4.7 Article

Forest structure and solar-induced fluorescence across intact and degraded forests in the Amazon

期刊

REMOTE SENSING OF ENVIRONMENT
卷 274, 期 -, 页码 -

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2022.112998

关键词

Amazon; Forest degradation; Selective logging; Forest fires; Forest structure; Solar-induced chlorophyll fluorescence

资金

  1. Estimativa de Biomassa da Amazonia project (EBA_BNDES-Amazon Fund)
  2. NAS [14.2.0929.1]
  3. USAID
  4. National Aeronautics and Space Administration [AID-OAA-A-11-00012]
  5. NASA Postdoctoral Program [14.2.0929.1]
  6. NASA
  7. U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research
  8. Australian Government Research Training Program Scholarship
  9. USDA Forest Service Pacific Northwest Research Station and International Programs
  10. [NNX15AH95G]

向作者/读者索取更多资源

This study explored the impact of tropical forest degradation on forest structure and function. Fire, logging, and time since disturbance were found to be major determinants of forest structure. Recently burned forests showed significantly depressed solar-induced chlorophyll fluorescence (SIF) during the dry season compared to intact forests. Canopy height and the vertical distribution of foliage were the best predictors of SIF. Surprisingly, wet-season SIF was higher in active regenerating forests compared to intact forests, despite lower leaf area index (LAI).
Tropical forest degradation (e.g., anthropogenic disturbances such as selective logging and fires) alters forest structure and function and influences the forest's carbon sink. In this study, we explored structure-function relationships across a variety of degradation levels in the southern Brazilian Amazon by 1) investigating how forest structural properties vary as a function of degradation history using airborne lidar data; 2) assessing the effects of degradation on solar-induced chlorophyll fluorescence (SIF) seasonality using TROPOMI data; and 3) quantifying the contribution of structural variables to SIF using multiple regression models with stepwise se-lection of lidar metrics. Forest degradation history was obtained through Landsat time-series classification. We found that fire, logging, and time since disturbance were major determinants of forest structure, and that forests affected by fires experienced larger variability in leaf area index (LAI), canopy height and vertical structure relative to logged and intact forests. Moreover, only recently burned forests showed significantly depressed SIF during the dry season compared to intact forests. Canopy height and the vertical distribution of foliage were the best predictors of SIF. Unexpectedly, we found that wet-season SIF was higher in active regenerating forests (~ 4 years after fires or logging) compared with intact forests, despite lower LAI. Our findings help to elucidate the mechanisms of carbon accumulation in anthropogenically disturbed tropical forests and indicate that they can capture large amounts of carbon while recovering.

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