Thursday, April 20, 2017

Burn Scar

This Image was created on ArcPro and in the middle of the red circle there is a burn scar. The burn scar is almost completely cover by new vegetation and this means that the fire happen a long time ago.  

Thursday, April 13, 2017

LULC Accuracy

This map is a land use land cover classification of Pascagoula Mississippi. A stratified random method was used for sampling. After selecting 30 points the accuracy of the LULC classification was 70%.

Friday, April 7, 2017

Land Use

This image was crated on ArcMap and it shows a level two land use land cover classification of an areal photograph.

Tuesday, April 4, 2017

Supervised Classification

This image is a land classification for Germantown, MD. This supervised land classification was done on ERDAS and the map was crated on arc map. The Land was classified in eight different classes and area of each class is given in acres.  

Wednesday, March 29, 2017

Unsupervised Classification

This image was created using ERDAS and Arcmap. This map shows five classes of surface area. The area of each class was calculated suing a tool on ERDAS and then the total area was calculated from these five classes. These five classes were split into permeable and impermeable surface area and then the percentage was determined.  In this area there is a lot more permeable surface area.    

Thursday, March 9, 2017

Photo Enhancement

This image is a result of the use of ERDAS and ArcMap. A sharpen 3x3 image was opened in ArcMap and the focal statistics tool was used to reduce the stripping in the image. The majority method of the focal statistic tool was used twice with the width and height set to 3x3 and 2x2. These two layers reduce the stripping and keep details of the image.  

Thursday, March 2, 2017

Thermal Analysis

This images is a product of multisprctral thermal analysis. This image shows two locations of fires and the RGB band combination makes these features stand out. These features were located on the on a gray scale layer- 6 image. These features were brighter than the surrounding pixels in the image.