Sand Mining - Plugging a Critical Data Gap

May 14, 2024

Sand Mining - Plugging a Critical Data Gap

“Sand .. is the essential ingredient that makes modern life possible. Without sand, we couldn’t have contemporary civilization” – Vince Beiser, "The World in a Grain"

Sand is a fundamental building block of modern life – it is a key ingredient in concrete, asphalt, glass, and many other items of daily use. After water, sand is perhaps the world’s most valuable natural resource, accounting for 85% of all mineral extraction (Pearce, 2019). Given its importance in construction, sand is vital to economic growth, especially in the emerging economies of the world which are experiencing rapid urbanization. Year after year, the demand for sand is growing dramatically. In 2021, over 50 billion tons of construction-grade sand and gravel were mined from rivers and beaches across the world in 2021 alone (Fritts, 2019).

The rate of extraction across the world is increasingly unsustainable (UNEP, 2020) and has severe environmental and socio-economic consequences. While governments across the world have attempted to regulate and monitor sand mining, it remains incredibly challenging to do so. As a result, illicit extraction is rampant and widespread, and the global black market for sand could be worth USD 300 billion annually (Taylor, 2024). It is hardly surprising that the UNEP refers to sustainable sand mining as one of the most pressing ecological challenges currently facing the planet (UNEP, 2021; Peduzzi et al. 2019).

The Environmental and Human Costs of Sand Mining

As one might imagine, there are many different types of sand grains, each having various physical properties that make them suitable and desirable for specific industrial processes. Construction sand is primarily quartz and can be found across the world’s riverbeds and beaches, and in some cases, in inland quarries. Excessive sand mining in these settings can have devastating consequences: it hastens erosion, threatens local biodiversity and livelihoods, and accelerates the effects of climate change.

In Monterey County (California) - where coastal sand mining was prevalent till 2021 (when the last mine was shut down) – it is estimated that the mining activities contributed to coastal erosion rates ranging from 0.5 meters/year to 1.5 meters/year (Thornton et al, 2006). In India, excessive sand mining in riverbeds is widely associated with groundwater depletion and the widespread destruction of riparian habitats. While rigorous evidence on the human and socioeconomic costs of sand mining is limited, the growing demand for sand is likely to trigger conflicts (Beiser 2017, Salopek 2019), increase violence against civil society and journalists (Schilis-Gallego 2019, Ghosh 2022), and disrupt rural livelihoods. It's important to note that other forms of sand mining too are associated with substantial human costs. For example, high-purity quartz (used for fracking) is extensively mined in Wisconsin, Illinois, Minnesota, and Michigan, and is believed to damage agricultural land, increase air pollution, and contaminate surface water.

A Critical Data Gap

Effective regulation, monitoring, and enforcement are vital if we are to manage our dwindling sand resources in a sustainable manner. However, any such efforts are impeded by a lack of reliable data on the location of existing sand deposits and the scale and scope of sand mining activities. There is also little accountability: across the world, we have very few systems in place to track the consumption and demand for sand in a manner that allows us to verify provenance. This is particularly salient in the Indian context, where illicit mining is rampant. In some locations, the true amount of sand mined is estimated to be over 15 times the amount recorded in official statistics (Arasu, 2017). This lack of suitable data on sand mining is highlighted in the UNEP’s Sand and Sustainability Report (UNEP 2019), with the need of the hour being “comprehensive knowledge on occurrence and distribution, composition, and dynamics” of sand mining.

A number of ongoing efforts attempt to plug these critical data gaps; for instance, India Sand Watch (by Veditum India Foundation) enables the “collection, annotation & archiving of data related to sand mining in India”. Similarly, the UNEP’s Marine Sand Watch provides a means to track marine dredging using data from the “automatic identification system (AIS)” emitted by dredging vessels. My own ongoing research (in collaboration with Ando Shah and Joshua Blumenstock) complements these efforts by building a set of open-source tools which enable the production of high-resolution maps of sand-mining activity around the world.

Sand Mining Watch

Our work automates the detection of sand mining activity in near-real time, thus offering a pathway to improving monitoring, transparency, and accountability. In order to do this, we rely on multi-spectral satellite imagery (Sentinel 2, 10 m resolution), which is available (on average) for most locations across the globe at least twice a month. The macro-signatures of sand mines (scarring, pitting, etc.) are easily identifiable in this imagery, as highlighted in Figure 1 below. We combine this imagery with state-of-the-art machine learning algorithms, which have been pre-trained on a large amount of earth observation data. While satellite imagery and machine learning have been used to detect and monitor mining activity in other contexts (Gallwey et al. 2020, Boakye et al., 2019; Obodai et al., 2019, Saavedra., 2023, and Amazon Mining Watch), sand mining has a number of distinct characteristics that require a more customized approach.

First, sand mining is highly dynamic in nature: the exact location of a mine often changes depending on the season, and the water levels in a river. Second, sand mines can range in size from a few square meters to several hectares, posing a substantial challenge for accurate prediction. The tools we build are thus optimized to account for these peculiarities. Our initial results are promising, and suggest that we can build a relatively powerful sand mine detection model by leveraging a relatively small dataset (~ 50 mining sites, spanning a few hundred square kilometers) of hand-labeled high-quality ground-truth data. Our models use knowledge and data from India Sand Watch (ISW), and we are currently piloting and evaluating our results in India in collaboration with ISW / Veditum India Foundation (our research partners). In addition to generating maps, we also aim to generate a panel dataset of mining activity, which then enables us to pursue a rigorous analysis of the socio-economic consequences of sand mining.

You can read more about our ongoing work here, and view recent updates from our recent field trip here. We are grateful to the Mozilla Foundation and CEGA for their generous support, and Veditum India Foundation for sharing their knowledge and expertise with us.

Fig 1. Sand mines (within red circles) on the Sone River, near Patna, Bihar. Sentinel 2 satellite imagery, June 2023. 


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