Ann Krogman Twin Cities Urban Lakes Project

Background Information… 100 lakes throughout the Twin Cities Metro Area Sampled in 2002 Land-use around each of the 100 lakes classified using 30m LandSat 5 and LandSat 7 imagery Re-sampled lakes in 2010 for my Masters project. Need to update land-use around each of the 100 lakes 2002 procedure laid out in Yuan et al, 2005

Original Goals of the Project Complete updated Land-use classification around all 100 sites in buffer zones.5, 1, 2, and 3 times the radius of the lake Produce a chart of % land cover surrounding each lake Have accuracy equal or exceed that of the accuracy reached by Dr. Fei Yuan (93.2% total and 91.6% kappa). [Accuracy assessment done by randomly placing ~363 polygons with at least 100 pixels on to the classification.] Land cover class2002 Producer’s User’s Agriculture Forest Grass Urban Water Wetland Overall accuracy93.2 Kappa statistic91.6

Example % Land Cover Chart Lake ID High Intensity Urban Low Intensity UrbanTransportationCropsGrassConiferDeciduousWaterMeadowLow ShrubsWetlandImpervious N N N N N N N N N

Refined Goals of the Project Update land-use classifications for Anoka County (14 lakes) using 1m NAIP data Focus classifications only within the 3r buffer Produce % land-use classification chart Perform an accuracy assessment with at least 50 points in each of the classes

Goals Met… None of them!! Project is not yet a success but not a complete failure either… Learned a lot through trial and error – unfortunately a lot of error….

Data Obtained Summer 2010 National Agriculture Imagery Program (NAIP) 1 meter resolution image of Anoka County (Red, Green, Blue bands only) Obtained July 2010 LandSat 5 30 meter resolution images of Anoka County (7 bands) Obtained July 2002 LandSat 5 30 meter resolution images of Anoka County (7 bands) Obtained Summer 2003 NAIP 1 meter resolution image of Anoka County (Red, Green, Blue bands only)

Changes to the 2002 method Yuan et al 2005 is a classification for entire metro area; no specific guidance for lake classifications Planned to meet with her 10/31 but meeting cancelled instead could not meet until 12/10 Used primarily 1 meter data instead of 30 meter data since lakes ranged in size from.003ha to 94.7ha – LandSat mmu too big Broken into 7 classes in paper, 12 classes in lab excel file – I feel 5 classes: lawn, impervious, trees, wetland, and water are sufficient (based on ground reference and analysis) LandSat 30mNAIP 1m Lake N-12: ha

Plan of Action Subset all images to include only the areas within the 3 radius buffer zone around each lake Classify all images using unsupervised classification (All images need to be classified first because there was 14% haze over the 2010 LandSat image for which I did not radiometrically correct) Determine percent accuracy for all images using NAIP data for reference Compare the percent accuracies between the 30 m and 1 m resolution Do change detection between the 30 m 2002 and 2010 images

Subsetting images Using ArcMap, add XY coordinates of the center points of each lake (given in lab excel file) Buffer each center point by 3 times the radius of the lake (buffer distance given by lab excel file) Merge all buffered files Add merged buffer file in Erdas Copy to a area of interest layer Extract the AOI from the County File

Issues The center points from the files were not at the center points of the lake When the.sid file AOI file was extracted from the.sid none of the.sid file went with it so I just had empty circles The extraction took two hours so it was difficult to replicate. Ran it twice with same result.

Solution Loaded all county rasters into Erdas Imagine in different viewers. Added inquire cursor to one view and linked all views Added an inquire box and used Google Earth to locate all of the lakes on the m NAIP raster Recorded the center XY coordinates in meters of each lake Used the subset feature to cut out a box about five times the size of each lake in each view. The coordinates of the inquire box were used for each subset Opened up the box subset for each lake in each view in ArcMap Used the extract by circle feature with the new center point coordinates and existing buffer radius to extract the area of interest for each lake (added all areas from each image at to the viewer at the same time to check for geometric correctness) Then attempted to use the MosaicPro from 2D feature in Erdas Imagine to put the images back together. Worked for the 30m resolution images. Did not work for the 1m resolution images. Took four hours and at the end was too much space for my flash drive to handle. Unadvisedly mapped new network drive on computer in lab and reran merge of 1 meter data.

Classifying Images Originally tried a supervised classification on the 1 meter NAIP imagery With no IR band to detect water, the classifier was confused. Major problems misclassifying water and wetland. Decided an unsupervised classification with many classes would be the best option.

Issues with Unsupervised Classification Ran an Unsupervised 60 class classification on the entire merged 1 meter NAIP image. Because of large blank spaces between lakes difficult to ensure classes were being accurately identified at each of the lakes At the end of classification recode failed – possible source of failure a repeated message to close attribute editor prior to saving before reopening for the recode. Attribute editor was not open so I didn’t know what to close so I would force save by closing the classification and then reopen and recode. Not sure if this was source of error. After doing classification, realized that I needed to exclude the water in the lake from the classification so that it would not be included in the classification scheme. Also realized that I wanted to get individual lake statistics so classifying all lakes together may not be the best option. Additionally, unsupervised classifications with 60 classes did not work for the 30 meter resolution subsets because many of them had so few pixels that I would be searching for one 1 pixel in a lake area for some classes. Needed fewer classes and to also exclude the water in the lake for these classifications

Excluding the Water…. Opened the 1 meter 2010 NAIP imagery in ArcMap. Digitized the exterior boundary of the lake Converted the drawing to a feature (shapefile) Extracted the shapefile from the 1 meter circular subset Exported the data as an image Loaded the extracted image in Erdas Imagine In a separate viewer loaded the shapefile in Erdas Imagine and a new aoi layer. Copied the shapefile into the new aoi layer and saved the aoi. Radiometrically correct the extracted image by rescaling; rescale from 254 to 254 to give the lake a unique spectral signature and rescale by the just saved aoi. This produces a white water body. Mosaic the circularly extracted 1 meter resolution NAIP image and the rescaled water body. Then use unsupervised classification. This method did not work to exclude the water in the 30 meter imagery because for many of the lakes the lake was indiscernible due to the resolution so digitizing was not possible Tried to mosaic the rescaled 1 meter lakes with the 30 meter data and that did not work because they contained different numbers of layers

1 meter classification without lake Unsupervised classification with the rescaled lake worked better to distinguish non lake of interest water and wetlands. The 60 class classification had one or two classes that were tree and building shadows (primarily trees). Problem with recode Each subset image was classified individually

Overall Problems Things took a long time. I didn’t really know what I was doing so I had to do a lot of trial and error to get things into the correct file types and do the necessary subsetting and merging Erdas is very finicky. For example MosaicPro by 2D would not open three out of four times so I would have to end program and restart often Because of the large data volume involved with the 1 meter resolution imagery functions took a long time and were sometimes lost if there was insufficient storage space The issue with recode is really the straw that broke the camels back. I spent a lot of time classifying and then all of the classification were lost when I tried to recode. When from 61 classes to two or five but not the two or five that I was interested in

Future Plans… I still need to finish these classifications in order to finish my Masters project I plan to work on them more over winter break Anticipated issues remain with the recode

Not a total failure… We had been basing our analysis of our original 2002 data on the 30 meter resolution classifications provided to the lab by Dr. Yuan this study makes me wonder if they are completely appropriate for the study areas and if we should not be basing them on the 2002 and 2003 NAIP 1 meter NAIP imagery Accuracy assessment will provide more insight into whether the 1 meter resolution imagery provides more accurate detail than the 30 meter imagery

Thank You! Questions? References: Yuan F, Sawaya K, Loeffelholz B, and M Bauer Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Arae by multitemporal Landsat remote sensing. Remote Sensing of Environment 98: