Blog Posts: data

Collecting field data on trees has various challenges, such as being time-consuming and requiring a lot of effort. In addition, the GPS signal on the device used can also experience interference if the weather conditions are bad, so it can reduce the accuracy of tree locations. The deep learning method as a method that relies on artificial intelligence, can help the process of collecting data for tree inventory, tree maintenance management, and preventing land fires in urban areas.

To understand Indonesia's climate prognosis, look at the sectors and provinces where emissions originate.

Indonesia sees more than 24 million hectares of degraded land, equivalent to almost twice the size of England, with such large size of degraded land, restoration is inevitable. But, we could save 2 million hectares of degraded land with good data. Read more in this article.

Countries considering open data policies have to guard against falsification and misuse, but there are clear ways to avoid these problems.

Disclosure of public information enables public to monitor illegal activities and ensure that natural resources are used equitably for all Indonesians.

An accurate, actual, complete and transparent data is a necessity for a well-informed decision-making process.

In Part 1 of the GLAD tree cover data accuracy blog series, we explained two different accuracy assessments of the global tree cover loss and gain data produced by Dr. Matt Hansen and collaborators at the University f Maryland’s Global Land Analysis and Discovery group (GLAD), Google, the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) . Today, we’ll dig into factors that increase errors and how this impacts the use of the data.

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Part 2 of this blog series available here.

In 2013, Dr. Matt Hansen and collaborators at the University of Maryland’s Global Land Analysis and Discovery group (GLAD), Google, U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) released the first global scale method for annually monitoring changes in tree cover using 30-meter resolution Landsat imagery, revolutionizing the way we measure and monitor forests. The GLAD tree cover change data (formerly referred to as the Hansen tree cover change data) set consists of two maps: one of annual tree cover loss from 2001 to 2014, and the other of cumulative tree cover gain during the 2000-2012 time period.

For further reading, see our op-ed in the Jakarta Post.

Less than four months ago, millions of people across Singapore, Malaysia and Indonesia were choking on the worst air pollution ever recorded in Southeast Asia as hundreds of fires burned across Sumatra. The fires caused serious damage, eliciting a public health emergency, closing schools and harming tourism and other businesses.

This week the Sultan of Brunei is hosting many of Asia’s heads of state for the 23rd Association of Southeast Asian Nations (ASEAN) Summit. Preventing new fires and haze are high on the agenda. Key decisions and actions are urgently needed from the presidents and prime ministers this week.