João Roberto dos Santos

Savanna and tropical rainforest biomass estimation and spatialization using JERS-1 data

The objective of this study is to show the relation among backscatter signals of JERS-1 images and biophysical parameters (biomass values) of forest and savanna formations. Two contact zones involving these vegetation units in Brazilian Amazonia (Roraima and Mato Grosso States) were selected. A regression model was applied during the analysis of these two variables, based on the best fit function and taking into account the data dispersion. Maps were generated showing biomass spatialization of the vegetation typology found in the study areas. The importance of this study is the innovation referring to the joint analysis of JERS-1 data of these two contact zones in Amazonia, representing both an abrupt contact and a smooth contact along a transition zone of savanna/tropical rainforests formations.

Area and Age of Secondary Forests in Brazilian Amazonia 1978–2002: An Empirical Estimate

In quantifying the carbon budget of the Amazon region, temporal estimates of the extent and age of regenerating tropical forests are fundamental. However, retrieving such information from remote-sensing data is difficult, largely because of spectral similarities between different successional stages and variations in the reflectance of forests following different pathways of regeneration. In this study, secondary-forest dynamics in Brazilian Amazonia were modeled for the 1978–2002 period to determine area and age on a grid basis. We modeled the area, age, and age class distribution of secondary forests using empirical relationships with the percentage of remaining primary forest, as determined from large-area remote-sensing campaigns (the Pathfinder and Prodes projects). The statistical models were calibrated using detailed maps of secondary-forest age generated for seven sites in the Brazilian Legal Amazon. The area–age distribution was then specified from mean age by a distribution assumption. Over the period 1978–2002, secondary-forest area was shown to have increased from 29,000 to 161,000 km2 (that is, by a factor of 5). The mean age increased from 4.4 to 4.8 years. We generated a time series of secondary-forest area fractions and successional stages that provides wall-to-wall coverage of the Brazilian Amazon at a spatial resolution of 0.1 decimal degrees (approximately 11 km). Validation against reference data yielded root mean squared errors of 8% of the total area for estimate of secondary-forest area and 2.4 years for mean secondary-forest age. Using this approach, we provide the first published update on the state of secondary forests in Amazonia since the early 1990s and a time series of secondary-forest area over the 25-year period.

Eucalyptus Biomass and Volume Estimation Using Interferometric and Polarimetric SAR Data

Abstract: This work aims to establish a relationship between volume and biomass with interferometric and radiometric SAR (Synthetic Aperture Radar) response from planted Eucalyptus saligna forest stands, using multi-variable regression techniques. X and P band SAR images from the airborne OrbiSAR-1 sensor, were acquired at the study area in the southeast region of Brazil. The interferometric height (Hint = difference between interferometric digital elevation model in X and P bands), contributed to the models developed due to fact that Eucalyptus forest is composed of individuals whose structure is predominantly cylindrical and vertically oriented, and whose tree heights have great correlation with volume and biomass. The volume model showed that the stand volume was highly correlated with the interferometric height logarithm (Log 10 Hint), since Eucalyptus tree volume has a linear relationship with the vegetation height. The biomass model showed that the combination of both Hint

Tropical-Forest Biomass Estimation at X-Band From the Spaceborne TanDEM-X Interferometer

This letter reports the sensitivity of X-band interferometric synthetic aperture radar (InSAR) data from the first dual-spacecraft radar interferometer, TanDEM-X, to variations in tropical-forest aboveground biomass (AGB). It also reports the first tropical-forest AGB estimates from TanDEM-X data. Tropical forests account for about 50% of the worlds forested biomass and play critical roles in the control of atmospheric carbon dioxide by emission through deforestation and uptake through forest growth. The TanDEM-X InSAR data used in this analysis were taken over the Tapajós National Forest, Pará, Brazil, where field measurements from 30 stands were acquired. The magnitude of the InSAR normalized complex correlation, which is called coherence, decreases by about 25% as AGB increases from 2 to 430 Mg-ha-1, suggesting more vertically distributed return-power profiles with increasing biomass. Comparison of InSAR coherences to those of small-spot (15 cm) lidar suggests that lidar penetrates deeper into the canopies than InSAR. Modeling InSAR profiles from InSAR coherence and lidar profiles yields an estimate of 0.29 dB/m for the X-band extinction coefficient relative to that of lidar. Forest AGB estimated from InSAR observations on 0.25-ha plots shows RMS scatters about the field-estimated AGB between 52 and 62 Mg-ha-1, which is between 29% and 35% of the average AGB of 179 Mg-ha-1, depending on the data analysis mode. The sensitivity and biomass-estimation performance suggest the potential of TanDEM-X observations to contribute to global tropical-forest biomass monitoring.

View-illumination effects on hyperspectral vegetation indices in the Amazonian tropical forest

Abstract Because of the pointing capability of the Hyperion/Earth Observing-One (EO-1) to improve the revisit time of the scene, temporal series of narrowband vegetation indices (VIs) can be generated to study the phenology of the Amazonian tropical forests. In this study, 10 selected narrowband VIs calculated from Hyperion nadir and off-nadir data and from different view directions (forward scattering and backscattering) were analyzed for their sensitivity to view-illumination effects along the dry season on the Seasonal Semi-deciduous Forest. Data analysis was also supported by PROSAIL modeling to simulate the spectral response of this forest type in both directions. Hyperion and PROSAIL results showed that the Enhanced Vegetation Index (EVI) and Photochemical Reflectance Index (PRI) were the two more anisotropic VIs, whereas the Normalized Difference Vegetation Index (NDVI), Structure Insensitive Pigment Index (SIPI) and the Vogelmann Red Edge Index (VOG) were comparatively less sensitive to view-illumination effects. When compared to the other VIs and because of the greater dependence on the near-infrared (NIR) reflectance, EVI showed a different spectral behavior. EVI increased from forward scattering to backscattering and with decreasing solar zenith angle (SZA) towards the end of the local dry season, due to reduction in shading and enhancement of the illumination effects. On the other hand, PRI was higher with increasing shading in the forward scattering direction, as deduced from the PROSAIL simulation. Results emphasized the importance of taking into account bidirectional effects when analyzing temporal series of VIs collected over tropical forests by imaging spectrometers with pointing capability or even by multispectral sensors with large field-of-view (FOV).

Stem volume of tropical forests from polarimetric radar

In this study, we investigated the potential of polarimetric synthetic aperture radar (PolSAR) data for the estimation of stem volume in tropical forests. We used calibrated L-band, high incidence angle data from the airborne system SAR-R99B, acquired over an experimental area in the Tapajós National Forest, Pará, Brazil. To evaluate the potential of PolSAR data for this application we used regression analysis, in which first-order models were fit to predict stem volume per hectare, as determined from field measurements. Unlike previous studies in tropical forests, the set of potential explanatory variables included a series of PolSAR attributes based on phase information, in addition to power measurements. Model selection techniques based on coefficient of determination (R 2) and mean square error (MSE) identified several useful subsets of explanatory variables for stem volume estimation, including backscattering coefficient in HH polarization, cross-polarized ratio, HH-VV phase difference, polarimetric coherence, and the volume scatter component of the Freeman decomposition. Evaluation of the selected models indicated that PolSAR data can be used to quantify stem volume in the study site with a root mean square error (RMSE) of about 20–29 m3 ha−1, corresponding to 8–12% of the mean stem volume. External validation using independent data showed average prediction errors of less than 14%. Saturation effects in measured versus modelled volume were not observed up to volumes of 308 m3 ha−1 (biomasses of ∼357 Mg ha−1). However, no formal assessment of saturation was possible due to limitations of the volume range of the dataset.

Implications of land-use history for forest regeneration in the Brazilian Amazon

Understanding the dynamics of forest regeneration on abandoned agricultural land in the Amazon has often been restricted by limited knowledge of historical land use. This study compared time-series classifications of mature forest, nonforest, and regrowth generated from Landsat sensor data for areas north of the Brazilian cities of Manaus (1973–2003) and Santarém (1984–2003) to chronicle land histories and forest age. At Manaus, active land use prior to abandonment ranged from <1 year with no burning to >10–15 years with burning. Few forests were recleared on more than three occasions. From the mid-1980s, land was increasingly abandoned and, in 2003, over 75% of the deforested area supported regenerating forests, with several being older than 20 years. South of Santarém, forests were cleared up to seven times. In 2003, few regenerating forests were older than 10 years, and all land covers, but particularly mature forest, were damaged by extensive wildfires in 1993 and 1998. Based on previous research, the study concludes that the capacity of regenerating forests to recover biomass and tree species diversity will be reduced where prior land use is more intense, as in Santarém and some clearings north of Manaus.

Land-use and land-cover change processes in the Upper Uruguay Basin: linking environmental and socioeconomic variables

Land-use and land-cover change affects both ecological and socioeconomic processes, motivating the integration of environmental and socioeconomic data to help understand this change. In this study, we propose a method for the characterisation and spatial analysis of land use and cover change in the Upper Uruguay River Basin (Brazil) based on (i) the characterisation of six LUCC processes—degradation, regeneration, intensification, extensification, silviculture expansion and urbanisation—by the combination of 2002 and 2008 land-use and land-cover classifications of Landsat/TM imagery and on (ii) the investigation of the relationships between the LUCC processes and environmental and socioeconomic variables via the combination of canonical correspondence analysis, linear and local spatial regression models (OLS and GWR) and spatial clustering procedures (SKATER), using environmental data, including geomorphometric data, landscape metrics and census socioeconomic statistics. The LUCC processes could be explained in terms of the associations between the selected physical, ecological and social variables that allowed the terrain, landscape fragmentation and socioeconomic characteristics to be related to various LUCC processes.

Mapping recent deforestation in the Brazilian Amazon using simulated L-band MAPSAR images

Brazilian Amazon Forest biomes are presently under intensive land cover conversion from natural vegetation to agriculture. Timely detection of recent deforestation through orbital remote sensing is a critical requirement for an operational land cover monitoring system in order to provide information to the regulatory systems and decision makers. Optical images present drawbacks for operation in the moist tropics and synthetic aperture radar (SAR) data are a real alternative. The feasibility of using multipolarized L‐band images simulating the Multi‐Application Purpose SAR (MAPSAR) satellite was examined for the detection of recent deforestation in the Tapajós region. The discrimination of recent deforestation from other land cover classes was evaluated through a quantitative analysis based on Jeffreys–Matusitas (JM) distances derived from training samples using amplitude values and supported by field survey. The investigation confirmed the possibility of the discrimination of recently deforested classes from other classes based on the L‐band images as proposed in the MAPSAR.

Change vector analysis technique to monitor selective logging activities in Amazon

The lack of sustainability in the exploitation of the tropical Amazon forest has caused a severely impacted environment and biodiversity. In this context, the objective of this study is to detect, characterize and quantify the forest areas affected by timber exploitation in the years 2001 and 2002. This research was developed in the north of the Mato Grosso state, Brazil, utilizing fraction images that were generated by a linear spectral mixture model referring to the mentioned years. Change vector analysis was applied to the difference image of the fraction images from the two years. One image representing magnitude of change, and two images representing the angles of the change vectors were generated. These images are efficient for detection of intensity and type of change that occurred in the forested areas, which are subject to selective logging.