Lauren Chaphe's Geographic Information Science (GIS) Portfolio

  Supervised classification involves manually choosing representative areas ("signatures") in order to tell the program to classify all areas with similar pixel values to the same class. Unsupervised classification is the opposite, in that the program sorts through and assigns a set number of categories based on pixel values. After this, the categories are grouped manually. For this map of Germantown, Maryland, the categories were determined via supervised classification methods from a image with a band combo of 5, 4, 3 (Red, Green, Blue). This band combo was chosen because it differentiated the vegetation from the urban or fallow areas by including the Near Infrared band (Band 5), which is reflected by healthy vegetation and shows as red areas in the image. Using this image, signature areas were chosen for each of the 8 categories of land use, such as the lake, urban areas, and deciduous forested areas. The image was then classified based on these chosen signatures and the n...

 This week's lab continued with ERDAS Imagine to add various filters to raster imagery data, as well as editing the band color combinations. Available filters that can be applied include high pass, low pass, mean, range, etc. Each filter produces different variation to the photo and can be useful to highlight certain features in an image. For example, the high pass filter created a photo with more detail and more defined edges, while the low pass filter created an image with softer, smoother edges. Band combinations can also be changed. For the images above, we were given clues on what pixel values to look for when identifying an unknown feature. The first feature was a river with had pixel values between 12 and 18 on layer 4 and I chose to use a band combo of 4, 2, 1 which made the forested areas appear red and the water appear black. The second feature was the snow capped mountains. A band combo of 5, 4, 3 showed the snow as bright blue and the forested areas as a bright green. T...

  This week focused on the the introduction of a new digital image processing software, ERDAS Imagine. The goal was to take a raster dataset and process the data so that it could be uploaded to ArcGIS Pro and to learn the basic tools for ERDAS Imagine.  An Advanced Very High Resolution Radiometer (AVHRR) image was used to create a multispectral land cover image of a mountain range within Washington State. Using the newly introduced tools and menus within ERDAS Imagine, this image was processed to show pseudo color thematic symbology, or when each land cover class is assigned a different color. The attribute table was also modified to calculate the area of each land cover class. Then, a subset image was chosen from the original image. I chose this particular area of the image because of its visible mountain ridges and valleys, seen as the different colored NW vegetation and the SE vegetation layers. In addition, water or riparian areas are also clearly visible running in the va...

  During this week's lab in Photo Interpretation and Remote Sensing, we focused on Florida Land Use and Land Cover. The main goal was to assign all of the areas shown in the provided aerial photo of Pascagoula, MS to the second level of the Florida Land Use/Land Cover codes. There are nine primary Level I Codes, specifically:  Urban and Built Up Land Agricultural Land Rangeland Forest Land Water Wetland Barren Land Tundra Perennial Snow or Ice Each of these class are then subdivided into Level II classes, which are then subdivided further into higher levels that become more specific at each level. For example, Urban and Built Up Land is further subdivided into the following classes: 11 Residential 12 Commercial and Services 13 Industrial 14 Transportation, Communications, and Utilities 15 Industrial and Commercial Complexes 16 Mixed Urban or Built-up Land 17 Other Urban or Built-up Land In order to create the final map, a new feature class was created and named LULC. Then the ...

This week marked the beginning of a new course, GIS 4035 Photo Interpretation and Remote Sensing! To introduce aerial photo interpretation and evaluation techniques, the first lab involved examining aerial photos and identifying land areas or features using the following techniques:  Tone Texture Shape Size Shadows Pattern Associated features The first map identifies five areas with varying tone, ranging from very light, light, medium, dark, and very dark, shown as yellow polygons. Notable areas included a unvegetated, sandy area that showed as very light color, while the forested area towards the top right corner of the photo appeared very dark. Additionally, five other areas were identified with varying textures, ranging from very fine, fine, mottled, course, and very course. These areas are shown as purple polygons. The finest texture was seen for the water features, followed by sand, sparsely vegetated areas, forested areas, and then highly developed areas.  The second map...

For the final project, the assignment was to perform GIS analysis on a proposed FPL transmission corridor. The objective was to use ArcGIS Pro to quantify key parameters that are typically evaluated to determine the proposed corridor's impact on nearby homes, schools, land owners, and environmentally sensitive areas. The map results can be seen above.  Based on the evaluation, the transmission corridor has the following impacts:  No schools within corridor vicinity 43 homes within vicinity of corridor 255 parcels within vicinity of corridor 13.9% of the corridor is wetlands 163.4 Acres of conservation lands within corridor For more information, a link to the final presentation with transcript can be found below:   https://docs.google.com/presentation/d/1Ar-MLLFwTLOwoGqDCe9b4KIlx-xDZLUkDIkuOx1xMM8/edit?usp=sharing

  This week's lab module topic was on georeferencing. The goal for this module was to georeference current aerial photographs and one copy of a building plan to update the UWF building dataset. Additionally, a Bald Eagle nest is located near the campus, so a 330-ft and 660-ft regulatory buffer was also created surrounding the nest location.  Data:  The two current aerial images and one engineering building plan was provided as JPEG images, along with a Roads, Buildings, and the Bald Eagle Nest Location. The images were added as raster data to the project map. Since I wanted the imagery to be placed exactly where it was taken on earth, I had to assign to the geographic coordinate system used for this map so that it matches the real world placement, which is also known as georeferencing. To do this, the image, with unknown coordinate system, was related to the world imagery basemap with the known coordinate system via ground control points. The points were added to the exac...

This week's module was focused on the process of geocoding and adding point feature data from a data table with a spatial component (i.e. coordinates or street address).  Part 1: Adding XY data Part one included adding coordinate data from a table. The following coordinates were taken from available Bald Eagle nest location data for Santa Rosa county:  LATITUDE LONGITUDE 30°33’50”N 87°08’33”W 30°31’43”N 87°06’59”W 30°31’02”N 87°05’54”W These coordinates are written in Degrees, Minutes, Seconds format. In order to plot these points with ArcGIS, the coordinates were converted to Decimal Degrees format with the following formulas: ycoord = (latDD + (latMM/60) + (latSS/3600)) xcoord = - (longDD + (longMM/60) + (longSS/3600)) (Note the negative) The conversion was completed using Microsoft Excel, saved as a .csv file, and then added to ArcGIS Pro. Finally, the "Add XY Point Data" tool was used to create points based on the tables x (longitude) and y (latitude) coordinates. See...

  This week, the lab was focused on vector analysis, specifically overlay tools and attribute table modifications. The main purpose of this map was to identify potential campsites that meet all of the following parameters:  -Within 300 m of a road AND -Within 150 m of a lake OR Within 500 m of a river AND -Not within a conservation area Data :  Road, Water, and Conservation Area data was provided. The BUFFER tool was used to create a 300 m buffer surrounding the roads polyline data. A variable distance buffer was created to do two different buffer distances within the water data. Rivers received a 500 m buffer and lakes received a 150 m buffer. The UNION tool was used to combine the water and roads buffers to create data that show the areas that overlap (i.e. meet the roads AND the water criteria). Then the ERASE tool was used to exclude the areas that overlap with the conservation areas. This produced only the areas that meet all three parameters.  Symbology : ...

  This week was focused on the introduction of projected coordinate systems. In this map, three different coordinate systems was used to display the same data, specifically counties in the state of Florida. This map is intended to compare the differences in data distortions for different coordinate systems.  Data:  A Florida County Boundary shapefile was downloaded from the online Florida Geographic Data Library.  This shapefile was then re-projected in three different projected coordinate systems,  Albers Conical Equal Area, NAD 1983 HARN State Plane Florida North FIPS 0903 (US Feet), and NAD 1983 UTM Zone 16N  using the Project tool in ESRI ArcGIS Pro.  Symbology:   Four counties were selected: Alachua, Escambia, Miami-Dade, and Polk county. Each county was highlighted with a different color, while the counties that were not selected were set to white.  Layout:  The required map features learned from the previous week was also applied ...

This week's lab focused on the topic of cartography, or the practice of creating maps. We were tasked to create a map showing the location of the University of West Florida's main campus location. Data : We were provided shapefile data of Escambia County, Florida Interstates, Florida Major City Locations, and Major Florida Rivers. Since we only wanted the data specifically within Escambia County, the  CLIP tool in ArcGIS Pro was used to clip all these data to the Escambia county boundary shapefile, like a cookie cutter. In this case, our final "cookie" is shaped like Escambia County and decorated with cities, interstates, rivers, and the UWF main campus location. Yum! Symbology:   Cartography often uses symbology to convey information. To start with, choosing symbology that the viewer can recognize is helpful. Water is usually shown as blue in most maps, so the rivers were changed to blue . The main focus was the location of the UWF Main Campus, so the point was chan...

This week's lab focused on the introduction of ESRI's ArcGIS Pro software. The main goal for this lab was to learn the ArcGIS Pro interface and create a simple map of the world's city locations and differentiate countries based on their population density.   Map Setup: In order to keep the map uncluttered and keep the focus on the intended map's purpose, the city and country locations, I chose a world topographic basemap. As for the map orientation, a landscape orientation seemed to fit the map extent the best. Data: To start out, we were given two shapefiles, a "Cities" shapefile (point data) and a "World Countries" shapefile (polygon data). These were layered so that the country polygons were overlaid by the city locations. If the layer order was reversed, the city locations would have been hidden underneath the country polygons. Therefore, this shows the importance of choosing the correct layer order for your map.  Symbology: The symbology for thi...