Project 5: Presentation

Calculating Standing Timber Values

Below you'll find the links to my final presentation and abstract on calculating standing timber values, enjoy.

Abstract

Presentation

Project 4: Forestry - Report Week

Forestry Poster

This week we finished up our forestry project by bringing together our preparation and analysis into one poster. This poster sums up a pro-clearcutting view of forestry management from the economic, ecological and aesthetic viewpoints.

Project 4: Forestry - Analyze Week

Impact Summaries

Ecological Summary:

The greatest ecological impact of clear-cut logging in North America occurred at the turn of the nineteenth century. Since then, foresters have gained a greater understanding of reforestation, best management practices and wildlife habitats. With proper forest management, clear-cutting is now an integral part of a forest’s biodiversity.

Economic Summary:

Clear-cut logging is the most financially efficient method of forest management because the greatest volume of wood is harvested at one time. Clear-cutting also requires fewer logging roads because it relies mainly on a cable logging system for transporting timber from the stump to the loading yard. Cleared sites are also less expensive to prepare for planting seedlings which regenerate faster than natural regeneration.

Aesthetic Summary:

The greatest objective to clear-cutting is the impact it has visually. No one wants to drive through a forest and see bald spots on the side of a mountain. But the visual impact of clear-cut logging can be predicted and reduced with the use of GIS. Knowing the view shed of public roads can help forest managers select future clear-cuts with the least visual impact. 

Project 4: Forestry - Prepare Week

Recent Clearcuts

This week we determined the aesthetic impact of clearcuts along major roadways in a 1,400-hectare woodlot in the Acadian-New England forest.

This was accomplished by identifying clearcuts no older than 5 years that shared a boundary with a major road.

Frequency Distribution

A frequency distribution of shared lengths between major roads and the clearcuts was created to show the distribution of clearcut distances (in kilometers) along the major roads. 

Aesthetic Impact

A view shed was created and reclassified according to the areas of the woodlot that are visible from a major road. The viewshed was combined with the previously identified clearcuts and used to calculate the total area of visible clearcuts.

Lab 10: Supervised Classification

Supervised Classification

This week we performed a supervised classification using tools in ERDAS Imagine. The final layout was created in ArcMap with the resulting Land Cover and Distance Images. The Distance Image is used to identify the brighter pixels that are more likely to have the wrong classification in the output image. Samples of spectral signatures were collected of known features and then used to create the Eight classes shown in the map. When applied properly, the supervised classification method produces a more accurate land cover map than the unsupervised method.

Lab 9: Unsupervised Classification

Unsupervised Classification

This week we performed an unsupervised classification using tools in both ArcMap and ERDAS Imagine. The image in this map is the result of an unsupervised classification in ERDAS Imagine. The final layout was created in ArcMap. Classifying this image provides a relatively accurate means of calculating surface areas. Classified images can also be used to extract vector data and create layers for features such as, buildings, roads and even telephone poles. 

Project 3: Web Applications - Report Week

A Visitor's Walking Tour of Downtown

This week we continued with web applications and finished up the final touches to our story maps. I made a few configuration changes to the template to "own my map" and dress it up some. My story map is a walking tour for visitors to the downtown area of Hinesville, Georgia. My goal with this assignment was to bring to life the history that is still evident downtown. In my humble opinion, I've succeeded in barely scratching the surface. As brief as it may seem, it is my pleasure to share my journey with you. I hope to use this map to demonstrate one of the benefits of GIS among the local governing authorities.
My Story Map

Lab 8: Thermal & Multispectral Analysis

Wildfire Hotspots

This week we used ArcMap and EDRAS Imagine to create composite multispectral images. Then we adjusted the images band combinations and symbology in order to identify features in the image. In this image of the Ecuador Coast, I adjusted the bands to contrast wildfire hotspots. In the true color image, two of the five hotspots had no smoke plume visible and may have gone unnoticed with out the aid of thermal imagery.

Project 3: Web Applications - Analyze Week

"Working" Tour Map

This week we completed a "working" version of our story map using a web map template from Esri. My map is a walking tour of downtown Hinesville, Georgia. The tour highlights buildings and monuments of historic significance. You can check out my progress here.

Project 3: Web Applications - Prepare Week

Story Map

This week we started on our third project where we learned about story maps. When my wife asked me what a story map was, I jokingly replied, "it's a map, that tells...a story". Yeah, she rolled her eyes at me. According to Esri's website, a story map is a web map that incorporates text, multimedia and interactive functions to inform, educate, entertain and inspire people about a wide variety of topics. For this project I will create a walking tour of the downtown area of Hinesville, Georgia. The map will highlight points of interest within walking distance of the courthouse square. Check out this story map of the recent flood in Fort Collins, Colorado.

Lab 7: Multispectral Analysis

Water Feature

This week we used clues to identify and locate examples of different features within EDRAS Imagine. In this map, identified the feature in the area of interest as water. I chose a color band combination of Red - Layer 6, Green - Layer  5 and Blue - Layer 3 to contrast the water against the other features in the image.

 Snow Feature

In this map, I used multiple views to identify the mystery feature. From the clues led me to this snow capped area where I chose a false color band combination to contrast the snow from surrounding vegetation.

 Variations in Water

And finally, in this map we were instructed to select a color band combination that clearly shows variations in water. I determined the best color band combination to show these features is Red - Layer 3, Green - Layer 2 and Blue - Layer 1.

Lab 6: Spatial Enhancement

Image Enhancement

This map was created in ArcMap with an image that was enhanced in ERDAS Imagine. The original image was striped with diagonal black lines that narrowed from left to right. Using the tools available in ERDAS Imagine, I was able to reduce the striping while retaining most of the detail in the original image.

Project 2: Bonus Assignment

HURREVAC

HURREVAC version 1.3.3, released on August 28, 2013, is a free computer-based program used to assist government emergency managers with hurricane evacuation decisions for their area. The program originated in 1987 as ‘Decide’, with the purpose of computing evacuation decision times for South Carolina. HURREVAC routinely checks for updates from the National Hurricane Center and displays this information in the programs interactive interface. Pre-determined clearance times for the user’s local area are automatically checked against a storms projected path. If the user’s area falls within the storms cone of probability, HURREVAC will prompt the emergency manager and notify them of the danger. The user can easily print maps from the program and use them in planning meetings. This software can be a very useful tool for local governments that do not have a GIS staff. You can obtain more information on HURREVAC at www.hurrevac.com

Project 2: Network Analyst - Report Week

Presentation of shelter locations to public

Hurricane Amber is expected to make landfall in the Tampa area on the evening of Thursday October 17, 2013. The National Weather Service is predicting heavy rain, along with a storm surge which may result in 5.5 ft of standing water in the South Tampa area. The Tampa Bay Blvd, Middleton HS and Oak Park storm shelters will be open before, during and after the storm. It is important to inform the public which shelter is closest to their location and what better way to so than a map. An evacuation service area map was designed in Adobe Illustrator including a map created in ArcMap. The evacuation service area polygons were built utilizing the Network Analyst extension. Network Analyst created each polygon by determining those routes that required the least amount of time to get to one of three storm shelters. The map also includes some helpful reminders for preparing for the storm. This map can easily be distributed to local and national television news agencies, as well as print media and the internet.  Please note that neither shelter capacity nor population density was considered for this study. As a result, overcrowding may become an issue at one or more of the shelters.

Project 2: Network Analyst - Analyze Week

Evacuation Service Areas and Emergency Routes

This week we focused on the Network Analyst extension of ArcGIS. Using point features and the roads feature class we prepared last week, I created two routes to aid in the evacuation of a hospital and three routes to aid in the delivery of emergency supplies to storm shelters. The road network and shelter feature points were also used to create Evacuation Service Areas. This data can be used to create information pamphlets for the public before a storm and driving directions for emergency workers before, during and after a storm.

Lab 5a: Intro to ERDAS Imagine and Digital Data 1

Classified Image of Forested Land in Washington State

This map was created from a subset of Landsat Thematic Mapper (TM) imagery of forested land in Washington State. The TM imagery was processed using EDRAS Imagine where an Area field was added to the image attributes in order to calculate the total acreage of each colorband. The final layout was created in ArcMap.

Project 2: Network Analyst – Prepare Week

Hurricane Evacuation Route Planning: Tampa, FL 

This week we prepared data for use with the Network Analyst extension of ArcGIS. This base map shows classified DEM polygons which were used to create a flood zone feature class (not pictured). The flood zone feature class was then used to identify which roads are likely to be flooded in the event of a hurricane impact. Knowing which roads are more likely to be affected are important when creating evacuation routes. This information can then be distributed to residents and local authorities.

Lab 4: Ground Truthing and Accuracy Assessment

Ground Truthing and Accuracy Assessment

This week I conducted a ground truthing and accuracy assessment of land use/land cover map I produced last week in lab 3. I created a point feature class to capture 30 samples in a stratified random pattern. Then I verified the accuracy of the assigned land use/land classification at each point using Google Street View. Those points that were accurate are marked with a green dot, and those points that were not accurate are marked with a red dot. The overall accuracy was calculated by dividing the total number of correct samples by the total number of samples.

Lab 3: Land Use/Land Cover Classification Mapping

Land Use/Land Cover Classification

This is a land use/land cover classification map classified to level 2 of 4 of the USGS Standard Land Use/Land Cover Classification System. This map was created by systematically digitizing all areas of an aerial photograph according its land cover and land use. The only resource for the classification was the photograph itself.

Project 1: Analyze Week

OLS Results

This is a screen shot of the Ordinary Least Squares (OLS) results window after performing what Esri calls "The Six Checks". The checks are necessary to determine which explanatory/independent variables are unneeded in the model.

Standard Residuals Results

This map shows the standard residuals results of a regression model used to predict meth lab locations in  West Virginia. The model predicted fewer meth labs in areas where the standard deviation is less than -0.5 than actually are there. In areas where the standard deviation is greater than 0.5, the model predicted there would be more.