Exercise 2: Incremental pixel greenness while moving away from refugee settlement boundaries

• The question I asked centered on how the pixel greenness / NDVI varied in buffered increments around settlements within BidiBidi, Imvepi, and Rhino Refugee camps. I wanted to compare the settlements in Bidi Bidi and Imvepi, which have a larger settlements, to Rhino, which tended to have smaller settlements more uniformly spaced and spread out. Given the more uniformed and wider spacing of Rhino, I expect that green-up will happen more quickly in comparison to the settlements in Bidi Bidi and Imvepi, which are closer together and varying in size and development pattern. This leads me to believe that there’s more cleared or available land and that
  

  Map of Regions of Interest & Buffers

  settlement size is based on political organization of camp blocks rather than natural boundaries that might exist already. One of the questions that I’m asking with these settlements and the geography of exclusion is essentially why different settlement areas and parts of these areas are included or excluded from global settlement datasets. One of the factors that contributes to this is a spectral and spatial distinction – that is, how might the green space in and around a settlement change as you move away from said settlement? With this exercise, I wanted to compare the settlements in Rhino to the settlements in Bidi Bidi and Imvepi to see if the pixel greenness changed at a different rate or in a different pattern as one moved away from the settlement center.

• I used multiple different tools, including the Multi-Ring Buffer tool in QGIS (since I was working with export JSONs), EarthEngine to extract NDVI mean values from Landsat 8 satellite images at these settlement locations, and the smoothing factor in ggplot in R to plot and statistically examine the way NDVI changed.
• I first needed to buffer the regions of interest that I wanted to study, of which there were 44 in Bidibidi and Impvipi and 41 in Rhino. I performed this buffering in QGIS using the Multi-Ring Buffer plugin and made buffers at 100-meter increments from 0 to 1000 meters. Some of the buffers overlapped, but for the sake of simplicity of this assessment, I ignored this. Within EarthEngine, I pulled Landsat8 images from 2018 that covered these settlements. After adding NDVI and NDBI calculated bands to the image collection of 2018 images, I performed a quality mosaic to compress the image collection into one image. I based this quality mosaic on NDVI, meaning that the pixel chosen from the image collection would be the pixel with the highest NDVI. While this can sometimes pull pixels from different dates, it does exclude the possibility of clouds and seasonality affecting the dataset by comparing just the most vegetated pixel that occurred in that area. If I were to re-do this, I might choose a single date image to capture phenological nuance. After reducing the image across all of the buffers (that is, calculating the mean NDVI within each buffer), I exported the geoJSONs, brought them back into QGIS, ensured that there was a spatial selection component linking all of the buffers and regions of interest together, and brought this data into R to plot the NDVI change over distance for all regions of interest.

Bidi Bidi Settlement; Imveppi Settlement Buffers

Rhino Settlement Buffers

• The pattern of greening in the buffers around settlements in Rhino versus Imvipi and BidiBidi did present different patterns, but not particularly significant different patterns. It appears that the Rhino settlements had a faster rate of increase in greenness while moving away from the settlements especially in the first 500 meters, whereas Bidi Bidi and Imveppi showed a more gradual green-up, although there also seems to be a small shift at 500 meters. These results are somewhat expected, but also not very drastic. It would be interesting to see how the green-up changes if I increased my buffer extent or decreased my buffer increments.

• I think that looking at NDVI in buffers was an interesting approach, but as I said above, my choice of pixel quality selection (highest NDVI) could alter a neutral selection of data. Also, what buffers I chose were relatively arbitrary – I chose equal intervals, but this does mean that when the mean NDVI is calculated, the mean is reduced across a larger area as each buffer gets further from the center. I could also try testing with larger buffers (200 or 500 meter buffers) that extend beyond 1000 meters from the settlement edge. Further, some of my buffers overlapped and encroached on other actual boundaries: this means that the buffers sometimes contained pixels from other identified settlements. For this reason, I chose to present the data in a smoothing trend. I will likely need to fix some of these errors for the final project, because I do think that this is a typical and useful spatial analysis to perform on this type of data and some of the errors are relatively easy to fix and would show stronger data integrity.