Developing web-based geographical services for temporally vegetation status monitoring using satellite imagery
Studying vegetation status is one of the most important environmental issues. Using contemporary and field methods in this regard will cost a lot of time and money. Remote sensing technology and satellite image processing is a solution that can facilitate such difficult processes. In this study, remote sensing technology will be integrated with web geographic services to provide users with adequate analysis of the growth of the plants and their changes based on vegetation indices in the shortest possible time. Important technologies used for this purpose are ENVI IDL, Google Map API, JavaScript, and ASP.NET. The service was successfully implemented and was employed for Landsat satellite images. In order to evaluate the performance of the service at different points and with a different number of images, the timeliness of the NDVI vegetation index was prepared. The processing time evaluation also showed that by increasing the number of satellite images the processing time will increase linearly.
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Travel time prediction with machine learning: competition of linear regression, multivariate regression, random forest and deep neural network
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