Frédéric Achard
Orcid: 0000-0001-7281-9582
According to our database1,
Frédéric Achard
authored at least 31 papers
between 1998 and 2024.
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Bibliography
2024
Proceedings of the IGARSS 2024, 2024
2022
Processed Data for Figures of "Declining Amazon biomass due to deforestation and subsequent degradation losses exceeding gains".
Dataset, November, 2022
Mapping Canopy Cover in African Dry Forests from the Combined Use of Sentinel-1 and Sentinel-2 Data: Application to Tanzania for the Year 2018.
Remote. Sens., 2022
2021
Performance Assessment of Recent Tropical Forest Monitoring Products for REDD+ Operational Services.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021
2019
Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon.
Remote. Sens., 2019
2017
An integrated remote sensing and GIS approach for monitoring areas affected by selective logging: A case study in northern Mato Grosso, Brazilian Amazon.
Int. J. Appl. Earth Obs. Geoinformation, 2017
2016
The Potential of Sentinel Satellites for Burnt Area Mapping and Monitoring in the Congo Basin Forests.
Remote. Sens., 2016
Urbanization and forest degradation in east Africa - a case study around Dar es Salaam, Tanzania.
Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, 2016
2015
Estimating Burned Area in Mato Grosso, Brazil, Using an Object-Based Classification Method on a Systematic Sample of Medium Resolution Satellite Images.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2015
Consistent forest change maps 1981-2000 from the AVHRR time series: Case studies for South America and Indonesia.
Proceedings of the 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images, 2015
Assessing forest degradation from selective logging using time series of fine spatial resolution imagery in Republic of Congo.
Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, 2015
Combining available land cover and tree cover maps for producing a hybrid forest map of Africa at 30M.
Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, 2015
Harmonization of pan-tropical biomass maps using an R2-weighted data fusion approach - A case study for the Amazon biome.
Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, 2015
A multidisciplinary approach for assessing forest degradation in the Brazilian Amazon.
Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, 2015
2014
Assessment of burned areas in Mato Grosso State, Brazil, from a systematic sample of medium resolution satellite imagery.
Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, 2014
2013
Combining Landsat TM/ETM+ and ALOS AVNIR-2 Satellite Data for Tropical Forest Cover Change Detection.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2013
Automatic Updating of an Object-Based Tropical Forest Cover Classification and Change Assessment.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2013
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2013
2012
Forest Cover Changes in Tropical South and Central America from 1990 to 2005 and Related Carbon Emissions and Removals.
Remote. Sens., 2012
2009
Remote. Sens., 2009
2007
Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, 2007
2004
A novel approach to the classification of regional-scale Radar mosaics for tropical vegetation mapping.
IEEE Trans. Geosci. Remote. Sens., 2004
2003
The GBFM radar mosaic of the Eurasian Taiga: a groundwork for the bio-physical characterization of an ecosystem with relevance to global change studies.
Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium, 2003
Use of data from the VEGETATION instrument for global environmental monitoring: some lessons from the GLC 2000 and the GBA 2000 projects.
Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium, 2003
2002
Contextual clustering for image labeling: an application to degraded forest assessment in Landsat TM images of the Brazilian Amazon.
IEEE Trans. Geosci. Remote. Sens., 2002
2001
1999
1998
Bioinform., 1998