The cross disciplinary inclusion of location analytics and remote sensing data in biological data sets has provided a rich catalyst in biology research towards a more comprehensive understanding of the data generated in the study of living systems. Through a lens focused on case examples, we provide two examples of research methods that integrate remotely sensed data coupled with machine learning in discovering relationships between location analytics and biological phenomena. Our case studies include spatial dynamics in bird songs throughout disparate breeding geographies across Canada and the Northern US, and orchid life cycles in the White Mountains of New Hampshire. We added Landsat, Lidar and Hyperspectral data to existing biological data sets to determine the impact of vegetation and ground characteristics on outcomes. Our results demonstrate the viability of adding location-based data to biological data sets in collaboration with the original researchers. We explain the methods used in both case studies and postulate that a continued exploration of interdisciplinary collaborations may prove beneficial in the biological fields where spatial data exists or can be collected.