Seeing the forest beyond the trees

Saatchi, Sassan; Mascaro, Joseph; Xu, Liang; Keller, Michael; Yang, Yan; Duffy, Paul; Espírito-Santo, Fernando Del Bon; Baccini, Alessandro; Chambers, Jeffrey Q.; Schimel, David

doi:10.1111/geb.12256

Cited by 64 publications

(82 citation statements)

References 43 publications

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“…1). In the light of the considerable uncertainties between RS products, as well as with site data [79][80][81] , we accepted the uncertainty we introduce through tree-height-based downscaling for the advantage of consistency, because national forest C-stock data are available with global coverage 26 . Other approaches that have been proposed for downscaling national carbon stock information, for example, following the pattern of NPP 82 , would result in one national τbact value for forest.…”

Section: Carbon Stock Of the Actual Vegetation (Scact )mentioning

confidence: 99%

Biomass turnover time in terrestrial ecosystems halved by land use

Erb¹,

Fetzel²,

et al. 2016

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The terrestrial carbon cycle is not well quantified1. Biomass turnover time is a crucial parameter in the global carbon cycle2–4, and contributes to the feedback between the terrestrial carbon cycle and climate2–7. Biomass turnover time varies substantially in time and space, but its determinants are not well known8,9, making predictions of future global carbon cycle dynamics uncertain5,10–13. Land use—the sum of activities that aim at enhancing terrestrial ecosystem services14—alters plant growth15 and reduces biomass stocks16, and is hence expected to aect biomass turnover. Here we explore land-use-induced alterations of biomass turnover at the global scale by comparing the biomass turnover of the actual vegetation with that of a hypothetical vegetation state with no land use under current climate conditions. We find that, in the global average, biomass turnover is 1.9 times faster with land use. This acceleration aects all biomes roughly equally, but with large dierences between land-use types. Land conversion, for example fromforests to agricultural fields, is responsible for59%of the acceleration; the use of forestsand natural grazing land accounts for 26% and 15% respectively. Reductions in biomass stocks are partly compensated by reductions in net primary productivity. We conclude that land use significantly and systematically aects the fundamental trade-off between carbon turnover and carbon stocks

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Section: Carbon Stock Of the Actual Vegetation (Scact )mentioning

confidence: 99%

Biomass turnover time in terrestrial ecosystems halved by land use

Erb¹,

Fetzel²,

et al. 2016

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“…Mitchard et al [129] found that these maps do not agree with the spatial distribution of AGB in permanent Amazon field plots and that the uncertainties quantified in a comparison with 413 ground plots far exceed those reported by the studies. Saatchi et al [131] responded to Mitchard et al [129] arguing that, aside from methodological flaws in the interpolation approach, using 413 plots from different periods ). Adapted with permission from Balzter et al [68] between 1956 and 2013 only represent 404.6 ha out of 650 million ha of forest in the Amazon and without a rigorously designed and extensive in situ forest inventory strategy are not representative of the AGB trends in the region.…”

Section: Large Mapping Scalementioning

confidence: 99%

Quantifying Forest Biomass Carbon Stocks From Space

et al. 2017

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Purpose of Review This review presents cutting-edge methods and current and forthcoming satellite remote sensing technologies to map aboveground biomass (AGB). Recent Findings The monitoring of carbon stored in living AGB of forest is of key importance to understand the global carbon cycle and for the functioning of international economic mechanisms aiming to protect and enhance forest carbon stocks. The main challenge of monitoring AGB lies in the difficulty of obtaining field measurements and allometric models in several parts of the world due to geographical remoteness, lack of capacity, data paucity or armed conflicts. Space-borne remote sensing in combination with ground measurements is the most cost-efficient technology to undertake the monitoring of AGB. Summary These approaches face several challenges: lack of ground data for calibration/validation purposes, signal saturation in high AGB, coverage of the sensor, cloud cover persistence or complex signal retrieval due to topography. New space-borne sensors to be launched in the coming years will allow accurate measurements of AGB in high biomass forests (>200 t ha −1 ) for the first time across large areas.

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“…This problem has been observed in most forest types globally [6][7][8]. In addition, the paucity of field inventory plots in most of the unmanaged forests due to the difficulty of the access and cost of establishing large plots has been a source of uncertainty in the use of regression model techniques [9,10]. Reducing such uncertainty would require the establishment of larger and more accurately located plots in global forests.…”

Section: Introductionmentioning

confidence: 99%

Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory

et al. 2016

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Abstract:The scientific community involved in the UN-REDD program is still reporting large uncertainties about the amount and spatial variability of CO 2 stored in forests. The main limitation has been the lack of field samplings over space and time needed to calibrate and convert remote sensing measurements into aboveground biomass (AGB). As an alternative to costly field inventories, we examine the reliability of state-of-the-art lidar methods to provide direct retrieval of many forest metrics that are commonly collected through field sampling techniques (e.g., tree density, individual tree height, crown cover). AGB is estimated using existing allometric equations that are fed by lidar-derived metrics at either the individual tree-or forest layer-level (for the overstory or underneath layers, respectively). Results over 40 plots of a multilayered forest located in northwest Portugal show that the lidar method provides AGB estimates with a relatively small random error (RMSE = of 17.1%) and bias (of 4.6%). It provides local AGB baselines that meet the requirements in terms of accuracy to calibrate satellite remote sensing measurements (e.g., the upcoming lidar GEDI (Global Ecosystem Dynamics Investigation), and the Synthetic Aperture Radar (SAR) missions NISAR (National Aeronautics and Space Administration and Indian Space Research Organization SAR) and BIOMASS from the European Space Agency, ESA) for AGB mapping purposes. The development of similar techniques over a variety of forest types would be a significant improvement in quantifying CO 2 stocks and changes to comply with the UN-REDD policies.

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Seeing the forest beyond the trees

Cited by 64 publications

References 43 publications

Biomass turnover time in terrestrial ecosystems halved by land use

Biomass turnover time in terrestrial ecosystems halved by land use

Quantifying Forest Biomass Carbon Stocks From Space

Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory

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