An introduction to the spatio-temporal analysis of satellite remote sensing data for geostatisticians

Satellite remote sensing data have become available in meteorology, agriculture, forestry, geology, regional planning, hydrology or natural environment sciences since several decades ago, because satellites provide routinely high quality images with different temporal and spatial resolutions. Joining, combining or smoothing these images for a better quality of information is a challenge not always properly solved. In this regard, geostatistics, as the spatiotemporal stochastic techniques of georeferenced data, is a very helpful and powerful tool not enough explored in this area yet. Here, we analyze the current use of some of the geostatistical tools in satellite image analysis, and provide an introduction to this subject for potential researchers.This research was supported by the Spanish Ministry of Economy, Industry and Competitiveness (Project MTM2017-82553-R), the Government of Navarra (Project PI015, 2016 and Project PI043 2017), and by the Fundación Caja Navarra-UNED Pamplona (2016 and 2017)

Monitoring and forecasting nitrate concentration in the groundwater using statistical process control and time series analysis: a case study

Contaminated water resources have important implications on health and the environment. Nitrate contamination of the groundwater is a serious problem in the European Union. A method based on the statistical process control (SPC) and time series analysis is developed to monitoring and to predict the concentration evolution of nitrate (NO 3 -) in groundwater. In many pumping wells the NO 3 -concentration ([NO 3 -]) increases and approaches or even passes the European Community standard of 50 mg l -1. The objective of this paper is to show the application of statistical process control as a monitoring tool for groundwater pollution from agricultural practices. We propose the autoregressive integrated moving average (ARIMA) model as a management tool to monitoring and reduction of the intrusion of nitrate into the groundwater. This tool should help in setting up useful guidelines for evaluating actual environmental performance against the firm's environmental objectives and targets and regulatory requirements. We concluded that the statistical process control method may be a potentially important way of monitoring groundwater quality that also permits rapid response to serious increases in pollutants concentrations. In doing so, the paper fills an important gap in the water pollution standards and emerging polices (Water Framework directives). © 2010 Springer-Verlag.The author is grateful to the anonymous referees and the editor for several constructive comments that have improved this paper. The author acknowledge the financial support of Programa de Apoyo a la Investigacion y Desarrollo (PAID-06-08) of the Universidad Politecnica de Valencia.García-Díaz, JC. (2011). Monitoring and forecasting nitrate concentration in the groundwater using statistical process control and time series analysis: a case study. Stochastic Environmental Research and Risk Assessment. 25(3):331-339. https://doi.org/10.1007/s00477-010-0371-6S331339253Alwan LC, Roberts HV (1989) Time series modelling for statistical process control. In: Keats JB, Hubele NF (eds) Automated manufacturing. Marcel Dekker Inc., New York, pp 87–95Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden Day, San FranciscoCanter LW (1997) Nitrates in groundwater. CRC Press, Boca Raton, pp 73–143CEC (2000) Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Off J Eur Union L 327:1–73CEC (2006) Directive 2006/118/EC of the European Parliament and of the Council of 12 December 2006 on the protection of groundwater against pollution and deterioration. Off J Eur Union L 372:19–31Chistensen OF, Cassiani G, Diggle PJ, Ribero P, Andreotti G (2004) Statistical estimation of the relative efficiency of natural attenuation mechanisms in contaminated aquifers. Stoch Environ Res Risk Assess 18:339–350Cook DF, Zobel CW, Wolfe ML (2006) Environmental statistical process control using an augmented neural network classification approach. Eur J Oper Res 174:1631–1642De Paz JM, Ramos C (2004) Simulation of nitrate leaching for different nitrogen fertilization rates in a region of Valencia (Spain) using a GIS–GLEAMS system. Agric Ecosyst Environ 103(1):59–73European Commission (1998) The Implementation of Council Directive 91/676/EEC concerning the protection of waters against pollution caused by nitrates from agricultural sources. Office for Official Publications of the European Communities, LuxembourgGeneralitat Valenciana (2000) Decreto 13/2000 del 25-enero de 2000, del gobierno valenciano por el que se designan, en el ámbito de la Comunidad Valenciana, determinados municipios como zonas vulnerables a la contaminación de las aguas por nitratos procedentes de fuentes agrarias. DOGV (Diario Oficial de la Generalitat Valenciana) No. 3677. Valencia, Spain, pp 1511–1515Harris TJ, Ross WH (1991) Statistical process control procedures for correlated observations. Can J Chem Eng 69:48–57ITGME (1998) Mapa de contenido en nitrato de las aguas subterráneas en España. Instituto Tecnológico GeoMinero de España. Ministerio de Medio Ambiente, MadridLi N, Liang X, Li X, Wang C, Wu DD (2009) Network environment and financial risk using machine learning and sentiment analysis. Hum Ecol Risk Assess 15(2):227–252Militino AF, Ugarte MD, Ibáñez B (2008) Longitudinal analysis of spatially correlated data. Stoch Environ Res Risk Assess 22:49–57Montgomery DC, Mastrangelo CM (1991) Some statistical process control methods for autocorrelated data. J Qual Technol 23:179–268Ramos C, Agut A, Lidón A (2002) Nitrate leaching in important crops of the Valencian Community region (Spain). Environ Pollut 118:215–223Sanchis EJ (1991) Estudio de la contaminación por nitratos de las aguas subterráneas de la Provincia de Valencia. Origen, balance y evolución espacial y temporal. Graficuatre SL, ValenciaUSEPA (Unites States Environmental Protection Agency) (1990) The drinking water criteria document on nitrate/nitrite. National Technical Information Service document no. PB91-142836Vigil J, Warburton S, Haynes WS, Kaiser LR (1965) Nitrates in municipal water supply cause metahemoglobinemia in infant. Public Health Rep 80:12Wardell DG, Moskowitz H, Plante RD (1992) Control charts in the presence of data correlation. Manage Sci 38(8):1084–1105Wardell DG, Moskowitz H, Plante RD (1994) Run length distributions of special-cause control charts for correlated processes. Technometrics 36:3–17WHO (World Health Organization) (1993) Guidelines for drinking water quality, 2nd edn (vol 1: Recommendations). WHO, GenevaWorrall F, Burt TP (1999) A univariate model of river water nitrate time series. J Hydrol 214:74–90Xu J, Li X, Wu DD (2009) Optimizing circular economy planning and risk analysis using system dynamics. Hum Ecol Risk Assess 15(2):316–33

A unified approach on residuals, leverages and outliers in the linear mixed model

Best linear unbiased predictor (BLUP), Case deletion, Leverages, Outliers, Predicted residuals, Variance ratios, 62J20, 62J99,

Hedonic approaches based on spatial econometrics and spatial statistics: application to evaluation of project benefits

Benefits evaluation, Spatial hedonic approach, Spatial error model, Spatial process model, Anisotropy, C21, R19,