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Brochure, flyer, fact-sheetDrones for Community Monitoring of Forests
New Technologies for self-management of indigenous territories in Panama Language
2018Also available in:
No results found.In 1950, approximately 70% of the Panamanian territory was covered with forests, a figure that fell to 60% of the area in 2012, and which is still decreasing. Indigenous people are the main forest inhabitants and they play an invaluable role in monitoring and conserving forests, a fundamental resource for biodiversity and food security. To strengthen the natural resource management capacities of indigenous territories, FAO, with support of the UN-REDD programme, implemented a community forest-monitoring project. The project had as strong focus on capacity development of members of the indigenous communities. The training included the preparation of flight plans, arming and flying drones, image processing and mapping with high-resolution images. The main objective of the project was to identify changes in specific points of forest cover undergoing deforestation and degradation processes, to monitor the status of crops and to monitor invasions of territory. The introduction of drones made the whole process a lot easier. -
DocumentDrones - A feasible way to revive forests
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.The role of forests in human survival is inevitable but the forest cover decreases by deforestation increased wildfires and unpredictable climate change. To regrow forest we need a lot of manpower and as per some estimates a human can plant about 1500 trees a day and there are many inaccessible places like mountains, river beds, which is not easy for human planters to go, carry, and plant trees. To combat this we need to find out effective mechanisms to plant a large volume of tree seeds in a stipulated period over a mass area. The feasible solution for this is the usage of drones in reviving forests. Drones are unmanned aerial vehicles (UAV), they are like small helicopters which can be flown by a person standing on the ground using a remote.
Drones can fly and drop seeds at places that were difficult to reach earlier. They can map out the territory, carry the seeds, and drop the load at the identified spots, and go back to check the progress at frequent intervals and creating a large-scale green landscape. The built of the drone for planting trees are designed to be durable enough to lift the high quantity of seeds and they mark the areas suitable for dropping the seeds using machine learning technologies and 3D imaging. The seeds used in the drones are highly recommended to use a protected nutrient coating that acts as a safe shell to bury them in the ground, protect them from animals, and be flown away.
Forests still cover about 30 percent of the world’s land area, but they are disappearing at an alarming rate. About 17 percent of the Amazonian rainforest has been destroyed over the past 50 years, and losses recently have been on the rise. Given the ferocity of the devastation, we need hundreds of companies, individuals, and groups to come forward, leverage the technology, take these aerial vehicles to the sky and make the planet green again. Keywords: Adaptive and integrated management, Biodiversity conservation, Climate change, Sustainable forest management ID: 3616686 -
Book (stand-alone)USE OF NOAA REMOTE SENSING DATA FOR ASSESSMENT OF THE FOREST AREA OF LIBERIA 1993
Also available in:
No results found.In the context of developing a practicable and cost effective method for obtaining a country’s forest area by remote sensing, the computer processing of NOAA AVHRR HRPT data covering Liberia was investigated. The only cloud-free scene then recorded turned out to be severely and unevenly affected by atmospheric haze. To mitigate the effects of this, the country was divided into six areas (strata) of more uniform haze conditions. Pixel DN values were obtained for forest and adjoining formations on transects within each stratum, for the first four AVHRR channels and three transforms: NDVI (2-1/2 + 1), IND3 (3-2/3 + 2), IND4(4-2/4 + 2). After analyzing the transects and comparing them with the available reference data (a mixture of large scale colour composite Landsat TM and MSS images for 1989 and 1986 respectively), channels 2, 3, 4 and IND3 were retained for processing. This was done by applying three methods to each stratum - thresholding, maximum likelihood classification using cluste ring signatures (hybrid), m.l.c. using training area signatures - and directed at separating the 5 main classes distinguishable on the Landsat images: Closed forest, Disturbed forest, Shifting cultivation and regrowth, Cultivation and Other.
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