35th FEFCO （森林生態系機能コロキウム）
日時：2017/2/27 Mon. 16:00-18:30
場所：旧演習林事務室 会議室 Former Head Office of Forest Research Station, Meeting room
Marc Padilla-Parellada (Centre for Landscape and Climate Research, Department of Geography, University of Leicester)
Valentin Louis (Centre for Landscape and Climate Research, Department of Geography, University of Leicester)
＊We will have small party after seminar, everybody is welcome !
1. Earth Observation of Forests by the Centre for Landscape and Climate Research (CLCR) – University of Leicester
Dr. Pedro Rodríguez-Veiga
The CLCR at the University of Leicester is a Research Centre of Excellence created in 2011 and specialising in Earth Observation of ecosystem services. The centre is part of the UK’s National Centre for Earth Observation (NCEO), which provides the UK’s Natural Environment Research Council with national capability in Earth observation science. The CLCR undertakes interdisciplinary research into how human land use and climate change affect ecosystems using Earth observation data. In recent years, the centre has also been involved in several official development assistance (ODA) projects from the UK government aimed to improve forest monitoring. We develop and deploy products and services derived from satellite enabled data to address the practical challenges of environmental management - as faced by global agencies and national states. The focus is addressing the monitoring and management of forests to stabilise the world’s climate and protect biodiversity. This presentation will include examples of CLCR research projects focused on Earth Observation of Forests. Our research uses space, airborne and terrestrial platforms with passive and active sensors to tackle the detection of forest disturbances (logging, degradation, fires, etc), the estimation of carbon stocks, and the assessment of forest health and forest structure. see the detail: http://www2.le.ac.uk/colleges/scieng/research/centres/clcr?uol_r=e4cc66eb
2. Daily mapping of forest fires at global scale and its validation.
Dr. Marc Padilla-Parellada
Biomass burning is one of the most important processes impacting the Earth system and one of the main sources of gases and aerosols emissions to the atmosphere. The Global Climate Observing System program identified Fire disturbance as an Essential Climate Variable, commonly characterized through burned are (BA) products, which provide the location and dates of burn surfaces at a coarse spatial resolution. This talk will show how BA can be mapped, by using optical and thermal information at global scale. Special focus will be on the BA validation, and the importance of efficient probability sampling designs to collect reference BA data and obtain precise accuracy estimates.
3. Analysis of forest-plantation dynamics using multi-sensor and multi-temporal remotely sensed data in West Kalimantan, Indonesia.
Mr. Valentin Louis
South-East Asia, in particular Indonesia and Malaysia are the biggest producers of palm oil in the world and their tropical forest and peatlands are threatened by high deforestation rates and plantation expansion. This results in loss of biodiversity and loss of a significant amount of carbon pools. In order to reduce biodiversity losses and carbon emissions the major aim is to develop an accurate method to map the extent of oil palm plantations and generate insight about regions of low and high yields to improve plantation monitoring towards a more sustainable plantation management. The objectives of the research project are; (1) to identify suitable vegetation indices and sensors for the mapping of oil palm plantations, (2) to identify their different growth stages, (3) to estimate the potential yields and to (4) to (semi-) automate the above mentioned processes. In order to achieve these objectives a broad range of available satellite data will be used. Multiple data types of satellite imagery are used for the land cover classification maps. The satellite data used include optical and radar images as well as a SRTM digitial eklevation model (DEM). Satellite images in the optical spectrum are commonly used to identify surface features due to their reflectance characteristics but they can also be identified by their shape and texture. Synthetic Aperture Radar (SAR) imagery is used in addition which has the advantage that it is independent from cloud and light conditions. The main focus lays on actual and archived Landsat and Sentinel-2 data for the optical spectrum and Sentinel-1 and ALOS PALSAR-2 for the SAR spectrum.
In order to distinguish between oil palm plantations and other land cover classes, the random forest classifier is applied to perform the land cover classification. It is an algorithm which creates an ensemble of decision trees to assign pixels or objects to the respective class. The oil palm maps will be used as a land use maps to exclude all other land use and land cover classes from further analysis such as estimation of growth stages and productivty. In addition to the project of oil palm plantation mapping, the presentation will include also several projects of a range of applications of Sentinel-1 images in areas such as deforestation and flood mapping.
4. ESA GlobBiomass project
Dr. Pedro Rodríguez-Veiga
The ESA Data User Element (DUE) project GlobBiomass aims to reduce uncertainties in current estimates of above-ground biomass (AGB) of forests by developing innovative synergistic mapping approaches in five regional case studies for the epochs 2005, 2010 and 2015 and for one global map for the year 2010. AGB stock and change maps with spatial resolution of < 150 m and with a multi-temporal approach are developed for these regions. The five regions cover the main forest biomes: Poland (Temperate forest), Sweden (Boreal forest), Kalimantan-Indonesia (Tropical forest), Mexico (Tropical forest-woodland transition), and South Africa (Forest-savanna mosaic). Each region relied on different ground truething, from plot data to airborne LiDAR observations, to train and evaluate five different state of the art approaches for retrieving AGB: Random Forests, k Nearest Neighbour (kNN), inversion of a Water Cloud Model (WCM), regression modelling, Maximum Entropy algorithm (MaxEnt); and inversion of the Multistatic Interferometric Polarimetric ElectroMagnetic (MIPERS) model in combination with the WCM. Despite the diversity of methods, each approach retrieved not only a spatially explicit regional biomass estimate but also associated uncertainties. The results emphasize the strengths and weaknesses of the different mapping approaches which should be considered for the generation of global AGB estimates.