Over the past several weeks my placement has gained momentum and gone through several stages. Once we had the datasets we needed it, was time to build and train a model to best identify varying types of tree mortality.

Mucho Mortality Dataset

The leaders at Salo created early-stage mortality datasets for large areas of California using LiDAR tree hight in conjuncture with high resolution NAIP and Rapideye spectral data. We decided our next step would be to improve the irregularities and inconsistencies in these datasets and get one, really beautiful, dataset to describe forest mortality in Sierra National Forest. Firstly, I worked to assess the agreement between the NAIP and Rapideye datasets. I found that the NAIP data had higher success in identifying mortality within forested areas on the scale of individual tree crowns, but struggled to identify larger areas of dead trees from old fires or beetle outbreaks. Fortunately, the Rapideye dataset was more successful in these areas (Photos below).