Barriers to remote sensing

Barriers to using remote sensing data and tools for forest governance in the tropics (Preprint in Global Environmental Change)

Remote sensing underpins tropical forest governance, from deforestation alerts to carbon accounting and territorial monitoring. But many of the actors who most need these tools struggle to use them. We asked: What barriers actually block uptake? How do they vary by context and actor? And do common conceptions of the remote sensing “value chain” even include the real end users and hurdles?

What we did. Between June 2023 and August 2024 we conducted 38 semi-structured interviews spanning providers, facilitators, and users (with emphasis on civil servants and Indigenous/local communities), and thematically analyzed transcripts. We also reviewed 19 published/online remote-sensing “value chain” conceptualizations and compared them to interview evidence. 

What we found. Six recurring sets of challenges limit effective use: 

(1) quality of accessible data (resolution, continuity/accuracy, cloud cover, limited ground-truthing)
(2) resources & expertise (hardware, connectivity, training, funding)
(3) retention of staff & institutional knowledge (turnover, short contracts, loss of champions)
(4) unclear institutional mandates (low leadership buy-in, weak coordination)
(5) bureaucracy & regulations (procurement hurdles, reliance on outdated methods, sovereignty rules that restrict external data)
(6) access & integration (indirect access via intermediaries, weak feedback loops to providers, and data-sovereignty concerns among Indigenous communities). These institutional barriers disproportionately affect under-resourced agencies and communities and often matter more than technology per se. 

What’s missing in current “value chains”. Most diagrams portray a one-way pipeline from satellites to generic “users,” rarely depicting diverse end users, barriers, or the role of facilitators. We propose a revised value chain with bi-directional flows, contextualized end users, explicit barriers, and facilitators (space agencies, development banks, NGOs) as first-class actors.

Why it matters. Democratizing remote sensing requires more than new data products: it needs user-centered training and tools, durable mandates and documentation to survive turnover, procurement reforms, and careful handling of data sovereignty alongside open-science goals. Practical steps include train-the-trainer models, curriculum pipelines, simpler interfaces (including NLP/AI aids), and direct relationships between subnational users and data providers (not only national-level agreements).

Many thanks to my great team of co-authors and to everyone who consulted on and participated in the study - this would not have been possible without you!

For the full picture check out the preprint of the publication below.