首页    期刊浏览 2024年12月04日 星期三
登录注册

文章基本信息

  • 标题:Going beyond the green: senesced vegetation material predicts basal area and biomass in remote sensing of tree cover conditions in an African tropical dry forest (miombo woodland) landscape
  • 本地全文:下载
  • 作者:Marc Mayes ; John Mustard ; Jerry Melillo
  • 期刊名称:Environmental Research Letters
  • 印刷版ISSN:1748-9326
  • 电子版ISSN:1748-9326
  • 出版年度:2017
  • 卷号:12
  • 期号:8
  • 页码:085004
  • DOI:10.1088/1748-9326/aa7242
  • 语种:English
  • 出版社:IOP Publishing Ltd
  • 摘要:In sub-Saharan Africa (SSA), tropical dry forests and savannas cover over 2.5 million km2 and support livelihoods for millions in fast-growing nations. Intensifying land use pressures have driven rapid changes in tree cover structure (basal area, biomass) that remain poorly characterized at regional scales. Here, we posed the hypothesis that tree cover structure related strongly to senesced and non-photosynthetic (NPV) vegetation features in a SSA tropical dry forest landscape, offering improved means for satellite remote sensing of tree cover structure compared to vegetation greenness-based methods. Across regrowth miombo woodland sites in Tanzania, we analyzed relationships among field data on tree structure, land cover, and satellite indices of green and NPV features based on spectral mixture analyses and normalized difference vegetation index calculated from Landsat 8 data. From satellite-field data relationships, we mapped regional basal area and biomass using NPV and greenness-based metrics, and compared map performances at landscape scales. Total canopy cover related significantly to stem basal area (r 2 = 0.815, p r 2 = 0.635, p 60%) at all sites. From these two conditions emerged a key inverse relationship: skyward exposure of NPV ground cover was high at sites with low tree basal area and biomass, and decreased with increasing stem basal area and biomass. This pattern scaled to Landsat NPV metrics, which showed strong inverse correlations to basal area (Pearson r = −0.85, p r = −0.86, p
国家哲学社会科学文献中心版权所有