- Title Information
- Title
- Forest nutrient cycling and remote sensing of land cover in Miombo Woodlands: Insights for biogeochemistry and environmental monitoring of African tropical dry forest landscapes
- Name:
Personal
- Name Part
- Mayes, Marc T
- Role
- Role Term:
Text
- creator
- Origin Information
- Copyright Date
- 2017
- Physical Description
- Extent
- 36, 161 p.
- digitalOrigin
- born digital
- Note
- Thesis (Ph.D.)--Brown University, 2017
- Name:
Personal
- Name Part
- Mustard, John
- Role
- Role Term:
Text
- Director
- Name:
Personal
- Name Part
- Melillo, Jerry
- Role
- Role Term:
Text
- Director
- Name:
Personal
- Name Part
- Neill, Christopher
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Lee, Jung-Eun
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Milliken, Ralph
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Estes, Lyndon
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Geological Sciences
- Role
- Role Term:
Text
- sponsor
- Genre (aat)
- theses
- Abstract
- Tropical dry forests are an extensive biome that supports biodiversity, food, water and energy resources for millions of people. Among global tropical dry forests, the Miombo Woodlands, which span 2.5 million km2 from Angola to Tanzania across sub-Saharan Africa, face acute forest conservation challenges from growing land-use and climate change pressures. To date, there has been little research on ecosystem processes powering Miombo forest growth, including water and nutrient cycles. Established methods for landscape-scale forest monitoring using satellite remote sensing struggle to characterize forest structure and disturbance at the small (<2 ha) spatial scales of most land cover changes. Improved knowledge of biogeochemical cycles and land cover features is necessary to understand land use and climate change effects and inform conservation management of Miombo forests.
This thesis addresses questions about ecosystem nitrogen (N) cycling during forest regrowth and land cover-forest structure relationships across Miombo landscapes to meet ecosystem science needs for conservation management. Its research spans fields of terrestrial biogeochemistry and environmental remote sensing. The first chapter examines trends in ecosystem N availability across a forest regrowth chronosequence with data from vegetation and soil indicators. Findings include evidence of persistent N scarcity with forest regrowth, the dominance of tree biomass accumulation as a driver for ecosystem N accumulation, and sustained N fixation in a known N fixing tree species across young and mature forest sites. The second and third chapters interrogate relationships among field data on forest structure and optical remote sensing metrics derived from Landsat satellite data. These chapters document a mechanism by which senesced vegetation ground cover materials have strong inverse correlations to forest structure, and model forest structure with improved performance compared to existing remotely sensed methods, particularly for low-biomass regions. Key findings that bear on forest management include the importance of limiting woody biomass removal and protecting N-fixing trees to conserve N during forest regrowth, and demonstration that non-green land cover materials relate inversely to tree structure, and can improve remote sensing methods for monitoring tree structure across Miombo landscapes.
- Subject
- Topic
- Africa
- Subject
- Topic
- Miombo Woodlands
- Subject
- Topic
- terrestrial biogeochemistry
- Subject
- Topic
- remote sensing
- Subject
- Topic
- carbon
- Subject
- Topic
- nitrogen
- Subject
- Topic
- nutrient cycling
- Subject
- Topic
- sustainable development
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20170531
- Identifier:
DOI
- 10.7301/Z0639N6X
- Access Condition:
rights statement
(href="http://rightsstatements.org/vocab/InC/1.0/")
- In Copyright
- Access Condition:
restriction on access
- Collection is open for research.
- Type of Resource (primo)
- dissertations