This paper presents the analysis of a time series of hyperspectral images collected with the Hyperion sensor on board EO-1 to demonstrate how hyperspectral imaging can be used for studying seasonal variations of vegetation cover over the Guánica Dry Forest in Puerto Rico. The approach is based on a local unmixing procedure that splits the hyperspectral scene into tiles and performs endmember extraction on each tile. The main assumption is that within a tile, a single spectral signature is an adequate representation of an endmember. Local endmember signatures from each tile are then clustered to extract endmember classes that better account for endmember spectral variability across the scene and provide a better global description of the full forest scene. Within a scene, abundances are computed using all extracted spectral endmembers and the abundance of an endmember class is computed as the sum of the abundances for the spectral endmembers belonging to that class. Variations in abundance maps are used to understand seasonal changes in forest cover. The procedure was performed using eleven near-cloud-free Hyperion images collected in different months in 2008. Results from the analysis agreed with published knowledge of the phenological changes for this forest. Correlation analyses with NDVI and rainfall time series are used to understand variations in coverage of certain endmember classes with weather. Mangrove was shown to be uncorrelated with rainfall, whereas the upland forest endmember was highly correlated with rain. This study shows the potential for unmixing methods to exploit hyperspectral data for temporal analysis.

Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be effective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI; the normalized difference vegetation index, NDVI; the normalized difference water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ field measurements. The results showed that on bare soil, the indices that best fit the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates significantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.

Various methods and specialized software programs are available for processing two-dimensional gel electrophoresis (2-DGE) images. However, due to the anomalies present in these images, a reliable, automated, and highly reproducible system for 2-DGE image analysis has still not been achieved. The most common anomalies found in 2-DGE images include vertical and horizontal streaking, fuzzy spots, and background noise, which greatly complicate computational analysis. In this paper, we review the preprocessing techniques applied to 2-DGE images for noise reduction, intensity normalization, and background correction. We also present a quantitative comparison of non-linear filtering techniques applied to synthetic gel images, through analyzing the performance of the filters under specific conditions. Synthetic proteins were modeled into a two-dimensional Gaussian distribution with adjustable parameters for changing the size, intensity, and degradation. Three types of noise were added to the images: Gaussian, Rayleigh, and exponential, with signal-to-noise ratios (SNRs) ranging 8–20 decibels (dB). We compared the performance of wavelet, contourlet, total variation (TV), and wavelet-total variation (WTTV) techniques using parameters SNR and spot efficiency. In terms of spot efficiency, contourlet and TV were more sensitive to noise than wavelet and WTTV. Wavelet worked the best for images with SNR ranging 10–20 dB, whereas WTTV performed better with high noise levels. Wavelet also presented the best performance with any level of Gaussian noise and low levels (20–14 dB) of Rayleigh and exponential noise in terms of SNR. Finally, the performance of the non-linear filtering techniques was evaluated using a real 2-DGE image with previously identified proteins marked. Wavelet achieved the best detection rate for the real image.

The transformations in educational environments due to the immersion of information and communication technologies (ICT) make it necessary to analyze the limits and possibilities of the assessment of the virtual training process. This paper presents an analysis of the meanings of ICT-mediated assessment, establishing what kinds of knowledge are suitable for this type of evaluation, and the challenges and possibilities of virtual tools. For this, we present a systematic review of ICT-mediated evaluation and assessment according to the educational paradigms and their implementation. We highlight that contemporary pedagogical models and their implementation in ICT mediation tools show a trend towards quantitative and summative valuation. The commonly used learning management systems (LMS) include several types of questions oriented to quantitative evaluation, with multiple-choice being the most common. However, new technological approaches like gamification, virtual reality and mobile learning open new assessment possibilities. The ICT educational platforms and new technologies demand new skills for all educational actors, such as digital literacy.

This paper presents an unsupervised unmixing approach that takes advantage of multiscale representation based on nonlinear diffusion to integrate the spatial information in the spectral endmembers extraction from a hyperspectral image. The main advantages of unsupervised unmixing based on multiscale representation (UUMR) are the avoidance of matrix rank estimation to determine the number of endmembers and the use of spatial information without employing spatial kernels. Multiscale representation builds a family of smoothed images where locally spectrally uniform regions can be identified. The multiscale representation is extracted solving a nonlinear diffusion partial differential equation (PDE). Locally, homogeneous regions are identified by taking advantage of an algebraic multigrid method used to solve the PDE. Representative spectra for each region are extracted and then clustered to build spectral endmember classes. These classes represent the different spectral components of the image as well as their spectral variability. The number of spectral endmember classes is estimated using the Davies and Bouldin validity index. A quantitative assessment of unmixing approach based on multiscale representation is presented using an AVIRIS image captured over Fort. A.P. Hill, Virginia. A comparison of UUMR results with others unmixing techniques is included.

The avocado cv. Hass requires a suitable rootstock for optimal development under water stress. This study evaluated the performance of two avocado rootstocks (ANRR88 and ANGI52) grafted onto cv. Hass under four water stress conditions, 50% and 25% deficit, and 50% and 25% excess during the nursery stage. Plant height, leaf area (LA), dry matter (DM), and Carbon (OC) content in the roots, stems, and leaves were measured. Root traits were evaluated using digital imaging, and three vegetation indices (NDVI, CI_RE, and MTCI) were used to quantify stress. The results showed that genotype significantly influenced the response to water stress. ANRR88 exhibited adaptation to moderate to high water deficits. ANGI52 adapted better to both water deficit and excess, and showed greater root exploration. LA and DM reductions of up to 60% were observed in ANRR88, suggesting a higher sensitivity to extreme changes in water availability. More than 90% of the total OC accumulation was observed in the stem and roots. The NDVI and the MTCI quantified the presence and levels of stress applied, and the 720 nm band provided high precision and speed for detecting stress. These insights are crucial for selecting rootstocks that ensure optimal performance under varying water availability, enhancing productivity and sustainability.