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satellites , which collect imagery of the entire globe every five days at the equator and every two to three days at midlatitudes , amounting to more than 5,000 images per day . These are streamed into the Google Earth Engine ’ s AI platform for analysis .
As new satellite images become available , the AI system classifies land cover types in near-real-time by detecting combinations of nine different land cover types — water , flooded vegetation , built-up areas , trees , crops , bare ground , grass , shrub / scrub , and snow / ice — in the images and calculating which types are most representative within each 10-by-10-meter pixel .
The continual updates mean the data set is extremely up-to-date , and users can also compare land-use maps for specific areas across chosen time periods between 2015 and two days ago . The data set is open-access and freely available on monitoring platforms Google Earth Engine and Resource Watch .
Speaking at the launch event , Craig Hanson , vice president of food , forests , water and the ocean at the World Resources Institute , said the new tool will enable public , private and nonprofit groups to make wiser decisions to protect , manage and restore our forests , nature and ecosystems , as well as create sustainable food systems and alert people to unforeseen changes to land .
“ This is particularly important because we live in a world facing great land squeeze ,” Hanson said . “ The world is experiencing growing demand for food , for timber , for bioenergy , for urban expansion . All the while , we need to be conserving land for nature , biodiversity and climate .”
Unlike most land cover platforms , which typically display a static view of locations , Dynamic World displays “ the pulsation of life ” throughout the year , Hanson said , making it useful for understanding longer-term trends of seasonal ecosystem change .
For example , as landscapes are seasonally flooded , the land cover can switch from grassland or trees to wetland and water . In agricultural landscapes , Dynamic World is able to detect the presence and proliferation of agroforestry systems , which would have once been classified simply as cropland .
“ Given the importance of restoration for the global agenda , such monitoring abilities are increasingly important and valuable , and will empower government , NGO and village efforts to advance restoration of their landscapes ,” Hanson said .
The African Forest Landscape Restoration Initiative ( AFR100 ), which aims to restore 100 million hectares ( 247 million acres ) of land in Africa by 2030 , is applying Dynamic World to keep track of progress toward its restoration goals , according to Mamadou Diakhité , leader of the initiative . Speaking at the online launch event , he said AFR100 is aiming to establish a credible monitoring platform that leverages satellite land cover data sets “ to bring in more investment from the private sector , from non-state actors , from the government to really embrace land restoration .”
For Wanjira Mathai , vice president and regional director for Africa at WRI , the new tool essentially brings landscapes to life . “ To understand the vulnerability of this precious planet is to understand how dynamic and how fragile it truly , truly is ,” Mathai said at the online launch . By showing the complex details of how land on Earth is used in a continually updating manner , she said , Dynamic World “ activates an emotional connection ” that has the potential to “ trigger a level of action that we have never seen before .”
Citation
Brown , C . F ., Brumby , S . P ., Guzder- Williams , B ., Birch , T ., Brooks Hyde , S ., Mazzariello , J ., Tait , A . M . ( 2022 ). Dynamic World , Near real-time global 10 m land use land cover mapping . Scientific Data , 9 ( 1 ), 251 . doi : 10.1038 / s41597- 022-01307-4
Figure 5 . Forest clearing for soy in Parecis in the state of Rondonia , Brazil . Image by Microsoft Zoom Earth
41 Grassroots Vol 22 No 2 July 2022