TY - JOUR
T1 - Nation-wide mapping shows linkages between sand mining activities, human land use, and hydrology in Rwanda
AU - Huang, Ke
AU - Iversen, Lars Lønsmann
AU - Fensholt, Rasmus
AU - Gominski, Dimitri
AU - Brandt, Martin
AU - Mugabowindekwe, Maurice
AU - Strange, Niels
AU - Bendixen, Mette
N1 - Publisher Copyright:
© 2025
PY - 2025
Y1 - 2025
N2 - Mining of sand, gravel, and stone, collectively referred to as aggregates, has increased in recent years due to growing usage of these materials in developing urban landscapes and increasing living standards, especially in African countries that are pursuing rapid economic growth. The rise in demand, however, has wide-ranging consequences for the environment from which the material is extracted. Nevertheless, our knowledge of where the material is extracted from remains insufficient, which hinders assessments of the overall sustainability of extraction. Here, we provide a nationwide distribution of sand extraction in Rwanda by applying deep learning on highly refined resolution aerial imagery. We show that mining sites increased by 18% to 3,751 between 2008 and 2023, which are spatially linked to river valleys and urban development at the sub-watershed level. We further reveal that the expansion of sand extraction over the past decade often encroached into agricultural areas. Our approach offers a scalable way to monitor sand extraction activities to assess their implications on the natural and built environment.
AB - Mining of sand, gravel, and stone, collectively referred to as aggregates, has increased in recent years due to growing usage of these materials in developing urban landscapes and increasing living standards, especially in African countries that are pursuing rapid economic growth. The rise in demand, however, has wide-ranging consequences for the environment from which the material is extracted. Nevertheless, our knowledge of where the material is extracted from remains insufficient, which hinders assessments of the overall sustainability of extraction. Here, we provide a nationwide distribution of sand extraction in Rwanda by applying deep learning on highly refined resolution aerial imagery. We show that mining sites increased by 18% to 3,751 between 2008 and 2023, which are spatially linked to river valleys and urban development at the sub-watershed level. We further reveal that the expansion of sand extraction over the past decade often encroached into agricultural areas. Our approach offers a scalable way to monitor sand extraction activities to assess their implications on the natural and built environment.
KW - deep learning
KW - highly refined resolution satellite imagery
KW - mining
KW - natural resources
KW - resource extraction
KW - sand extraction
U2 - 10.1016/j.oneear.2025.101236
DO - 10.1016/j.oneear.2025.101236
M3 - Journal article
AN - SCOPUS:105000226254
SN - 2590-3330
VL - 8
JO - One Earth
JF - One Earth
IS - 3
M1 - 101236
ER -