Mobile Devices for Community-Based REDD+ Monitoring: A Case Study for Central Vietnam

Sensors

Arun Kumar Pratihast²  Martin Herold²  Valerio Avitabile²  Sytze de Bruin²  Harm Bartholomeus²  Carlos M. Souza¹ 

[1]Instituto do Homem e Meio Ambiente da Amazônia—Imazon, Caixa Postal 5101, Belém, PA 66613-397, Brazil

[2] E-Mail:

[3]Centre for Geo-Information, Wageningen University, P.O. Box 47, 6700 AA, Wageningen, The Netherlands

[4] E-Mails:

关键词: mobile devices;    REDD+;    MRV;    community based monitoring;    forest carbon;    forest change;   

DOI  :  10.3390/s130100021

来源: mdpi

【 摘 要 】

Monitoring tropical deforestation and forest degradation is one of the central elements for the Reduced Emissions from Deforestation and Forest Degradation in developing countries (REDD+) scheme. Current arrangements for monitoring are based on remote sensing and field measurements. Since monitoring is the periodic process of assessing forest stands properties with respect to reference data, adopting the current REDD+ requirements for implementing monitoring at national levels is a challenging task. Recently, the advancement in Information and Communications Technologies (ICT) and mobile devices has enabled local communities to monitor their forest in a basic resource setting such as no or slow internet connection link, limited power supply, etc. Despite the potential, the use of mobile device system for community based monitoring (CBM) is still exceptional and faces implementation challenges. This paper presents an integrated data collection system based on mobile devices that streamlines the community-based forest monitoring data collection, transmission and visualization process. This paper also assesses the accuracy and reliability of CBM data and proposes a way to fit them into national REDD+ Monitoring, Reporting and Verification (MRV) scheme. The system performance is evaluated at Tra Bui commune, Quang Nam province, Central Vietnam, where forest carbon and change activities were tracked. The results show that the local community is able to provide data with accuracy comparable to expert measurements (index of agreement greater than 0.88), but against lower costs. Furthermore, the results confirm that communities are more effective to monitor small scale forest degradation due to subsistence fuel wood collection and selective logging, than high resolution remote sensing SPOT imagery.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.