Blanes I., Zabala A., Moré G., Pons X., Serra-Sagristà J. (2008) Classification of hyperspectral images compressed through 3D-JPEG2000. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5179 LNAI: 416-423.EnllaçDoi: 10.1007/978-3-540-85567-5-52
Classification of hyperspectral images is paramount to an increasing number of user applications. With the advent of more powerful technology, sensed images demand for larger requirements in computational and memory capabilities, which has led to devise compression techniques to alleviate the transmission and storage necessities. Classification of compressed images is addressed in this paper. Compression takes into account the spectral correlation of hyperspectral images together with more simple approaches. Experiments have been performed on a large hyperspectral CASI image with 72 bands. Both coding and classification results indicate that the performance of 3d-DWT is superior to the other two lossy coding approaches, providing consistent improvements of more than 10 dB for the coding process, and maintaining both the global accuracy and the percentage of classified area for the classification process. © 2008 Springer-Verlag Berlin Heidelberg.
Cristóbal J., Ninyerola M., Pons X. (2008) Modeling air temperature through a combination of remote sensing and GIS data. Journal of Geophysical Research Atmospheres. 113: 0-0.EnllaçDoi: 10.1029/2007JD009318
Air temperature is involved in many environmental processes such as actual and potential evapotranspiration, net radiation and species distribution. Ground meteorological stations provide important local data of air temperature, but a continuous surface for large and heterogeneous areas is also needed. In this paper we present a hybrid methodology between Remote Sensing and Geographical Information Systems to retrieve daily instantaneous, mean, maximum and minimum air temperatures (2002-2004) as well as monthly and annual mean, maximum and minimum air temperatures (2000-2005) on a regional scale (Catalonia, northeast of the Iberian Peninsula) by means of multiple regression analysis and spatial interpolation techniques. To perform multiple regression analysis we have used geographical and multiresolution remotely sensed variables as predictors. The geographical variables we have included are altitude, latitude, continentality and solar radiation. As remote sensing predictors, we have selected those variables that are most closely related with air temperature such as albedo, land surface temperature (LST) and NDVI obtained from Landsat-5 (TM), Landsat-7 (ETM+), NOAA (AVHRR) and TERRA (MODIS) satellites. The best air temperature models are obtained when remote sensing variables are combined with geographical variables: averaged R2 = 0.60 and averaged root mean square error (RMSE) = 1.75°C for daily temperatures, and averaged R2 = 0.86 and averaged RMSE = 1.00°C for monthly and annual temperatures. The results also show that combined models appear in a higher frequency than only geographical or only remote sensing models (87%, 11 % and 2% respectively) and that LST and NDVI are the most powerful remote sensing predictors in air temperature modeling. Copyright 2008 by The American Geophysical Union.
Olivet M., Aloy J., Prat E., Pons X. (2008) Health services provision and geographic accessibility [Oferta de servicios de salud y accesibilidad geográfica]. Medicina Clinica. 131: 16-22.EnllaçDoi: 10.1016/S0025-7753(08)76470-4
This study describes the health services available in Catalonia, Spain as part of the situation analysis of the healthcare map, setting a starting point for the process of adapting services to the needs of the population. It also includes an analysis of the geographic accessibility to healthcare centres in the public health system, through the use of a geographic information system (GIS), with geo-referencing variables and calculations of travel times and distances. The principal results show,on one hand, the adaptation of the Catalan healthcare network to the distribution of the population, with a high level of geographic proximity of the services to the population, and a high degree of capillarity, principally in primary healthcare; and on the other hand, the importance that GIS tools and procedures may acquire in healthcareplanning is highlighted. © 2008 Elsevier España S.L.
Serra P., Pons X. (2008) Monitoring farmers' decisions on Mediterranean irrigated crops using satellite image time series. International Journal of Remote Sensing. 29: 2293-2316.EnllaçDoi: 10.1080/01431160701408444
The main purpose of this study is to present a methodology for mapping and monitoring temporal signatures of Mediterranean crops over several years in irrigated areas, and to study their inter-annual dynamics. These goals were achieved by remote sensing using 36 Landsat images from 2002 to 2005. Four crop maps, one for each year, with six agricultural categories and a thematic accuracy of 93%, 95%, 96% and 94% were obtained using a hybrid classifier. A mean of nine images produced these highly accurate results, but the absence of one image in the growth period of 2002 resulted in lower accuracies, particularly in fruit trees (85% of user accuracy). This highlights the importance of a multi-temporal approach based on a relatively large number of images. After the classification results were validated, two parameters were used to characterize the dynamics of the four crops (rice, maize, alfalfa and fruit trees): greenness, extracted from the Normalized Difference Vegetation Index (NDVI), and wetness, calculated from the Tasselled Cap Wetness (TCW) Index. In order to differentiate the wetness origin of crops, an analysis of local daily precipitation (which could cause significant anomalies in the TCW coefficients) and water stored in the Susqueda reservoir (which may result in farmers making important management decisions when water is limited) was conducted during this four-year period. After applying statistical analysis, the results showed that, of the four crops analysed, rice, alfalfa and fruit trees had more stable dynamics than maize, which was planted later in case of water deficit at the beginning of the irrigation campaign (in 2002) and earlier when the deficit occurred later (in 2005).
Serra P., Pons X., Saurí D. (2008) Land-cover and land-use change in a Mediterranean landscape: A spatial analysis of driving forces integrating biophysical and human factors. Applied Geography. 28: 189-209.EnllaçDoi: 10.1016/j.apgeog.2008.02.001
This article develops a spatial analysis applied to examine the main driving forces of land-cover and land-use (LCLU) change in a Mediterranean region. Three different tools have been used in order to differentiate LCLU changes, driving forces and landscape dynamics. LCLU changes have been quantified with remote sensing techniques, driving forces have been analysed with multiple logistic regressions combining biophysical and human variables, whereas landscape dynamics have been quantified using different metrics. Results show the intensification of subsidised herbaceous crops on the coastal agricultural plain, the abandonment of olive trees and vineyards in the transitional area and forest restoration in the mountainous subregion. © 2008 Elsevier Ltd. All rights reserved.
Zabala A., Pons X., Aulí-Llinàs F., Serra-Sagristà J. (2008) Image compression effects in visual analysis. Proceedings of SPIE - The International Society for Optical Engineering. 7084: 0-0.EnllaçDoi: 10.1117/12.798572
This study deals with the effects of lossy image compression in the visual analysis of remotely sensed images. The experiments consider two factors with interaction: the type of landscape and the degree of lossy compression. Three landscapes and two areas for each landscape (with different homogeneity) have been selected. For every of the six study area, color 1:5000 orthoimages have been submitted to a JPEG2000 lossy compression algorithm at five different compression ratios. The image of every area and compression ratio has been submitted to on-screen photographic interpretation, generating 30 polygon layers. Maps obtained using compressed images with a high compression ratio present high structural differences regarding to maps obtained with the original images. On the other hand, the compression of 20% obtains values only slightly different from those of the original photographic interpretation, but these differences seem owed to the subjectivity of the photographic interpretation. Therefore, this compression ratio seems to be the optimum since it implies an important reduction of the image size without determining changes neither in the topological variables of the generated vector nor in the obtained thematic quality.
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