Review of Remote Sensing Methods to Map Coffee Production Systems
The coffee sector is working towards sector-wide commitments for sustainable production. Yet, knowledge of where coffee is cultivated and its environmental impact remains limited, in part due to the challenges of mapping coffee using satellite remote sensing. We recognize the urgency to capitalize o...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/12/2041 |
| _version_ | 1797564224179798016 |
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| author | David A. Hunt Karyn Tabor Jennifer H. Hewson Margot A. Wood Louis Reymondin Kellee Koenig Mikaela Schmitt-Harsh Forrest Follett |
| author_facet | David A. Hunt Karyn Tabor Jennifer H. Hewson Margot A. Wood Louis Reymondin Kellee Koenig Mikaela Schmitt-Harsh Forrest Follett |
| author_sort | David A. Hunt |
| collection | DOAJ |
| description | The coffee sector is working towards sector-wide commitments for sustainable production. Yet, knowledge of where coffee is cultivated and its environmental impact remains limited, in part due to the challenges of mapping coffee using satellite remote sensing. We recognize the urgency to capitalize on recent technological advances to improve remote sensing methods and generate more accurate, reliable, and scalable approaches to coffee mapping. In this study, we provide a systematic review of satellite-based approaches to mapping coffee extent, which produced 43 articles in the peer-reviewed and gray literature. We outline key considerations for employing effective approaches, focused on the need to balance data affordability and quality, classification complexity and accuracy, and generalizability and site-specificity. We discuss research opportunities for improved approaches by leveraging the recent expansion of diverse satellite sensors and constellations, optical/Synthetic Aperture Radar data fusion approaches, and advances in cloud computing and deep learning algorithms. We highlight the need for differentiating between production systems and the need for research in important coffee-growing geographies. By reviewing the range of techniques successfully used to map coffee extent, we provide technical recommendations and future directions to enable accurate and scalable coffee maps. |
| first_indexed | 2024-03-10T18:54:20Z |
| format | Article |
| id | doaj.art-7c6f2f05e3594def8b9f2ae5e4588fba |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:54:20Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7c6f2f05e3594def8b9f2ae5e4588fba2023-11-20T04:54:41ZengMDPI AGRemote Sensing2072-42922020-06-011212204110.3390/rs12122041Review of Remote Sensing Methods to Map Coffee Production SystemsDavid A. Hunt0Karyn Tabor1Jennifer H. Hewson2Margot A. Wood3Louis Reymondin4Kellee Koenig5Mikaela Schmitt-Harsh6Forrest Follett7Conservation International, 2011 Crystal Dr. #600, Arlington, VA 22202, USAConservation International, 2011 Crystal Dr. #600, Arlington, VA 22202, USAConservation International, 2011 Crystal Dr. #600, Arlington, VA 22202, USAConservation International, 2011 Crystal Dr. #600, Arlington, VA 22202, USAAlliance of Biodiversity International and CIAT, Asia—Hanoi Hub, Agricultural Genetics Institute, Pham Van Dong Street, Bac Tu Liem District, Hanoi 100000, VietnamConservation International, 2011 Crystal Dr. #600, Arlington, VA 22202, USADepartment of Interdisciplinary Liberal Studies, James Madison University, Maury Hall, 800 S. Main Street, Harrisonburg, VA 22801, USAThe Sustainability Consortium, Sam M. Walton College of Business, University of Arkansas, Fayetteville, AR 72701, USAThe coffee sector is working towards sector-wide commitments for sustainable production. Yet, knowledge of where coffee is cultivated and its environmental impact remains limited, in part due to the challenges of mapping coffee using satellite remote sensing. We recognize the urgency to capitalize on recent technological advances to improve remote sensing methods and generate more accurate, reliable, and scalable approaches to coffee mapping. In this study, we provide a systematic review of satellite-based approaches to mapping coffee extent, which produced 43 articles in the peer-reviewed and gray literature. We outline key considerations for employing effective approaches, focused on the need to balance data affordability and quality, classification complexity and accuracy, and generalizability and site-specificity. We discuss research opportunities for improved approaches by leveraging the recent expansion of diverse satellite sensors and constellations, optical/Synthetic Aperture Radar data fusion approaches, and advances in cloud computing and deep learning algorithms. We highlight the need for differentiating between production systems and the need for research in important coffee-growing geographies. By reviewing the range of techniques successfully used to map coffee extent, we provide technical recommendations and future directions to enable accurate and scalable coffee maps.https://www.mdpi.com/2072-4292/12/12/2041coffeeremote sensingagricultureagroforestryproduction systemmapping |
| spellingShingle | David A. Hunt Karyn Tabor Jennifer H. Hewson Margot A. Wood Louis Reymondin Kellee Koenig Mikaela Schmitt-Harsh Forrest Follett Review of Remote Sensing Methods to Map Coffee Production Systems Remote Sensing coffee remote sensing agriculture agroforestry production system mapping |
| title | Review of Remote Sensing Methods to Map Coffee Production Systems |
| title_full | Review of Remote Sensing Methods to Map Coffee Production Systems |
| title_fullStr | Review of Remote Sensing Methods to Map Coffee Production Systems |
| title_full_unstemmed | Review of Remote Sensing Methods to Map Coffee Production Systems |
| title_short | Review of Remote Sensing Methods to Map Coffee Production Systems |
| title_sort | review of remote sensing methods to map coffee production systems |
| topic | coffee remote sensing agriculture agroforestry production system mapping |
| url | https://www.mdpi.com/2072-4292/12/12/2041 |
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