Tracking Cloud Forests With Cloud Technology and Random Forests

Frontiers in Environmental Science

卷:9

Pasky Pascual¹  Cam Pascual² 

[1] United States Environmental Protection Agency (Retired), Washington, DC, United States;

[2] World Wildlife Fund, Washington, DC, United States;

关键词: cloud forest;    random forest;    artificial intelligence;    machine learning;    cloud technology;    Sentinel-2;   

DOI  :  10.3389/fenvs.2021.800179

来源: DOAJ

【 摘 要 】

Hotspots of endemic biodiversity, tropical cloud forests teem with ecosystem services such as drinking water, food, building materials, and carbon sequestration. Unfortunately, already threatened by climate change, the cloud forests in our study area are being further endangered during the Covid pandemic. These forests in northern Ecuador are being razed by city dwellers building country homes to escape the Covid virus, as well as by illegal miners desperate for money. Between August 2019 and July 2021, our study area of 52 square kilometers lost 1.17% of its tree cover. We base this estimate on simulations from the predictive model we built using Artificial Intelligence, satellite images, and cloud technology. When simulating tree cover, this model achieved an accuracy between 96 and 100 percent. To train the model, we developed a visual and interactive application to rapidly annotate satellite image pixels with land use and land cover classes. We codified our algorithms in an R package—loRax—that researchers, environmental organizations, and governmental agencies can readily deploy to monitor forest loss all over the world.

【 授权许可】

Unknown