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Book (stand-alone)Good practices in sample-based area estimation 2024
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Reducing Emissions from Deforestation and Forest Degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+), as well as greenhouse gas reporting for the agriculture, forestry and other land use sector, requires land use changes to be characterized to estimate the associated greenhouse gas emissions or absorptions. It is becoming increasingly common to generate these estimates using sample-based area estimation (SBAE). This technique has been widely used in recent years in the generation of activity data – particularly for estimating areas of deforestation – for REDD+ measuring, reporting and verification. However, implementing countries and agencies have repeatedly highlighted the lack of guidance on how to address certain frequently encountered issues with this approach. This paper seeks to enable donors, academia, and countries that currently use or want to use SBAE for generating activity data for REDD+ or for other national or international reporting purposes, to delve into current good practice and existing literature, as well as gain a better understanding of the most pressing research needs in the area. The paper moreover will give non-experts an overview of area estimation, as well as its applications and limitations.Published by FAO with the collaborative support of several partners in the Global Forest Observations Initiative (GFOI), the World Bank and the Department for Energy Security and Net Zero of the United Kingdom of Great Britain and Northern Ireland, the paper is expected to contribute to improved forest data. -
DocumentThe effects of ignoring clustered data structure in allometric biomass models on large forest area biomass estimation
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
The aim of this study was to assess the effects of ignoring the clustered data structure on large area biomass estimation, when model uncertainty is included or not in the biomass prediction process. We used a Monte Carlo error propagation procedure to combine the uncertainty from allometric model predictions with the uncertainty from plot-to-plot variation, to produce estimates of mean AGB per hectare and standard error of the mean. An alternative procedure that ignores model prediction uncertainty was also used, therefore, showing only uncertainty due to differences between plots. Ignoring the clustered data structure, (i.e., fitting allometric models using ordinary least squares), the estimates of mean biomass per hectare were approximately 11% less than the estimates based on mixed effects models (that accounted for the clustered data structure), regardless of including or not the model prediction uncertainty. The estimates of uncertainty were also affected by ignoring the clustered data structure. When including model prediction uncertainty, ignoring the clustered data structure resulted in underestimation of standard error by 30%, whereas when model uncertainty was not included, the underestimation was 13%. Therefore, ignoring the clustered data structure, may affect both, the accuracy and the precision of biomass estimations over large forest areas. Keywords: Monitoring and data collection, Climate change ID: 3616826 -
ArticleDoes independent forest monitoring reduce forest infringement? Insights from Ghana’s collaborative mobile-based IFM system
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
Independent Forest Monitoring (IFM) has been a feature of international effort to improve forest governance since its beginning in Cambodia in 1999. Today, IFM has gained traction and is an integral element of emerging forest governance schemes such as voluntary partnership agreement (VPA) which seeks to promote trade in legal timber between EU member countries and timber-producing countries in the global south. Within the VPA, IFM aims to complement the national due diligence mechanisms by flagging illegalities and providing opportunities for redress. Ghana is one such country where IFM is emerging within the country's VPA to address perennial forest governance challenges including corruption. This is often done through projects that develop and train communities on forest laws and provide them with mobile phones and appropriate software applications to monitor and flagged illegalities within their localities. Although this has been done over the years little insights are available on how this IFM architecture has performed. Such analysis is required to understand if IFM presents any hope for sanitizing the forest sector. On the back of this, this paper review community IFM monitoring reports identify key trends on forest illegalities and how they were addressed or otherwise. We found that the real-time monitoring platform has generated 747 alerts as of December 2019. Nearly 72% of them have been verified with most Social Responsibility Agreement (SRA) related infractions resulting in some 32 communities receiving SRA for the first time or on a continuous basis. The study concludes that communities are now protecting their forest as a result of compliance from timber companies which has generated revenue in the form of social responsibility agreements for community projects. Managers of the forest reserves are now responsive to queries as a result of the digital nature of the alerts. Keywords: Monitoring and data collection, Deforestation and forest degradation, Sustainable forest management, Governance ID: 3470164
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