Remote Sensing and Forest Carbon Monitoring—a Review of Recent Progress, Challenges and Opportunities

doi:10.11947/j.JGGS.2022.0212

Abstract

Abstract:

Remote sensing provides key inputs to a wide range of models and methods developed for quantifying forest carbon. In particular, carbon inventory methods recommended by IPCC require biomass data and a suite of forest disturbance products. Significant progress has been made in deriving these products by leveraging publicly available remote sensing assets, including observations acquired by the legendary Landsat mission and new systems launched within the past decade, including Sentinel-2, Sentinel-1, GEDI, and ICESAT-2. With the L-band NISAR and P-band BIOMASS missions to be launched in 2023, the Earth’s land surfaces will be imaged by optical and multi-band (including C-, L-, and P-bands) radar systems that can provide global, sub-weekly observations at sub-hectare spatial resolutions for public use. Fine scale products derived from these observations will be crucial for developing monitoring, reporting, and verification (MRV) capabilities needed to support carbon trade, REDD+, and other market-driven tools aimed at achieving climate mitigation goals through forest management at all levels. Following a brief discussion of the roles of forests in the global carbon cycle and the wide range of models and methods available for evaluating forest carbon dynamics, this paper provides an overview of recent progress and forthcoming opportunities in using remote sensing to map forest structure and biomass, detect forest disturbances, determine disturbance attribution, quantify disturbance intensity, and estimate harvested timber volume. Advances in these research areas require large quantities of well—distributed reference data to calibrate remote sensing algorithms and to validate the derived products. In addition, two of the forest carbon pools-dead organic matter and soil carbon—are difficult to monitor using modern remote sensing capabilities. Carefully designed inventory programs are needed to collect the required reference data as well as the data needed to estimate dead organic matter and soil carbon.

Key words: carbon models; forest disturbance; growth; structure biomass

Chengquan HUANG,Weishu GONG,Yong PANG. Remote Sensing and Forest Carbon Monitoring—a Review of Recent Progress, Challenges and Opportunities[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(2): 124-147.

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Model name	Developers/References
BEPS	Chen et al.^[40];Ju et al.^[53]
Biome-BGC	Bond-Lamberty et al.^[54]
Can-IBIS	Foley et al.^[45];Kucharik et al.^[55]
CASA	Randerson et al.^[43]
CASA GFEDv2	van der Werf et al.^[41,42]
CENTURY	Parton et al.^[56]
CLM-CASA	Randerson et al.^[57]
CLM-CN	Thornton et al.^[46];Randerson et al.^[57]
DLEM	Tian et al.^[47]
DNDC	Li et al.^[58]
ED	Hurtt et al.^[59]; Moorcroft et al.^[60]
EDCM	Liu et al.^[61]
FIRE-BGC	Keane et al.^[62]
FORCLIM	Bugmann^[63]
FOREST-BGC	Running and Gower^[64]
FVS	Dixon^[65]
HYBRID	Friend et al.^[66]
InTEC	Chen et al.^[67]
ISAM	Jain and Yang^[44]
LANDIS	Mladenoff^[68]
LINKAGES	Pastor and Post^[69]
LPJ-wsl	Sitch et al.^[48]; Bondeau et al.^[70]
ORCHIDEE	Krinner et al.^[49]
SiB3	Baker et al.^[50]
SORTIE	Pacala et al.^[71]
TEM	Raich et al.^[51]; McGuire et al.^[72]

General category	Variable list
Plant characteristics	Foliar nitrogen, chlorophyll, lignin concentration, leaf area, leaf water content, stress/drought
Vegetation status	Stand age, species composition, canopy cover, height, volume, biomass
Vegetation dynamics	Land cover/use change, disturbance, management, harvested wood products, growth rates
Ecosystem fluxes	SIF, GPP, NPP, NEP, NBP/NEE, FAPAR, ER
Soil properties	Soil moisture, nutrient, soil organic carbon
Meteorological variables	Precipitation, temperature (including LST), ET, VPD, PAR
Atmospheric carbon	CO₂, CH₄

Carbon pools	Estimation methods/input data
Aboveground biomass	Ground measurements, remote sensing, land use/disturbance
Belowground biomass	No change, or modeled based on aboveground biomass
Dead organic matter	No change, or modeled based on land use/disturbance data
Soil	No change, or modeled based on land use/disturbance data
Harvested wood products	FAO database, survey data, remote sensing based