Thermal and spectral remotely sensed data make the monitoring from flux energy variables in the land atmosphere
interface possible. Therefore, remotely sensed data can be used as an alternative to estimate actual evapotranspiration
(ET) by applying the energy balance equation. In order to test the applicability of this approach in Mexico, MODIS
(Moderate Resolution Imaging Spectroradiometer) estimations from land surface variables are used at 16-day intervals of
composite data. Ancillary information is collected from 2000 ground stations. The methodology includes the Simplified
Surface Energy Balance model (SSEB) and its intercomparison with a combined model from the Surface Energy Balance
Algorithm (SEBAL) and the Two Source Energy Balance (TSEB) procedures. Preliminary results applied to one 16-day
interval during winter, 2002, showed that ET is spatially structured at a landscape level. The most significant
discrepancies between estimations are found due to the general assumptions applied to each model. Secondly, the use of
interpolated ancilliary data from local observations, along with remote sensing data, provides a better representation of
spatial variations of ET with SEBAL-TSEB model for the study period. There is not enough evidence to asses
objectively the performance of both applied procedures. Further testing is required to evaluate at a local scale the
reliability from estimations.
Nowadays, it is very common to have readily available remotely-sensed spatial information, at different resolutions,
thanks to the different satellite sensors that are acquiring multispectral images at both low and high resolutions. Fusion
techniques have then arisen as an alternative to integrate this information, which result in new images that contain better
spectral and spatial information in terms of contents and resolution.
Several fusion techniques based on the Wavelet transformation have been developed, in which the "à trous" algorithm
stands out as one of the most important tool that is able to preserve spectral and spatial properties. As an alternative, we
have introduced an algorithm based on an undecimated Hermite transform (HT) that preserves these properties, with
better image quality. In this paper, fused images are analyzed in the framework of biophysical-variables such as leaf-area-
index and sparse-fractional-vegetation-cover, all of them derived from reflectance values in the visible-red and
near-infrared bands, from multi-temporal SPOT-5 images [2005-2007]. Multi-temporal analyses are conducted to test
the consistency of these variables for different illumination conditions, and vegetation amount, in order to determine
indicators of land-cover-change. Results were used to characterize a change vector analysis, by differentiating land
transformation from modifications based on the results with fused and original images. Results also showed how the HT
algorithm resulted in the smallest modification of the bi-dimensional space of the vegetation and soil isolines after
fusion. This method also preserves the information integrity necessitated to obtain similar biophysical variable values.
By improving spatial resolution, while preserving spectral characteristics of the resulting images, the HT-based
algorithm is able to better characterize land-cover-change.
The monitoring of short-term changes in structural characteristics of forests is important to understand mechanisms of vegetation loss that can be associated with deforestation, and illegal logging. These changes, however, should be differentiated from variations in vegetation activity due to interannual variability. Change detection based on thematic information is limited for this purpose because it depends highly on classification accuracies, and it does not allow a quantitative evaluation of biomass loss. The definition of bio-indicators associated with structural characteristics (such as, leaf area index, vegetation fraction) is at present, the only way to monitor such changes. We developed an evaluation system consisting of 4 bio-physical variables, estimated from visible red and near-infrared observations of the Enhanced Thematic Mapper, to monitor changes in forest biomass. The system is based in the application of algorithms to estimate leaf area index, the fractional vegetation cover, leaf vegetation index, and a sparse vegetation cover index, from radiometrically and atmospherically calibrated data. The algorithms were applied to individual scene images acquired during the dry season (April-May) to maximize the forest vegetation signal, and in order to identify areas of change due to changes in forest biomass rather than changes in understory vegetation conditions. The change detection analysis consisted in comparing pixel-by-pixel scenes of such variables, and the results indicated that changes in structural characteristics of forest can be monitored with Landsat-7, being leaf area index, and fractional vegetation cover the most significant in identifying changes along roadsides and population centers that indicate biomass extraction.
Mapping and characterization of forest and vegetation are particularly challenging in urban areas. High resolution imagery is needed for mapping and characterization purposes, due to the areal extent of urban forests, parks and recreational areas. Fusion techniques of panchromatic (1m resolution) and multiband (4m resolution) IKONOS data were used for mapping and characterization of land covering characteristics of urban green areas, allowing the identification of parks, tree areas and fields with a minimal mapping unit of 160 m2. Techniques, that integrate the fine details of the input data into the fused image, are used. Experimental results for different image fusion methods (Laplacian, Gradient pyramids, Principal Component Analysis and Wavelet transform) are also demonstrated in order to improve spatial resolution. Classification of urban areas, mapped with fused data, results in higher accuracies than when using a multiband approach with 4 m data alone. Furthermore, high spatial resolution data permitted to obtain new areal extents of green areas of the city, giving a better estimate of international indicators for a suitable green areas policy. Vegetation indexes derived from red and near infrared data IKONOS are used to evaluate vegetation conditions, which, along with their distribution, location and urban context, resulted in better indicators of green areas.