zbMATH — the first resource for mathematics

Using remote sensing for agricultural statistics. (English) Zbl 1105.62111
Summary: Remote sensing can be a valuable tool for agricultural statistics when area frames or multiple frames are used. At the design level, remote sensing typically helps in the definition of sampling units and the stratification, but can also be exploited to optimise the sample allocation and size of sampling units. At the estimator level, classified satellite images are generally used as auxiliary variables in a regression estimator or for estimators based on confusion matrices. The most often used satellite images are LANDSAT-TM and SPOT-XS. In general, classified or photo-interpreted images should not be directly used to estimate crop areas because the proportion of pixels classified into the specific crop is often strongly biased. Vegetation indexes computed from satellite images can give in some cases a good indication of the potential crop yield.
Reviewer: Reviewer (Berlin)

62P12 Applications of statistics to environmental and related topics
Full Text: DOI
[1] Allen, A Look at the Remote Sensing Applications Program of the National Agricultural Statistics Service., Journal of Official Statistics 6 pp 393– (1990)
[2] Allen, The Remote Sensing Applications Programme of The National Agricultural Statistics Service. (1988)
[3] Ambrosio Flores, Land cover estimation in small areas using ground survey and remote sensing., Remote Sensing of the Environment 74 pp 240– (2000)
[4] Arbia, The use of GIS in spatial statistical surveys., International Statistical Review 63 (2) pp 339– (1993) · doi:10.2307/1403632
[5] Avenier, Méthodologie de stratification sur images satellitaires et utilisation d’un système d’information géographique; mise en place du plan d’échantillonnage. (1992)
[6] Battese, Prediction of county crop areas using survey and satellite data. Survey section proceedings pp 500– (1981)
[7] Battese, An error-components model for prediction of county crop areas using survey and satellite data., Journal of the American Statistical Association 83 pp 28– (1988)
[8] Bellow, Application of satellite data to crop area estimation at the county level (1994)
[9] Benedetti, Towards crop yield estimate and forecast by Remote Sensing: the use of NOAA/NDVI data pp 4– (1993)
[10] Brun, Pilot use of the TER-UTI data in agricultural statistics procedure using remote sensing (1992)
[11] Card, Using Known Map Category Marginal Frequencies to Improve Estimates of Thematic Map Accuracy., Photogrammetric Engineering and Remote Sensing 48 pp 431– (1982)
[12] Carfagna, Proc. of Agricultural Statistics 2000 pp 261– (1998)
[13] Carfagna, Meeting on Food and Agricultural Statistics in Europe (1999)
[14] Carfagna , E. 2001a Note sull’uso dell’autocorrelazione spaziale nel disegno campionario areale Atti della XL riunione scientifica della Societí Italiana di Statistica 169 182
[15] Carfagna, Cost-effectiveness of remote sensing in agricultural and environmental statistics, Proc. of the Conference on Agricultural and Environmental Statistical Applications in Rome (CAESAR). 3 pp 618– (2001b)
[16] Chhikara, Effect of mixed (boundary) pixels in crop proportion estimation., Remote Sensing of the Environment 14 pp 207– (1984)
[17] Chhikara, Crop acreage estimation using a LANDSAT based estimator as an auxiliary variable., IEEE transactions on Geoscience and Remote Sensing GE 24 (1) pp 157– (1986)
[18] Cochran, Sampling Techniques (1977)
[19] Congalton, Assessing the accuracy of remotely sensed data: principles and practices. (1999)
[20] Consorzio, Telerilevamento in Agricoltura, Previsione delle Produzioni di Frumento in Tempo Reale e Sviluppi Tecnologici. (1987)
[21] Cotter, An Image Analysis System to Develop Area Sampling Frames for Agricultural Surveys., Photogrammetric Engineering and Remote Sensing 60 pp 299– (1994)
[22] Czaplewski, Calibration of remotely sensed proportion or area estimates for misclassification error., Remote Sensing of Environment 39 pp 29– (1992)
[23] Delincé, A European approach to area frame survey., Proc. of the Conference on Agricultural and Environmental Statistical Applications in Rome (CAESAR) 2 pp 463– (2001)
[24] Deville, Calibration estimators in survey sampling., Journal of the American Statistical Association 87 pp 376– (1992) · Zbl 0760.62010
[25] Dymond, How accurately do image classifiers estimate area?, International Journal of Remote Sensing 13 pp 1735– (1992)
[26] Efron, An introduction to the Bootstrap. (1993) · Zbl 0835.62038 · doi:10.1007/978-1-4899-4541-9
[27] FAO, Multiple frame agricultural surveys, Vol. 1: current surveys based on area and list sampling methods. (1996)
[28] FAO, Multiple frame agricultural surveys, Vol. 2 (1998)
[29] FAS, Proc. of the seminar on crop yield forecasting methods. pp 111– (1997)
[30] Fuller, Small area statistics for agriculture from a national survey 2 pp 397– (1999)
[31] Gallego, Remote sensing and land cover area estimation., International Journal of Remote Sensing 25 pp 3019– (2004)
[32] Gallego, Problemi statistici nel telerilevamento (1998)
[33] Gallego, The use of CORINE Land Cover to improve area frame survey estimates in Spain., Research in Official Statistics 2 pp 99– (1999a)
[34] Gallego, Using a Confusion Matrix for Area Estimation with Remote Sensing pp 99– (1994)
[35] Gallego, Crop area estimates through remote sensing: stability of the regression correction., International Journal of Remote Sensing 14 pp 3433– (1993)
[36] Gallego, GeoENV II- Geostatistics for Environmental applications. 10 pp 393– (1999b) · doi:10.1007/978-94-015-9297-0_33
[37] Genovese, A methodology for a combined use of normalised difference vegetation index and CORINE L and Cover data for crop yield monitoring and forecasting. A case study in Spain., Agronomie 21 pp 91– (2001)
[38] González, Remote sensing and agricultural statistics: crop area estimation through regression estimators and confusion matrices., International Journal of Remote Sensing 14 (6) pp 1215– (1993)
[39] Gonzalez, Small-area estimation with application to unemployment and housing estimates., Journal of the American Statistical Association 73 (361) pp 7– (1978)
[40] Hansen, Sample Survey Methods and Theory (1953)
[41] Hanuschak , G. Hale , R. Craig , M. Mueller , R. Hart , G. 2001 The new economics of remote sensing for agricultural statistics in the United States. Proc. of the Conference on Agricultural and Environmental Statistical Applications in Rome (CAESAR). 2 XXII 10
[42] Hay, The derivation of global estimates from a confusion matrix., International Journal of Remote Sensing 9 pp 1395– (1988)
[43] Kumar, Plants and the daylight spectrum. pp 133– (1981)
[44] Lillesand, Remote sensing and image interpretation pp 750– (1999)
[45] Malingreau, AVHRR for monitoring global tropical deforestation., International Journal of Remote Sensing 10 pp 855– (1989)
[46] Martino, The Agrit system for short-term estimates in agriculture (2003)
[47] Mayaux, Estimation of tropical forest area from coarse spatial resolution data: a two-step correction function for proportional errors due to spatial aggregation., Remote sensing of environment 53 pp 1– (1995)
[48] McNairn, Providing crop information using Radarsat-1 and satellite optical imagery., International Journal of Remote Sensing 23 (5) pp 851– (2002)
[49] Meyer-Roux, The MARS Project: Overview and Perspectives pp 33– (1994)
[50] Ministry of Food, Agriculture and Forestry, Remote sensing in agriculture. Crop acreage estimate and crop production forecasts. (1996)
[51] Pinty, Towards a quantitative interpretation of vegetation indices. Part 1: Biophysical canopy properties and classical indices., Remote Sensing Reviews 7 pp 127– (1993) · doi:10.1080/02757259309532171
[52] Priesley, Using Classification Error Matrices to Improve the Accuracy of Weighted Land-Cover Models., Photogrammetric Engineering and Remote Sensing 53 pp 1258– (1987)
[53] Rasmussen, Developing simple, operational consistent NDVI-vegetation models by applying environmental and climatic information. Part II: Crop yield assessment., International Journal of Remote Sensing 19 pp 119– (1998)
[54] Roesch, A comparison of various estimators for updating forest area coverage using AVHRR and forest inventory data., Photogrammetric Engineering and Remote Sensing 61 pp 307– (1995)
[55] Ruimy, CO2 Fluxes over Plant Canopies and Solar Radiation: A Review., Advances in Ecological Research 26 pp 2– (1995)
[56] Schmidt, Exploring spectral discrimination of grass species in African rangeland., International Journal of Remote Sensing 22 pp 3421– (2001)
[57] Seber, Multivariate observations. (1984) · Zbl 0627.62052
[58] Singh, Small area estimation of crop yield using remote sensing satellite data., International Journal of Remote Sensing 23 (1) pp 49– (2002)
[59] Sridhar, Wheat production forecasting for a predominantly unirrigated region in Madhya Pradesh (India)., International Journal of Remote Sensing 15 pp 1307– (1994)
[60] Taylor , J. Sannier , C. Delincé , J. Gallego , F. J. 1997 Regional Crop Inventories in Europe Assisted by Remote Sensing
[61] Taylor, Vegetation conditions and yield indicators in England and Wales using NOAA-AVHRR data pp 249– (1992)
[62] Thompson, Sampling. (1992)
[63] Walker, The use of Landsat for County estimates of crop areas., Communications in Statistics - Theory and Methods 23 pp 2975– (1984) · Zbl 0567.62091
[64] Walsh, Calibration of satellite classification of land area., Remote Sensing of Environment 46 pp 281– (1993)
[65] Wishahy, Evaluation of area calculation from SPOT imageries pp 1296– (1994)
[66] Yuan, A simulation comparison of three marginal area estimators for image classification., Photogrammetric Engineering and Remote Sensing 63 pp 385– (1997)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.