Ricardo Dal’Agnol da Silva

Spectral/textural attributes from ALI/EO-1 for mapping primary and secondary tropical forests and studying the relationships with biophysical parameters

We analysed spectral and textural attributes from the Advanced Land Imager (ALI)/EO-1 for land-cover mapping and inspected their correlation with biophysical parameters of primary and secondary forests from Eastern Amazon. An artificial neural network (ANN) technique selected the most relevant spectral/textural attributes, which were combined for classification of the ALI scene. From the ANN land-cover map, areas classified as primary forest (PF), initial (SS1), intermediate (SS2) and advanced (SS3) stages of secondary succession were studied. Biophysical parameters were determined from field inventory of 40 sample plots. Results showed an overall classification accuracy of 79% using reflectance and 89% using the combined data set. The combined data set included the reflectance of ALI bands 3–9 and the texture metrics mean (bands 3–4; 6–8) and dissimilarity (band 8). The reflectance of the near-infrared/shortwave infrared bands and their texture mean decreased from SS1 to SS3/PF. The gradient between primary and secondary forests controlled the correlations of reflectance with biophysical parameters. While the aboveground biomass, basal area, leaf area index, tree height and canopy cover increased from SS1 to SS3/PF, the reflectance decreased with the development of canopy structure and the resultant canopy shadows. The mean was the only texture metric correlated with biophysical parameters.

Following a site-specific secondary succession in the Amazon using the Landsat CDR product and field inventory data

Secondary forests cover large areas and are strong carbon sinks in tropical regions. They are important for ecosystem functioning, biodiversity conservation, watershed protection, and recovery of soil fertility. In this study, we used the Surface Reflectance Climate Data Record (CDR) product from 16 Thematic Mapper (TM)/Landsat-5 images (1984–2010) to continuously track the secondary succession (SS) of a forest following land abandonment in 1980. Changes in canopy structure and floristic composition were analysed using data from four field inventories (1995, 2002, 2007, and 2012). To characterize variations in brightness, greenness, spectral reflectance, and shadows with the natural regeneration of vegetation, we applied tasselled cap transformations, principal component analysis (PCA), and linear spectral mixture models to the TM datasets. Shade fractions were plotted over time and correlated with the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). Because image texture may reflect the variability of the successional process, eight co-occurrence-based filter metrics were calculated for selected TM bands and plotted as a function of time since abandonment. The successional forest was compared to a nearby primary reference forest (PF) and had differences in the spectral and textural means evaluated using analysis of variance (ANOVA). The results showed increases of 35% and 10.4% over time in basal area and tree height, respectively. Species richness within the assemblage of sampling units increased from 14 to 71 between 1995 and 2012, and this trend was also confirmed using an individual-based rarefaction analysis. Species richness in 2012 was still lower than that observed in the PF site, which presented greater amounts of aboveground biomass (336.4 ± 17.0 ton ha−1 for PF versus 98.5 ± 21.4 ton ha−1 for SS in 2012). Brightness and greenness tasselled cap differences between the SS and PF rapidly decreased from 1984 (SS at the age of 4 years) to 1991 (age of 11 years). Brightness also decreased from 1997 to 2003, as indicated by PC1 scores and surface reflectance of the TM bands 4 (near infrared) and 5 (shortwave infrared). Spectral mixture shade fraction increased from young to old successional stages with strata composition and canopy structure development, whereas NDVI and EVI decreased over time. Because EVI was strongly dependent on near infrared reflectance (r = + 0.96), it was also much more strongly correlated with the shade fraction (r = −0.93) than NDVI. Except for the image texture mean that decreased from young to old successional stages in TM bands 4 and 5, no clear trend was observed in the remaining texture metrics over the time period of vegetation regeneration. Overall, due to structural-floristic and spectral/textural differences with the PF, the SS site was still distinguishable using Landsat data 30 years after land abandonment. Most of the spectral metric means between PF and SS were significantly different over time at 0.01 significance level, as indicated by ANOVA.

Assessment of two techniques to merge ground-based and TRMM rainfall measurements: a case study about Brazilian Amazon Rainforest

The availability of accurate rainfall data with high spatial resolution, especially in vast watersheds with low density of ground-measurements, is critical for planning and management of water resources and can increase the quality of the hydrological modeling predictions. In this study, we used two classical methods: the optimal interpolation and the successive correction method (SCM), for merging ground-measurements and satellite rainfall estimates. Cressman and Barnes schemes have been used in the SCM in order to define the error covariance matrices. The correction of bias in satellite rainfall data has been assessed by using four different algorithms: (1) the mean bias correction, (2) the regression equation, (3) the distribution transformation, and (4) the spatial transformation. The satellite rainfall data were provided by the Tropical Rainfall Measuring Mission, over the Brazilian Amazon Rainforest. Performances of the two merging data techniques are compared, qualitatively, by visual inspection and quantitatively, by a statistical analysis, collected from January 1999 to December 2010. The computation of the statistical indices shows that the SCM, with the Cressman scheme, provides slightly better results.

Eficácia da arquitetura MLP em modo closed-loop para simulação de um Sistema Hidrológico

Estimatives of hydrological responses are needed for the watershed planning. The aim of this study was to evaluate the hydrological behavior simulation of the Upper Canoas basin using artificial neural networks Multi Layer Perceptron (MLP) method, as well as to analyze the contribution of the input variables for modeling. It were tested 12 treatments with combinations of variables such as precipitation, evapotranspiration (ET0) and discharge, as well as transformations and temporal displacements of these variables, in order to determine the variables that promoted the better performance on discharge modeling. The MLP was trained in open-loop mode using part of the observed discharges. The discharges for the whole series were simulated in closed-loop, using the discharge simulated on the previous time step as input. The learning algorithm used was the Levenberg-Marquardt. The treatment with the best performance (NS = 0.9119, RMS = 14.29 m3/s) employed the daily precipitation of the four rainfall stations (Urubici, Vila Canoas, Lomba Alta e Anitapolis), precipitation of the four stations with -2 days of response time, and simulated discharge from the previous day. Despite the low RMS, the modeled discharge using MLP was generally overestimated.