Spatial patterns of malaria in a land reform colonization project, Juruena municipality, Mato Grosso, Brazil

Background: In Brazil, 99% of malaria cases are concentrated in the Amazon, and malaria's spatial distribution is commonly associated with socio-environmental conditions on a fine landscape scale. In this study, the spatial patterns of malaria and its determinants in a rural settlement of the Brazilian agricultural reform programme called ""Vale do Amanhecer"" in the northern Mato Grosso state were analysed. Methods: In a fine-scaled, exploratory ecological study, geocoded notification forms corresponding to malaria cases from 2005 were compared with spectral indices, such as the Normalized Difference Vegetation Index (NDVI) and the third component of the Tasseled Cap Transformation (TC_3) and thematic layers, derived from the visual interpretation of multispectral TM-Landsat 5 imagery and the application of GIS distance operators. Results: Of a total of 336 malaria cases, 102 (30.36%) were caused by Plasmodium falciparum and 174 (51.79%) by Plasmodium vivax. Of all the cases, 37.6% (133 cases) were from residents of a unique road. In total, 276 cases were reported for the southern part of the settlement, where the population density is higher, with notification rates higher than 10 cases per household. The local landscape mostly consists of open areas (38.79 km(2)). Training forest occupied 27.34 km(2) and midsize vegetation 7.01 km(2). Most domiciles with more than five notified malaria cases were located near areas with high NDVI values. Most domiciles (41.78%) and malaria cases (44.94%) were concentrated in areas with intermediate values of the TC_3, a spectral index representing surface and vegetation humidity. Conclusions: Environmental factors and their alteration are associated with the occurrence and spatial distribution of malaria cases in rural settlements.

Utilização de imagem LISS III para análise de espaços verdes em Lisboa

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Leishmaniasis transmission: distribution and coarse-resolution ecology of two vectors and two parasites in Egypt

Introduction: In past decades, leishmaniasis burden has been low across Egypt; however, changing environment and land use has placed several parts of the country at risk. As a consequence, leishmaniasis has become a particularly difficult health problem, both for local inhabitants and for multinational military personnel. Methods: To evaluate coarse-resolution aspects of the ecology of leishmaniasis transmission, collection records for sandflies and Leishmania species were obtained from diverse sources. To characterize environmental variation across the country, we used multitemporal Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for 2005-2011. Ecological niche models were generated using MaxEnt, and results were analyzed using background similarity tests to assess whether associations among vectors and parasites (i.e., niche similarity) can be detected across broad geographic regions. Results: We found niche similarity only between one vector species and its corresponding parasite species (i.e., Phlebotomus papatasi with Leishmania major), suggesting that geographic ranges of zoonotic cutaneous leishmaniasis and its potential vector may overlap, but under distinct environmental associations. Other associations (e.g., P. sergenti with L. major) were not supported. Mapping suitable areas for each species suggested that northeastern Egypt is particularly at risk because both parasites have potential to circulate. Conclusions: Ecological niche modeling approaches can be used as a first-pass assessment of vector-parasite interactions, offering useful insights into constraints on the geography of transmission patterns of leishmaniasis.

Deteção remota na avaliação das alterações da cobertura do solo no Parque Nacional da Peneda-Gerês

Dissertação de mestrado integrado em Engenharia Civil

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.

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.

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

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).

Analysis of vineyard differential management zones and relation to vine development, grape maturity and quality

The objective of research was to analyse the potential of Normalized Difference Vegetation Index (NDVI) maps from satellite images, yield maps and grapevine fertility and load variables to delineate zones with different wine grape properties for selective harvesting. Two vineyard blocks located in NE Spain (Cabernet Sauvignon and Syrah) were analysed. The NDVI was computed from a Quickbird-2 multi-spectral image at veraison (July 2005). Yield data was acquired by means of a yield monitor during September 2005. Other variables, such as the number of buds, number of shoots, number of wine grape clusters and weight of 100 berries were sampled in a 10 rows × 5 vines pattern and used as input variables, in combination with the NDVI, to define the clusters as alternative to yield maps. Two days prior to the harvesting, grape samples were taken. The analysed variables were probable alcoholic degree, pH of the juice, total acidity, total phenolics, colour, anthocyanins and tannins. The input variables, alone or in combination, were clustered (2 and 3 Clusters) by using the ISODATA algorithm, and an analysis of variance and a multiple rang test were performed. The results show that the zones derived from the NDVI maps are more effective to differentiate grape maturity and quality variables than the zones derived from the yield maps. The inclusion of other grapevine fertility and load variables did not improve the results.

Protocolo para la zonificación intraparcelaria de la viña para vendimia selectiva a partir de imágenes multiespectratrales

En este trabajo se presenta un protocolo para la zonificación intraparcelaria de la viña con la finalidad de vendimia selectiva. Se basa en la adquisición de una imagen multiespectral detallada en el momento del envero, a partir de la cual se obtiene el índice de vegetación de la diferencia normalizada (NDVI). Este índice se clasifica en áreas de vigor alto y bajo mediante un proceso de clasificación no supervisada (algoritmo ISODATA). Las zonas resultantes se generalizan y se transfieren al monitor de cosecha de una máquina vendimiadora para realizar la recolección selectiva. La uva recolectada según este protocolo en parcelas control ha mostrado diferenciación en cuanto a parámetros de calidad como el pH, la acidez total, el contenido de polifenoles y el color. La imagen multiespectral utilizada fue adquirida por el satélite Quickbird-2. Los datos de calidad de la uva fueron muestreados según una malla regular de 5 filas por 10 cepas, procediendo a un test estadístico de rangos múltiples para analizar la separación de medias de las variables analizadas en cada zona de NDVI.

Análise da dinâmica sazonal e separabilidade espectral de algumas fitofisionomias do cerrado com índices de vegetação dos sensores MODIS/TERRA e AQUA

Composições de 16 dias de índices de vegetação do sensor MODerate resolution Imaging Spectroradiometer (MODIS), com resolução espacial de 1km, a bordo dos satélites TERRA e AQUA, foram usadas para caracterizar a dinâmica sazonal em 2004 de cinco fitofisionomias de Cerrado e analisar a sua separabilidade espectral. Os índices Normalized Difference Vegetation Index (NDVI) e Enhanced Vegetation Index (EVI), calculados a partir dos dados dos sensores de ambas as plataformas e de uma base comum de pixels, foram comparados entre si. Os resultados indicaram que: (a) dentre as fitofisionomias estudadas, a Floresta Estacional decídua apresentou uma dinâmica sazonal muito marcante em função da perda de folhas da estação chuvosa para a seca (substancial redução nos índices) e do rápido verdejamento com o início da precipitação no final de outubro (rápido incremento de NDVI e EVI); (b) o NDVI mostrou maior variabilidade entre as classes de vegetação do que o EVI apenas na estação seca; (c) a discriminação entre as fitofisionomias melhorou da estação chuvosa para a seca; (d) o NDVI foi mais eficiente do que o EVI para discriminar as classes de vegetação na estação seca, ocorrendo o contrário na estação chuvosa; e (e) na maioria das datas selecionadas para estudo, não houve diferenças estatisticamente significativas entre os índices de vegetação gerados de ambas as plataformas, apesar das variações na qualidade dos pixels selecionados para as composições de 16 dias e na geometria de iluminação e de visada.

Influence of data acquisition geometry on soybean spectral response simulated by the prosail model

View angle and directional effects significantly affect reflectance and vegetation indices, especially when daily images collected by large field-of-view (FOV) sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) are used. In this study, the PROSAIL radiative transfer model was chosen to evaluate the impact of the geometry of data acquisition on soybean reflectance and two vegetation indices (Normalized Difference Vegetation Index - NDVI and Enhanced Vegetation Index -EVI) by varying biochemical and biophysical parameters of the crop. Input values for PROSAIL simulation were based on the literature and were adjusted by the comparison between simulated and real satellite soybean spectra acquired by the MODIS/Terra and hyperspectral Hyperion/Earth Observing-One (EO-1). Results showed that the influence of the view angle and view direction on reflectance was stronger with decreasing leaf area index (LAI) and chlorophyll concentration. Because of the greater dependence on the near-infrared reflectance, the EVI was much more sensitive to viewing geometry than NDVI presenting larger values in the backscattering direction. The contrary was observed for NDVI in the forward scattering direction. In relation to the LAI, NDVI was much more isotropic for closed soybean canopies than for incomplete canopies and a contrary behavior was verified for EVI.

Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

Several tools of precision agriculture have been developed for specific uses. However, this specificity may hinder the implementation of precision agriculture due to an increasing in costs and operational complexity. The use of vegetation index sensors which are traditionally developed for crop fertilization, for site-specific weed management can provide multiple utilizations of these sensors and result in the optimization of precision agriculture. The aim of this study was to evaluate the relationship between reflectance indices of weeds obtained by the GreenSeekerTM sensor and conventional parameters used for weed interference quantification. Two experiments were conducted with soybean and corn by establishing a gradient of weed interference through the use of pre- and post-emergence herbicides. The weed quantification was evaluated by the normalized difference vegetation index (NDVI) and the ratio of red to near infrared (Red/NIR) obtained using the GreenSeekerTM sensor, the visual weed control, the weed dry matter, and digital photographs, which supplied information about the leaf area coverage proportions of weed and straw. The weed leaf coverage obtained using digital photography was highly associated with the NDVI (r = 0.78) and the Red/NIR (r = -0.74). The weed dry matter also positively correlated with the NDVI obtained in 1 m linear (r = 0.66). The results indicated that the GreenSeekerTM sensor originally used for crop fertilization could also be used to obtain reflectance indices in the area between rows of crops to support decision-making programs for weed control.

Precision mapping

The nondestructive determination of plant total dry matter (TDM) in the field is greatly preferable to the harvest of entire plots in areas such as the Sahel where small differences in soil properties may cause large differences in crop growth within short distances. Existing equipment to nondestructively determine TDM is either expensive or unreliable. Therefore, two radiometers for measuring reflected red and near-infrared light were designed, mounted on a single wheeled hand cart and attached to a differential Global Positioning System (GPS) to measure georeferenced variations in normalized difference vegetation index (NDVI) in pearl millet fields [Pennisetum glaucum (L.) R. Br.]. The NDVI measurements were then used to determine the distribution of crop TDM. The two versions of the radiometer could (i) send single NDVI measurements to the GPS data logger at distance intervals of 0.03 to 8.53 m set by the user, and (ii) collect NDVI values averaged across 0.5, 1, or 2 m. The average correlation between TDM of pearl millet plants in planting hills and their NDVI values was high (r^2 = 0.850) but varied slightly depending on solar irradiance when the instrument was calibrated. There also was a good correlation between NDVI, fractional vegetation cover derived from aerial photographs and millet TDM at harvest. Both versions of the rugged instrument appear to provide a rapid and reliable way of mapping plant growth at the field scale with a high spatial resolution and should therefore be widely tested with different crops and soil types.