Examinando por Autor "Tariq, Aqil"

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Analyzing urban expansion and land use dynamics in Bagua Grande and Chachapoyas using cloud computing and predictive modeling
(Springer Nature, 2024-09-26) Barboza, Elgar; Turpo, Efrain Y.; Salas Lopez, Rolando; Silva López, Jhonsy O.; Cruz Luis, Juancarlos Alejandro; Vásquez, Héctor V.; Purohit, Sanju; Aslam, Muhammad; Tariq, Aqil
Urban growth and Land Use/Land Cover (LULC) changes have increased in recent decades due to anthropogenic activities. This study explored past and projected future LULC changes and urban growth patterns in the Bagua Grande and Chachapoyas districts using Landsat imagery, cloud computing, and predictive models for 1990 to 2031. The analysis of satellite images was grouped into four time periods (1990–2000, 2000–2011, 2011–2021 and 2021–2031). The Google Earth Engine (GEE) cloud-based system facilitated the classification of Landsat 5 ETM (1990, 2000, and 2011) and Landsat 8 OLI (2021) images using the Random Forest (RF) model. A simulation model integrating Cellular Automata (CA) and an Artificial Neural Network (ANN) Multilayer Perceptron (MLP) in the MOLUSCE plugin of QGIS was used to forecast urban sprawl to 2031. The resulting maps showed an overall accuracy (OA) of over 92%. A decrease in forested area was observed, from 20,807.97 ha in 1990 to 14,629.44 ha in 2021 in Bagua Grande and from 7,796.08 ha to 3,598.19 ha in Chachapoyas. In contrast, urban areas experienced a significant increase, from 287.49 to 1,128.77 ha in Bagua Grande and from 185.65 to 924.50 ha in Chachapoyas between 1990 and 2021. By 2031, the urban area of Bagua Grande is expected to increase from 1,128.77 to 1,459.25 ha (29%) in a southeast, south, southwest, west, and northwest direction. Chachapoyas expanded from 924.50 to 1138.05 ha (23%) in the southwest, north, northeast, and southeast directions. The study presents an analytical method integrating cloud processing, GIS, and change simulation modeling to evaluate urban growth spatio-temporal patterns and LULC changes. This approach effectively identified the main LULC changes and trends in the study area. In addition, potential urbanization areas are highlighted where there are still opportunities for developing planned and managed urban settlements.
Mapping of coffee cultivation based on land use/land cover in the tropics of Cajamarca (Peru): An integration of Sentinel data, machine learning, and Google Earth Engine
(Elsevier B.V., 2026-08-12) Barboza, Elgar; Aragon, Amanda; Pizarro, Samuel; Roman Peña, Alcides; Adams, Brandon; Delgado, Ellen; Cotrina Sanchez, Alexander; Tariq, Aqil; Medina Medina, Angel; Tuesta Trauco, Katerin; Rivera Fernandez, Abner; Zabaleta Santisteban, Jhon; Ocaña, Candy; Madden, Marguerite
Coffee cultivation is a strategic economic activity in the Andean-Amazonian region of Peru, characterized by its diversity of production systems and complex landscape. In this study, a methodological approach based on multitemporal mapping of land cover and land use (LULC) was developed to identify coffee crops by integrating optical satellite imagery (Sentinel-2) and radar data (Sentinel-1), as well as topographic variables, processed on the Google Earth Engine (GEE) platform, with supervised classification based on the Random Forest (RF) algorithm. The study area covered the provinces of Jaen and San Ignacio (Cajamarca), the main coffee-growing areas in the country's north. A time series of satellite data from 2019 to 2024 was used, considering two climatic seasons (dry and wet). The 69 spectral, textural, and topographic variables were evaluated but subsequently reduced to 22 using the Variance Inflation Factor (VIF). The classification distinguished 11 LULC classes, including shade-grown and non-shade-grown coffee, achieving an overall accuracy of over 84% and a Kappa index of 0.90. The binary coffee maps made it possible to quantify and analyze the spatial dynamics of the crop in both provinces, showing a growing trend toward shade-grown coffee, especially in San Ignacio. The proposed approach proved to be operational for mountainous tropical areas with high cloud cover.