Final Project

Goal: To build a model to predict light levels based off of Landsat data, and field data.

Base files used

>UW

FieldOBS.csv (converted to feature class through ARCmap XYFieldOBS.csv

Landsat data (sept022006utm.img)

Studyarea.shp

Base Setup

Basemap= Imagery with Labels

XYFieldOBS symbology changed to 

Base image

>Spatial analyst tool>multivariate>Iso Cluster Unsupervised Classification=ISOcluster layer

Created an Cluster classification and then using reclassify reduced the classification from 13 to 10 categories that i felt represented the areas well. 

Reclass images timeline

13->12->10

Using Sept0220065utm and Study area made a clip of the study area using image analysis toolbar and created an NDVI>clip_sept022006utm.img>NDVI_slip_sept02006utm.img>and exported the raster data (file: NDVI) which allowed me to create a dat table that I joined with the original XYfieldOBS to take NDVI data and compare it to the original data in excel. Light and NDVI were graphed using a scatter plot with a trendline

Data quite spread out from trendline note: r^2 = 0.10 and the trendline equation y 241.08x+8767.6 

The equation allows an individual to predict light intensity using the equation above x=NDVI value and y is predicted light intensity with a low goodness to fit (R value) of .10 percent. I am no sure why it eded up in such a low R value maybe if I had relassified it more times it would have resluted in a higher goodness to fit. 

The ovserved NDVI and Predicted (created using raster calc with the values (NDVI * 114.65) + 38957 ) shown below (file name P_lightraster)

Observed

Predicted

Using>Spatial analyst tool>Zonal> Zonal Histogram, and Zonal Statistics as table I was able to view that data

Next I wanted to combine other areas of study from the class so I added the Dem30_uw and using the spatial analyst tools>Surface>Hillsshade to give more depth to the map, 

Then defning roadways that were not clear changing the symbology to represent each type of raodway. 

Finaly overlaying the basemap referrencces and ISO4

The ISO4 I changed the display to have a transparency of 50% to help display the hills and roads below

Lab 8

Working with ArcMap to manipulate and work with IMG files taking a basemap and overlaying and image file.

To sattelite image and overlayed it on the basemap and accentuated a portion of them map by using image analysis toolbar. Tools: DRA (exentuate the part of the map I wanted to focus, and clip to take out the piece I wanted then overlaying that on the original. Shown is how to export the clip into a raster file since the clip is not saved just heald in the clipboard. (**important** clips need to be exported this way so they are not lost)

ISODATA cluster unsupervised classification
>spatial analysis tool>multivariate>ISODATA cluster unsupervised classification
made multiple with 16/17(originaly 20 but would only allow 17)/8 to see how each one limits

ISOCLUSTER 1: 16

ISOCLUSTER 2: 17

ISOCLUSTER 3: 8

Each version allowing designated # of categories to the data though lower numbers would not allow for much manipulation later on so if I got farther through my work at need more categories to display would have to start over so planning is important.

Reclassify allowed me to take similiar attributes and combine them results before and after below

Played with display so that I could see the base map underneath changed to a transperancy of 60%

The program manipulated to color but that was maintained in the legend, was not sure how it was done in the lab slides but I think this would be close.

NDVI

Final with addition of data collected during the fieldtrip setup in layout view and added north arrow, legend, scale, and title. 

lab 7 Working Rasters

Rasters:

Extensions: Spatial Analysis (added toolbar for easy access)
Introduced Raster calculator (Spatial Analyst Tools>Map Algebra>Raster Calculator

Raster Calculator>environments>Processing extent/Raster Analysis: allow to select area that the calculator will focus.

Selected the Dem 30 which after the Calculator was converted into rastercal 1 (see below)

Wanted to add in road data (UWroad_clip) and used selection tool to select "Type_L"=  'Rd' and "Type_L" = "Blvd" then used analysis Tools>overlay>intersect with Studyarea similar to lab6 for practice and to follow closer to the clips in the power point

Selection shows designated area selected by the view of the window

Next selection >Raster calc>enviroments>raster analysis allowing to select a specific feature, was not sure of the selection that it made. Did not quite remeber this from class and without more information created a selection but not sure how it differed. Played around with it setting the Maximum inputs with the mask of layer 3. Selection the same as seen above. though I am not quite sure how to interpret the difference. 

>spatial analysis>surface>hillshade: input raster rastercalc5 created above with stretched symbology overlayed over the intersect clip created earlier. 

Took selection and converted into an ASPECT (>spacial analysis tools>surface>Aspect) input raster the hillshade created earlier. 

Using the raster calculator and sin("aspect_hills1" * 3.1459/180) using basic numerical functions. (note: resuls look much like the hillshade effect 

Results gave a depth to the map though i honestly do not understand all the conversions just need more time working with them. Basic understanding is useful though. I understand though it is useful for highlighting and area and bringing and individuals attention to an important area but leaving surrounding information available for reference. 

Lab 3

The lab was on overview of ArcGIS introduction to the tools associated with creating spatial maps and managing data associated with it. 

Our main focuses
ArcToolbox- Tools to work with spatial data
ArcMap- Viewing, manipulating, and quantifying spatial data
ArcCatalog- Organizing spatial data.

ArcGIS Geodatabase: "At its most basic level, the geodatabase is a container for storing spatial and attribute data and the relationships that exist among them. Geodatabases are created, edited, and managed using the standard menus and tools in ArcCatalog™ and ArcMap™." 
(Note: not widely used in the sciences though Miles notes that one day it will be due to it's efficiency The slides give a lot of information surrounding the database but it will not be covered much in the class)

Geographic data (2d, 3d)
Spatial: Location, shape, relating features (Layers: Vectors, Rasters, surface, images)
Descriptive data: defining attributes and characteristics of spatial features. 

Lab 2

Lab 2 was an overview of spatial elements
( Most information and pictures taken directly from lecture slides (lecture 2 SPIT 2015) 

Points- a point has no area but is a fixed position (coordinates)
Lines- a connection between 2 or more points
Area- the space enclosed by multiple points and lines. 

Raster (see picture) 

The numbers represent a spatial value no matter how small your "pixel" is never an exact representation of actual geography

Vector (see picture)

Represented by coordinate data taken by GPS measurements x, y coordinates

GPS section- An overview of the history of gps and it's limitations


Why use GPS
(1)
•Availability:
–1995, DoD NAVSTAR, civilian use foreseeable future
•Accuracy: Factors
–work with “primary” data sources
–High inherent accuracy (2.5m medium-quality properly corrected receiver)
–Time Corrected to 1/1 billionth of a second

Differential GPS:  An extension of the GPS system that uses land-based radio beacons to transmit position corrections to GPS receivers. DGPS reduces the effect of selective availability, propagation delay, etc. and can improve position accuracy to better than 10 meters.of a second (notes) I understand this is mainly used for marine navigation I am not sure if it is useful for land based mesurements. Possibly for air. 

Sources of error

Satellite clock errors  < 1 meter

Ephemeris errors   (satellite position)  < 1 meter

Receiver errors  < 2 meters

Ionosphere errors (upper atmos.)  < 2 meters

Troposphere errors (lower atmos.)  < 2 meters

Most of these seem to be out of the users control though need to be taken into account when anomolies are found within the data. 

Multipath errors (bounced signals)  ???- seems to be the most controllable, being able to recognize areas that could potentially reflect signals causing error within how your receiver interprets them. I believe this was similar to what we were going over in the example when were taking data. The instructors had us take reading next to the building which would likely reflect the signals manipulating our data of the "square" 

Considered the most accurate representation of space with the least amount of error. (note: the distortion at the poles. The farther you get away from the equator the more distortion of information. 

Remote Sensing/Landsat 

Representing the reflection of solar radiation off the earth with satellite instruments. Multiple satellites all with different capabilities that cover different wavelengths of light. Important to note that what we see is what is not absorbed by the surface example (we see green when we look at plants due to the absorption of other light waves) Important to pay attention to the wavelengths that are covered and how often the satellite can pass over the area that you want. often takes multiple passes due to possible cloud coverage. 

landsat-etm has a 30m resolution and repeats every 16 days. 

First launch in 1972

•Landsat 1 – 7, today 5 TM and 7 ETM+ still fly

–15m, 30m, & 60m pixel resolution

–7 spectral bands, 1 panchromatic band

•3 Visible, 2 NIR, 1 MIR, 1 Thermal Low/High Gain

–Every 16 days

–Tuned for looking at the land

–Broad spectral bands

–Can “see” into estuaries and rivers

Working Enviroment

ArcGIS 10.3.1 for desktop under student license provided by UW

Lab 6

Objective:
Start to take the functions we have already used like symbology, selection, and tools in the toolbox to learn how to navigate and select different options in the data files.

ArcMap

Every lab I am amazed how much detail goes into each of these projects. The complexity seems to intimidating right now but I am starting to become more comfortable. Worked with the selection "select By Attibutes" using Method: Create a selection and add to current selection  selecting "ways" within the road data (UW>UWroad_clip.shp). Started working with statiscs to analayze the selctions using ROLL_LEN feature see below:

Used the selection tool to select within the university district then with that selection used the select by location that allowed me to select all of the roads within the university district allowing me to run any available statistics I wanted. 

Started working with the union function in the toolbar to "unionise" to layers and create a selection from that, 

Worked with the interect tool to select within not including the "water ways"

and Identity within the (analysis tools>overlay> identity) 

Clip (Analysis tools>extract>clip input:road, and clip:"neighborhood"

New section Raster maniulation

Raster calculator ("dem30_uw" * 2) converts multiplies map values by 2 you can see the high value doubling from 1428 to 2856. 

Final areas of the lab focus on pulling statistics from the map. Functions give a huge range of functions Focal/Zonal/Global statistics to pull information out of the data. (Spatial Analyst>

Reclasify is useful allowing new categories to make data easier to work with. Especially useful if to areas are similar and you want to combine them into one making the data easier to read or it was not interpreted correctly