Network Analysis

Watershed Analysis of the Tibetan Plateau

          The Tibetan Plateau has some of the world’s largest group of high altitude endorheic lakes and it is very important to analyze water balance in these lakes. This lab introduces us on how to perform a watershed analysis in order to derive topographic features such as stream features. A watershed is an area that drains water to a common outlet as concentrated drainage. In order to create a watershed analysis the step that need to be taken are as followed: make a fill DEM, derive a flow direction raster, derive a basin, derive a flow accumulation raster, derive a stream network, stream order and then convert that into a feature.

          The first step in creating a watershed analysis is getting data. Luckily for this lab, the data was provided on the S: drive. The data we need is a DEM and a lakes layer of the Tibetan Plateau. Also provided was a Landsat image for later comparing an analysis with the final map. The DEM might have some error that will affect the outcome of the watershed so we need to create a DEM that has filled in any depressions. Located under hydrology under the spatial analysis section in the toolbox in ArcMap, I used the fill tool to create a DEM that has its depressions filled. I set the z value as 15, instead of using the default. This puts a limit on how many meters can be filled up. Now that I have an “error free” DEM, I can create a flow direction raster with the input being the filled DEM. This shows the direction water will flow out of each cell. There is a special formula using an eight-neighborhood grid in calculating which way water will flow. You take the elevation number in the middle and subtract that number from the numbers around it. The number with the highest remaining number is the direction of where the water will flow. However the different cells are weighted differently. The immediate neighbor cells are divided by 1 and the corner neighbors are divided by 1.414. This is how the water flow direction is determined in ArcMap.

          The next task to do in the analysis is to create a basin analysis. By using the direction flow raster as the input feature, I created a basin raster using the basin tool found under the hydrology tab. However, we do not need it to be a raster so from there I changed the raster basin into a polygon feature and made the color hollow with black outline to show where the drainage basins are located and where the water flows into. Next, I created a flow accumulation using the flow direction raster as the input. The flow accumulation raster tabulates for each cell the number of cells that will flow to it. From the flow accumulation raster I can now create a stream network by applying threshold value to it. I chose to use a threshold value of 500 (meters). In order to do this I went into the reclassify tool in the spatial analysis. I chose to reclassify the flow accumulation into equal interval and one class. I kept the maximum value at the value it has and then I changed the zero to 500. So it read 500-1469259. Before I set the output and clicked okay, I made sure that the “change missing values to NoData” was checked. I now had a reclassified flow accumulation and a stream network. Next, I created a stream order, which is a method for identifying and classifying types of streams based on their number of tributaries. I used the stream order tool under hydrology tool and used the reclass of flow accumulation as the input stream raster and input the flow direction as well. The method for stream order I used was Strahler’s method. So far I have made a fill DEM, a flow direction, a basin polygon feature, flow accumulation, and a stream order.

          In order for the stream network (the reclassified flow accumulation raster) to look like kind of real streams I had to convert raster to a polyline feature. Now that I have all the necessary features and raster data to see the watershed and do an analysis, I can compare it to thedata I downloaded offline from the http://hydrosheds.cr.usgs.gov. From this website I downloaded 30 second DEMs of the research area of the Tibetan Plateau and also flow direction, basin polygon and stream layers. The comparison and analysis is explored in the next paragraphs.

          From the HydroSHEDS website I downloaded the available data sets: DEM, flow direction, streams, and the basins. In the top output maps I have my final watershed analysis and the HydroSHEDS analysis on the first page, showing the flow direction, basins and streams combined, along with the landsat image to compare. However, I will discuss the maps in detail step by step on the second page.

First maps to compare on the second page are the filled DEMs.(I’m not sure how far you can zoom in) If you look closely, you can already see that my fill DEM is more detailed than the HydroSHEDS. This may because my fill DEM is at a lower second. The fill DEM I downloaded was a 30 second fill DEM, whereas my fill DEM is probably a 15 second of maybe even a 3 second. Also you can tell that the resolution of my fill DEM is at a lower resolution than HydroSHEDS DEM. This may be because the seconds are lower and more detailed. The resolution and quality of the fill DEM will affect the detail of the final watershed analysis. Also, there could be some differences in the z limit that was used creating the fill. The next map I produced and compared was the flow direction. Here, it looks as if my map is more detailed and HydroSHEDS flow direction is more generalized. This is obviously because the 30 second fill DEM is more general than my fill DEM, resulting in a more general flow direction. Furthermore, there are discrepancies using GIS when it comes down to what algorithm was used in computing the flow direction. It could have been the D8 algorithm which uses the eight neighboring cells, or the D-infinity algorithm could have been used, which uses a infinite number of directions. This could cause problems on choosing which algorithm to use.

           The basins in each map differ from one another. My basins map is again, more detailed while the HydroSHEDS basins are more generalized. Again, this is most likely because the fill DEM to create the flow direction was a 30 second fill DEM and less detailed than my fill DEM. Therefore it created a less detailed flow dire3ction raster. As a result, the basins were also less detailed. Therefore, by having a more detailed fill DEM, my resulting maps were also more detailed. The streams polyline feature is another example of a more detailed result of the lower resolution DEM. As you can see my streams feature is more detailed then HydroSHEDS streams. This is because HydroSHEDS streams were created from a 30 second DEM and mine was created from a 15 second of maybe even smaller a 3 second. This is the reason for some discrepancies. Furthermore, when creating the streams by reclassifying the flow accumulation, I had to put a threshold value. I put 500, but the HydroSHEDS user could have put a different threshold. This could also lead to the differences between my results of the streams and HydroSHEDS streams.

          As you can see there are many discrepancies when doing watershed analysis and it all depends on what data the map make decides to use and what he/she decides to put as the z value or threshold value. This will make the maps look different and unique. Furthermore, we can use the landsat image as an informational tool in validating the extracted drainage networks. As you can see in the image, shows in red the lakes in which the water drains into. It also shows the contours of the land showing how the water drains down into those lakes.

By

Chelsea Kemp

May 9, 2011

Georeferencing

          In order to better understand how we can get an image projected a certain way, we can

use GIS. Georeferencing is used to apply coordinates to a map, an aerial photographs, or digital image. With this technique, we can use real life maps in ArcMap and overlay different layers, like streets or buildings to answer spatial analysis questions or get directions. In order for learning purposes, we did an assignment to georeference an image of the UCLA campus.

First, we needed to find coordinates of certain points on the map. We divided the map up into five sections and divided the class into two groups. Each group assigned a few people to go to each section to obtain coordinates of specific points in their section. My group, in team 2, went to section 2, which is the general area of “South Campus.” We collected UTM coordinates from five different locations: the northwest corner of parking lot 2, the southwest corner of lot 2, the southwest corner of another lot, the bruin bear, and the center of the circle near Jan’s steps. When standing in those locations, we used the GPS device to locate our UTM coordinates and recorded them. Then we emailed the 5 coordinates and put together a compilation of all the coordinates collected.

         I then went into ArcMap and georeferenced the points to apply them to the image. I used the georeference tool found in the toolbar. I zoomed into each of the 24 GCP’s that my team located and found the column and row that the location was at. When I found it, I clicked the area and the right clicked it to add the control points. I did this for each of the 24 points. I then checked my points and deleted the points with the highest residuals. As a result, I had a map that had 15 points that all had a RMS error around 8 as shown below.I then rectified the georeferenced data as a tif file. However, after much frustration, it took me awhile, and the help of some friends, to figure out how to project the image and the streets correctly. With their help, I was able to figure out how to get the map to project what I wanted to see. The resulting map has the georeferenced image of UCLA, a street layer, and the GCP’s with graduating symbols showing the residual size.

          From this assignment, I have come to find the discrepancies between GIS and GPS. First of all, when using the GPS locator, it was not precisely accurate. It did not show the locations with decimal points and it therefore rounds the area to the nearest northing and easting. This allows for a large margin of error. This means that a precise location of the Bruin Bear or the corner of parking structure 2 may not be entirely accurate to begin with and therefore could cause misalignment when projecting. Some of the points could be meters off. Furthermore, There were many errors when adding the data into the georeferencing tool. I did not know that I did not need to the “11” in the x data row when entering the coordinates, so therefore I was having problems with projecting the streets and data points. Also, what was really confusing was that there was no description of where the locations were coordinated. It would have been a lot clearer if there was an additional column in the excel file that said something like “northeast corner of drake stadium” or the like, because knowing where the actual point is supposed to go is always helpful.

          Furthermore, there are some irregularities with collecting the data. Some groups did a really good job a collecting data, and I ended up using most of their points. But the group that went to section 5, must have had a hard time because I only used one of their GCP’s. This may be because the landscape of the campus up in that section is very hilly and elevated and therefore the group’s coordinates are less accurate. However, if I were to get a more accurate point, then my maps RMS error would be lower. Also, because the points were generally located near the middle of campus, the accuracy is more precise in that area. If you look at the streets layer, the red lines (the streets) line up more precisely on campus. However, if you look outside of campus in the residential area, the streets are not aligned as well. This is because there are no coordinate points outside of campus and therefore those areas of the street layer do not line up exactly with the image. However, my image was pretty accurate when comparing them to other images my friends showed me.

          In conclusion, GIS and GPS can work together to georeference places on an image and apply real coordinates to them. This can be very helpful when looking at images and applying them to real life situations like finding directions. However, as I have come to find, there is some uncertainty and error when using GIS and GPS because some technologies may not be as accurate or the picture may be pixilated. However, when done correctly, we can have georeferenced maps from any picture we obtain if we can just get enough accurate information.

Sources: http://gis.ats.ucla.edu/mapshare/

By

Chelsea Kemp

May 3, 2011

Suitability Analysis for UCLA Satellite Campus

          UCLA’s campus has been growing and growing every year because of the high acceptance into the institution. However, UCLA’s campus can only hold 31,000 and is currently at its capacity. Each class size is getting larger and effecting the ability for students to learn. In order to promote high education with smaller class sizes, the UC Regents and Los Angeles County have decided that UCLA is in need of an extra satellite campus that will serve an additional 5,000 students. They have decided that certain criteria should apply. Besides the fact that the second campus (UCLA2) should be located within Los Angeles County, it has also been agreed that it should be on relatively flat land; within close proximity (1-3 miles) of other major universities (including the mothers campus); be within a distance of 2 miles of major highways and freeways; and should also be on suitable land types. By using GIS and technologies we can create a maps for each criteria ranking the most suitable areas and then combining them all to create a final suitability analysis.

Besides the fact that UCLA2 must be located in Los Angeles County, another important criteria is that it must be on relatively flat land. The campus needs to be on a relatively flat surface because flat land is easier to work with and maneuver around than steep hills. It is easier to build on flat surface and easier to maintain it. Therefore, I created a slope map of Los Angeles county using a digital elevation model. The resulting map gives me values from 0 to about around 73 in degrees slope. First, in order for the final suitability analysis to work, I need to reclassify to values of slope. I decided to have five classes that will determine how suitable to land is. One will be ranked the least suitable and 5 will be ranked the most suitable. I then reclassify the slope values into those five classes, 5 being the areas that are relatively flat surface and 1 ranking as the steepest and least suitable for a new campus. The resulting map’ labeled “Slope’” is shown as the first map on the second page of maps. Class 5, the most suitable, is shown in green. As we can see, most of the flat land is located in the LA Basin area in the middle of the county and also north-west of that in The Valley. We can expect the UCLA campus to go somewhere in the area on the green.

          Furthermore, it is necessary for UCLA2 to be located with a close proximity of other major Universities. These seven universities include: UCLA, USC, Cal State Northridge, Cal State Long Beach, Cal State Domiguez Hills, and Cal State Los Angeles. This promotes strong interaction between the campuses including combined campus events like seminars, lectures, fundraisers, parties, positive social interaction and friendly rivalries. By being close to other campuses, UCLA2 will be given the opportunity to learn from and interact with students from different these universities. Therefore, it is most important that the new UCLA campus is close to any of these schools. I created a multiple ring buffer around these schools to show the areas around those campuses. There are four rings each at a distance of 1 mile, 2 miles, 3 miles, and 4 miles away from the universities. I next had to convert the data into a raster feature in order to reclassifiy. I then reclassified those rings to be more suitable than the others. I classified being within a 1 mile radius of one of the campuses as being a highly suitable area for UCLA2 therefore giving it class five. I gave the 2 mile ring a class 4, 3 mile ring class 3, and so one. Anything that was outside of a 4 mile buffer is considered to be the least suitable and therefore is considered class one. The results are projected in the second map on the second page. As shown, class 5 suitability is at a one miles radius around all the major university campuses mapped and

Chelsea Kemp Lab 4 April 26, 2011is represented in orange. As you go farther out, the suitability class changes and the land becomes less suitable because the areas closer to the university are the most suitable.

          Other than close proximity to other university locations, it is necessary that UCLA2 must be within a close distance of major freeways and highways. Los Angeles is a very motor driven county and to get anywhere one must travel either by car or bus. It is necessary that UCLA2 must be located within a 2 mile distance from all major freeways and highways. This would ensure that students, who are commuting from home or the main UCLA campus, will have an easy and more direct drive. I created a 2 mile buffer around all major freeways and highways in Los Angeles County and then converted the features to a raster in order to reclassify the values. Since there is only one buffer (of 2 miles) this buffer is put into the most suitably class (class five) because it is the most important area for UCLA2 to be. Anything outside of the two mile barrier is a class one, the least suitable. As shown in the third map, the 2 mile buffer is shown around all major freeways and highways and is the most suitable land for UCLA2 to be located. All areas around the 2 miles buffer are not nearly as suitable and are only in suitability class 1.

          Additionally and arguably most importantly, we need to look at what land is available to build, and assess the land that would need to be converted and the expenses. Taking all of this into account, we can look at landuse in Los Angeles County and rank the types of land by suitability class. First, I converted the features to raster in order to reclassify the data. Then, I reclassified the data assessing whether or not the landuse would be suitable to build on or convert into a campus. The land use that were put into a class five (most suitable) were vacant land not being used, underdeveloped areas or the like, whereas landuse put into class 1 (least suitable) were things like fire stations, airports, etc. Landuse that was in between and given classes ranging from 4 to 2 were landuse like, apartments and condominiums, office buildings, elementary, junior, and high schools, parks, malls, etc. Furthermore, landuse that will definitely not be used for UCLA’s second campus received NoData and these areas include landuse such as beaches or harbors. The results are shown in the forth map. Most of the areas are scattered and show no overall pattern, but now we know areas that are vacant and available to build and the areas that would cost money to convert to a new UCLA campus. We are now ready to combine all of maps and create a final suitability analysis map for UCLA2.

          Using the raster calculator, I combined the four reclassified data or slope, university buffers, highway buffers, and landuse types. The resulting data projected gave me ranging numbers from 7 to 20, 7 being the least suitable and 20 being the most suitable. I changed the symbology into classified data with each number having its own value and use a white-pink-blue to yellow-orange-red color scheme. The dark red spots on the map show the land that is most suitable with the combined criteria. I circled 4 places of the darkest red clusters of areas of which the second UCLA campus can be located. It looks like the area that is furthest north closest to Northridge and the two further south of that near UCLA’s primary campus, are the locations that are the most suitable locations. Therefore, according to the criteria that UCLA2 must be located in Los Angeles County, be on flat land, be in close proximity to major universities and major freeways and highways and also be on suitable landuse type, the most suitable locations have been mapped. Now, the UC Regents and Los Angeles County will need to look further into those locations and take into account the cost it will be to build a new campus.

All data found is from UCLA GIS Data Repository.

By

Chelsea Kemp

April 26, 2011

Terrain Analysis of the Santa Monica Mountains

Terrain Analysis of the Santa Monica Mountains

          California is very diverse in vegetation, having many various different types of grasses, forest, deserts, agriculture etc. How California’s vegetation cover is laid out can be analyzed by many different factors such as elevation, slope, aspect, and the solar radiation hitting the earth’s horizontal surface, or insolation. In order to completely understand the insolation, we need to create a hillshade of the Mountains for all four seasons of the year. GIS can be a very helpful tool when analyzing the terrain of the Santa Monica Mountains. By obtaining digital elevation models of the Santa Monica Mountains from http://seamless.usgs.gov and clipping the California vegetation shape file found on the classes\\kilimanjaro.labs drive, a terrain analysis can be created and analyzed.

          After obtaining the DEM of the Santa Monica Mountains and clipping the vegetation cover from California to fit the Santa Monica Mountains, I created a slope and aspect of the elevation. From there, we have the slope in degrees of the Mountain sides and also the breakdown of which sides of the mountains face north, south, etc. Next I created a hillshade for each season. Using the http://susdesign.com/sunangle/ website, I typed in each solstice and equinox occurring at noon for the year 2011 and as a result got: Vernal Equinox (March 20) – altitude 55.82 – azimuth -1.43... 358.57; Summer solstice (June 21) – altitude 79.30 – azimuth 3.08; Autumnal Equinox (Sept 23) – altitude 55.59 – azimuth 5.28; Winter solstice (Dec 22) – altitude 31.01 – azimuth -14.55... 345.45. For the negative numbers, I added 365. I put the azimuth and altitude into the hillshade calculator and got the hillshade of the Santa Monica Mountains for each season. But now I need to calculate the insolation of the mountains for each season. By using the website http://edmall.gsfc.nasa.gov/inv99Project.Site/Pages/science- briefs/ed-stickler/ed-irradiance.html, I found that the equation to find out the insolation of the sun is the hillshade of the specific season x1000/255. Therefore, I went into raster calculator and entered the calculation for each equinox and solstice. The results shown in the maps are the insolation for those seasons. In the final map, I created a table showing the mean seasonal insolation and topography for each vegetation type.

          The results are shown in the final maps and tables. As shown in the resulting table, elevation has a role in what vegetation type is located at a specific elevation. Most notably, agricultural vegetation is at lower elevation because people chose to have agriculture were it is easily accessible and therefore cannot put it at high elevations. Also, most agriculture will not grow up in such high elevation. What does grow in the higher elevations is mixed chaparral and Chemise Redshank Chaparral, shown in the table. This is dry brush and trees that commonly grow in higher elevation in California’s Mediterranean climate. Coastal scrub grows at a lower elevation and the Annual grass grows at an even lower elevation. Furthermore, slope has a lot to do with where vegetation grows. Again, agricultural vegetation will not be located on high slopes because that makes it hard to grow and not easily accessible. Also, we can see that both of the Chaparral vegetations grow best on higher slopes on the Mountain. The coastal scrub and annual grass shows that it can grow at the middle elevations. When analyzing the aspect of where vegetation grows, most of the results are near the same value. Therefore, I can conclude that aspect is not a significant determination of where vegetation grows.

          When analyzing the seasonal insolation, you can see right away the summer months show more of the high insolation because it gets the most direct solar radiation because of the tilt of earth’s axis. Therefore, we can see that in the table, the mean values for insolation for all vegetation types are higher in summer. Therefore, when looking at the winter solstice, the mean values are all lower. This is because the Santa Monica Mountains get the most sun light in summer and then it goes progressively down in Autumn, it is at its lowest radiation in winter, and then begins to get progressively higher in spring. In the overall seasonal analysis, the Mixed Chaparral vegetation grows where there is the most radiation except in the summer, where its mean radiation is less than the other vegetation types. This may be because it grows where there is sun all year around, and the other vegetation types are seasonal.

In conclusion, we are able to see that depending on the elevation, slope, aspect, and seasonal insolation, we can analyze where different types of vegetation types grow. Agriculture is mainly on low elevation and flat surfaces, where as the Chaparral vegetation types grow in the higher elevation and steeper slopes. The coastal scrub and grass grow on the in between slopes and elevation. Also, insolation determines where vegetation grows. Chaparral grows in places that get more sun all year around, whereas the agriculture and other vegetations grow seasonally in areas that get a lot of radiation in summer. The elevation, slope and the seasonal insolation can determine where vegetation grows on the Santa Monica Mountains and GIS technology can help project this information.

Sources:

http://susdesign.com/sunangle/ http://edmall.gsfc.nasa.gov/inv99Project.Site/Pages/science-briefs/ed-stickler/ed-irradiance.html http://gis.ats.ucla.edu/mapshare/ http://seamless.usgs.gov

By

Chelsea Kemp April 12, 2011

Viewshed Analysis of Cell Towers

          Because of the growing dependence on cell phones in today’s society, especially in large cities, cell phone reception is very important. In Los Angeles, many people use cell phones to keep in touch with families, friends, coworkers and clients. However, according to the map, only 55.1% on Los Angeles county area has cell phone coverage. We are always looking for ways to improve cell coverage in the County. With only a $30,000 budget, creating maps that show either three additional towers, towers with extended height, and towers with extended range, will help us figure out which way to improve cell performance will be the most effective. From the calculations taken from the map, adding three additional towers will increase the percent of cell coverage in Los Angeles County.

          To begin with making the maps, I first acquired a digital elevation model of Los Angeles County and the cell tower coordinates from the Universal Licensing system from the FCC website (www.fcc.gov). I added data to the excel table so that all fields have 20 meter tower height, 2 meter cell phone user height and have a tower radius of 30,000 meters. From there I added the DEM into ArcMap and set the projection to UTM Zone 11N and coordinate system to WGS 1984. Then I added XY data, the table with the cell tower locations and set the projection and coordinate system accordingly. Next, I created the viewshed using the spatial analysis toolbar. Then, I converted the raster of the viewshed into a polygon layer to represent the colors better. In the attribute table, there were numbers ranging from 0-12, 0 being anything not visible, and 1-12 being anything visible. I changed all the numbers 2-12 to 1 and then dissolved the polygon layer. From there, I went into the symbology tab under properties of the polygon viewshed and changed the “not visible” color to white and the “visible” color to a green. Now I have the areas of cell phone coverage for Los Angeles County. For the other three maps, I had the same procedures, except I computed the excel table differently to project different data. I added three new towers at these coordinates: 34.066, -118.346; 33.881, -118.395; and 34.079, - 118597. I chose these locations because these where areas that had patchy coverage but had high populations. For the extended tower height map I changed the height from 20 meters to 30, and for the extended range map I changed the radius from 30,000 to 35,000.

          As a result, I calculated the area of each coverage map in the attribute table and added them up and divided to find the percentage of covered area. I came to find that right now, 55.1% of Los Angeles has cell phone coverage. With the three new towers it is increased to 58%. The extended height map increased to 56.5% and the extended range increased to 56.1%. Therefore, my conclusion is that adding three cell towers to the map would add the most amount of covered area. Most importantly, I chose the cell tower locations to be in area that have people living in those areas so that the added cell coverage is not in vain. In conclusion, by adding three tower locations to Los Angeles County, cell coverage will be increased the most and therefore people of the area will benefit the greatest.

Sources:

http://seamless.usgs.gov/website/seamless/viewer.htm 

http://wireless.fcc.gov/

By Chelsea Kemp

April 19, 2011