Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud
| Autor: | Sreenath Dixit, Adam J. Oliphant, Murali Krishna Gumma, Chandra Giri, Vineetha Pyla, Anthony M. Whitbread, Jun Xiong, Prasad S. Thenkabail, P Teluguntla |
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| Rok vydání: | 2019 |
| Předmět: |
education.field_of_study
Food security 010504 meteorology & atmospheric sciences business.industry Population Big data 0211 other engineering and technologies Cloud computing 02 engineering and technology 01 natural sciences Agricultural economics Random forest Unit (housing) Geography Agriculture General Earth and Planetary Sciences Satellite business education 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | GIScience & Remote Sensing. 57:302-322 |
| ISSN: | 1943-7226 1548-1603 |
| DOI: | 10.1080/15481603.2019.1690780 |
| Popis: | The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per Unit... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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