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This document contains a list of names separated into groups, suggesting slides or pictures are being shared among various people. The names John MacD; Jeff S; Spencer P; Natasha F & Matt B are in one group, Sarah S & Mel B in another, Mel M in another, and Jean-Yves L in the final group.
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This document summarizes an aerial survey and mapping project of the topography and land cover of Sable Island, Nova Scotia. It provides background on the island and previous surveys. It describes the 2014 aerial survey conducted using LiDAR and aerial photographs. The objectives were to produce updated topography and land cover atlases of the island and make the data available online. ArcGIS and Python scripts were used to analyze the data, produce elevation profiles and land cover maps for each island section, and automate the generation of the atlases. An online map was also created to display the topography and land cover data using ArcGIS Server and Flash Builder.
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Back in the 1920s, Mr. Murdoch and his crew surveyed the entire city of Saint John with great precision. The original sheets were scanned, which were black and white, and were made available on the Saint John open source website. Unfortunately these were not registered and individual sheets. These survey sheets would be very useful for City employees and interesting for the public.
After georeferencing each sheet to its correct location and scale, they were reference to the city property lines, with the help of ortho photos and city streets. Then each sheet was cropped to remove the border and surrounding text, using the Image Analysis window clip tool, and added to a mosaic dataset. The areas of overlap were clipped in the same way so that the areas of more detail was visible. This dataset was the input for the Copy Raster tool, which created one tiff file for all the sheets in 1 bit. The final mosaic was cleaned with the Raster Painting Tool to remove any redundant street names. This cleaned mosaic would then be uploaded to the online interactive City of Saint John Map as a layer for the public to see.
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This document summarizes a research project using LiDAR data and permanent sample plot (PSP) field data to generate models for estimating forest attributes. The following steps were completed: 1) LiDAR data was acquired and processed, 2) LiDAR metrics were extracted for PSP locations, 3) models were developed in R relating forest attributes (average height, basal area, biomass) to LiDAR metrics, 4) models were applied to a larger LiDAR dataset, and 5) results were assessed. Models achieved R-squared values from 0.73-0.80 and error margins from 10-28% depending on the attribute. The document discusses limitations and conclusions.
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The document describes a project to model the distribution of karst topography in Nova Scotia, Canada. It acknowledges the assistance of various groups in providing data and expertise. It then discusses what karst topography is, why it is important to study, and presents photos of examples in Nova Scotia. The document outlines the study area, objectives, approach, datasets used, and the data preparation process. It describes the variables chosen for the model and the automated model that was created. The document discusses the final predictive karst distribution output, validation of the model, and presents results for various regions of Nova Scotia. It concludes with recommendations to improve the model and further understanding of karst distribution.
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The document provides an overview of Ryan Leach's project to compile and visualize historic drillhole data from the Dominique Property in Yarmouth County, Nova Scotia. The project evolved from constructing a 3D resource model to a data collection and compilation exercise due to insufficient available data. Key steps included researching reports, georeferencing maps, digitizing drillhole collars and local grids, compiling assay data, creating cross-sections and 3D models in MapInfo, and addressing issues around missing or outdated drillhole data formats. The end result was a compilation of available data but no meaningful 3D model or interpretation could be completed without complete downhole data.
Southwest mongolia multispectral program title
Southwest Mongolia contains mineral resources that can be explored using multispectral remote sensing. Peter Dalton and his advisor Rob Hodder are conducting a program applying multispectral techniques to identify mineralization in southwest Mongolia. Their work uses multispectral data to explore the mineral resource potential of southwest Mongolia.
Remote Sensing Field Camp 2016
The 2016 Remote Sensing Field camp will take the form of two projects.
A low tech, low cost aerial photography project using visible spectrum UAV/Ultralight Aircraft mounted cameras as the sensor to demonstrate that relatively low tech, low cost solutions can achieve surprisingly good results when compared to more commercial systems.
A more high tech, high cost terrestrial LiDAR collect of a building or structure of historical or architectural significance.
The scope of a project will influence all other aspects of the project, including its cost, timing, quality and risk.
Test2016
This document appears to be a test presentation uploaded to SlideShare on April 26, 2016 by Gabe Solomon. It contains one slide titled "Some FortMac Smoke" which is an image from NASA with labels added by DaveM, likely related to forest fire smoke from Fort McMurray, Alberta, Canada.
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The Processing of the 1920's Survey Sheets of the City of Saint John, NB for ...
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Reeves: Modelling & Estimating Forest Structure Attributes Using LiDAR
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The document describes using GRASS GIS to detect land cover change over 13 years at a mining site in British Columbia. Atmospherically corrected Landsat images from 2001-2014 were analyzed using image differencing of NDVI, TCT, and PCA outputs. Thresholding identified significant change areas. NDVI detected over 2300 ha of change, while TCT and PCA detected over 2000 ha. The open source and automated nature of GRASS GIS makes it suitable for replicable change detection.
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The document discusses a community mapping project in Annapolis County, Nova Scotia led by the Age Advantage Association with support from COGS students. The project's objectives were to: 1) Consult with the Association to understand their needs; 2) Provide ArcGIS training to members; 3) Prepare and organize data; 4) Configure a prototype website to display the data; and 5) Develop documentation to guide the Association's future goals. Results included online maps of pre-confederation properties, post-confederation properties, and tourism sites. The importance of community mapping is that it empowers local residents to be involved in community planning and decision making.
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This document outlines Millbrook Minerals' plan to map and develop industrial mineral occurrences in Cuba. The project involves georeferencing historical maps of 654 deposits, digitizing the locations, and performing network analysis to identify targets suitable for extraction and transport. Key steps include compiling deposit data, creating a geodatabase, analyzing infrastructure connections, and modeling transport routes to ports for export. The goal is to utilize improved US-Cuba relations to explore opportunities in Cuba's decorative building stone industry.
Geovisualisation and Analysis of the Good Country Index
The Good Country Index is developed by Mr Simon Anholt, a policy advisor and strategist. It measures the cumulative contribution of 125 countries towards the benefit of the world in the technological, cultural, peace-related, world orderliness, environmental, prosperous, and health wellbeing aspects, thus making up the seven categories of the Good Country Index (Figure 1). Countries included in the index do not necessarily have to be good for their own citizens, because the index does not look at the existing infrastructure that make up the development and dynamics of countries, but rather, it only focuses on countries’ external outputs and resultant motives. On the contrary, there are plenty of other global indices (for example, Human Development Index, or Social Progress Index) that measure and rank countries in terms of their existing infrastructure. If we can substitute the category components of the Good Country Index with other global indices that correspond to the respective category component, we may be able to use the global indices as drivers to evaluate a country’s ‘goodness’.
This project entails a number of statistical methods for analytical purposes. Two types of analysis are carried out, the first type is non-spatial analysis which employs bivariate correlation to test for the existence of relationship between the Good Country Index components, and other global indices. Regression curves and decision tree rules are also obtained to employ predictive analysis on the Good Country Index in relation to other global indices as explanatory variables, with the main focus being made on the ‘Most Good’, and ‘Least Good’ countries. The second type is spatial analysis which includes spatial autocorrelation to test for the presence of underlying spatial processes by the Good Country Index, and global indices. If clustering is present, the degree of clustering is displayed. The cluster and outlier analysis, as well as hot spot analysis geovisualise the clusters, outliers, and hot and cold spots on various maps. Finally, grouping analysis is carried out to group similar countries into geographically compact clusters, and mapped.
This project focuses on exploratory analysis to find out if the use of global indices may be suitable as drivers to account for countries’ ‘goodness’ in the Good Country Index, as well as geovisualisation through mapping of the Good Country Index, in relation to its corresponding global indices. It must be greatly emphasized that this project will attempt to investigate levels of association (or correlation) between variables, as the use of explanatory variables does not necessarily result in causation, hence no strong judgement is made when attempting to relate the Index with other global indices.
Flood susceptibility for the kennebecasis watershed
The document discusses analyzing restoration sites in the Kennebecasis watershed for their susceptibility to flood damage. It describes the Kennebecasis Watershed Restoration Committee, which aims to restore the watershed. Various data factors are considered to assess flood susceptibility, including soil, land use, and satellite imagery. Methods used include delineating sub-watersheds and river buffers. Next steps include further site visits, finalizing the analysis method, and incorporating existing site data.
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