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GIS404 Remote Sensing – Module 2 – Aerial Photography Basics & Visual Interpretation of Aerial Photography

Building off of the overview of remote sensing and history of aerial photograph provided in Module 1, we will now learn more about the types of and techniques for interpreting aerial photography. Modules 3 and 4 will continue to focus on the use of aerial photography but expand on the material covered in the first two modules.

The first slideshow (Lecture 2a: Aerial Photography basics) provides background on the types of aerial cameras, the different types of images they capture (oblique, vertical, stereo), the types of film used by traditional cameras, and understand how resolution (spatial, spectral, temporal) applies to aerial photography. This lecture (and Chapter 4) provides students the background material that form the foundation for understanding how to use aerial photos. The second slideshow (Lecture 2b: Aerial Photo Interpretation) goes into detail about the concepts, techniques, and application of visually interpreting aerial photos. Students will learn about the methods and techniques used to visually interpret aerial photos (i.e. recognition elements). These techniques form the basis for deriving geographic features and/or land use land cover types from aerial photos (which we will do in the next lab) and that are used in a wide variety of real world applications. This lecture (and Chapter 5) provide students an overview on the concepts and techniques of visual interpretation that will be applied in this week’s lab.

In the laboratory exercise, you will learn some basic principles of interpreting features found on aerial photographs. These principles range from concepts so basic that you might never have considered them, to quite obvious ideas, and finally some more advanced techniques.

Module 2 Topics

Topics covered in module 2, include:

1. Aerial Photography Basics

  • Types of Cameras
  • Types of Film
  • Types of Products

2. Basics of Visual (i.e. manual) Interpretation

  • Recognition Elements: 
    • shape, color, size, texture, pattern, shadow
    • Site and Association
  • Examples of Applications of Remote Sensing 
  • Forestry Interpretation Case Study
  • Sources for Aerial Photography

Module 2 Student Learning Outcomes

When you complete this module, you should be able to:

  • Recall the major types of aerial photos/cameras
  • Recall the types of films, resolution and products generally produced from aerial photos
  • Recall case study examples of the visual interpretation of aerial photos
  • Recall how to use various recognition elements to visually interpret aerial photographs

By the end of this lab, you should be able to:

  • Interpret the tone and texture of aerial photographs
  • Identify land features in an aerial photograph based on several visual attributes
  • Compare similar land features in true color and false color infrared (IR) photographs

Tanner Jessel Blog URL

Hello Diedra,

I am a new, first semester online GIS program student.

I had the same question as another new student that you answered August 29 concerning using a personal Google Account versus a UWF student Google Account.

If it is not required that I use the UWF student Google Account to host the blog, I think I would prefer to use my personal Google Account to host the blog; it is easier not to have to log out of my personal Google Account to log in to my Student UWF Google Account (Past experience is it’s not possible to use two Google Accounts at once within the same browser session).

The URL for my personal blog that I intend to use for the UWF online GIS program is:

(Please forgive the vainglorious branding, as I’m obviously a GIS data novice – call it wishful thinking).



INSC 553 Assignment 1: Career Pathfinder

IS 553 Assignment 1 is a career pathfinder.

Thoughts on Importance of Online Portfolio Building

Hi Dr. Bishop and Prof. Dodsworth:

I have a postcard in the mail informing me that a selection has been made for GIS Stormwater Administrator that I applied and interviewed for (it wasn’t me).

In a lot of ways, it was a GIS librarian position; they needed traditional GIS analysis skills but also data management and data curation skills, along with education and outreach skills.

There were 20 well qualified applicants and I think it reflects well on SIS that I even got an interview.

However, in reflection I feel I should have brought printouts of work I have done to provide concrete evidence in response to the question, “how are your map making skills.”  I’ve been told to bring along examples to interviews in my geography classes – I’m not sure why I didn’t do that for this interview.

The next best thing to a printed map would be a robust online portfolio.  To be honest I’ve made tons of nice maps in my GIS classes, but getting the labs done is so mentally draining that I rarely re-visit them to present in an online portfolio.

I’m trying to do a better job about getting my work online.

However, I wanted to encourage you both as GIS instructors to try and build creating an online portfolio into your coursework.

I saw this example from the graduate certificate program I’m getting into at UWF –

In sum, I think it would be very helpful for students to have a GIS online portfolio as part of the normal coursework.



Options for Open Source, Public Access for ATBI Data

When I worked for the NBII we used Rackspace and Amazon’s cloud service, along with MySQL for the “Species of Greatest Conservation Need” database that held a similar number of species, but no spatial data.
Since your data has a spatial component, something like PostgreSQL or SQLite might be worthwhile to look at. I took a class in environmental information management last summer and was introduced to SQLite – which apparently has some advantages over databases created with Access, including native support for GIS applications like GRASS and QGIS.
SQLite works with a CMS called Django, Scott Simmerman suggested I look at that for improvements on the ATBI mapping project’s Web interface.  There some free hosting services for small or dev projects: Might be worthwhile to look at that. Another “free” hosting site here:
Finally Tom Colson mentioned something he’s working on with GSMIT – the Otter Spotter. Tom said Google Earth Engine might be worthwhile looking at for that.  Google offers grants and hosting of data for non-profits to use Earth Engine, so I’m curious how much of the ATBI database data might be translatable to KML.
When I worked for the NBII I was interested in serving up spatial data in KML files – I like them because you can open KML in robust GIS packages along with popular virtual globe tools like Google Earth or ArcGIS Explorer – things the everyday person has access to.
Even if not all the data can translate, it’s another “view” of the ATBI data that’s pretty useful for outreach, if not doing actual science.
I will explore at this issue as I have time and hopefully help as I am able.  I am a student of RDBMS and the ATBI is one of the more interesting datasets to learn with.

Eastern Hemlock (Tsuga canadensis) model on PC versus Supercomputer

I recently ran a model of Hemlock on my personal computer.

I modeled ~489 records; the Nautilus model used over 2,000.

From the model output:

This is a representation of the Maxent model for Tsuga_canadensis. Warmer colors show areas with better predicted conditions. White dots show the presence locations used for training, while violet dots show test locations.


This slideshow requires JavaScript.

This is not really a fair comparison, but the difference between the models with 489 records and 2000+ is interesting for comparing the predictions.

EECS model for Eastern hemlock is different.  It uses more data, and it was run 20 times with 10% of the records reserved before being synthesized into one image.

I will post some additional comparisons for other trees in some later posts.

Possible Spatial Data Inputs for MaxEnt Species Distribution Models in Great Smoky Mountains N.P.

Environmental layers are available to the public via IRMA.

Source metadata are not available at

I have attempted to map or cross-walk the layers listed by the Simmerman et. al paper to the names of datasets available for download from IRMA.

Table. Mapping from UTK names to IRMA names.

1 Soil Organic Type Soil Classification
2 Topographic Convergence Index Topographic Wetness Index
3 Solar Radiation Data 30 -m Potential Solar Radiation
4 Terrain Shape Index 30-m Topographic Shape Index
5 Terrain Shape Index 30-m Topographic Ruggedness Index Model
6 Digital Elevation Model 30-m Lidar Digital Elevation Model
7 Slope in Degrees 30-m Lidar Slope Model
8 Understory Density Classes Understory Vegetation at GRSM
9 Leaf On Canopy Cover Overstory Vegetation at GRSM
10 Vegetation Classes Vegetation Classification Great Smoky Mountains NP Vegetation Classification

Note: I am grateful to which made it possible to easily create this table from plain text.  I expect to add this to my “toolkit” of useful items and it saved me a lot of time.

Spatial Data Diversity Supporting Herpetofaunal Research in Great Smoky Mountains National Park

2014 North Carolina PARC Poster.

Reduced file size image of poster presented at North Carolina Partners in Amphibian and Reptile Conservation (NCPARC) meeting, March 2014.

Poster presented at 2014 North Carolina Partners in Amphibian and Reptile Conservation Meeting.

              Jessel, Tanner; Super, Paul E.; Colson, Thomas (2014): Spatial Data Diversity Supporting Herpetological Research in Great Smoky Mountains National Park. figshare.

Spring 2014 Practicum Application

Student’s SIS Advisor’s Name: Dr. Suzie Allard
Name Student’s Practicum Supervisor: Tom Colson; Scott Simmerman
Number of Credit Hours for which you wish to be enrolled in the practicum: 3 hours
Semester during which you wish to be enrolled in the practicum: Spring 2014
Briefly describe prior and/or current information or library work experience:
I am interested in a career in environmental information management,
particularly in a governmental natural resource management agency. My course work to date includes classes in geographic information science,
environmental information management, and data visualization for
environmental science.
Indicate the type of information organization in which you wish to take the
practicum: Environmental Information Management Org
Practicum Location: Great Smoky Mountains National Park; National Institute for Computational Sciences

Practicum Objectives:
I would like to develop advanced environmental information processing and
data visualization skills by working with species occurrence records and a
high performance computing environment as part of a technology transfer
project between the University of Tennessee and the National Park Service.

The following four practicum goals and associated outcomes are proposed for
this project:

(1) Develop proficiency in running the MaxEnt species distribution modelling
program in a PC environment for determining probability of species
distribution given environmental variables and demonstrate acquired
proficiency by providing training and instruction to Park Service staff in
use of the MaxEnt program on Park resources configured to run MaxEnt.
Training materials and sessions will be produced as an outcome of the
(2) Gain skills with workflow and parallel processing in a high performance
computing environment on a single-system-image supercomputer and demonstrate these skills by generating species distribution models as requested by practicum supervisor. There are currently 540 species models out of ~36,000 species in the park. A collection of new models will demonstrate the outcome of the practicum.
(3) Create documentation for running the MaxEnt model in a PC environment using appropriate technology such as a wiki with walkthroughs, screen captures, or video screencasts as appropriate. A URL will be provided to the final online documentation to demonstrate the outcome of the practicum.
(4) Practice sound data curation principles in managing both model inputs and model outputs by successfully building on the store of models available at . An HPC data management system such as XSEDE ( will be used to manage the inputs and outputs to demonstrate the outcome of the practicum.

In a rough estimation, I expect to spend about a 1/3 of the required 150
hours learning MaxEnt on PC and HPC environments, 1/3 writing documentation, and 1/3 creating and delivering training (to commence in March, 2014) to enable NPS staff to implement MaxEnt modelling on both PC and HPC platforms.

Spatial Data Infrastructure Usability Comparison Assignment

Submission Materials

Submission Field :

Student Comments : Hi Dr. Bishop, I apologize for the late submission. I mistakenly thought I had more time to work on this. I appreciate the opportunity to submit this work with a penalty deduction for lateness. I am a bit concerned about how to incorporate literature, which I did not do. If I need to add in references to literature on online mapping usability, perhaps I could use some additional time this evening? In for a penny, in for a pound after all. Thanks, Tanner

Attached Files : INSC590-GIL-JesselT-UsabilityAssn.pdf

Instructor Feedback

Grade : 52.00 out of 60

Comments :

This was done well. Future work would benefit from positioning your thoughts within existing literature. In that way, work you do matters to others. I will make it more clear in future versions of the course by assigning more required readings about usability in the GeoWeb week, including the best example from this class, and providing examples of what I mean by referencing literature. I did expect course materials to inform this first assignment (e.g., Harley, Crampton), but I will work better to help connect the dots in future assignments. For example, using another researchers framework would have been one way to do a usability assignment. Also, headings and subheadings help organize a paper, so you may want to use that structure in future work. The instructions also asked for screen shots and I think you could have used more of those. At least one for each application is what I will add to the directions.