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Contacts

Irstea
Author
Cirad
Author
AgroParisTech
Author

Credit

BioSceneMada, ReCaREDD

Forest biomass for Madagascar

Type: mapLicense: not specifiedCategory: imagery, base maps, earth coverLast update: 3 years ago
Land cover

A biomass map for Madagascar obtained using the data fusion of LiDAR, optical and other climatic datasets (spatial resolution = 250 m x 250 m). The precision of the map by comparison to field estimates is around 74 Mg/ha.

Data years : Lidar spaceborne data (GLAS-ICESat): 2003-2009 Optical data: 2000-2010 SRTM DEM: 2000 Field inventories: 1995-2013

Data origin:
not specified

Purpose: not specified

Data life cycle, according to producer:
Creation date: January 1, 2017
Update frequency: not planned
State: completed

Resources

Carte_Biomasse_Mada_AGB_2010.zip

Interest of Integrating Spaceborne LiDAR Data to Improve the Estimation of Biomass in High Biomass Forested Areas

Mapping forest AGB (Above Ground Biomass) is of crucial importance to estimate the carbon emissions associated with tropical deforestation. This study proposes a method to overcome the saturation at high AGB values of existing AGB map (Vieilledent’s AGB map) by using a map of correction factors generated from GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data. The Vieilledent’s AGB map of Madagascar was established using optical images, with parameters calculated from the SRTM Digital Elevation Model, climatic variables, and field inventories. In the present study, first, GLAS LiDAR data were used to obtain a spatially distributed (GLAS footprints geolocation) estimation of AGB (GLAS AGB) covering Madagascar forested areas, with a density of 0.52 footprint/km2. Second, the difference between the AGB from the Vieilledent’s AGB map and GLAS AGB at each GLAS footprint location was calculated, and additional spatially distributed correction factors were obtained. Third, an ordinary kriging interpolation was thus performed by taking into account the spatial structure of these additional correction factors to provide a continuous correction factor map. Finally, the existing and the correction factor maps were summed to improve the Vieilledent’s AGB map. The results showed that the integration of GLAS data improves the precision of Vieilledent’s AGB map by approximately 7 t/ha. By integrating GLAS data, the RMSE on AGB estimates decreases from 81 t/ha (R2 = 0.62) to 74.1 t/ha (R2 = 0.71). Most importantly, we showed that this approach using LiDAR data avoids underestimating high biomass values (new maximum AGB of 650 t/ha compared to 550 t/ha with the first approach).

Citer la ressource :El Hajj, M., Baghdadi, N., Fayad, I., Vieilledent, G., Bailly, J.-S., & Ho Tong Minh, D. (2018). Forest biomass map in Madagascar (p. ). Irstea. https://doi.org/10.17180/forest-biomass-map-madagascar-2017

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