I created a model to detect the change between the two images. The next step for this section of the lab is to develop a From-to change map from the two images. 4) Percent change between the 20 image of the Milwaukee MSA area. Once the values were converted into Hectares I was able to calculate the percent of change for each class for the Milwaukee MSA area (Fig. 3) Histogram values input in to Excel spreadsheet converted to Hectares. The next step was to convert the histogram values to square meters and then to hectares. The next step was to obtain the histogram values from the raster attribute table and input the values in to an Excel spreadsheet with the proper classes (Fig 1).
2) 2001 classified image (Left) and 2011 classified image (Right) of the Milwaukee MSA in Erdas. I brought the two image in to separate viewers in Erdas (Fig. The first objective for this section of the lab is to quantify the change which has occurred in the Milwaukee MSA in hectares between the classified image from 2001 and classified image from 2011. I was provided previously classified images from my professor Dr.
Post-classification comparison change detectionįor this section of the lab I will be conduction change detection of the Milwaukee Metropolitan Statistical Area (MSA) between 20. The bright red areas are where change has occurred in the images between the two dates. 1) Results from performing Write Function Memory Insertion. I opened the layer stacked image in a viewer in Erdas to see the results (Fig. The last step was to layer stack the images together. I opened the an image containing the red band (band 3) from 2011 and then 2 images of near-infrared band of the same area from 1991. The pixles within the images which have changed will be displayed as a different color against those which remained the same.įor this section of the lab I utilized images of Eau Claire and surrounding counties provided to me by my professor Dr. The analyst simply opens the near-infrared bands from the two dates of imagery in the red, green, and blue color guns. Utilizing Write Function Memory Insertion is a simple yet powerful method to detect changes between images of the same area from different time frames. Additionally, I will be creating a model which will allow me to display the result of the change detection on a map.Īll of the following step were performed in Erdas Imagine 2015.Ĭhange detection using Write Function Memory Insertion I will be conducting a quick qualitative change detection method along with a quantifying post-classification changed detection method. The purpose of this lab is to gain knowledge and skills in measuring and identifying change of LULC over a specified time period utilizing remotely sensed imagery.