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.
