Frontiers in Environmental Science (Nov 2019)

Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities

  • David Saah,
  • David Saah,
  • David Saah,
  • Karis Tenneson,
  • Mir Matin,
  • Kabir Uddin,
  • Peter Cutter,
  • Peter Cutter,
  • Ate Poortinga,
  • Ate Poortinga,
  • Quyen H. Nguyen,
  • Quyen H. Nguyen,
  • Matthew Patterson,
  • Gary Johnson,
  • Kel Markert,
  • Kel Markert,
  • Africa Flores,
  • Africa Flores,
  • Eric Anderson,
  • Eric Anderson,
  • Amanda Weigel,
  • Amanda Weigel,
  • Walter L. Ellenberg,
  • Walter L. Ellenberg,
  • Radhika Bhargava,
  • Aekkapol Aekakkararungroj,
  • Aekkapol Aekakkararungroj,
  • Biplov Bhandari,
  • Biplov Bhandari,
  • Nishanta Khanal,
  • Ian W. Housman,
  • Peter Potapov,
  • Alexandra Tyukavina,
  • Paul Maus,
  • David Ganz,
  • Nicholas Clinton,
  • Farrukh Chishtie,
  • Farrukh Chishtie,
  • Farrukh Chishtie

DOI
https://doi.org/10.3389/fenvs.2019.00150
Journal volume & issue
Vol. 7

Abstract

Read online

Land cover maps are a critical component to make informed policy, development, planning, and resource management decisions. However, technical, capacity, and institutional challenges inhibit the creation of consistent and relevant land cover maps for use in developing regions. Many developing regions lack coordinated capacity, infrastructure, and technologies to produce a robust land cover monitoring system that meets land management needs. Local capacity may be replaced by external consultants or methods which lack long-term sustainability. In this study, we characterize and respond to the key land cover mapping gaps and challenges encountered in the Lower Mekong (LMR) and Hindu Kush-Himalaya (HKH) region through a needs assessment exercise and a collaborative system design. Needs were assessed using multiple approaches, including focus groups, user engagement workshops, and online surveys. Efforts to understand existing limitations and stakeholder needs resulted in a co-developed and modular land cover monitoring system which utilizes state-of-the-art cloud computing and machine learning which leverages freely available Earth observations. This approach meets the needs of diverse actors and is a model for transnational cooperation.

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