Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda

Avitabile Valerio, Herold Martin, Henry Matieu, Schmullius, Schmullius Christiane

Description

Assessing biomass is gaining increasing interest mainly for bioenergy, climate change research and mitigation activities, such as reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+). In response to these needs, a number of biomass/carbon maps have been recently produced using different approaches but the lack of comparable reference data limits their proper validation. The objectives of this study are to compare the available maps for Uganda and to understand the sources of variability in the estimation. Uganda was chosen as a case-study because it presents a reliable national biomass reference dataset. Results The comparison of the biomass/carbon maps show strong disagreement between the products, with estimates of total aboveground biomass of Uganda ranging from 343 to 2201 Tg and different spatial distribution patterns. Compared to the reference map based on country-specific field data and a national Land Cover (LC) dataset (estimating 468 Tg), maps based on biome-average biomass values, such as the Intergovernmental Panel on Climate Change (IPCC) default values, and global LC datasets tend to strongly overestimate biomass availability of Uganda (ranging from 578 to 2201 Tg), while maps based on satellite data and regression models provide conservative estimates (ranging from 343 to 443 Tg). The comparison of the maps predictions with field data, upscaled to map resolution using LC data, is in accordance with the above findings. This study also demonstrates that the biomass estimates are primarily driven by the biomass reference data while the type of spatial maps used for their stratification has a smaller, but not negligible, impact. The differences in format, resolution and biomass definition used by the maps, as well as the fact that some datasets are not independent from the reference data to which they are compared, are considered in the interpretation of the results. Conclusions The strong disagreement between existing products and the large impact of biomass reference data on the estimates indicate that the first, critical step to improve the accuracy of the biomass maps consists of the collection of accurate biomass field data for all relevant vegetation types. However, detailed and accurate spatial datasets are crucial to obtain accurate estimates at specific locations.

Sujets

• Forestry
• Global change
• Carbón
• Sub-Saharan Africa
• Land cover
• Landsat
• Moderate-Resolution Imaging Spectroradiometer
• Bioenergy

Informations

• Publié par: biomed
• Publié le: 01 janvier 2011
• Nombre de lectures: 7
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Avitabileet al.Carbon Balance and Management2011,6:7 http://www.cbmjournal.com/content/6/1/7

R E S E A R C HOpen Access Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda 1,2* 13,4,5 2 Valerio Avitabile, Martin Herold , Matieu Henryand Christiane Schmullius

Abstract Background:Assessing biomass is gaining increasing interest mainly for bioenergy, climate change research and mitigation activities, such as reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+). In response to these needs, a number of biomass/carbon maps have been recently produced using different approaches but the lack of comparable reference data limits their proper validation. The objectives of this study are to compare the available maps for Uganda and to understand the sources of variability in the estimation. Uganda was chosen as a casestudy because it presents a reliable national biomass reference dataset. Results:The comparison of the biomass/carbon maps show strong disagreement between the products, with estimates of total aboveground biomass of Uganda ranging from 343 to 2201 Tg and different spatial distribution patterns. Compared to the reference map based on countryspecific field data and a national Land Cover (LC) dataset (estimating 468 Tg), maps based on biomeaverage biomass values, such as the Intergovernmental Panel on Climate Change (IPCC) default values, and global LC datasets tend to strongly overestimate biomass availability of Uganda (ranging from 578 to 2201 Tg), while maps based on satellite data and regression models provide conservative estimates (ranging from 343 to 443 Tg). The comparison of the maps predictions with field data, upscaled to map resolution using LC data, is in accordance with the above findings. This study also demonstrates that the biomass estimates are primarily driven by the biomass reference data while the type of spatial maps used for their stratification has a smaller, but not negligible, impact. The differences in format, resolution and biomass definition used by the maps, as well as the fact that some datasets are not independent from the reference data to which they are compared, are considered in the interpretation of the results. Conclusions:The strong disagreement between existing products and the large impact of biomass reference data on the estimates indicate that the first, critical step to improve the accuracy of the biomass maps consists of the collection of accurate biomass field data for all relevant vegetation types. However, detailed and accurate spatial datasets are crucial to obtain accurate estimates at specific locations. Keywords:forestry, global change, carbon, REDD+, subSaharan Africa, Land Cover, Landsat, MODIS, bioenergy

Background The accurate estimation of forest biomass is crucial for many applications, from monitoring fuelwood availabil ity [1] to reducing uncertainties in global carbon (C) modeling [24]. Accurate biomass estimates are also

* Correspondence: [e-mail] 1 Department of Environmental Science, Wageningen University, 6708 PB Wageningen, The Netherlands Full list of author information is available at the end of the article

required for the implementation of a reliable mechanism to reduce emissions from tropical deforestation and for est degradation (REDD+) under the United Nations Framework Convention on Climate Change (UNFCCC) [5]. While there is high interest in seeing such initiatives take form, monitoring forest biomass stocks and stock changes is identified as a key challenge for developing countries wishing to take part in the expected REDD+ mechanism [6,7].

© 2011 Avitabile et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.