Analytical Support to Decision Making
We locate, analyse, and layer data to inform intelligent action and provide analytical support to decision-makers at every level. We do this in a variety of ways, depending on the needs of the partner and the conservation context.
The following case studies illustrate some of our work.
Case Study 1: Baseline Analysis of a National Park in Central Africa.
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Connect with Local Partner
Langland is approached by a local partner with management authority over a Protected Area.
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Data Collection and Collation
We combine any and all sources of available data; including patrol data, remote sensor data, wildlife telemetry, satellite and drone imagery, and an ever increasing range of publicly available and subscription based open-source datasets.
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Data Analysis
Using the data gathered and considering the requirements of the partner we conduct in-depth analysis on the protected area. Here we take into account the many factors influencing conservation outcomes, including; environmental, communities, biodiversity, criminality and threat.
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Synthesis
Outputs are bespoke to the partner’s needs, but typically we produce map based illustrations of the findings and recommendations from analysis to identify areas of vulnerability, recommendations on optimising resource allocation and evidence based predictions of likely future trends that may affect conservation efforts both within the park and in neighbouring communities that co-exist with conservation operations.
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Ongoing Support
We aim to pass knowledge and capability to partners at the frontline of conservation efforts. To that end we provide in-country analytical training to ensure that local partners have the tools and capabilities to both collect and analyse their data more effectively.
Case Study 2: Protecting Pangolins in Kenya
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Connect with Local Partner
Langland is approached by a regional conservation organization to assist their work in addressing critical challenges in a Giant Pangolin habitat within a key forest ecosystem. The project aims to support the protection for this rare and endangered species using cutting-edge remote sensing technologies and AI.
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Data Collection and Collation
Working through key technology partners, high-resolution multispectral satellite imagery is sourced in an area 1000km2. A detailed training dataset is prepared for machine learning models, annotated to identify key landscape features such as forest cover, linear partitions, and human structures.
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Data Analysis
A bespoke deep learning model is trained to analyze the satellite imagery, detecting patterns of habitat degradation such as deforestation and the construction of barriers. Advanced mapping tools are used to quantify changes to the landscape, highlighting risks, conservation opportunities, and priority areas. Connectivity analysis identifies critical wildlife corridors and fragmentation.
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Synthesis
A detailed report is produced, providing tailored conservation advice. Strategic recommendations are included to guide conservation actions and resource allocation. High-resolution maps are provided delineating tree cover, barriers, and settlements to support operational planning.
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Future Application
Building on the project’s success, Langland Conservation is working with partners to expand this methodology to neighboring conservation areas, adapting and refining the model to address regional ecological challenges and support larger-scale conservation goals.
