18 October 2016 Accounting for ecosystem assets using remote sensing in the Colombian Orinoco River basin lowlands
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In many parts of the world, ecosystems change compromises the supply of ecosystem services (ES). Better ecosystem management requires detailed and structured information. Ecosystem accounting has been developed as an information system for ecosystems, using concepts and valuation approaches that are aligned with the System of National Accounts (SNA). The SNA is used to store and analyse economic data, and the alignment of ecosystem accounts with the SNA facilitates the integrated analysis of economic and ecological aspects of ecosystem use. Ecosystem accounting requires detailed spatial information at aggregated scales. The objective of this paper is to explore how remote sensing images can be used to analyse ecosystems using an accounting approach in the Orinoco river basin. We assessed ecosystem assets in terms of extent, condition and capacity to supply ES. We focus on four specific ES: grasslands grazed by cattle, timber and oil palm harvest, and carbon sequestration. We link ES with six ecosystem assets; savannahs, woody grasslands, mixed agro-ecosystems, very dense forests, dense forest and oil palm plantations. We used remote sensing vegetation, surface temperature and productivity indexes to measure ecosystem assets. We found that remote sensing is a powerful tool to estimate ecosystem extent. The enhanced vegetation index can be used to assess ecosystems condition, and net primary productivity can be used for the assessment of ecosystem assets capacity to supply ES. Integrating remote sensing and ecological information facilitates efficient monitoring of ecosystem assets, in particular in data poor contexts.
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Leonardo Vargas, Leonardo Vargas, Lars Hein, Lars Hein, Roy P. Remme, Roy P. Remme, } "Accounting for ecosystem assets using remote sensing in the Colombian Orinoco River basin lowlands", Proc. SPIE 10005, Earth Resources and Environmental Remote Sensing/GIS Applications VII, 1000510 (18 October 2016); doi: 10.1117/12.2245293; https://doi.org/10.1117/12.2245293

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