Mapping Regional Landscape by Using OpenstreetMap (OSM): A Case Study to Understand Forest Patterns in Maya Zone, Mexico

A forest patterns map over a large extent at a high spatial resolution is a heavily computation task but is critical to most regions. There are two major difficulties in generating the classification maps at regional scale: large training point sets and expensive computation cost in classifier modelling. As one of the most well-known Volunteered Geographic Information (VGI) initiatives, OpenstreetMap contributes not only on road network distributions, but the potential of justify land cover and land use. Google Earth Engine is a platform designed for cloud-based mapping with a strong computing power. In this study, we aim to prove OpenStreetmap’s potential capability in mapping forestland cover in a regional scale. We proposed a new approach to generating forest cover map and quantifying road-caused forest fragmentations by using OpenstreetMap in conjunction with remote sensing dataset stored in Google Earth Engine. Additionally, the landscape metrics produced after incorporating OpenStreetMap (OSM) with the forest spatial pattern layers from our output indicated significant levels of forest fragmentations in Yucatan peninsula. By investigating the spatial size-frequency distribution of regional forest patterns, Yucatán State has 45% more forest patches than Quintana Roo State. We also discussed the social driver of those difference based on 9 sites sub-case studies at Quintana Roo State and the border between two States.