Uso de imagens TST do sensor MODIS/AQUA como indicativo da ocorrência de geadas no RS

Although frost occurrence causes severe losses in agriculture, especially in the south of Brazil, the data of minimum air temperature (Tmin) currently available for monitoring and predicting frosts show insufficient spatial distribution. This study aimed to evaluate the MDY11A1 (LST – Land Surface Temperature) product, from the MODIS sensor on board the AQUA satellite as an estimator of frost occurrence in the southeast of the state of Rio Grande do Sul, Brazil. LST images from the nighttime overpass of the MODIS/AQUA sensor for the months of June, July and August from 2006 to 2012, and data from three conventional weather stations of the National Institute of Meteorology (INMET) were used. Consistency was observed between Tmin data measured in weather stations and LST data obtained from the MODIS sensor. According to the results, LSTs below 3 ºC recorded by the MODIS/AQUA sensor are an indication of a favorable scenario to frost occurrence.Apesar da ocorrência de geadas causar severas perdas à agricultura, em especial no Sul do Brasil, os dados de temperatura mínima do ar atualmente disponíveis para o monitoramento e previsão deste fenômeno apresentam distribuição espacial insuficiente. O objetivo deste trabalho foi avaliar o produto MDY11A1 (TST - Temperatura da Superfície Terrestre), do sensor MODIS a bordo do satélite AQUA como estimador da ocorrência de geadas sobre o Sudeste do Estado do Rio Grande do Sul. Utilizaram-se imagens de TST da passagem noturna do sensor MODIS/AQUA dos meses de junho, julho e agosto de 2006 a 2012 e dados de três estações meteorológicas convencionais do INMET. Verificou-se coerência entre os dados de Temperatura mínima do ar medidos em estações meteorológicas e os dados de Temperatura da superfície da terra obtidos do sensor MODIS. Resultados desta pesquisa apontam que as TSTs registradas pelo sensor MODIS/AQUA inferiores a 3 °C são indicativas de situação favorável à ocorrência de geadas

New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar

A large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly owing to the lack of measurements. To date, accessible peer-reviewed data are available for just 10 large tropical trees in the Amazon that have been harvested and directly measured entirely via weighing. Here, we harvested four large tropical rainforest trees (stem diameter: 0.6–1.2 m, height: 30–46 m, AGB: 3960–18 584 kg) in intact old-growth forest in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We first present rare ecological insights provided by these data, including unsystematic intra-tree variations in density, with both height and radius. We also found the majority of AGB was usually found in the crown, but varied from 42 to 62%. We then compare non-destructive approaches for estimating the AGB of these trees, using both classical allometry and new lidar-based methods. Terrestrial lidar point clouds were collected pre-harvest, on which we fitted cylinders to model woody structure, enabling retrieval of volume-derived AGB. Estimates from this approach were more accurate than allometric counterparts (mean tree-scale relative error: 3% versus 15%), and error decreased when up-scaling to the cumulative AGB of the four trees (1% versus 15%). Furthermore, while allometric error increased fourfold with tree size over the diameter range, lidar error remained constant. This suggests error in these lidar-derived estimates is random and additive. Were these results transferable across forest scenes, terrestrial lidar methods would reduce uncertainty in stand-scale AGB estimates, and therefore advance our understanding of the role of tropical forests in the global carbon cycle

Correlations between spectral and biophysical data obtained in canola canopy cultivated in the subtropical region of Brazil

O objetivo deste trabalho foi a identificação das bandas espectrais, dos índices de vegetação e dos períodos do ciclo da canola em que a correlação entre os dados espectrais e os indicadores biofísicos (matéria seca total da parte aérea e rendimento de grãos) é mais significativa. Os experimentos foram conduzidos nas safras de 2013 e 2014, na Embrapa Trigo, no Estado do Rio Grande do Sul. Utilizou-se o delineamento experimental em blocos ao acaso, com quatro repetições, e os tratamentos foram cinco doses de nitrogênio em cobertura. Foram determinados a matéria seca das plantas, o rendimento de grãos e a fenologia. A resposta espectral da canola foi avaliada por medições de reflectância do dossel, com espectrorradiômetro, e, a partir desses dados, foram calculados os índices de vegetação SR, NDVI, EVI, SAVI e GNDVI. As correlações de Pearson entre as variáveis espectrais e biofísicas da canola mostraram que as melhores bandas para estimativa da matéria seca são as do vermelho (620 a 670 nm) e do infravermelho próximo (841 a 876 nm). O período vegetativo é o mais indicado para obtenção de correlações mais significativas para a canola. Todos os índices de vegetação utilizados são adequados para estimativas da matéria seca e do rendimento de grãos da canola.The objective of this work was to identify the spectral bands, vegetation indices, and periods of the canola crop season in which the correlation between spectral data and biophysical indicators (total shoot dry matter and grain yield) is most significant. The experiment was carried out during the 2013 and 2014 crop seasons at Embrapa Trigo, in the state of Rio Grande do Sul, Brazil. A randomized complete block design was used, with four replicates, and the treatments consisted of five doses of nitrogen topdressing. Plant dry matter, grain yield, and phenology were measured. The canola spectral response was evaluated by measuring the canola canopy reflectance using a spectroradiometer, and, with this data, the SR, NDVI, EVI, SAVI, and GNDVI vegetation indices were determined. Pearson’s correlations between the spectral and biophysical variables of canola showed that the red (620 to 670 nm) and near-infrared (841 to 876 nm) bands were the best to estimate the dry matter. The vegetative period is the most indicated to obtain the most significant correlations for canola. All the used vegetation indices are adequate for estimating the dry matter and grain yield of canola

Performance of Laser-Based Electronic Devices for Structural Analysis of Amazonian Terra-Firme Forests

Tropical vegetation biomass represents a key component of the carbon stored in global forest ecosystems. Estimates of aboveground biomass commonly rely on measurements of tree size (diameter and height) and then indirectly relate, via allometric relationships and wood density, to biomass sampled from a relatively small number of harvested and weighed trees. Recently, however, novel in situ remote sensing techniques have been proposed, which may provide nondestructive alternative approaches to derive biomass estimates. Nonetheless, we still lack knowledge of the measurement uncertainties, as both the calibration and validation of estimates using different techniques and instruments requires consistent assessment of the underlying errors. To that end, we investigate different approaches estimating the tropical aboveground biomass in situ. We quantify the total and systematic errors among measurements obtained from terrestrial light detection and ranging (LiDAR), hypsometer-based trigonometry, and traditional forest inventory. We show that laser-based estimates of aboveground biomass are in good agreement (<10% measurement uncertainty) with traditional measurements. However, relative uncertainties vary among the allometric equations based on the vegetation parameters used for parameterization. We report the error metrics for measurements of tree diameter and tree height and discuss the consequences for estimated biomass. Despite methodological differences detected in this study, we conclude that laser-based electronic devices could complement conventional measurement techniques, thereby potentially improving estimates of tropical vegetation biomass

On leaf and wood separation from Terrestrial LiDAR data

TLS can provide high resolution measurements to calibrate/validate remote sensing (RS) efforts to monitor forests and to extend the knowledge underpinning many ecological theories. The application of current TLS methods is hampered by the effects of material mixtures in TLS data. Studies in the literature suggest that mixture of materials in point clouds can lower the accuracy of TLS estimates. Leaf-wood separation methods have previously been proposed in the literature. Given their need for manual input, they are restricted to a small number of trees. Also, approaches used to test these methods are subjective and hard to reproduce. The impact of leaf-wood separation is, so far, poorly understood, as very few quantitative analyses of pre- and post-separation TLS estimates are available in the literature. Reports found in the literature highlight the need for an automated separation method that is able to accurately separate leaf and wood points from TLS data. There is also a gap with regards to reproducing tests that are necessary to validate these separation methods. The final issue identified here is the gap in the knowledge about the impact of leaf-wood separation on TLS estimates. The main objective of this thesis is to propose a method to accurately separate leaf-wood material from TLS data in an automated fashion. Two other methods were proposed: a testing framework to validate the leaf-wood separation method; and a method to estimate leaf angle distribution from leaf-separated point clouds. An initial attempt to quantify the impact of leaf-wood separation on LAD and wood volume estimates from TLS was presented. The method proposed in this thesis is able to automatically separate leaf and wood with accuracy above 80%. The testing framework provided the tools to quantify separation accuracy. LAD estimates from TLS were shown to be accurate. Finally, the leaf-wood separation was found to improve TLS estimates

Estimativas de variáveis biofísicas da canola com dados espectrais multisensor

Esse trabalho utilizou sensores remotos, em escala local e regional, para caracterizar o padrão espectral da canola e propor metodologias de criação de máscaras de cultivo, através da classificação de imagens de satélite, e de geração de estimativas de variáveis biofísicas, a partir de índices de vegetação medidos ao longo do ciclo. As medições das variáveis biofísicas foram realizadas em parcelas experimentais, na Embrapa-Trigo no município de Coxilha, e em lavouras monitoradas nas mesorregiões Nordeste e Noroeste do Rio Grande do Sul, para as safras 2013 e 2014. As variáveis biofísicas medidas foram altura de plantas, matéria seca das folhas, da haste e das síliquas e, também, foi estimado o índice de área foliar. Os dados espectrais para as parcelas experimentais foram obtidos através de um espectrorradiômetro. Para as lavouras monitoradas, os dados espectrais foram obtidos dos produtos MCD43B4 e MOD09A1, medidos pelo sensor MODIS (satélites Terra/Aqua), e de imagens do sensor OLI (satélite Landsat 8). A partir destes foi realizada a caracterização espectral da canola ao longo do seu ciclo de desenvolvimento, gerando perfis completos e perfis das bandas espectrais. Os índices de vegetação foram utilizados para caracterizar o padrão espectral e para a criação de modelos de estimativas das variáveis biofísicas, os quais foram calculados usando as bandas espectrais simuladas. Os índices de vegetação foram utilizados para classificar as áreas cultivadas com canola para as mesorregiões Nordeste e Noroeste do Rio Grande do Sul e, posteriormente, aplicados os modelos de estimativas de variáveis biofísicas. A caracterização do padrão espectral da canola foi consistente entre os dois anos avaliados e para todos os sensores, com variação temporal semelhante a outras culturas agrícolas, exceto pela redução nos índices de vegetação durante a floração da cultura. Os modelos de estimativa das variáveis biofísicas, apresentaram coeficientes de determinação elevados, com exceção das variáveis matéria seca das folhas e índice de área foliar. A classificação da área cultivada com canola, utilizando os produtos MODIS, apresentou resultados coerentes com o esperado de acordo com dados de série histórica, apresentados pela CONAB. As estimativas de variáveis biofísicas mostraram coerência com os obtidos pelas medições nas lavouras monitoradas. Os resultados obtidos nesse estudo demonstram, portanto, o potencial da utilização de dados espectrais multisensor para o mapeamento de lavouras e realização de estimativas de variáveis biofísicas da cultura da canola.This study used remote sensors, at local and regional levels, in order to characterize the spectral pattern of canola and propose methodologies to create crop masks, through satellite image classification, and generation of estimates of biophysical variables, from vegetation indices measured along the cycle. The measurements of biophysical variables were performed on experimental plots at Embrapa Trigo in Coxilha, and in crop sites monitored in the mesoregions Northeast and Northwest of Rio Grande do Sul, in 2013 and 2014. The biophysical variables measured were plant height, dry matter of the leaves, stem and pods and also, the leaf area index was estimated. The spectral data for the experimental plots were obtained using a spectroradiometer. For monitored crop fields, spectral data were obtained from the products MCD43B4 and MOD09A1, measured by MODIS (Terra / Aqua satellite) sensor, and images from the OLI sensor (Landsat 8). These data were used to perform the spectral characterization of canola along its development cycle, generating full spectral profiles and spectral bands profiles. The vegetation indices were used to characterize the spectral pattern and creating models to estimate the biophysical variables, which have been calculated using the simulated spectral bands. The vegetation indices were used to classify the areas planted with canola for the mesoregions Northeast and Northwest and then applied to the models for estimates of biophysical variables. The characterization of the canola's spectral pattern was consistent between the two years and for all sensors with temporal variation similar to other agricultural crops, except for the reduction in the vegetation indices during the flowering phase of culture. The biophysical variables estimation models showed high correlation coefficients, except for the variables dry matter of leaves and leaf area index. The canola classification using MODIS products, showed results consistent with the expected according to historical data series presented by CONAB. Estimates of biophysical variables were consistent with those obtained by measurements in the monitored fields. The results of this study show, therefore, the potential of using multi-sensor data for the spectral mapping of canola the estimation of biophysical variables

Assessing Approaches to 3D Tree Reconstruction from Terrestrial Laser Scanning Data

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Uso de imagens TST do sensor MODIS/AQUA como indicativo da ocorrência de geadas no RS

Although frost occurrence causes severe losses in agriculture, especially in the south of Brazil, the data of minimum air temperature (Tmin) currently available for monitoring and predicting frosts show insufficient spatial distribution. This study aimed to evaluate the MDY11A1 (LST – Land Surface Temperature) product, from the MODIS sensor on board the AQUA satellite as an estimator of frost occurrence in the southeast of the state of Rio Grande do Sul, Brazil. LST images from the nighttime overpass of the MODIS/AQUA sensor for the months of June, July and August from 2006 to 2012, and data from three conventional weather stations of the National Institute of Meteorology (INMET) were used. Consistency was observed between Tmin data measured in weather stations and LST data obtained from the MODIS sensor. According to the results, LSTs below 3 ºC recorded by the MODIS/AQUA sensor are an indication of a favorable scenario to frost occurrence.Apesar da ocorrência de geadas causar severas perdas à agricultura, em especial no Sul do Brasil, os dados de temperatura mínima do ar atualmente disponíveis para o monitoramento e previsão deste fenômeno apresentam distribuição espacial insuficiente. O objetivo deste trabalho foi avaliar o produto MDY11A1 (TST - Temperatura da Superfície Terrestre), do sensor MODIS a bordo do satélite AQUA como estimador da ocorrência de geadas sobre o Sudeste do Estado do Rio Grande do Sul. Utilizaram-se imagens de TST da passagem noturna do sensor MODIS/AQUA dos meses de junho, julho e agosto de 2006 a 2012 e dados de três estações meteorológicas convencionais do INMET. Verificou-se coerência entre os dados de Temperatura mínima do ar medidos em estações meteorológicas e os dados de Temperatura da superfície da terra obtidos do sensor MODIS. Resultados desta pesquisa apontam que as TSTs registradas pelo sensor MODIS/AQUA inferiores a 3 °C são indicativas de situação favorável à ocorrência de geadas