Using Satellite Imagery to Build Foundation of Elephant Corridors in Namibia

Introduction

Land conversion is driving habitat loss and fragmentation on a global scale, with Sub-Saharan Africa experiencing significant impacts due to the expansion of agricultural development corridors. These corridors, while boosting economic growth, are fragmenting dryland and savanna ecosystems and disrupting critical wildlife migration routes and ecological connectivity. In Namibia’s Kunene region, this issue is particularly pronounced, as desert-adapted elephants, already navigating one of the most arid landscapes on Earth, are increasingly forced into human-dominated areas in search of food and water. According to Weidlich (2008) and Hauptfleisch (2018), this growing overlap between elephant habitats and farmlands has intensified human- elephant conflict, leading to crop destruction, water source depletion and, in some cases, retaliatory killings.

Failure to understand migration dynamics and their drivers endangers the successful protection of animals and is likely to exacerbate HEC (Bohrer et al., 2014). Establishing and restoring elephant corridors is crucial, and satellite imagery has proven to be a powerful tool in identifying, planning, and monitoring these vital pathways.

This project focuses on Namibia's southernmost population of desert-adapted elephants, which comprises just 21 individuals. Since 2003, the Elephant-Human Relations Aid (EHRA) has collaborated with local communities and landowners to protect these iconic elephants, striving to ensure their survival beyond the boundaries of Namibia's national parks.

This research aims to support EHRA by analysing elephant movements and their motivating factors and studying the environmental characteristics of the movements in commercial farms of the Kunene and Erongo regions using Geographic Information System (GIS) and Remote Sensing (RS) techniques. This project has been supported by the Airbus Foundation and the Connected Conservation Foundation. Their contributions include HR satellite imagery, funding and expert guidance in GIS, machine learning and Earth Observation throughout the study.

Methodology and analysis

Data 

Satellite imagery for this study was obtained from the Airbus Pleiades satellite constellation that consists of Pleaides-1A and Pleiades-1B, which were launched in 2011 and 2012, respectively. The satellites provide very-high-resolution (VHR) imagery at 50cm with a 20 km swath width. The images were captured between July 2023 and April 2024. Other datasets include topography, river network, population density, fence lines, farms and water points. EHRA provided elephant movement data captured by GPS collars between January and April 2024. 

Tables 1.1 and 1.2 below describe imagery, ancillary and elephant telemetry data, respectively.

Table 1.1: Satellite imagery and ancillary data

Table 1.2: Elephant movement data

Feature extraction

i. Land use, land cover and habitat mapping

Airbus Pleiades images were classified using Random Forest (RF) and Support Vector Machine (SVM) algorithms. The classification was done in Google Earth Engine. The table below shows the parameters used in the classification for each algorithm.

The resulting land cover map included a total of 4 land cover classes: Trees, Shrubland, Grassland and Bare areas. Based on the land cover map, a habitat map was derived according to the International Union for Conservation of Nature (IUCN) Habitat Classification Scheme by relating European Space Agency (ESA) Climate Change Initiative (CCI) land cover classes to IUCN Level 2 habitat types.

ii. Home range

Using the GPS collar data, a heatmap was created for each elephant at a radius of 1 kilometre using Kernel Density Estimation (KDE). The layers were analysed to identify motivators in the areas of high density. 

iii. NDVI

A Normalised Difference Vegetation Index was computed using Red and Near Infrared bands of the Airbus Pleiades imagery.

iv. Slope 

The Slope was derived from the SRTM elevation data and classified into 3 categories: steep, moderate, and gentle slopes.

V. Human population density

The population density of the study area was calculated from the global dataset Gridded Population of World (GPW) and categorised into four classes: high, medium, low, and very low.

Vi. River network

Rivers with an order of 2 were selected for this analysis primarily because the region is characterised by arid conditions, making these secondary streams critical sources of water and food in such a dry environment.

An analysis of VHR Airbus Pleiades imagery was conducted in Google Earth Engine to detect water sources using the Modified Normalized Difference Water Index (MNDWI) complemented by extensive field validation to ensure accurate detection and verification of water bodies. The combination of high-resolution satellite imagery and ground truthing provided a comprehensive approach to identifying water sources in the study area.

Analysis and Results

All the layers were loaded and overlaid in the Google Earth Engine platform for easier analysis. Elephant collar data were compared with each environmental and ancillary variable to identify patterns.

b. NDVI

NDVI analysis shows that elephants frequently move across landscapes in search of healthy vegetation. Their movement is often concentrated in regions with abundant green vegetation as these areas provide better food resources. This suggests that elephants are selective in their movements, optimising their routes based on vegetation availability and health.

c. Slope

Analysis shows that elephants prefer moving across gentle flats rather than steep slopes. Flat terrain allows for easier movement and access to resources, while steep slopes may present physical challenges and energy costs, so elephants avoid them. This tendency to navigate through flat areas helps optimise their energy use as they search for food and water across the landscape.

d. Population density

These Elephants tended to avoid densely populated areas, with their movement concentrating in regions of low population density. This behaviour likely reflects their efforts to avoid human settlements, reducing the risk of conflict with people. Encounters with human populations can lead to harmful situations, such as crop raiding or retaliatory harm, which elephants instinctively seek to avoid. Consequently, their movement patterns highlight a preference for safer, less disturbed areas where human presence is minimal, reducing potential conflicts.

e. Man-made water points and fence lines

Elephants in the study area are frequently observed moving across the landscape in search of water points, which are vital for their survival. Nearly all man-made water sources in the region have been visited multiple times by elephants tracked during the study. In some cases, elephants have been observed breaking electric fences to gain access to water points located within commercial farms.

However, in areas where high-voltage electric fences are present, elephants appear to be restricted, as these fences prevent them from crossing. Instead, they often move along the fence lines, attempting to find an entry point or alternative access to water sources. This behaviour underscores the importance of access to water availability and the lengths elephants will go to meet their hydration needs. In the left image, you can see how the elephant's movement is repeated and restricted by fencing. 

f. Home range

Based on the heat density analysis, elephant movement from January to April 2024 was concentrated in areas with healthy vegetation and near water sources. Elephants repeatedly sought out regions with abundant resources, indicating patterns of movement and their reliance on productive landscapes for sustenance.

Map output and review

EHRA has conducted community workshops with key stakeholders, using identified motivating factors and elephant movement patterns to inform collaborative efforts in delineating elephant corridors and integrating farm planning, including:

Commercial farmers: EHRA, commercial farmers and the Ministry of Environment, Forestry and Tourism (MEFT) convened to address the significant financial impact of elephants traversing commercial farms. Farmers validated conflict hotspots, water points and fences, providing key data that was digitised for analysis. EHRA also set up an EarthRanger platform, giving farmers controlled access to live elephant collar data to deter elephants sustainably and repair fences quickly. These initiatives have greatly improved collaboration and conflict mitigation.

Communal farmers, conservancies and traditional authorities: The maps highlighting elephant movement and conflict areas helped stakeholders fairly prioritise mitigation projects for maximum impact.

Tourism industry: Using the movement, situational and satellite data, EHRA demonstrated how unethical tourism threatens this vulnerable population. In partnership with TOSCO, major tourism associations, and MEFT, EHRA is leading the Ethical Elephant Tourism campaign to promote responsible practices that support conservation.