Overall, we found the Collect Earth tool and Mapathon data to be a creative and useful way to collect a lot of sample data for land use classifications. However, there were a few aspects of the survey that were unclear or buggy. One of the most common sources of confusion was how to count trees…Continue Reading Thoughts on Collect Earth and Mapathon Data
In Rwanda, most forests are located far away from urban settlements. By far the largest shares of forest cover were located farther than 2km from urban points, with the most forests found in the 5km to 10km range. This is partially because these zones were the largest in the analysis. However, even when zooming in…Continue Reading Forests in Relation to Urban Settlements
For this exercise, I created gap-filled images of Rwanda from Landsat-8 and Sentinel-2 imagery during the summer months from 2017-2020. These months were chosen to correspond with Rwanda’s dry season, when there is less cloud cover, which reduces the opportunity for gaps in the composite image. The range of years was extended in an attempt…Continue Reading Comparing Forest Classifications with Landsat and Sentinel Imagery
Comparing my classification with the satellite imagery provided by Google shows that in general, I was generally able to identify obvious, unclouded forest cover and categorize it as such. However, this is about the only thing that my classification was able to do properly. When compared to the built in satellite visualization, it seems I…Continue Reading Comparing Hansen Canopy Thresholds with a Supervised Classification
On the surface, there appears to be little difference in deforestation rates between Rwandan parks with different levels of protection. Using data from the Hansen Global Forest Change in 2019, the most strictly protected parks lost 0.04% of their tree cover, whereas those that were not as strictly protected lost only 0.03%. However, extending the…Continue Reading Assessment of Tree Cover Loss in Protected Areas in Rwanda