Assessing the status and changes in tree cover and land use
![Assessing the status and changes in tree cover and land use](https://www.fao.org/images/faoraplibraries/news-import/20190409-fra-pr.jpg?sfvrsn=46797daf_0)
With improved access to an ever-growing archive of satellite imagery and new tools to facilitate image processing and interpretation, it is now easier to benefit from the full potential of remote sensing for the assessment of the status and changes in tree cover and land use.
The five days China National Remote Sensing Survey workshop is being organized jointly by the Food and Agriculture Organization of the United Nations (FAO) and the National Forestry and Grassland Administration of the People´s Republic of China as part of the Global Forest Resources Assessment 2020 (FRA 2020).
The workshop will develop national capacities in using the FRA 2020 Remote Sensing methodology through a learning-by-doing training session on data collection, using remote sensing tools specifically developed for this purpose. The training sessions and data collection are part of the upcoming FRA 2020 Global Remote Sensing Survey.
“We are very excited to have the opportunity to work closely with national experts to collect this type of data” said Mr. Anssi Pekkarinen, Senior Forestry Officer and FRA Team Leader. “This close collaboration with our Chinese colleagues will allow us to collect data of a higher quality than that which would have been possible without this collaboration,” he added.
The collected data will become part of the global data set, which will be used to assess forest area and its changes at regional and global levels.
As reported in the Global Forest Resources Assessment 2015, China has the 5th largest forest area and the biggest forest area increase in the past ten years of any country in the world, therefore it is of great importance that part of the global assessment is taking place today in China.
How does Global Forest Resources Assessment work?
FAO collects data for the Global Forest Resources Assessment through a global network of officially nominated National Correspondents. Combining their knowledge of the forest resources in their countries with data from remote sensing and other sources allows FAO to provide information, which can be used to draw up recommendations for governments, civil society and the private sector. The FRA process also plays central part in monitoring progress towards the Sustainable Development Goal 15 – Life on Land - as it is used to collect information and report on the indicators on forest area change and on area of forest under sustainable management.
FRA covers all countries and territories and the reports contains a wealth of information structured according to seven thematic elements of Sustainable Forest Management (SFM). With the large amount of detailed data collected and made available by the countries and analyzed by FAO and its partner organizations, we now know a lot about the world’s forests resources and how they are changing.
The FRA 2020 Remote Sensing Survey
With the aim of providing a more comprehensive overview of the global forest resources, the FRA 2020 statistical data collection will be complemented by a new and improved global remote sensing survey.
The objectives set out for the FRA 2020 Remote Sensing Survey are multiple. It will provide independent and consistent estimates of forest area and changes at global, regional and biome level as well as assess drivers of deforestation. The survey uses a methodology that can be easily up-scaled for use at country level to produce national estimates in those countries that wish to do so, which will be a tremendous help for developing countries with limited resources as a first step towards more detailed information about their forests.
The data collection will be done through participatory and collaborative approaches, engaging national experts in interpretation of the remote sensing images and thereby generating ownership of the data from their country and developing national capacities in remote sensing assessment.