Author Archives: zochg

Examining the Spatial Patterns of NDVI Change near ASGM and non-ASGM villages in rural Senegal

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!

Ex3: Correlation of Mining and Non-Mining Villages’ NDVI Loss with Distance to Nearest Primary Road

Following up from Exercise 2, where I determined how NDVI loss relates to distance from village center, I now wanted to explore whether or not the NDVI loss I observed could be explained or attributed to another factor: the distance of a village from a primary road.

To do this, I acquired a shapefile of roads from OpenStreetMaps for Senegal, which I used alongside my locations of mining and non-mining villages, ArcGIS Pro for some simple spatial work, and Excel to calculate a simple correlation.

First, I imported the shapefile of roads in Senegal to ArcGIS Pro, and first determined what the nearest actual road to each village was (fig. 1). Based on this first-pass glance, it appears the more mining sites in my sample than non-mining sites are nearest to “primary” paved roads, and that the majority of non-mining villages are near “tertiary” roads. This simply means that, out of my sample, more mining happens closer to paved roads, which would make sense: gold needs to be transported from gold sites, and equipment/supplies need to be transported in.

Fig 1

Next, I manipulated the shapefile to only include features which were designated “primary” roads (fig. 2). I then used ArcGIS Pro’s “Near” tool to generate new attributes for each set of villages which listed the shortest distance to the nearest primary road in meters.

Fig 2: Green dots are non-mining villages; red dots are mining villages

With these distances in hand, I plotted them in excel against the NDVI % loss values for the first ring (0-250m) around each village, as a simple first pass of understanding how distance from a primary road may be related to the amount of NDVI loss being experienced at a particular road (fig. 3).

Fig 3

Then, within Excel, I calculated a simple correlation (a la fig. 4) to determine how distance from a nearest primary road relates to amount of NDVI loss experienced by a village.

Fig 4: x, in this case, is distance (m) from nearest primary road and y is NDVI loss at the first ring.

For mining villages, I calculated a correlation of -.804, and for non-mining villages, I calculated a correlation of .686. This means that, for this sample at least, the NDVI loss experienced at a mining village is negatively correlated with distance from a primary road; that is, as a mining village’s distance from a primary road increases, its NDVI loss should decrease. This makes perfect sense: mining villages closer to primary roads are easier to access both by miners and by other users, and to leave as well, encouraging larger groups of people to live there, which would decrease NDVI as a result of their activities.

Non-mining villages, on the other hand, are more difficult to explain. One explanation, which may be plausible, is that more distant villages have less opportunities for other livelihoods which are not environmentally dependent, and so are more obligated to work in and with the environment, perhaps encouraging vegetation loss as users rely on and drawdown more on their environment.

Overall, I think this was a useful approach for examining another possible explanation for why mining villages may have higher rates of NDVI loss than non-mining villages. Mining villages are closer to primary roads, which means they are more accessible to potential miners, and thus more likely to grow larger than non-mining villages, and drawdown more on their environment. That being said, as can be seen in Fig. 3, this is not a perfect relationship by any means, and a more robust analysis would be useful to say much more. But, as a first pass, it’s useful for illustrating some additional explanation.

Spatial Pattern of NDVI change from the Center of an Artisanal Gold Mine

For Exercise 2, I wanted to explore how the spatial distribution of land use/land cover (LULC) varied in relation to an artisanal, small-scale gold mine (ASGM). To do this, I took the NDVI change maps which I had generated for Exercise 1 (modified slightly to produce better, more accurate results), as well as my dataset of mapped footprints of known areas of ASGM activity, found the median center of those areas, generated buffers/donuts at 250m apart, from 250m to 2000m, clipped the NDVI change layer to each buffer, and counted the amount of each type of NDVI pixel contained within each buffer. In addition, I performed this same analysis around non-mining villages, to examine how the spatial pattern of NDVI loss changes with distance from the center of non-mining villages. With this data, I could generate a chart showing how the percentage of pixels representing loss or decrease in NDVI changed as you moved further away from the center of mining activity. This examination looked at nine mining villages and nine non-mining villages

In order to perform this analysis, I continued my work using ArcGIS Pro, in addition to Excel for a bit of charting work.

To begin, I imported my NDVI change map, which detailed increases and decreases in NDVI values between imagery taken in April 2018 with Landsat 8, and imagery taken in April 2007 with Landsat 5, representing 11 years of NDVI change. I also imported my shapefile containing polygons which I had digitized over my high-resolution satellite imagery depicting areas of known ASGM activity. With this shapefile, I used ArcGIS’s Median Center tool, which found the median center of the mining areas near the village of Douta (fig. 1). From there, I generated buffers/rings at 250m intervals (e.g. 0-250m, 251-500m, 501-750m, etc.), from 250m to 2000m around this median center. I then used the clip tool to clip the overall NDVI change layer to each buffer, resulting in NDVI change for 0m to 250m from center, NDVI change for 251m to 500m from center, and so on.

Fig 1: Center of the village of Douta with a 2km NDVI buffer; the blue buffer shows the 1750m extent, whose values were subtracted from the 2000m values, to only give values between 1751 and 2000m

Once I had accomplished this, I generated histograms for each buffered NDVI change layer in order to count the amount of pixels contained within each buffer, and assign them to one of two classes: negative values, representing overall NDVI decrease from 2007 to 2018, and positive values, representing overall NDVI increase from 2007 to 2018. I did not account for magnitude of change, as I wanted a general idea of how NDVI was changing from the center. Fig. 2 shows an example of these histograms, specifically for the 500m buffer. The values from the previous buffers, e.g. the one to 250m, were subtracted to only show values from 251 to 500m.

Fig. 2: Douta histogram

I entered all the pixel values for negative change at each distance into Excel, as well as all of the pixel values for positive change, and was able to generate a chart showing how the percentage of the overall pixels in each subsequent buffer from center representing NDVI decrease change over distance (fig. 3)

Fig. 3: Pale blue lines indicate individual mining villages. The dark blue line indicates the average of those villages. Thin red lines indicate non-mining villages. The dark red line indicates the average for those villages.

On the whole, this exercise was useful for illustrating the problem I’m attempting to grapple with. I was frustrated with the lack of Landsat imagery from the late 2000s — I was unable to find any Landsat 5 imagery corrected for surface reflection aside from the year 2007. Additionally, there are problems with comparing this 2007 image to the 2018 image. I found that the rainy season before the 2018 image was taken, in 2017, was wetter than average, while I was unable to determine the rain fall that preceded the image in 2007. As such, it is possible that the 2018 Landsat 8 image shows a non-normal vegetative pattern — or it’s even possible that the 2007 image is showing a non-normal vegetative pattern! I require some more investigation into the historical meteorology of the area before I can say either way. Regardless, I feel that this is a useful first step in investing how LULC change relates to the establishment of ASGM.

Grant Z’s Exercise 1: Determining Moran’s I of LULC/NDVI change in rural Senegal

For this first exercise, I wanted to determine how land use/land cover (LULC) was spatially auto-correlated with itself in my region of interest. In order to do this, I acquired two Landsat images, one from the past and one from present, conducted an NDVI (normalized difference vegetation index) analysis on each, determined the difference between the two, and then ran a Moran’s I function over that image to determine how changes in NDVI are related to each other. By understanding this, I can know better how patterns of LULC manifest in the landscape, and their spatial pattern.

The software I used to approach this problem were ArcGIS Pro to import imagery, clip imagery, and perform raster calculations for computing NDVI, and RStudio to import the NDVI raster and run a Moran’s I function on it.

Firstly, I downloaded Landsat imagery from GloVis, the USGS Global Visualization Viewer, which is a repository for all Landsat data, as well as some imagery from other satellites. I selected my area of interest and searched for Landsat 5 imagery from ~2008 and Landsat 8 imagery from 2018 — I avoided Landsat 7 as a malfunction on that sensor has led to gaps in its data. Ultimately, I downloaded one Landsat 5 image from January 2010 (the only one available which had no cloud cover) and one Landsat 8 image from January 2018, to determine 8 years of change.

I then added the red and near-infrared (NIR) bands for each image into ArcGIS Pro. I first performed an intersect over all the layers to generate a common footprint. From there, I performed an NDVI analysis using the raster calculator tool on each image set (Landsat 5 and Landsat 8), using the classic NDVI formula (NIR – red)/(NIR + red). I then subtracted the 2010 NDVI raster from the 2018 NDVI raster to determine areas of change. The figure below shows the ultimate 8 year difference NDVI image I output. Areas of red represent declines in vegetation between the two images; yellow areas represent no change; green areas represent growth in vegetation.


Overall NDVI

With this raster depicting NDVI change in my AOI, I then wanted to know how the pattern of change related to itself. To do this, I performed a spatial auto-correlation function on both the large image, and a subset image, to find out its Moran’s I. I examined two images in order to superficially examine how scale affected the spatial auto-correlation of LULC change.

My first Moran’s I, of the overall image, was 0.6716816. As a positive number, this indicates that there is some amount of spatial auto-correlation taking place; that is, areas of vegetation change tend to occur near one another. The code I used is below.


Moran’s I of overall image

Next, I performed the exact same analysis with a subset image of the overall image, to explore how Moran’s I changed with scale. I explored a large area surrounding a village I’m familiar with. The Moran’s I for this analysis was 0.8079745, which is higher than the overall image. This indicates that, potentially, there is stronger spatial auto-correlation at smaller scales.

Overall, I feel that this approach is a good jumping off point into further exploring how LULC changes in my area of interest are related to other processes. Ultimately, I’m curious as to whether these LULC changes can be attributed in some way to the establishment of artisanal gold mining in the area. One good control for this would be to examine LULC change between years without establishment of gold mines, to see if it follows a similar pattern to the years of change, and if it is spatially auto-correlated as in this exercise.

Grant Z’s Spatial Problem

My spatial problem is about land use/land cover (LULC) change associated with the establishment of artisanal, small-scale gold mines (ASGM) in rural Senegal. Put in the vernacular we’ve learned in the course, I’d phrase my question this way:

How is the spatial pattern of LULC change related to the spatial pattern of ASGM establishment via the mechanism of deforestation?

As part of their establishment, ASGM requires clearing the land where the mines will be, as well as additional timber harvesting to build the homes where the miners will live while working and additionally to bolster the mines shafts themselves. As such, I’m curious as to what the exact change is that accompanies ASGM establishment, as this is a sub-set of my graduate thesis which seeks to understand more broadly how ASGM impacts the environment and the lives of the miners themselves, to understand better if a household diversifying into ASGM is better suited towards adapting to future climate change than if they hadn’t.

The dataset I have is very high resolution (VHR) satellite imagery (panchromatic and multispectral) courtesy of the Digital Globe Foundation. The panchromatic imagery has a resolution of .3m while the MS imagery is around 1.24m — as part of the preprocessing I’ve pansharpened the images so the overall imagery is stills sub-meter, which is necessary to investigate ASGM as its footprint is too small for detection with Landsat or other sensors. The imagery covers about 16 gold mines I’ve identified, and has imagery from 2018 and 2009/2010.

My present hypothesis is that, while obviously there will be a decrease in LULC at the mine in general, the area around the mine will also be indicative of some change — in my literature review, I’ve found some information that the environment up to 20km around a gold mine can be impacted. I think in this case the impact won’t be as drastic, but it’s what I’m expecting.

Currently I’m not sure how to approach the problem. The first step will be to just map out the mining locations first in ArcPro and from there try to see how the forest cover has changed between present and past. Beyond that I’m not sure how to answer the question.

Ultimately I’d like to produce maps, to demonstrate the environmental impact that ASGM has (or not, potentially!). I’ve also considered producing similar maps showing the relative impact subsistence agriculture has had in non-mining villages as a comparison.

I feel somewhat prepared for this task. I’m comfortable working with ArcGIS and have some knowledge of ArcPy (though I’m a bit rusty). I can use ModelBuilder pretty competently and feel that I have a good grasp of what to do in that arena. My major hurdle right now is not knowing what I don’t know — I need some more exposure to the tools available to me for addressing this problem. However, I feel confident that with enough time and work, this problem is not intractable!

One problem is that, given the rural area and part of the world I’m looking at, Digital Globe does not frequently sample the area and so the imagery I have is not necessarily on the anniversary date. Given the drastic climate differences (rainy season and dry season), the vegetation may look drastically different.

As an example, bellow is Kharakhena in November 2008, with the original village highlighted in red.

Here is the village of Kharakhena in March 2017. The village is highlighted in yellow and the mine in blue.

The difference is very dramatic, but I’m not sure how to approach this analytically.

This is my spatial problem! I believe that this is significant as a part of my thesis work exploring livelihood diversification in rural Kedougou. As 70% of the population in the region lives below the poverty line, and most people engage in subsistence farming as their primary livelihood, I am interested in how ASGM operates as a livelihood diversification option. Specifically, I’m interested in assessing whether it is an “erosive” coping strategy: one which households undertake out of lack of other options and one which may diminish the household’s overall capacity to react to future stresses and shocks. I’m assessing this through an evaluation of the “Three Capitals” (sometimes five) which constitute a household’s assets. The three capitals are Economic, Environmental, and Social. I’m assessing the impact of ASGM on miners’ Social and Economic capital through interviews which I’ve already conducted, and I’m assessing the impact of ASGM on miners’ Environmental capital through remote sensing. Together, I hope to paint a holistic picture of ASGM’s impact on miners’ livelihood capitals in the region in order to better understand if it is indeed an “erosive” coping strategy, which, if it is, needs to be known in order to help miners and farmers find different ways to adapt to future climate change.