EVALUACIÓN ESPACIO TEMPORAL DE LA COBERTURA VEGETAL MEDIANTE IMÁGENES SATELITALES LANDSAT 8 EN LA UNIVERSIDAD TÉCNICA DE COTOPAXI, CAMPUS SALACHE ENTRE LOS AÑOS 2015 AL 2024

  • Alex Mauricio Chicaiza Tipanguano 1Universidad Técnica de Cotopaxi, Facultad de CAREN, Ingeniería Agronómica, Latacunga, Cotopaxi, Ecuador
  • David Santiago Carrera Molina 1Universidad Técnica de Cotopaxi, Facultad de CAREN, Ingeniería Agronómica, Latacunga, Cotopaxi, Ecuador

Abstract

El presente artículo tiene como objetivo evaluar la evolución de la cobertura vegetal en el Campus Salache, utilizando el Índice de Vegetación de Diferencia Normalizada (NDVI) mediante imágenes satelitales Landsat 8. La metodología incluyó la delimitación del área de estudio siendo un total de 89.43 hectáreas, la descarga y procesamiento de las imágenes fueron desde el 2015 hasta el 2024, además se procedió hacer el cálculo del NDVI para identificar la salud y la productividad de los cultivos. Se aplicaron técnicas como la extracción por máscara y clasificación supervisada, asimismo se analizó los cambios en la cobertura vegetal. Los resultados mostraron que el área de infraestructura aumentó de 1.130 ha en el 2015 a 1.477 ha en el 2024, lo que representa un incremento de 0.347 ha, mientras que el área de cultivo disminuyó de 4.359 ha a 4.012 ha, evidenciando una reducción de 0.347 ha. El análisis de tendencias reveló que, algunas zonas mostraron mejoras en la salud de la cobertura vegetal, las áreas más altas enfrentaron un aumento en el suelo desnudo y plantas enfermas. En conclusión, la investigación destacó la efectividad de las intervenciones de conservación en ciertas áreas, pero también subrayó la necesidad de un manejo adaptativo para abordar los problemas en las zonas más vulnerables. La evaluación del NDVI y el análisis de tendencias nos resalta la importancia de continuar con estrategias de gestión sostenible para mitigar la pérdida por lo que es fundamental dar un monitoreo para ajustar las prácticas que favorecen los ecosistemas agrícolas.

Downloads

Download data is not yet available.

References

Abdul Athick, A. S. M., Shankar, K., & Naqvi, H. R. (2020). Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve Wereda of Ethiopia using mono window, split window algorithm and spectral radiance model. Data in Brief, 27. https://doi.org/10.1016/j.dib.2019.104773

Ahmed, Z., Nalley, L., Brye, K., Steven Green, V., Popp, M., Shew, A. M., & Connor, L. (2023). Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation. International Journal of Applied Earth Observation and Geoinformation, 125. https://doi.org/10.1016/j.jag.2023.103564

Alemu, M., Warkineh, B., Lulekal, E., & Asfaw, Z. (2024). Analysis of land use land cover change dynamics in Habru District, Amhara Region, Ethiopia. Heliyon, 10(19). https://doi.org/10.1016/j.heliyon.2024.e38971

Alicia Arcos, M., Balaguer-Beser, Á., & Ángel Ruiz, L. (2024). Evaluating the performance of spectral indices and meteorological variables as indicators of live fuel moisture content in Mediterranean shrublands. Ecological Indicators, 169. https://doi.org/10.1016/j.ecolind.2024.112894

Annan, E., Amponsah, W., Adjei, K. A., Disse, M., Hounkpè, J., Biney, E., Agbenorhevi, A. E., & Agyare, W. A. (2024). Spatio-temporal land use and land cover change assessment: Insights from the Ouémé River Basin. Scientific African, 25. https://doi.org/10.1016/j.sciaf.2024.e02262

Barbosa, H. A., Lakshmi Kumar, T. V., Paredes, F., Elliott, S., & Ayuga, J. G. (2020). Assessment of Caatinga response to drought using Meteosat-SEVIRI Normalized Difference Vegetation Index (2008–2016). ISPRS Journal of Photogrammetry and Remote Sensing, 148, 235–252. https://doi.org/10.1016/j.isprsjprs.2018.12.014

Botella-Campos, M., Romero-Huedo, J., Mora, J., & Ortega, B. (2025). Convergent optical fronthaul link for wireless access over different spectral bands. Optical Fiber Technology, 90. https://doi.org/10.1016/j.yofte.2024.104114

Cantini, C., Nepi, P. E., Giovanni Avola, & Riggi, E. (2023). Direct and indirect ground estimation of leaf area index to support interpretation of NDVI data from satellite images in hedgerow olive orchards. Smart Agricultural Technology, 5. https://doi.org/10.1016/j.atech.2023.100267

Caruso, G., Palai, G., Tozzini, L., D’Onofrio, C., & Gucci, R. (2023). The role of LAI and leaf chlorophyll on NDVI estimated by UAV in grapevine canopies. Scientia Horticulturae, 322. https://doi.org/10.1016/j.scienta.2023.112398

Centorame, L., Ilari, A., Del Gatto, A., & Foppa Pedretti, E. (2024). A systematic review on precision agriculture applied to sunflowers, the role of hyperspectral imaging. In Computers and Electronics in Agriculture (Vol. 222). Elsevier B.V. https://doi.org/10.1016/j.compag.2024.109097

Chu, D., Shen, H., Guan, X., & Li, X. (2022). An L1-regularized variational approach for NDVI time-series reconstruction considering inter-annual seasonal similarity. International Journal of Applied Earth Observation and Geoinformation, 114. https://doi.org/10.1016/j.jag.2022.103021

Debie, E. (2024). Analysis of the decision to convert croplands into E. Camaldulensis woodlot and its impact on income diversification in Mecha district, Northwest Ethiopia. Trees, Forests and People, 17. https://doi.org/10.1016/j.tfp.2024.100636

Dembélé, F., Guuroh, R. T., Ansah, P. B., Asare, D. C. B. M., Da, S. S., Aryee, J. N. A., & Adu-Bredu, S. (2024). Land use land cover change and intensity analysis of land transformation in and around a moist semi-deciduous forest in Ghana. Trees, Forests and People, 15. https://doi.org/10.1016/j.tfp.2024.100507

Diykh, M., Ali, M., Jamei, M., Abdulla, S., Uddin, M. P., Farooque, A. A., Labban, A. H., & Alabdally, H. (2024a). Empirical curvelet transform based deep DenseNet model to predict NDVI using RGB drone imagery data. Computers and Electronics in Agriculture, 221. https://doi.org/10.1016/j.compag.2024.108964

Diykh, M., Ali, M., Jamei, M., Abdulla, S., Uddin, M. P., Farooque, A. A., Labban, A. H., & Alabdally, H. (2024b). Empirical curvelet transform based deep DenseNet model to predict NDVI using RGB drone imagery data. Computers and Electronics in Agriculture, 221. https://doi.org/10.1016/j.compag.2024.108964

Galaszkiewicz, A., Delaney, K. B., & Steelman, C. M. (2024). Identifying sulphurous water discharge from legacy oil and gas wells using spectral band analysis of aerial and satellite imagery. Geomatica, 76(2). https://doi.org/10.1016/j.geomat.2024.100024

Gaso, D. V., Paudel, D., de Wit, A., Puntel, L. A., Mullissa, A., & Kooistra, L. (2024). Beyond assimilation of leaf area index: Leveraging additional spectral information using machine learning for site-specific soybean yield prediction. Agricultural and Forest Meteorology, 351. https://doi.org/10.1016/j.agrformet.2024.110022

Gemeda, D. O., Kenea, G., Teshome, B., Daba, G. L., Argu, W., & Roba, Z. R. (2024). Impact of land use and land cover change on land surface temperature: Comparative studies in four cities in southwestern Ethiopia. Environmental Challenges, 16. https://doi.org/10.1016/j.envc.2024.101002

Guo, Y., Fu, Y. H., Chen, S., Hao, F., Zhang, X., de Beurs, K., & He, Y. (2024). Predicting grain yield of maize using a new multispectral-based canopy volumetric vegetation index. Ecological Indicators, 166. https://doi.org/10.1016/j.ecolind.2024.112295

Hasan, I., Dey, J., Munna, M. M. R., Preya, A., Nisanur, T. B., Memy, M. J., & Zeba, M. Z. S. (2024). Morphological changes of river Bank Erosion and channel shifting assessment on Arial Khan River of Bangladesh using Landsat satellite time series images. Progress in Disaster Science, 24. https://doi.org/10.1016/j.pdisas.2024.100381

He, H., Fischer, C., Darsow, U., Aguirre, J., & Ntziachristos, V. (2024). Quality control in clinical raster-scan optoacoustic mesoscopy. Photoacoustics, 35. https://doi.org/10.1016/j.pacs.2023.100582

He, P., Xu, L., Liu, Z., Jing, Y., & Zhu, W. (2021). Dynamics of NDVI and its influencing factors in the Chinese Loess Plateau during 2002–2018. Regional Sustainability, 2(1), 36–46. https://doi.org/10.1016/j.regsus.2021.01.002

Hu, P., Zheng, B., Chen, Q., Grunefeld, S., Choudhury, M. R., Fernandez, J., Potgieter, A., & Chapman, S. C. (2024). Estimating aboveground biomass dynamics of wheat at small spatial scale by integrating crop growth and radiative transfer models with satellite remote sensing data. Remote Sensing of Environment, 311. https://doi.org/10.1016/j.rse.2024.114277

Ibarra-Bonilla, J. S., Pinedo-Alvarez, A., Prieto-Amparán, J. A., Siller-Clavel, P., Santellano-Estrada, E., Álvarez-Holguín, A., & Villarreal-Guerrero, F. (2024). Post-fire vegetation dynamics of a temperate mixed forest: An assessment based on the variability of Landsat spectral indices. Trees, Forests and People, 17. https://doi.org/10.1016/j.tfp.2024.100648

Ilbay, M., Ruiz, J., Cueva, E., Ortiz, V., & Morales, D. (2021). Empirical Model for Estimating the Ecological Footprint in Ecuador Based on Demographic, Economic and Environmental Indicators. Journal of Ecological Engineering, 22(5), 59–67. https://doi.org/10.12911/22998993/135868

Ilbay-Yupa, M., Lavado-Casimiro, W., Rau, P., Zubieta, R., & Castillón, F. (2021). Updating regionalization of precipitation in Ecuador. https://doi.org/10.1007/s00704-020-03476-x/Published

Illán-Fernández, E. J., Tiede, D., & Sudmanns, M. (2024). Consistent land use and land cover classification across 20 years of various high-resolution images for detecting soil sealing in murcia, Spain. Remote Sensing Applications: Society and Environment, 35. https://doi.org/10.1016/j.rsase.2024.101223

Imtiaz, F., Farooque, A. A., Randhawa, G. S., Wang, X., Esau, T. J., Acharya, B., & Hashemi Garmdareh, S. E. (2024). An inclusive approach to crop soil moisture estimation: Leveraging satellite thermal infrared bands and vegetation indices on Google Earth engine. Agricultural Water Management, 306. https://doi.org/10.1016/j.agwat.2024.109172

Karalasingham, S., Deo, R. C., Casillas-Pérez, D., Raj, N., & Salcedo-Sanz, S. (2024). Wavelet-fusion image super-resolution model with deep learning for downscaling remotely-sensed, multi-band spectral albedo imagery. Remote Sensing Applications: Society and Environment, 36. https://doi.org/10.1016/j.rsase.2024.101333

Khormizi, H. Z., Ghafarian Malamiri, H. R., Alian, S., Stein, A., Kalantari, Z., & Ferreira, C. S. S. (2023). Proof of evidence of changes in global terrestrial biomes using historic and recent NDVI time series. Heliyon, 9(8). https://doi.org/10.1016/j.heliyon.2023.e18686

Khosravi, Y., Homayouni, S., & Ouarda, T. B. M. J. (2024). Spatio-temporal evaluation of MODIS temperature vegetation dryness index in the Middle East. Ecological Informatics, 84. https://doi.org/10.1016/j.ecoinf.2024.102894

Laroche-Pinel, E., Cianciola, V., Singh, K., Vivaldi, G. A., & Brillante, L. (2024). Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation. Agricultural Water Management, 306. https://doi.org/10.1016/j.agwat.2024.109163

Lee, C. C., Koo, V. C., Lim, T. S., Lee, Y. P., & Abidin, H. (2022). A multi-layer perceptron-based approach for early detection of BSR disease in oil palm trees using hyperspectral images. Heliyon, 8(4). https://doi.org/10.1016/j.heliyon.2022.e09252

Li, J., Li, C., Xu, W., Feng, H., Zhao, F., Long, H., Meng, Y., Chen, W., Yang, H., & Yang, G. (2022). Fusion of optical and SAR images based on deep learning to reconstruct vegetation NDVI time series in cloud-prone regions. International Journal of Applied Earth Observation and Geoinformation, 112. https://doi.org/10.1016/j.jag.2022.102818

Li, P., Li, W., Shi, D., & Nath, A. J. (2024). Normalized Difference Red-NIR-SWIR: A new Sentinel-2 three-band spectral index for mapping freshly-opened swiddens in the tropics. Ecological Informatics, 82. https://doi.org/10.1016/j.ecoinf.2024.102775

Liu, Q., Yao, F., Garcia-Garcia, A., Zhang, J., Li, J., Ma, S., Li, S., & Peng, J. (2023). The response and sensitivity of global vegetation to water stress: A comparison of different satellite-based NDVI products. International Journal of Applied Earth Observation and Geoinformation, 120. https://doi.org/10.1016/j.jag.2023.103341

Mann, & Kendall. (1945). Combination of modified Mann-Kendall method and Şen innovative trend analysis. Econometrica

Marino, S. (2023). Understanding the spatio-temporal behaviour of the sunflower crop for subfield areas delineation using Sentinel‐2 NDVI time-series images in an organic farming system. Heliyon, 9(9). https://doi.org/10.1016/j.heliyon.2023.e19507

Martin, E. R., Godwin, I. A., Cooper, R. I., Aryal, N., Reba, M. L., & Bouldin, J. L. (2021). Assessing the impact of vegetative cover within Northeast Arkansas agricultural ditches on sediment and nutrient loads. Agriculture, Ecosystems and Environment, 320. https://doi.org/10.1016/j.agee.2021.107613

Martins, G. D., Sousa Santos, L. C., dos Santos Carmo, G. J., da Silva Neto, O. F., Castoldi, R., Machado, A. I. M. R., & de Oliveira Charlo, H. C. (2023). Multispectral images for estimating morphophysiological and nutritional parameters in cabbage seedlings. Smart Agricultural Technology, 4. https://doi.org/10.1016/j.atech.2023.100211

Miah, M. T., Fariha, J. N., Kafy, A. Al, Islam, R., Biswas, N., Duti, B. M., Fattah, M. A., Alsulamy, S., Khedher, K. M., & Salem, M. A. (2024). Exploring the nexus between land cover change dynamics and spatial heterogeneity of demographic trajectories in rapidly growing ecosystems of south Asian cities. Ecological Indicators, 158. https://doi.org/10.1016/j.ecolind.2023.111299

Milella, A., & Reina, G. (2024). Consumer-grade imaging system for NDVI measurement at plant scale by a farmer robot. Measurement: Journal of the International Measurement Confederation, 234. https://doi.org/10.1016/j.measurement.2024.114817

Mu, X., Yang, Y., Xu, H., Guo, Y., Lai, Y., McVicar, T. R., Xie, D., & Yan, G. (2024). Improvement of NDVI mixture model for fractional vegetation cover estimation with consideration of shaded vegetation and soil components. Remote Sensing of Environment, 314. https://doi.org/10.1016/j.rse.2024.114409

Newete, S. W., Abutaleb, K., Chirima, G. J., Dabrowska-Zielinska, K., & Gurdak, R. (2024). Phenology-based winter wheat classification for crop growth monitoring using multi-temporal sentinel-2 satellite data. Egyptian Journal of Remote Sensing and Space Science, 27(4), 695–704. https://doi.org/10.1016/j.ejrs.2024.10.001

Niu, T., Hou, Z., Yu, J., Lu, J., Yu, Q., Yang, L., Ma, J., Liu, Y., Shi, H., & Jin, X. (2024). Construction of prediction model for water retention of forest ecosystem in alpine region based on vegetation spectral features. Ecological Indicators, 169. https://doi.org/10.1016/j.ecolind.2024.112889

Panhelleux léa, Rapinel Sébastien, & Humbert-Moy Laurence. (2023). Specification Table. https://doi.org/10.5281/zenodo.7895449

Pérez-García, Á., van Emmerik, T. H. M., Mata, A., Tasseron, P. F., & López, J. F. (2024). Efficient plastic detection in coastal areas with selected spectral bands. Marine Pollution Bulletin, 207. https://doi.org/10.1016/j.marpolbul.2024.116914

Puttipipatkajorn, A., & Puttipipatkajorn, A. (2024). Development of low-cost portable spectrometer equipped with 18-band spectral sensors using deep learning model for evaluating moisture content of rubber sheets. Smart Agricultural Technology, 9. https://doi.org/10.1016/j.atech.2024.100562

Qian, H., Bao, N., Meng, D., Zhou, B., Lei, H., & Li, H. (2024). Mapping and classification of Liao River Delta coastal wetland based on time series and multi-source GaoFen images using stacking ensemble model. Ecological Informatics, 80. https://doi.org/10.1016/j.ecoinf.2024.102488

Qiao, K., Zhu, W., Xie, Z., Wu, S., & Li, S. (2024). New three red-edge vegetation index (VI3RE) for crop seasonal LAI prediction using Sentinel-2 data. International Journal of Applied Earth Observation and Geoinformation, 130. https://doi.org/10.1016/j.jag.2024.103894

Qu, C., Li, P., & Zhang, C. (2021). A spectral index for winter wheat mapping using multi-temporal Landsat NDVI data of key growth stages. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 431–447. https://doi.org/10.1016/j.isprsjprs.2021.03.015

Rahman, G., Kim, J. Y., Kim, T. W., Park, M., & Kwon, H. H. (2025). Spatial and temporal variations in temperature and precipitation trends in South Korea over the past half-century (1974–2023) using innovative trend analysis. Journal of Hydro-Environment Research, 58, 1–18. https://doi.org/10.1016/j.jher.2024.11.002

Recuero, L., Maila, L., Cicuéndez, V., Sáenz, C., Litago, J., Tornos, L., Merino-de-Miguel, S., & Palacios-Orueta, A. (2023). Mapping Cropland Intensification in Ecuador through Spectral Analysis of MODIS NDVI Time Series. Agronomy, 13(9). https://doi.org/10.3390/agronomy13092329

Ren, Y., Zhang, F., Zhao, C., & Cheng, Z. (2023). Attribution of climate change and human activities to vegetation NDVI in Jilin Province, China during 1998–2020. Ecological Indicators, 153. https://doi.org/10.1016/j.ecolind.2023.110415

Roznik, M., Boyd, M., & Porth, L. (2022). Improving crop yield estimation by applying higher resolution satellite NDVI imagery and high-resolution cropland masks. Remote Sensing Applications: Society and Environment, 25. https://doi.org/10.1016/j.rsase.2022.100693

RYU, J. H., OH, D., & CHO, J. (2021). Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor. Journal of Integrative Agriculture, 20(7), 1969–1986. https://doi.org/10.1016/S2095-3119(20)63410-4

Saha, K. K., Weltzien, C., Bookhagen, B., & Zude-Sasse, M. (2024). Chlorophyll content estimation and ripeness detection in tomato fruit based on NDVI from dual wavelength LiDAR point cloud data. Journal of Food Engineering, 383. https://doi.org/10.1016/j.jfoodeng.2024.112218

Sandonís-Pozo, L., Oger, B., Tisseyre, B., Llorens, J., Escolà, A., Pascual, M., & Martínez-Casasnovas, J. A. (2024). Leafiness-LiDAR index and NDVI for identification of temporal patterns in super-intensive almond orchards as response to different management strategies. European Journal of Agronomy, 159. https://doi.org/10.1016/j.eja.2024.127278

Sapkota, A., Roby, M., Peddinti, S. R., Fulton, A., & Kisekka, I. (2024). Comparative analysis of evapotranspiration (ET), crop water stress index (CWSI), and normalized difference vegetation index (NDVI) to delineate site-specific irrigation management zones in almond orchards. Scientia Horticulturae, 339. https://doi.org/10.1016/j.scienta.2024.113860

Sayre, R., Frye, C., Breyer, S., Roehrdanz, P. R., Elsen, P. R., Butler, K., Brown, C., Cress, J., Karagulle, D., Martin, M., Sangermano, F., Smyth, R. L., Sohl, T. L., Wolff, N. H., Wright, D. J., & Wu, Z. (2024). Potential 2050 Distributions of World Terrestrial Ecosystems from Projections of Changes in World Climate Regions and Global Land Cover. Global Ecology and Conservation, e03370. https://doi.org/10.1016/j.gecco.2024.e03370

Shammi, S. A., & Meng, Q. (2021). Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecological Indicators, 121. https://doi.org/10.1016/j.ecolind.2020.107124

Sharma, N., Bhattacharjee, S., Garg, R. D., Sharma, K., & Salim, M. (2024). Sustainable management and agriculture resource technology system using remote sensing descriptors and IoT. Geomatica, 76(2). https://doi.org/10.1016/j.geomat.2024.100040

Sorkhabi, O. M. (2024). An LSTM deep learning framework for history-based tornado prediction using meteorological data and damage assessment using NDVI anomalies. Results in Earth Sciences, 2, 100040. https://doi.org/10.1016/j.rines.2024.100040

Sotille, M. E., Bremer, U. F., Vieira, G., Velho, L. F., Petsch, C., & Simões, J. C. (2020). Evaluation of UAV and satellite-derived NDVI to map maritime Antarctic vegetation. Applied Geography, 125. https://doi.org/10.1016/j.apgeog.2020.102322

Tian, J., Su, S., Tian, Q., Zhan, W., Xi, Y., & Wang, N. (2021). A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite. International Journal of Applied Earth Observation and Geoinformation, 101. https://doi.org/10.1016/j.jag.2021.102361

Trevisiol, F., Mandanici, E., Pagliarani, A., & Bitelli, G. (2024). Evaluation of Landsat-9 interoperability with Sentinel-2 and Landsat-8 over Europe and local comparison with field surveys. ISPRS Journal of Photogrammetry and Remote Sensing, 210, 55–68. https://doi.org/10.1016/j.isprsjprs.2024.02.021

Wang, Z., Wang, Y., Liu, Y., Wang, F., Deng, W., & Rao, P. (2023). Spatiotemporal characteristics and natural forces of grassland NDVI changes in Qilian Mountains from a sub-basin perspective. Ecological Indicators, 157. https://doi.org/10.1016/j.ecolind.2023.111186

Wu, S., Zhang, Y., & Kang, W. (2024). Employing NDVI as vegetation correction variable to improve soil moisture measurements of mobile cosmic-ray neutron sensor near the Qilian Mountains. Geoderma, 441. https://doi.org/10.1016/j.geoderma.2023.116764

Xu, Y., Dai, Q. Y., Lu, Y. G., Zhao, C., Huang, W. T., Xu, M., & Feng, Y. X. (2024). Identification of ecologically sensitive zones affected by climate change and anthropogenic activities in Southwest China through a NDVI-based spatial-temporal model. Ecological Indicators, 158. https://doi.org/10.1016/j.ecolind.2023.111482

Yang, J., Yan, D., Yu, Z., Wu, Z., Wang, H., Liu, W., Liu, S., & Yuan, Z. (2024). NDVI variations of different terrestrial ecosystems and their response to major driving factors on two side regions of the Hu-Line. Ecological Indicators, 159. https://doi.org/10.1016/j.ecolind.2024.111667

Yao, B., Gong, X., Li, Y., Li, Y., Lian, J., & Wang, X. (2024). Spatiotemporal variation and GeoDetector analysis of NDVI at the northern foothills of the Yinshan Mountains in Inner Mongolia over the past 40 years. Heliyon, 10(20). https://doi.org/10.1016/j.heliyon.2024.e39309

Zhang, K., Zhu, C., Li, J., Shi, K., & Zhang, X. (2024). Reconstruction of dense time series high spatial resolution NDVI data using a spatiotemporal optimal weighted combination estimation model based on Sentinel-2 and MODIS. Ecological Informatics, 82. https://doi.org/10.1016/j.ecoinf.2024.102725

Zhao, C., Pan, Y., Ren, S., Gao, Y., Wu, H., & Ma, G. (2024). Accurate vegetation destruction detection using remote sensing imagery based on the three-band difference vegetation index (TBDVI) and dual-temporal detection method. International Journal of Applied Earth Observation and Geoinformation, 127. https://doi.org/10.1016/j.jag.2024.103669

Zhihao, W., & Wei, F. (2024). UV-NDVI for real-time crop health monitoring in vertical farms. Smart Agricultural Technology, 8. https://doi.org/10.1016/j.atech.2024.100462
Published
2025-02-07
How to Cite
Chicaiza TipanguanoA. M., & Carrera MolinaD. S. (2025). EVALUACIÓN ESPACIO TEMPORAL DE LA COBERTURA VEGETAL MEDIANTE IMÁGENES SATELITALES LANDSAT 8 EN LA UNIVERSIDAD TÉCNICA DE COTOPAXI, CAMPUS SALACHE ENTRE LOS AÑOS 2015 AL 2024. Revista Recursos Naturales Producción Y Sostenibilidad, 4(1), 131-151. https://doi.org/10.61236/renpys.v4i1.1037
Section
Artículos de investigación