Evaluating Long-Term Land Cover Changes for Malheur Lake, Oregon Using ENVI and ArcGIS

• Main Author: Woods, Ryan Joseph
• Format: Others
• Published: BYU ScholarsArchive 2015
• Subjects: watershed; land cover; Landsat; remote sensing; GIS; ISODATA; maximum likelihood; minimum distance; Mahalanobis distance; Malheur Lake; Civil and Environmental Engineering
• Online Access: https://scholarsarchive.byu.edu/etd/5743; https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6742&context=etd

Land cover change over time can be a useful indicator of variations in a watershed, such as the patterns of drought in an area. I present a case study using remotely sensed images from Landsat satellites for over a 30-year period to generate classifications representing land cover categories, which I use to quantify land cover change in the watershed areas that contribute to Malheur, Mud, and Harney Lakes. I selected images, about every 4 to 6 years from late June to late July, in an attempt to capture the peak vegetation growth and to avoid cloud cover. Complete coverage of the watershed required that I selected an image that included the lakes, an image to the North, and an image to the West of the lakes to capture the watershed areas for each chosen year. I used the watershed areas defined by the HUC-8 shapefiles. The relevant watersheds are called: Harney-Malheur Lakes, Donner und Blitzen, Silver, and Silvies watershed. To summarize the land cover classes that could be discriminated from the Landsat images in the area, I used an unsupervised classification algorithm called Iterative Self-Organizing Data Analysis Technique (ISODATA) to identify different classes from the pixels. I then used the ISODATA results and visual inspection of calibrated Landsat images and Google Earth imagery, to create Regions of Interest (ROI) with the following land cover classes: Water, Shallow Water, Vegetation, Dark Vegetation, Salty Area, and Bare Earth. The ROIs were used in the following supervised classification algorithms: maximum likelihood, minimum distance, and Mahalanobis distance, to classify land cover for the area. Using ArcGIS, I removed most of the misclassified area from the classified images by the use of the Landsat CDR, combined the main, north, and west images and then extracted the watersheds from the combined image. The area in acres for each land cover class and watershed was computed and stored in graphs and tables.After comparing the three supervised classifications using the amount of area classified into each category, normalized area in each category, and the raster datasets, I determined that the minimum distance classification algorithm produced the most accurate land cover classification. I investigated the correlation of the land cover classes with the average precipitation, average discharge, average summer high temperature, and drought indicators. For the most part, the land cover changes correlate with the weather. However, land use changes, groundwater, and error in the land cover classes may have accounted for the instances of discrepancy. The correlation of land cover classes, except Dark Vegetation and Bare Earth, are statistically significant with weather data. This study shows that Landsat imagery has the necessary components to create and track land cover changes over time. These results can be useful in hydrological studies and can be applied to models.