Multitemporal analysis with statistical models: influence of the atmospheric condition on urban concentrations of particulate matter

This paper shows a multitemporal analysis with autoregressive integrated moving average models of the influence of atmospheric condition on concentrations of particulate matter ≤ 10 µm in Bogotá city, Colombia. Information was collected from six monitoring stations distributed throughout the city. The study period was nine years. Autoregressive component of the models suggests that urban areas with greater atmospheric instability show a lower hourly persistence of particulate matter (one hour) compared to urban areas with lower atmospheric instability (two hours). Moving average component of the models hints those urban areas with greater atmospheric instability show greater hourly variability in particulate matter concentrations (5-10 hours). The models also suggest that a high degree of air pollution decreases the temporal influence of the atmospheric condition on particulate matter concentrations; in this case, the temporal behavior of particulate matter possibly depends on the urban emission sources of this pollutant rather than on the existing atmospheric condition. This study is relevant to deepen the knowledge in relation to the following aspects of atmospheric physics: The use of statistical models for the time series analysis of atmospheric condition, and the analysis by statistical models of the influence of atmospheric condition on air pollutant concentrations.

Evaluación espacial del comportamiento fenológico de la vegetación mediante imágenes satelitales distrito de chiara-ayacucho 2000-2015

La presente investigación se realizó el análisis de las imágenes satelitales MODIS-Terra 250m x 250m de resolución espacial y determinar el comportamiento fenológico de la cobertura vegetal del distrito de Chiara. El objetivo de esta investigación es dar a conocer y demostrar la importancia de la metodología utilizada (imágenes de satélite MOD13Q1) para el análisis multitemporal de la vegetación, mediante el uso del software ENVI, IDL, ArcGIS. En este sentido, se ha logrado describir el comportamiento de la vegetación en el periodo de los dieciséis años, y determinar una variabilidad del área de vegetación en las estaciones anuales de verano, otoño, invierno y primavera y estimar mediante métodos estadísticos la variabilidad del NDVI. Los principales resultados obtenidos son: La variación fenológicos de los ecosistemas, según la estacionalidad climática de las áreas estudiadas. En este sentido, los cambios y variaciones son explicados en base a las características de pixeles de colores. Los resultados obtenidos pueden servir como insumo para mejorar el proceso y clasificación de las imágenes de satélite con el objeto de elaborar mapas temáticos y contribuir a la mejor toma de decisiones en cuanto al uso adecuado de los calendarios agrícolas definidos para cultivos anuales y/o transitorios instalados en dicho distrito. The present research was the analysis of the satellite imagery MODIS-Terra 250m x 250m of spatial resolution and determine the phenological behavior of the vegetation cover of the district of Chiara. The objective of this research is to make known and demonstrate the importance of the methodology used (satellite images MOD13Q1) for multitemporal analysis of vegetation, using the software ENVI, IDL, ArcGIS. In this sense, it has been possible to describe the behavior of vegetation in the sixteen-year period, and to determine a variability of the vegetation area in the annual seasons of summer, autumn, winter and spring and to estimate by means of statistical methods the variability of NDVI. The main results obtained are the phenological variation of the ecosystems, according to the climatic seasonality of the studied areas. In this sense, changes and variations are explained based on the characteristics of colored pixels. The results obtained can serve as input to improve the processing and classification of satellite images in order to produce thematic maps and contribute to better decision making regarding the proper use of the defined agricultural calendars for annual and / or transient crops Installed in that district.

Vineyard yield estimation using 2-D proximal sensing: a multitemporal approach

Vineyard yield estimation is a fundamental aspect in precision viticulture that enables a better understanding of the inherent variability within a vineyard. Yield estimation conducted early in the growing season provides insightful information to ensure the best fruit quality for the maximum desired yield. Proximal sensing techniques provide non-destructive in situ data acquisition for yield estimation during the growing season. This study aimed to determine the ideal phenological stage for yield estimation using 2-dimensional (2-D) proximal sensing and computer vision techniques in a vertical shoot positioned (VSP) vineyard. To achieve this aim, multitemporal digital imagery was acquired weekly over a 12-week period, with a final acquisition two days prior to harvest. Preceding the multitemporal analysis for yield estimation, an unsupervised k-means clustering (KMC) algorithm was evaluated for image segmentation on the final dataset captured before harvest, yielding bunch-level segmentation accuracies as high as 0.942, with a corresponding F1-score of 0.948. The segmentation yielded a pixel area (cm2), which served as input to a cross-validation model for calculating bunch mass (g). The ‘calculated mass’ was linearly regressed against the ‘actual mass’, indicating the capability for estimating vineyard yield. Results of the multitemporal analysis showed that the final stage of berry ripening was the ideal phenological stage for yield estimation, achieving a global r2 of 0.790.

Assessment of Depressional Wetland Degradation, Spatial Distribution, and Geological Aspects in Southern Brazil

This paper presents a procedure to study depressional wetlands in southern Brazil and focuses on the mechanisms controlling water dynamics and environmental degradation due to anthropogenic interference. The study is based on an inventory of wetlands, a digital elevation model, the geological and geotechnical characteristics of geological materials, a multitemporal analysis of satellite images, the distribution of land use types, and onsite monitoring of water level and rainfall data. One hundred and twelve depressional wetlands were identified with a total area of 902 ha and a catchment area of 5456.8 ha. These wetlands were grouped into two classes with different hydrological control mechanisms. From the water level monitoring, the wetlands were found to present different hydrological conditions. Before rainy periods, the wetlands were almost dry or had little water; after rainy periods, over half of the wetlands were still dry or had groundwater levels below the surface, and the water levels of the other wetlands increased. The multitemporal analysis showed a reduction in the wetland water surface area from 270 ha in 1991 to 60 ha in 2019, which confirms the monitoring result that the amount of stored water is decreasing because of anthropogenic activities. Anthropogenic activities affect wetland water dynamics because of changes in the landscape and soil characteristics of the catchment area, and drainage of wetland areas by ditches for agricultural water supply; more than 50% of wetlands showed a high degree of change (environmental degradation), with conditions that make restoration or remediation very difficult.