Each month, the Elsevier Atlas Award recognizes research that could significantly impact people's lives around the world. The May 2020 award goes to an article that explores mountain green cover index mapping:
Jinhu Bian, Ainong Li, Guanbin Lei, Zhengjian Zhang, Xi Nan for their 18 February 2020 article in ISPRS Journal of Photogrammetry and Remote Sensing: Global high-resolution mountain green cover index mapping based on Landsat images and Google Earth Engine
Mountains provide essential resources of many kinds for billions of people around the world. They’re also important biodiversity hotspots that are uniquely sensitive to climate and other environmental changes. Now, an Atlas award-winning team of researchers has developed a new and more precise method for tracking changes in mountain ecosystems by combining satellite images going back to the 1980s with Google Earth Engine, a state-of the-art platform for cloud computing.
“Our objective was to develop a new global high resolution gridded-mountain green cover index dataset to enable detection of changes at a much higher degree of detail than previously possible in both spatial and temporal domains,” said Ainong Li of the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences. Their findings are reported in ISPRS Journal of Photogrammetry and Remote Sensing.
Li’s work, conducted with study first author Jinhu Bian and their colleagues, was inspired by the United Nation’s Sustainable Development Goal 15: to “Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.” Within this goal, SDG 15.4 particularly highlights the importance of protecting and sustainably managing mountain ecosystems. To assist in that effort, the mountain green cover index (MGCI)—which measures change in green vegetation in mountain areas—is one of two key indicators for assessing the status of mountains around the world over time.
However, Bian and Li’s team saw limitations in the previously available MGCI data. For one thing, those data were calculated at the country level, making it hard to make comparisons at sub-national regions. The measures also were relatively low resolution and lacked detail on mountain’s three-dimensional features.
To develop a new, high-res MGCI method in the new study, the researchers took advantage of publicly available Landsat images, which were made freely available by the United States Geological Survey (USGS) in 2008. Those images allowed mapping of historical changes in the MGCI going back to the 1980s. Bian and Li noted that this Landsat data archived on the Google Earth Engine platform offered a unique opportunity to monitor MGCI year by year at higher spatial resolution and on various scales, from local to global.
The researchers found that the new method reached a high level of accuracy—more than 95 percent. The global grid further showed that about 70 percent of global mountain area had an MGCI greater than 90 percent. Another 17 percent had no vegetation cover at all, and about 4 percent had less than 10 percent green cover.
“We found that at the global scale, most mountain regions were covered by different degrees of vegetation, except for the area such as middle Asia, North Africa, Greenland, and Antarctica, where water shortages or low temperatures are the main constraints on vegetation growth and therefore mountains had barren surfaces,” Li said.
With a relatively high resolution of 500 meters, the researchers noted they could capture spatial details that had not been observable before in the official country-level data. Additionally, their results could easily be aggregated into different administrative units or regional scales—to explore, for example, a particular watershed or other area of interest. They report that such information will be useful and informative in the future to support sustainable development in mountain ecosystems and in the development of protection strategies and decision-making for mountain environments.
“Using all available high spatial resolution satellite images and the cloud-computing platform will be highly beneficial for the monitoring of the mountain ecosystem in times to come,” Li said.
The researchers have made their datasets available to the public through the big Earth data engine data portal developed by an effort known as Big Earth Data Science Engineering (CASEarth) for use by researchers around the world. They say they now need to create a time series by calculating historical MGCI at a high spatial resolution from historical data to understand changes over time and identify the driving factors for those changes in detail. Once validated, those datasets can then be provided to local governments for protection, conservation and reforestation activities as well as sustainable planning.
A Conversation with Ainong Li and Jinhu Bian
I talked with Ainong Li, the corresponding author of the paper, from the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, and his assistant Jinhu Bian, who is also the first author of the paper, about their approach to global high-resolution mountain green cover index mapping.Listen now.
What is the importance of mountains when it comes to conservation of ecosystems?
Ainong Li: Looking at the World Conservation Monitoring Centre (WCMC) mountain classification system, developed by the United Nations Environment Programme (UNEP), mountains account for about 24% of global land surface. In addition to their coverage, mountains are recognized to be a critical component of Earth’s surface due to their ecological, cultural, social, and economic benefits. They provide essential ecosystem services to billions of people and are host majority of the global biodiversity hotspots. Mountain areas guarantee ecosystem security and therefore form a key natural resource for human development and quality of life for all living beings. Therefore, the conservation of mountain ecosystems is of great importance for all humans.
Why are mountains especially sensitive areas?
Ainong Li: Mountains are categorized as ecologically fragile areas, and they are highly sensitive to climate change. Mountains apparently have three-dimensional characteristics. The environmental gradient of mountains is highly concentrated due to the unique vertical zone spectrums of climate and vegetation. Within the context of global warming, mountains are warming faster than low-lying areas owing to elevation-dependent warming. A lot of sensitive elements such as alpine cryosphere and tree line in mountain area have changed apparently under the influence of climate change in the past.
Tell me about the SDG goal 15.4 and its importance.
Ainong Li: It’s an SDG target specifically designed for conservation of mountain ecosystems. The SDG target 15.4 calls for ensuring conservation of the mountain ecosystem that includes biodiversity to enhance its benefits and ensure sustainable development. It is a very important and necessary SDG Target because in the past few decades, mountains are being increasingly exposed to changes in climate and human disturbance, such as mountain hazards, large-scale socio-economic transformations, and unsustainable management of natural resources. This SDG target highlights, priorities and motivates necessary and sustained actions towards conservation of mountain ecosystems and provide important guidance in terms of relevant actions needed through identifying important indicators for monitoring progress and change.
What is the mountain green cover index?
Ainong Li: This index is used by the International Mountain Science Committee as an important indicator for reflecting the state of environmental protection in mountain areas, and has also been incorporated in the SDG 15 indicator system. The Mountain Green Cover Index is defined as the ratio between green plants located on mountains (e.g., forests, shrubs, woodlands, pastures, farmland) with the total mountain area. Currently, it is widely agreed that there is a direct link between the Mountain Green Cover Index and the health and function of the mountain ecosystems. The conservation capacity and health status of mountain ecosystems can be diagnosed by monitoring this index over time.
Why is it hard to use this data?
Ainong Li: Currently, the official MGCI data has been released at the national, continental, and global scales. It is difficult to observe and study changes at sub-national or even much higher resolution which is always highly desired. At local scale, it is important for decision makers to understand changes in vegetation with location, the intensity and history of change. Therefore, mapping MGCI changes at high spatial resolution is critically important for the protection and development of mountains at local region or for use by local administrative units.
What did you propose in your study?
Jinhu Bian: In our study we have proposed a model to generate a grid-based MGCI that considers the true surface area of each mountain pixel. First the global green vegetation cover was extracted using all available 30-m Landsat-8 satellite observations within the calendar year by a new frequency- and phenology-based algorithm. The global mountain true surface area was then calculated and introduced into the MGCI calculation model for the global MGCI mapping.
Were there any surprises?
Ainong Li: Yes. As our results were generated at much higher resolution of 500 meters, there were several spatial details which were not observable in the official country-level data. Additionally our results can also be easily aggregated into different administrative units or regional scales such as the watershed scale, which will be very useful and informative in the future to support sustainable development in mountain ecosystems and in development of protection strategy and decision-making for mountain environments.
What needs to happen now? How can this be put to work?
Ainong Li: Now we need to create a MGCI time series by calculating historical MGCI at a high spatial resolution from historical data and understand the temporal change and identify the driving factors for these changes in detail. Higher resolution satellite data can be used to validate the results. The validated datasets can be then provided to the local governments for protection, conservation and reforestation activities and sustainable planning.
What's next for you?
Ainong Li: My institute and my group have been devotedly working on remote sensing application for mountain research for decades. We are focusing on developing and innovating new Digital Mountain and remote sensing technologies on the monitoring of mountain ecosystem and environment. Although we have developed the new MGCI method and have achieved encouraging results, there is still a lot of work to do in this domain. For example, the current definition of MGCI does not allow recording transformations from one green cover ecosystems to another such as development of cropland resulting from deforestation as they are all green covers. Identifying and studying the driving factors for the mountain green cover change is another important work to advance our understanding of global mountain changes. We will keep on working towards exploring these questions in future studies.
What would you like for the general public to know?
Ainong Li: Scientists around the world are working together to realize sustainable development. The importance of mountains and green sustainable development of mountain ecosystem has been gradually recognized by the general public. In the current era of big Earth data, we could better understand environmental changes than any time in the past. The mountain ecosystem has been undergoing rapid changes in the past few decades, the protection and conservation of these fragile ecosystem needs support from all parts of our society including support and encouragement from general public.
Anything else you'd like for us to know?
Ainong Li: In 2018, The Chinese Academy of Sciences launched the Strategic Priority Research program entitled “Big Earth Data Science Engineering (CASEarth)”. This program utilizes the large-scale and dynamic monitoring capabilities of Big Earth Data to measure and monitor SDG indicators. Prof. Guo Huadong, Chair of this program, is a member of the UN 10-member Group to support the Technology Facilitation Mechanism (TFM). Under Prof. Guo’s leadership, CASEarth concentrates on the sustainability assessments of six selected SDGs and our work is one of them focusing on using Big Earth Data technology for mountain-related studies. We are looking forward to working together with our colleagues to accelerate the achievement of SDGs, and we also call on all of us to pay attention to the sustainable development of our mountains and our planet.
- Sustainable Development Goal 15 (United Nations)
- Sustainable Development Goal 15.4: Mountains (United Nations)
- Big Earth Data Science Engineering Program
- Big Earth Data Engine Data Portal
- Ainong Li
The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) is the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). The Journal provides a channel of communication for scientists and professionals in all countries working in the many disciplines that employ photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The Journal is designed to serve as a source reference and archive of advancements in these disciplines.
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