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dc.contributorDe Pauw, Eddyen_US
dc.contributorHellden, Ulfen_US
dc.creatorWu, Weichengen_US
dc.date2013-03-21en_US
dc.date.accessioned2017-07-23T22:09:08Z
dc.date.available2017-07-23T22:09:08Z
dc.identifierhttps://mel.cgiar.org/reporting/download/hash/8OjcjZesen_US
dc.identifier.citationWeicheng Wu, Eddy De Pauw, Ulf Hellden. (21/3/2013). Assessing woody biomass in African tropical savannahs by multiscale remote sensing. International Journal of Remote Sensing, 34 (13), pp. 4525-4549.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/7192
dc.description.abstractWoody biomass production is a critical indicator in evaluation of land use management and the dynamics of the global carbon cycle (sequestration/emission) in terrestrial ecosystems. The objective of the present study was to develop, through a case study in Sudan, an operational multiscale remote-sensing-based methodology for largescale estimation of woody biomass in tropical savannahs. Woody biomass estimation models obtained by different authors from destructive field measurements in different tropical savannah ecosystems were expressed as functions of tree canopy cover (CC). The field-measured CC data were used for developing regression equations with atmospherically corrected and reflectance-based vegetation indices derived from Landsat ETM+ (Enhanced Thematic Mapper Plus) imagery. Among a set of vegetation indices, the normalized difference vegetation index (NDVI) provided the best correlation with CC (R2 = 0.91) and was hence selected for woodland woody biomass estimation. After validation of the CC-NDVI model and its applicability to Moderate Resolution Imaging Spectroradiometer (MODIS) data, time-series MODIS NDVI data (MOD13Q1) were used to partition the woody component from the herbaceous component for sparse woodlands, woodlands and forests defined by the Food and Agriculture Organization (FAO) of the United Nations Land Cover Map. Following the weighting of the estimation models based on the dominant woody species in each vegetation community, NDVI-based woody biomass models were applied according to their weighted ratios to the decomposed summer and autumn woody NDVI images in all vegetation communities in the whole of Sudan taking the year 2007, for example. The results were found to be in good agreement with those from other authors obtained by either field measurements or other remote sensing methods using MODIS and lidar data. It is concluded that the proposed approach is operational and can be applied for a reliable large-scale assessment of woody biomass at a ground resolution of 250 m in tropical savannah woodlands of any month or season.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherTaylor & Francis: STM, Behavioural Science and Public Health Titlesen_US
dc.rightsCC-BY-NC-4.0en_US
dc.sourceInternational Journal of Remote Sensing;34,(2013) Pagination 4525,4549en_US
dc.subjectnormalized difference vegetation index (ndvi)en_US
dc.subjecttropical savannahsen_US
dc.titleAssessing woody biomass in African tropical savannahs by multiscale remote sensingen_US
dc.typeJournal Articleen_US
cg.subject.agrovocremote sensingen_US
cg.subject.agrovocafricaen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerIndependent / Not associateden_US
cg.contributor.centerLund Universityen_US
cg.contributor.crpCRP on Dryland Systems - DSen_US
cg.contributor.funderInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.projectCommunication and Documentation Information Services (CODIS)en_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.regionNorthern Africaen_US
cg.coverage.countrySDen_US
cg.contactw.wu@cgiar.orgen_US
cg.identifier.doihttps://dx.doi.org/10.1080/01431161.2013.777487en_US
dc.identifier.statusOpen accessen_US
mel.impact-factor1.724en_US


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