Use of Current Remote Sensing Methods for Biodiversity Monitoring
and Conservation of Mount Kilimanjaro National Park Ecosystems
Fortunata Msoffe
1,2
, Thomas Nauss
1
and Dirk Zeuss
1
1
Philipps Universitat, Umweltinformatik, Deutschhausstrasse, 12, 35032, Marburg-Hessen, Germany
2
Tanzania National Parks, P.O. Box 3134, Arusha, Tanzania
Keywords: Climate-land-use Change, Kilimanjaro-Mountain National Park, Remote Sensing, Sentinel-2,
Vegetation-indices.
Abstract: Climate and land use change have become serious challenges facing protected areas globally, more so those
in the tropical forest ecosystems. Kilimanjaro-Mountain National Park was specifically designated to protect
and safeguard the highest free-standing mountain in the tropics. The park attracts thousands of national and
international tourists annually because of its snow capped-summit and the altitudinal gradients, representing
the different eco-climatic zones of the world. Earnings from tourism boost the country’s economy while
ensuring the sustainability of this unique glacial-tropical mountainous forest ecosystem park. Conventional
monitoring of key biodiversity and environmental parameters are carried out by park staff, following
established guidelines by Tanzania National Parks. However, given the park’s geo-morphological nature of
mountainous terrain, efficient implementations of the labor intensive in-situ observations are hardly
feasible. This research explored the use of Remote Sensing data from the European Satellite Agency–
Sentinel- 2 Multi-Spectral Instrument, in developing a state-of-the-art monitoring protocol. The developed
methodology ensures that essential biodiversity parameters, including Vegetation Indices, required in
monitoring the vast areas of the park and its surroundings in the short-term and long-term, using up to date,
high resolutions and frequently available Remote Sensing data from the Sentinel-2 Sensors are captured.
1 INRODUCTION
It is well known that protected areas particularly
those in the tropics face key challenges linked to loss
of wildlife habitats, mainly due to land use changes
in their surrounding ecosystems whilst exacerbated
by the increasing impacts of global climate change
(Peters, et al. 2016; Burgess, et al., 2017; Tabor, et
al., 2018). Kilimanjaro Mountain National Park and
its associated ecosystem represents such a world-
wide unique and diverse habitat, with an altitudinal
range of over 5,800 m and associated with climate
and vegetation zones changing from the tropical
savannas at the lowlands to the afro-alpine
grasslands at the top (Hemp, 2009). Apart from the
natural ecosystems within the national park, several
land use types occur in the vicinity, including
intensive annual monocultures (maize and other
cereals), perennial coffee-plantations and diverse
traditional agro-forestry systems of Chagga-Home-
gardens which to some extent retain a semi-natural
forestry structure (Hemp and Hemp, 2018).
This highest free-standing mountain in Africa
acts as a water tower by feeding major river systems
in the region. The tropical mountainous forest
ecosystem plays a major role in the regional climate
regulation, while providing many other important
ecosystem services to the locals and beyond (Hemp
and Hemp, 2018). Its melting “ice cap”, which is an
important tourism attraction by mountaineers and
tourists visiting the park every year, though caused
by decreasing precipitation (Hemp, 2005;
Thompson, et al. 2002) rather than by increasing
temperature, has become a global symbol for the
accelerating trend of global warming. The KiLi-
Project, funded by the German Research Foundation
(DFG) studied the influence of climate and land-use
change on biodiversity and multiple ecosystems
processes on Mount Kilimanjaro from 65 established
plots, across twelve different land covers and land
uses along the elevation gradient (vertically from the
lowlands of Colline savannas to the highest peak of
vegetation layer dominated by the Hellichrysum)
and across the land use gradient from the protected
Msoffe, F., Nauss, T. and Zeuss, D.
Use of Current Remote Sensing Methods for Biodiversity Monitoring and Conservation of Mount Kilimanjaro National Park Ecosystems.
DOI: 10.5220/0009357701750183
In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020), pages 175-183
ISBN: 978-989-758-425-1
Copyright
c
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
175
tropical montane forest of Kilimanjaro National Park
to the disturbed lowlands of the savannas currently
converted to intensive monoculture crops cultivation
(Appelhans, et al., 2016).
This study, capitalizes on the recently concluded
“KiLi1”-Project (2010-2018), with the main
objective of a follow-up monitoring strategy for the
Kilimanjaro National Park, being the custodian in
ensuring the continuity of the ecosystem services
provided by the park to the local, national and the
international community at large. Apart from the
direct ecosystem services provided by the park in its
natural settings, it is particularly a key tourist
destination in the country, contributing to the local
and national economy from the foreign currency
accrued through the tourism business and its tripling
effects to the local communities surrounding the
park. In doing so, Kilimanjaro National Park is
vested with the responsibility of protecting this
uniquely massif standing tropical montane cloud
forest in the long run, at the face of its increasingly
isolation from its surroundings, mainly through
habitat conversions from the natural forest
vegetation to croplands because of the adjacent
intensifying land uses spearheaded by the increasing
human population pressure (Hemp and Hemp,
2018).
The study explored the use of remote sensing by
deploying the current state of the art from the
Sentinel-2 Multi-Spectral Instrument (MSI) satellite
of the European Satellite Agency (ESA), in
developing a workflow protocol data-model tool.
The implementation of the workflow protocol will
enable “in-situ repeated observations up-scale”,
which are hardly feasible in such a large protected
area’s challenging ecosystem by park staff. The
currently available data from the Sentinel-2 MSI
provides multi-spectral bands with high spatial
resolutions and quick revisit time of five days for
both sentinel 2 A & B (ESA, 2017). The Sentinel- 2
MSI is comprised of 13 spectral bands ranging in
resolutions from 10 m, (four bands) inclusive of the
visible wavelengths; 20 m (six bands) inclusive of
the new “Red-Edge”, near-infra red and short-wave
infra-red wavelengths; important for vegetation
monitoring and with high capabilities for use in
ecosystem, biodiversity and conservation monitoring
(Drusch, et al. 2012). The other three bands are of 60
m resolution including the aerosol, water vapor and
cirrus bands (Table 1).
Table 1: Spectral bands available from the Sentinel- 2 A
(since June 2015) and Sentinel- 2 B (since March 2017)
NIR = near infra-red; SWIR = short wave infra-red;
(Credit: ESA, 2017).
Sentinel -2 MSI
Bands
Central
Wavelengths
(µm)
Resolution
(in m)
Band 1 – Coastal
Aerosol
0.443 60
Band 2 – Blue 0.490 10
Band 3 - Green 0.560 10
Band 4 – Red 0.665 10
Band 5-Red edge 1 0.705 20
Band 6-Red edge 2 0.740 20
Band 7-Red edge 3 0.783 20
Band 8- NIR 0.842 10
Band 8A – NIR 0.865 20
Band 9-Water vapor 0.945 60
Band 10 – SWIR-
Cirrus
1.375 60
Band 11-SWIR- 1 1.610 20
Band 12-SWIR- 2 2.190 20
Spectral signatures and derived indices like the
Normalized Difference Vegetation Index (NDVI)
are used as a standardized way to measure the health
of vegetation by quantifying the ratio of the
difference between the NIR (strongly reflected by
vegetation) and Red (strongly absorbed by
vegetation). NDVI values ranges from -1 to + 1,
with a distinct boundary for each type of land cover.
Negatives likely represent water, while positives
close to one indicate dense green leaves. However,
values close to zeros represent no leaves (green
vegetation) or degraded forest (Figure 1). In this
research project, Sentinel – 2 MSI spectral bands
and derived products, including the vegetation and
biophysical indices such as NDVI and leaf area
index (LAI), were explored and analyzed with the
following objectives;
Explore current RS data and methods in
developing a biodiversity and conservation
monitoring tool in the Kilimanjaro National
Park and its surrounding ecosystems
Link remote sensing derived information
with in-situ data and predict/retrieve
spatially explicit biodiversity measures
Demonstrate the explanatory power in
biodiversity analysis and its contribution to
a deeper understanding of the biodiversity
GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management
176
and potential linkages to ecosystem
functions along climatic and anthropogenic
disturbance gradients at Mt. Kilimanjaro.
Figure 1: Spectral Bands Reflectance’s Mapping
Application of the Sentinel-2 MSI (Credit: ESA, 2017).
2 MATERIALS AND METHODS
2.1 Study Area
The Kilimanjaro Mountain National Park and its
ecosystems, is located in the north-east of Tanzania
(Figure 2) and spans an elevation gradient from the
colline savanna plains (~ 700 m a.s.l.) to the
glaciated areas encircling Kibo summit (5895 m
a.s.l.). Its equatorial daytime climate is shaped by
the passing of the inter-tropical convergence zone,
with more than half of the annual rainfall occurring
during the so called long-rainy season (March-May),
(Appelhans, et al., 2016). While annual precipitation
amounts to more than 2500 mm in the southern
montane forest belt, the northern mountain side (lee
ward) receives hardly more than 1000 mm (Hemp
2005; Detsch, et al., 2017).
The mountain’s belt-like vegetation zonation
(Figure 3) is characterized by major land-cover
transitions at short horizontal distances resulting
from changing climatic conditions and
anthropogenic interferences (Hemp & Hemp 2018).
This study covered the land-cover distributional
zones which were marked by the 65 Kili-Research
Project Plots (Detsch, et al., 2017; Appelhans, et al.
2016), which were distributed along the mountain
elevation gradient; from the lowlands of savanna
vegetation to the top elevation zone covered by the
Hellichrysum spp. Plots were also selected to cover
twelve land-cover uses, from the total protected
forests in the park, dominated by Ocotea,
Podocarpus and Helichrysum vegetation to the
intensive monoculture crop cultivation dominated by
Maize and the Coffee plantations.
Figure 2: Study Area- the Kilimanjaro Mountain Ecosystems in Africa-Tanzania-(inset), showing the distribution of the
sampled plots along the elevation and land-cover/use types gradient (Appelhans, et al., 2016).
Use of Current Remote Sensing Methods for Biodiversity Monitoring and Conservation of Mount Kilimanjaro National Park Ecosystems
177
Figure 3: Map of Kilimanjaro Mountain Ecosystems showing land-cover/use vegetation types (Hemp and Hemp, 2018).
2.2 Sentinel 2 MSI Data Sets
Sentinel- 2 datasets used in this project were
downloaded from the ESA-Copernicus website,
https://sentinels.copernicus.eu/web/sentinel/sentinel-
data-access/registration. The
Kilimanjaro is covered
with two-sentinel 2 tiles area wide; the T37MBS on
the western side and the T37MCS on the eastern part
according to the date of acquisition, in this case
scenes taken between 2016 and 2019. Sentinel 2 A
and B image data were searched and downloaded for
the study area. The selected scenes were chosen
based on cloud cover percentages in order to obtain
good quality images. However, given the nature of
the Kilimanjaro, being a tropical cloud montane
forest, it is almost impossible to acquire cloud-free
scenes at any time of the year. Images with less than
10 percent of cloud covers were downloaded from
both sentinel 2 A and B, as Level 1 C Top of
Atmosphere (TOA) products, through the Sentinel
Application Platform (SNAP) Software. SNAP is
also available for free through downloading at the
ESA-Copernicus website, with its associated plug-
ins, for the pre-processing of the Sentinel-2 data.
The downloaded images were further processed
for atmospheric effects from the top of atmosphere
Level 1C to bottom of atmosphere (BOA) Level 2A
products, using the Sen2Cor Algorithm-plug-in from
SNAP (Wilm, 2018). In order to automate the
process, the image products (L1C) were exported
from the SNAP- graphical processing tool (.gpt) into
the R software, as .xml files for commands and
functions creation using the R Studio processing
environment. In R, image files were further
corrected for atmospheric effects, to Level 2A
products using the Sen2Cor algorithm. Image
outputs were then used for the follow-up stages of
extracting the various vegetation and biophysical
indices from the sentinel-2 imagery data. In order to
create a cloudless image, the T37-MBS and T37-
MCS tiles had to be processed separately before
mosaicking of the tiles using the Cloud-Mask layer,
also produced as part of the level 2A processing
output, performed in R as shown in Figure4below.
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Figure 4: Workflows for access, pre- and processing,
analyses and sharing Outputs of Image Data from
Sentinel-2 through the TANAPA GIS Server.
2.3 Vegetation and Biophysical Indices
Derived from Sentinel-2 Data
Creation of commands-data models for indices
extraction automation was performed after the
atmospheric corrections for each of the scenes,
according to the date of acquisition, as year, month
and day for the matching tiles. In order to ensure that
the sampled indices from the Level 2A image
products were not affected by clouds, further
processing of the Level 2A image scenes using
cloud masks application, as a byproduct of sentinel-
2 data from the classification, was performed in R.
Several vegetation and biophysical indices including
the NDVI and its derivatives, and the leaf area index
(LAI) were extracted using the created “Snap
Models” function in R-Software environment. The
output products (images), were exported as image
files (Geotiff) into a GIS (QGIS/ArcGIS) software
for visualizations and further analyses, including
overlaying with the plots (sampled sites of the KiLi-
project) for final products output and sharing with
park staff as a web-service maps through the
Tanzania National Parks (TANAPA) Server, located
at the headquarters office in Arusha.
3 ANALYSES AND RESULTS
Sentinel-2 data used here, provides the ability to
carry out consistent monitoring work at high spatial
resolutions ranging from 10m, while providing a
wide opportunity for the use of the Red-Edge bands
(bands 5, 6 & 7) for a vast of biodiversity
measurements for ecosystem monitoring (Rocchini,
et al. 2015). This is because RS allows
measurements of large regions in a short period of
time thereby providing continuous information about
vegetation status. The reflectance and emission of
light from the Earth‘s surface can be directly related
to physiological, morphological and structural
composition of plants (Jetz et al., 2016).
Several studies have proven a significant
correlation between species richness and spectral
indices (Peters, et al. 2016). The most common used
indices are NDVI, capturing the greenness and
chlorophyll content. The green normalized
difference vegetation index (GNDVI), a modified
form of NDVI (Clevers, et al. 2002), which linearly
correlates with LAI, the transformed soil adjusted
index (TSAVI), which corrects the variations of soil
background (Huete, 1988) and a simple ratio
between Red and NIR, RVI; have also been used in
various studies for assessing and monitoring
biodiversity in tropical forest ecosystems (Rocchini,
et al. 2015). All these indices were explored in the
study and proved to be useful in monitoring the key
biodiversity parameters at Kilimanjaro and its
ecosystems, where frequent in-situ observations by
field staff may be difficult given the nature of the
terrain of this unique cloud montane forest.
Several vegetation and biophysical indices were
derived from the final Sentinel- 2 images that were
chosen based on the quality of their scenes output
from the processes described in 2 above, with their
descriptions in Table 2, and a few of the selected
indices are presented in Figure 5.
3.1 Arvi Maps (2019, 2018 and 2017)
The Atmospherically Resistant vegetation index
(Arvi), resistant to the atmospheric effects (in
comparison to the NDVI) is accomplished by a self-
correction process for the atmospheric effects on the
red channel. Arvi takes advantages of the different
scattering responses from the blue and red bands to
Use of Current Remote Sensing Methods for Biodiversity Monitoring and Conservation of Mount Kilimanjaro National Park Ecosystems
179
Table 2: Vegetation indices derived from Sentinel 2-MSI sensor products (images) used in this study between 2017 and
2019, for both tiles T37-MBS and T37-MCS of the Kilimanjaro Mountain ecosystems.
Index Index Application Derived formula from image bands
NDVI
Normalized Difference Vegetation Index (The most
commonly used in RS studies) its values range between -1
and +1 (Rouse, et al. 1973)
NIR-RED
NIR+RED
RVI
Ratio Vegetation Index, also known as Simple Ratio has
high reflectance for vegetation than soil, water and snow-
preferred for mapping vegetation (Jordan, 1969)
NIR
RED
ARVI
Atmospherically Resistant Vegetation Index, accomplishes
self-atmospheric corrections in the red channel (Kaufman &
Tanre, 1992)
NIR-RED-y(RED-BLUE)
NIR+RED-y(RED-BLUE)
TNDVI
Transformed NDVI- preferred because it’s the square root of
NDVI and so its values are always positive (can be larger
than 1) (Senseman, et al. 1996)
√ (NIR-RED + 0.5)
NIR+RED
IRECI
Inverted Red Edge Chlorophyll Index utilizes the Red-Edge
bands (bands 5, 6 & 7) currently present in sentinel 2 data
(Clevers, et al. 2002)
(NIR *NIR-RED1*RED1)
(RED2*RED2/RED3*RED3)
GNDVI
Green Normalized Difference Vegetation Index is strongly
correlated to the leaf area index and hence chlorophyll
content (Gitelson, et al. 1996)
NIR- GREEN
NIR+GREEN
NDI45
Normalized Difference Index is more linearly, less saturated
at higher values than NDVI, (Delegido, et al. 2011b)
(NIR*NIR- RED*RED)
(NIR*NIR+RED*RED)
TSAVI
Transformed Soil Adjusted Vegetation Index is used to
correct the effects of soil line arbitrary values due to
slope/terrain of the area on vegetation (Baret & Guyot, 1991)
(NIR*NIR-s *RED*RED-a)
(a*NIR*NIR+RED*RED-a)
retrieve information regarding the atmospheric
opacity (the blue sky). These properties therefore
have determined that Arvi, has a similar dynamic
range to the NDVI, but is on average four times less
sensitive to the atmospheric effects than the NDVI
(Jetz, et al., 2012). Areas of dense green vegetation
(the montane forest belt) showed clearly high
reflectance (more brightness) compared to non-
vegetated areas of the Kilimanjaro, as shown in
Figure 5.
3.2 Ireci Maps (2019, 2018 and 2017)
The Inverted Red Edge index (Ireci) algorithm
incorporates the reflectance in four bands to estimate
canopy chlorophyll content. The “red edge” is the
name given to the abrupt reflectance change in the
680±740 nm region of vegetation spectra that is
caused by the combined effects of strong chlorophyll
absorption and leaf internal scattering. The position
of the red edge has been used as an indication of
stress and scene sense of vegetation, (Clevers, et al.,
2002). The time series images show darker tones in
areas void of vegetation compared with areas of the
montane forest with brighter tones, for all the years;
2017 to 2019 (Figure 5).
3.3 Tndvi Maps (2019, 2018 and 2017)
The transformed normalized difference vegetation
index (Tndvi) algorithm indicates a relation between
the amount of green biomass that is found in a pixel
and it’s the square root of NDVI. It is superior to
NDVI in that it has higher coefficient of
determination for the same variable and always has
positive values and the variances of the ratio are
proportional to the mean values. Due to limitations
from effects of clouds for obtaining more quality
data, our results indicate that Tndvi could be better
in determining changes of time series related to
monitoring tropical cloud forests, but additional
analyses are needed in the future (seeFigure5).
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Figure 5: Derived vegetation indices from the sentinel-2 data for the Kilimanjaro between 2017 and 2019. Bright areas
represent high values and dark areas low values of vegetation coverage. (Arvi= atmospherically resistant vegetation index;
Ireci=Inverted red-edge chlorophyll index; Tndvi= Transformed normalize difference vegetation index).
4 DISCUSSION AND
CONCLUSIONS
This study attempts to show the use of current
remote sensing sensors capabilities, in particular the
Sentinel- 2 MSI sensor of ESA, in providing data for
ecological, biodiversity and conservation
monitoring, much needed in managing vast
protected areas such as Kilimanjaro Mountain and
its ecosystems. Previous research work indicated
that future climatic characteristics of the Kilimanjaro
ecosystems are mainly determined by the local land-
use and global climate change (Thompson, et al.
2002; Detsch, et al. 2017; Hemp & Hemp, 2018),
and therefore it is imperative that Kilimanjaro
Mountain National Park management is able to
consistently carry out a monitoring program for the
key biodiversity and environmental parameters in
for both current and long-term plans. A workflow
(protocol) developed here, (Figure 4) which is
automated has been customized in such a way that it
can be easily followed up, even with non-RS
specialists, which is usually the case in many
protected areas. Just with a minimum of a computer
that is connected to the ArcGIS Server at TANAPA
headquarters office in Arusha, through the internal
network, the data model workflows output is
uploaded, ran and results can be accessed for sharing
using the available web-services in the ArcGIS
Server network, through the publishing services
(ESRI, ArcGIS Vers. 10.6) .
Results from the derived indices ascertain that it
is possible to monitor key biodiversity changes using
the workflows data-protocols developed here, given
the fact that Sentinel-2 data are also available for
free downloads through the ESA website, and
expected to continue its operations for free data
availability at least until 2028 (Skidmore, et al.
2015). The time-series images indicated that this
technique can be used to show areas of change
(increase or decrease in cover), either through
anthropogenic disturbances such as land-cover
conversions for cultivation and other illegal
activities in the forest and/ or natural phenomenon
associated with climate change, such as recurring
wild fires in the montane forest in previous decades
(Hemp, 2009; 2005). The outputs derived from the
data workflows model will provide early warnings
on the environmental conditions and lead in carrying
out more detailed ground surveys, at focused areas.
Such surveys will guide management decisions for
quick interventions using the established
protocols/methodologies while ensuring cost-
effective park operation undertakings.
The use of the Sentinel-2 data products studied
here will enable an integrated ecosystem
Use of Current Remote Sensing Methods for Biodiversity Monitoring and Conservation of Mount Kilimanjaro National Park Ecosystems
181
measurement and monitoring, through the derived
indices of vegetation like the NDVI- and its
derivatives including TNDVI, ARVI, IRECI,
TSAVI and more as well as the biophysical indices,
such as LAI and the fraction of vegetation cover
(FVC), (Wang, et al. 2018). These indices provide a
bird’s eye view snapshot urgently needed to monitor
these vast areas, while contributing to the global
biodiversity conservation and monitoring agenda,
especially needed in achieving the Aichi
Conservation Targets (2011-2020), in developing
essential biodiversity variables (EBV) from RS data
(Alleaume, et al., 2018; Skidmore, et al., 2015).
Different spectral bands combination derived from
Sentinel-2 sensors, ranging from the Visible, Red-
Edge, Near and Short Infra-red spectra, important
for biodiversity monitoring will provide data from
consistent images of time series indices like NDVI
and its derivatives for rigorous analyses in
biodiversity monitoring of the park and its
surrounding ecosystems.
In order to overcome limitations from accessing
enough continuous image data from scenes that are
cloud free, caused by the nature of the cloud forests,
like in the Kilimanjaro Mountain ecosystems, the
developed data-model protocols provide for
automation of step by step in the selection of
available scenes and enhancement techniques
needed to obtain the final products for further
analyses. Further work in this study would explore
the variances in the ratios obtained for the different
indices’ derived here in relation to each of the
research plots/sampled sites, along the elevation and
across the different land cover/use types gradients in
the study area.
ACKNOWLEDGEMENTS
This work was supported by the German Research
Foundation (DFG), through the KiLi1-Project
(2010-2018), as part of the post-project synthesis
phase for monitoring key biodiversity aspects in the
Kilimanjaro Mountain Ecosystems. F. Msoffe’s time
at Marburg was supported through the Katholischer
Akademischer Auslander Dienst (KAAD)-
Stipendiatum (Scholarship) between 2018 and 2020
as a Post-doc Researcher at the department of
Physical Geography, Umwelti-informatik, Philipps
University, Marburg, Germany.
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