Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
LiDAR (Light Detection And Ranging) remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and above ground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution.In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data (e.g. geology, slope, vegetation indices, etc.). The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of random forest regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on the precision of canopy height estimates, six subsets were derived from the initial airborne LiDAR dataset with flight line spacing of 5, 10, 20, 30, 40 and 50 km (corresponding to 0.29, 0.11, 0.08, 0.05, 0.04, and 0.03 points/km² respectively). Results indicated that using the regression-kriging approach achieved a precision of 1.8 m on the canopy height map with flight line spacing of 5 km and achieved an average RMSE of 4.8m for the configuration for the 50 km flight line spacing.
ISSN: 2072-4292

hal-01361343
https://hal.archives-ouvertes.fr/hal-01361343
https://hal.archives-ouvertes.fr/hal-01361343/document
https://hal.archives-ouvertes.fr/hal-01361343/file/mt2016-pub00048036.pdf
DOI : 10.3390/rs8030240

Éditeur(s) : HAL CCSD
Résumé : IGARSS 2015, Milan, ITA, 26-/07/2015 - 31/07/2015
International audience
LiDAR remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and aboveground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution.In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data. The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of RF regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset.
Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International
Milan, Italy

hal-01244677
https://hal.archives-ouvertes.fr/hal-01244677
https://hal.archives-ouvertes.fr/hal-01244677/document
https://hal.archives-ouvertes.fr/hal-01244677/file/mt2015-pub00044297.pdf
DOI : 10.1109/IGARSS.2015.7326729

Éditeur(s) : HAL CCSD Association Internationale de Climatologie
Résumé : 6 pages
International audience
This work presents an overview of spatio-temporal quality andspatial density of rainfall networks of Cameroon, Gabon, Congo, CAP and DRC, particularly in the recentperiod (1973-2014) through the GSOD international database. Faced with enormous gaps observed, theseresults will help to select metrological and analytical orientations in FORGREENE project (Tropical forestsgreeness and cloudiness in Central Africa: present conditions and future evolution). A comparison with rainfallsatellite estimates (RFE type) also indicates their great potential, provided they are based on in situ validations.
Ce travail fait un état des lieux de la qualité temporelle et de la densité spatiale des sériespluviométriques du Cameroun, Gabon, Congo, RCA et RDC, en particulier sur la période récente (1973-2014)grâce à la base de données internationale GSOD. Face aux énormes lacunes observées, ces résultats vontpermettre d’orienter les choix métrologiques et analytiques in situ du programme FORGREENE (Tropicalforests greeness and cloudiness in Central Africa: present conditions and future evolution). Une comparaisonavec des estimations satellitaires (de type RFE) indique aussi leur potentiel intéressant, à condition qu’elless’appuient sur des validations in situ.
Climat et pollution de l'air
Besançon, France

hal-01360178
https://hal.archives-ouvertes.fr/hal-01360178

Éditeur(s) : HAL CCSD Elsevier Masson
Résumé : 14 pages
International audience
Central Africa hosts the world's second largest tropical forest after the Amazonian basin. However, as compared to its Amazonian counterpart, the Central Africa forests receive much less rain (∼1500 mm/year in mean spread over two rainy seasons in March-May and September-November, and two dry seasons). They also experience a slower deforestation rate, so that the main threat for the next decades might come from climate variations. Nonetheless, their response to the annual cycle of solar radiation and rainfall/clouds is still poorly known. Analysing high resolution remote sensing data of Enhanced Vegetation Index, rainfall, cloudiness, and solar radiation for a target region located between 0 and 5°N and 12–19°E, we explore the climatic drivers of the forests greenness mean annual cycle. Three main points emerge; first, the diurnal cycle is a key-scale for understanding the mean annual cycles of rainfall and incoming solar radiation at surface, then how climate shapes the greenness mean annual evolution; second, neither the two dry seasons nor the two rainy seasons resemble each other in terms of cloud cover, solar radiation and rainfall, and their links with greenness levels; third, whereas the first rainy season (March-May) appears optimal for greenness especially because of favorable light conditions, water availability is the main controlling factor during the main dry season and at the start of the first vegetative season (February). Regarding the little dry season (mid-June-mid-August) and the second rainy season (September-October), light availability might be the main limiting factor. These findings pave the way for further studies of the climate interannual variability and change impacts on the Central Africa forests, taking into account time-scale interactions.
ISSN: 0168-1923

hal-01309565
https://hal.archives-ouvertes.fr/hal-01309565
DOI : 10.1016/j.agrformet.2016.04.005