The migration of vegetation under the influence of climate change is of great interest to ecologists, but can be difficult to quantify—especially in less accessible landscapes. Monitoring land cover change through remote sensing has become the best solution, especially with the use of unmanned aerial systems (UASs; drones) as low-cost remote sensing platforms are able to collect data at high spatial and spectral resolutions. Unfortunately, in the context of climate change studies, the lack of comparative UAS data sets over decadal timescales has been limiting. Here, we describe a technique for the integration of historical, low spatial resolution satellite-based Normalized Difference Vegetation Index (NDVI) data with short-term high-resolution multispectral UAS data to track the vegetation changes in a Costa Rican rainforest over a 33-year time frame. The study reveals the transition of a mixed forest from strongly deciduous to weakly deciduous phenology in the Hacienda Barú National Wildlife Refuge (HBNWR), southwestern Costa Rica. This case study presents an approach for researchers and forest managers to study and track vegetation changes over time in locations that lack detailed historical vegetation data. Vegetation migration due to climate change is not well documented and difficult to monitor, especially in remote or inaccessible locations. This case study presents researchers, students, and forest managers an approach for leveraging freely available satellite imagery and UASs to track these changes over time.
A novel remote sensing ecological vulnerability index on large scale: A case study of the China-Pakistan Economic Corridor region
