J.R. dos Santos

Bio: J.R. dos Santos is an academic researcher from National Institute for Space Research. The author has contributed to research in topics: Land cover & Synthetic aperture radar. The author has an hindex of 9, co-authored 30 publications receiving 308 citations.

Vegetation profiles in tropical forests from multibaseline interferometric synthetic aperture radar, field, and lidar measurements

Abstract: [1] This paper addresses the estimation of vertical vegetation density profiles from multibaseline interferometric synthetic aperture radar (InSAR) data from the AirSAR aircraft at C band over primary, secondary, and abandoned-pasture stands at La Selva Biological Station, Costa Rica in 2004. Profiles were also estimated from field data taken in 2006 and lidar data taken with the LVIS, 25 m spot instrument in 2005. After motivating the study of tropical forest profiles based on their role in the global carbon cycle, ecosystem state, and biodiversity, this paper describes the InSAR, field, and lidar data acquisitions and analyses. Beyond qualitative agreement between profiles from the 3 measurement techniques, results show that InSAR and lidar profile-averaged mean height have RMS scatters about field-measured means of 3.4 m and 3.2 m, 16% and 15% of the average mean height, respectively. InSAR and lidar standard deviations of the vegetation distribution have RMS scatters about the field standard deviations of 1.9 m and 1.5 m, or 27% and 21%, respectively. Dominant errors in the profile-averaged mean height for each measurement technique were modeled. InSAR inaccuracies, dominated by ambiguities in finding the ground altitude and coherence calibration, together account for about 3 m of InSAR error in the mean height. The dominant, modeled error for the field measurements was the inaccuracy in modeling the trees as uniformly filled volumes of leaf area, inducing field errors in mean height of about 3 m. The dominant, modeled lidar error, also due to finding the ground, was 2 m.

Land Use and Land Cover Mapping in the Brazilian Amazon Using Polarimetric Airborne P-Band SAR Data

Abstract: In September 2000, an airborne synthetic aperture radar (SAR) mission acquired unprecedented full polarimetric P-band data over the Tapajos National Forest (Para State), which is an area in the Brazilian Amazon which has been continuously monitored in the last three decades. Eight land use/cover classes were identified, namely, primary forest, regeneration older than 25 years, regeneration between 12 and 25 years, regeneration between 6 and 12 years, regeneration younger than six years, crops/pasture, bare soil, and floodplain (FP). The objective of this paper is to analyze the potential of full polarimetric P-band data in distinguishing different land use/cover classes with a minimum established Kappa value of 75%, using the latest development on SAR statistical characterization. The iterated conditional mode (ICM) contextual classifier was applied to amplitude, intensity images, biomass index, and some polarimetric parameters (entropy, alpha angle, and anisotropy) extracted from the polarimetric P-band data. As the accuracy obtained for eight classes was not acceptable, another two sets, with five and four classes, were formed by the combination of the previous ones. They were defined by confusion matrix analysis and by the graphical analysis of average backscatter values, entropy, [alpha] angle, and anisotropy images and by the H/alpha plans of the land use samples. The classification accuracy with four classes (three levels of biomass plus FP) was then considered acceptable with a Kappa value of 76.81%, using the ICM classification with the adequate bivariate distribution for the HV and VV channels.

Biomass estimation in a tropical wet forest using Fourier transforms of profiles from lidar or interferometric SAR

Abstract: [1] Tropical forest biomass estimation based on the structure of the canopy is a burgeoning and crucial remote sensing capability for balancing terrestrial carbon budgets. This paper introduces a new approach to structural biomass estimation based on the Fourier transform of vertical profiles from lidar or interferometric SAR (InSAR). Airborne and field data were used from 28 tropical wet forest stands at La Selva Biological Station, Costa Rica, with average biomass of 229 Mg-ha−1. RMS scatters of remote sensing biomass estimates about field measurements were 58.3 Mg-ha−1, 21%, and 76.1 Mg-ha−1, 26%, for lidar and InSAR, respectively. Using mean forest height, the RMS scatter was 97 Mg-ha−1, ≈34% for both lidar and InSAR. The confidence that Fourier transforms are a significant improvement over height was >99% for lidar and ≈90% for InSAR. Lidar Fourier transforms determined the useful range of vertical wavelengths to be 14 m to 100 m.

Land use dynamics in the semi-arid region of Brazil (Quixaba, PE): Characterization by principal component analysis (PCA)

Abstract: The changes caused by the pressure of human occupation can be observed by non-sustainable land use forms, in the Brazilian semi-arid region. The multitemporal analysis of changes in the caatinga land cover provides sufficient information about the dynamics of this typical land use. Within this frame, principal component analysis (PCA) was applied in combination with field survey data, which permitted estimation of point samples of new recovery/degradation. The area selected for this study is located in the central-southern semi-arid section of Pernambuco State, north-east Brazil. It covers an area of about 190 km 2 and is located between geographical coordinates 8° 00' to 8° 07' S and 39° 45' to 39° 53' W. Five classes of changed and unchanged multitemporal effects were discriminated. The results of this study were evaluated using combined Geographical Information System (GIS) and field survey techniques. This work was less time-consuming and economically significant for the time period 1983-96. An altern...

Power spectrum analysis of SAR data for spatial forest characterization in Amazonia

Abstract: Power spectrum analysis was used for the analysis of spatial forest features from airborne X‐band synthetic aperture radar (SAR) data in the Brazilian Amazon. Spectral estimates were arrived at empirically by periodograms and correlograms, and from autoregressive moving‐average (ARMA) models. The spectral estimates derived from SAR data were validated by those derived from ground data with locational match. The results obtained by ARMA modelling revealed particularly good correspondence between remote sensing and reference data: repeating patterns at pixel level could be detected in the images. These patterns were shown to arise from canopy structure and distances between major tree individuals; and thus allowed the extraction of parameters of spatial forest structure, particularly of forest density. The method was applied to an example area of primary tropical forest, and its spatial patterns were modelled.

A tutorial on synthetic aperture radar

Abstract: Synthetic Aperture Radar (SAR) has been widely used for Earth remote sensing for more than 30 years. It provides high-resolution, day-and-night and weather-independent images for a multitude of applications ranging from geoscience and climate change research, environmental and Earth system monitoring, 2-D and 3-D mapping, change detection, 4-D mapping (space and time), security-related applications up to planetary exploration. With the advances in radar technology and geo/bio-physical parameter inversion modeling in the 90s, using data from several airborne and spaceborne systems, a paradigm shift occurred from the development driven by the technology push to the user demand pull. Today, more than 15 spaceborne SAR systems are being operated for innumerous applications. This paper provides first a tutorial about the SAR principles and theory, followed by an overview of established techniques like polarimetry, interferometry and differential interferometry as well as of emerging techniques (e.g., polarimetric SAR interferometry, tomography and holographic tomography). Several application examples including the associated parameter inversion modeling are provided for each case. The paper also describes innovative technologies and concepts like digital beamforming, Multiple-Input Multiple-Output (MIMO) and bi- and multi-static configurations which are suitable means to fulfill the increasing user requirements. The paper concludes with a vision for SAR remote sensing.

Carbon emissions from land use and land-cover change

Abstract: . The net flux of carbon from land use and land-cover change (LULCC) accounted for 12.5% of anthropogenic carbon emissions from 1990 to 2010. This net flux is the most uncertain term in the global carbon budget, not only because of uncertainties in rates of deforestation and forestation, but also because of uncertainties in the carbon density of the lands actually undergoing change. Furthermore, there are differences in approaches used to determine the flux that introduce variability into estimates in ways that are difficult to evaluate, and not all analyses consider the same types of management activities. Thirteen recent estimates of net carbon emissions from LULCC are summarized here. In addition to deforestation, all analyses considered changes in the area of agricultural lands (croplands and pastures). Some considered, also, forest management (wood harvest, shifting cultivation). None included emissions from the degradation of tropical peatlands. Means and standard deviations across the thirteen model estimates of annual emissions for the 1980s and 1990s, respectively, are 1.14 ± 0.23 and 1.12 ± 0.25 Pg C yr−1 (1 Pg = 1015 g carbon). Four studies also considered the period 2000–2009, and the mean and standard deviations across these four for the three decades are 1.14 ± 0.39, 1.17 ± 0.32, and 1.10 ± 0.11 Pg C yr−1. For the period 1990–2009 the mean global emissions from LULCC are 1.14 ± 0.18 Pg C yr−1. The standard deviations across model means shown here are smaller than previous estimates of uncertainty as they do not account for the errors that result from data uncertainty and from an incomplete understanding of all the processes affecting the net flux of carbon from LULCC. Although these errors have not been systematically evaluated, based on partial analyses available in the literature and expert opinion, they are estimated to be on the order of ± 0.5 Pg C yr−1.

Polarimetric SAR interferometry

Abstract: In this paper, we examine the role of polarimetry in synthetic aperture radar (SAR) interferometry. We first propose a general formulation for vector wave interferometry that includes conventional scalar interferometry as a special case. Then, we show how polarimetric basis transformations can be introduced into SAR interferometry and applied to form interferograms between all possible linear combinations of polarization states. This allows us to reveal the strong polarization dependency of the interferometric coherence. We then solve the coherence optimization problem involving maximization of interferometric coherence and formulate a new coherent decomposition for polarimetric SAR interferometry that allows the separation of the effective phase centers of different scattering mechanisms. A simplified stochastic scattering model for an elevated forest canopy is introduced to demonstrate the effectiveness of the proposed algorithms. In this way, we demonstrate the importance of wave polarization for the physical interpretation of SAR interferograms. We investigate the potential of polarimetric SAR interferometry using results from the evaluation of fully polarimetric interferometric shuttle imaging radar (SIR)-C/X-SAR data collected during October 8-9, 1994, over the SE Baikal Lake Selenga delta region of Buriatia, Southeast Siberia, Russia.