Data Article: Dataset on woody aboveground biomass, disturbance losses, and wood density from an African savanna ecosystem

This page lists all metadata that was entered for this dataset. You can download the dataset.

Citation Options
Title:Main Title: Data Article: Dataset on woody aboveground biomass, disturbance losses, and wood density from an African savanna ecosystem
Description:Abstract: This dataset comprises tree inventories and damage assessments performed in Namibia's semi-arid Zambezi Region. Data were sampled in savannas and savanna woodlands along steep gradients of elephant population densities to capture the effects of those (and other) disturbances on individual-level and stand-level aboveground woody biomass (AGB). The dataset contains raw data on dendrometric measures and processed data on specific wood density (SWD), woody aboveground biomass, and biomass losses through disturbance impacts. Allometric proxies (height, canopy diameters, and in adult trees also stem circumferences) were recorded for n = 6,179 tree and shrub individuals. Wood samples were taken for each encountered species to measure specific wood density. These measurements have been used to estimate woody aboveground biomass via established allometric models, advanced through our improved methodologies and workflows that accounted for tree and shrub architecture shaped by disturbance impacts. To this end, we performed a detailed damage assessment on each woody individual in the field. In addition to estimations of standing biomass, our new method also delivered data on biomass losses to different disturbance agents (elephants, fire, and others) on the level of plant individuals and stands. The data presented here have been used within a study published with Ecological Indicators (Kindermann et al., 2022) to evaluate the benefits of our improved methodology in comparison to a standard reference method of aboveground biomass estimations. Additionally, it has been employed in a study on carbon storage and sequestration in vegetation and soils (Sandhage-Hofmann et al., 2021). The raw data of dendrometric measurements can be subjected to other available allometric models for biomass estimation. The processed data can be used to analyze disturbance impacts on woody aboveground biomass, or for regional carbon storage estimates. The data on species-specific wood density can be used for application to other dendrometric datasets to (re-) estimate biomass through allometric models requiring wood density. It can further be used for plant functional trait analyses.
Identifier:10.1016/j.dib.2022.108155 (DOI)
Related Resource:Describes Dataset 10.17632/3cs85wd3gb.5 (DOI)
Citation Advice:Kindermann L, Dobler M, Niedeggen D, Fabiano EC, Linstädter A (2022). Dataset on woody aboveground biomass, disturbance losses, and wood density from an African savanna ecosystem. Data in Brief, 108155,
Responsible Party
Creators:Liana Kindermann (Author), Magnus Dobler (Author), Daniela Niedeggen (Author), Ezequiel Chimbioputo Fabiano (Author), Anja Linstädter (Author)
Publication Year:2022
TRR228 Topic:Ecology
Related Subproject:A1
Subjects:Keywords: Biomass, Carbon, Carbon Storage Dynamics, Conservation Areas, Ecology, Ecosystem, National Park, Vegetation, Vegetation Structure, Wildlife
Geogr. Information Topic:Biota
File Details
Data Type:Data Paper - Scientific Publication
File Size:1.9 MB
Dates:Submitted: 16.03.2022 (Submission to Data in Brief)
Other: 01.04.2022 (Revised)
Accepted: 01.04.2022
Available: 11.04.2022 (Available online)
Issued: 26.04.2022 (in its final version of record)
Mime Type:application/pdf
Data Format:PDF
Download Permission:Free
Download Information:Paper was published open access with Data in Brief journal and can be downloaded from:
General Access and Use Conditions:According to the TRR228DB data policy agreement.
Access Limitations:According to the TRR228DB data policy agreement.
Licence:[Creative Commons] Attribution-NonCommercial-NoDerivs 4.0 Unported (CC BY-NC-ND 4.0)
Specific Information - Publication
Publication Status:Published
Review Status:Peer reviewed
Publication Type:Article
Article Type:Journal
Source:Data in Brief
Number of Pages:1 (32767 - 32767)
Metadata Details
Metadata Creator:Liana Kindermann
Metadata Created:16.05.2022
Metadata Last Updated:16.05.2022
Funding Phase:1
Metadata Language:English
Metadata Version:V50
Metadata Export
Metadata Schema:
Dataset Statistics
Page Visits:120
Metadata Downloads:0
Dataset Downloads:1
Dataset Activity