2
ensure a more comprehensive understanding of the long-
term consequences on land quality. This data is expensive
and time-consuming to acquire, which has led to the lack
of data.
A robust land use impact assessment method requires
good reliable data on the footprint of the mine and the
biodiversity attributes of the geographic area over time.
The use of satellite imagery to acquire such time series data
for mineral extraction sites has emerged as a more accurate
alternative to manual biodiversity surveys, offering supe-
rior spatial and temporal resolution in assessing biodiversity
(land use) impacts. By leveraging high-resolution time-
lagged remote sensing imagery, researchers can quantify
mine land use intensity and its corresponding biodiversity
impacts to acceptable accuracy.
Remote sensing techniques, leveraging satellite multi-
spectral platforms such as Sentinel‑2 and the Landsat mis-
sion series, have emerged as powerful tools for biodiversity
assessment and quantification. Landsat 7 is equipped with
the Enhanced Thematic Mapper Plus (ETM+) sensor, which
can capture both multispectral and panchromatic imagery.
The satellite provides data in multiple spectral bands, spe-
cifically bands 1 through 8, each with a spectral resolution
of 30 meters. Among these, Band 3 (red) and Band 4 (near-
infrared, NIR) are particularly significant for applications
such as distinguishing between vegetation types, monitor-
ing plant health, and detecting cultural features. Remote
sensing indices are mathematical combinations of different
spectral bands from satellite imagery, designed to emphasize
specific features or conditions on the Earth’s surface. These
indices simplify the analysis and interpretation of remote
sensing data by focusing on characteristics, such as vege-
tation health, water bodies, or soil properties. One com-
monly used index is the Normalized Difference Vegetation
Index (NDVI), which is utilized to assess vegetation health
and density. NDVI values range from –1 to 1, with higher
values indicating healthier and denser vegetation. Here we
seek to integrate mining activities into the life cycle impact
assessment of critical mineral supply chains. Specifically, we
focus on utilizing remote sensing indices noted earlier to
estimate the diversity of plant and animal species in areas
impacted by mining operations. NDVI, a remote sensing
tool, is used to assess vegetation health and density, which
indirectly reflects biodiversity and the presence of various
plant species (Vinod Janse et al., 2018 Xue and Su, 2017).
We propose to leverage this approach to evaluate species
richness in mining- affected regions, thereby facilitating the
assessment of mining’s life cycle land use impact.
The objective of this paper is to present a novel frame-
work for assessing and quantifying land use impacts based
on interdecadal Earth observation data and mathematical
algorithms. This paper presents a comparative analysis of
multispectral satellite data and mining production data to
evaluate the viability of remote sensing in assessing mining-
related biodiversity impacts. By integrating satellite-derived
environmental indicators with ground-based production
metrics, the study aims to identify key spectral signatures
indicative of vegetation loss, habitat degradation, and other
biodiversity changes.
The significance of this work lies in its potential to
transform the way mining-related environmental impacts
are monitored and managed. By leveraging multispectral
satellite data, the study introduces a scalable, cost-effective,
and non-invasive approach to assess biodiversity changes,
addressing a critical need for more efficient and accurate
environmental monitoring tools in the mining sector. The
integration of remote sensing with mining production data
provides a novel framework for quantifying the ecological
footprint of mining activities, offering valuable insights for
stakeholders, including policymakers, environmental man-
agers, and industry leaders. This research contributes to the
advancement of remote sensing applications and supports
global efforts toward sustainable mining practices and bio-
diversity conservation.
PROPOSED FRAMEWORK
To achieve the stated objectives of this study, we employ a
systematic modeling framework, comprising satellite imag-
ery collection, geographic information of selected mines,
cobalt ore production, Normalized Difference Vegetation
Index (NDVI) estimation, visualization, and statistical
evaluation of results. We leveraged Google Earth Engine
(GEE) (Gorelick et al., 2017 Amani et al., 2020 Chen
et al., 2021), a cloud- based platform for geospatial data
analysis. Developed by google, GEE offers computing plat-
form and access to a large-scale satellite imagery for vari-
ous application. This platform was employed for time series
analysis of the land use and vegetation cover changes within
the study area using Landsat imagery from 2000 to 2023.
Through GEE platform, we processed large Landsat data-
sets via scripts written within the platform, allowing effi-
cient computation and extraction of meaningful insights
suitable for the study objectives from the Landsat satellite
images. We characterized the individual mines using their
location coordinates and base polygons data that were
sourced from S&P Global platform (S&P Global,2024),
this platform provides comprehensive global geospatial
ensure a more comprehensive understanding of the long-
term consequences on land quality. This data is expensive
and time-consuming to acquire, which has led to the lack
of data.
A robust land use impact assessment method requires
good reliable data on the footprint of the mine and the
biodiversity attributes of the geographic area over time.
The use of satellite imagery to acquire such time series data
for mineral extraction sites has emerged as a more accurate
alternative to manual biodiversity surveys, offering supe-
rior spatial and temporal resolution in assessing biodiversity
(land use) impacts. By leveraging high-resolution time-
lagged remote sensing imagery, researchers can quantify
mine land use intensity and its corresponding biodiversity
impacts to acceptable accuracy.
Remote sensing techniques, leveraging satellite multi-
spectral platforms such as Sentinel‑2 and the Landsat mis-
sion series, have emerged as powerful tools for biodiversity
assessment and quantification. Landsat 7 is equipped with
the Enhanced Thematic Mapper Plus (ETM+) sensor, which
can capture both multispectral and panchromatic imagery.
The satellite provides data in multiple spectral bands, spe-
cifically bands 1 through 8, each with a spectral resolution
of 30 meters. Among these, Band 3 (red) and Band 4 (near-
infrared, NIR) are particularly significant for applications
such as distinguishing between vegetation types, monitor-
ing plant health, and detecting cultural features. Remote
sensing indices are mathematical combinations of different
spectral bands from satellite imagery, designed to emphasize
specific features or conditions on the Earth’s surface. These
indices simplify the analysis and interpretation of remote
sensing data by focusing on characteristics, such as vege-
tation health, water bodies, or soil properties. One com-
monly used index is the Normalized Difference Vegetation
Index (NDVI), which is utilized to assess vegetation health
and density. NDVI values range from –1 to 1, with higher
values indicating healthier and denser vegetation. Here we
seek to integrate mining activities into the life cycle impact
assessment of critical mineral supply chains. Specifically, we
focus on utilizing remote sensing indices noted earlier to
estimate the diversity of plant and animal species in areas
impacted by mining operations. NDVI, a remote sensing
tool, is used to assess vegetation health and density, which
indirectly reflects biodiversity and the presence of various
plant species (Vinod Janse et al., 2018 Xue and Su, 2017).
We propose to leverage this approach to evaluate species
richness in mining- affected regions, thereby facilitating the
assessment of mining’s life cycle land use impact.
The objective of this paper is to present a novel frame-
work for assessing and quantifying land use impacts based
on interdecadal Earth observation data and mathematical
algorithms. This paper presents a comparative analysis of
multispectral satellite data and mining production data to
evaluate the viability of remote sensing in assessing mining-
related biodiversity impacts. By integrating satellite-derived
environmental indicators with ground-based production
metrics, the study aims to identify key spectral signatures
indicative of vegetation loss, habitat degradation, and other
biodiversity changes.
The significance of this work lies in its potential to
transform the way mining-related environmental impacts
are monitored and managed. By leveraging multispectral
satellite data, the study introduces a scalable, cost-effective,
and non-invasive approach to assess biodiversity changes,
addressing a critical need for more efficient and accurate
environmental monitoring tools in the mining sector. The
integration of remote sensing with mining production data
provides a novel framework for quantifying the ecological
footprint of mining activities, offering valuable insights for
stakeholders, including policymakers, environmental man-
agers, and industry leaders. This research contributes to the
advancement of remote sensing applications and supports
global efforts toward sustainable mining practices and bio-
diversity conservation.
PROPOSED FRAMEWORK
To achieve the stated objectives of this study, we employ a
systematic modeling framework, comprising satellite imag-
ery collection, geographic information of selected mines,
cobalt ore production, Normalized Difference Vegetation
Index (NDVI) estimation, visualization, and statistical
evaluation of results. We leveraged Google Earth Engine
(GEE) (Gorelick et al., 2017 Amani et al., 2020 Chen
et al., 2021), a cloud- based platform for geospatial data
analysis. Developed by google, GEE offers computing plat-
form and access to a large-scale satellite imagery for vari-
ous application. This platform was employed for time series
analysis of the land use and vegetation cover changes within
the study area using Landsat imagery from 2000 to 2023.
Through GEE platform, we processed large Landsat data-
sets via scripts written within the platform, allowing effi-
cient computation and extraction of meaningful insights
suitable for the study objectives from the Landsat satellite
images. We characterized the individual mines using their
location coordinates and base polygons data that were
sourced from S&P Global platform (S&P Global,2024),
this platform provides comprehensive global geospatial