Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenario forecasting-Reference-Cited by-同舟云学术

Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenario forecasting

Published:2024-01-16 Issue:1 Volume:61 Page:
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Parracciani Cecilia¹,Gigante Daniela¹,Mutanga Onisimo²,Bonafoni Stefania³,Vizzari Marco¹

Affiliation:

1. Department of Agricultural, Food, and Environmental Sciences, University of Perugia, Perugia, Italy

2. Department of Geography, University of KwaZulu-Natal, Pietermaritzburg, South Africa

3. Department of Engineering, University of Perugia, Perugia, Italy

Funder

the European Union - LIFE Project “LIFE IMAGINE UMBRIA”

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2024.2302221

Reference138 articles.

1. Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

2. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

3. Exploring LULC changes in Pakhal Lake area, Telangana, India using QGIS MOLUSCE plugin

4. Climate change will increase savannas at the expense of forests and treeless vegetation in tropical and subtropical Americas

5. Future land use land cover scenario simulation using open source GIS for the city of Warangal, Telangana, India

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