Survey of Raster Datasets-Reference-Cited by-同舟云学术

Survey of Raster Datasets

Published:2023-10-02 Issue: Volume: Page:41-66
ISSN:
Container-title:Cloud-Based Remote Sensing with Google Earth Engine
language:
Short-container-title:

Author:

Nicolau Andréa Puzzi^ORCID,Dyson Karen^ORCID,Saah David^ORCID,Clinton Nicholas^ORCID

Abstract

AbstractThe purpose of this chapter is to introduce you to the many types of collections of images available in Google Earth Engine. These include sets of individual satellite images, pre-made composites (which merge multiple individual satellite images into one composite image), classified land use and land cover (LULC) maps, weather data, and other types of datasets. If you are new to JavaScript or programming, work through Chaps. F1.0 and F1.1 first.

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-26588-4_3

Reference4 articles.

1. Chander G, Huang C, Yang L et al (2009a) Developing consistent Landsat data sets for large area applications: the MRLC 2001 protocol. IEEE Geosci Remote Sens Lett 6:777–781. https://doi.org/10.1109/LGRS.2009.2025244

2. Chander G, Markham BL, Helder DL (2009b) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens Environ 113:893–903. https://doi.org/10.1016/j.rse.2009.01.007

3. Hansen MC, Potapov PV, Moore R et al (2013) High-resolution global maps of 21st-century forest cover change. Science 342:850–853. https://doi.org/10.1126/science.1244693

4. Sulova A, Arsanjani JJ (2021) Exploratory analysis of driving force of wildfires in Australia: an application of machine learning within Google Earth Engine. Remote Sens 13:1–23. https://doi.org/10.3390/rs13010010