Remote Sensing Applications and Decision Support

Accounting for ecosystem assets using remote sensing in the Colombian Orinoco River Basin lowlands

[+] Author Affiliations
Leonardo Vargas, Lars Hein, Roy P. Remme

Wageningen University and Research, Environmental Systems Analysis Group, Wageningen, The Netherlands

J. Appl. Remote Sens. 11(2), 026008 (Apr 19, 2017). doi:10.1117/1.JRS.11.026008
History: Received August 22, 2016; Accepted March 28, 2017
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Abstract.  Worldwide, ecosystem change compromises the supply of ecosystem services (ES). Better managing ecosystems requires detailed information on these changes and their implications for ES supply. Ecosystem accounting has been developed as an environmental-economic accounting system using concepts aligned with the System of National Accounts. Ecosystem accounting requires spatial information from a local to national scale. The objective of this paper is to explore how remote sensing can be used to analyze 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 harvesting, oil palm fresh fruit bunches harvesting, and carbon sequestration. We link ES with six ecosystem assets: savannahs, woody grasslands, mixed agroecosystems, very dense forests, dense forest, and oil palm plantations. We used remote sensing vegetation 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.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Leonardo Vargas ; Lars Hein and Roy P. Remme
"Accounting for ecosystem assets using remote sensing in the Colombian Orinoco River Basin lowlands", J. Appl. Remote Sens. 11(2), 026008 (Apr 19, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.026008


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