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.

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.

Phlebotominae spatial distribution asssociated with a focus of tegumentary leishmaniasis in Las Lomitas, Formosa, Argentina, 2002

Las Lomitas, Formosa, Argentina, reported 96 cases of tegumentary leishmaniasis during 2002. The urban transmission was suggested although previous outbreaks were related with floods of the Bermejo river (BR) 50 km from the village. Phlebotomine collections were performed during March 2002 to define the spatial distribution of risk, together with satellite imaginery. The phlebotomine/trap obtained was 1679.5 in the southern BR shore, 1.1 in the periruban-rural environment and 2.3 in the northern Pilcomayo river marshes. Lutzomyia neivai was the prevalent species (91.1%) among the 2393 phlebotomine captured, and it was only found in the BR traps. The other species were L. migonei (7.9%), L. cortelezzii (0.9%), and Brumptomyia guimaraesi (0.1%). The satellite images analysis indicates that the fishing spots at the BR were significantlyoverflowed during the transmission peak, consistent with fishermen recollections. This spatial restricted flood might concentrate vectors, reservoirs, and humans in high places. Therefore, both the spatial distribution of vectors and the sensor remoting data suggests that in Las Lomitas area the higher transmission risk it is still related with the gallery forest of the BR, despite of the urban residence of the cases. The surveillance and control implications of these results are discussed.

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil

Geographical information systems (GIS) are tools that have been recently tested for improving our understanding of the spatial distribution of disease. The objective of this paper was to further develop the GIS technology to model and control schistosomiasis using environmental, social, biological and remote-sensing variables. A final regression model (R² = 0.39) was established, after a variable selection phase, with a set of spatial variables including the presence or absence of Biomphalaria glabrata, winter enhanced vegetation index, summer minimum temperature and percentage of houses with water coming from a spring or well. A regional model was also developed by splitting the state of Minas Gerais (MG) into four regions and establishing a linear regression model for each of the four regions: 1 (R² = 0.97), 2 (R² = 0.60), 3 (R² = 0.63) and 4 (R² = 0.76). Based on these models, a schistosomiasis risk map was built for MG. In this paper, geostatistics was also used to make inferences about the presence of Biomphalaria spp. The result was a map of species and risk areas. The obtained risk map permits the association of uncertainties, which can be used to qualify the inferences and it can be thought of as an auxiliary tool for public health strategies.

The Estrada Real project and endemic diseases: the case of schistosomiasis, geoprocessing and tourism

Geographical Information System (GIS) is a tool that has recently been applied to better understand spatial disease distributions. Using meteorological, social, sanitation, mollusc distribution data and remote sensing variables, this study aimed to further develop the GIS technology by creating a model for the spatial distribution of schistosomiasis and to apply this model to an area with rural tourism in the Brazilian state of Minas Gerais (MG). The Estrada Real, covering about 1,400 km, is the largest and most important Brazilian tourism project, involving 163 cities in MG with different schistosomiasis prevalence rates. The model with three variables showed a R² = 0.34, with a standard deviation of risk estimated adequate for public health needs. The main variables selected for modelling were summer vegetation, summer minimal temperature and winter minimal temperature. The results confirmed the importance of Remote Sensing data and the valuable contribution of GIS in identifying priority areas for intervention in tourism regions which are endemic to schistosomiasis.

A comparative evaluation of endemic and non-endemic region of visceral leishmaniasis (Kala-azar) in India with ground survey and space technology

In visceral leishmaniasis, phlebotomine vectors are targets for control measures. Understanding the ecosystem of the vectors is a prerequisite for creating these control measures. This study endeavours to delineate the suitable locations of Phlebotomus argentipes with relation to environmental characteristics between endemic and non-endemic districts in India. A cross-sectional survey was conducted on 25 villages in each district. Environmental data were obtained through remote sensing images and vector density was measured using a CDC light trap. Simple linear regression analysis was used to measure the association between climatic parameters and vector density. Using factor analysis, the relationship between land cover classes and P. argentipes density among the villages in both districts was investigated. The results of the regression analysis indicated that indoor temperature and relative humidity are the best predictors for P. argentipes distribution. Factor analysis confirmed breeding preferences for P. argentipes by landscape element. Minimum Normalised Difference Vegetation Index, marshy land and orchard/settlement produced high loading in an endemic region, whereas water bodies and dense forest were preferred in non-endemic sites. Soil properties between the two districts were studied and indicated that soil pH and moisture content is higher in endemic sites compared to non-endemic sites. The present study should be utilised to make critical decisions for vector surveillance and controlling Kala-azar disease vectors.

Biotic factors and occurrence of Lutzomyia longipalpis in endemic area of visceral leishmaniasis, Mato Grosso do Sul, Brazil

The relationships between environmental exposure to risk agents and health conditions have been studied with the aid of remote sensing imagery, a tool particularly useful in the study of vegetation cover. This study aims to evaluate the influence of environmental variables on the spatial distribution of the abundance of Lutzomyia longipalpis and the reported canine and human visceral leishmaniasis (VL) cases at an urban area of Campo Grande, state of Mato Grosso do Sul. The sandfly captures were performed in 13 residences that were selected by raffle considering four residences or collection station for buffer. These buffers were generated from the central house with about 50, 100 and 200 m from it in an endemic area of VL. The abundance of sandflies and human and canine cases were georreferenced using the GIS software PCI Geomatica. The normalized difference vegetation index (NDVI) and percentage of land covered by vegetation were the environmental variables extracted from a remote sensing IKONOS-2 image. The average NDVI was considered as the complexity of habitat and the standard deviation as the heterogeneity of habitat. One thousand three hundred sixty-seven specimens were collected during the catch. We found a significant positive linear correlation between the abundance of sandflies and the percentage of vegetation cover and average NDVI. However, there was no significant association between habitat heterogeneity and the abundance of these flies.

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.

The combined use of reflectance, emissivity and elevation Aster/Terra data for tropical soil studies

Reflectance, emissivity and elevation data of the sensor ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)/Terra were used to characterize soil composition variations according to the toposequence position. Normalized data of SWIR (shortwave infrared) reflectance and TIR (thermal infrared) emissivity, coupled to a soil-fraction image from a spectral mixture model, were evaluated to separate bare soils from nonphotosynthetic vegetation. Regression relationships of some soil properties with reflectance and emissivity data were then applied on the exposed soil pixels. The resulting estimated values were plotted on the ASTER-derived digital elevation model. Results showed that the SWIR bands 5 and 6 and the TIR bands 10 and 14 measured the clay mineral absorption band and the quartz emissivity feature, respectively. These bands improved also the discrimination between nonphotosynthetic vegetation and soils. Despite the differences in pixel size and field sampling size, some soil properties were correlated with reflectance (R² of 0.65 for Al2O3 in band 6; 0.61 for Fe2O3 in band 3) and emissivity (R² of 0.65 for total sand fraction in the 10/14 band ratio). The combined use of reflectance, emissivity and elevation data revealed variations in soil composition with topography in specific parts of the landscape. From higher to lower slope positions, a general decrease in Al2O3 and increase in total sand fraction was observed, due to the prevalence of Rhodic Acrustox at the top and its gradual transition to Typic Acrustox at the bottom.

Soil spectral library and its use in soil classification

Soil science has sought to develop better techniques for the classification of soils, one of which is the use of remote sensing applications. The use of ground sensors to obtain soil spectral data has enabled the characterization of these data and the advancement of techniques for the quantification of soil attributes. In order to do this, the creation of a soil spectral library is necessary. A spectral library should be representative of the variability of the soils in a region. The objective of this study was to create a spectral library of distinct soils from several agricultural regions of Brazil. Spectral data were collected (using a Fieldspec sensor, 350-2,500 nm) for the horizons of 223 soil profiles from the regions of Matão, Paraguaçu Paulista, Andradina, Ipaussu, Mirandópolis, Piracicaba, São Carlos, Araraquara, Guararapes, Valparaíso (SP); Naviraí, Maracajú, Rio Brilhante, Três Lagoas (MS); Goianésia (GO); and Uberaba and Lagoa da Prata (MG). A Principal Component Analysis (PCA) of the data was then performed and a graphic representation of the spectral curve was created for each profile. The reflectance intensity of the curves was principally influenced by the levels of Fe2O3, clay, organic matter and the presence of opaque minerals. There was no change in the spectral curves in the horizons of the Latossolos, Nitossolos, and Neossolos Quartzarênicos. Argissolos had superficial horizon curves with the greatest intensity of reflection above 2,200 nm. Cambissolos and Neossolos Litólicos had curves with greater reflectance intensity in poorly developed horizons. Gleisols showed a convex curve in the region of 350-400 nm. The PCA was able to separate different data collection areas according to the region of source material. Principal component one (PC1) was correlated with the intensity of reflectance samples and PC2 with the slope between the visible and infrared samples. The use of the Spectral Library as an indicator of possible soil classes proved to be an important tool in profile classification.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

ABSTRACT In recent years, geotechnologies as remote and proximal sensing and attributes derived from digital terrain elevation models indicated to be very useful for the description of soil variability. However, these information sources are rarely used together. Therefore, a methodology for assessing and specialize soil classes using the information obtained from remote/proximal sensing, GIS and technical knowledge has been applied and evaluated. Two areas of study, in the State of São Paulo, Brazil, totaling approximately 28.000 ha were used for this work. First, in an area (area 1), conventional pedological mapping was done and from the soil classes found patterns were obtained with the following information: a) spectral information (forms of features and absorption intensity of spectral curves with 350 wavelengths -2,500 nm) of soil samples collected at specific points in the area (according to each soil type); b) obtaining equations for determining chemical and physical properties of the soil from the relationship between the results obtained in the laboratory by the conventional method, the levels of chemical and physical attributes with the spectral data; c) supervised classification of Landsat TM 5 images, in order to detect changes in the size of the soil particles (soil texture); d) relationship between classes relief soils and attributes. Subsequently, the obtained patterns were applied in area 2 obtain pedological classification of soils, but in GIS (ArcGIS). Finally, we developed a conventional pedological mapping in area 2 to which was compared with a digital map, ie the one obtained only with pre certain standards. The proposed methodology had a 79 % accuracy in the first categorical level of Soil Classification System, 60 % accuracy in the second category level and became less useful in the categorical level 3 (37 % accuracy).

Radarsat backscattering from an agricultural scene

Orbital remote sensing in the microwave electromagnetic region has been presented as an important tool for agriculture monitoring. The satellite systems in operation have almost all-weather capability and high spatial resolution, which are features appropriated for agriculture. However, for full exploration of these data, an understanding of the relationships between the characteristics of each system and agricultural targets is necessary. This paper describes the behavior of backscattering coefficient (sigma°) derived from calibrated data of Radarsat images from an agricultural area. It is shown that in a dispersion diagram of sigma° there are three main regions in which most of the fields can be classified. The first one is characterized by low backscattering values, with pastures and bare soils; the second one has intermediate backscattering coefficients and comprises well grown crops mainly; and a third one, with high backscattering coefficients, in which there are fields with strong structures causing a kind of double bounce effect. The results of this research indicate that the use of Radarsat images is optimized when a multitemporal analysis is done making the best use of the agricultural calendar and of the dynamics of different cultures.

Characterization and discrimination of soils by their reflected electromagnetic energy

The objective of this work was to verify if reflected energy of soils can characterize and discriminate them. A spectroradiometer (Spectral reflectance between: 400-2,500 nm) was utilized in laboratory. The soils evaluated are located in Bauru region, SP, Brazil, and are classified as Typic Argiudoll (TR), Typic Eutrorthox (LR), Typic Argiudoll (PE), Typic Haplortox (LE), Typic Paleudalf (PV) and Typic Quartzipsamment (AQ). They were characterized by their spectral reflectance as for descriptive conventional methods (Brazilian and International) according to the types of spectral curves. A method for the spectral descriptive evaluation of soils was established. It was possible to characterize and discriminate the soils by their spectral reflectance, with exception for LR and TR. The spectral differences were better identified by the general shape of spectral curves, by the intensity of band absorption and angle tendencies. These characteristics were mainly influenced by organic matter, iron, granulometry and mineralogy constituents. A reduction of iron and clay contents, which influenced higher reflectance intensity and shape variations, occurred on the soils LR/TR, PE, LE, PV and AQ, on that sequence. Soils of the same group with different clay textures could be discriminated. The conventional descriptive evaluation of spectral curves was less efficient on discriminating soils. Simulated orbital data discriminated soils mainly by bands 5 and 7.

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.