Content uploaded by Jose Sobrino
Author content
All content in this area was uploaded by Jose Sobrino on Apr 07, 2017
Content may be subject to copyright.
A preview of the PDF is not available
Second Recent Advances in Quantitative Remote Sensing
Abstract
This Symposium addressed the scientific advances in quantitative remote sensing in connection with real applications. Its main goal was to assess the state of the art of both theory and applications in the analysis of remote sensing data, as well as to provide a forum for researcher in this subject area to exchange views and report their latest results. In this book 176 contributions presented in both plenary and poster sessions are arranged according to the scientific topics selected. José A. Sobrino Symposium Chairperson Global Change Unit, Universitat de València Valencia, November 2006
Mapping forest composition is a major concern for forest management, biodiversity assessment and for understanding the potential impacts of climate change on tree species distribution. In this study, the suitability of a dense high spatial resolution multispectral Formosat-2 satellite image time-series (SITS) to discriminate tree species in temperate forests is investigated. Based on a 17-date SITS acquired across one year, thirteen major tree species (8 broadleaves and 5 conifers) are classified in a study area of southwest France. The performance of parametric (GMM) and nonparametric (k-NN, RF, SVM) methods are compared at three class hierarchy levels for different versions of the SITS: (i) a smoothed noise-free version based on the Whittaker smoother; (ii) a non-smoothed cloudy version including all the dates; (iii) a non-smoothed noise-free version including only 14 dates. Noise refers to pixels contaminated by clouds and cloud shadows. The results of the 108 distinct classifications show a very high suitability of the SITS to identify the forest tree species based on phenological differences (average κ = 0 . 93 estimated by cross-validation based on 1235 field-collected plots). SVM is found to be the best classifier with very close results from the other classifiers. No clear benefit of removing noise by smoothing can be observed. Classification accuracy is even improved using the non-smoothed cloudy version of the SITS compared to the 14 cloud-free image time series. However conclusions of the results need to be considered with caution because of possible overfitting. Disagreements also appear between the maps produced by the classifiers for complex mixed forests, suggesting a higher classification uncertainty in these contexts. Our findings suggest that time-series data can be a good alternative to hyperspectral data for mapping forest types. It also demonstrates the potential contribution of the recently launched Sentinel-2 satellite for studying forest ecosystems.
This study compared three broadband emissivity (BBE) datasets from satellite observations. The first is a new global land surface BBE dataset known as the Global Land Surface Satellite (GLASS) BBE. The other two are the North American ASTER Land Surface Emissivity Database (NAALSED) BBE and University of Wisconsin Global Infrared Land Surface Emissivity Database (UWIREMIS) BBE, which were derived from two independent narrowband emissivity products. Firstly, NAALSED BBE was taken as the reference to evaluate the GLASS BBE and UWIREMIS BBE. The GLASS BBE was more close to NAALSED BBE with a bias and root mean square error (RMSE) of -0.001 and 0.007 for the summer season, -0.001 and 0.008 for the winter season, respectively. Then, the spatial distribution and seasonal pattern of global GLASS BBE and UWIREMIS BBE for six dominant land cover types were compared. The BBE difference between vegetated areas and non-vegetated areas can be easily seen from two BBEs. The seasonal variation of GLASS BBE was more reasonable than that of UWIREMIS BBE. Finally, the time series were calculated from GLASS BBE and UWIREMIS BBE using the data from 2003 through 2010. The periodic variations of GLASS BBE were stronger than those of UWIREMIS BBE. The long time series high quality GLASS BBE can be incorporated in land surface models for improving their simulation results.
The aim of this study was to combine the FAO-56 dual approach and remotely-sensed data for mapping water use (ETc) in irrigated wheat crops of a semi-arid region. The method is based on the relationships established between Normalized Difference Vegetation Index (NDVI) and crop biophysical variables such as basal crop coefficient, cover fraction and soil evaporation. A time series of high spatial resolution SPOT and Landsat images acquired during the 2002/2003 agricultural season has been used to generate the profiles of NDVI in each pixel that have been related to crop biophysical parameters which were used in conjunction with FAO-56 dual source approach. The obtained results showed that the spatial distribution of seasonal ETc varied between 200 and 450 mm depending to sowing date and the development of the vegetation. The validation of spatial results showed that the ETc estimated by FAO-56 corresponded well with actual ET measured by eddy covariance system over test sites of wheat, especially when soil evaporation and plant water stress are not encountered.
ResearchGate has not been able to resolve any references for this publication.