The Ten Commandments of GIS

Ten Commandments of GIS

1. Thou shalt be humble and patient in all things, and thankful this stuff works at all; for lo, there are many paths to wisdom, and if one module faileth to work as advertised, thou shalt figure out a workaround.
2. Thou shalt manage thine geodatabases, and follow the paths to thy data files and be a good shepherd unto them, and not forsake critical files in the temp directory. 
3. Thou shalt not rename, move or copy thine GIS data files except with Arc Catalog lest they come to grief. 
4. Thou shalt set thy data frame to an appropriate coordinate system. 
5. Thou shalt specify the cellsize of thine rasters, and attend to their other Environment settings, and not blindly accept default setting lest thy project founder in confusion. 
6. Thou shalt pay attention to database field types, neither confusing integer with double nor double with integer.
7. Thou shalt not depend on corrupt Document files after thy project has crashed, lest it crash upon thee yet again; yea verily, thou shalt open a blank new map and add thine data to it, knowing where to find them.
8. Thou shalt create maps both simple and fancy, but they shall represent thy research steps forthrightly.   Thou shalt not lie with maps, neither shalt thou idolize eye-candy, even when creating TIN or 3D images to please the Philistines in charge of thy research funding.
9. Thou shalt not abuse the lab equipment, even if wroth unto blasphemy.
10. Thou shalt accept success with grace, and disappointment also, and if thy best efforts reward thee not, thou shalt retire for beer, and return to the lab another day. Thou shalt not weep over GIS.

Used from UDEL GIS in Natural Resource Management Website

Imagery Resolution Types: Spatial, Spectral, Radiometric

In trying to completely understand some readings that Prof. Cardille assigned for me, I came across this GIS website from Penn State which explained the spatial, radiometric, and spectral resolution concepts in a perfectly comprehensive manner...

These charts were what helped me understand them the best:

Spatial Resolution: Spatial resolution is equated to the size of the pixel versus the amount of data collected. Depending on the speed of the imagery platform (satellite, airplane speed etc.) and the speed at which the images are collected, this can contribute to the spatial resolution. This is a good way to think about spatial resolution in terms of data collection.

Used from Penn State's Nature of Geographic Website

Spectral Resolution: Spectral resolution is measuring the wavelength that is reflected by the targeted object. For example, lower spectral resolution results in fewer large-width bands that fall at fewer increments and therefore more wavelengths are grouped into the band. Higher spectral resolution is a result of many small-width bands that fall at more increments are therefore each band contains fewer wavelengths (think.... more precision value-wise of the wavelengths captured). 

Used from Penn State's Nature of Geographic Website

Radiometric Resolution: This was the hardest resolution conceptually for me to grasp. Radiometric resolution falls upon the same style of bands like Spectral resolution, however in this case they are called "levels". Lower radiometric resolution results in fewer far-spanning levels that fall at fewer increments and therefore more radiometric values are grouped into the level. Higher radiometric resolution is a result of many small-width levels that fall at more increments are therefore each level contains fewer radiometric values (think.... more precision value-wise of the radiometric values captured). Specifically, the radiometric values measure the amplitude of the reflected light waves, and are often referred to as measuring the intensity of the light source, or the energy levels. Bits are a binary method of explaining the precision of the resolution, in terms of possible levels that the DN values can fall within. For example, 8-bit resolution is equivalent of 2^18 available values, or 256 DNs being available to fall within for the levels.

Used from Penn State's Nature of Geographic Website

4 Days, 16 Hours, 35 Minutes, 58 seconds...

Landsat 8. 

Meeting with Jeff 2/1/2013

For next week, I will read two relevant papers that Jeff will send me. He will also find the model builder stuff from his former Master's Student, in order to re-evaluate it for the Landsat image. This model was created with ArcGIS 9.3 and may have to be updated for version 10.1.

The two relevant papers are by Kutser et al. and the research took place in Sweden. The first is Kutser et al. (2004) entitled Mapping lake CDOM by satellite remote sensing. The second is Kutser et al. (2005) Using Satellite Remote Sensing to Estimate the Colored Dissolved Organic Matter Absorption Coefficient in Lakes. Both rely on radiometric resolution in order to get CDOM values from satellite, and experiment with the experimental satellite, ALI. Atmospheric correction was found to be unimportant, and they extrapolate and determine expected results for future satellites. 

We will meet next on Wednesday morning at 9 am.