Course Context
Remote sensing has fundamentally changed humanity. It has transformed our scientific insight, geographic knowledge, and cultural perceptions of Earth. The highly symbolic photograph “The Blue Marble” is not only a byproduct, or legacy of the space age, but the promise of that synoptic view, afforded from above, was a primary force prompting the investments necessary to access space beyond Earth’s atmosphere and gravitational dominance. Access to space meant release from the limits imposed by the mismatch between the individual human scale of experience and the geographic scope of the Earth system. In fact, remote sensing has, in part, coevolved with the vast research domains enveloped within “Earth System Science,” which will form the context for many of the examples and applications discussed in this course.
Military and strategic interests, have, of course, been tantamount in the pursuit of space, including the development of remote sensing technology. However that legacy is dying, if not dead. Satellite remote sensing technology is now driven by federal and international programs funding space and Earth system science research, by public and private land management interests, by demand from extractive and recreational industry, federally funded meteorological and climate observation, and by demand relating to management of human infrastructure. We are approaching a critical threshold at which the Earth system – the atmosphere, climate, ocean, ice, and land processes – will be replicated continuously and digitally in real time using gargantuan hypervolumes of data generated through the coalescence of what the National Academies has referred to as “an intelligent sensor web[1].”
In the meantime, satellite remote sensing plays an extensive role in a wide range of fields, including forestry, ecosystem ecology, geophysics, climatology and oceanography, to name a few. Meanwhile, the quality, variety and availability of satellite- (and aircraft-) based data is expanding rapidly, along with the computing technology necessary to integrate, analyze, and synthesize them. Naturally, there is a corresponding demand for personnel who understand and are able to work with these data: particularly those who combine remote sensing expertise with a background in other systematic fields.
Course Description
This course covers the basic theory and mechanics of the remote sensing process and related theoretical aspects of radiation and the environment. We study a tiny bit of history, but will focus on a selective subset of the network of currently active sensors on board Earth-orbiting platforms. We will work with data from these sensors and learn their basic characteristics and applications. Although we will focus on visible to near-infrared spectra, we will also review RaDAR, LiDAR, and thermal-infrared methods and applications. In order to extract information from satellite data, students will use software to apply the digital image analysis methods discussed in class. This will also involve learning (or relearning) some statistics. Topics will be covered in the context of a variety of environmental science applications and examples.
Through this course, students should gain the basic concepts and language skills necessary to understand and communicate with others about remote sensing, as well as the background necessary to begin using remote sensing for research.
Your Professor
I am an associate professor in the Department of Geography at UNC-Chapel Hill. I arrived here in 1994 after receiving my Ph.D. in Geography from Boston University under the direction of Alan Strahler and Curtis Woodcock. Prior to that, I received bachelors and masters degrees in Geography from UC Santa Barbara, and an associate degree in Music from Santa Barbara City College. I study biogeography, landscape ecology, and ecosystem ecology, and apply a variety of approaches, theories and tools in my research. My work has examined processes and patterns across a broad range of geographic and temporal scales. Some of the main themes in my research include plant-water relations in California chaparral, ecosystem response to climate variability, geographic determinants of species richness, and habitat conservation.
Goals and Grading
Class lectures will be supplemented by readings, quizzes, lab exercises, one midterm exam, and a cumulative final exam. Grading will be based on the following elements and their corresponding percentages:
Labs: 20% Assigned on Tuesdays – Due upon assignment of following lab.
Quizzes: 30% Given on Tuesdays before lecture.
Midterm Exam: 20%
Final Exam: 25% (cumulative)
Participation and Engagement: 5%
See schedule below for important dates.
Review the following resource each week: http://earthobservatory.nasa.gov/.
Class Schedule
Week / Topics / Main Themes / Notes1 8/24 – 8/29 – 8/31
Jensen RSE Ch. 1, 3 / Introduction & Background Resolutions & Trends / Pigeons to Payloads, Current Systems, Resolutions & Trade-offs, State of the Art, Trends / Lab 1
2 9/5 – 9/7
Jensen RSE Chapter 2 / Basics of EMR Matter / Definitions, Units, Spectrum, Major Spectral Regions, Laws, Fates, Energy Balance
3 9/12 – 9/14
Lillesand Ch. 1 and appendix 1 / Radiative Transfer / Atmosphere, Canopy, Water, Terrain, BRDF / Lab 2
4 9/19 – 9/21
Jensen RSE Ch. 7
Robinson Ch. 2 p26-51 / Sensors & Orbits / Types, Specs, Design, Orbital Geometry, Space Junk / Quiz 1
5 9/26 – 9/28
Jensen DIP Ch. 2 to p141 / Scene-to-Image; Image Properties, Structures & Formats / Pixels, Bits/Bytes/Binary, Scale, Spectral Mixing, Image Data Properties, Image Choice, Data Storage & Formats, Data Access / Lab 3
6 10/3 – 10/5
Jensen DIP Ch. 4 / Image Statistics / Summary Statistics, Σ, μ, PDFs, Set up for classification / Quiz 2
7 10/10 – 10/12 / Lab 4
8 10/17 – Break / Exam I (through Week 7)
9 10/24 – 10/26
Jensen DIP Ch. 5, 7, 8 / Display, Contrast & Geometric Enhancement, Geometric Correction / Histogram Equalization Etc., Hi/Low Pass Filters, Moving Window Operations, GCPs and Polynomial Regression, Resampling
10 10/31 – 11/2
Jensen DIP Ch. 9, 13 / Supervised Classification / Lab 5
11 11/7 – 11/9 / Unsupervised Classification & Change Detection / Quiz 3
12 11/14 – 11/16
Jensen RSE Ch. 9, 10 / Other Sensor Types / LiDAR, RADAR, Thermal
13 11/21 – Break / Catch up
14 11/28 – 11/30
Jensen RSE Ch. 11, 12 / Advanced Products / Vegetation Indices, LAI, fAPAR, Pnet, NPP, Atmosphere, Ocean, Geophysics
15 12/5 / Wrap Up
Final: 12/11 @ 4:00 / Final Exam
[1] http://darwin.nap.edu/openbook.php?record_id=10658&page=35