Thermal Earth observation for monitoring livestock enclosures and livestock numbers in Kenya
The topic is also suitable for GEM students in track 3 – GEM for Ecosystems & Natural Resources.
Suggested elective courses:
- Thermal Infrared remote sensing
- Scientific programming for geosciences
- UAV for Earth observation
Fieldwork:
Fieldwork is foreseen, particularly in case chosen by RANGE-funded MSc students. A key desire is to replicate some of the findings in the north; there could be potential to perform another UAV-based study, pending on funding and authorizations. However, proper visiting and mapping of a good number of bomas is required. The study can strongly link to work by one of the PhD students in Samburu, Kenya (Benjamin Loloju). A separate option for this thesis could be to continue the work on Kapiti Research Station, but then with sourcing of a multi-temporal dataset of thermal satellite imagery. Supervisors will in the coming six months also explore the potential for this.
The livelihoods of many people in the East African drylands heavily depend on livestock. Monitoring livestock populations is important for assessing food security and the effects of droughts, flagging potential areas of conflict over limited resources, and supporting policy making and governance. However, detecting individual animals from space is challenging due to their small size and dispersed distribution. In the East African drylands, livestock are typically secured at night in enclosures known as bomas for protection against predation and theft. Previous studies used daytime spectral imagery to map bomas based on the different spectral response of the accumulated manure (e.g. Vrieling et al. 2022). However, due to the dynamic nature of bomas, this may not give precise information on their active use. Thermal imagery is an alternative, making use of the thermal contrast between the dozens to hundreds of animals within a confined area and the colder surrounding at night. This study evaluates the potential of current and future thermal satellite missions to detect active (occupied) livestock enclosures.
To assess the potential of mapping livestock bomas and assessing livestock numbers, controlled experiments were carried out in 2025 with a thermal sensor onboard an Uncrewed Aerial Vehicle (UAV) at the Kapiti Research Station and Wildlife Conservancy near Nairobi, Kenya. The experiments spanned a total of eight nights: five at the end of the dry season (September 2025) and three during the wet season (November 2025). Both campaigns employed a DJI Mavic 3T Enterprise drone with a thermal camera (8-14 μm) to map bomas and surrounding landscape over the course of several night-time flight missions. The experimental setup included six bomas constructed for this study of three size classes, each accommodating 0 to 150 cows (weaners, i.e. young cows of 8-12 months old). Animal numbers in the bomas were varied across the eight nights, representing empty enclosures with zero animals and stepwise increasing animal numbers to very dense concentrations of up to 150 animals per enclosure. In addition, other existing bomas in Kapiti containing cattle, sheep, goats, and camels, i.e. livestock species typical of East African pastoral systems. The study bomas were constructed with metal fencing, and their dimensions reflected the range of typical boma sizes across larger dryland regions.
Initial experimental results indicate that thermal contrast between bomas and surroundings allows for their detection, also at coarser spatial resolutions. This offers potential for boma mapping with new and future satellite missions (including ESA’s LSTM, ConstellR’s HiVE, SatVu’s HotSat). Nonetheless, further evaluation is needed to 1) assess if beyond the experimental conditions (mobile) bomas in the wider East African rangelands, such as in northern Kenya, can effectively be detected with thermal imagery, and 2) evaluate or confirm if thermal acquisitions from satellite platforms indeed are effective in detecting bomas within larger landscapes.
This MSc project links to the RANGE (Resilient Approaches in Natural ranGeland Ecosystems) Project, which supports resilient livelihoods in rangeland ecosystems, as well as to an ongoing collaboration with the International Livestock Research Institute. The research will be conducted in close collaboration with local partners, aligning with ongoing initiatives to build a comprehensive understanding of dryland dynamics.
The main objective of this research is to assess if livestock enclosures (bomas) can be reliably mapped at landscape level in Kenya based on thermal satellite imagery. The study can benefit from existing experiments with thermal UAV acquisitions over bomas, but will aim to a) move beyond experimental conditions to bomas in practical use by pastoralists in northern Kenya, and b) assess boma identification with thermal satellite acquisitions. Depending on the quality and amount of field data that can be collected, a further objective intends to relate the intensity (and size) of thermal contrast between animals and surroundings to animal numbers inside a boma. Together with supervisors, the student will acquire new-generation thermal satellite imagery for specific areas of interest.
Although the MSE student should have a well-developed technical focus to succeed, the topic has a strong interdisciplinary nature. Wickedness is guaranteed by the fact that currently information on livestock numbers across landscapes is very scarce across Africa, but also that information on livestock numbers may face privacy issues and could potentially induce conflict. The SIS part is well-covered, given that collection, processing, analyzing, and visualization of thermal imagery is a key component of the intended work. The TE aspect would consist of understanding and modelling the thermal signals and developing an innovative tool for livestock monitoring. Lastly, SPG elements are critical in the reflection on how improved insights on livestock distribution may affect stakeholders’ behaviour and whether and at what scale such data could be openly published and shared.