Question

My research question for this project was to investigate how spatial patterns of land use/land cover (LULC) change were related and/or associated with spatial patterns of artisanal, small-scale gold mining (ASGM) establishment. A possible mechanism I considered as influencing these patterns would be deforestation associated with the establishment of ASGM operations, as miners require significant amounts of timber to bolster mine shafts, in addition to constructing shelters around the mine shafts and for the miners themselves. As such, my research question can be phrased thusly: “How is the spatial pattern of LULC change related to the spatial pattern of ASGM establishment via the mechanism of deforestation?” Additionally, I wanted to examine whether alternative factors may affect LULC change around sites; in this case, I examined the proximity of a site to a primary road and whether or not that related to rates of LULC change.

Dataset

My dataset for this project had several components. In previous work, I used very high resolution satellite imagery to map out the locations and spatial extent of nine ASGM sites, as well as nine non-ASGM sites to compare to (these sites are shown in fig. 1). After having mapped these sites, I acquired Landsat 5 and Landsat 8 imagery at 30m spatial resolution from the USGS for April 2007 and April 2018, respectively. Using this Landsat imagery, I calculated normalized difference vegetation indices (NDVIs) for each year, and then subtracted the former from the latter to obtain a dataset showing NDVI loss between each timeframe. This dataset enabled me to examine NDVI change around each of my 18 sites to evaluate LULC change in the form of NDVI change. In addition to these datasets, I also acquired a road network layer from OpenStreetMaps for Senegal, which I selected to only include “primary” roads in Kedougou. Together, these datasets enabled to me to investigate LULC change around ASGM sites via NDVI change and also how this change relates to proximity to primary roads.

Fig 1: Study Site

Hypotheses

My two hypotheses for this project are that 1) NDVI would be more negatively impacted near ASGM sites compared to non-ASGM sites as a result of deforestation, and 2) NDVI losses at ASGM sites could be attributed in part to each site’s proximity to primary roads.

Patterns of higher NDVI loss as a percentage of the total would occur at ASGM sites compared to non-ASGM sites. This may be because, as the larger populations near ASGM sites and also the activity itself would necessitate more timber harvesting, there would be more negative NDVI change in the immediate surroundings compared to sites without ASGM activity. The spatial process I would expect to see would be areas of higher NDVI loss nearer to the center of ASGM villages. These areas would be fragmented, indicating differences in vegetative cover around the village, but, overall, more negative than around non-ASGM villages. This pattern results from miners and villagers harvesting the nearby timber to bolster mine shafts, as well as for construction of structures around each mine and their own huts. This hypothesis is an explanation for the pattern of NDVI loss around a village.
ASGM sites may be, overall, closer to primary roads than non-ASGM sites, and so their growth and attendant NDVI loss is in part attributable to proximity to major travel corridors and the relative ease of reaching the sites. If a mine is closer to a primary road than another mine, it’s possible that this proximity may result in larger spatial patterns of NDVI loss. This hypothesis is an explanation for the process of deforestation around different sites.

The first hypothesis here is an attempt at understanding what spatial patterns may be occurring around ASGM sites; that is, it is an attempt to understand what NDVI losses may look like. The second hypothesis is an attempt to understand why those patterns may be occurring, and in finding some sort of correlation between the observed pattern and an alternative possible explanation for that pattern.

Approaches
Exercise 1: Moran’s I

Before examining how patterns of NDVI loss are shaped around ASGM and non-ASGM sites, I first wanted to examine how the patterns of NDVI loss and gain were self-related; that is, I wanted to know whether or not patterns of NDVI change occur near areas of similar change (spatially autocorrelated or clustered) or whether or not these patterns occur away from self-similar patterns (dispersed). To do this, I took my layer of NDVI change and ran a script in Rstudio to generate a Moran’s I value for the global layer, which is a measure of spatial autocorrelation: it results in values between -1 and 1, where values closer to -1 indicate negative spatial autocorrelation (dispersion) and values closer to 1 indicate positive spatial autocorrelation (clustering). I also performed this analysis at a smaller scale around one mining village to see how Moran’s I may change on a different scale.

Exercise 2: Neighborhood Analysis

After determining the spatial autocorrelation of patterns of NDVI change, I could examine how those patterns were related to and varied between ASGM sites and non-ASGM sites. To do this, I performed a neighborhood analysis. At each village, I found its center using the Mean Center tool in ArcGIS Pro. Around each center point, I generated “donut” buffers using the Buffer tool at 250 meter increments, up to 2 kilometers, for eight different donuts (e.g. 0-250m, 251-500m, 501-750m, etc.). I then used each of these buffers to Clip the global NDVI layer (which I had also edited to omit values over rivers and Mali), generating eight raster layers representing NDVI change values between each spatial step. From there, I simply generated histograms for each step and counted the values below 0, indicating NDVI loss from 2007 to 2018, and found their percentage of the total. With these values, I could then see how NDVI loss as a percentage of total pixels in each donut varied with distance from the center of each village, and could more readily understand how NDVI values change with distance from both ASGM and non-ASGM villages.

Exercise 3: Pearson’s R Correlation

After examining how NDVI change across space near ASGM and non-ASGM villages, I wanted to further understand how these patterns may be related to another factor: proximity to a primary road. To do this, I used my road network layer from OpenStreetMaps showing locations of all primary roads in Kedougou and used the Near tool in ArcGIS Pro to find the shortest distance from each of my 18 sites to the nearest primary road. Then, I took only the first values of NDVI change (from 0m to 250m), and, using these values, along with each site’s distance to the nearest primary road, I performed a Pearson’s R calculation (fig. 2). This approach thus attempts to explain how values of NDVI loss around both ASGM and non-ASGM sites may be related to a site’s proximity to a primary road, providing some more information on how different rates of NDVI loss may be affected by the ability of people to access a particular village.

Fig 2: Pearson’s R Correlation. x, in this case, is distance (m) from nearest primary road and y is NDVI loss at the first ring.

Results

I had several results from these three approaches.

Firstly, I produced a global Moran’s I of 0.6716816. The second Moran’s I produced, at a smaller scale, was .8079745. These results helped me to understand that, generally, patterns of NDVI loss are spatially autocorrelated, meaning that areas of loss and growth are clustered near self-similar areas. However, this analysis occurred over such a large area that it is possible the global Moran’s I may be accounting more for patterns of near riparian corridors, which are of course autocorrelated, especially areas where water bodies are visible. As such, this result needs to be interpreted with caution.

Secondly, the results from my neighborhood analysis are presented in fig. 3 [insert graph of NDVI changes for fig. 3; include key in caption]. These results indicate that, on average, villages which have ASGM activity present see more NDVI loss in their immediate surroundings than non-ASGM villages, up to about 2km radially from their center. This means that, generally, villages with ASGM activities experience reduced NDVI, which may indicate vegetation loss or loss healthy vegetation, than villages without ASGM present.

Fig 3: NDVI results. Light blue lines indicate ASGM mines; the dark blue line is their average. Light red lines indicate non-ASGM villages; the dark red line is their average.

Finally, the results of my Pearson’s R correlation indicate relatively weak correlations between a village’s NDVI loss (at least, within the first ring) and its proximity to the nearest primary road. The results are shown in table 1 [insert table screen snip]. For ASGM villages, their correlation was -0.1418, whereas non-ASGM villages have a Pearson’s R of -0.0578. Both of these are weak correlations, there is some indication that ASGM villages and their attendant NDVI losses are more correlated with proximity to a primary road (i.e., the closer they are to a primary road, the more NDVI loss they experience) than non-ASGM villages.

Table 1: Pearson’s R Results

Significance

These results are significant for several reasons. These showed to me that there is an observable pattern of NDVI loss that is greater around villages which develop or engage in ASGM than in villages which do not engage in ASGM. However, there is still some work to do in order to understand the process by which NDVI varies, and it may not be explainable by a village’s proximity to a primary road. There are several possibilities that may explain the process: deforestation is more prevalent around the gold mines for reasons to do with securing timber for construction, or because an inherently larger population at gold mines necessitates more forest product harvesting. More work should be undertaken to understand these processes. On the whole, however, these results are useful for governments or NGOs which seek to help better the conditions which ASGM practitioners participate and engage in, now that they know that ASGM villages experience greater rates of NDVI loss, which may indicate environmental degradation.

Learning

This process taught me several techniques. Firstly, I had no experience at all with R and R Studio, though I do understand some coding. As such, this helped me learn more about a really useful language that I did not have any experience with. Secondly, any practice with ArcGIS Pro is good practice for me, and so I got a lot of time and work done using that piece of software. In particular, I go some good time in with the model builder to help expedite work, as generating eight different ring buffers and then clipping them to an NDVI layer for 18 different villages would have been very time-consuming otherwise. Finally, I gained some good experience performing a statistical analysis, in the form of Pearson’s R. I have very little experience performing statistics, so this was really useful for me to help understand and interpret statistical analyses and results

Statistics

With regard to statistics, I learned several things. The three statistical analyses I used were Moran’s I for spatial autocorrelation, neighborhood analyses for comparing spatial patterns, and Pearson’s R for calculating a simple statistical correlation.

With regards to Moran’s I, I learned the importance of examining patterns at different scales, as a pattern which is apparent at one scale may not be so at another scale, and so interpretation and set-up must be conducted carefully.

Concerning neighborhood analyses, I learned the importance of dilligent workflow and juggling several different variables without muddying the results. This also demonstrated how to examine and structure a problem which seeks to understand change both across space and scross time.

Finally, conducting a Pearson’s R helped me to understand better how statistical analyses are constructed and what their significance is. Additionally, this taught me the value of interpreting a statistical result, as their significance may not always be indicative of anything. Null results or negative results are just as valuable as positive ones, and this analysis showed me that. Now I know more about what process to examine and how to examine them!