Spatiotemporal Modeling of Rural Agricultural Land Use Change and Area Forecasts in Historical Time Series after COVID-19 Pandemic, Using Google Earth Engine in Peru

Segundo G. Chavez, Jaris Veneros, Nilton B. Rojas-Briceño, Manuel Oliva-Cruz, Grobert A. Guadalupe and Ligia García ()
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Segundo G. Chavez: Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, 342 Higos Urco, Chachapoyas 01001, Peru
Jaris Veneros: Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, 342 Higos Urco, Chachapoyas 01001, Peru
Nilton B. Rojas-Briceño: Grupo de Investigación en Ciencia de la Información Geoespacial, Instituto de Investigación para el Desarrollo del Perú, Universidad Nacional de Moquegua, Pacocha 18610, Peru
Manuel Oliva-Cruz: Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, 342 Higos Urco, Chachapoyas 01001, Peru
Grobert A. Guadalupe: Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, 342 Higos Urco, Chachapoyas 01001, Peru
Ligia García: Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, 342 Higos Urco, Chachapoyas 01001, Peru

Sustainability, 2024, vol. 16, issue 17, 1-19

Abstract: Despite the importance of using digital technologies for resource management, Peru does not record current and estimated processed data on rural agriculture, hindering an effective management process combined with policy. This research analyzes the connotation of spatiotemporal level trends of eight different land cover types in nine rural districts representative of the three natural regions (coast, highlands, and jungle) of Peru. The effect of change over time of the COVID-19 pandemic was emphasized. Then, forecast trends of agricultural areas were estimated, approximating possible future trends in a post-COVID-19 scenario. Landsat 7, Landsat 8, and Sentinel 2 images (2017–2022) processed in the Google Earth Engine platform (GEE) and adjusted by random forest, Kappa index, and Global Accuracy. To model the forecasts for 2027, the best-fit formula was chosen according to the criteria of the lowest precision value of the mean absolute percentage error, the mean absolute deviation, and the mean squared deviation. In the three natural regions, but not in all districts, all cover types suggested in the satellite images were classified. We found advantageous situations of agricultural area dynamics (2017–2022) for the coast of up to 80.92 km 2 (Guadalupe, 2022), disadvantageous situations for the Sierra, and indistinct situations for the Selva: between −91.52 km 2 (Villa Rica, 2022) and 22.76 km 2 (Santa Rosa, 2022). The trend analysis allows us to confirm the effects of the COVID-19 pandemic on the extension dedicated to agriculture. The area dedicated to agriculture in the Peruvian coast experienced a decrease; in the highlands, it increased, and in the jungle, the changes were different for the districts studied. It is expected that these results will allow progress in the fulfillment of the 2030 Agenda in its goals 1, 2, and 17.

Keywords: land cover mapping; land cover forecasting; GEE; pandemic; food security (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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