Search

Diante da complexa relação entre reflectância foliar e aspectos fisiológicos da planta em dias e diferentes horários do dia, a pesquisa foi desenvolvida para averiguar e mensurar o grau da influência do dia e o período do dia em espectros de reflectância foliar de árvores de eucalipto. O experimento foi conduzido em um plantio de eucalipto clonal, híbrido espontâneo de Eucalyptus grandis com centroide nas coordenadas 18º 12’ 12,19’’S e 43º 34’ 20,28’’O. Em cada uma das cinco árvores-amostra escolhidas aleatoriamente, amostraram-se quatro folhas distribuídas em cada ponto cardeal. Todas as folhas selecionadas foram avaliadas nos dias 7, 8, 9 e 10 de dezembro de 2013, em dois períodos do dia: entre 8 e 9 horas e entre 15 e 16 horas. A leitura da reflectância foliar foi realizada utilizando um mini-espectrometro com posterior suavização e dessa forma realizou-se a análise de componentes principais e com posterior análise de agrupamento por dendrograma. No geral, a média das reflectâncias foliares no segundo dia foram as menores e as médias do terceiro dia maiores. Em média a reflectância foliar foi menor no período da manhã do que no período da tarde. O dia e o período do dia em que foi realizada a leitura influenciaram de forma aleatória na reflectância foliar de eucalipto. A reflectância foliar do eucalipto pode se alterar em um curto período de tempo, inclusive em um mesmo dia, o que indica a importância da padronização no horário de coleta para futuros estudos.

ABSTRACT: Leaf hyperspectral reflectance has been used to estimate nutrient concentrations in plants in narrow bands of the electromagnetic spectrum. The aim of this study was to estimate leaf nutrient concentrations using leaf hyperspectral reflectance and verify the variable selection methods using the partial least squares regression (PLSR). Two studies were carried out using stands with Eucalyptus clones. Study I was established in Eucalyptus stands with three clones, classifying leaves into five colour patterns using the Munsell chart for plant tissues. Immediately after leaf collection, leaf reflectance was read and the chemical analysis was performed. Study II was carried out in commercial clonal stands of Eucalyptus performing the same leaf sampling and chemical analysis as used in Study I. All leaf reflectance spectra were smoothed and three more pre-processing procedures were applied. In addition, three methods of PLSR were tested. The first derivative was more accurate for predicting nitrogen ( R c v 2 = 0.95), phosphorous ( R c v 2 = 0.93), and sulphur concentration ( R c v 2 = 0.85). The estimates for concentrations of calcium ( R c v 2 = 0.81), magnesium ( R c v 2 = 0.22), and potassium ( R c v 2 = 0.76) were more accurate using the logarithm transformation. Only the estimates for iron concentrations were performed with higher accuracy ( R c v 2 = 0.35) using the smoothed reflectance. The copper concentrations were more accurate ( R c v 2 = 0.78) using the logarithm transformation. Concentrations of boron ( R c v 2 = 0.68) and manganese ( R c v 2 = 0.79) were more accurate using the first derivative, while zinc ( R c v 2 = 0.31) concentration was most accurate using the second derivative.

Unmanned aircraft systems (UAS) have advanced rapidly enabling low-cost capture of high-resolution images with cameras, from which three-dimensional photogrammetric point clouds can be derived. More recently UAS equipped with laser scanners, or lidar, have been employed to create similar 3D datasets. While airborne lidar (originally from conventional aircraft) has been used effectively in forest systems for many years, the ability to obtain important tree features such as height, diameter at breast height, and crown dimensions is now becoming feasible for individual trees at reasonable costs thanks to UAS lidar. Getting to individual tree resolution is crucial for detailed phenotyping and genetic analyses. This study evaluates the quality of three three-dimensional datasets from three sensors—two cameras of different quality and one lidar sensor—collected over a managed, closed-canopy pine stand with different planting densities. For reference, a ground-based timber cruise of the same pine stand is also collected. This study then conducted three straightforward experiments to determine the quality of the three sensors’ datasets for use in automated forest inventory: manual mensuration of the point clouds to (1) detect trees and (2) measure tree heights, and (3) automated individual tree detection. The results demonstrate that, while both photogrammetric and lidar data are well-suited for single-tree forest inventory, the photogrammetric data from the higher-quality camera is sufficient for individual tree detection and height determination, but that lidar data is best. The automated tree detection algorithm used in the study performed well with the lidar data, detecting 98% of the 2199 trees in the pine stand, but fell short of manual mensuration within the lidar point cloud, where 100% of the trees were detected. The manually-mensurated heights in the lidar dataset correlated with field measurements at r = 0.95 with a bias of −0.25 m, where the photogrammetric datasets were again less accurate and precise.

ABSTRACT Nitrogen (N) has commonly been applied in Eucalyptus stands in Brazil and it has a direct relation with biomass production and chlorophyll content. Foliar N concentrations are used to diagnose soil and plant fertility levels and to develop N fertilizer application rates. Normally, foliar N is obtained using destructive methods, but indirect analyses using Vegetation Indexes (VIs) may be possible. The aim of this work was to evaluate VIs to estimate foliar N concentration in three Eucalyptus clones. Lower crown leaves of three clonal Eucalyptus plantations (25 months old) were classified into five color patterns using the Munsell Plant Tissue Color Chart. For each color, N concentration was determined by the Kjeldahl method and foliar reflectance was measured using a CI-710 Miniature Leaf Spectrometer. Foliar reflectance data were used to obtain the VIs and the VIs were used to estimate N concentrations. In the visible region, the relationship between N concentration and reflectance percentage was negative. The highest correlations between VIs and N concentrations were obtained by the Inflection Point Position (IPP, r = 0.97), Normalized Difference Red-Edge (reNDVI, r = 0.97) and Modified Red-Edge Normalized Difference Vegetation Index (mNDI, r = 0.97). Vegetation indexes on the red edge region provided the most accurate estimates of foliar N concentration. The reNDVI index provided the best N concentration estimates in leaves of different colors of Eucalyptus urophylla × grandis and Eucalyptus urophylla × urophylla (R2 = 0.97 and RMSE = 0.91 g kg−1).

O gênero Hymenaea pertence à família Leguminosae, subfamília Caesalpinoideae, com nome popular de jatobá. No gênero Hymenaea, foram descritas aproximadamente 25 espécies. Já no Brasil, verifica-se a presença de 13 espécies, com destaque para a Hymenaea martiana Hayne e a Hymenaea courbaril Linneaus. Na literatura, há vários trabalhos que tratam principalmente da quebra de dormência das sementes e da produção de mudas, sobretudo da espécie Hymenaea courbaril Linneaus, porém essas avaliações por matriz ainda são incipientes. Este trabalho teve por objetivo avaliar e comparar a germinação e o crescimento inicial de mudas entre as espécies e as matrizes de Hymenaea martiana Hayne e Hymenaea courbaril Linneaus. Foram desenvolvidos dois experimentos: o primeiro avaliou a germinação, e o segundo, o crescimento inicial de mudas. As duas espécies avaliadas apresentaram comportamentos distintos entre a germinação e o crescimento inicial das mudas, sendo a Hymenaea courbaril Linneaus a espécie com maior média para ambas variáveis, porém não apresentou variabilidade fenotípica entre matrizes.