LiDAR-based reference aboveground biomass maps for tropical forests of South Asia and Central Africa

被引：2

作者：

Rodda, Suraj Reddy ^{[1
,2
]}

Fararoda, Rakesh ^{[1
]}

Gopalakrishnan, Rajashekar ^{[1
]}

Jha, Nidhi ^{[3
]}

Rejou-Mechain, Maxime ^{[4
]}

Couteron, Pierre ^{[4
]}

Barbier, Nicolas ^{[4
]}

Alfonso, Alonso ^{[5
]}

Bako, Ousmane ^{[6
]}

Bassama, Patrick ^{[6
]}

Behera, Debabrata ^{[7
]}

Bissiengou, Pulcherie ^{[8
]}

Biyiha, Herve ^{[6
]}

Brockelman, Warren Y. ^{[9
]}

Chanthorn, Wirong ^{[10
]}

Chauhan, Prakash ^{[1
]}

Dadhwal, Vinay Kumar ^{[11
]}

Dauby, Gilles ^{[4
,12
,13
]}

Deblauwe, Vincent ^{[14
,15
]}

Dongmo, Narcis ^{[6
]}

Droissart, Vincent ^{[4
,12
]}

Jeyakumar, Selvaraj ^{[7
]}

Jha, Chandra Shekar ^{[1
]}

Kandem, Narcisse G. ^{[12
]}

Katembo, John ^{[16
]}

Kougue, Ronald ^{[6
]}

Leblanc, Hugo ^{[4
]}

Lewis, Simon ^{[17
,18
]}

Libalah, Moses ^{[12
]}

Manikandan, Maya ^{[1
]}

Martin-Ducup, Olivier ^{[19
]}

Mbock, Germain ^{[6
]}

Memiaghe, Herve ^{[8
]}

Mofack, Gislain ^{[12
]}

Mutyala, Praveen ^{[1
]}

Narayanan, Ayyappan ^{[7
]}

Nathalang, Anuttara ^{[9
]}

Ndjock, Gilbert Oum ^{[20
]}

Ngoula, Fernandez ^{[12
]}

Nidamanuri, Rama Rao ^{[2
]}

Pelissier, Raphael ^{[4
]}

Saatchi, Sassan ^{[21
]}

Sagang, Le Bienfaiteur ^{[12
,15
]}

Salla, Patrick ^{[12
]}

Simo-Droissart, Murielle ^{[12
]}

Smith, Thomas B. ^{[15
]}

Sonke, Bonaventure ^{[12
,13
]}

Stevart, Tariq ^{[22
]}

Tjomb, Daniele ^{[6
]}

Zebaze, Donatien ^{[12
]}

机构：

[1] ISRO, Forestry & Ecol Grp, Natl Remote Sensing Ctr, Hyderabad 500037, India

[2] Indian Inst Space Sci & Technol IIST, Thiruvananthapuram, Kerala, India

[3] Oregon State Univ, Coll Forestry, Corvallis, OR 97331 USA

[4] Univ Montpellier, CNRS, IRD, INRAE,CIRAD,AMAP, Montpellier, France

[5] Smithsonian Natl Zoo & Conservat Biol Inst, Ctr Conservat & Sustainabil, Washington, DC USA

[6] Minist Forets & Faune, Ecole Natl Eaux & Forets Mbalmayo, Mbalmayo, Cameroon

[7] French Inst Pondicherry, Dept Ecol, Pondicherry 605001, India

[8] CENAREST, Inst pharmacopee & med tradit Herbier Natl Gabon, Libreville, Gabon

[9] Natl Sci & Technol Dev Agcy, Natl Biobank Thailand NBT, Klongluang, Pathum Thani, Thailand

[10] Kasetsart Univ, Fac Environm, Dept Environm Technol & Management, Bangkok 10900, Thailand

[11] Natl Inst Adv Studies NIAS, Bangalore, India

[12] Univ Yaounde I, Higher Teachers Training Coll, Plant Systemat & Ecol Lab, POB 047, Yaounde, Cameroon

[13] IRGM, Int Joint Lab DYCOFAC, IRD, UYI, POB 1857, Yaounde, Cameroon

[14] Int Inst Trop Agr IITA, BP 2008 Messa, Yaounde, Cameroon

[15] Univ Calif Los Angeles, Inst Environm & Sustainabil, Ctr Trop Res, Los Angeles, CA USA

[16] Inst Super Etud Agron Bengamisa, Kisangani, DEM REP CONGO

[17] Univ Coll London UCL, Dept Geog, London, England

[18] Univ Leeds, Sch Geog, Leeds, England

[19] URFM, INRAE, Ecol Forets Mediterraneennes, F-84914 Avignon, France

[20] Minist Forestry & Wildlife, Dja Wildlife Reserve, Yaounde, Cameroon

[21] CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA

[22] Missouri Bot Garden, Africa & Madagascar Program, 4344 Shaw Blvd, St Louis, MO 63110 USA

来源：

SCIENTIFIC DATA | 2024年 / 11卷 / 01期

关键词：

MODELS;

D O I：

10.1038/s41597-024-03162-x

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Accurate mapping and monitoring of tropical forests aboveground biomass (AGB) is crucial to design effective carbon emission reduction strategies and improving our understanding of Earth's carbon cycle. However, existing large-scale maps of tropical forest AGB generated through combinations of Earth Observation (EO) and forest inventory data show markedly divergent estimates, even after accounting for reported uncertainties. To address this, a network of high-quality reference data is needed to calibrate and validate mapping algorithms. This study aims to generate reference AGB datasets using field inventory plots and airborne LiDAR data for eight sites in Central Africa and five sites in South Asia, two regions largely underrepresented in global reference AGB datasets. The study provides access to these reference AGB maps, including uncertainty maps, at 100 m and 40 m spatial resolutions covering a total LiDAR footprint of 1,11,650 ha [ranging from 150 to 40,000 ha at site level]. These maps serve as calibration/validation datasets to improve the accuracy and reliability of AGB mapping for current and upcoming EO missions (viz., GEDI, BIOMASS, and NISAR).

引用

页数：15

共 39 条

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