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[1] Lehmann, C.E.R., Archibald, S.A., Hoffmann, W.A., Bond, W.J. (2011). Deciphering the distribution of the savanna biome. New Phytologist, 191(1): 197-209. [Crossref]
[2] Miles, L., Newton, A.C., DeFries, R.S., Ravilious, C., May, I., Blyth, S., Kapos, V., Gordon, J.E. (2006). A global overview of the conservation status of tropical dry forests. Journal of Biogeography, 33(3): 491-505. [Crossref]
[3] Sullivan, M.J.P., Talbot, J., Lewis, et al. (2017). Diversity and carbon storage across the tropical forest biome. Scientific Reports, 7: 1-12. [Crossref]
[4] Pötzschner, F., Baumann, M., Gasparri, N.I., Conti, G., Loto, D., Piquer-Rodríguez, M., Kuemmerle, T. (2022). Ecoregion-wide, multi-sensor biomass mapping highlights a major underestimation of dry forests carbon stocks. Remote Sensing of Environment, 269: 1-10. [Crossref]
[5] An, Y., Zhao, W., Li, C., Sofia Santos Ferreira, C. (2022). temporal changes on soil conservation services in large basins across the world. Catena, 209: 105793. [Crossref]
[6] Keys, P.W., Wang-Erlandsson, L., Gordon, L.J. (2016). Revealing invisible water: Moisture recycling as an ecosystem service. PLoS One, 11: 1-16. [Crossref]
[7] Stefanidis, S., Alexandridis, V., Ghosal, K. (2022). Assessment of water-induced soil erosion as a threat to natura 2000 protected areas in Crete Island, Greece. Sustainability, 14: 1-22. [Crossref]
[8] Powers, J.S., Feng, X., Sanchez-Azofeifa, A., Medvigy, D. (2018). Focus on tropical dry forest ecosystems and ecosystem services in the face of global change. Environmental Research Letters, 13. [Crossref]
[9] Reyes-Palomeque, G., Dupuy, J.M., Portillo-Quintero, C.A., Andrade, J.L., Tun-Dzul, F.J., Hernández-Stefanoni, J.L. (2021). Mapping forest age and characterizing vegetation structure and species composition in tropical dry forests. Ecological Indicators, 120. [Crossref]
[10] Singh, C., Karan, S.K., Sardar, P., Samadder, S.R. (2022). Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis. Journal of Environmental Management, 308: 114639. [Crossref]
[11] Qarallah, B., Al-Ajlouni, M., Al-Awasi, A., Alkarmy, M., Al-Qudah, E., Naser, A.B., Al-Assaf, A., Gevaert, C.M., Al Asmar, Y., Belgiu, M., Othman, Y.A. (2021). Evaluating post-fire recovery of latroon dry forest using landsat ETM+, unmanned aerial vehicle and field survey data. Journal of Arid Environments, 193: 104587. [Crossref]
[12] Winkler, K., Fuchs, R., Rounsevell, M., Herold, M. (2021). Global land use changes are four times greater than previously estimated. Nature Communications, 12: 1-10. [Crossref]
[13] Mendoza-Ponce, A., Corona-Núñez, R., Kraxner, F., Leduc, S., Patrizio, P. (2018). Identifying effects of land use cover changes and climate change on terrestrial ecosystems and carbon stocks in Mexico. Global Environmental Change, 53: 12-23. [Crossref]
[14] Corona-Núñez, R.O., Mendoza-Ponce, A.V., Campo, J. (2021). Assessment of above-ground biomass and carbon loss from a tropical dry forest in Mexico. Journal of Environmental Management, 282. [Crossref]
[15] Foley, J.A., DeFries, R., Asner, et al. (2005). Global consequences of land use. Science, 309: 570-574. https://www.science.org/doi/10.1126/science.1111772.
[16] Netzer, M.S., Sidman, G., Pearson, T.R.H., Walker, S.M., Srinivasan, R. (2019). Combining global remote sensing products with hydrological modeling to measure the impact of tropical forest loss on water-based ecosystem services. Forests, 10: 1-20. [Crossref]
[17] Zhu, E., Deng, J., Zhou, M., Gan, M., Jiang, R., Wang, K., Shahtahmassebi, A.R. (2019). Carbon emissions induced by land-use and land-cover change from 1970 to 2010 in Zhejiang, China. Science of the Total Environment, 646: 930-939. [Crossref]
[18] Rojas, N.B., Barboza, E., Maicelo, J.L., Oliva, S.M., Salas, R. (2019). Deforestación en la Amazonía Peruana: Índices de cambios de cobertura y uso del suelo basado en SIG. Boletín de la Asociación de Geógrafos Españoles, 81: 1-34. [Crossref]
[19] Iqbal, M.F., Khan, I.A. (2014). Spatiotemporal land use land cover change analysis and erosion risk mapping of Azad Jammu and Kashmir, Pakistan. Egyptian Journal of Remote Sensing and Space Science, 17: 209-229. [Crossref]
[20] Achmad, A., Ramli, I., Sugiarto, S., Irzaidi, I., Izzaty, A. (2024). Assessing and forecasting carbon stock variations in response to land use and land cover changes in central Aceh, Indonesia. International Journal of Design & Nature and Ecodynamics, 19: 465-475. https://www.iieta.org/journals/ijdne/paper/10.18280/ijdne.190212.
[21] Yahya, N., Bekele, T., Gardi, O., Blaser, J. (2020). Forest cover dynamics and its drivers of the arba gugu forest in the eastern highlands of Ethiopia during 1986 - 2015. Remote Sensing Applications: Society and Environment, 20: 100378. [Crossref]
[22] Edewede, D., Onojiede, E., Peace, N. (2019). Effect of urban centre growth on vegetation cover: A case study of Ebony State, South-Eastern, Nigeria. Environmental Earth Sciences Research Journal, 6: 51-58. https://www.iieta.org/journals/eesrj/paper/10.18280/eesrj.060201.
[23] de Luque-Villa, M., Acosta-Santos, C., Vargas-Cediel, A., Robledo-Buitrago, D. (2020). Noise impact assessment using corine land cover methodology: A case study in Funza, Colombia. International Journal of Sustainable Development and Planning, 15: 857-863. https://www.iieta.org/journals/ijsdp/paper/10.18280/ijsdp.150609
[24] Andrade, J., Cunha, J., Silva, J., Rufino, I., Galvão, C. (2021). Evaluating single and multi-date landsat classifications of land-cover in a seasonally dry tropical forest. Remote Sensing Applications: Society and Environment, 22: 100515. [Crossref]
[25] Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 152-170. [Crossref]
[26] Man, C.D., Nguyen, T.T., Bui, H.Q., Lasko, K., Nguyen, T.N.T. (2018). Improvement of land-cover classification over frequently cloud-covered areas using landsat 8 time-series composites and an ensemble of supervised classifiers. International Journal of Remote Sensing, 39: 1243-1255. [Crossref]
[27] Wulder, M.A., White, J.C., Loveland, T.R., Woodcock, C.E., Belward, A.S., Cohen, W.B., Fosnight, E.A., Shaw, J., Masek, J.G., Roy, D.P. (2016). The global landsat archive: status, consolidation, and direction. Remote Sensing of Environment, 185: 271-283. [Crossref]
[28] Teluguntla, P., Thenkabail, P., Oliphant, A., Xiong, J., Gumma, M.K., Congalton, R.G., Yadav, K., Huete, A. (2018). A 30-m landsat-derived cropland extent product of australia and china using random forest machine learning algorithm on google earth engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144: 325-340. [Crossref]
[29] Belgiu, M., Drăgu, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31. [Crossref]
[30] Melo, M.V.N. de, Oliveira, M.E.G. de, Almeida, G.L.P. de, Gomes, N.F., Montalvo Morales, K.R., Santana, T.C., Silva, P.C., Moraes, A.S., Pandorfi, H., Silva, M.V. (2022). Spatiotemporal characterization of land cover and degradation in the agreste region of pernambuco, brazil, using cloud geoprocessing on Google Earth Engine. Remote Sensing Applications: Society and Environment, 26: 00756. [Crossref]
[31] Huang, H., Chen, Y., Clinton, N., Wang, J., Wang, X., Liu, C., Gong, P., Yang, J., Bai, Y., Zheng, Y., Zhu, Z. (2017). Mapping major land cover dynamics in beijing using all landsat images in Google Earth Engine. Remote Sensing of Environment, 202: 166-176. [Crossref]
[32] Na, X., Zhang, S., Li, X., Yu, H., Liu, C. (2010). Improved land cover mapping using random forests combined with landsat thematic mapper imagery and ancillary geographic data. Photogrammetric Engineering and Remote Sensing, 76: 833-840. [Crossref]
[33] Gonzáles, P., Neri, L. (2015). El ecoturismo como alternativa sostenible para proteger el bosque seco tropical peruano: El caso de proyecto hualtaco, tumbes. PASOS Revista de Turismo y Patrimonio Cultural, 13: 1437-1449. https://www.redalyc.org/articulo.oa?id=88143407012.
[34] Mercado, W., Rimac, D. (2019). Comercialización de miel de abeja del bosque seco, distrito de Motupe, Lambayeque, Perú. Natura@economía, 4: 24. [Crossref]
[35] Zorogastúa, P., Quiroz, R., Garatuza, J. (2011). Evaluación de cambios en la cobertura y uso de la tierra con imágenes de satélite en Piura - Perú. Ecología Aplicada, 10: 13-22. http://www.scielo.org.pe/scielo.php?pid=S1726-22162011000100002&script=sci_abstract.
[36] Aldana, C., Revilla, M., Gonzales, J., Saavedra, Y., Moncada, W., Maicelo, J. (2020). Relación de firmas espectrales para la identificación de bosque seco en imágenes de satélite Sentinel 2, cuenca baja del río Chira, región Piura. Revista de Teledetección, 147. [Crossref]
[37] Pennington, R.T., Prado, D.E., Pendry, C.A., Garden, R.B. (2001). Neotropical seasonally dry forests and quaternary vegetation changes. Journal of Biogeography, 27: 261-273. [Crossref]
[38] MINAM. (2018). Línea de base de los bosques secos de la costa norte del Perú al 2018. Ministerio del Ambiente, Lima, Perú. Available online: http://www.bosques.gob.pe/archivo/Apuntes-del-bosque-4.pdf.
[39] Rodríguez, A., Álvarez, R. (2005). Uso múltiple del bosque seco del norte del Perú: análisis del ingreso y autoconsumo. Zonas Áridas, 8921: 131-148.
[40] Ruíz, L., Quijandría, G., Otárola, E., Rios, S., Álvarez, J., Núñez, F. (2019). Mapa nacional de ecosistemas del Perú: memoria descriptiva. Ministerio del Ambiente, Lima, Perú. Available online: http://www.bosques.gob.pe/archivo/Apuntes-del-bosque-4.pdf.
[41] Moro, M.F., Silva, I.A., De Araújo, F.S., Lughadha, E.N., Meagher, T.R., Martins, F.R. (2015). The role of edaphic environment and climate in structuring phylogenetic pattern in seasonally dry tropical plant communities. PLoS One, 10: 1-18. [Crossref]
[42] Chuvieco, E. (2020). Fundamentals of satellite remote sensing: An environmental approach. CRC Press, Boca Raton.
[43] Parente, L., Ferreira, L. (2018). Assessing the spatial and occupation dynamics of the brazilian pasturelands based on the automated classification of modis images from 2000 to 2016. Remote Sensing, 10: 606. [Crossref]
[44] Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. (2017). Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18-27. [Crossref]
[45] Breiman, L. (2001). Random Forests. Machine Learning, 45: 5-32. [Crossref]
[46] Hosseiny, B., Abdi, A.M., Jamali, S. (2022). Urban land use and land cover classification with interpretable machine learning - A case study using Sentinel-2 and auxiliary data. Remote Sensing Applications: Society and Environment, 28. [Crossref]
[47] Traganos, D., Reinartz, P. (2018). Mapping mediterranean seagrasses with Sentinel-2 imagery. Marine Pollution Bulletin, 134: 197-209. [Crossref]
[48] Tsai, Y.H., Stow, D., Chen, H.L., Lewison, R., An, L., Shi, L. (2018). Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine. Remote Sensing, 10(6): 927. [Crossref]
[49] Foody, G.M. (1992). On the compensation for chance agreement in image classification accuracy assessment. Photogrammetric Engineering and Remote Sensing, 58: 1459-1460. https://www.asprs.org/wp-content/uploads/pers/1992journal/oct/1992_oct_1459-1460.pdf.
[50] Cochran, W.G. (1997). Sampling Techniques. John Wiley, Hoboken.
[51] Acharki, S. (2022). PlanetScope contributions compared to Sentinel-2, and Landsat-8 for LULC mapping. Remote Sensing Applications: Society and Environment, 27: 100774. [Crossref]
[52] Padilla, M., Stehman, S.V., Chuvieco, E. (2014). Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling. Remote Sensing of Environment, 144: 187-196. [Crossref]
[53] Pontius, R.G., Shusas, E., McEachern, M. (2004). Detecting important categorical land changes while accounting for persistence. Agriculture, Ecosystems & Environment, 101: 251-268. [Crossref]
[54] FAO. (1996). Forest Resources Assessment 1990. Survey of Tropical Forest Cover and Study of Change Processes. Rome, Italy. https://www.fao.org/4/w0015e/w0015e00.htm
[55] Zhang, S., Guan, Z., Liu, Y., Zheng, F. (2022). Land use/cover change and its relationship with regional development in Xixian New Area, China. Sustainability, 14: 6889. [Crossref]
[56] Zhuang, D., Liu, J. (1997). Modeling of regional differentiation of land-use degree in China. Chinese Geographical Science, 7: 302-309. https://link.springer.com/article/10.1007/s11769-997-0002-4
[57] FAO. (2007). Situación de los Bosques del Mundo. Rome, Italy. https://www.fao.org/4/a0773s/a0773s00.htm.
[58] Cui, J., Zhu, M., Liang, Y., Qin, G., Li, J., Liu, Y. (2022). Land use/land cover change and their driving factors in the yellow river basin of shandong province based on Google Earth Engine from 2000 to 2020. ISPRS International Journal of Geo-Information, 11(3): 163. [Crossref]
[59] Heredia-R, M., Torres, B., Cabrera-Torres, F., Torres, E., Díaz-Ambrona, C.G.H., Pappalardo, S.E. (2021). land use and land cover changes in the diversity and life zone for uncontacted indigenous people: deforestation hotspots in the Yasuní Biosphere Reserve, Ecuadorian Amazon. Forests, 12(11): 1539. [Crossref]
[60] Xie, H. (2017). Towards sustainable land use in china: A collection of empirical studies. Sustainability, 9(11): 2129. [Crossref]
[61] Zuo, Q., Li, X., Hao, L., Hao, M. (2020). Spatiotemporal evolution of land-use and ecosystem services valuation in the belt and road initiative. Sustainability, 12: 6583. [Crossref]
[62] Bertrand, R., Lenoir, J., Piedallu, C., Dillon, G.R., De Ruffray, P., Vidal, C., Pierrat, J.C., Gégout, J.C. (2011). Changes in plant community composition lag behind climate warming in lowland forests. Nature, 479: 517-520. [Crossref]
[63] Khanal, S., Timilsina, R., Behroozian, M., Peterson, A.T., Poudel, M., Alwar, M.S.S., Wijewickrama, T., Osorio-Olvera, L. (2022). Potential impact of climate change on the distribution and conservation status of pterocarpus marsupium, a near threatened South Asian medicinal tree species. Ecological Informatics, 70: 101722. [Crossref]
[64] Pécastaing, N., Chávez, C. (2020). The impact of el niño phenomenon on dry forest-dependent communities’ welfare in the Northern Coast of Peru. Ecological Economics, 178: 106820. [Crossref]
[65] Rosa, I.M.D., Purves, D., Souza, C., Ewers, R.M. (2013). Predictive modelling of contagious deforestation in the Brazilian Amazon. PLoS One, 8: e77231. [Crossref]
[66] Steege, H. Ter, Pitman, N.C.A., Killeen, et al. (2015). Estimating the global conservation status of more than 15,000 amazonian tree species. Science Advances, 1: 9-11. https://www.science.org/doi/10.1126/sciadv.1500936.
[67] Cotrina, A., Bandopadhyay, S., Rojas, N.B., Banerjee, P., Torres, C., Oliva, M. (2021). Peruvian Amazon disappearing: Transformation of protected areas during the last two decades (2001-2019) and potential future deforestation modelling using cloud computing and MaxEnt Approach. Journal of Nature Conservation, 64: 126081. [Crossref]
[68] Paiva, P.F.P.R., de Lourdes, M., da Silva, O.M., de Nazaré, M., Braga, T.G.M., de Andrade, M.M.N., dos Santos, P.C., da Rocha, E.S., de Freitas, T.P.M., da Silva, et al. (2020). Deforestation in Protect areas in the Amazon: A threat to biodiversity. Biodiversity and Conservation, 29: 19-38. [Crossref]
[69] Cotrina, A., Barboza, E., Rojas Briceño, N.B., Oliva, M., Torres, C., Amasifuen, C.A., Bandopadhyay, S. (2020). Distribution models of timber species for forest conservation and restoration in the Andean-Amazonian Landscape, North of Peru. Sustainability, 12(19): 7945. [Crossref]
[70] Bonilla-Bedoya, S., Estrella-Bastidas, A., Molina, J.R., Herrera, M.Á. (2018). Socioecological system and potential deforestation in Western Amazon forest landscapes. Science of the Total Environment, 644: 1044-1055. [Crossref]
[71] Geist, H.J., Lambin, E.F. (2002). Proximate causes and underlying driving forces of tropical deforestation. BioScience, 52(2): 143-150. [0143:PCAUDF]2.0.CO;2 [Crossref]
[72] Samsammurphy. (2023). Cloud Masking with Sentinel 2. GitHub Repository. https://github.com/samsammurphy/cloud-masking-sentinel2/blob/master/cloud-masking-sentinel2.ipynb.
[73] Liu, S., Li, X., Chen, D., Duan, Y., Ji, H., Zhang, L., Chai, Q., Hu, X. (2020). Understanding land use/land cover dynamics and impacts of human activities in the mekong delta over the last 40 years. Global Ecology and Conservation, 22: e00991. [Crossref]
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Open Access
Research article

Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru

elgar barboza1,2*,
wilian salazar3,
david gálvez-paucar4,
lamberto valqui-valqui1,
leandro valqui1,
luis h. zagaceta1,
jhony gonzales4,
héctor v. vásquez1,
carlos i. arbizu5
1
Agrostology Laboratory, Research Institute for Livestock and Biotechnology, National University Toribio Rodríguez de Mendoza de Amazonas (UNTRM), 01001 Chachapoyas, Peru
2
Professional School of Environmental Engineering and Natural Resources, Faculty of Engineering, University Technological of the Andes (UTEA), 03001 Abancay, Peru
3
Vista Florida Agricultural Experiment Station, National Institute of Agricultural Innovation (INIA), 14300 Chiclayo, Peru
4
Research Institute for Sustainable Development and Climate Change, National University of Frontera (UNF), 20103 Sullana, Peru
5
Faculty of Engineering and Agricultural Sciences, National University Toribio Rodríguez de Mendoza de Amazonas (UNTRM), 01001 Chachapoyas, Peru
International Journal of Environmental Impacts
|
Volume 7, Issue 3, 2024
|
Pages 505-514
Received: 07-26-2024,
Revised: 09-14-2024,
Accepted: 09-21-2024,
Available online: 09-29-2024
View Full Article|Download PDF

Abstract:

Dry forests are ecosystems of great importance worldwide, but in recent decades they have been affected by climate change and changes in land use. In this study, we evaluated land use and land cover changes (LULC) in dry forests in Peru between 2017 and 2021 using Sentinel-2 images, and cloud processing with Machine Learning (ML) models. The results reported a mapping with accuracies above 85% with an increase in bare soil, urban areas and open dry forest, and reduction in the area of crops and dense dry forest. Protected natural areas lost 2.47% of their conserved surface area and the areas with the greatest degree of land use impact are located in the center and north of the study area. The study provides information that can help in the management of dry forests in northern Peru.

Keywords: Remote Sensing (RS), Biodiversity, Random Forest (RF), Forest monitoring, Google Earth Engine (GEE)
Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Acknowledgments

We would like to thank the Dirección de Desarrollo Tecnológico Agropecuario – DDTA of the Instituto Nacional de Innovación Agraria – INIA. This research was conducted and financed mainly by CUI Project No 2253484 “Creación de un Servicio de Laboratorio de Agrostología de la Universidad Nacional Toribio Rodríguez de Mendoza” that was financed by Sistema Nacional de Inversión Pública (SNIP) of the Ministry of Economy and Finance (MEF) of Peru. Also, was supported by the Vice-Rectorate of Research of the Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas - UNTRM.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References
[1] Lehmann, C.E.R., Archibald, S.A., Hoffmann, W.A., Bond, W.J. (2011). Deciphering the distribution of the savanna biome. New Phytologist, 191(1): 197-209. [Crossref]
[2] Miles, L., Newton, A.C., DeFries, R.S., Ravilious, C., May, I., Blyth, S., Kapos, V., Gordon, J.E. (2006). A global overview of the conservation status of tropical dry forests. Journal of Biogeography, 33(3): 491-505. [Crossref]
[3] Sullivan, M.J.P., Talbot, J., Lewis, et al. (2017). Diversity and carbon storage across the tropical forest biome. Scientific Reports, 7: 1-12. [Crossref]
[4] Pötzschner, F., Baumann, M., Gasparri, N.I., Conti, G., Loto, D., Piquer-Rodríguez, M., Kuemmerle, T. (2022). Ecoregion-wide, multi-sensor biomass mapping highlights a major underestimation of dry forests carbon stocks. Remote Sensing of Environment, 269: 1-10. [Crossref]
[5] An, Y., Zhao, W., Li, C., Sofia Santos Ferreira, C. (2022). temporal changes on soil conservation services in large basins across the world. Catena, 209: 105793. [Crossref]
[6] Keys, P.W., Wang-Erlandsson, L., Gordon, L.J. (2016). Revealing invisible water: Moisture recycling as an ecosystem service. PLoS One, 11: 1-16. [Crossref]
[7] Stefanidis, S., Alexandridis, V., Ghosal, K. (2022). Assessment of water-induced soil erosion as a threat to natura 2000 protected areas in Crete Island, Greece. Sustainability, 14: 1-22. [Crossref]
[8] Powers, J.S., Feng, X., Sanchez-Azofeifa, A., Medvigy, D. (2018). Focus on tropical dry forest ecosystems and ecosystem services in the face of global change. Environmental Research Letters, 13. [Crossref]
[9] Reyes-Palomeque, G., Dupuy, J.M., Portillo-Quintero, C.A., Andrade, J.L., Tun-Dzul, F.J., Hernández-Stefanoni, J.L. (2021). Mapping forest age and characterizing vegetation structure and species composition in tropical dry forests. Ecological Indicators, 120. [Crossref]
[10] Singh, C., Karan, S.K., Sardar, P., Samadder, S.R. (2022). Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis. Journal of Environmental Management, 308: 114639. [Crossref]
[11] Qarallah, B., Al-Ajlouni, M., Al-Awasi, A., Alkarmy, M., Al-Qudah, E., Naser, A.B., Al-Assaf, A., Gevaert, C.M., Al Asmar, Y., Belgiu, M., Othman, Y.A. (2021). Evaluating post-fire recovery of latroon dry forest using landsat ETM+, unmanned aerial vehicle and field survey data. Journal of Arid Environments, 193: 104587. [Crossref]
[12] Winkler, K., Fuchs, R., Rounsevell, M., Herold, M. (2021). Global land use changes are four times greater than previously estimated. Nature Communications, 12: 1-10. [Crossref]
[13] Mendoza-Ponce, A., Corona-Núñez, R., Kraxner, F., Leduc, S., Patrizio, P. (2018). Identifying effects of land use cover changes and climate change on terrestrial ecosystems and carbon stocks in Mexico. Global Environmental Change, 53: 12-23. [Crossref]
[14] Corona-Núñez, R.O., Mendoza-Ponce, A.V., Campo, J. (2021). Assessment of above-ground biomass and carbon loss from a tropical dry forest in Mexico. Journal of Environmental Management, 282. [Crossref]
[15] Foley, J.A., DeFries, R., Asner, et al. (2005). Global consequences of land use. Science, 309: 570-574. https://www.science.org/doi/10.1126/science.1111772.
[16] Netzer, M.S., Sidman, G., Pearson, T.R.H., Walker, S.M., Srinivasan, R. (2019). Combining global remote sensing products with hydrological modeling to measure the impact of tropical forest loss on water-based ecosystem services. Forests, 10: 1-20. [Crossref]
[17] Zhu, E., Deng, J., Zhou, M., Gan, M., Jiang, R., Wang, K., Shahtahmassebi, A.R. (2019). Carbon emissions induced by land-use and land-cover change from 1970 to 2010 in Zhejiang, China. Science of the Total Environment, 646: 930-939. [Crossref]
[18] Rojas, N.B., Barboza, E., Maicelo, J.L., Oliva, S.M., Salas, R. (2019). Deforestación en la Amazonía Peruana: Índices de cambios de cobertura y uso del suelo basado en SIG. Boletín de la Asociación de Geógrafos Españoles, 81: 1-34. [Crossref]
[19] Iqbal, M.F., Khan, I.A. (2014). Spatiotemporal land use land cover change analysis and erosion risk mapping of Azad Jammu and Kashmir, Pakistan. Egyptian Journal of Remote Sensing and Space Science, 17: 209-229. [Crossref]
[20] Achmad, A., Ramli, I., Sugiarto, S., Irzaidi, I., Izzaty, A. (2024). Assessing and forecasting carbon stock variations in response to land use and land cover changes in central Aceh, Indonesia. International Journal of Design & Nature and Ecodynamics, 19: 465-475. https://www.iieta.org/journals/ijdne/paper/10.18280/ijdne.190212.
[21] Yahya, N., Bekele, T., Gardi, O., Blaser, J. (2020). Forest cover dynamics and its drivers of the arba gugu forest in the eastern highlands of Ethiopia during 1986 - 2015. Remote Sensing Applications: Society and Environment, 20: 100378. [Crossref]
[22] Edewede, D., Onojiede, E., Peace, N. (2019). Effect of urban centre growth on vegetation cover: A case study of Ebony State, South-Eastern, Nigeria. Environmental Earth Sciences Research Journal, 6: 51-58. https://www.iieta.org/journals/eesrj/paper/10.18280/eesrj.060201.
[23] de Luque-Villa, M., Acosta-Santos, C., Vargas-Cediel, A., Robledo-Buitrago, D. (2020). Noise impact assessment using corine land cover methodology: A case study in Funza, Colombia. International Journal of Sustainable Development and Planning, 15: 857-863. https://www.iieta.org/journals/ijsdp/paper/10.18280/ijsdp.150609
[24] Andrade, J., Cunha, J., Silva, J., Rufino, I., Galvão, C. (2021). Evaluating single and multi-date landsat classifications of land-cover in a seasonally dry tropical forest. Remote Sensing Applications: Society and Environment, 22: 100515. [Crossref]
[25] Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 152-170. [Crossref]
[26] Man, C.D., Nguyen, T.T., Bui, H.Q., Lasko, K., Nguyen, T.N.T. (2018). Improvement of land-cover classification over frequently cloud-covered areas using landsat 8 time-series composites and an ensemble of supervised classifiers. International Journal of Remote Sensing, 39: 1243-1255. [Crossref]
[27] Wulder, M.A., White, J.C., Loveland, T.R., Woodcock, C.E., Belward, A.S., Cohen, W.B., Fosnight, E.A., Shaw, J., Masek, J.G., Roy, D.P. (2016). The global landsat archive: status, consolidation, and direction. Remote Sensing of Environment, 185: 271-283. [Crossref]
[28] Teluguntla, P., Thenkabail, P., Oliphant, A., Xiong, J., Gumma, M.K., Congalton, R.G., Yadav, K., Huete, A. (2018). A 30-m landsat-derived cropland extent product of australia and china using random forest machine learning algorithm on google earth engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144: 325-340. [Crossref]
[29] Belgiu, M., Drăgu, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31. [Crossref]
[30] Melo, M.V.N. de, Oliveira, M.E.G. de, Almeida, G.L.P. de, Gomes, N.F., Montalvo Morales, K.R., Santana, T.C., Silva, P.C., Moraes, A.S., Pandorfi, H., Silva, M.V. (2022). Spatiotemporal characterization of land cover and degradation in the agreste region of pernambuco, brazil, using cloud geoprocessing on Google Earth Engine. Remote Sensing Applications: Society and Environment, 26: 00756. [Crossref]
[31] Huang, H., Chen, Y., Clinton, N., Wang, J., Wang, X., Liu, C., Gong, P., Yang, J., Bai, Y., Zheng, Y., Zhu, Z. (2017). Mapping major land cover dynamics in beijing using all landsat images in Google Earth Engine. Remote Sensing of Environment, 202: 166-176. [Crossref]
[32] Na, X., Zhang, S., Li, X., Yu, H., Liu, C. (2010). Improved land cover mapping using random forests combined with landsat thematic mapper imagery and ancillary geographic data. Photogrammetric Engineering and Remote Sensing, 76: 833-840. [Crossref]
[33] Gonzáles, P., Neri, L. (2015). El ecoturismo como alternativa sostenible para proteger el bosque seco tropical peruano: El caso de proyecto hualtaco, tumbes. PASOS Revista de Turismo y Patrimonio Cultural, 13: 1437-1449. https://www.redalyc.org/articulo.oa?id=88143407012.
[34] Mercado, W., Rimac, D. (2019). Comercialización de miel de abeja del bosque seco, distrito de Motupe, Lambayeque, Perú. Natura@economía, 4: 24. [Crossref]
[35] Zorogastúa, P., Quiroz, R., Garatuza, J. (2011). Evaluación de cambios en la cobertura y uso de la tierra con imágenes de satélite en Piura - Perú. Ecología Aplicada, 10: 13-22. http://www.scielo.org.pe/scielo.php?pid=S1726-22162011000100002&script=sci_abstract.
[36] Aldana, C., Revilla, M., Gonzales, J., Saavedra, Y., Moncada, W., Maicelo, J. (2020). Relación de firmas espectrales para la identificación de bosque seco en imágenes de satélite Sentinel 2, cuenca baja del río Chira, región Piura. Revista de Teledetección, 147. [Crossref]
[37] Pennington, R.T., Prado, D.E., Pendry, C.A., Garden, R.B. (2001). Neotropical seasonally dry forests and quaternary vegetation changes. Journal of Biogeography, 27: 261-273. [Crossref]
[38] MINAM. (2018). Línea de base de los bosques secos de la costa norte del Perú al 2018. Ministerio del Ambiente, Lima, Perú. Available online: http://www.bosques.gob.pe/archivo/Apuntes-del-bosque-4.pdf.
[39] Rodríguez, A., Álvarez, R. (2005). Uso múltiple del bosque seco del norte del Perú: análisis del ingreso y autoconsumo. Zonas Áridas, 8921: 131-148.
[40] Ruíz, L., Quijandría, G., Otárola, E., Rios, S., Álvarez, J., Núñez, F. (2019). Mapa nacional de ecosistemas del Perú: memoria descriptiva. Ministerio del Ambiente, Lima, Perú. Available online: http://www.bosques.gob.pe/archivo/Apuntes-del-bosque-4.pdf.
[41] Moro, M.F., Silva, I.A., De Araújo, F.S., Lughadha, E.N., Meagher, T.R., Martins, F.R. (2015). The role of edaphic environment and climate in structuring phylogenetic pattern in seasonally dry tropical plant communities. PLoS One, 10: 1-18. [Crossref]
[42] Chuvieco, E. (2020). Fundamentals of satellite remote sensing: An environmental approach. CRC Press, Boca Raton.
[43] Parente, L., Ferreira, L. (2018). Assessing the spatial and occupation dynamics of the brazilian pasturelands based on the automated classification of modis images from 2000 to 2016. Remote Sensing, 10: 606. [Crossref]
[44] Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. (2017). Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18-27. [Crossref]
[45] Breiman, L. (2001). Random Forests. Machine Learning, 45: 5-32. [Crossref]
[46] Hosseiny, B., Abdi, A.M., Jamali, S. (2022). Urban land use and land cover classification with interpretable machine learning - A case study using Sentinel-2 and auxiliary data. Remote Sensing Applications: Society and Environment, 28. [Crossref]
[47] Traganos, D., Reinartz, P. (2018). Mapping mediterranean seagrasses with Sentinel-2 imagery. Marine Pollution Bulletin, 134: 197-209. [Crossref]
[48] Tsai, Y.H., Stow, D., Chen, H.L., Lewison, R., An, L., Shi, L. (2018). Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine. Remote Sensing, 10(6): 927. [Crossref]
[49] Foody, G.M. (1992). On the compensation for chance agreement in image classification accuracy assessment. Photogrammetric Engineering and Remote Sensing, 58: 1459-1460. https://www.asprs.org/wp-content/uploads/pers/1992journal/oct/1992_oct_1459-1460.pdf.
[50] Cochran, W.G. (1997). Sampling Techniques. John Wiley, Hoboken.
[51] Acharki, S. (2022). PlanetScope contributions compared to Sentinel-2, and Landsat-8 for LULC mapping. Remote Sensing Applications: Society and Environment, 27: 100774. [Crossref]
[52] Padilla, M., Stehman, S.V., Chuvieco, E. (2014). Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling. Remote Sensing of Environment, 144: 187-196. [Crossref]
[53] Pontius, R.G., Shusas, E., McEachern, M. (2004). Detecting important categorical land changes while accounting for persistence. Agriculture, Ecosystems & Environment, 101: 251-268. [Crossref]
[54] FAO. (1996). Forest Resources Assessment 1990. Survey of Tropical Forest Cover and Study of Change Processes. Rome, Italy. https://www.fao.org/4/w0015e/w0015e00.htm
[55] Zhang, S., Guan, Z., Liu, Y., Zheng, F. (2022). Land use/cover change and its relationship with regional development in Xixian New Area, China. Sustainability, 14: 6889. [Crossref]
[56] Zhuang, D., Liu, J. (1997). Modeling of regional differentiation of land-use degree in China. Chinese Geographical Science, 7: 302-309. https://link.springer.com/article/10.1007/s11769-997-0002-4
[57] FAO. (2007). Situación de los Bosques del Mundo. Rome, Italy. https://www.fao.org/4/a0773s/a0773s00.htm.
[58] Cui, J., Zhu, M., Liang, Y., Qin, G., Li, J., Liu, Y. (2022). Land use/land cover change and their driving factors in the yellow river basin of shandong province based on Google Earth Engine from 2000 to 2020. ISPRS International Journal of Geo-Information, 11(3): 163. [Crossref]
[59] Heredia-R, M., Torres, B., Cabrera-Torres, F., Torres, E., Díaz-Ambrona, C.G.H., Pappalardo, S.E. (2021). land use and land cover changes in the diversity and life zone for uncontacted indigenous people: deforestation hotspots in the Yasuní Biosphere Reserve, Ecuadorian Amazon. Forests, 12(11): 1539. [Crossref]
[60] Xie, H. (2017). Towards sustainable land use in china: A collection of empirical studies. Sustainability, 9(11): 2129. [Crossref]
[61] Zuo, Q., Li, X., Hao, L., Hao, M. (2020). Spatiotemporal evolution of land-use and ecosystem services valuation in the belt and road initiative. Sustainability, 12: 6583. [Crossref]
[62] Bertrand, R., Lenoir, J., Piedallu, C., Dillon, G.R., De Ruffray, P., Vidal, C., Pierrat, J.C., Gégout, J.C. (2011). Changes in plant community composition lag behind climate warming in lowland forests. Nature, 479: 517-520. [Crossref]
[63] Khanal, S., Timilsina, R., Behroozian, M., Peterson, A.T., Poudel, M., Alwar, M.S.S., Wijewickrama, T., Osorio-Olvera, L. (2022). Potential impact of climate change on the distribution and conservation status of pterocarpus marsupium, a near threatened South Asian medicinal tree species. Ecological Informatics, 70: 101722. [Crossref]
[64] Pécastaing, N., Chávez, C. (2020). The impact of el niño phenomenon on dry forest-dependent communities’ welfare in the Northern Coast of Peru. Ecological Economics, 178: 106820. [Crossref]
[65] Rosa, I.M.D., Purves, D., Souza, C., Ewers, R.M. (2013). Predictive modelling of contagious deforestation in the Brazilian Amazon. PLoS One, 8: e77231. [Crossref]
[66] Steege, H. Ter, Pitman, N.C.A., Killeen, et al. (2015). Estimating the global conservation status of more than 15,000 amazonian tree species. Science Advances, 1: 9-11. https://www.science.org/doi/10.1126/sciadv.1500936.
[67] Cotrina, A., Bandopadhyay, S., Rojas, N.B., Banerjee, P., Torres, C., Oliva, M. (2021). Peruvian Amazon disappearing: Transformation of protected areas during the last two decades (2001-2019) and potential future deforestation modelling using cloud computing and MaxEnt Approach. Journal of Nature Conservation, 64: 126081. [Crossref]
[68] Paiva, P.F.P.R., de Lourdes, M., da Silva, O.M., de Nazaré, M., Braga, T.G.M., de Andrade, M.M.N., dos Santos, P.C., da Rocha, E.S., de Freitas, T.P.M., da Silva, et al. (2020). Deforestation in Protect areas in the Amazon: A threat to biodiversity. Biodiversity and Conservation, 29: 19-38. [Crossref]
[69] Cotrina, A., Barboza, E., Rojas Briceño, N.B., Oliva, M., Torres, C., Amasifuen, C.A., Bandopadhyay, S. (2020). Distribution models of timber species for forest conservation and restoration in the Andean-Amazonian Landscape, North of Peru. Sustainability, 12(19): 7945. [Crossref]
[70] Bonilla-Bedoya, S., Estrella-Bastidas, A., Molina, J.R., Herrera, M.Á. (2018). Socioecological system and potential deforestation in Western Amazon forest landscapes. Science of the Total Environment, 644: 1044-1055. [Crossref]
[71] Geist, H.J., Lambin, E.F. (2002). Proximate causes and underlying driving forces of tropical deforestation. BioScience, 52(2): 143-150. [0143:PCAUDF]2.0.CO;2 [Crossref]
[72] Samsammurphy. (2023). Cloud Masking with Sentinel 2. GitHub Repository. https://github.com/samsammurphy/cloud-masking-sentinel2/blob/master/cloud-masking-sentinel2.ipynb.
[73] Liu, S., Li, X., Chen, D., Duan, Y., Ji, H., Zhang, L., Chai, Q., Hu, X. (2020). Understanding land use/land cover dynamics and impacts of human activities in the mekong delta over the last 40 years. Global Ecology and Conservation, 22: e00991. [Crossref]
Cite this:
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Barboza, E., Salazar, W., Gálvez-paucar, D., Valqui-valqui, L., Valqui, L., Zagaceta, L. H., Gonzales, J., Vásquez, H. V., & Arbizu, C. I. (2024). Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru. Int. J. Environ. Impacts., 7(3), 505-514. https://doi.org/10.18280/ijei.070312
E. Barboza, W. Salazar, D. Gálvez-paucar, L. Valqui-valqui, L. Valqui, L. H. Zagaceta, J. Gonzales, H. V. Vásquez, and C. I. Arbizu, "Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru," Int. J. Environ. Impacts., vol. 7, no. 3, pp. 505-514, 2024. https://doi.org/10.18280/ijei.070312
@research-article{Barboza2024CloudCA,
title={Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru},
author={Elgar Barboza and Wilian Salazar and David GáLvez-Paucar and Lamberto Valqui-Valqui and Leandro Valqui and Luis H. Zagaceta and Jhony Gonzales and HéCtor V. VáSquez and Carlos I. Arbizu},
journal={International Journal of Environmental Impacts},
year={2024},
page={505-514},
doi={https://doi.org/10.18280/ijei.070312}
}
Elgar Barboza, et al. "Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru." International Journal of Environmental Impacts, v 7, pp 505-514. doi: https://doi.org/10.18280/ijei.070312
Elgar Barboza, Wilian Salazar, David GáLvez-Paucar, Lamberto Valqui-Valqui, Leandro Valqui, Luis H. Zagaceta, Jhony Gonzales, HéCtor V. VáSquez and Carlos I. Arbizu. "Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru." International Journal of Environmental Impacts, 7, (2024): 505-514. doi: https://doi.org/10.18280/ijei.070312
BARBOZA E， SALAZAR W， GÁLVEZ-PAUCAR D， et al. Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru[J]. International Journal of Environmental Impacts， 2024， 7(3)： 505-514. https://doi.org/10.18280/ijei.070312