Module 5: Supervised Image Classification

 

This week I completed a supervised land-use classification for Germantown, Maryland. Using ERDAS Imagine, I collected spectral signatures, evaluated their accuracy and recoded the results into eight major land-cover types. The final map created shows the distribution of these land classes, as well as a distance file to highlight areas of potential misclassification. 

Comments

Post a Comment

Popular posts from this blog

Module 3: Error Handling and Debugging

Image
 Module 3 focused on debugging Python scripts. The lab taught me how to identify syntax, runtime and file path-related errors while improving my understanding of troubleshooting techniques and exception handling. Three scripts were successfully debugged.  Script 1  For the first script, I first received a name error stating that ‘fc’ is not defined. To fix this, I changed it to ‘FC’. The second error I received was another Name error stating that field is not defined. To fix this error, I switched ‘fields’ to ‘field’. The script then ran successfully, as shown above. Script 2  The first error received on the second script was a syntax warning on line 15, saying there was an invalid escape sequence (/p). To fix this, I simply fixed the backslash to the proper form. The second error was a name error on line 14, stating that ‘Filepath’ was not described. To fix this, I just had to change ‘Filepath’ to ‘filepath’ because Python is case-sensitive. The third error encounte...
Read more

Module 1: Crime Analysis

Image
    In this lab, Washington D.C. burglary data were analyzed by selecting burglary incidents, performing a spatial join to count burglaries within census tracts, joining housing data, and calculating burglary rates per 1,000 housing units. The results were displayed as a graduated color choropleth map after excluding outlier tracts.        A kernel density analysis was then completed for assault incidents using a 100-foot cell size and a 1320 foot search radius. The raster was classified into six classes based on multiples of the mean density to identify hotspot areas.       For the Chicago homicide analysis, three hotspot mapping techniques were compared. The grid overlay method counted homicides within grid cells and selected the top 20% of cells. Kernel density was performed using a 100-foot cell size and 2630 foot search radius, with hotspots defined as areas greater than 3 times the mean density. Local ...
Read more

Module 2: LiDAR

Image
   This lab used LiDAR data to create a Digital Elevation Model (DEM), Digital Surface Model (DSM), forest height raster, and canopy density map. LiDAR point clouds were converted into raster datasets, and spatial analysis tools were used to calculate vegetation height and canopy density. The resulting maps and histogram illustrate the distribution of forest structure and demonstrate how LiDAR can be used to assess terrain and support forest management and conservation efforts. 
Read more