Tiffield

Transcription

This video will provide you with a basic introduction to remote sensing workflows using ArcGIS. The example we'll use is mapping lakes in Kenya, using Landsat satellite imagery. The desired end state of our project is to produce updated lake boundaries for a few lakes that lie in the Great Rift Valley region in Kenya. We'll begin by outlining our remote sensing workflow. We've already defined the task, which is to create lake polygon boundaries. Next we need to determine our data needs. Data needs of course are a balance between what you'd like to have, what's available, and what you can afford. In this particular case, we're going to use freely available Landsat satellite imagery. We're going to obtain that data from the USGS GloVis site. We're going to assemble the separate Landsat bands into a single composite image and overlay those bands in ArcGIS to take a look at our data to better understand it. We're then going to carry out an unsupervised classification using a nicer data classification algorithm. And then finally, we're going to do a quick evaluation of our data to understand its strengths and weaknesses. There are two reasons for selecting Landsat imagery for this project. First, its acquired at regular intervals. Second, it's freely available from the USGS. We need to obtain the appropriate Landsat satellites scene. But before we do that, let's get oriented in ArcGIS. We're going to load in an ArcGIS basemap. In this case, the Imagery with Labels basemap. And use it to zoom into our area of interest. Here we are on the Great Rift Valley. And you can see there are two major lakes here that are labeled. Lake Nakuru and then Lake Naivasha. Landsat scenes are organized by their path and row numbers, so let's head over to our search engine to see if we can locate a Landsat path row shapefile. Sure enough, we can find one from the USGS. Now land areas are typically imaged when the satellite is in it's descending orbit. So we can get the wrs2, the world reference system 2, descending shape file. This is for the most recent Landsat satellites. We're going to save that to our local drive, and unzip it so that we can bring it into ArcGIS. Moving back into ArcMap we've loaded in the wrs2 shapefile. Let's use the Identify tool to click on the Landsat scene corresponding to our area of interest. And in the Identify tool you'll notice that we want the Landsat scene with path 169, row 60. Landsat imagery can be obtained freely from the USGS Global Visualization Viewer. We'll first want to specify the collection. We're going to go for Landsat 8, which was launched in February of 2013. Once we specify the sensor, we're going to go and enter the path/row. Remember this was 169 and 60. Once we're entered the path/row, we're going to click on the Go button. And GloVis is going to transfer us over to that portion of the globe. Scrolling down, you can see that we can view the previous and next scene. This will scroll through all the Landsat scenes available for that particular area. Once we've found a scene of interest, we can click on the Go button and add it to our cart. Once we've added it to our cart, we can download the data by sending it over to EarthExplorer. You're going to need to login with your EarthExplorer ID, but once you've done that, you can go ahead and click on the little download icon, and you'll be able to download the full geoTIFF product. Once we've uncompressed the file we've downloaded, let's head over to our catalog. Here we see that each of the 11 Landsat bands is a separate raster file. In addition, we have a QA, or quality assurance, band. And a metadata text file. Now we're not going to need all of these Landsat bands for our work. If we look at the Landsat spectral coverage for each band from the USGS website, it looks like bands 2 through 7 are probably going to be optimal for our work. In order to more effectively work with our Landsat image, we're going to want to combine bands 2 through 7 into a single multi-spectral image raster file. To do this, we're going to use the Composite Bands tool. We're going to select bands 2 through 7, and drag them into the Composite Bands tool. The Composit Bands tool is going to produce a single output file that has all those bands. In this example, we're saving it with the dot TIF extension, meaning it's going to be a geoTIFF file. To check our progress from the Geoprocessing menu, we can choose Results. And once the process is complete, we can preview the results in our catalog. Now let's head over to ArcMap to do some data exploration. The first thing that we're going to do is switch up the band combinations. We're going to create a color infrared composite by assigning bands 4, 3, and 2 to the red, green, and blue color cones, respectively. Band 4 corresponds to the near-infrared band, and this color infrared composite will really help distinguish water, because water absorbs practically all near-infrared energy. We can also make use of some of ArcGISs Image Analysis tools. By selecting the image in the Image Analysis window, we can activate the DRA-- or Dynamic Range Adjust-- and also play around with the contrast and brightness sliders. Adjusting the digital imagery will help us identify certain features of interest. All the work we've done up until this point of time is leading us to the data analysis phase. Where we're going to use an unsupervised isodata classification in an attempt to map water. The isodata classification simply takes our multi-spectral image, and groups it into a set number of classes based on the digital values of the pixels. We're going to use 20 classes in this case, and give the output a new file name. So we can expect a new raster file, in which we have 20 classes based on the similarity of the spectral values of the pixels. When we look at the output, we see that we only have 13 classes. This means the algorithm could only identify 13 unique clusters of data. In examining the results of isodata classification, it looks like class 4, the bright pink class, best corresponds to water. And that all other classes are not of interest to us. To confirm this, we can double click on our isodata classification layer, to access the Layer Symbology properties. Under the Symbology tab, we can remove all those classes, except class 4. This doesn't remove those values from the raster, it simply hides them for display purposes. Now we're going to want to create a vector layer that contains only those pixels corresponding to class 4. This is going to be a 2-step process. In the first step, we're going to create a new raster that only contains those pixels with class four. All other pixels are going to be no data values. We're going to do this using the Reclassify tool. Within the Reclassify tool, we're going to load in our raster data, turn all pixels with a value of 4 to 1, click on the check box that says Change Missing Values to no data. We're going to say this is a new geoTIFF file, and then run the reclassify tool. Our new raster layer only contains pixels with ones, and no data. However further exploration of this data set yields some problems. You can see that we've got a lot of shadows that fell into our water class. We're going to deal with these false positives by converting the raster data to a vector layer, and then querying by size. Let's create a new file geodatabase to store the vector output in. Then we're going to use our conversion tools to convert the raster data to a polygon. By storing the vector data in our geodatabase we'll make sure that the area field, the shape area field, more specifically, is populated automatically. The vectorized version of our classification really illustrates the problem that we have with false positives. As you can see, we have all these small, quote unquote, water polygons, that are actually shadows. Because we stored our vector output in a shapefile, it contains the shape area field. The shape area field contains the area of each polygon in map units, because our Landsat satellite was in UTM, this means it's going to be square meters. It looks like somewhere around 20 million square meters is a good cut off. Using Select by Attributes, we can select only those polygons that exceed 20 million square meters. Our query is going to be shape area is greater than 20 million. Once we click OK, we'll have selected only those polygons that fit that criteria. Right clicking on the layer, we can go to Selection, and choose Create Layer from Selected Features. To make this layer permanent, we're going to right click on it, and go to Data, and export our data to a new feature class. This new feature class will only contain those polygons that exceeded 20 million square meters. We're going to finish up by doing a very quick evaluation of our classification. Flipping back and forth between our lake polygons and the imagery, you can see that we have some issues. Both with respect to the lake edges, and with some clouds and haze that were in the middle of the lakes. It seemed to throw off the classification. In this video, we walked you through the entire remote sensing workflow. Everything from defining the task and obtaining the data, to extracting features and doing a course evaluation.