Abstract
BackgroundRemote sensing products can provide regular and consistent observations of the Earth´s surface to monitor and understand the condition and change of forest ecosystems and to inform estimates of terrestrial carbon dynamics. Yet, challenges remain to select the appropriate satellite data source for ecosystem carbon monitoring. In this study we examine the impacts of three attributes of four remote sensing products derived from Landsat, Landsat-SPOT, and MODIS satellite imagery on estimates of greenhouse gas emissions and removals: (1) the spatial resolution (30 vs. 250 m), (2) the temporal resolution (annual vs. multi-year observations), and (3) the attribution of forest cover changes to disturbance types using supplementary data.ResultsWith a spatially-explicit version of the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3), we produced annual estimates of carbon fluxes from 2002 to 2010 over a 3.2 million ha forested region in the Yucatan Peninsula, Mexico. The cumulative carbon balance for the 9-year period differed by 30.7 million MgC (112.5 million Mg CO2e) among the four remote sensing products used. The cumulative difference between scenarios with and without attribution of disturbance types was over 5 million Mg C for a single Landsat scene.ConclusionsUncertainty arising from activity data (rates of land-cover changes) can be reduced by, in order of priority, increasing spatial resolution from 250 to 30 m, obtaining annual observations of forest disturbances, and by attributing land-cover changes by disturbance type. Even missing a single year in the land-cover observations can lead to substantial errors in ecosystems with rapid forest regrowth, such as the Yucatan Peninsula.
Highlights
Remote sensing products can provide regular and consistent observations of the Earths surface to monitor and understand the condition and change of forest ecosystems and to inform estimates of terrestrial carbon dynamics
Systematic forest monitoring, reporting and verification (MRV) systems are required to aid in the successful development of national and regional strategies for REDD+, and to ensure long-term commitments to preserve forests [25, 40, 67]
This can only be achieved by implementing Remote sensing (RS) observations that will allow monitoring of large areas of land in a regular, consistent, and cost-efficient way and by developing modelling tools that can translate activity data derived from RS products into policy-relevant estimates of greenhouse gas (GHG) emissions and removals
Summary
Remote sensing products can provide regular and consistent observations of the Earths surface to monitor and understand the condition and change of forest ecosystems and to inform estimates of terrestrial carbon dynamics. In this study we examine the impacts of three attributes of four remote sensing products derived from Landsat, LandsatSPOT, and MODIS satellite imagery on estimates of greenhouse gas emissions and removals: (1) the spatial resolution (30 vs 250 m), (2) the temporal resolution (annual vs multi-year observations), and (3) the attribution of forest cover changes to disturbance types using supplementary data. Natural and anthropogenic disturbances are one of the main drivers that alter the forest structure over time, and understanding their impacts is critical for quantifying carbon stock changes and associated emissions into and removals from the atmosphere [10,11,12,13]. The CBM-CFS3 is used as a decision support tool for forests managers to quantify the ecosystem carbon dynamics at the landscape level
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