30 Aug
2023
30 Aug
'23
5:25 a.m.
Annonce postée par : BETBEDER Julie (julie.betbeder(a)cirad.fr)
----------------------------------------
18 months contract
Leveraging on free, broad scale earth observation data to characterize forest types and
degradation gradients
We encourage applications from young scientists with expertise and interest in remote
sensing and geomatics applied to continental vegetation monitoring
Job requirements
— PhD degree in geography/remote sensing or Master degree with considerable experience on
the subject
— Knowledge and experience on forestry will be an asset.
— Good spoken and written English. Knowledge of French would be useful.
— Good knowledge of programming languages (e.g. R, Python) is mandatory, spatial analysis
and GIS. Experience with GEE would be an asset
— High motivation to developing research activities
Workplace: Cirad – UPR Forests and Societies (UPR F&S), Montpellier, France. Part-time
remote work acceptable (8 working days/month)
The supervisory team associates CIRAD UPR F&S, IRD UMR AMAP in Montpellier and CNRS
UMR LETG in Rennes (France). They have expertise on remote sensing, tropical forest
ecology and geography.
Contacts : julie.betbeder(a)cirad.fr ; lilian.blanc(a)cirad.fr ; pierre.couteron(a)ird.fr
Application deadline: september 20, 2023
Background and stakes
Containing tropical deforestation is a fundamental issue for the future of the climate and
biodiversity. France and the EU have designed a framework to combat ‘imported
deforestation’ (SNDI in France), namely imported agricultural products (commodities, e.g.
soybeans, cocoa, rubber, etc) that would be farmed on recently deforested lands.
Implementing effective measures in this direction demands qualifying what is meant by
“forest” through biophysical or floristic variables/indicators that could be reliably
quantified and mapped at pantropical scale through freely available earth observation
data.
Indeed, defining a "forest" is a complex task because this term encompasses very
diverse vegetation types (rainforest, dry forest, woodlands, etc.) related to both natural
(e.g. climate) and disturbance-induced gradients. To date, there is a multitude of
national definitions of the forest: 800 have been identified (Lund and Gyde, 2018).
Worldwide definitions recommended by FAO and UNFCCC are based on “structural” variables
(canopy cover or height, etc). Such definitions are pivotal but display certain
limitations. Notably, they do not account for the natural (e.g. climate-driven)
differences between forest types: a same low canopy cover value may as well correspond to
preserved vegetation in drylands yet to heavily degraded areas under a humid climate.
Principles and tools for meaningfully monitoring the degradation level of wooded
vegetation are thus critical to let the new European framework meet its goal of obviating
importation-induced deforestation. That calls for a thorough
assessment of the global or pantropical data sources providing forest biophysical
variables, along with a parsimonious refinement of existing ecological zonation systems
(FAO, WWF, etc).
Objectives and activities
The general objective of this project is to propose principles, methods and tools for the
classification of tropical forests adapted to the diversity of ecological contexts. It
will rely on existing classifications (FAO’s world’s ecological zones, WWF’s Global
ecological classification, IUCN’s global ecosystem typology …), combine and/or refine them
considering gradients of forest degradation in order to apply the High Carbon Stock
Approach (HCS, 2015).
This will entail:
1) Establish a new forest typology based on vegetation maps (e.g. based on national forest
inventories), WWF ecoregions (Olson et al. 2001), IUCN ecoregions (Keith et al. 2020),
ecozones (FAO, 2012) and/or other sources of information. The aim is to produce a
‘structural forest typology’ map that represents forest formations according to ecological
environment and forest structure on a national scale;
2) Identify areas of undisturbed forest within each of the previously defined types. The
identification of undisturbed forest will consist of analyzing the absence of
anthropogenic impacts by combining ancillary data (national parks locations and global
databases such as ‘Intact Forest Landscape’ (Potapov et al. 2017) or ‘Landscape Integrity
Index’ (Grantham et al., 2020)) and remote sensing data (e.g. quantification of canopy
cover, disturbance history, Vancutsem et al., 2021).
3) Analyze the gradients of forest structure within each of the types defined above. The
aim is to identify threshold values in order to discriminate forests with few or no
disturbance from degraded forests. This approach corresponds to a large-scale
implementation of the HCS method. The structural parameters measured will be mainly the
height (Potapov et al., 2021) and the forest cover (Hansen et al., 2013). This work will
be based on remote sensing data (existing databases such as Lidar data on canopy height,
https://glad.umd.edu/dataset/gedi/). To quantify the uncertainties associated with the use
of satellite data we will use field data such as drone images, Lidar data or forest
inventories available on some areas.
This work will focus on two biomes (tropical rainforests and dry forests) and two
countries (Brazil and Cameroon).
Reference list
FAO. 2012. Global ecological Zones for FAO forest reporting: 2010 update (179). Rome : 52
p.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A.,
... & Townshend, J. (2013). High-resolution global maps of 21st-century forest cover
change. science, 342(6160), 850-853.
Grantham, H. S., Duncan, A., Evans, T. D., Jones, K. R., Beyer, H. L., Schuster, R., ...
& Watson, J. E. M. (2020). Anthropogenic modification of forests means only 40% of
remaining forests have high ecosystem integrity. Nature communications, 11(1), 5978.
Vancutsem, C., Achard, F., Pekel, J. F., Vieilledent, G., Carboni, S., Simonetti, D., ...
& Nasi, R. (2021). Long-term (1990–2019) monitoring of forest cover changes in the
humid tropics. Science Advances, 7(10), eabe1603.
HCS Approach Steering Group. (2015). The HCS Approach Toolkit.
Keith, D.A., Ferrer-Paris, J.R., Nicholson, E. and Kingsford, R.T. (eds.) (2020). The IUCN
Global Ecosystem Typology 2.0: Descriptive profiles for biomes and ecosystem functional
groups. Gland, Switzerland: IUCN.
Lund, H. Gyde. 2018 rev. Definitions of Forest State, Stage, Origin, and Management.
[[Online publication], Gainesville, VA: Forest Information Services.
DOI10.13140/RG.2.2.14093.03042.
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N.,
Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks,
C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W.,
Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on
Earth. Bioscience 51(11):933-938.
Potapov, P., Hansen, M. C., Laestadius, L., Turubanova, S., Yaroshenko, A., Thies, C., ...
& Esipova, E. (2017). The last frontiers of wilderness: Tracking loss of intact forest
landscapes from 2000 to 2013. Science advances, 3(1), e1600821.
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A.,
... & Hofton, M. (2021). Mapping global forest canopy height through integration of
GEDI and Landsat data. Remote Sensing of Environment, 253, 112165.
----------------------------------------
L'annonce est située https://georezo.net/forum/viewtopic.php?pid=362334#p362334
Pour vous désabonner connectez-vous sur le forum puis Profil / Abonnement
--
Association GeoRezo - le portail géomatique
https://georezo.net
(Message généré automatiquement. Ne pas répondre à ce message, utiliser les coordonnées
contenues dans l'annonce)