Aerial Photography and Remote Sensing - Module Lab 1 - Visual Interpretation

Hello Everyone!

This semester I am starting Aerial Photography and Remote Sensing. For this week's lab, I learned all about identifying various features and aspects of aerial images. I essentially had three primary exercises, I will show you the final maps for the first two and talk briefly about the third.

For the first exercise, I was able to distinguish two aspects of aerial photographs. Tone and Texture. The tone in an aerial image is how bright or how dark a section of an aerial image is while the texture is how smooth or how coarse a surface of the image area is. For this map, I selected 5 areas of tone (in red) that range from very light, light, medium, dark, and very dark. For texture, additionally, I selected 5 areas of texture (in blue) that ranged from very fine, fine, mottled coarse and very coarse. The results of my first map can be seen below:

For the second exercise, I distinguished aerial photography features based on four specific criteria: These criteria are a shadow, pattern, shape and size, and association. For shadow, I used the shadow of the object to identify features (such as a power pole). For shape and size, I used just that such as the shape and size of a vehicle. Pattern, I used identified clusters such as a cluster of buildings or trees, and for association I used a grouping and contextual location such as buildings with a big parking lot being a potential mall. 

Finally, for the third and final exercise, I picked points on a true-color image and compared them to the same locations on a false-color infrared image. This was a really cool exercise as true color locations showed up opposite as false-color (an example being a forest that is green in the true-color image and red in the false-color infrared image.

~Map On!

Special Topics in GIS - Module 3.1: Scale Effect and Spatial Data Aggregation

Hello Everyone!

It really is hard to believe, but here we are at the end of Special Topics in GIS, it has bee a great course and I have learned so much in the past 8 weeks. From data accuracy to spatial data assessment, we have covered so much this semester. For this final lab, we covered a huge topic that is very relevant in GIS today and that is the effect of scale on various types of data in addition to the Modifiable Areal Unit Problem and how it pertains to congressional district gerrymandering. I'd like to break down the effect of scale and resolution on the two types of spatial data: Vector and Raster.

When vector data is created at different scales there will obviously be a difference in detail of the data. For the lab, we were given multiple hydrographic features taken at three scales:

1:1200
1:24000
1:100000

At the 1:1200 scale, hydrographic lines and polygons can be expected to reflect the nature of the real world in the data. Polylines at this level will be very detailed and have a high number of vertexes. Polygons will be more inclusive in features.

At the 1:24000 scale, feature detail begins to drop. Polylines have less detail and shorter total lengths to account for the less number of features illustrated. Polygons drop off at this level of scale, and polygons compared to the 1:1200 scale are not accurate reflections of the features.

At the 1:100000 scale, data detail becomes even less than at the 1:24000 scale. Polylines are as minimal as possible with very little detail and polygons are missing even more since features are too small to draw by eye. The data very minimally reflects the real world.

For raster surface data, the difference in resolution can heavily impact the data. For this lab, I resampled a raster surface (1-meter cell size) DEM of a watershed surface feature at five different resolutions:

2-meter cell size
5-meter cell size
10-meter cell size
30-meter cell size
90-meter cell size

Using a bilinear sampling method designed specifically for this type of continuous data, with each resample to the next resolution size up, the quality of the raster surface decreased. Larger cell values did not reflect the values accurately of the initial 1-meter cell size values when calculating the average slope of each raster.

These two typed of spatial data with there issues point to a problem well known in the GIS world known as the Modifiable Areal Unit Problem. This 'problem' essentially arises when you create new boundaries to aggregate smaller features together into new areas. These 'new boundaries' are usually always arbitrary and in no way reflect the data of the smaller pieces within them and can be deceiving. A great modern GIS issue of this is Gerrymandering. Gerrymandering is a political strategy to redraw boundaries of districts so that they favor one particular political party or class. In the state of Florida, gerrymandering has been under the spotlight for some years. While gerrymandering is tough to measure by the human eye (with some exceptions), it can statistically be measured. Through a method called the Polsby-Popper Score, congressional district compactness can be measured. This value is calculated by multiplying the area of the district by 4pi and then dividing that number by the perimeter squared of the district. The values returned can range from a value of 0 to 1. Values closer to 1 reflect districts that are more compact while values closer to 0 reflect districts that are very 'loose'. The looser the district, the higher the likelihood it has been gerrymandered.

Below is the district (in red) that received the lowest Polsby-Popper Score value of 0.029

I hope you have enjoyed keeping up with my learning this semester. Next semester I will be taking on Advanced Topics in GIS and Remote Sensing. I look forward to sharing these next moments with you and as always...

~Map On!

Special Topics in GIS - Module 2.3: Surfaces - Accuracy In DEMs

Hello Everyone!

This week's lab focused all on assessing the vertical accuracy of DEMs. For the lab this week I analyzed the vertical accuracy of a DEM of river tributaries within North Carolina. To assess the DEM's vertical accuracy, I was given reference point data taken by high accuracy surveying equipment. The 287 reference points were taken at five various landcover type locations. These land cover types are as follows:

• A - Bare Earth/Low Grass
• B - High Grass/Weeds/Crops
• C - Brushland/Low Trees
• D - Fully Forested
• E - Urban

To assess the vertical accuracy of the DEM I extracted the elevation values from each cell that where each reference survey point fell. Now that I had the actual elevation points and a set of reference points for each actual value, I could compare the results. To assess accuracy, I calculated 4 statistical numbers for each land cover type and the dataset as a whole. My four statistical numbers included accuracy at the 68th percent confidence interval, the accuracy at the 95th percent confidence interval, the RMSE (Root Mean Square Error), and the Bias (Mean Error). The Root Mean Square Error is the most used metric to assess and calculate accuracy. Higher RMSE values mean lower accuracy and lower RMSE values mean higher accuracy. My results can be seen below.

For my analysis, the Bare Earth has the highest accuracy with the fully forested land cover having the lowest. This is no surprise as creating a digital elevation model of the varying land cover of the earth's surface can be quite challenging since various land cover types can vary in terms of accuracy. 

~Map On!

Special Topics in GIS - Module 2.2: Surface Interpolation

Hello Everyone!

This week's lab was all about interpolation methods for surface data. Interpolation of spatial data is essentially assigning values across surface data at unmeasured/unvalued locations based on the values at measured/values sampling locations. During this lab, I worked with three primary forms of surface data interpolation. Thiessen Polygons, IDW or Inverse Distance Weight and Spline Interpolation. For the Spline interpolation method, there are two types of Spline Interpolation: Regular and Tension.

For this week's lab I worked in two parts. For part A, I compared Spline and IDW interpolation to create a Digital Elevation Model. While this part of the lab and assessing the differences between the two data methods was interesting, I'm going to share with you Part B. For Part B, I was provided 41 sample station water quality measurement points sampled in Tampa Bay, Florida. These data points essentially focus on the water quality and specifically the Biochemical Oxygen Demand (mg/L) at each sample.

Thiessen Polygons

The Thiessen Polygon interpolation method is fairly straight forward. Thiessen polygons contruct polygon boundaries where the value throughout the polygon is equal to the value of the sample point. Overall this method is fairly simple and widely used but for the nature of water quality data, the drastic shifts in polygon values and their clunky look do not reflect the data.

IDW (Inverse Distance Weight)

This method is much better for the nature of the data I was interpolating. Essentially, the values associated with the points directly affect the interpolation while the value decreases the further it gets away from the points. Points that are clustered together tend to push the overall data distribution higher in the clustered areas of concentration. For this data, this method still felt too clunky and did not reflect water quality.

Spline (Regular and Tension)

Spline Interpolation, the smoothest method employed in this lab essentially tries to smoothly go through the data sample points while reducing the curvature of the surface data. Regular spline interpolation is much more dynamic with value ranges (I had negative values even though my data contained none) with lower lows and higher highs. Tension spline interpolation attempts to reduce the factor of data values outside the initial range. For the nature of the data, I believe that Tension spline interpolation (Below) is the best method to visualize the surface data. Water is a smooth continuous medium and water quality can change constantly. Interpolation of this quality of data needs to be loose, but not exceed the data itself making tension spline interpolation the best method for this week.

~Map On!