{"id":5728,"date":"2023-09-12T18:03:00","date_gmt":"2023-09-12T18:03:00","guid":{"rendered":"https:\/\/new.www.purdue.edu\/newsroom\/?p=5728"},"modified":"2024-07-16T18:18:58","modified_gmt":"2024-07-16T18:18:58","slug":"study-improves-accuracy-of-planted-forest-locations-in-east-asia","status":"publish","type":"post","link":"https:\/\/www.purdue.edu\/newsroom\/2023\/Q3\/study-improves-accuracy-of-planted-forest-locations-in-east-asia","title":{"rendered":"Study improves accuracy of planted forest locations in East Asia"},"content":{"rendered":"

WEST LAFAYETTE, Ind. —<\/p> \n

An international team led by Purdue University scientists has created the first spatial database of planted forests in East Asia by combining data collected on-site and via satellite. Produced with the assistance of artificial intelligence, the database contains forest location maps at a resolution of one kilometer (six-tenths of a mile) and lists the most common tree species growing there, such as pine and eucalyptus.<\/p>\n<\/div>\n\n\n

The Food and Agriculture Organization of the United Nations<\/a> (FAO) estimates that East Asia holds 36% of the world\u2019s planted forests. Africa, meanwhile, has 19%; Europe, 7%; and the United States, 9%. Within East Asia, 87.0% of planted forest is in China, followed by 11.1% in Japan, 1.0% in South Korea and 0.9% in North Korea.<\/p>\n\n\n\n

Previous maps of East Asia\u2019s planted forests covered only parts of the region spanning China, Japan, North Korea and South Korea. And the data sources for those maps are inconsistent and unverified.<\/p>\n\n\n\n

\u201cWe used an AI approach to help us understand a massive amount of data, measured both from the ground and also from remote-sensing sources,\u201d said\u00a0Jingjing Liang<\/a>, associate professor of quantitative forest ecology at Purdue. \u201cThis study complements the research portfolio of the Institute for Digital Forestry with an international perspective, enriching our understanding of global forest ecosystems and their sustainable management.\u201d<\/p>\n\n\n\n

\"A
Panorama of a newly forested region in northeastern China. Before forestation efforts, a sandy desert blanketed the area. (Photo provided by Jingjing Liang)<\/figcaption><\/figure>\n\n\n\n

A collaboration consisting of Liang, lead author Akane Abbasi, a PhD student in forestry and natural resources, and 15 co-authors recently presented their results<\/a> in the journal Scientific Data.<\/p>\n\n\n\n

The project collaborators are members of Science-i<\/a> and the Global Forest Biodiversity Initiative<\/a> (GFBI). Science-i is a web-based platform involving more than 300 scientists worldwide. GFBI has built a database of 1.3 million sample plots and 55 million trees.<\/p>\n\n\n\n

\u201cAt Land & Carbon Lab<\/a>, we believe what gets measured gets managed,\u201d said study co-author Nancy Harris<\/a>, research director of Land & Carbon Lab and Global Forest Watch at World Resources Institute<\/a>. \u201cOur partnership with Purdue and FAO embodies our mission to deploy breakthroughs in geospatial monitoring that power solutions for sustainable landscapes. Seeing where these different types of forests are on a map revolutionizes the options for interpretation and decision-making that go beyond simply knowing how much forest is in a country.\u201d<\/p>\n\n\n\n

Integrating the various data sources was one of the project\u2019s big challenges. The team started with data that came in different formats, types and spatial extents.<\/p>\n\n\n\n

\u201cSome data cover only China. Some data cover only Japan. Some data cover China and South Korea,\u201d Abbasi said. One dataset based on satellite images covered China, Japan and South Korea but not North Korea. \u201cThey differ in spatial extent. They also differ in relatability in terms of whether it\u2019s measured on the ground or from space.\u201d<\/a><\/p>\n\n\n\n

\"A
This newly planted forest in China is an example of afforestation \u2014 growing forest where there was none before. (Photo provided by Jingjing Liang)<\/figcaption><\/figure>\n\n\n\n

The researchers integrated data collected on-site, by satellite and through an approach called \u201censemble machine learning.\u201d This method entailed using AI to train three different machine learning models.<\/p>\n\n\n\n

\u201cThe ensemble machine learning method is developed to account for imperfections in both data and the model,\u201d said Liang, who also is co-director of the Forest Advanced Computing and Artificial Intelligence Lab<\/a> (FACAI).<\/p>\n\n\n\n

The team used the Bell Community Cluster<\/a> at Purdue\u2019s Rosen Center for Advanced Computing<\/a> to process and analyze the massive amount of data involved in the project.<\/p>\n\n\n\n

\u201cWhen I say I study forest ecology, people assume that I go to the forest, measure something, then come back and analyze the data,\u201d Abbasi said. \u201cBut this is not what we\u2019re doing here. We deal with very large spatial extents, and we study nature using cutting-edge AI and machine learning.\u201d<\/p>\n\n\n\n

Scientists know from official, country-led estimates that East Asia by far contains the world\u2019s largest proportion of planted forest, said study co-author Javier Gamarra of FAO. \u201cYet most of the region has traditionally tended to be less prone to follow open data policies about forest information, which makes the exercise of estimating such area particularly challenging,\u201d he said.<\/p>\n\n\n\n

The results confirm previous data shown by the FAO\u2019s Global Forest Resource Assessment (FRA). \u201cBut in this case, we can obtain spatial locations of where forest plantations exist at small resolutions, something that FRA cannot show,\u201d Gamarra said.<\/p>\n\n\n\n

Gamarra lauded Purdue\u2019s use of AI to combine data collected both via satellites and on the ground to produce robust estimates that could prove especially useful in making vast amounts of forest resource information widely available.<\/p>\n\n\n\n

\u201cIn fact, FAO\u2019s Forestry Division is actively collaborating with Professor Liang\u2019s FACAI Lab and the World Resources Institute to expand the use of artificial intelligence to obtain ecozone-based estimates of forest growth across the globe,\u201d he said.<\/p>\n\n\n\n

\"A
A planted forest in Japan consisting of Japanese cedar or hinoki cypress or both. Planted forests like this one tend to include one or only a few species. Natural forests tend to have more species and more trees of different ages. (Photo provided by PhotoAC)<\/figcaption><\/figure>\n\n\n\n

China has planted more forest in recent decades than any other nation, covering an area of more than 324,000 square miles. That area is greater than the square mileage of the states of Texas and New York combined. Japan and South Korea, meanwhile, planted many of their forests 50 or 60 years ago.<\/p>\n\n\n\n

After World War II and the Korean War, demand was high for forest products, so people started planting forest plots. \u201cThat was a huge contribution to most of the planted forests in those countries,\u201d Abbasi said.<\/p>\n\n\n\n

Sandstorms regularly pelt Beijing, which stands less than 60 miles from the nearest desert. This has led the Chinese government to begin planting trees on sand dunes, a costly and labor-intensive process.<\/p>\n\n\n\n

\u201cLearning from what China did would be one thing we can do to help the world plant more trees to stem deforestation,\u201d Liang said.<\/p>\n\n\n\n

The co-authors include Purdue\u2019s Ankita Mitra, a PhD student in forestry and natural resources<\/a>; and Mohammad Jahanshahi<\/a>, associate professor in the Lyles School of Civil Engineering<\/a>.<\/p>\n\n\n\n

This work received funding from the World Resources Institute and Purdue\u2019s Department of Forestry and Natural Resources.<\/p>\n","protected":false},"excerpt":{"rendered":"

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