Topology, homogeneity and scale factors for object detection: application of eCognition software for urban mapping using multispectral satellite image

The research scope of this paper is to apply spatial object based image analysis (OBIA) method for processing panchromatic multispectral image covering study area of Brussels for urban mapping. The aim is to map different land cover types and more specifically, built-up areas from the very high resolution (VHR) satellite image using OBIA approach. A case study covers urban landscapes in the eastern areas of the city of Brussels, Belgium. Technically, this research was performed in eCognition raster processing software demonstrating excellent results of image segmentation and classification. The tools embedded in eCognition enabled to perform image segmentation and objects classification processes in a semi-automated regime, which is useful for the city planning, spatial analysis and urban growth analysis. The combination of the OBIA method together with technical tools of the eCognition demonstrated applicability of this method for urban mapping in densely populated areas, e.g. in megapolis and capital cities. The methodology included multiresolution segmentation and classification of the created objects.Comment: 6 pages, 12 figures, INSO2015, Ed. by A. Girgvliani et al. Akaki Tsereteli State University, Kutaisi (Imereti), Georgi

Discriminant Analysis with Spatial Weights for Urban Land Cover Classification

Classifying urban area images is challenging because of the heterogeneous nature of the urban landscape resulting in mixed pixels and classes with highly variable spectral ranges. Approaches using ancillary data, such as knowledge based or expert systems, have shown to improve the classification accuracy in urban areas. Appropriate ancillary data, however, may not always be available. The goal of this study is to compare the results of the discriminant analysis statistical technique with discriminant analysis with spatial weights to classify urban land cover. Discriminant analysis is a statistical technique used to predict group membership for a target based on the linear combination of independent variables. Strict per pixel statistical analysis however does not consider the spatial dependencies among neighbouring pixels. Our study shows that approaches using ancillary data continue to outperform strict spectral classifiers but that using a spatial weight improved the results. Furthermore, results show that when the discriminant analysis technique works well then the spatially weighted approach performs better. However, when the discriminant analysis performs poorly, those poor results are magnified in the spatially weighted approach in the same study area. The study shows that for dominant classes, adding spatial weights improves the classification accuracy.

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

An analysis of the spatial and temporal changes in the Riparian zone of the Berg River in the vicinity of Hermon: implications for governance

Riparian zones broadly refer to the interface between terrestrial and aquatic systems. It is widely acknowledged that riparian zones provide a number of services including that of an ecological corridor for migration of animal species; a habitat; food for aquatic macro invertebrates in the form of organic matter; stabilising river banks; filtering nutrients and sediments from water that discharges off surface slopes and land; and protecting and improving water quality of river systems. This study examines how the Berg River riparian zone has changed over the past few decades and then considers the implications for governance of these zones in South Africa. The study identifies changes in vegetation composition and spatial extent of the riparian zone. The study site is a stretch of the Berg River in the Hermon area. Changes in vegetation and the spatial extent of the riparian zone over time were identified and mapped using aerial photographs of the study area spanning a period from 1955 to 2012. The results of the study showed that the spatial extent of the riparian zone decreased by 29.3% from 55 ha in 1955 to 39 ha in 2012. At the same time the area covered by trees (Eucalyptus globules) increased from 3.84 ha in 1955 to 35.94 ha while the area covered by shrubs that could be detected from the sources, decreased from 46.10 ha in 1955 to close to zero in 2012. The results of this study reveal a lack of governance in the river system. The lack of governance is attributed to the fact that the Berg River Catchment Management Agency is not operational. In South Africa weak governance in the management and responsible care in safeguarding riparian zones has compromised water quality, ecological integrity and habitat of the river system

Determining Exurbia: Is It Really Its Own Entity or Merely An Extension of Americas Growing Suburbia

The purpose for studying exurbanization is to evaluate the spatial spread of metropolitan areas into their immediate hinterlands through remote sensing satellite imagery. This includes addressing scholar\u27s inability to define exurbia, along with plausible reasons people move into exurbia. In addition, determination will include consideration on the possibility that exurbia has become an extension of America\u27s growing and increasingly independent suburbia or; recognize that exurbia exists, but within various geographic locations. In return to the former, an analytical approach was taken to investigate scholar\u27s inability to provide a definition to this phenomena; as well as inconsistent results on the push and pull factors behind people\u27s desire to move further outward. Therefore, the need for remote sensing, GIS analysis that includes statistical implementation is necessary to determine the potential location of exurban developments. This includes three study areas, which are St. Louis, Twin Cities and Los Angeles. Each city were evaluated through 1990 and 2000 census data information and satellite imagery. The level of impervious verses non-impervious land area for selected census tracts were determined, as the main findings indicate a substantial increase of urban sprawl within St. Louis and Twin Cities. As for Los Angeles, this area maintained a level of compact urban development from 1990 and 2000 census tract data, satellite imagery and geospatial analysis. These findings indicate two different results: (1) Los Angeles has a high probability that exurbia is an extension to suburbia; (2) St. Louis and Twin Cities experienced substantial increase of urban sprawl that suggests the possibility of continued exurban existence

Determining Exurbia: Is It Really Its Own Entity or Merely An Extension of Americas Growing Suburbia

The purpose for studying exurbanization is to evaluate the spatial spread of metropolitan areas into their immediate hinterlands through remote sensing satellite imagery. This includes addressing scholar\u27s inability to define exurbia, along with plausible reasons people move into exurbia. In addition, determination will include consideration on the possibility that exurbia has become an extension of America\u27s growing and increasingly independent suburbia or; recognize that exurbia exists, but within various geographic locations. In return to the former, an analytical approach was taken to investigate scholar\u27s inability to provide a definition to this phenomena; as well as inconsistent results on the push and pull factors behind people\u27s desire to move further outward. Therefore, the need for remote sensing, GIS analysis that includes statistical implementation is necessary to determine the potential location of exurban developments. This includes three study areas, which are St. Louis, Twin Cities and Los Angeles. Each city were evaluated through 1990 and 2000 census data information and satellite imagery. The level of impervious verses non-impervious land area for selected census tracts were determined, as the main findings indicate a substantial increase of urban sprawl within St. Louis and Twin Cities. As for Los Angeles, this area maintained a level of compact urban development from 1990 and 2000 census tract data, satellite imagery and geospatial analysis. These findings indicate two different results: (1) Los Angeles has a high probability that exurbia is an extension to suburbia; (2) St. Louis and Twin Cities experienced substantial increase of urban sprawl that suggests the possibility of continued exurban existence

Understanding Open Spaces in an Arid City

abstract: This doctoral dissertation research aims to develop a comprehensive definition of urban open spaces and to determine the extent of environmental, social and economic impacts of open spaces on cities and the people living there. The approach I take to define urban open space is to apply fuzzy set theory to conceptualize the physical characteristics of open spaces. In addition, a 'W-green index' is developed to quantify the scope of greenness in urban open spaces. Finally, I characterize the environmental impact of open spaces' greenness on the surface temperature, explore the social benefits through observing recreation and relaxation, and identify the relationship between housing price and open space be creating a hedonic model on nearby housing to quantify the economic impact. Fuzzy open space mapping helps to investigate the landscape characteristics of existing-recognized open spaces as well as other areas that can serve as open spaces. Research findings indicated that two fuzzy open space values are effective to the variability in different land-use types and between arid and humid cities. W-Green index quantifies the greenness for various types of open spaces. Most parks in Tempe, Arizona are grass-dominant with higher W-Green index, while natural landscapes are shrub-dominant with lower index. W-Green index has the advantage to explain vegetation composition and structural characteristics in open spaces. The outputs of comprehensive analyses show that the different qualities and types of open spaces, including size, greenness, equipment (facility), and surrounding areas, have different patterns in the reduction of surface temperature and the number of physical activities. The variance in housing prices through the distance to park was, however, not clear in this research. This dissertation project provides better insight into how to describe, plan, and prioritize the functions and types of urban open spaces need for sustainable living. This project builds a comprehensive framework for analyzing urban open spaces in an arid city. This dissertation helps expand the view for urban environment and play a key role in establishing a strategy and finding decision-makings.Dissertation/ThesisPh.D. Geography 201

Vulnerability assessment of wetland ecosystems to water demand, climate variability and land-use/cover change: The case of Die Vlei wetland, Eastern Cape province, South Africa

Water scarcity is a major challenge in many different countries, particularly arid and or semi-arid like South Africa. Wetlands are one of the freshwater ecosystems that may assist in alleviating water scarcity because they are valuable not only as a water source for humans but also as an ecosystem of animals and plant species. However, wetlands have been experiencing rapid rates of vulnerability/risk due to alterations by population growth leading to enhanced water demand, climate variability, and human activities leading to land cover/land-use changes. Geographical Information Systems (GIS) and Remote Sensing (RS) are less financially taxing methods useful in studying water scarcity, as shown in this study. The study begins with a literature review presentation based on a desk study from predominantly academic publications and additional municipal and consultancy reports on the wetland ecosystem’s vulnerability/risk and focuses on applying GIS & RS in related studies. After that, the study conducts a vulnerability assessment using the Ramsar Convention’s wetland vulnerability assessment using the theoretical framework stages using GIS and RS technologies. The study hypothesizes that water demand, climate variability, and land-use/cover changes (LULC) are the tri-factor responsible for wetland vulnerability. It begins the assessment by first quantifying wetland water demand using the wetland water budget, ecosystem services and the Penman-Montheith-FAO (ETo) evapotranspiration index. Secondly, objectively representing climate variability on wetland vulnerability using trend analysis to measure rainfall and temperature variability. Thirdly, reconstructing LULC changes from multi-date remotely sensed SPOT imagery over ten years from 2007 to 2017 to identify and monitor impacts of trends. The vulnerability was assessed through a Principle Component Analysis (PCA) that identified relevant variables and Multi-Criteria Evaluation (MCE) to evaluate the wetland’s exposure. The study concludes that there is evidence of a possible increase in water demand whilst climate variability, which is estimated to have a 39% contribution to the wetland dynamics, is characterised by a decrease in precipitation and an increase in temperatures. Lastly, LULC trends showed a marked increase in domestic and commercial farming, and farming has been identified as a wetland stressor of note.Thesis (MPhil) -- Faculty of Science and Agriculture, 202

Vulnerability assessment of wetland ecosystems to water demand, climate variability and land-use/cover change: The case of Die Vlei wetland, Eastern Cape province, South Africa

Water scarcity is a major challenge in many different countries, particularly arid and or semi-arid like South Africa. Wetlands are one of the freshwater ecosystems that may assist in alleviating water scarcity because they are valuable not only as a water source for humans but also as an ecosystem of animals and plant species. However, wetlands have been experiencing rapid rates of vulnerability/risk due to alterations by population growth leading to enhanced water demand, climate variability, and human activities leading to land cover/land-use changes. Geographical Information Systems (GIS) and Remote Sensing (RS) are less financially taxing methods useful in studying water scarcity, as shown in this study. The study begins with a literature review presentation based on a desk study from predominantly academic publications and additional municipal and consultancy reports on the wetland ecosystem’s vulnerability/risk and focuses on applying GIS & RS in related studies. After that, the study conducts a vulnerability assessment using the Ramsar Convention’s wetland vulnerability assessment using the theoretical framework stages using GIS and RS technologies. The study hypothesizes that water demand, climate variability, and land-use/cover changes (LULC) are the tri-factor responsible for wetland vulnerability. It begins the assessment by first quantifying wetland water demand using the wetland water budget, ecosystem services and the Penman-Montheith-FAO (ETo) evapotranspiration index. Secondly, objectively representing climate variability on wetland vulnerability using trend analysis to measure rainfall and temperature variability. Thirdly, reconstructing LULC changes from multi-date remotely sensed SPOT imagery over ten years from 2007 to 2017 to identify and monitor impacts of trends. The vulnerability was assessed through a Principle Component Analysis (PCA) that identified relevant variables and Multi-Criteria Evaluation (MCE) to evaluate the wetland’s exposure. The study concludes that there is evidence of a possible increase in water demand whilst climate variability, which is estimated to have a 39% contribution to the wetland dynamics, is characterised by a decrease in precipitation and an increase in temperatures. Lastly, LULC trends showed a marked increase in domestic and commercial farming, and farming has been identified as a wetland stressor of note.Thesis (MPhil) -- Faculty of Science and Agriculture, 202

Rangeland degradation assessment using remote sensing and vegetation species.

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2011.The degradation of rangeland grass is currently one of the most serious environmental problems in South Africa. Increaser and decreaser grass species have been used as indicators to evaluate rangeland condition. Therefore, classifying these species and monitoring their relative abundance is an important step for sustainable rangelands management. Traditional methods (e.g. wheel point technique) have been used in classifying increaser and decreaser species over small geographic areas. These methods are regarded as being costly and time-consuming, because grasslands usually cover large expanses that are situated in isolated and inaccessible areas. In this regard, remote sensing techniques offer a practical and economical means for quantifying rangeland degradation over large areas. Remote sensing is capable of providing rapid, relatively inexpensive, and near-real-time data that could be used for classifying and monitoring species. This study advocates the development of techniques based on remote sensing to classify four dominant increaser species associated with rangeland degradation namely: Hyparrhenia hirta, Eragrostis curvula, Sporobolus africanus and Aristida diffusa in Okhombe communal rangeland, KwaZulu-Natal, South Africa. To our knowledge, no attempt has yet been made to discriminate and characterize the landscape using these species as indicators of the different levels of rangeland degradation using remote sensing. The first part of the thesis reviewed the problem of rangeland degradation in South Africa, the use of remote sensing (multispectral and hyperspectral) and their challenges and opportunities in mapping rangeland degradation using different indicators. The concept of decreaser and increaser species and how it can be used to map rangeland degradation was discussed. The second part of this study focused on exploring the relationship between vegetation species (increaser and decreaser species) and different levels of rangeland degradation. Results showed that, there is significant relationship between the abundance and distribution of different vegetation species and rangeland condition. The third part of the study aimed to investigate the potential use of hyperspectral remote sensing in discriminating between four increaser species using the raw field spectroscopy data and discriminant analysis as a classifier. The results indicate that the spectroscopic approach used in this study has a strong potential to discriminate among increaser species. These positive results prompted the need to scale up the method to airborne remote sensing data characteristics for the purpose of possible mapping of rangeland species as indicators of degradation. We investigated whether canopy reflectance spectra resampled to AISA Eagle resolution and random forest as a classification algorithm could discriminate between four increaser species. Results showed that hyperspectral data assessed with the random forest algorithm has the potential to accurately discriminate species with best overall accuracy. Knowledge on reduced key wavelength regions and spectral band combinations for successful discrimination of increaser species was obtained. These wavelengths were evaluated using the new WorldView imagery containing unique and strategically positioned band settings. The study demonstrated the potential of WorldView-2 bands in classifying grass at species level with an overall accuracy of 82% which is only 5% less than an overall accuracy achieved by AISA Eagle hyperspectral data. Overall, the study has demonstrated the potential of remote sensing techniques to classify different increaser species representing levels of rangeland degradation. In this regard, we expect that the results of this study can be used to support up-to-date monitoring system for sustainable rangeland management