Mapeo de la vulnerabilidad a la degradación de pastizales mediante AHP-GIS & RPAS en la microcuenca Pomacochas - Perú

Autores/as

  • Jhonsy O. Silva-López Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza (UNTRM), Chachapoyas 01001, Perú
  • Héctor Vladimir Vázquez Pérez Facultad de Ingeniería Zootecnista, Agronegocios y Biotecnología, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Chachapoyas 01001, Perú.
  • Jhon A. Zabaleta-Santisteban Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza (UNTRM), Chachapoyas 01001, Perú

DOI:

https://doi.org/10.55996/dekamuagropec.v4i1.136

Palabras clave:

Vulnerabilidad, degradación de pastizales, NDVI, RPAS.

Resumen

En Perú, realizar un monitoreo de pastizales es cada vez más esencial para apoyar a los productores agropecuarios y fortalecer nuevas políticas públicas enmarcadas a un manejo sostenible a nivel de cuencas hidrográficas. En esta investigación se buscó mapear la vulnerabilidad a la degradación de pastizales en la microcuenca de Pomacochas, Amazonas − Perú. Para ello, se utilizaron criterios (NDVI, precipitación, MOS, textura del suelo, pH y pendiente). También, basado en consulta a expertos y el Proceso de Jerarquía Analítica (AHP), se sopeso la importancia de los criterios. Luego, se generó el mapa de aptitud del territorio para evaluar la vulnerabilidad de pastizales mediante superposición ponderada de los mapas de criterios. NDVI fue el criterio más importante, mientras que, la pendiente del terreno fue el menos importante. El modelado basado en AHP y SIG muestra que alrededor de 4012.08 km2 (62.98%) del área total de estudio se encuentran en la categoría “ligeramente vulnerable” (C3) a la degradación de pastizales. Asimismo, se validó los resultados mediante cuatro parcelas de validación empleando imágenes de un Aeronave Piloteada Remotamente (RPA). El estudio brindará apoyo para la toma de decisiones en torno al manejo de los pastizales en microcuencas.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

AbdelRahman, M. A. E., Shalaby, A., Aboelsoud, M. H., & Moghanm, F. S. (2018). GIS spatial model based for determining actual land degradation status in Kafr El-Sheikh Governorate, North Nile Delta. Modeling Earth Systems and Environment, 4(1), 359–372. https://doi.org/10.1007/s40808-017-0403-z

Abuzaid, A. S., Mazrou, Y. S. A., Baroudy, A. A. El, Ding, Z., & Shokr, M. S. (2022). Multi-Indicator and Geospatial Based Approaches for Assessing Variation of Land Quality in Arid Agroecosystems. Sustainability.

Abuzaid, A. S., Mazrou, Y. S. A., El Baroudy, A. A., Ding, Z., & Shokr, M. S. (2022). Multi-Indicator and Geospatial Based Approaches for Assessing Variation of Land Quality in Arid Agroecosystems. Sustainability (Switzerland), 14(10). https://doi.org/10.3390/su14105840

Achu, A. L., Thomas, J., & Reghunath, R. (2020). Multi-criteria decision analysis for delineation of groundwater potential zones in a tropical river basin using remote sensing, GIS and analytical hierarchy process (AHP). Groundwater for Sustainable Development, 100365. https://doi.org/10.1016/j.gsd.2020.100365

Ajibade, F. O., Olajire, O. O., Ajibade, T. F., Nwogwu, N. A., Lasisi, K. H., Alo, A. B., Owolabi, T. A., & Adewumi, J. R. (2019). Combining multicriteria decision analysis with GIS for suitably siting landfills in a Nigerian state. Environmental and Sustainability Indicators, 3–4(May), 100010. https://doi.org/10.1016/j.indic.2019.100010

Al Raisi, S., Sulaiman, H., Abdallah, O., & Suliman, F. (2014). Landfill Suitablity Analysis Using Ahp Method and State of Heavy Metals Pollution in Selected Landfills in. European Scientific Journa, 10(17), 309–326.

Amadi, D., Kwaha, J. D., & Riki, J. (2021). Deforestation: Athreat to rural development in Michika Local Govrnment area of Adamawa State, Nigeria. Science & Technology Journal, April 2022.

Beckmann, V. (2022). Transitioning to Sustainable Life on Land. In Transitioning to Sustainable Life on Land. https://doi.org/10.3390/books978-3-03897-879-4-1

Caman Aliaga, D. O. (2020). Dinámica Multitemporal Del Lago Pomacochas Y De Las Lagunas Burlan Y De Los Cóndores En El Departamento De Amazonas (1988 – 2031). Universidad Nacional Toribio Rodríguez De Mendoza De Amazonas-(Untrm), 68. http://repositorio.untrm.edu.pe/bitstream/handle/UNTRM/1482/CHAPA GRANDEZ SALLY PATRICIA.pdf?sequence=1&isAllowed=y

Cao, J., Adamowski, J. F., Deo, R. C., Xu, X., Gong, Y., & Feng, Q. (2019). Grassland Degradation on the Qinghai-Tibetan Plateau: Reevaluation of Causative Factors. Rangeland Ecology and Management, 72(6), 988–995. https://doi.org/10.1016/j.rama.2019.06.001

Castillo, E. B., Turpo Cayo, E. Y., De Almeida, C. M., López, R. S., Rojas Briceño, N. B., Silva López, J. O., Gurbillón, M. Á. B., Oliva, M., & Espinoza-Villar, R. (2020). Monitoring wildfires in the northeastern peruvian amazon using landsat-8 and sentinel-2 imagery in the GEE platform. ISPRS International Journal of Geo-Information, 9(10), 1–22. https://doi.org/10.3390/ijgi9100564

Castro, W. F. (2011). Geología. Convenio Suscrito Entre el Instituto de Investigaciones de la Amazonía Peruana y el Gobierno Regional de Amazonas; Estudios Temáticos para Zonificación Ecológia y Económica del Departamento de Amazonas (pp. 1–76).

Chávez Ortiz, J., Leiva Tafur, D., Rascón, J., Hoyos, I., & Corroto, F. (2014). Estado trófico del lago Pomacochas a través de parámetros fisicoquímicos y bacteriológicos. Rev. Indes, 2(2), 70–78. https://doi.org/10.25127/indes.20140

Dagnachew, M., Kebede, A., Moges, A., & Abebe, A. (2020). Effects of Climate Variability on Normalized Difference Vegetation Index ( NDVI ) in the Gojeb River Catchment , Omo-Gibe Basin , Ethiopia. Advances in Meteorology, 2020, 16. https://doi.org/https://doi.org/10.1155/2020/8263246 Research

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086

Fonte, S. J., Nesper, M., Hegglin, D., Velásquez, J. E., Ramirez, B., Rao, I. M., Bernasconi, S. M., Bünemann, E. K., Frossard, E., & Oberson, A. (2014). Pasture degradation impacts soil phosphorus storage via changes to aggregate-associated soil organic matter in highly weathered tropical soils. Soil Biology and Biochemistry, 68, 150–157. https://doi.org/10.1016/j.soilbio.2013.09.025

Hengl, T., Jesus, J. M. De, Heuvelink, G. B. M., Ruiperez, M., Kilibarda, M., Blagoti, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-marschallinger, B., Guevara, M. A., Vargas, R., Macmillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., & Kempen, B. (2017). SoilGrids250m : Global gridded soil information based on machine learning. PLoS ONE, 1–40. https://doi.org/10.1371/journal.pone.0169748

Hereher, M., & Kenawy, A. El. (2021). Assessment of Land Degradation in Northern Oman Using Geospatial Techniques. Earth Systems and Environment, Tucker 1979. https://doi.org/10.1007/s41748-021-00216-7

Hott, M. C., Carvalho, L. M. T., Antunes, M. A. H., Resende, J. C., & Rocha, W. S. D. (2019). Analysis of grassland degradation in Zona da Mata, MG, Brazil, based on NDVI time series data with the integration of phenological metrics. Remote Sensing, 11(24). https://doi.org/10.3390/rs11242956

INEI (Instituto Nacional de Estadística e Informática). (2017). Perú: Crecimineto y distribución de la población total, 2017.

Ingram, J. S. I., & Fernandes, E. C. M. (2001). Managing carbon sequestration in soils : concepts and terminology. Agriculture, Ecosystems and Environment, 87, 111–117.

Inzerillo, L., Acuto, F., Mino, G. Di, & Uddin, M. Z. (2022). Super-Resolution Images Methodology Applied to UAV Datasets to Road Pavement Monitoring. Drones.

Karaca, S., Dengiz, O., Demirağ Turan, İ., Özkan, B., Dedeoğlu, M., Gülser, F., Sargin, B., Demirkaya, S., & Ay, A. (2021). An assessment of pasture soils quality based on multi-indicator weighting approaches in semi-arid ecosystem. Ecological Indicators, 121. https://doi.org/10.1016/j.ecolind.2020.107001

Kartic Kumar, M., Annadurai, R., Ravichandran, P. T., & Arumugam, K. (2015). Landslide susceptibility mapping using analytical hierarchy process and artificial neural network at Kothagiri Taluk, India. International Journal of Earth Sciences and Engineering, 8(2), 775–787.

Knight, J., & Harrison, S. (2013). The impacts of climate change on terrestrial Earth surface systems. Nature Climate Change, 3(1), 24–29. https://doi.org/10.1038/nclimate1660

Kwon, H.-Y., Nkonya, E., Johnson, T., Graw, V., & Kihiu, E. (2015). Economics of land degradation and improvement - A global assessment for sustainable development. Economics of Land Degradation and Improvement - A Global Assessment for Sustainable Development, August 2020, 1–686. https://doi.org/10.1007/978-3-319-19168-3

Le, Q., Ephraim, N., & Alisher, M. (2016). Biomass Productivity-Based Mapping of Global Land Degradation Hotspots. Economics of Land Degradation and Improvement—A Global Assessment for Sustainable Development, 55–84. https://doi.org/10.1007/978-3-319-19168-3_3

Lemmessa, F. (2011). Rangeland suitability analysis for livestock production using GIS and Remote sensing: The case of Yabello Woreda, Southern Ethiopia. Addis Ababa University.

Logan, T. A., Nicoll, J., Laurencelle, J., Hogenson, K., Gens, R., & Buechler, B. (2014). Radiometrically Terrain Corrected ALOS PALSAR Data Available from the Alaska Satellite Facility - NASA/ADS. AGU FALL MEETING.

López, R. S., Fernández, D. G., Silva López, J. O., Rojas Briceño, N. B., Oliva, M., Terrones Murga, R. E., Trigoso, D. I., Castillo, E. B., & Barrena Gurbillón, M. Á. (2020). Land suitability for coffee (coffea arabica) growing in Amazonas, Peru: Integrated use of AHP, GIS and RS. ISPRS International Journal of Geo-Information, 9(11), 1–21. https://doi.org/10.3390/ijgi9110673

Malav, L. C., Yadav, B., Tailor, B. L., Pattanayak, S., Singh, S. V., Kumar, N., Reddy, G. P. O., Mina, B. L., Dwivedi, B. S., & Jha, P. K. (2022). Mapping of Land Degradation Vulnerability in the Semi-Arid Watershed of Rajasthan, India. Sustainability (Switzerland), 14(16), 1–16. https://doi.org/10.3390/su141610198

Malczewski, J., & Rinner, C. (2015). Multicriteria Decision Analysis in Geographic Information Science.

Martínez-Murillo, J. F., Nadal-Romero, E., Regüés, D., Cerdà, A., & Poesen, J. (2013). Soil erosion and hydrology of the western Mediterranean badlands throughout rainfall simulation experiments: A review. Catena, 106, 101–112. https://doi.org/10.1016/j.catena.2012.06.001

Mekuria, W., & Aynekulu, E. (2013). Exclosure land management for restoration of the soils in degraded communal grazing lands in northern ethiopia. Land Degradation and Development, 24(6), 528–538. https://doi.org/10.1002/ldr.1146

MINEDU (Ministerio de Educación). (2020). Descarga de información espacial del Ministerio de Educación. Disponible En Linea.

Monfreda, C., Ramankutty, N., & Foley, J. A. (2008). Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles, 22(1). https://doi.org/10.1029/2007GB002947

MTC (Ministerio de Trasmportes y Comunicaciones). (2021). Descarga de Datos Espaciales-Transporte Terrestre por Carretera. En Linea.

Naegeli de Torres, F., Richter, R., & Vohland, M. (2019). A multisensoral approach for high-resolution land cover and pasture degradation mapping in the humid tropics: A case study of the fragmented landscape of Rio de Janeiro. International Journal of Applied Earth Observation and Geoinformation, 78(September 2018), 189–201. https://doi.org/10.1016/j.jag.2019.01.011

Oliveira, C. F., do Valle Junior, R. F., Valera, C. A., Rodrigues, V. S., Sanches Fernandes, L. F., & Pacheco, F. A. L. (2019). The modeling of pasture conservation and of its impact on stream water quality using Partial Least Squares-Path Modeling. Science of the Total Environment, 697, 134081. https://doi.org/10.1016/j.scitotenv.2019.134081

Pacheco, F. A. L., & Van der Weijden, C. H. (2014). Role of hydraulic diffusivity in the decrease of weathering rates over time. Journal of Hydrology, 512, 87–106. https://doi.org/10.1016/j.jhydrol.2014.02.041

Parmar, M., Bhawsar, Z., Kotecha, M., Shukla, A., & Rajawat, A. S. (2021). Assessment of Land Degradation Vulnerability using Geospatial Technique: A Case Study of Kachchh District of Gujarat, India. Journal of the Indian Society of Remote Sensing, 49(7), 1661–1675. https://doi.org/10.1007/s12524-021-01349-y

Pokhriyal, P., Rehman, S., Areendran, G., Raj, K., Pandey, R., Kumar, M., Sahana, M., & Sajjad, H. (2020). Assessing forest cover vulnerability in Uttarakhand, India using analytical hierarchy process. Modeling Earth Systems and Environment, 6(2), 821–831. https://doi.org/10.1007/s40808-019-00710-y

Posgrado, E. DE, & -Perú, L. (2016). Universidad Nacional Agraria La Molina "Influencia De Factores Socio-Económicos En La. Universidad Nacional Agraria La Molina. http://repositorio.lamolina.edu.pe/handle/UNALM/2710

Romshoo, S. A., Amin, M., Sastry, K. L. N., & Parmar, M. (2020). Integration of social, economic and environmental factors in GIS for land degradation vulnerability assessment in the Pir Panjal Himalaya, Kashmir, India. Applied Geography, 125(September), 102307. https://doi.org/10.1016/j.apgeog.2020.102307

Saaty, R. W. (1987). The analytic hierarchy process-what it is and how it is used. Mathematical Modelling, 9(3–5), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8

Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281. https://doi.org/10.1016/0022-2496(77)90033-5

Saaty, T. L. (1980a). Analytic Hierarchy Process. https://doi.org/10.1201/9780429504419-2

Saaty, T. L. (1980b). The Analytic Hierarchy Process. McGraw-Hill, New York.

Saaty, T. L. (2008). Decision making with the Analytic Hierarchy Process. Scientia Iranica, 1(1), 215–229. https://doi.org/10.1504/ijssci.2008.017590

Sandeep, P., Reddy, G. P. O., Jegankumar, R., & Arun Kumar, K. C. (2021). Modeling and Assessment of Land Degradation Vulnerability in Semi-arid Ecosystem of Southern India Using Temporal Satellite Data, AHP and GIS. Environmental Modeling and Assessment, 26(2), 143–154. https://doi.org/10.1007/s10666-020-09739-1

Sar, N., Chatterjee, S., & Das Adhikari, M. (2015). Integrated remote sensing and GIS based spatial modelling through analytical hierarchy process (AHP) for water logging hazard, vulnerability and risk assessment in Keleghai river basin, India. Modeling Earth Systems and Environment, 1(4), 1–21. https://doi.org/10.1007/s40808-015-0039-9

Senapati, U., & Das, T. K. (2020). Assessment of potential land degradation in akarsa watershed, west bengal, using gis and multi-influencing factor technique. Advances in Science, Technology and Innovation, 187–205. https://doi.org/10.1007/978-3-030-23243-6_11

Silva López, J. O., Salas López, R., Rojas Briceño, N. B., Gómez Fernández, D., Terrones Murga, R. E., Iliquín Trigoso, D., Barboza Castillo, E., Oliva Cruz, M., & Barrena Gurbillón, M. Á. (2022). Analytic Hierarchy Process (AHP) for a Landfill Site Selection in Chachapoyas and Huancas (NW Peru): Modeling in a GIS-RS Environment. Advances in Civil Engineering, 2022. https://doi.org/10.1155/2022/9733322

Swain, K. C., Singha, C., & Nayak, L. (2020). Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS International Journal of Geo-Information, 9(12). https://doi.org/10.3390/ijgi9120720

Tepanosyan, G. H., Asmaryan, S. G., Muradyan, V. S., & Saghatelyan, A. K. (2017). Mapping man-induced soil degradation in Armenia’s high mountain pastures through remote sensing methods: A case study. Remote Sensing Applications: Society and Environment, 8(September 2016), 105–113. https://doi.org/10.1016/j.rsase.2017.08.006

Tolche, A.-D., Gurara, M.-A., Pham, Q.-B., & Anh, D.-T. (2021). Modelling and accessing land degradation vulnerability using remote sensing techniques and the analytical hierarchy process approach. Geocarto International, 0(0), 1–21. https://doi.org/10.1080/10106049.2021.1959656

Turan, İ. D., Dengiz, O., & Özkan, B. (2019a). Spatial assessment and mapping of soil quality index for desertification in the semi-arid terrestrial ecosystem using MCDM in interval type-2 fuzzy environment. Computers and Electronics in Agriculture, 164(June). https://doi.org/10.1016/j.compag.2019.104933

Turan, İ. D., Dengiz, O., & Özkan, B. (2019b). Spatial assessment and mapping of soil quality index for desertification in the semi-arid terrestrial ecosystem using MCDM in interval type-2 fuzzy environment. Computers and Electronics in Agriculture, 164(March). https://doi.org/10.1016/j.compag.2019.104933

Valera, C. A., Pissarra, T. C. T., Martins Filho, M. V., Valle Junior, R. F., Sanches Fernandes, L. F., & Pacheco, F. A. L. (2017). A legal framework with scientific basis for applying the ‘polluter pays principle’ to soil conservation in rural watersheds in Brazil. Land Use Policy, 66(April), 61–71. https://doi.org/10.1016/j.landusepol.2017.04.036

Valera, C. A., Valle Junior, R. F., Varandas, S. G. P., Sanches Fernandes, L. F., & Pacheco, F. A. L. (2016). The role of environmental land use conflicts in soil fertility: A study on the Uberaba River basin, Brazil. Science of the Total Environment, 562, 463–473. https://doi.org/10.1016/j.scitotenv.2016.04.046

Valle Júnior, R. F. do, Siqueira, H. E., Valera, C. A., Oliveira, C. F., Sanches Fernandes, L. F., Moura, J. P., & Pacheco, F. A. L. (2019). Diagnosis of degraded pastures using an improved NDVI-based remote sensing approach: An application to the Environmental Protection Area of Uberaba River Basin (Minas Gerais, Brazil). Remote Sensing Applications: Society and Environment, 14(December 2018), 20–33. https://doi.org/10.1016/j.rsase.2019.02.001

Vargas Rivera, J. (2010). Clima, informe temático. Proyecto Zonificación Ecológica y Económica del departamento de Amazonas, convenio entre el IIAP y el Gobierno Regional de Amazonas.

Wang, J., Xiao, X., Bajgain, R., Starks, P., Steiner, J., Doughty, R. B., & Chang, Q. (2019). Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing, 154(June), 189–201. https://doi.org/10.1016/j.isprsjprs.2019.06.007

Yalew, S. G., van Griensven, A., Mul, M. L., & van der Zaag, P. (2016). Land suitability analysis for agriculture in the Abbay basin using remote sensing, GIS and AHP techniques. Modeling Earth Systems and Environment, 2(2), 1–14. https://doi.org/10.1007/s40808-016-0167-x

Yousefi, S., Avand, M., Yariyan, P., Pourghasemi, H. R., Keesstra, S., Tavangar, S., & Tabibian, S. (2020). A novel GIS-based ensemble technique for rangeland downward trend mapping as an ecological indicator change. Ecological Indicators, 117(March). https://doi.org/10.1016/j.ecolind.2020.106591

Zhao, T., & Iwaasa, A. D. (2022). Rotational grazing increases purple prairie clover frequency in the rangeland plant communities under semi-arid environment. Canadian Journal of Plant Science, 103(2). https://doi.org/https://doi.org/10.1139/cjps-2021-0141

Zhao, X., Xia, H., Pan, L., Song, H., Niu, W., Wang, R., & Li, R. (2021). Drought Monitoring over Yellow River Basin from 2003 – 2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine. Remote Sensing. https://doi.org/https://doi.org/ 10.3390/rs13183748

Descargas

Archivos adicionales

Publicado

2023-06-28

Cómo citar

Silva-López, J. O., Vázquez Pérez, H. V. ., & Zabaleta-Santisteban, J. A. (2023). Mapeo de la vulnerabilidad a la degradación de pastizales mediante AHP-GIS & RPAS en la microcuenca Pomacochas - Perú. Revista Científica Dékamu Agropec, 4(1), 1–25. https://doi.org/10.55996/dekamuagropec.v4i1.136