Beyond MRV: combining remote sensing and ecosystem modeling for geospatial monitoring and attribution of forest carbon fluxes over Maryland, USA

被引：0

作者：

Hurtt, George C ^{[1
]}

Ma, Lei ^{[1
]}

Lamb, Rachel ^{[1
,2
]}

Campbell, Elliot ^{[3
]}

Dubayah, Ralph O ^{[1
]}

Hansen, M. ^{[1
]}

Huang, Chengquan ^{[1
]}

Leslie-Bole, Haley ^{[4
]}

Lister, Andrew ^{[5
]}

Lu, Jiaming ^{[6
]}

Panday, Frances Marie S ^{[1
,2
]}

Shen, Quan ^{[1
]}

Silva, Carlos E ^{[7
]}

Tang, H. ^{[8
]}

机构：

[1] Department of Geographical Sciences, University of Maryland, College Park,MD, United States

[2] Department of Environment, Baltimore,MD, United States

[3] Department of Natural Resources, Annapolis,MD, United States

[4] World Resources Institute, Washington,DC,20002, United States

[5] Northern Research Station, Forest Inventory and Analysis, National Inventory & Monitoring Applications Center (NIMAC), U. S. Forest Service, York,PA,17402, United States

[6] School of Biological Sciences, University of Utah, Salt Lake City,UT,84112, United States

[7] Pachama, Inc., San Francisco,CA, United States

[8] Department of Geography, National University of Singapore, Singapore,117570, Singapore

来源：

Environmental Research Letters | 2024年 / 19卷 / 12期

关键词：

Members of the U.S. Climate Alliance; a coalition of 24 states committed to achieving the emissions reductions outlined in the 2015 Paris Agreement; are considering policy options for inclusion of forest carbon in climate mitigation plans. These initiatives are generally limited by a lack of relevant data on forest carbon stocks and fluxes past-to-future. Previously; we developed a new forest carbon modeling system that combined high-resolution remote sensing; field data; and ecological modeling to estimate contemporary above-ground forest carbon stocks; and projected future forest carbon sequestration potential for the state of Maryland. Here we extended this work to provide a consistent geospatial approach for monitoring changes in forest carbon stocks over time. Utilizing the same data and modeling system developed previously for planning; we integrated historical input data on weather and disturbance to reconstruct the history of vegetation dynamics and forest above-ground carbon stocks annually over the period 1984-2016 at 30 m resolution and provided an extension to 2023. Statewide; forested land had an average annual net above ground carbon sink of 1.37 TgC yr−1; comparable to prior estimates. However; unlike the prior estimates; there was considerable variation around this mean. The statewide net above ground flux ranged interannually from −0.65 to 2.77 Tg C yr−1. At the county scale; the average annual net above ground flux ranged spatially from 0.01 to 0.13 Tg C yr−1 and spatiotemporally from −0.43 to 0.24 Tg C yr−1. Attribution analyses indicate the primary importance of persistent and regrowing forests; vegetation structure; local disturbance; and rising CO2 to the mean flux; and the primary importance of weather to the large-scale interannual variability. These results have important implications for state climate mitigation planning; reporting and assessment. With this approach; it is now possible to monitor changes in forest carbon stocks spatiotemporally over policy relevant domains with a consistent framework that is also enabled for future planning. © 2024 The Author(s). Published by IOP Publishing Ltd;

D O I：

10.1088/1748-9326/ad9035

中图分类号：

学科分类号：

摘要：

引用

共 10 条

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Hurtt, G.
Zhao, M.
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ENVIRONMENTAL RESEARCH LETTERS, 2019, 14 (04)
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Sanchez Ruiz, Sergio
REVISTA DE TELEDETECCION, 2019, (53): : 86 - 90
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David P. Turner
Michael Guzy
Michael A. Lefsky
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Environmental Management, 2004, 33 : 457 - 466
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Turner, DP
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Lefsky, MA
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VAN Tuyl, S
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ENVIRONMENTAL MANAGEMENT, 2004, 33 (04) : 457 - 466
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Collatz, G. James
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, 2006, 111 (G1)
[6] Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
Xie, Mingjuan
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Kruijt, Bart
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Heinesch, Bernard
Chojnicki, Bogdan
Helfter, Carole
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SCIENTIFIC DATA, 2023, 10 (01)
[7] Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
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Yuangang Wang
Chaofan Li
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Chunbo Chen
Wenqiang Zhang
Chen Zhang
Qing Ling
Ruixiang Gao
Yu Zhang
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Philippe De Maeyer
Nina Buchmann
Iris Feigenwinter
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Andrea Pitacco
Andrej Varlagin
Ankit Shekhar
Annalea Lohila
Arnaud Carrara
Aurore Brut
Bart Kruijt
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Bernard Heinesch
Bogdan Chojnicki
Carole Helfter
Caroline Vincke
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Christian Brümmer
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Eiko Nemitz
Franco Meggio
Gang Dong
Gary Lanigan
Georg Niedrist
Georg Wohlfahrt
Guoyi Zhou
Ignacio Goded
Thomas Gruenwald
Janusz Olejnik
Joachim Jansen
Scientific Data, 10
[8] Use of Remote Sensing and Biogeochemical Modeling to Simulate the Impact of Climatic and Anthropogenic Factors on Forest Carbon Fluxes
Chiesi, Marta
Fibbi, Luca
Vanucci, Silvana
Maselli, Fabio
REMOTE SENSING, 2024, 16 (02)
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Netzer, Michael S.
Sidman, Gabriel
Pearson, Timothy R. H.
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FORESTS, 2019, 10 (05):
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Leuning, R
Cleugh, HA
Zegelin, SJ
Hughes, D
AGRICULTURAL AND FOREST METEOROLOGY, 2005, 129 (3-4) : 151 - 173

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