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Remote Sensing Assessment of Tropical Forest Canopy Height, Aboveground Biomass, and Regrowth in Mai Ndombe Province, Democratic Republic of the Congo.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Remote Sensing Assessment of Tropical Forest Canopy Height, Aboveground Biomass, and Regrowth in Mai Ndombe Province, Democratic Republic of the Congo./
Author:	Kashongwe, Herve.
Description:	1 online resource (162 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
Subject:	Geography. -
Online resource:	click for full text (PQDT)
ISBN:	9798380851640

Remote Sensing Assessment of Tropical Forest Canopy Height, Aboveground Biomass, and Regrowth in Mai Ndombe Province, Democratic Republic of the Congo.
Kashongwe, Herve.

Remote Sensing Assessment of Tropical Forest Canopy Height, Aboveground Biomass, and Regrowth in Mai Ndombe Province, Democratic Republic of the Congo. - 1 online resource (162 pages)

Source: Dissertations Abstracts International, Volume: 85-05, Section: B.

Thesis (Ph.D.)--Michigan State University, 2023.

Includes bibliographical references

Tropical forests account for about half the world's terrestrial carbon, primarily stored in aboveground biomass (AGB), and so play a key role in the terrestrial carbon cycle. Central African tropical forests constitute the second largest continuous block of tropical forests in the world, and the majority are in the Democratic Republic of the Congo (DRC). Despite this, they are relatively understudied compared to other tropical regions and their contribution to the global terrestrial carbon cycle is not well unknown. The overarching research goal of this dissertation is to quantify AGB storage in Central African tropical forest regrowth following forest cover loss, to improve our understanding of the carbon sequestration potential of forest regrowth in Central Africa. The following three research hypotheses, each comprising a dissertation chapter (Chapters 2-4) and all concerning DRC Mai Ndombe province, are addressed:#1. Using dry and wet season Landsat 8 imagery will significantly improve forest height prediction (with airborne LiDAR training data) compared to using single season images.#2. Using a six month time period of Global Ecosystem Dynamics Investigation (GEDI) observations is sufficient for forest AGB assessment with 10% uncertainty at Reducing Emission from Deforestation and Degradation (REDD+) project scale.#3. The impact of regrowth species differences on mature tree AGB (at 25 m GEDI footprint scale) will be less than the 10% REDD+ forest AGB reporting uncertainty.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380851640Subjects--Topical Terms:

654331
Geography.
Subjects--Index Terms:

Aboveground biomassIndex Terms--Genre/Form:

554714
Electronic books.

Remote Sensing Assessment of Tropical Forest Canopy Height, Aboveground Biomass, and Regrowth in Mai Ndombe Province, Democratic Republic of the Congo.
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504 $a Includes bibliographical references
520 $a Tropical forests account for about half the world's terrestrial carbon, primarily stored in aboveground biomass (AGB), and so play a key role in the terrestrial carbon cycle. Central African tropical forests constitute the second largest continuous block of tropical forests in the world, and the majority are in the Democratic Republic of the Congo (DRC). Despite this, they are relatively understudied compared to other tropical regions and their contribution to the global terrestrial carbon cycle is not well unknown. The overarching research goal of this dissertation is to quantify AGB storage in Central African tropical forest regrowth following forest cover loss, to improve our understanding of the carbon sequestration potential of forest regrowth in Central Africa. The following three research hypotheses, each comprising a dissertation chapter (Chapters 2-4) and all concerning DRC Mai Ndombe province, are addressed:#1. Using dry and wet season Landsat 8 imagery will significantly improve forest height prediction (with airborne LiDAR training data) compared to using single season images.#2. Using a six month time period of Global Ecosystem Dynamics Investigation (GEDI) observations is sufficient for forest AGB assessment with 10% uncertainty at Reducing Emission from Deforestation and Degradation (REDD+) project scale.#3. The impact of regrowth species differences on mature tree AGB (at 25 m GEDI footprint scale) will be less than the 10% REDD+ forest AGB reporting uncertainty.
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653 $a Mai Ndombe province
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