???ENUM_LANGUAGE_JA???
 
???mainMenu_lnkPrivacyPolicy??? ???mainMenu_lnkPolicy???

???ViewItemPage???

???ViewItemFull_lblItemActions??????List_lblExportOptions???
???ViewItemFull_lblAddToBasket???
  ???ViewItemFull_lblSubHeaderLocalTags??????ViewItemFull_btnItemVersions??????ViewItemFull_btnItemView??????ViewItemOverview_lblLinkOverviewPage???

???ENUM_STATE_RELEASED???
???ENUM_GENRE_ARTICLE???

Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory

???ViewItemOverview_lblSpecificAuthorsSection???

Rödig,  E.
External Organizations;

Cuntz,  M.
External Organizations;

/persons/resource/Jens.Heinke

Heinke,  Jens       
Potsdam Institute for Climate Impact Research;

/persons/resource/Anja.Rammig

Rammig,  Anja
Potsdam Institute for Climate Impact Research;

Huth,  A.
External Organizations;

???ViewItemOverview_lblExternalResourceSection???
???ViewItemOverview_noExternalResourcesAvailable???
???ViewItemOverview_lblRestrictedFulltextSection???
???ViewItemOverview_noRestrictedFullTextsAvailable???
???ViewItemOverview_lblFulltextSection???
???ViewItemOverview_noFullTextsAvailable???
???ViewItemOverview_lblSupplementaryMaterialSection???
???ViewItemOverview_noSupplementaryMaterialAvailable???
???ViewItemOverview_lblCitationSection???

Rödig, E., Cuntz, M., Heinke, J., Rammig, A., Huth, A. (2017): Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory. - Global Ecology and Biogeography, 26, 11, 1292-1302.
https://doi.org/10.1111/geb.12639


???ViewItemOverview_lblCiteAs???: https://publications.pik-potsdam.de/pubman/item/item_22299
???ViewItemOverview_lblAbstractSection???
Estimating the current spatial variation of biomass in the Amazon rain forest is a challenge and remains a source of substantial uncertainty in the assessment of the global carbon cycle. Precise estimates need to consider small‐scale variations of forest structures resulting from local disturbances, on the one hand, and require large‐scale information on the state of the forest that can be detected by remote sensing, on the other hand. In this study, we introduce a novel method that links a forest gap model and a canopy height map to derive the biomass distribution of the Amazon rain forest.