Published February 20, 2023 | Version V1
Dataset Restricted

Case data: modeling spatial determinants of sugarcane abandonment in Rio de Janeiro

  • 1. University of Bonn

Description

This repository encompasses datasets for modeling spatial determinants of sugarcane abandonment in Rio de Janeiro, Brazil. Data includes previously published datasets and other publicly available data. Sugarcane mapping datasets should be referred to as outcomes from publication: https://doi.org/10.1016/j.rse.2022.113194 and might be used freely. The other datasets come from secondary sources and might be used for reproducibility. Further uses depend on the original data source policy. 

MSWEP data is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. Please get in touch with the authors if you are affiliated with a commercial entity and want to try MSWEP.  If you do not have a commercial affiliation and you intend to use the product for non-commercial purposes, please send the authors a request using the form on MSEP webpage: http://www.gloh2o.org/mswep/

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This repository encompasses datasets for modeling spatial determinants of sugarcane abandonment in Rio de Janeiro, Brazil. Data includes previously published datasets and other publicly available data. Sugarcane mapping datasets should be referred to as outcomes from publication: https://doi.org/10.1016/j.rse.2022.113194 and might be used freely. The other datasets come from secondary sources and might be used for reproducibility. Further uses depend on the original data source policy. 

MSWEP data is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. Please get in touch with the authors if you are affiliated with a commercial entity and want to try MSWEP.  If you do not have a commercial affiliation and you intend to use the product for non-commercial purposes, please send the authors a request using the form on MSEP webpage: http://www.gloh2o.org/mswep/

 

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Is cited by
Journal article: 10.1016/j.rse.2022.113194 (DOI)