Twin Cities Urban Lakes Project

Ann KrogmanTwin Cities Urban Lakes Project

Background Information100 lakes throughout the Twin Cities Metro Area Sampled in 2002Land-use around each of the 100 lakes classified using 30m LandSat 5 and LandSat 7 imageryRe-sampled lakes in 2010 for my Masters project. Need to update land-use around each of the 100 lakes2002 procedure laid out in Yuan et al, 2005

Original Goals of the ProjectComplete updated Land-use classification around all 100 sites in buffer zones .5, 1, 2, and 3 times the radius of the lakeProduce a chart of % land cover surrounding each lakeHave 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 class2002Producers UsersAgriculture 95.896.4Forest 97.388.8Grass 98.178.9Urban89.699.8Water95.996.7Wetland 81.984.3Overall accuracy93.2Kappa statistic 91.6

Example % Land Cover ChartLake IDHigh Intensity UrbanLow Intensity UrbanTransportationCropsGrassConiferDeciduousWaterMeadowLow ShrubsWetlandImpervious1-252567100007000702-12364012204211005202-13222932021502700252-16371818003160800582-1803300394700082N-0013703102218090012N-02000006630190130N-12011033005600002N-17000000000000N-2500090181171010N-2632293130000230051N-35132506300000001N-4141131704161726018N-54104071221511110029Refined Goals of the ProjectUpdate land-use classifications for Anoka County (14 lakes) using 1m NAIP dataFocus classifications only within the 3r bufferProduce % land-use classification chartPerform an accuracy assessment with at least 50 points in each of the classes

Goals MetNone 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.

DataObtained 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 methodYuan et al 2005 is a classification for entire metro area; no specific guidance for lake classificationsPlanned to meet with her 10/31 but meeting cancelled instead could not meet until 12/10Used primarily 1 meter data instead of 30 meter data since lakes ranged in size from .003ha to 94.7ha LandSat mmu too bigBroken 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 30m NAIP 1mLake N-12: 0.099 haPlan of ActionSubset all images to include only the areas within the 3 radius buffer zone around each lakeClassify 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 referenceCompare the percent accuracies between the 30 m and 1 m resolution Do change detection between the 30 m 2002 and 2010 imagesSubsetting imagesUsing 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 filesAdd merged buffer file in ErdasCopy to a area of interest layerExtract the AOI from the County FileIssuesThe center points from the files were not at the center points of the lakeWhen the .sid file AOI file was extracted from the .sid none of the .sid file went with it so I just had empty circlesThe extraction took two hours so it was difficult to replicate. Ran it twice with same result.SolutionLoaded all county rasters into Erdas Imagine in different viewers. Added inquire cursor to one view and linked all viewsAdded an inquire box and used Google Earth to locate all of the lakes on the 2010 1m NAIP rasterRecorded the center XY coordinates in meters of each lakeUsed 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 subsetOpened up the box subset for each lake in each view in ArcMapUsed 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 ImagesOriginally tried a supervised classification on the 1 meter NAIP imageryWith 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 ClassificationRan 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 lakesAt 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 didnt 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 lakeConverted the drawing to a feature (shapefile)Extracted the shapefile from the 1 meter circular subsetExported the data as an imageLoaded the extracted image in Erdas ImagineIn 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 possibleTried 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 lakeUnsupervised 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 recodeEach subset image was classified individually

Overall ProblemsThings took a long time. I didnt 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 mergingErdas 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 oftenBecause 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 spaceThe 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 PlansI still need to finish these classifications in order to finish my Masters projectI plan to work on them more over winter breakAnticipated issues remain with the recode

Not a total failureWe 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 imageryThank You!Questions?

References:Yuan F, Sawaya K, Loeffelholz B, and M Bauer. 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Arae by multitemporal Landsat remote sensing. Remote Sensing of Environment 98:317-328