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Cycle 6 (2017 Deadline)

Unmanned aerial systems-based assessment of tree cover and deforestation dynamics in Bangladesh

PI: A.B.M Kamal Pasha,, Daffodil International University
U.S. Partner: Demetrios Gatziolis, The United States Forest Service
Project dates: December 2017 - November 2019

Project Overview:

6-009 Pasha Proposal_Typical conditions in the Sundarbans Forest  6-009 Study area
 Typical conditions in the Sundarbans Forest Bangladesh [Photo credit]Study area (red oval) in the Sundarbans (mangrove) forest of Bangladesh [courtesy of Dr. Pasha]
The main advantages of photogrammetry based on unmanned aerial vehicles (UAVs) compared to traditional aircraft imaging campaigns are low cost and flexibility. In addition, aircraft-based image acquisition usually requires months of planning and must cover a substantial area to justify the cost. UAVs can be deployed at a moment’s notice and require practically no maintenance. They can also be deployed at easily customizable above-canopy altitude, camera orientation, trajectory, and speed. They are thus ideal for imaging small areas of forests, such as over individual tree stands, management units or inventory plots (Gatziolis et al., 2015). Most developing countries do not yet have the capacity or knowledge base to support modern forest inventories and typically depend on satellite imagery and applications developed elsewhere to meet their needs, including the obligation to report on the status of their forests if they participate in the international REDD initiative. Satellite-based assessment is adequate in many instances—for example, to assess deforestration in the Amazon basin. It can be problematic and biased, however, when the anthropogenic interventions and ensuing disturbances are either gradual or have a spatial footprint much smaller than the resolution of the satellite imagery employed. The advent of novel, inexpensive technologies, including UAVs, and the development of affordable software capable of performing complex photogrammetric tasks hold promising potential into assisting the assessment of forest resources in developing countries and facilitating their verifiable participation in efforts to mitigate the effects of deforestation and forest degradation.

In this project, the PI and his team will devise and optimize a fully-automated UAV-based image acquisition protocol compatible with generating comprehensive (gap-free), high-density, precisely geo-referenced point clouds representing forest canopies for areas centered on selected locations in the Sal and Sundarbans forest types that host national forest inventory plots of the Bangladesh Forest Department (BFD). The researchers will generate canopy surface and canopy height rasters for each selected inventory plot and its surroundings, as well as estimates of canopy cover for each subplot of every selected inventory plot, and compare them to those obtained by inventory personnel during field visits. Using the data they will gather, they will develop forest type-specific models of forest biomass and outline a framework for forest change detection and quantification based on periodic satellite imagery and incorporating UAV-derived information over selected areas. Methodologies, findings, and results will be shared with the United Nations Food and Agriculture Organization, representatives of the U.S. Government technical cooperative program SilvaCarbon in Bangladesh, BFD officials, and ultimately the public.

Project updates

6-009 Pasha Drone
Although the project officially began in January 2018, preparatory project activities began soon the announcement of the grant award in fall of 2017.  With USAID/Bangladesh assistance, the PEER team was introduced early on to other colleagues in the field who are working on forest monitoring efforts. USAID/Bangladesh convened USAID-funded forest conservation representatives from the UNREDD program, SilvaCarbon, the Food and Agriculture Organization of the United Nations (FAO), the RIMS unit of the Bangladesh Forest Department, and other specialists from this sector. Although unable to attend in person, U.S. partner Dr. Demetrios Gatziolis participated at the meeting via Skype as well.  Since the PEER team is required to obtain a permit from Ministry of Environment and Forest (MoEF) to conduct their data collection campaign in the forests and a permit from Civil Aviation Authority of Bangladesh (CAAB) to fly drones for the data collection, the process also began soon after the award was announced, and the process is still ongoing. USAID/Bangladesh Mission sent letters of support to relevant governmental offices to facilitate the team’s permit applications. 

Another USAID-funded project under the International Maize and Wheat Improvement Center (CIMMYT) which uses drones for their research will be assisting the PEER team until they obtain their permits to operate in the Sundarbans and Sal forests of Bangladesh by allowing  the PEER team to collect data from drone flights over the CIMMYT’s area within the boundaries of Borishal, where the permit to fly drones is in effect, which is helpful, as the Kolapara sub-district has some coastal forests within its boundary. To be fully compliant with the current legal requirements, CIMMYT will operate the UAV with the PEER team on-site, and the PEER team will collect the needed data. Sample data collection campaigns in the forests of Kolapara in Borishal are anticipated to commence in April 2018. 

With the permits obtained,  the main part of the research will begin. The forest plots will be selected in consultation with the U.S. partner and the Forest Department of Bangladesh, and several data collection campaigns in the Sal forest and Sundarbans will be conducted. By September, the team anticipates to have enough data to work on. Dr. Demetrios has been communicating the PEER team online from Portland, Oregon, and the team expects him to visit their sites in Bangladesh in fall of 2018. During the visit, he will also conduct training on photogrammetry and GIS with support for his travel provided under a PEER Partner Cooperation Supplement.

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