7
a need for unified standards that integrate high- resolution,
time-series remote sensing data for more accurate, localized
and direct land use impact assessments (at lease within the
mineral extraction sites).
Evidence (as presented in this study) exist that show that
mining operations in the Democratic Republic of Congo’s
Cobalt-Copper Belt region, has significantly impacted local
ecosystems. This is supported by vegetation loss and habi-
tat degradation, as shown by negative correlations between
cobalt production and vegetation health (estimated via
NDVI). For example, a negative correlation (e.g., Ruashi
mine’s –0.7588 correlation) suggest that increased produc-
tion intensifies biodiversity degradation. Conversely, weak
or positive correlations (e.g., at Kamoto mine) indicate that
reduced mining activity or other mitigating factors (e.g.,
mining in conjunction with environmental conservation)
can limit negative impacts on vegetation health. Mines
can also leverage this insight to develop targeted restora-
tion strategies. The strategic integration of remote sensing
methodologies (as demonstrated in this study) into min-
ing production processes could facilitate the development
of comprehensive, data-driven environmental manage-
ment approaches, thereby ensuring both ecological conser-
vation and strategic ore extraction or mine planning. By
incorporating advanced methodologies and additional data
sources, future studies can further enhance our ability to
monitor, understand and mitigate land use impact induced
by ore extraction intensity at the study location.
REFERENCES
[1] Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei,
M., Moghimi, A., Mirmazloumi, S. M., Moghaddam,
S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S.,
Wu, Q., &Brisco, B. (2020). Google Earth Engine
Cloud Computing Platform for Remote Sensing
Big Data Applications: A Comprehensive Review.
IEEE Journal of Selected Topics in Applied Earth
Observations and Remote Sensing, 13, 5326–5350.
[2] Chen, S., Woodcock, C. E., Bullock, E. L., Arévalo,
P., Torchinava, P., Peng, S., &Olofsson, P. (2021).
Monitoring temperate forest degradation on Google
Earth Engine using Landsat time series analysis.
Remote Sensing of Environment, 265, 112648.
https://doi.org/10.1016/j.rse.2021.112648.
[3] Dong, J., Metternicht, G., Hostert, P., Fensholt, R.,
&Chowdhury, R. R. (2019). Remote sensing and
geospatial technologies in support of a normative
land system science: status and prospects. In Current
Figure 6. Time series comparison of cobalt production data for Kamoto mine with the mean and standard deviation of the
NDVI
a need for unified standards that integrate high- resolution,
time-series remote sensing data for more accurate, localized
and direct land use impact assessments (at lease within the
mineral extraction sites).
Evidence (as presented in this study) exist that show that
mining operations in the Democratic Republic of Congo’s
Cobalt-Copper Belt region, has significantly impacted local
ecosystems. This is supported by vegetation loss and habi-
tat degradation, as shown by negative correlations between
cobalt production and vegetation health (estimated via
NDVI). For example, a negative correlation (e.g., Ruashi
mine’s –0.7588 correlation) suggest that increased produc-
tion intensifies biodiversity degradation. Conversely, weak
or positive correlations (e.g., at Kamoto mine) indicate that
reduced mining activity or other mitigating factors (e.g.,
mining in conjunction with environmental conservation)
can limit negative impacts on vegetation health. Mines
can also leverage this insight to develop targeted restora-
tion strategies. The strategic integration of remote sensing
methodologies (as demonstrated in this study) into min-
ing production processes could facilitate the development
of comprehensive, data-driven environmental manage-
ment approaches, thereby ensuring both ecological conser-
vation and strategic ore extraction or mine planning. By
incorporating advanced methodologies and additional data
sources, future studies can further enhance our ability to
monitor, understand and mitigate land use impact induced
by ore extraction intensity at the study location.
REFERENCES
[1] Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei,
M., Moghimi, A., Mirmazloumi, S. M., Moghaddam,
S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S.,
Wu, Q., &Brisco, B. (2020). Google Earth Engine
Cloud Computing Platform for Remote Sensing
Big Data Applications: A Comprehensive Review.
IEEE Journal of Selected Topics in Applied Earth
Observations and Remote Sensing, 13, 5326–5350.
[2] Chen, S., Woodcock, C. E., Bullock, E. L., Arévalo,
P., Torchinava, P., Peng, S., &Olofsson, P. (2021).
Monitoring temperate forest degradation on Google
Earth Engine using Landsat time series analysis.
Remote Sensing of Environment, 265, 112648.
https://doi.org/10.1016/j.rse.2021.112648.
[3] Dong, J., Metternicht, G., Hostert, P., Fensholt, R.,
&Chowdhury, R. R. (2019). Remote sensing and
geospatial technologies in support of a normative
land system science: status and prospects. In Current
Figure 6. Time series comparison of cobalt production data for Kamoto mine with the mean and standard deviation of the
NDVI