Remote Sensing in Land Science Studies

Mapping Land-Use Land-Cover Change (LULCC) through satellite remote sensing offers valuable insights into understanding the trajectories, patterns, drivers, and consequences of land-cover alterations. Throughout this course, we will delve into the evolution of classification and change detection techniques designed to map LULCC and gauge land-use intensity.

The course curriculum encompasses non-parametric machine learning classification methods, including SVM, Random Forests, hybrid classifications. We will explore techniques to enhance classifications and boost classification accuracies, state-of-the-art accuracy assessment methods, multisource image fusion techniques, and the analysis of large data composites, including cloud computation environments like Google Earth Engine. Our primary focus will be on the utilization of freely accessible datasets, including optical imagery such as Landsat, Sentinel-2, and MODIS, as well as radar data from Sentinel-1 and microsatellites like PlanetScope. Additionally, we will cover applications in socio-economic and environmental domains, such as mapping wildfires, floods, land-cover changes, and socio-economic footprints, as seen through night-time lights (VIIRS and DMSP OLS).

This course assumes a prior knowledge and experience of working with satellite imagery, equivalent to completing undergraduate-level classes like Introduction to Remote Sensing and Classification of Spatial Data. However, we are committed to accommodating students without prior remote sensing experience. Moreover, this course complements the Remote Sensing of the Biogeosphere class and serves as a stepping stone into more advanced course Satellite Image Processing and Analysis in the Big Data Era. The course is particularly tailored for students who anticipate utilizing satellite remote sensing in interdisciplinary studies of the Biogeosphere and Anthroposphere (examining the human dimensions of land-cover change).

Our primary focus will be on applying remote sensing to investigate land-cover transformations, including urban sprawl and decline, as well as agricultural and forestry dynamics. Nonetheless, students are encouraged to bring their own research projects or propose alternative topics that align with their personal interests. We also highly encourage students to integrate knowledge gained from other courses and embark on interdisciplinary projects. Here are some examples of interdisciplinary projects that have evolved into published research papers:

https://doi.org/10.1016/j.rse.2019.03.013

https://doi.org/10.1016/j.rsase.2021.100647

https://doi.org/10.1038/s43016-021-00417-3

https://doi.org/10.1016/j.srs.2023.100092

https://doi.org/10.25518/0770-7576.6653