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Method: In this retrospective study, the reliability analyses were conducted with paired t-tests considering a short (mean 14d) and a long (mean 120d) time in between two assessment periods. In addition, an intraclass correlation coefficient (ICC) was used to assess the level of congruency. The responsiveness to therapy was conducted with a paired t-test in the whole sample regarding the age, the manual ability level as classified with the Manual Ability Classification System (MACS), and the topography.

Results: Our main results confirmed the tests' reliability in a short time period for the JTTHF in both hands and for the BBT on the less affected hand. These results were consistent with the ICC. The responsiveness was confirmed, except on the less affected hand for the JTTHF, with similar results for age, MACS, and topography approach.

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We compared spectral density profiles and the ability to discriminate between SWS and REM across (A) the raw signal, (B) the independent component (IC) that maximally expresses brain activity, and (C) the raw signals pruned with ICs that minimally express brain activity (e.g., heart signals or electrical noise). We determined the maximal brain IC visually by selecting the IC that (1) allowed visual and quantitative discrimination between SWS and REM and (2) was generated by one of four EEG electrode locations in 2D topographic maps. We always kept the intact ECG channel (without ECG IC(s) removed) and the IC that maximally expressed heart activity for heart rate analysis. We determined the maximal heart IC by locating the highest amplitude ECG signal generated by the posterior location in 2D topographic maps. Because the ECG waveform was often separated into two or more ICs, the ECG channel often yielded the cleanest heart rate signal.

After running ICA on a subset of our data while the animal was stationary in the water, we applied those weights to entire recordings and inspected the resulting ICs. In all cases, we were able to identify ICs that maximally expressed contaminating artifacts (e.g., IC1, IC2, and IC9 in Fig. 10B) and one IC that maximally expressed brain activity (e.g., IC5 in Fig. 10B). The maximal heart ICs were identified visually as containing recognizable ECG components (e.g., IC1 & IC2 in Fig. 10B) and confirmed in topographic maps (Fig. 10A and C). Any identifiable contaminating electrical signals were removed (constant frequency; e.g., IC9 in Fig. 10B). We visually identified the IC that maximally expressed brain activity (IC5) as the one with distinct slow waves during SWS and low-voltage activity during REM. Maximal brain components were then confirmed with topographic maps relating to the four EEG electrodes (Fig. 10D).

Abstract: Assessing the status of tropical dry forest habitats using remote sensing technologies is one of the research priorities for Neotropical forests. We developed a simple method for mapping vegetation and habitats in a tropical dry forest reserve, Mona Island, Puerto Rico, by integrating the Normalized Difference vegetation Index (NDvI) from Landsat, topographic information, and high-resolution Ikonos imagery. The method was practical for identifying vegetation types in areas with a great variety of plant communities and complex relief, and can be adapted to other dry forest habitats of the Caribbean Islands. NDvI was useful for identifying the distribution of forests, woodlands, and shrubland, providing a natural representation of the vegetation patterns on the island. The use of Ikonos imagery allowed increasing the number of land cover classes. As a result, sixteen land-cover types were mapped over the 5 500 ha area, with a kappa coefficient of accuracy equal to 79 %. This map is a central piece for modeling vertebrate species distribution and biodiversity patterns by the Puerto Rico Gap Analysis Project, and it is of great value for assisting research and management actions in the island. Rev. Biol. Trop. 56 (2): 625-639. Epub 2008 June 30.

Another problem for mapping tropical vegetation using satellite data is the spectral confusion, in which more than one vegetation type shows similar spectral responses. Segmenting the image into regions with potentially different vegetation using variables such as topography, temperature, rainfall, substrate, etc, has shown its value for alleviating this problem (Vogelman et al. 1998, Helmer et al. 2002). In the case of Mona, relief is the principal variable explaining the distribution of the plant communities (Cintron and Rogers 1991).

In this paper we evaluate the use of Landsat NDVI, topographic information, and high-resolution remotely sensed data for mapping land cover and habitats on Mona Island, and we evaluated how this technology can be applied to other regions.

Additional information included a previous vegetation map by Cintrn and Rogers (1991) made by visual interpretation of blackand-white aerial photos from the 1960s and 1970s, and digitized by Ramos (2004) at a scale 1:35.000. ADD structural parameters. We also used a layer of the depression forests developed from the Ikonos as part of ongoing research of vegetation in Mona (Martinez et al. 2005), but we only considered depressions larger than the Landsat pixel. Although Cintrn and Roger (1991) vegetation map did not overlay well with the rest of the data, it was still functional for visually interpreting the vegetation patterns and the topography of the island. Finally, we used the Sensitivity of Coastal and Inland Resources to Spilled Oil Atlas for Puerto Rico (NOAA et al. 2000) to classify the coastal shorelines. Remote sensing analysis was conducted using ERDAS 8.7 software (Leica Geosystems GIS & Mapping LLC).

First, we segmented the island into regions with distinctive groups of vegetation. For this, we developed a map of the landforms of Mona, including the plateau, cliffs, and coastal plain by visual interpretation of the Ikonos imagery and assisted by the previous land cover map from Cintrn and Rogers (1991) (we used this map as a general guide for interpreting the landforms, but the final interpretation and digitalization was conducted on the Ikonos imagery). We previously evaluated the 30-meter digital elevation model (DEM) available for Mona (from USGS), but the resolution was no adequate for capturing the strong topographic changes, over shot distances, that occur in Mona.

Finally, we analyzed the vegetation-NDVI samples within each topographic unit in order to identify ranges in NDVI that correspond to the specific vegetation classes. In case of NDVI values shared by different plant communities, we resolved to combine the classes into mixed or inclusive units if the Ikonos wouldnt allow us to separate them visually. For separating the plant communities in the cliffs, where no samples were collected, we used the same NDVI ranges as in the plateau, since the vegetation in the cliffs (including the tree density) is more similar to the platform than to the coastal lowland (Table 1). Ultimately, we used the Ikonos imagery to add by visual interpretation the mangrove patch, few built-up pixels (field station facilities and a light house), small patches of grasses, the two types of shorelines, and the areas covered by clouds in the Landsat data.

Combining Landsat NDVI, topographic information, and high-resolution imagery, represented a valuable tool for mapping tropical dry forest habitats. Previous studies have shown the importance of the NDvI for characterizing different structural aspects of tropical dry forests (Oza et al. 1996, Sanches-Aofelia et al. 2003, Krishnawsamy et al. 2004, Feeley et al. 2005, Gillespie et al. 2006); nevertheless, in areas with a great variability in vegetation and a strong influence of relief such as Mona, NDVI alone was not sufficient to separate the entire collection of plant communities. For example, lowland forests and some platform forests showed similar NDVI values, making it impossible to separate them from a spectral basis. As a result, topographic information was needed first in order to segment the island into regions with distinct vegetation, corresponding to the plateau, cliffs, and coastal plain. NDVI was used within each of these units and allowed to separate the major groups of vegetation, including forests, woodlands, shrubland, and some grasses, by capturing their differences in "green" structure and canopy closure. NDVI was practical even if the extent of the plant communities allowed for the identification of just few samples. The different classes of vegetation exhibited a variable range of NDVI values (mean and std. dev.), reflecting internal variations in structure that have been associated with changes in micro-relief or soil depth (Cintrn and Rogers 1991). In the case of low growing vegetation, including grasses and dwarf shrub, the presence of bare rock and soil might enhanced this variability.

The methods developed in this study can be applied to other tropical dry-forest regions when mapping of land cover and habitats is desired. Although the need of auxiliary information (topographic and high-resolution imagery) could limit the possibilities, this concern can be addressed in diverse ways depending on the study area, level of mapping desired, and available resources. In Mona, topography is the principal variable explaining the distribution of vegetation (Cintrn and Rogers 1991), but in other places variables such as geology or land use history may be better used. For areas where topography is important for vegetation, information can be derived from digital elevation models (DEMs), using the slope, elevation, aspect, or combination of them (Manis et al. 2001, Martinuzzi el al. 2007a). Unfortunately, we found that the spatial resolution of the 30 m DEM for Mona was not adequate for separating the narrow cliffs of Mona; and because the topographic changes in the island are exceptionally strong, it was easier to interpret the landforms from Ikonos data. On the other hand, high-resolution imagery might be specially needed if a detailed mapping and habitat classification is desired. Although aerial photographs can be used instead of Ikonos, for new acquisitions, Ikonos data has proved to be of greater value due to the lower cost coordination effort required in the collection, and the high temporal resolution that might help finding images with low cloud cover (Morisette et al. 2003). In addition, Ikonos data is available globally, while some areas not covered by aerial survey companies. Finally, while the presence of clouds is a common problem of optical remote sensing in tropical areas, dryforest regions are characterized by a lower presence of clouds, which might facilitate the acquisition of cloud-free scenes (or circa). In addition, new approaches have been developed to produce cloud-free images in tropical landscapes shedding a new light to the cloud problem (Helmer and Ruefenacht 2005, Martinuzzi et al. 2007b). be457b7860