Automated Forest Restoration: Could Robots Revive Rain Forests?
Elliott, S., G. Gale & M. Robertson (Eds), 2020. Automated Forest Restoration: Could Robots Revive Rain Forests? Proceedings of a brain-storming workshop. FORRU-CMU, Chiang Mai Thailand, 2015, 254 pp.
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The proceedings of a brainstorming workshop - "Automated Forest Restoration; Could Robots Revive Rain Forests?" - held in Chiang Mai Thailand in October 2015, hosted by FORRU-CMU. Comprises 14 original papers on various technologies, currently used or under development, to perform forest restoration tasks. The volume culminates in an agenda, which proposes priority research topics, to advance new technologies or create new ones that will facilitate cost-effective restoration of forest ecosystems, over large areas or on remote sites. Technologies covered include drones, imaging, artificial intelligence, bio-herbicides, GIS software and camera traps. Foreword by David Lamb.
INTRODUCTION: In 2014, the UN New York Climate Summit set a goal to restore forest to 350 million hectares of degraded land by 2030, to counter climate change. Conventional tree-planting with human labour is unlikely to achieve this goal, due to the inaccessibility of most sites available for restoration and limited labour availability. This volume, therefore, establishes the basic concepts of forest restoration (ecological restoration), summarizes the tasks necessary to achieve it and the potential for emerging technologies to carry them out. Drones, with tree recognition software, could rapidly provide GPS coordinates of native seed trees, in natural forest, to facilitate seed collection, or they might collect seeds autonomously, using robotic arms, suction tubes or rotating brushes. Drones are already being used to carry out aerial seeding. The need is to develop rapidly bio-degradable “designer seed-bombs”, which protect seeds from desiccation with hydrogels, whilst also providing them with nutrients, growth promoters and micro-organisms, to promote seedling establishment. Combined with plant-recognition technology, drones might also be able to spray herbicides to control weeds, whilst avoiding killing trees, and accurately deliver fertilizer around tree seedlings These processes could be fully automated, by recharging drone batteries with solar-powered inductive charging pads. Monitoring forest canopy closure is already possible with drone-mounted sensors. Advances in plant recognition software will probably enable auto-monitoring of recovery of plant species diversity soon, whilst recovery of bird or mammal communities could be recorded by remote microphones and camera traps. Data from such devices could be transmitted via the telephone network or by using drones as “data mules”. Many of these technologies already exist, but to develop practical auto-restoration systems, they must be improved (e.g. longer drone-battery life), made cheaper and more rugged, to operate for long periods in tropical climates. Intensive collaboration among ecologists and technologists, will be essential, to achieve viable and cost-effective auto-restoration systems.
CONTENTS (click on each chapter for more details):
Chapter 1 - Forest restoration: Concepts and the Potential for its Automation
Stephen Elliott
Chapter 2 - Unmanned Aerial Vehicles for Automated Forest Restoration
Pimonrat Tiansawat & Stephen Elliott
Chapter 5 - Developing aerial seeding by UAVs: lessons from direct seeding
Dia Panitnard Shannon & Stephen Elliott
Chapter 7 - Aerial Robotics for Forestry Sowing and Management
Lot Amoros & Jesus Ledesma
Chapter 8 - Smart seed for automated forest restoration
Pedrini. S., D. Merritt & K. Dixon
Chapter 9 - Innovation and Robotics in Forestry Weed Management
Bruce A. Auld
Chapter 11 - Basics of Automated Plant Identification
Pierre Bonnet & Dawn Frame
Chapter 12 - Automated Vegetation Monitoring for Forest Restoration
Ryan Chisholm & Tom Swinfield
Chapter 13 - Auto-Monitoring of Wildlife Recovery
George A. Gale & Sara Bumrungsri
Chapter 14 - Social, Economic and Legal Issues of Automated Forest Restoration
Pimonrat Tiansawat, Jacob Zott, and Prasit Wangpakapattanawong.
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