Design and Development of a Google Earth Engine-Based Environmental Data Monitoring Web Application with a Client-Server Architecture
TL;DR
These results show that separating the client, server, and cloud computing layers enables security and optimization mechanisms unavailable in approaches based on the built-in ecosystem.
Abstract
The use of Google Earth Engine (GEE) for environmental data monitoring is generally limited to users proficient in script-based programming environments. Prior studies have bridged this constraint through web interfaces, but were built on top of GEE's built-in application ecosystem, leaving the software architecture without room for independent design. This study designs and builds a web application with a decoupled client-server architecture using the Next.js framework, treating GEE solely as a spatial processing engine. The method used is Research and Development, covering requirements analysis, architecture and interface design, and functional and user experience testing. The system was applied to a case study extracting Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) in Bali, and was successfully realized with historical study, comparison view, and predictive modeling modes. Black Box testing showed that all thirteen scenarios succeeded, including rate limiting and caching that reduced response time from 9,000–12,000 milliseconds to 200–2,000 milliseconds. A user experience evaluation involving 27 respondents obtained a mean value of 1.979, with four of the six UEQ scales rated Excellent. These results show that separating the client, server, and cloud computing layers enables security and optimization mechanisms unavailable in approaches based on the built-in ecosystem.