Camilo Daleles Rennó

Exploratory study of the relationship between tropical forest regeneration stages and SIR-C L and C data☆

Abstract In this article, the relationship between secondary forest regrowth stage and SIR-C SAR data is assessed, for an area located near to the Tapajos National Forest, south of Para State, in the Amazon region. These regeneration stages are mapped by making use of a consecutive annual sequence of Landsat TM (Thematic Mapper) images. Using this map as a mask over the radar images, the tonal means (expressed in dB) and coefficient of variation (CV) for several second-growth succession stages classes are calculated. It is shown that the discrimination between regeneration stages is difficult when individual ″small areas are used, but this discrimination might be possible when L-band means over a “large area are computed. In particular, the LHV band seems to carry more information. The maximum difference of means among classes occurred in this band and it is of about 5 dB. The CV appeared to be less well suited than the mean value for regeneration stage discrimination, although some discrimination among early stages of regeneration may be possible using this measure at L-band.

Atmosphere and hydrological controls of the evapotranspiration over a floodplain forest in the Bananal Island region, Amazonia

influenced the energy exchange. Soil moisture, which was substantially depleted during the dry season, and adaptative vegetation mechanisms such as leaf senescence contributed to limit the dry season ET. Strong drainage within permeable sandy soils helped to explain the soil moisture depletion. These results suggest that the Bananal flooding area shows a different pattern in relation to the upland Amazon forests, being more similar to the savanna strictu senso areas in central Brazil. For example, seasonal ET variation was not in phase with Rn; the wet season ETwas higher than the dry season ET; and the system stored only a tiny memory of the flooding period, being sensitive to extended drought periods.

Vertical distance from drainage drives floristic composition changes in an Amazonian rainforest

Background: Plant composition changes with topography and edaphic gradients that correlate with soil-water and nutrient availability. Data on soil water for the Amazon Basin are scarce, limiting the possibility of distinguishing between soil and soil-water influences on plant composition. Aim: We tested a new proxy for water table depth, the terrain height above nearest drainage (HAND), as a predictor of composition in trees, lianas, palms, shrubs, and herbs and compared HAND to conventional measures of height above sea level (HASL) and horizontal distances from nearest drainage (HDND). Methods: Plant-species composition in 72 plots distributed across 64 km2 of lowland evergreen terra firme forest was summarised using non-metric multidimensional scaling (NMDS). NMDS scores were regressed against estimates of HAND, HASL and HDND. Results: Plant composition was highly correlated with the vertical distance from water table, capturing up to 82% of variation. All life forms showed highest turnover rates in the zone with seasonally water-saturated soils, which can extend 350 m from stream margins. Conclusions: Floristic composition is closely related to water table depth, and HAND appears to be the most robust available topographical metric of soil-water gradients. Brazilian conservation laws protecting 30-m-wide riparian buffers are likely to be too narrow to encompass the full zone of highest floristic turnover and may be ineffective in safeguarding riparian plant diversity.

An investigation of the selection of texture features for crop discrimination using SAR imagery

This article presents a methodology for selecting texture measures to maximize the discrimination of agricultural land use classes in SAR images. The images were acquired during the first flight of the Shuttle Imaging Radar-C (SIR-C) experiment, in April 1994. L (24 cm)- and C (5 cm)-band SAR data at HH (horizontal transmitting and receiving), HV (horizontal transmitting, vertical receiving), and VV (vertical transmitting and receiving) polarizations both in ground range and slant range and in two different passes were analyzed. The kappa statistic was used to identify meaningful texture measures to discriminate seven classes. The results show that the classifications of land use based only on tonal averages produced a kappa coefficient only slightly higher than 0.50. A kappa threshold of 0.90 was reached with the simultaneous inclusion of 15 texture measures for the six images (two bands, three polarizations). It was also found that the inclusion of texture features when only one band and one polarization was used could produce kappa values higher than 0.85.

Heterogeneity of soils and vegetation in an eastern Amazonian rain forest: Implications for scaling up biomass and production

AbstractTransferring fine-scale ecological knowledge into an understanding of earth system processes presents a considerable challenge to ecologists. Our objective here was to identify and quantify heterogeneity of, and relationships among, vegetation and soil properties in terra firme rain forest ecosystems in eastern Amazonia and assess implications for generating regional predictions of carbon (C) exchange. Some of these properties showed considerable variation among sites; soil textures varied from 11% to 92% clay. But we did not find any significant correlations between soil characteristics (percentage clay, nitrogen [N], C, organic matter) and vegetation characteristics (leaf area index [LAI], foliar N concentration, basal area, biomass, stem density). We found some evidence for increased drought stress on the sandier sites: There was a significant correlation between soil texture and wood δ13C (but not with foliar δ13C); volumetric soil moisture was lower at sandier sites; and some canopy foliage had large, negative dawn water potentials (ψld), indicating limited water availability in the rooting zone. However, at every site at least one foliage sample indicated full or nearly full rehydration, suggesting significant interspecific variability in drought vulnerability. There were significant differences in foliar δ15N among sites, but not in foliar % N, suggesting differences in N cycling but not in plant access to N. We used an ecophysiological model to examine the sensitivity of gross primary production (GPP) to observed inter- and intrasite variation in key driving variables—LAI, foliar N, and ψld. The greatest sensitivity was to foliar N; standard errors on foliar N data translated into uncertainty in GPP predictions up to ±10% on sunny days and ±5% on cloudy days. Local variability in LAI had a minor influence on uncertainty, especially on sunny days. The largest observed reductions in ψld reduced GPP by 4%–6%. If uncertainty in foliar N estimates is propagated into the model, then GPP estimates are not significantly different among sites. Our results suggest that water restrictions in the sandier sites are not enough to reduce production significantly and that texture is not the key control on plant access to N.

Estimativa de área de vegetação secundária na Amazônia Legal Brasileira

Secondary vegetation has many relevant functions to the ecosystems such as atmospheric carbon fixation , maintenance of biodiversity, establishment of connectivity among forest remnants, maintenance of hydrological regime, and restoration of soil fertility. The objective of this work is to estimate the area occupied by secondary vegetation in the Brazilian Legal Amazon (BLA) for 2006 using a sampling scheme. The sampling is based on a stratified approach according to the degree of deforestation observed in the 229 TM-Landsat scenes that cover the BLA. Thus, 26 scenes were selected for 2006 and distributed into seven strata, according to their degree of deforestation, in which secondary vegetation areas were mapped. A regression model was constructed to estimate secondary vegetation area in the remaining images using deforestation area, hydrographic area, agrarian structure , and area of conservation units, as independent variables. The regression analysis found an adjusted R2 of 0.84 and positive coefficients for the proportion of hydrography in the image (2.055) and for the agrarian structure (0.197), while negative coefficients for the degree of deforestation in the image (-0.232) as well as for the proportion of Conservation Unity(-0.262). Using the multivariate regression model, an area of 131,873 km2 of secondary vegetation was estimated for the year of 2006. Applying a Monte Carlo simulation we estimated an uncertainty of approximately 12,445 km2 .

A user-friendly system for synthetic aperture radar image classification based on grayscale distributional properties and context

The purpose of this paper is to present a system for the analysis and classification of Synthetic Aperture Radar (SAR) images. This system, unlike most of its competitors, allows a careful modeling of the statistical properties of the data beyond the usual Gaussian hypothesis. The modeling tools include basic descriptive measures and the choice of suited distributions, through goodness-of-fit tests, to model the data. The classification tools offer the choice between pointwise and contextual (Markovian) techniques, and the quantitative assessment of the quality of the results. The system is goal-driven, and its interfaces are solely based on pull-down menus; the user is prompted with the correct sequence of operations, whenever an invalid option is invoked. An example of the use of this system for the classification of a SAR image is presented.

Window Regression: A Spatial-Temporal Analysis to Estimate Pixels Classified as Low-Quality in MODIS NDVI Time Series

MODerate resolution Imaging Spectroradiometer (MODIS) data are largely used in multitemporal analysis of various Earth-related phenomena, such as vegetation phenology, land use/land cover change, deforestation monitoring, and time series analysis. In general, the MODIS products used to undertake multitemporal analysis are composite mosaics of the best pixels over a certain period of time. However, it is common to find bad pixels in the composition that affect the time series analysis. We present a filtering methodology that considers the pixel position (location in space) and time (position in the temporal data series) to define a new value for the bad pixel. This methodology, called Window Regression (WR), estimates the value of the point of interest, based on the regression analysis of the data selected by a spatial-temporal window. The spatial window is represented by eight pixels neighboring the pixel under evaluation, and the temporal window selects a set of dates close to the date of interest (either earlier or later). Intensities of noises were simulated over time and space, using the MOD13Q1 product. The method presented and other techniques (4253H twice, Mean Value Iteration (MVI) and Savitzky–Golay) were evaluated using the Mean Absolute Percentage Error (MAPE) and Akaike Information Criteria (AIC). The tests revealed the consistently superior performance of the Window Regression approach to estimate new Normalized Difference Vegetation Index (NDVI) values irrespective of the intensity of the noise simulated.

Evaluation of the Tropical Rainfall Measuring Mission 3B43 product over an inland area in Brazil and the effects of satellite boost on rainfall estimates

Abstract The main objective of this study was to evaluate the monthly mean areal rainfall estimated using the Tropical Rainfall Measuring Mission (TRMM) 3B43 version 7 product over an inland area in Central Brazil. Furthermore, we investigated the effect of TRMM orbit boost (in August 2001) over the 3B43 estimates. The TRMM 3B43 estimates were compared to reference rainfall data, collected at 67 rain gauge stations irregularly distributed in the study area. The results showed a good agreement between the TRMM 3B43 areal monthly mean rainfall estimations and reference data ( r > 0.97 ). The error analysis showed that the TRMM 3B43 product tends to overestimate the areal monthly mean rainfall at approximately 1.24%. The root-mean-square error (RMSE) for the entire period was 19.66     mm   month − 1 (15.75%). A Monte Carlo simulation and Wilcoxon statistical test showed that the RMSE increased significantly ( p -value < 0.01 ) after the boost, rising from 15.20 to 23.06     mm   month − 1 . However, the increase in the RMSE does not preclude the use of the TRMM 3B43 product for estimating the monthly mean areal rainfall over the Upper Paraná watershed. The impacts of boost on TRMM 3B43 estimates were observed only for rainfall rates higher than 250     mm   month − 1 .

Evaluating the discrimination of Cu-mineralized rock alteration products from simulated MAPSAR Images in the Curaçá Valley, Brazil

L‐band images simulating MAPSAR satellite sensor data were used to identify Cu‐mineralized rock alteration products in the Curaçá Valley, Brazil. The area is characterized by a semi‐arid terrain, with residual soils and xerophytic vegetation. The mineralizations are mainly associated with pyroxenites but are also found in norites, gabbro‐norites, and anorthosites. These mafic–ultramafic rocks produce characteristic Vertisols. A quantitative analysis for the rock discrimination was based on JM distances derived from training samples (amplitude values of L‐HH, L‐VV, and L‐HV). The investigation showed the possibility of the discrimination of ore‐bearing intrusives from the host rocks, with the exception of amphibolite. Variations in the surface moisture affected the SAR responses, prior to the data acquisition, and caused the higher returns created by wet Vertisols, favouring the discrimination of the rock alteration products.