Question that I asked?

Could I identify functional tree species with supervised image-classification in my stands?

The reason I asked this question was so that If I had to do a geographically weighted regression again it would be valuable to have deciduous or coniferous tree species in my point data for an added variable.

Name of the tool or approach that you used.

The main tool that I used for image classification was the maximum-likelihood classification in ArcMap. I also used the create accuracy assessment points to help create a confusion matrix in excel.

Brief description of steps you followed to complete the analysis.

I downloaded 2016 NAIP imagery in my area of interest and used the high resolution imagery to create a false colored image with the bands being arranged as NIR, Red, and Green. To help delineate broad-leaf vegetation from coniferous vegetation, I applied a histogram equalize stretch that enhanced my ability to identity conifers in the landscape. From there I created a maximum likelihood classification by drawing training data polygons on the false color imagery, which involved me using the Esri digital imagery base map as a reference image.

Once the image classification was complete, I used the create accuracy points on my stand and then extracted the raster values from the thematic map output to those points to create a confusion matrix in Excel. I clipped the thematic map raster to the watershed polygons I made when I did an individual tree segmentation to show what pixel classifications were assigned in my tree tops.

Brief description of results you obtained.

The thematic map output was 83% accurate with conifer and developed land covers performing the worst in the model. The developed  land cover is generally difficult to model in a landscape, and the variability in urban spectral reflectance leads to errors in modeling. The conifer land cover performed more poorly due to my trouble achieving accurate training data with the imagery resolution, and also with the model having trouble delineating conifers from grass and deciduous vegetation. Errors of commission on my part (65% accuracy), and errors of omission (75%  accuracy) lead to the lower accuracy of the conifer land cover (Table 1).  Despite these errors, the thematic map output performed well, and the land cover pixels in my stands showed that conifer trees were accurately assigned in the Saddleback stand (Figure 2). For the baker creek stand the large amount of shadows, sun glare on canopies, and classification cut off, lead to a poor classification of that stand.

Figure 1. The land cover thematic map for the entire NAIP image. The cyan blue color indicates the locations of Saddleback and Baker Creek stands.

Figure 2.The land cover classification output for the Saddleback stand.

Figure 3. The land cover classified output for Baker Creek Stand. Note that the NAIP imagery that was classified did not extend to cover the entire stand. The tree crown polygons were laid below the output to show where the land cover cuts off.

Table 1. Confusion matrix for the thematic map output.

Critique of the method – what was useful, what was not?

Some critiques about this process was that it was time consuming to create training data detailed enough to capture the variation in the scene for my desired accuracy. Sources of errors in the thematic map include shadows, resolution and variable spectral response signatures in the remotely sensed vegetation. Shadows occluded trees that would otherwise stand out, and distorted the classification enough for me to have to add in a land cover classification for shadows to mask them out of the scene. The issue of resolution just means that NAIP imagery was not detailed enough for the applications I asked. Imagery taken from unmanned aerial drones may be a potential avenue for acquiring a more  higher resolution data set. The confusion matrix highlights this issue, with an omission error of 65% for conifers and 75% commission error. It was difficult to determine conifer trees accurately in the training data from the variability of the spectral reflectance and the blurred crowns from the 1 meter resolution.

Since I only did a classification, I didn’t attempt to classify tree functional species to my tree polygons. The process that comes to mind on how to do that is to visibly determine which classification color is more pronounced in a tree top, and then placing that species in the point data as an attribute. This process would be highly time consuming and developing a methodology to streamline the classification of functional tree species to my tree points would be potential future work.Overall, the thematic map outputs are useful for areas like the Saddleback stand that have less shadows and distortion. The map is less useful for areas with high distortion like my Baker Creek stand.