Develop Matlab program with function to analyze lidar data? Part 1: lidar1.txt a

Question

Develop Matlab program with function to analyze lidar data?

Part 1:

lidar1.txt and lidar2.txt that are single lines of sight taken with a vertically staring lidar. Column 1 is the height above the ground and column 2 is the range-corrected lidar return. The goal of part 1 is to develop a function that will provide a smoothed version of the lidar data for visualization of the signal. This function should take a 1D array that is one lidar line of sight and smooth the data.

a. Write a matlab function to provide a smoothed version of the lidar data with height. Document your code as to which method is being used to smooth the data and how it is implemented. You may smooth the data or fit it to an appropriate function as you see fit. The goal is to produce a visualization that preserves as much as possible of the original variations, but not be confused by a large amount of noise.

b. Show that your code works on the data sets given. Plot the raw data and the smoothed version and put the plots in a Word document (or other word processor).

Part 2:

There are two files 24JUN015.TDA and 24JUN019.TDA from which the files above were taken. Those files are single lines of sight taken with a vertically staring lidar. The TDA files consist of many lines of sight taken sequentially (a full image consists of several hundred of these data sets). The goal of this effort is to plot the two dimensional data and use the function that developed in Part 1 to enhance visualization.

a. Read in and plot the data in the two files identified above. Use the matlab function you developed in Part 1 to smooth the data. Do this for both data sets.

b. The format for the TDA files is that the first horizontal line of data are the altitudes for each of the corresponding data points. The following lines are individual lines of sight. The first column in the data are the times at which each line of sight was taken. This means that the first row and the first column should not be included in your plot of lidar data nor in your analysis. Plot all of the lidar data in time and from 1 to 1100 m altitude. Include a color bar and adjust the displayed colors to produce an image that optimally shows the activity in the boundary layer.

Didn't attach files so that I can learn how to do it and check that it would work on my own. Any little bit of some code that should work would help; Matlab makes me tear my hair out! Thank you!

Explanation / Answer

The detection of impervious surfaces is an important issue in the study of urban and rural environments. Imperviousness refers to water's inability to pass through a surface. Although impervious surfaces represent a small percentage of the Earth's surface, knowledge of their locations is relevant to planning and managing human activities. Impervious structures are primarily manmade (e.g., roads and rooftops). Impervious surfaces are an environmental concern because many processes that modify the normal function of land, air, and water resources are initiated during their construction. This paper presents a novel method of identifying impervious surfaces using satellite images and light detection and ranging (LIDAR) data. The inputs for the procedure are SPOT images formed by four spectral bands (corresponding to red, green, near-infrared and mid-infrared wavelengths), a digital terrain model, and an .las file. The proposed method computes five decision indexes from the input data to classify the studied area into two categories: impervious (subdivided into buildings and roads) and non-impervious surfaces. The impervious class is divided into two subclasses because the elements forming this category (mainly roads and rooftops) have different spectral and height properties, and it is difficult to combine these elements into one group. The classification is conducted using a decision tree procedure. For every decision index, a threshold is set for which every surface is considered impervious or non-impervious. The proposed method has been applied to four different regions located in the north, center, and south of Spain, providing satisfactory results for every dataset.

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