa

Includes bibliography.Interpretation of remote sensing products as a procedure used during the process of vegetation mapping has developed from a purely visual process of image identification to one which can utilize computerised methods to aid consistent identification of vast quantities of digitally stored/recorded spectral information. A description of the Landsat satellite system which is currently providing imagery to potential South African users in the form of digitally stored data and photographic products is given and sources of digital spectral data at other than satellite scales of resolution are described. A brief description of some image processing systems already operational in South Africa and being utilized for land cover mapping is also included. An introduction to the concept of computer analysis of numerical spectral data is given. The difference in approach between workers interpreting geological or other surface features as opposed to those wishing to simplify an image into categories is emphasized. This explains the local effort being expended on development of computer 'classification' routines as opposed to other methods of computer based image processing in vegetation mapping. The first paper presents a review of current use of remote sensing products in vegetation mapping in South Africa and the potential of more recently available products and processes in this field. The relative merits of different film types is discussed, as is the problem of scale of survey, scale of remote sensing product and scale of final mapping. The position that computer analysis of spectral data occupies in a scheme designed to show the relationships between different scales of survey is described. The second paper describes an example of the application of computer classification techniques to Landsat data in mapping vegetation in the Langebaan area, South Africa, at a semi-detailed scale of operation. The results of this exercise are illustrated together with a map produced using visual air photo interpretation techniques, backed by field checking. More detail of the specific relationships between plant community structure, canopy cover, scale of survey and reflectance values in the map classes produced is then given in the third paper. Computer classification matches well with major structural divisions; finer structural sub-division descriptions of sample plots correlate well with floristic divisions. A combination of digital analysis of remote sensing products ·and field checking based on structural schemes is recommended as a rapid mapping process. A report compiled on the full range of investigations carried out up to the end of 1980 into the usefulness of various remote sensing products for studying and mapping the Fynbos Biome is included. The overall mapping objective of this investigation was to determine the extent of the Fynbos Biome and of the major land use types within it. It was decided that the 'reconnaissance' level of operation at 1:250 000 final mapping scale is best suited to meet the overall mapping objective. General conclusions are drawn as to the current status of remote sensing applications in vegetation/land cover mapping in South Africa and elsewhere. Developments and refinements in techniques subsequent to the carrying out of the investigations reported in this account are briefly discussed

คาดการณ์การใช้ที่ดินลุ่มน้ำลำตะคอง พ.ศ. 2567 ด้วยแบบจำลอง CA-MARKOV

Prediction of Lamtakong Watershed Land Use in 2024 with CA-MARKOV Mode

Urban scene description for a multi scale classication of high resolution imagery case of Cape Town urban Scene

Includes abstract.Includes bibliographical references.In this paper, a multi level contextual classification approach of the City of Cape Town, South Africa is presented. The methodology developed to identify the different objects using the multi level contextual technique comprised three important phases

Remote sensing and GIS for wetland vegetation study

Remote Sensing (RS) and Geographic Information System (GIS) approaches, combined with ground truthing, are providing new tools for advanced ecosystem management, by providing the ability to monitor change over time at local, regional, and global scales. In this study, remote sensing (Landsat TM and aerial photographs) and GIS, combined with ground truthing work, were used to assess wetland vegetation change over time at two contrasting wetland sites in the UK: freshwater wetland at Wicken Fen between 1984 and 2009, and saltmarsh between 1988 and 2009 in Caerlaverock Reserve. Ground truthing studies were carried out in Wicken Fen (UK National Grid Reference TL 5570) during 14th - 18th June 2010: forty 1 m2 quadrats were taken in total, placed randomly along six transects in different vegetation types. The survey in the second Study Area Caerlaverock Reserve (UK National Grid Reference NY0464) was conducted on 5th - 9th July 2011, with a total of forty-eight 1 m2 quadrats placed randomly along seven transects in different vegetation types within the study area. Two-way indicator species (TWINSPAN) was used for classification the ground truth samples, taking separation on eigenvalues with high value (>0.500), to define end-groups of samples. The samples were classified into four sample-groups based on data from 40 quadrats in Wicken Fen, while the data were from 48 quadrats divided into five sample-groups in Caerlaverock Reserve. The primary analysis was conducted by interpreting vegetation cover from aerial photographs, using GIS combined with ground truth data. Unsupervised and supervised classifications with the same technique for aerial photography interpretation were used to interpret the vegetation cover in the Landsat TM images. In Wicken Fen, Landsat TM images were used from 18th August 1984 and 23rd August 2009; for Caerlaverock Reserve Landsat TM imagery used was taken from 14th May 1988 and 11th July 2009. Aerial photograph imagery for Wicken Fen was from 1985 and 2009; and for Caerlaverock Reserve, from 1988 and 2009. Both the results from analysis of aerial photographs and Landsat TM imagery showed a substantial temporal change in vegetation during the period of study at Wicken Fen, most likely primarily produced by the management programme, rather than being due to natural change. In Cearlaverock Reserve, results from aerial photography interpretation indicated a slight change in the cover of shrubs during the period 1988 to 2009, but little other change over the study period. The results show that the classification accuracy using aerial photography was higher than that of Landsat TM data. The difference of classification accuracy between aerial photography and Landsat TM, especially in Caerlaverock Reserve, was due to the low resolution of Landsat TM images, and the fact that some vegetation classes occupied an area less than that of the pixel size of the TM image. Based on the mapping exercise, the aerial photographs produced better vegetation classes (when compared with ground truthing data) than Landsat TM images, because aerial photos have a higher spatial resolution than the Landsat TM images. Perhaps the most important conclusion of this study is that it provides evidence that the RS/GIS approach can provide useful baseline data about wetland vegetation change over time, and across quite expansive areas, which can therefore provide valuable information to aid the management and conservation of wetland habitats

Monitoring, modelling and managing urban growth in Alexandria, Egypt using remote sensing and GIS

Alexandria is the second largest urban governorate in Egypt and has seen significant urban growth in its modern and contemporary history. This study investigates the urban growth phenomenon in Alexandria, Egypt using the integration of remote sensing and GIS. The study has revealed some significant findings that can help in understanding the current and future trends of urban growth in Alexandria. For demographic analysis, growth rates dropped off between 1976 and 1996. In the same manner, Alexandria's population decreased from 6.33% of total country in 1976 to 5.6% in 1996. Family size and crowding rates are declining as well. Moreover, the role of internal migration has changed and the city sends out more population than it receives. In addition, there is a clear decline in population density in the city's core, while city fringes have witnessed increases in their density. For physical expansion, Alexandria experienced a long history of deterioration from the end of the Roman era until the French expedition's departure in the beginning of the 19`" century. Alexandria began to revive again from the first half of the 19`n century during Mohamed Ali era up to date. The city expanded in all available directions. Therefore, the side effects of urban growth commenced to develop in some parts such as informal housing on the cultivated land in the east and southeast of the city. The urban physical expansion and change were detected using Landsat satellite images. The satellite images of years 1984 and 1993 were first georeferenced, achieving a very small RMSE that provided high accuracy data for satellite image analysis. Then, the images were classified using a tailored classification scheme with accuracy of 93.82% and 95.27% for 1984 and 1993 images respectively. This high accuracy enabled detecting land use/cover changes with high confidence using a postclassification comparison method. One of the most important findings here is the loss of cultivated land in favour of urban expansion. If the current loss rates continued, 75% of green lands would be lost by year 2191. These hazardous rates call for an urban growth management policy that can preserve such valuable resources to achieve sustainable urban development. The starting point of any management programme will be based on the modelling of the future growth. Modelling techniques can help in defining the scenarios of urban growth. In this study, the SLEUTH urban growth model was applied to predict future urban expansion in Alexandria until the year 2055. The application of this model in Alexandria of Egypt with its different environmental characteristics is the first application outside USA and Europe. The results revealed that future urban growth would continue in the edges of the current urban extent, which means the cultivated lands in the east and the southeast of the city will continue to lose more day by day from their area. To deal with this crisis, there is a serious need for a comprehensive urban growth management programme that based on the best practices in similar situations. Good urban governance, public participation, using GIS and remote sensing, and decentralisation (among others) are found to be the most important principles for such programme.EThOS - Electronic Theses Online ServiceEgyptian Government, Ministry of Higher EducationGBUnited Kingdo

A Multi-temporal fusion-based approach for land cover mapping in support of nuclear incident response

An increasingly important application of remote sensing is to provide decision support during emergency response and disaster management efforts. Land cover maps constitute one such useful application product during disaster events; if generated rapidly after any disaster, such map products can contribute to the efficacy of the response effort. In light of recent nuclear incidents, e.g., after the earthquake/tsunami in Japan (2011), our research focuses on constructing rapid and accurate land cover maps of the impacted area in case of an accidental nuclear release. The methodology involves integration of results from two different approaches, namely coarse spatial resolution multi-temporal and fine spatial resolution imagery, to increase classification accuracy. Although advanced methods have been developed for classification using high spatial or temporal resolution imagery, only a limited amount of work has been done on fusion of these two remote sensing approaches. The presented methodology thus involves integration of classification results from two different remote sensing modalities in order to improve classification accuracy. The data used included RapidEye and MODIS scenes over the Nine Mile Point Nuclear Power Station in Oswego (New York, USA). The first step in the process was the construction of land cover maps from freely available, high temporal resolution, low spatial resolution MODIS imagery using a time-series approach. We used the variability in the temporal signatures among different land cover classes for classification. The time series-specific features were defined by various physical properties of a pixel, such as variation in vegetation cover and water content over time. The pixels were classified into four land cover classes - forest, urban, water, and vegetation - using Euclidean and Mahalanobis distance metrics. On the other hand, a high spatial resolution commercial satellite, such as RapidEye, can be tasked to capture images over the affected area in the case of a nuclear event. This imagery served as a second source of data to augment results from the time series approach. The classifications from the two approaches were integrated using an a posteriori probability-based fusion approach. This was done by establishing a relationship between the classes, obtained after classification of the two data sources. Despite the coarse spatial resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion-based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. This fusion thus contributed to classification accuracy refinement, with a few additional advantages, such as correction for cloud cover and providing for an approach that is robust against point-in-time seasonal anomalies, due to the inclusion of multi-temporal data