Good Practices In Sample Based Area Estimation
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Good practices in sample-based area estimation
Author: Jonckheere, I.
language: en
Publisher: Food & Agriculture Org.
Release Date: 2024-02-14
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.
FAO publications catalogue 2024
From the State of the World collection and other major reports to freshly penned technical studies , FAO publications cater to a diverse range of audiences. This catalogue presents a selection of FAO’s main publications, produced in 2024 or earlier, ranging from its global reports and general interest publications to numerous specialized titles. In addition to the major themes of agriculture, forestry and fisheries, it also includes thematic sections on climate change, economic and social development, and food safety and nutrition.
Optical and SAR Remote Sensing of Urban Areas
This book introduces remotely sensed image processing for urban areas using optical and synthetic aperture radar (SAR) data and assists students, researchers, and remote sensing practitioners who are interested in land cover mapping using such data. There are many introductory and advanced books on optical and SAR remote sensing image processing, but most of them do not serve as good practical guides. However, this book is designed as a practical guide and a hands-on workbook, where users can explore data and methods to improve their land cover mapping skills for urban areas. Although there are many freely available earth observation data, the focus is on land cover mapping using Sentinel-1 C-band SAR and Sentinel-2 data. All remotely sensed image processing and classification procedures are based on open-source software applications such QGIS and R as well as cloud-based platforms such as Google Earth Engine (GEE). The book is organized into six chapters. Chapter 1 introduces geospatial machine learning, and Chapter 2 covers exploratory image analysis and transformation. Chapters 3 and 4 focus on mapping urban land cover using multi-seasonal Sentinel-2 imagery and multi-seasonal Sentinel-1 imagery, respectively. Chapter 5 discusses mapping urban land cover using multi-seasonal Sentinel-1 and Sentinel-2 imagery as well as other derived data such as spectral and texture indices. Chapter 6 concludes the book with land cover classification accuracy assessment.