Estimation of surface roughness in a semiarid region from C-band ERS-1 synthetic aperture radar data

In this study, we investigated the feasibility of using the C-band European Remote Sensing Satellite (ERS-1) synthetic aperture radar (SAR) data to estimate surface soil roughness in a semiarid rangeland. Radar backscattering coefficients were extracted from a dry and a wet season SAR image and were compared with 47 in situ soil roughness measurements obtained in the rocky soils of the Walnut Gulch Experimental Watershed, southeastern Arizona, USA. Both the dry and the wet season SAR data showed exponential relationships with root mean square (RMS) height measurements. The dry C-band ERS-1 SAR data were strongly correlated (R² = 0.80), while the wet season SAR data have somewhat higher secondary variation (R² = 0.59). This lower correlation was probably provoked by the stronger influence of soil moisture, which may not be negligible in the wet season SAR data. We concluded that the single configuration C-band SAR data is useful to estimate surface roughness of rocky soils in a semiarid rangeland.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Schistosomiasis risk mapping in the state of Minas Gerais, Brazil, using a decision tree approach, remote sensing data and sociological indicators

Schistosomiasis mansoni is not just a physical disease, but is related to social and behavioural factors as well. Snails of the Biomphalaria genus are an intermediate host for Schistosoma mansoni and infect humans through water. The objective of this study is to classify the risk of schistosomiasis in the state of Minas Gerais (MG). We focus on socioeconomic and demographic features, basic sanitation features, the presence of accumulated water bodies, dense vegetation in the summer and winter seasons and related terrain characteristics. We draw on the decision tree approach to infection risk modelling and mapping. The model robustness was properly verified. The main variables that were selected by the procedure included the terrain's water accumulation capacity, temperature extremes and the Human Development Index. In addition, the model was used to generate two maps, one that included risk classification for the entire of MG and another that included classification errors. The resulting map was 62.9% accurate.

Structural changes in latosols of the cerrado region: II - soil compressive behavior and modeling of additional compaction

Currently in Brazil, as in other parts of the world, the concern is great with the increase of degraded agricultural soil, which is mostly related to the occurrence of soil compaction. Although soil texture is recognized as a very important component in the soil compressive behaviors, there are few studies that quantify its influence on the structural changes of Latosols in the Brazilian Cerrado region. This study aimed to evaluate structural changes and the compressive behavior of Latosols in Rio Verde, Goiás, through the modeling of additional soil compaction. The study was carried out using five Latosols with very different textures, under different soil compaction levels. Water retention and soil compression curves, and bearing capacity models were determined from undisturbed samples collected on the B horizons. Results indicated that clayey and very clayey Latosols were more susceptible to compression than medium-textured soils. Soil compression curves at density values associate with edaphic functions were used to determine the beneficial pressure (σ b) , i.e., pressure with optimal water retention, and critical pressure (σcrMAC), i.e., pressure with macroporosity below critical levels. These pressure values were higher than the preconsolidation pressure (σp), and therefore characterized as additional compaction. Based on the compressive behavior of these Latosols, it can be concluded that the combined preconsolidation pressure, beneficial pressure and critical pressure allow a better understanding of compression processes of Latosols.

Estimation of soil moisture in the root-zone from remote sensing data

Field-based soil moisture measurements are cumbersome. Thus, remote sensing techniques are needed because allows field and landscape-scale mapping of soil moisture depth-averaged through the root zone of existing vegetation. The objective of the study was to evaluate the accuracy of an empirical relationship to calculate soil moisture from remote sensing data of irrigated soils of the Apodi Plateau, in the Brazilian semiarid region. The empirical relationship had previously been tested for irrigated soils in Mexico, Egypt, and Pakistan, with promising results. In this study, the relationship was evaluated from experimental data collected from a cotton field. The experiment was carried out in an area of 5 ha with irrigated cotton. The energy balance and evaporative fraction (Λ) were measured by the Bowen ratio method. Soil moisture (θ) data were collected using a PR2 - Profile Probe (Delta-T Devices Ltd). The empirical relationship was tested using experimentally collected Λ and θ values and was applied using the Λ values obtained from the Surface Energy Balance Algorithm for Land (SEBAL) and three TM - Landsat 5 images. There was a close correlation between measured and estimated θ values (p<0.05, R² = 0.84) and there were no significant differences according to the Student t-test (p<0.01). The statistical analyses showed that the empirical relationship can be applied to estimate the root-zone soil moisture of irrigated soils, i.e. when the evaporative fraction is greater than 0.45.

Multispectral remote sensing for site-specific nitrogen fertilizer management

The objective of this work was to evaluate the use of multispectral remote sensing for site-specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn-planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (Savi), optimized soil-adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R²=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0-2.5 kg), medium (2.5-3 kg), and high (3-3.3 kg).

Compressive neuropathy of the first branch of the lateral plantar nerve: a study by magnetic resonance imaging

Abstract Objective: To assess the prevalence of isolated findings of abnormalities leading to entrapment of the lateral plantar nerve and respective branches in patients complaining of chronic heel pain, whose magnetic resonance imaging exams have showed complete selective fatty atrophy of the abductor digiti quinti muscle. Materials and Methods: Retrospective, analytical, and cross-sectional study. The authors selected magnetic resonance imaging of hindfoot of 90 patients with grade IV abductor digiti quinti muscle atrophy according to Goutallier and Bernageau classification. Patients presenting with minor degrees of fatty muscle degeneration (below grade IV) and those who had been operated on for nerve decompression were excluded. Results: A female prevalence (78.8%) was observed, and a strong correlation was found between fatty muscle atrophy and plantar fasciitis in 21.2%, and ankle varices, in 16.8% of the patients. Conclusion: Fatty atrophy of the abductor digiti quinti muscle is strongly associated with neuropathic alterations of the first branch of the lateral plantar nerve. The present study showed a significant association between plantar fasciitis and ankle varices with grade IV atrophy of the abductor digiti quinti muscle.

ANION-BINDING AND SENSING PROPERTIES OF NOVEL RECEPTORS BASED ON N-(INDOL-3-YLGLYOXYLYL)BENZYLAMINE

Indole-based receptors such as biindole, carbazole, and indolocarbazole are regarded as some of the most favorable anion receptors in molecular recognition. This is because indole groups possess N–H groups as hydrogen-bonding donors. The introduction of amide groups in the indole framework can induce strong binding properties and good water solubility. In this study, we designed and synthesized a series of N-(indol-3-ylglyoxylyl)benzylamine derivatives as novel and simple anion receptors. The receptors derived by aryl and aliphatic amines can selectively recognize F– based on a color change from colorless-to-yellow in DMSO. The receptors derived by hydrazine hydrate can recognize F–, AcO–, and H2PO4– by similar color changes in DMSO and can even enable the selective recognition of F– in a DMSO–H2O binary solution by the naked eye. Spectrographic data indicate that complexes are formed between receptors and anions through multiple hydrogen-bonding interactions in dual solutions.

Using an aerial system of remote sensing to detect different nutritional status in Brachiaria decumbens

The aim of this study was to use digital images acquired by cameras attached to a helium balloon to detect variation of the nutritional status in Brachiaria decumbens. The treatments consisted of five doses of nitrogen (0, 50, 100, 150 e 200kg ha-1) with six replications each, evaluated in a completely randomized statistical design. A remote sensing system composed of digital cameras and microcomputers was used for image acquisition, and a helium balloon lifted the cameras to the heights of 15, 20, 25 and 30m. A portable chlorophyll meter and analyses of leaf nitrogen content were used to make comparisons with data obtained by the remote sensing system. Data was acquired in two phases, in different climatic conditions. At the end of each phase, dry matter production was measured. Three vegetation indices were used to evaluate the detection of different nutritional status. The three indices were able to detect the effects of N doses. The indices constructed with the Green spectral band showed to be more efficient.

Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%.

The use of ALOS/PALSAR data for estimating sugarcane productivity

Some models have been developed using agrometeorological and remote sensing data to estimate agriculture production. However, it is expected that the use of SAR images can improve their performance. The main objective of this study was to estimate the sugarcane production using a multiple linear regression model which considers agronomic data and ALOS/PALSAR images obtained from 2007/08, 2008/09 and 2009/10 cropping seasons. The performance of models was evaluated by coefficient of determination, t-test, Willmott agreement index (d), random error and standard error. The model was able to explain 79%, 12% and 74% of the variation in the observed productions of the 2007/08, 2008/09 and 2009/10 cropping seasons, respectively. Performance of the model for the 2008/09 cropping season was poor because of the occurrence of a long period of drought in that season. When the three seasons were considered all together, the model explained 66% of the variation. Results showed that SAR-based yield prediction models can contribute and assist sugar mill technicians to improve such estimates.

Extracellular calcium-sensing receptor: structural and functional features and association with diseases

The recently cloned extracellular calcium-sensing receptor (CaR) is a G protein-coupled receptor that plays an essential role in the regulation of extracellular calcium homeostasis. This receptor is expressed in all tissues related to this control (parathyroid glands, thyroid C-cells, kidneys, intestine and bones) and also in tissues with apparently no role in the maintenance of extracellular calcium levels, such as brain, skin and pancreas. The CaR amino acid sequence is compatible with three major domains: a long and hydrophilic aminoterminal extracellular domain, where most of the activating and inactivating mutations described to date are located and where the dimerization process occurs, and the agonist-binding site is located, a hydrophobic transmembrane domain involved in the signal transduction mechanism from the extracellular domain to its respective G protein, and a carboxyterminal intracellular tail, with a well-established role for cell surface CaR expression and for signal transduction. CaR cloning was immediately followed by the association of genetic human diseases with inactivating and activating CaR mutations: familial hypocalciuric hypercalcemia and neonatal severe hyperparathyroidism are caused by CaR-inactivating mutations, whereas autosomal dominant hypoparathyroidism is secondary to CaR-activating mutations. Finally, we will comment on the development of drugs that modulate CaR function by either activating (calcimimetic drugs) or antagonizing it (calcilytic drugs), and on their potential therapeutic implications, such as medical control of specific cases of primary and uremic hyperparathyroidism with calcimimetic drugs and a potential treatment for osteoporosis with a calcilytic drug.

An acid-sensing ion channel that detects ischemic pain

Ischemic pain occurs when there is insufficient blood flow for the metabolic needs of an organ. The pain of a heart attack is the prototypical example. Multiple compounds released from ischemic muscle likely contribute to this pain by acting on sensory neurons that innervate muscle. One such compound is lactic acid. Here, we show that ASIC3 (acid-sensing ion channel #3) has the appropriate expression pattern and physical properties to be the detector of this lactic acid. In rats, it is expressed only in sensory neurons and then only on a minority (~40%) of these. Nevertheless, it is expressed at extremely high levels on virtually all dorsal root ganglion sensory neurons that innervate the heart. It is extraordinarily sensitive to protons (Hill slope 4, half-activating pH 6.7), allowing it to readily respond to the small changes in extracellular pH (from 7.4 to 7.0) that occur during muscle ischemia. Moreover, both extracellular lactate and extracellular ATP increase the sensitivity of ASIC3 to protons. This final property makes ASIC3 a "coincidence detector" of three molecules that appear during ischemia, thereby allowing it to better detect acidosis caused by ischemia than other forms of systemic acidosis such as hypercapnia.

Anti-quorum sensing activity of phenolic extract from Eugenia brasiliensis (Brazilian cherry)

Abstract The aim of this study was to assess the anti-quorum sensing activity of phenolic extracts from grumixama (Eugenia brasiliensis), also known as Brazilian cherry, in concentrations that did not interfere with bacterial growth. The pulp phenolic compounds were extracted by using solid phage extraction in a mini-collumn C18 and quantified by spectrophotometry. The anti-quorum sensing activity was evaluated by testing the inhibition of violacein production in Chromobacterium violaceum and by evaluating the swarming motility in Aeromonas hydrophila and Serratia marcescens, both phenotypes regulated by quorum sensing. The phenolic extract strongly inhibited the production of violacein in C. violaceum, reducing its production in comparison with a control with no extract. No inhibition of growth was observed at the concentrations tested for quorum sensing inhibition. Confirming the quorum sensing inhibition phenotype, the extract was also able to inhibit swarming motility in S. marcescens and in A. hydrophila, although in the later the effect was marginal. Overall, these results indicate that phenolic extract from E. brasiliensis presents quorum sensing inhibitory activity most likely due to the presence of fruit phenolics which have been implicated as quorum sensing inhibitors in Gram negative bacteria.