Efeito da fertilização e da disponibilidade hídrica na produtividade florestal e na eficiência do uso da luz de povoamentos clonais de eucalyptus spp. em Bocaiúva,MG

Pós-graduação em Ciência Florestal - FCA

Indices de vegetação na mensuração do estoque de carbono em áreas com cana-de-açúcar

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Diagnóstico da qualidade ambiental nas áreas verdes públicas em Presidente Prudente (SP)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Produtividade de biomassa de cana-de-açúcar em função dos índices de vegetação utilizando técnicas de sensoriamento remoto

Pós-graduação em Agronomia (Ciência do Solo) - FCAV

Imagens aéreas multiespectrais na identificação de zonas de manejo em áreas de algodão para aplicação localizada de insumos

Crop yield is influenced by several factors with variability in time and space that are associated with the variations in the plant vigor. This variability allows the identification of management zones and site-specific applications to manage different regions of the field. The purpose of this study was the use of multispectral image for management zones identification and implications of site-specific application in commercial cotton areas. Multispectral airborne images from three years were used to classify a field into three vegetation classes via the Normalized Difference Vegetation Index (NDVI). The NDVI classes were used to verify the potential differences between plant physical measurements and identify management zones. The cotton plant measurements sampled in 8 repetitions of 10 plants at each NDVI class were Stand Count, Plant Height, Total Nodes and Total Bolls. Statistical analysis was performed with treatments arranged in split plot design with Tukey’s Test at 5% of probability. The images were classified into five NDVI classes to evaluate the relationship between cotton plant measurement results and sampling location across the field. The results have demonstrated the possibility of using multispectral image for management zones identification in cotton areas. The image classification into three NDVI classes showed three different zones in the field with similar characteristics for the studied years. Statistical differences were shown for plant height, total nodes and total bolls between low and high NDVI classes for all years. High NDVI classes contained plants with greater height, total nodes and total bolls compared to low NDVI classes. There was no difference in Stand Count between low and high NDVI classes for the three studied years. The final plant stand was the same between all NDVI classes for 2001 and 2003 as it was expected due to the conventional seeding application with the same rate of seeds for the entire field.

Discriminação de classes de cobertura vegetal utilizando técnicas de classificação digital de imagens de sensoriamento remoto

The aim of this work is to discriminate vegetation classes throught remote sensing images from the satellite CBERS-2, related to winter and summer seasons in the Campos Gerais region Paraná State, Brazil. The vegetation cover of the region presents different kinds of vegetations: summer and winter cultures, reforestation areas, natural areas and pasture. Supervised classification techniques like Maximum Likelihood Classifier (MLC) and Decision Tree were evaluated, considering a set of attributes from images, composed by bands of the CCD sensor (1, 2, 3, 4), vegetation indices (CTVI, DVI, GEMI, NDVI, SR, SAVI, TVI), mixture models (soil, shadow, vegetation) and the two first main components. The evaluation of the classifications accuracy was made using the classification error matrix and the kappa coefficient. It was defined a high discriminatory level during the classes definition, in order to allow separation of different kinds of winter and summer crops. The classification accuracy by decision tree was 94.5% and the kappa coefficient was 0.9389 for the scene 157/128. For the scene 158/127, the values were 88% and 0.8667, respectively. The classification accuracy by MLC was 84.86% and the kappa coefficient was 0.8099 for the scene 157/128. For the scene 158/127, the values were 77.90% and 0.7476, respectively. The results showed a better performance of the Decision Tree classifier than MLC, especially to the classes related to cultivated crops, indicating the use of the Decision Tree classifier to the vegetation cover mapping including different kinds of crops.

Relação entre a resposta espectral da vegetação em diferentes cultivos e níveis freáticos em área de recarga do Sistema Aquífero Guarani

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Análise do potencial do sensor OLI (Operational Land Imager) a bordo do Landsat-8 em estimar a concentração de sólidos totais em suspensão no reservatório de Barra Bonita

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Recent shift from forest to savanna burning in the Amazon Basin observed by satellite

The numbers of fires detected on forest, savanna and transition lands during the 2002-10 biomass burning seasons in Amazonia are shown using fire count data and co-located land cover classifications from the Moderate Resolution Imaging Spectroradiometer (MODIS). The ratio of forest fires to savanna fires has varied substantially over the study period, with a maximum ratio of 0.65:1 in 2005 and a minimum ratio of 0.27:1 in 2009, with the four lowest years occurring in 2007-10. The burning during the droughts of 2007 and 2010 is attributed to a higher number of savanna fires relative to the drought of 2005. A decrease in the regional mean single scattering albedo of biomass burning aerosols, consistent with the shift from forest to savanna burning, is also shown. During the severe drought of 2010, forest fire detections were lower in many areas compared with 2005, even though the drought was more severe in 2010. This result suggests that improved fire management practices, including stricter burning regulations as well as lower deforestation burning, may have reduced forest fires in 2010 relative to 2005 in some areas of the Amazon Basin.

Assessing biophysical variable parameters of bean crop with hyperspectral measurements

Recently high spectral resolution sensors have been developed, which allow new and more advanced applications in agriculture. Motivated by the increasing importance of hyperspectral remote sensing data, the need for research is important to define optimal wavebands to estimate biophysical parameters of crop. The use of narrow band vegetation indices (VI) derived from hyperspectral measurements acquired by a field spectrometer was evaluated to estimate bean (Phaseolus vulgaris L.) grain yield, plant height and leaf area index (LAI). Field canopy reflectance measurements were acquired at six bean growth stages over 48 plots with four water levels (179.5; 256.5; 357.5 and 406.2 mm) and tree nitrogen rates (0; 80 and 160 kg ha-1) and four replicates. The following VI was analyzed: OSNBR (optimum simple narrow-band reflectivity); NB_NDVI (narrow-band normalized difference vegetation index) and NDVI (normalized difference index). The vegetation indices investigated (OSNBR, NB_NDVI and NDVI) were efficient to estimate LAI, plant height and grain yield. During all crop development, the best correlations between biophysical variables and spectral variables were observed on V4 (the third trifoliolate leaves were unfolded in 50 % of plants) and R6 (plants developed first flowers in 50 % of plants) stages, according to the variable analyzed.

Mapeamento do índice de vegetação da diferença normalizada em lavoura de algodão

Os dados de sensoriamento remoto em campo podem fornecer informações detalhadas sobre a variabilidade de parâmetros biofísicos ligados à produtividade em grandes áreas e apresentam potencial para o monitoramento destes parâmetros, ao longo de todo o ciclo de desenvolvimento da cultura. Este trabalho objetivou mapear a variabilidade espacial do índice de vegetação da diferença normalizada (NDVI) e seus componentes, em duas lavouras comerciais de algodão (Gossipium hirsutum L.), utilizando sensor óptico ativo, em nível terrestre. Os dados foram coletados utilizando-se sensor instalado em um pulverizador autopropelido agrícola. Um receptor GPS foi acoplado ao sensor, para a obtenção das coordenadas dos pontos de amostragem. As leituras foram realizadas em faixas espaçadas em 21,0 m, aproveitando-se as passadas do veículo no momento da pulverização de agroquímicos, e os dados submetidos à análise estatística clássica e geoestatística. Mapas de distribuição espacial das variáveis foram elaborados pela interpolação por krigagem. Observou-se maior variabilidade espacial do NDVI e da reflectância espectral da vegetação na região do infravermelho próximo (IVP) (880 nm) e do visível (590 nm) na lavoura com maior estresse fisiológico, devido ao ataque do percevejo castanho [Scaptocoris castanea (Hem.: Cydnidae)], em relação à lavoura sadia.

Aerosols in Amazonia: Natural Biogenic Particles and Large Scale Biomass Burning Impacts.

The Large Scale Biosphere Atmosphere Experiment in Amazonia (LBA) is a long term (20 years) research effort aimed at the understanding of the functioning of the Amazonian ecosystem. In particular, the strong biosphere-atmosphere interaction is a key component looking at the exchange processes between vegetation and the atmosphere, focusing on aerosol particles. Two aerosol components are the most visible: The natural biogenic emissions of aerosols and VOCs, and the biomass burning emissions. A large effort was done to characterize natural biogenic aerosols that showed detailed organic characterization and optical properties. The biomass burning component in Amazonia is important in term of aerosol and trace gases emissions, with deforestation rates decreasing, from 27,000 Km2 in 2004 to about 5,000 Km2 in 2011. Biomass burning emissions in Amazonia increases concentrations of aerosol particles, CO, ozone and other species, and also change the surface radiation balance in a significant way. Long term monitoring of aerosols and trace gases were performed in two sites: a background site in Central Amazonia, 55 Km North of Manaus (called ZF2 ecological reservation) and a monitoring station in Porto Velho, Rondonia state, a site heavily impacted by biomass burning smoke. Several instruments were operated to measured aerosol size distribution, optical properties (absorption and scattering at several wavelengths), composition of organic (OC/EC) and inorganic components among other measurements. AERONET and MODIS measurements from 5 long term sites show a large year-to year variability due to climatic and socio-economic issues. Aerosol optical depths of more than 4 at 550nm was observed frequently over biomass burning areas. In the pristine Amazonian atmosphere, aerosol scattering coefficients ranged between 1 and 200 Mm-1 at 450 nm, while absorption ranged between 1 and 20 Mm-1 at 637 nm. A strong seasonal behavior was observed, with greater aerosol loadings during the dry season (Jul-Nov) as compared to the wet season (Dec-Jun). During the wet season in Manaus, aerosol scattering (450 nm) and absorption (637 nm) coefficients averaged, respectively, 14 and 0.9 Mm-1. Angstrom exponents for scattering were lower during the wet season (1.6) in comparison to the dry season (1.9), which is consistent with the shift from biomass burning aerosols, predominant in the fine mode, to biogenic aerosols, predominant in the coarse mode. Single scattering albedo, calculated at 637 nm, did not show a significant seasonal variation, averaging 0.86. In Porto Velho, even in the wet season it was possible to observe an impact from anthropogenic aerosol. Black Carbon was measured at a high 20 ug/m³ in the dry season, showing strong aerosol absorption. This work presents a general description of the aerosol optical properties in Amazonia, both during the Amazonian wet and dry seasons.

Heat island simulations in Londrina city, Brazil

This work aims to study the urban heat island on North region of Parana state, Brazil and the influence of land use and urban settlements on the intensity and frequency of occurrence of these events. Through atmospheric modeling whith WRF/Chem model two simulations were made with different land and use files, one with the original land use another obtained from a composition of MODIS-Landsat imagery. The simulations showed good skills compared to observed data. Urban areas presented higher temperatures. Landsat land use has represented better urban heat islands (UHI), the gradient between urban and rural areas was well demonstrated and the correlation coefficient was above 0.92. The model underestimated the maximum values and overestimated the minimum compared with observed data in both simulations.

Air quality simulations for Londrina region, Brazil

The objective of this work were apply and provide a preliminary evaluation of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) performance, for Londrina region. We performed comparison with measurements obtained in meteorological stations. The model was configured to run with three domains with 27,9 and 3 km of grid resolution, using the ndown program and also was realized a simulation with the model configured to run with a single domain using a land use file based in a classified image for region of MODIS sensor. The emission files to supply the chemistry run were generated based in the work of Martins et al., 2012. RADM2 chemical mechanism and MADE/SORGAM modal aerosol models were used in the simulations. The results demonstrated that model was able to represent coherently the formation and dispersion of the pollution in Metropolitan Region of Londrina and also the importance of using the appropriate land use file for the region.

A new snowfall detection algorithm for high latitude regions based on a combination of active and passive sensors

Precipitation retrieval over high latitudes, particularly snowfall retrieval over ice and snow, using satellite-based passive microwave spectrometers, is currently an unsolved problem. The challenge results from the large variability of microwave emissivity spectra for snow and ice surfaces, which can mimic, to some degree, the spectral characteristics of snowfall. This work focuses on the investigation of a new snowfall detection algorithm specific for high latitude regions, based on a combination of active and passive sensors able to discriminate between snowing and non snowing areas. The space-borne Cloud Profiling Radar (on CloudSat), the Advanced Microwave Sensor units A and B (on NOAA-16) and the infrared spectrometer MODIS (on AQUA) have been co-located for 365 days, from October 1st 2006 to September 30th, 2007. CloudSat products have been used as truth to calibrate and validate all the proposed algorithms. The methodological approach followed can be summarised into two different steps. In a first step, an empirical search for a threshold, aimed at discriminating the case of no snow, was performed, following Kongoli et al. [2003]. This single-channel approach has not produced appropriate results, a more statistically sound approach was attempted. Two different techniques, which allow to compute the probability above and below a Brightness Temperature (BT) threshold, have been used on the available data. The first technique is based upon a Logistic Distribution to represent the probability of Snow given the predictors. The second technique, defined Bayesian Multivariate Binary Predictor (BMBP), is a fully Bayesian technique not requiring any hypothesis on the shape of the probabilistic model (such as for instance the Logistic), which only requires the estimation of the BT thresholds. The results obtained show that both methods proposed are able to discriminate snowing and non snowing condition over the Polar regions with a probability of correct detection larger than 0.5, highlighting the importance of a multispectral approach.