Identification and characterization of deforestation hot spots in Venezuela using MODIS satellite images

Land cover changes over time as a result of human activity. Nowadays deforestation may be considered one of the main environmental problems. The objective of this study was to identify and characterize changes to forest cover in Venezuela between 2005-2010. Two maps of deforestation hot spots were generated on the basis of MODIS data, one using digital techniques and the other by means of direct visual interpretation by experts. These maps were validated against Landsat ETM+ images. The accuracy of the map obtained digitally was estimated by means of a confusion matrix. The overall accuracy of the maps obtained digitally was 92.5%. Expert opinions regarding the hot spots permitted the causes of deforestation to be identified. The main processes of deforestation were concentrated to the north of the Orinoco River, where 8.63% of the country's forests are located. In this region, some places registered an average annual forest change rate of between 0.72% and 2.95%, above the forest change rate for the country as a whole (0.61%). The main causes of deforestation for the period evaluated were agricultural and livestock activities (47.9%), particularly family subsistence farming and extensive farming which were carried out in 94% of the identified areas.

Multi-temporal analysis of radiometric changes in satellite images of forest edges to infer selective-logging areas in the Amazon forest

Radiometric changes observed in multi-temporal optical satellite images have an important role in efforts to characterize selective-logging areas. The aim of this study was to analyze the multi-temporal behavior of spectral-mixture responses in satellite images in simulated selective-logging areas in the Amazon forest, considering red/near-infrared spectral relationships. Forest edges were used to infer the selective-logging infrastructure using differently oriented edges in the transition between forest and deforested areas in satellite images. TM/Landsat-5 images acquired at three dates with different solar-illumination geometries were used in this analysis. The method assumed that the radiometric responses between forest with selective-logging effects and forest edges in contact with recent clear-cuts are related. The spatial frequency attributes of red/near infrared bands for edge areas were analyzed. Analysis of dispersion diagrams showed two groups of pixels that represent selective-logging areas. The attributes for size and radiometric distance representing these two groups were related to solar-elevation angle. The results suggest that detection of timber exploitation areas is limited because of the complexity of the selective-logging radiometric response. Thus, the accuracy of detecting selective logging can be influenced by the solar-elevation angle at the time of image acquisition. We conclude that images with lower solar-elevation angles are less reliable for delineation of selecting logging.

Reproducibility of Left Ventricular Mass by Echocardiogram in the ELSA-Brasil

Background: Echocardiography, though non-invasive and having relatively low-cost, presents issues of variability which can limit its use in epidemiological studies. Objective: To evaluate left ventricular mass reproducibility when assessed at acquisition (online) compared to when assessed at a reading center after electronic transmission (offline) and also when assessed by different readers at the reading center. Methods: Echocardiographers from the 6 ELSA-Brasil study investigation centers measured the left ventricular mass online during the acquisition from 124 studies before transmitting to the reading center, where studies were read according to the study protocol. Half of these studies were blindly read by a second reader in the reading center. Results: From the 124 echocardiograms, 5 (4%) were considered not measurable. Among the remaining 119, 72 (61%) were women, mean age was 50.2 ± 7.0 years and 2 had structural myocardial abnormalities. Images were considered to be optimal/ good by the reading center for 110 (92.4%) cases. No significant difference existed between online and offline measurements (1,29 g, CI 95% −3.60-6.19), and the intraclass correlation coefficient between them was 0.79 (CI 95% 0.71-0.85). For images read by two readers, the intraclass correlation coefficient was 0.86 (CI 95% 0.78-0.91). Conclusion: There were no significant drifts between online and offline left ventricular mass measurements, and reproducibility was similar to that described in previous studies. Central quantitative assessment of echocardiographic studies in reading centers, as performed in the ELSA-Brasil study, is feasible and useful in clinical and epidemiological studies performed in our setting.

Ancient medical texts, modern reading problems

The word tradition has a very specific meaning in linguistics: the passing down of a text, which may have been completed or corrected by different copyists at different times, when the concept of authorship was not the same as it is today. When reading an ancient text the word tradition must be in the reader's mind. To discuss one of the problems an ancient text poses to its modern readers, this work deals with one of the first printed medical texts in Portuguese, the Regimento proueytoso contra ha pestenença, and draws a parallel between it and two related texts, A moche profitable treatise against the pestilence, and the Recopilaçam das cousas que conuem guardar se no modo de preseruar à Cidade de Lixboa E os sãos, & curar os que esteuerem enfermos de Peste. The problems which arise out of the textual structure of those books show how difficult is to establish a tradition of another type, the medical tradition. The linguistic study of the innumerable medieval plague treatises may throw light on the continuities and on the disruptions of the so-called hippocratic-galenical medical tradition.

Comparison of citrus rooting evaluation methods using root images in soil profiles and root weight

A digitized image method was compared with a standard washing technique for measuring citrus roots in the field. Video pictures of roots were taken in a soil profile. The profile area analyzed was defined by iron rings, which were also used to remove the roots to determine their dry weight. The roots presented in the pictures were quantified using SIARCS software developed by Embrapa. The root length and area determined by digital images provided a good estimate of root quantity present in the profile.

Description of a pure seed fraction of oat through usual evaluations and radiographic images

The objective of this work was to evaluate the width and length incidence in a single seed fraction of oat [Avena sativa (L.)] cv. Cristal. The seeds were selected by a mechanical divider and by hand, and their correspondence to radiographic images in seeds with glumes and their caryopses. The width and length of the seeds with glumes and their caryopses were measured with electronic calliper, and their weight, with precision balance. Radiographic images of seeds with glumes were taken with an X-ray experimental equipment. The analyst selected seeds with glumes by the width and by the length previously determined and so with more weight, than that obtained by hand selection was slightly narrower, larger and lighter. The presence of the glumes masked the caryopses real dimensions (width and length), and conduced the analyst to select seeds that differed more by the width than by the length. The radiographic images showed the presence, or not, of caryopses inside the seed and its real dimensions. The mechanical partition method for seeds showed to be more efficient because the analyst subjectivity was not considered when the selection upon its dimensions was done. The X-ray analysis was a useful tool that complements the pure seed fraction selection as another factor of seed quality.

Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil

The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.