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Remote Sensing Assessment of Tropica...

Kashongwe, Herve.

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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.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Remote Sensing Assessment of Tropical Forest Canopy Height, Aboveground Biomass, and Regrowth in Mai Ndombe Province, Democratic Republic of the Congo./
作者:	Kashongwe, Herve.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	162 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
標題:	Geography. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30694991
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. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 162 p.

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

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

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.

ISBN: 9798380851640Subjects--Topical Terms:

524010
Geography.
Subjects--Index Terms:

Aboveground biomass

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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245 1 0 $a 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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300 $a 162 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
500 $a Advisor: Roy, David.
502 $a Thesis (Ph.D.)--Michigan State University, 2023.
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.
590 $a School code: 0128.
650 4 $a Geography. $3 524010
650 4 $a Ecology. $3 516476
650 4 $a Remote sensing. $3 535394
650 4 $a Forestry. $3 895157
653 $a Aboveground biomass
653 $a Tropical forests
653 $a Terrestrial carbon cycle
653 $a Global Ecosystem Dynamics Investigation
653 $a Mai Ndombe province
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690 $a 0478
690 $a 0799
690 $a 0329
710 2 $a Michigan State University. $b Geography - Doctor of Philosophy. $3 2105007
773 0 $t Dissertations Abstracts International $g 85-05B.
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791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30694991