Expanding Access to Data-intensive Remote Sensing Algorithms through Collaboration with the Socio-Environmental Science Research Community

In the proposed Pursuit, we seek to overcome these long-standing, enviro-climatic and disciplinary limitations by convening a diverse group of interdisciplinary, socio-environmental researchers with ongoing projects in understudied regions. We have developed a cloud-resilient remote sensing algorithm (“NITA”) to distill dense time series of satellite imagery into metrics of protracted (e.g., drought) and acute (e.g., forest clearing) land-cover change. Socio-environmental researchers have voiced interest in implementing NITA but presently it is only available as a Matlab prototype and may not optimally satisfy the needs articulated by this diverse community. Thus, we propose this data-intensive SESYNC Pursuit to: 1) Refine NITA through collaboration with the SES community and SESYNC data scientists; 2) Broaden the reach of the algorithm by transitioning to open-source code; and 3) Implement NITA with invited participants to augment research coverage in difficult-to-access areas.

To learn more about the Pursuit, click here [1].