Portfolio Entry IV — December 7, 2020

How do patterns of tree cover change at different distances from settled areas?

We create 50 points within built-up areas across the study region in western Rwanda and calculate tree cover totals in concentric regions outside of the built-up areas. We discuss the shortcomings within our analysis, the possible societal factors influencing these tree cover patterns, and opportunities for future studies to better understand the relationship between tree cover, urban areas, and rural areas.

Tree cover across buffer regions

Figure 1: Concentric buffer regions around built-up point (Mabanza/Rubengera, Rwanda)

There is a slow increase in tree cover between 0.3 and 2 km away from built-up areas, spiking dramatically within the 5 and 10 km regions. Each increasing concentric buffer has a larger area than the prior region (Fig. 1; Fig. 2). To improve this analysis, each buffer region could be normalized to one square kilometer rather than showing the total tree cover in each concentric buffer, illustrating the proportion of trees within each region. Further, some of the buffers go beyond the boundaries of the 100km x 100km study region, meaning that the tree cover in the largest buffer regions could be even greater than realized in Figure 3. Similarly, the tree cover buffer regions overlap with one another, meaning that some tree cover is likely double-counted in our analysis.

There is a slow increase in tree cover between 0.3 and 2 km away from built-up areas, spiking dramatically within the 5 and 10 km regions. Each increasing concentric buffer has a larger area than the prior region (Fig. 1; Fig. 2). To improve this analysis, each buffer region could be normalized to one square kilometer rather than showing the total tree cover in each concentric buffer, illustrating the proportion of trees within each region. Further, some of the buffers go beyond the boundaries of the 100km x 100km study region, meaning that the tree cover in the largest buffer regions could be even greater than realized in Figure 3.

Figure 2: Forest cover within buffer regions

Tree cover and social forces

This pattern of increasing tree cover in concentric buffer regions can be explained by government afforestation efforts, planting of woodlots, and the presence of large forest preserves further away from built-up areas. This region encompasses two large forest preserves (Virunga National Park and Gishwati Forest National Park), making more expansive buffer regions from nearby urban areas capture dense forest cover within these protected parks (Fig. 2; Fig. 3). While government afforestation efforts are the leading cause of forest increase in the region, there are simultaneous efforts to clear natural forests outside of the parks and replace them with monoculture coffee, tea, and woodlot plantations (Akinyemi 2017). With over 90% of the Rwandan population using charcoal and fuel wood for cooking and heating, the pressures towards afforestation for sufficient fuelwood supplies, reducing soil erosion, and other ecosystem services makes trees a highly-valued resource in the region (Akinyemi 2017; Nambajimana 2019). This increasing commercialization and governmentalization of trees for fuel and charcoal has reduced opportunities for people to collect local fuel wood, further altering patterns of tree cover across the region (Dawson & Martin 2015). This complex push and pull of tree cover can not be entirely explained by this buffer region analysis, as changing market, governmental, and regional dynamics influence tree cover differently in different locations. 

Figure 3: Forest cover and national parks in study region

Analyzing the urban-rural divide

This study could be improved by dividing built-up areas into urban and rural categories. As this study captures the populous, dual-city region of Goma, D.R.C and Gisenyi, Rwanda, one could create two separate feature classes of points for urban and rural built-up areas. Creating these classes would require knowledge of the largest cities in the region (settlements larger than 20,000 people). From there, buffer regions could be made for each built-up point, calculated for the same intervals as above. These buffer regions would be proportionally scaled to one another, as the 10km buffer region encompasses far more area than any of the prior regions. Using tree cover derived from Sentinel-2 imagery, tree cover around urban and rural areas could be compared within the same chart to better predict trends in tree cover across the region. Further analyses could expand this urban and rural study by distinguishing natural forest from woodlots, exploring the interplay between how local and outsider communities interact with fuelwood and different types of tree cover.

Link to code: https://code.earthengine.google.com/f1c1635a23ae04555ee96e05354cb335

Works cited:

  • Akinyemi, F. O. (2017). Land change in the central Albertine rift: Insights from analysis and mapping of land use-land cover change in north-western Rwanda. Applied Geography, 87, 127–138. https://doi.org/10.1016/j.apgeog.2017.07.016
  • Dawson, N., & Martin, A. (2015). Assessing the contribution of ecosystem services to human wellbeing: A disaggregated study in western Rwanda. Ecological Economics, 117, 62–72. https://doi.org/10.1016/j.ecolecon.2015.06.018
  • Nambajimana, J. de D., He, X., Zhou, J., Justine, M., Lee, C., Khurram, D., Mind’je, R., & Nsabimana, G. (2019). Land Use Change Impacts on Water Erosion in Rwanda. Sustainability, 12, 50. https://doi.org/10.3390/su12010050