Using last week's data and map, this week's lab demosnstrated how to verify a land use/land cover map for accuracy. Typically, field work would be used for ground truthing, but in this instance Street View through Google Maps was useful in getting detailed information about points.
Thursday, April 6, 2017
This week's lab instructed us on how to classify an area that has diverse land cover. I ended with having eleven categories and found it easiest to first classify the larger features and then classify the smaller ones. I found the process to be lengthy but was good practice in classifying land use. I also realized how complex it can be because of sub-classifications.
Monday, April 3, 2017
The difference between the Unsupervised Classification from last week's lab and this weeks Supervised Classification is that the program uses numerical descriptors of pre-determined land cover types. While in unsupervised classification the computer program sorts pixels with similar spectral properties into groups according to a criteria chosen by the user. Overall I found the Supervised Classification to be easier, although there are a lot of small steps that can be easily overlooked, it was simpler than doing an unsupervised classification. I also thought that user error is less likely to occur in a supervised classification.
Thursday, March 23, 2017
Throughout this week's lab, we were required to completed an unsupervised classification of an aerial photo of UWF campus. At first, the lab seemed like it would be more challenging compared to prior labs but I found it to be relatively simple to comprehend. We had to turn in a map with the percentage of impervious and permeable surfaces depicted on the map, as well as calculate the total area and area of each feature that was classified. Overall this week's lab was useful and bettered my understanding of classification tools in both ArcMap and ERDAS and I find that I am able to navigate through ERDAS better compared to previous weeks.
Wednesday, March 8, 2017
This image has a high pass filter applied to it through using ERDAS Imagine and has been sharpened using the same program. Aside from the fact that the high pass filter looked better, it also allowed the rapidly changing data to pass from pixel to pixel. Throughout this lab we applied filtered and used spatial enhancement tools in both ERDAS Imagine and ArcMap. I found that because I was more familiar with how to navigate ArcMap, I used its spatial enhancement features for my final map instead of ERDAS Imagine.