Cristóbal J., Poyatos R., Ninyerola M., Llorens P., Pons X. (2011) Combining remote sensing and GIS climate modelling to estimate daily forest evapotranspiration in a Mediterranean mountain area. Hydrology and Earth System Sciences. 15: 1563-1575.EnllaçDoi: 10.5194/hess-15-1563-2011
Evapotranspiration monitoring allows us to assess the environmental stress on forest and agricultural ecosystems. Nowadays, Remote Sensing and Geographical Information Systems (GIS) are the main techniques used for calculating evapotranspiration at catchment and regional scales. In this study we present a methodology, based on the energy balance equation (B-method), that combines remote sensing imagery with GIS-based climate modelling to estimate daily evapotranspiration (ETd) for several dates between 2003 and 2005. The three main variables needed to compute ETd were obtained as follows: (i) Land surface temperature by means of the Landsat-5 TM and Landsat-7 ETM+ thermal band, (ii) air temperature by means of multiple regression analysis and spatial interpolation from meteorological ground stations data at satellite pass, and (iii) net radiation by means of the radiative balance. We calculated ETd using remote sensing data at different spatial and temporal scales (Landsat-7 ETM+, Landsat-5 TM and TERRA/AQUA MODIS, with a spatial resolution of 60, 120 and 1000 m, respectively) and combining three different approaches to calculate the parameter, which represents an average bulk conductance for the daily-integrated sensible heat flux. We then compared these estimates with sap flow measurements from a Scots pine (Pinus sylvestris L.) stand in a Mediterranean mountain area. This procedure allowed us to better understand the limitations of ETd modelling and how it needs to be improved, especially in heterogeneous forest areas. The method using Landsat data resulted in a good agreement, R2 test of 0.89, with a mean RMSE value of about 0.6 mm day-1 and an estimation error of ±30 %. The poor agreement obtained using TERRA/AQUA MODIS, with a mean RMSE value of 1.8 and 2.4 mm day-1 and an estimation error of about ±57 and 50 %, respectively. This reveals that ETd retrieval from coarse resolution remote sensing data is troublesome in these heterogeneous areas, and therefore further research is necessary on this issue. Finally, implementing regional GIS-based climate models as inputs in ETd retrieval have has provided good results, making possible to compute ETd at regional scales. © 2011 Author(s).
Moré G., Serra P., Pons X. (2011) Multitemporal flooding dynamics of rice fields by means of discriminant analysis of radiometrically corrected remote sensing imagery. International Journal of Remote Sensing. 32: 1983-2011.EnllaçDoi: 10.1080/01431161003645816
An automatic classifier based on a discriminant analysis (DA) was used to classify eight classes in relation to different stages of rice fields during the flooding season. This methodology is characterized by the fact that, once the training phase has been carried out, training areas are not required to perform new classifications. If the images have been radiometrically corrected in a consistent way, the classifier can be used in a retrospective mode using past images. For this study, the training phase was conducted with data taken in October 2006 and January 2007 while the automatic classifier was applied to a total of 10 Landsat-5 Thematic Mapper (TM) images from the 2004-05 and 2006-07 seasons. An average level of accuracy of 93.4% (range 89.7-98.7%) demonstrates the capability of the method to obtain high-quality and quasi-instantaneous classifications and to carry out retrospective studies even when training areas are not available for past dates. Two examples of how the method can be used are included in this article: (i) a study of the temporal evolution of flooding covers by period and (ii) the use of vector enrichment as a thematic updating tool for the cadastre. An additional objective of the study was to analyse the importance of the different bands to ascertain the suitability of alternative sensors with spectral configurations other than those provided by Landsat. This analysis demonstrates that the absence of shortwave infrared (SWIR) bands results in a decrease of almost nine percentage points in the accuracy levels of the classification while the blue band can be excluded with minimal impact on the results. © 2011 Taylor & Francis.
Zabala A., Pons X. (2011) Effects of lossy compression on remote sensing image classification of forest areas. International Journal of Applied Earth Observation and Geoinformation. 13: 43-51.EnllaçDoi: 10.1016/j.jag.2010.06.005
Lossy compression is being increasingly used in remote sensing; however, its effects on classification have scarcely been studied. This paper studies the implications of JPEG (JPG) and JPEG 2000 (J2K) lossy compression for image classification of forests in Mediterranean areas. Results explore the impact of the compression on the images themselves as well as on the obtained classification. The results indicate that classifications made with previously compressed radiometrically corrected images and topoclimatic variables are not negatively affected by compression, even at quite high compression ratios. Indeed, JPG compression can be applied to images at a compression ratio (CR, ratio between the size of the original file and the size of the compressed file) of 10:1 or even 20:1 (for both JPG and J2K). Nevertheless, the fragmentation of the study area must be taken into account: in less fragmented zones, high CR are possible for both JPG and J2K, but in fragmented zones, JPG is not advisable, and when J2K is used, only a medium CR is recommended (3.33:1 to 5:1). Taking into account that J2K produces fewer artefacts at higher CR, the study not only contributes with optimum CR recommendations, but also found that the J2K compression standard (ISO 15444-1) is better than the JPG (ISO 10918-1) when applied to image classification. Although J2K is computationally more expensive, this is no longer a critical issue with current computer technology. © 2010 Elsevier B.V.
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