Digital analysis of remotely sensed images for evaluating color in turfgrass

Most conventional approaches to rating turf color for different turf varieties grown under similar conditions or for the same variety grown under different cultural conditions employ a visual subjective rating. By digitizing remotely sensed images acquired by use of a helium filled blimp and a Canon EOS camera, we were able to quickly and inexpensively rate turf plots. Our classification techniques evaluate histograms of pixels within a defined area rather than classifying each individual pixel as in traditional classifications. Analysis of the digital images includes separation of the image into the distinct 8-bit planes (red, green and blue). A plot map is created such that each experimental unit is defined by a rectangle. When this plot map is superimposed over the registered red, green and blue images, the respective histograms are collected for all plots. Data from these histograms are related to visual ratings for each plot. Supervised classification employs least squares regression equations developed from the histogram data and visual evaluations for the respective reference plots. Relationships apparent in the reference plots are used to predict values for all plots. Unsupervised classification is achieved by applying principal components transformation to the histogram data. The transformed values are then scaled to fit within the traditional 1-10 turf rating system by supplying maximum and minimum values. Supervised classifications produce predicted color values that are similar to visual ratings, but with greater statistical significance and fewer violations of the assumptions for ANOVA. Unsupervised classifications were similar to supervised classifications in two of three trials, but not consistently related to visual ratings in a third

Interpreting and integrating landsat remote sensing image and geographic information system by fuzzy unsupervised clustering algorithm

Due to the resolution of Landsat images and the multiplicity of the terrain, it is improper to assign each pixel in an image to one of a number of land cover types by using the conventional remote sensing classification method. This is also known as the hard partition method. The concept of the fuzzy set provides the means to resolve this problem. This paper presents a two-pass-mode fuzzy unsupervised clustering algorithm. In the first passing, the cluster mean vectors which represent the geographic attributes or the land cover types are derived. In the second passing, the concept of fuzzy set is used. The cluster mean vectors which are obtained in the first passing are used to derive the membership function. The grade of memberships of each pixel to the land cover types are obtained according to the distance from the pixel to each cluster mean vector. The output of this algorithm can be used as the input of the Geographic Information System

Social media and GIScience: Collection, analysis, and visualization of user-generated spatial data

Over the last decade, social media platforms have eclipsed the height of popular culture and communication technology, which, in combination with widespread access to GIS-enabled hardware (i.e. mobile phones), has resulted in the continuous creation of massive amounts of user-generated spatial data. This thesis explores how social media data have been utilized in GIS research and provides a commentary on the impacts of this next iteration of technological change with respect to GIScience. First, the roots of GIS technology are traced to set the stage for the examination of social media as a technological catalyst for change in GIScience. Next, a scoping review is conducted to gather and synthesize a summary of methods used to collect, analyze, and visualize this data. Finally, a case study exploring the spatio-temporality of crowdfunding behaviours in Canada during the COVID-19 pandemic is presented to demonstrate the utility of social media data in spatial research

Urban growth in Southern Africa : comparing 30 years of decadal imagery to census data

The total urban area of each study site was calculated for each time slice and the results were represented as maps depicting urban expansion. Graphs were also created depicting the total urban area vs. total population for each time slice (1970s, 1990s and 2000s)

The utility of complex soil reflectance image properties for soil mapping

This investigation is concerned with the application of complex quantitative analysis to remotely sensed data for mapping soils. The major aim of this thesis is to examine, by means of illustrative examples, the utility of complex image metrics in the detection, differentiation, and partitioning of satellite images of soil landscapes. Satellite images have been widely used for soil mapping. In order to realise the maximum potential of satellite imagery, improvements are needed both in visual presentation of such images, and in their automatic classification, in order to reveal the complex properties of soil landscape. A Landsat TM image of the Al-Ahsa area of Saudi Arabia was used in the investigation. It presents an ideal region for remote sensing studies due to the absence of vegetation cover and the existence of different type of landforms in a region of low topography. Three techniques for modelling complex elements of images were used and evaluated; Fast Fourier Transform (FFT), Artificial Neural Network Analysis (ANN), Fractal and Multifractal Analysis. The FFT technique developed in this thesis isolates spatial frequency components in specific wavebands. The inverse FFT images are enhanced to (i) display optimised zoning of the image, and (ii) to display specific features. This technique partitions images into major zones that are different zones from the standard soil maps. The ANN technique developed is a non-linear measure of image texture. It shows difference within an image. The texture model is trained on areas selected on the basis of the existing soil map. Substitution analysis of training areas allows an assessment of image zones and boundaries. The texture image is displayed by linear contrast stretch. Zonation does not correspond with published maps or with FFT zonation. The fractal method is based on the local fractal dimension that is used as a texture measure based on a moving pre-set size filter over the entire image. The resulting images do not give zones but shows clear patterns of complexity such as spatial transitions. It is possible to derive areas of similar patterns of transition in complexity. There are implications of these results for soil mapping at the theoretical and practical levels. The implications of the theoretical level are about the existences of soil units defined following the classical approach. In the practical level, the classical approach would be abandoned. There is at present nowhere near the same support of the ideas to complement the traditional mapping approach and raise awareness that soils are inherently complex. The study has important implications for classical theory and practice of soil mapping

Development of a Multicriteria Spatial Decision Support System with Application to the Economic Optimization of Aircraft Based Weather Data Collection

This research is motivated by the economic optimization of the Troposherical Airborne Meteorological Data Reporting (TAMDAR), an aircraft-based meteorological data collection system. In the envisioned TAMDAR system, meteorological data collected by the onboard sensors of selected aircraft are transmitted to the ground as aircraft fly their missions. The data is processed by a national center, which disseminates the data to diverse users such as weather forecasters and aviation control centers. Substantial government funding is required for the implementation and operation of this new data acquisition system and data transmission expenditures constitute the largest portion of the costs. To achieve economic optimization of the data gathering activities, the TAMDAR system requires a multicriteria spatial decision support system (MC-SDSS) that facilitates the efficient selection of the most desirable data points to collect based on a limited budget. To optimize the data collection each data point must be assigned a value by the TAMDAR DSS and a specialized data valuation technique is developed for this purpose. This work presents a design methodology for practical integrated application of multi-attribute utility, simulation and spatial decision analysis techniques in the optimization of aircraft-based weather data collection systems. The TAMDAR DSS demonstrates tools to address a number of challenging decision support design problems such as inherent uncertainty, required subject-matter knowledge, geo-spatial data dimension, resolution of conflicting goals, reduction of complexity and, qualitative judgment. The developed model has wide application to other weather information and data gathering problems

CORSE-81: The 1981 Conference on Remote Sensing Education

Summaries of the presentations and tutorial workshops addressing various strategies in remote sensing education are presented. Course design from different discipline perspectives, equipment requirements for image interpretation and processing, and the role of universities, private industry, and government agencies in the education process are covered

A study of spatial data models and their application to selecting information from pictorial databases

People have always used visual techniques to locate information in the space surrounding them. However with the advent of powerful computer systems and user-friendly interfaces it has become possible to extend such techniques to stored pictorial information. Pictorial database systems have in the past primarily used mathematical or textual search techniques to locate specific pictures contained within such databases. However these techniques have largely relied upon complex combinations of numeric and textual queries in order to find the required pictures. Such techniques restrict users of pictorial databases to expressing what is in essence a visual query in a numeric or character based form. What is required is the ability to express such queries in a form that more closely matches the user's visual memory or perception of the picture required. It is suggested in this thesis that spatial techniques of search are important and that two of the most important attributes of a picture are the spatial positions and the spatial relationships of objects contained within such pictures. It is further suggested that a database management system which allows users to indicate the nature of their query by visually placing iconic representations of objects on an interface in spatially appropriate positions, is a feasible method by which pictures might be found from a pictorial database. This thesis undertakes a detailed study of spatial techniques using a combination of historical evidence, psychological conclusions and practical examples to demonstrate that the spatial metaphor is an important concept and that pictures can be readily found by visually specifying the spatial positions and relationships between objects contained within them

Transportation linear referencing toolboxes : a 'reflective practitioner's' design approach

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2000."September, 2000."Includes bibliographical references (p. 395-407).Seventy percent of the data of a typical transportation agency (e.g., bridges, accidents, etc.) has location as a primary reference. A Linear Referencing System (LRS) is the main way of identifying the location of this data and providing a storage key for it in a database. LRS is based on a one-dimensional offset on a predefined network. In theory, it is one of the simplest spatial cases. In reality, it can be spatially and analytically quite complex. LRS to quite recent date has been little formally researched. That research which has occurred has been the construction of large and comprehensive conceptual data models. This thesis is not primarily aimed at new "tool building research". The existing models have been based to only a limited extent on a fuller analysis of the nature of transportation and spatial data; they have not considered relevant field and wider methodological concerns (i.e., they followed a "model-driven" approach). The goal here is to create a more appropriate foundation and base from which LRS tools may be most appropriately built (i.e., a 'field-driven" approach). A "practitioners perspective" view of LRS was sought. Such a more holistic understanding was sought through the adoption of a "layered methodology" of research that involved gaining the perspectives of a variety of disciplinary viewpoints. This research framework was developed especially for this thesis based on the ideas and work of Schon and Reich. The approach involved in short a desk exercise in fundamental consideration of the nature of LRS, a deeper, cross-field synthesis and literature research, four in-depth state DOT LRS case studies, a panel of transportation field experts, a panel of national data model experts, and a limited object-orientated modeling exercise. The conclusion reached is that while LRS in the simple case can be modeled in general forms, it is also an "exception-driven" field. Thus, a "toolkit approach" may be more appropriate for LRS. It is inferred that this may hold for other similar application areas in transportation and planning. Further research would further develop the holistic layered methodology adopted here and further define the proposed LRS transportation application toolboxes.by Simon Lewis.Ph.D

Patterns in the diversity and distribution of flowering plant genera

Regional distributions of all vascular plant genera have been compiled from herbarium specimens at the Royal Botanic Gardens, Kew, and this data has then been analysed for large-scale patterns in the diversity and distribution of flowering plants, at both genus and family levels. A strong latitudinal gradient in diversity is apparent at family, genus and species levels, though while western South America is most diverse at species and genus levels, it is the SW. Pacific which is most diverse at family level. However, the number of families and genera per region is very strongly correlated, irrespective of the region. There is a very strong relationship between area and both family and genus diversity, though not for numbers of endemic genera. Analysing floristic similarity between different regions of the world reveals very strongly supported continental groups, since most genera are confined to particular continents, although the latitudinal difference between regions is a better predictor of floristic similarity than is simply distance between regions. Latitudinal range-size for genera increases towards the equator, although taxon-size in general decreases with increasing latitudinal range-size. For both families and genera, the range-size frequency distribution is highly skewed towards small range sizes (more so for genera than families), which account for the majority of taxa. Distribution patterns show strong regional clustering, with almost 40% of genera single-region endemics, and approximately 20% of world distribution patterns accounting for about 80% of total angiosperm genus diversity. Analysis of these distribution patterns reveals a strong correlation between diversity and the number of floristic elements, which intersect to form the diversity of a region. In general, though with many exceptions, there is a correlation between recency of evolutionary origin and the size (number of taxa) and spread (size of distribution) of flowering plant families. However, while a phylogenetic perspective becomes essential for addressing within-family patterns of distribution, it is argued that over the whole clade of flowering plants the resulting patterns of diversity are constrained more by large-scale ecological processes