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  • Periodic Reporting for period 1 - FOREST 3D - ECOCARB (Integration of innovative remote sensing techniques for optimum modelling of tropical forest primate habitat and carbon storage)
H2020

FOREST 3D - ECOCARB Report Summary

Project ID: 657607
Funded under: H2020-EU.1.3.2.

Periodic Reporting for period 1 - FOREST 3D - ECOCARB (Integration of innovative remote sensing techniques for optimum modelling of tropical forest primate habitat and carbon storage)

Reporting period: 2015-10-01 to 2017-09-30

Summary of the context and overall objectives of the project

Tropical forests are being destroyed at a rate of 1.5 acres every second due to human activities , thereby accelerating climate change through impacts on the carbon cycle, and causing the extinction of species dependent on these habitats. There is a need to develop methods that can rapidly assess tropical forest structure and relate this to carbon stocks stored in tree biomass and to habitat quality for keystone species, like primates.

The aim of this project was to utilise innovative methods of acquiring detailed 3-dimensional data of tropical forests at a landscape-scale, using remote sensing systems on aircraft and unmanned aerial vehicles (UAVs), to model primate habitat and to measure forest carbon stocks. The objectives of the project were to link forest structure in 3-D directly to primate behaviour and forest use for the first time, and to develop cost-effective remote sensing methods using UAVs for monitoring changes in habitats and forest carbon stocks. This project was part of the Landscape Ecology and Primatology (LEAP) project at Bournemouth University (BU), and ran from Oct 2015 to Sept. 2017.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Progress in Achieving Research Objectives
Data from Airborne Laser Scanners (ALS) for Batang Toru in Sumatra, were provided by the Sumatran Orangutan Conservation Program (SOCP). Data from Unmanned Aerial Vehicles (UAVs) were also provided by SOCP for Batang Toru and Sikundur; whilst primate data have been collected by SOCP and post-graduate students from BU in Sikundur (Fig. 1).

1. Work Package 1: Airborne Data Processing and Analysis
1.1 ALS Data Processing
ALS data points from the ground were processed using FUSION software to generate a Digital Terrain Model (DTM) with a cell-size of 1 m. The DTM made it possible to estimate the above-ground height of vegetation points, and a Canopy Height Model (CHM) was generated for the entire site (162 km2). More than 40 variables at a cell size of 20 m were generated from the ALS data for modelling the forest structure in the area.
Output:
• The DTM and the CHM were provided to SOCP as tools for the development of an Environmental Management Plan for Batang Toru.

1.2 Identifying homogeneous forest patches based on individual trees
A history of logging and clearing of land for agro-forestry, selective logging to establish ‘forest gardens’, and natural dynamics has created a mosaic of forest patches in the study area. Patches of similar composition of trees could be visually identified in the CHM, based mainly on the heights and density of trees. Individual canopy trees were detected from the CHM in FUSION software. A method to group trees with similar attributes into forest patches based on Thiessen polygons and k-medoids clustering was developed (Figs 2 & 3), combining the advantages of both raster and individual tree–based methods.
Outputs:
• The work was presented at the annual conference of the Remote Sensing and Photogrammetry Society (RSPSoc), UK in September 2016; winning one of the two Merit Awards
• The results were published in the International Journal of Earth Observation and Geo-information (Volume 55, March 2017, Pages 68–72).
• The results were discussed with researchers at SOCP while the Fellow was in Indonesia in January 2017.

1.3 Habitat Classification
In addition to the method based on Thiessen polygons, a grid-based solution (Fig 4) was also explored to delineate the forest patches over a large area, based on canopy cover and canopy height. The patches were clustered to identify natural forest classes in the study area (Fig 5); field data collected by SOCP were used for labelling the clusters.
Output:
• The initial results were shared with SOCP and, through them, with Sarulla Operations Ltd., a geo-thermal company that commissioned the collection of ALS data, for the preparation of an Environmental Management Plan. The habitat map was finalised during a visit to Indonesia in January 2017, after discussions with researchers at SOCP.
• A manuscript based on this work will be submitted to Remote Sensing in the last quarter of 2017.

2 Work Package 2: Primate Modelling
2.1 Generation of forest structure variables from UAV data
Three UAV datasets from Sikundur were acquired by Graham Usher (SOCP). The datasets were processed in Agisoft PhotoScan software to generate a Digital Surface Model (Fig 6). Variables related to canopy roughness were generated within 25×25 m grids. These were used by C. Marsh, a PhD student at BU, to explore the relationship between the variables generated from UAV data and field data on vegetation and micro-climate. A strong relationship was found between micro-climate data and UAV-based variables. Micro-climate is considered to be a determinant of the level of activity of orang-utans throughout the day. Variables within 10×10 m grids were also generated which will be used by J. Abernethy, a PhD student at LJMU, for his work on orang-utan density modelling.
Output:
• This work was presented by C. Marsh at the joint meeting of the International Primatological Society and the American Society of Primatolog

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

This is covered in the box above.

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