Identification of coffee agroforestry systems using remote sensing data: a review of methods and sensor data

References

• Baloloy AB, Blanco AC, Ana RRCS, Nadaoka K. 2020. Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS J Photogramm Remote Sens. 166:95–117. https://www.sciencedirect.com/science/article/pii/S0924271620301519. doi: 10.1016/j.isprsjprs.2020.06.001.
   Web of Science ®Google Scholar
• Beer J, Harvey C, Ibrahim M, Harmaand JM, Somarriba E, Jiménez F. 2003. Servicios ambientales de los sistemas agroforestales. Agroforestería en las América. 10(37–28):80–87.
   Google Scholar
• Bense T. 2007. Procesamiento de las imágenes satelitales. In: Sextas Jornadas de Educación en Percepción Remota en el Ámbito Del Mercosur.
   Google Scholar
• Bernardes T, Moreira MA, Adami M, Giarolla A, Rudorff BFT. 2012. Monitoring biennial bearing effect on coffee yield using MODIS remote sensing imagery. Remote Sens. 4(9):2492–2509. http://www.mdpi.com/2072-4292/4/9/2492. doi: 10.3390/rs4092492.
   Google Scholar
• Borelli S, Conigliaro M, Olivier E. 2017. Agroforesteria para la restauración del paisaje: explorando el potencial de la agroforesteria para mejorar la sostenibilidad y la resiliencia de los paisajes degradados. FAO.
   Google Scholar
• Castañeda-Castro O. 2018. Variaciones en Área Foliar y Concentraciones de Clorofilas y Nutrimentos Esenciales en Hojas de Café Robusta (Coffea Canephora P.) Durante un Ciclo Anual. Agro Productividad. 11(4):36–41.http://www.revista-agroproductividad.org/index.php/agroproductividad/article/view/266. doi: 10.32854/agrop.v11i4.266.
   Google Scholar
• Escobar-López A, Castillo-Santiago MA, Hernández-Stefanoni JL, Mas JF, López Martínez JO. 2022. Identifying coffee agroforestry system types using multitemporal sentinel-2 data and auxiliary information. Remote Sens. 14(16):3847. doi: 10.3390/rs14163847.
   Google Scholar
• Figueroa-Hernández E, Pérez-Soto F, Godínez-Montoya L. 2015. La producción y el consumo del café. Spain: ECORFAN, p. 182.
   Google Scholar
• Gomez C, Mangeas M, Petit M, Corbane C, Hamon P, Hamon S, De Kochko A, Le Pierres D, Poncet V, Despinoy M. 2010. Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones. Remote Sens Environ. 114(11):2731–2744. doi: 10.1016/j.rse.2010.06.007.
   Web of Science ®Google Scholar
• Hailu BT, Maeda EE, Pellikka P, Pfeifer M. 2015. Identifying potential areas of understorey coffee in Ethiopia’s highlands using predictive modelling. Int J Remote Sens. 36(11):2898–2919. doi: 10.1080/01431161.2015.1051631.
   Web of Science ®Google Scholar
• Hunt DA, Tabor K, Hewson JH, Wood MA, Reymondin L, Koenig K, Schmitt-Harsh M, Follett F. 2020. Review of remote sensing methods to map coffee production systems. Remote Sens. 12(12):2041. https://www.mdpi.com/2072-4292/12/12/2041. doi: 10.3390/rs12122041.
   Google Scholar
• Jensen J. 1996. Introductory digital image processing. Upper Saddle River: prentice Hall
   Google Scholar
• Jesus J, Kuplich TM. 2020. Applications of SAR data to estimate forest biophysical variables in Brazil. CERNE. 26(1):88–97. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-77602020000100088&tlng=en. doi: 10.1590/01047760202026012656.
   Web of Science ®Google Scholar
• Júnior AFC, de Carvalho Júnior OA, de Souza Martins O, Guerra AF. 2013. Phenological characterization of coffee crop (Coffea arabica L.) from MODIS time series. Rev Bras Geof. 31(4):569–578. doi: 10.22564/rbgf.v31i4.338.
   Google Scholar
• Kelley LC, Pitcher L, Bacon C. 2018. Using google earth engine to map complex shade-grown coffee landscapes in Northern Nicaragua. Remote Sens. 10(6):952. http://www.mdpi.com/2072-4292/10/6/952. doi: 10.3390/rs10060952.
   Google Scholar
• Lamparelli RAC, Johann JA, Santos É, Esquerdo JCDM, Rocha JV. 2012. Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants. Eng Agríc. 32(1):184–196. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162012000100019&lng=en&tlng=en. doi: 10.1590/S0100-69162012000100019.
   Web of Science ®Google Scholar
• Li M, Zang S, Zhang B, Li S, Wu C. 2014. A review of remote sensing image classification techniques: the role of spatio-contextual information. Eur J Remote Sens. 47(1):389–411. doi: 10.5721/EuJRS20144723.
   Web of Science ®Google Scholar
• Marín-Garza T, Gómez-Merino FC, Aguilar-Rivera N, Murguía-González J, Trejo-Téllez LI, Pastelín-Solano MC, Castañeda-Castro O. 2018. Composición Bioactiva De Hojas De Café Durante Un Ciclo Anual. RevFitotecMex. 41(4):365–372. http://167.172.243.173:80/index.php/RFM/article/view/121. doi: 10.35196/rfm.2018.4.365-372.
   Google Scholar
• Maurya K, Mahajan S, Chaube N. 2021. Remote sensing techniques: mapping and monitoring of mangrove ecosystem - a review. Complex Intell Syst. 7(6):2797–2818. doi: 10.1007/s40747-021-00457-z.
   Web of Science ®Google Scholar
• McKight PE, Najab J. 2010. Kruskal-wallis test. The corsini encyclopedia of psychology. Hoboken, NJ: Wiley; p. 1.
   Google Scholar
• Moguel P, Toledo V. 1999. Biodiversity conservation in traditional coffee systems of Mexico. Conserv Biol. 13(1):11–21. doi: 10.1046/j.1523-1739.1999.97153.x.
   Web of Science ®Google Scholar
• Nogueira Martins R, de Carvalho Pinto F, Marçal de Queiroz D, Magalhães Valente DS, Fim Rosas JT. 2021. A novel vegetation index for coffee ripeness monitoring using aerial imagery. Remote Sens. 13(2):263. doi: 10.3390/rs13020263.
   Google Scholar
• Olofsson P, Foody GM, Herold M, Stehman SV, Woodcock CE, Wulder MA. 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sens Environ. 148:42–57. https://linkinghub.elsevier.com/retrieve/pii/S0034425714000704. doi: 10.1016/j.rse.2014.02.015.
   Web of Science ®Google Scholar
• Olofsson P, Foody GM, Stehman SV, Woodcock CE. 2013. Making better use of accuracy data in land change studies: estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sens Environ. 129:122–131. https://linkinghub.elsevier.com/retrieve/pii/S0034425712004191. doi: 10.1016/j.rse.2012.10.031.
   Web of Science ®Google Scholar
• Ortega-Huerta MA, Komar O, Price KP, Ventura HJ. 2012. Mapping coffee plantations with Landsat imagery: an example from El Salvador. Int J Remote Sens. 33(1):220–242. doi: 10.1080/01431161.2011.591442.
   Web of Science ®Google Scholar
• Philpot W. 2015. Remote sensing fundamentals. Report No.
   Google Scholar
• Philpott SM, Bichier P. 2012. Effects of shade tree removal on birds in coffee agroecosystems in Chiapas, Mexico. Agric Ecosyst Environ. 149:171–180. https://linkinghub.elsevier.com/retrieve/pii/S0167880911000533. doi: 10.1016/j.agee.2011.02.015.
   Web of Science ®Google Scholar
• Podest E. 2017. Basics of Synthetic Aperture Radar (SAR). p. 1–56.
   Google Scholar
• Rizvi RH, Dhyani SK, Newaj SR, Karmakar PS. 2013. Mapping extent of agroforestry area through remote sensing: issues, estimates and methodology. Indian J Agrofor. 15(2):26–30.
   Google Scholar
• Sharma P, Bhardwaj DR, Singh MK, Nigam R, Pala NA, Kumar A, Verma K, Kumar D, Thakur P. 2022. Geospatial technology in agroforestry: status, prospects, and constraints. Environ Sci Pollut Res Int. 30(55):116459–116487. doi: 10.1007/s11356-022-20305-y.
   PubMedGoogle Scholar
• Tridawati A, Wikantika K, Susantoro TM, Harto AB, Darmawan S, Yayusman LF, Ghazali MF. 2020. Mapping the distribution of coffee plantations from multi-resolution, multi-temporal, and multi-sensor data using a random forest algorithm. Remote Sens. 12(23):3933. https://www.mdpi.com/2072-4292/12/23/3933. doi: 10.3390/rs12233933.
   Google Scholar