The NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument has performed tomographic SAR experiments over a number of study areas, including Rabi Forest in Gabon in 2016 and Sierra National Forest in California, USA in 2021. Tomographic SAR, or TomoSAR, is a technique enabling 3-D radar imaging with diverse applications including mapping of vegetation structure. Convolutional neural networks (CNNs) have shown widespread potential for many image processing and computer vision tasks such as image segmentation, classification, and object recognition. By using 3-D CNNs rather than 2-D CNNs, the filters can be applied to all three dimensions of a forest volume imaged by TomoSAR. We have trained 3-D CNN-based deep learning models to estimate canopy height and canopy cover from fully polarimetric UAVSAR TomoSAR images using lidar data as training and validation. When applied to canopy height estimation in the Rabi Forest study area, a trained network had root mean square error (RMSE) of 3.6 m (11%) compared to the validation dataset. For canopy cover estimation in the Sierra National Forest study area, the RMSE was 12%. Further work can be done to optimize the network architecture, improve the output spatial resolution, and to check if these methods can be applied to other study areas or to other vegetation structure parameters such as above-ground biomass. The results show the strong potential of 3-D CNNs for mapping wall-to-wall vegetation structure from tomographic SAR imagery using lidar training data.

Surface Topography and Vegetation (STV) is a NASA targeted observable for maturation into an observing system architecture. STV will acquire high-resolution, global height measurements, including bare surface land topography, ice topography, vegetation structure, and shallow water bathymetry. These measurements serve a broad range of science and applications objectives that span solid earth, cryosphere, biosphere and hydrosphere disciplines. A common set of measurements could meet many of the community needs. STV objectives would be best met by new observing strategies that employ flexible multi-source and sensor measurements from a variety of orbital and sub-orbital assets. Science and application objectives would be best met by new, 3-dimensional observations from lidar, radar, and stereoimaging. Simulations, experiments, data analysis and technology development in interferometric SAR, lidar and stereo photogrammetry approaches, platform options and system architectures will all mature STV toward an observing system.

Human modification of the landscape affects total suspended solids (TSS) concentrations in water. The quantitative extent of these changes remains poorly understood, partly because of the challenges associated with observing TSS dynamics in inland waters over large scales. While many current missions and sensors provide usable data to estimate inland water quality (e.g. Landsat series, VIIRS, Sentinel-2), future missions present the opportunity to increase transferability and accuracy of TSS estimation. Here we degrade assumed ideal spectral data to evaluate the optimal data quality for TSS retrieval using an optical sensor configuration. We also perform wavelet analysis and a river size distribution analysis to study temporal and spatial data quantity requirements, respectively. We find that while the highest resolution data always gives the best retrieval accuracy, some factors are more essential in TSS estimation than others and can simplify mission design. Specifically, fine hyperspectral resolution is key in improving retrieval accuracy and a finer spatial resolution allows exponentially more river surface area to be observed. A revisit period of approximately 5 days or less best captures TSS pulse events, such as floods. Understanding the optimal mission specifications for observing inland water quality, especially TSS, will assist in developing and proposing future optical satellite missions.

Coastal wetlands play a crucial role in supporting diverse ecosystems and providing numerous ecosystem services. The monitoring of wetland hydrodynamics is essential for understanding and assessing their vulnerability to environmental stressors. In recent years, InSAR (Interferometric Synthetic Aperture Radar) time series analysis has emerged as a valuable tool for studying wetland hydrodynamics. However, accurate wetland water level change monitoring is occasionally hindered by the presence of high amounts of atmospheric water vapor over coastal areas, which mislead the interpretation of InSAR retrievals.In this paper, we present a methodological approach based on Independent Component Analysis (ICA) combined with image segmentation as a blind source separation technique to discriminate between Water Level Change (WLC) related features and wet tropospheric delay features here referred to as 'cloud-induced features' in a UAVSAR WLC time series. Our findings provide a specific methodological case study towards addressing the challenges associated with wet tropospheric delay in Airborne InSAR, and a potential alternative solution for improved and more accurate water level change monitoring in coastal wetlands.

AirSWOT is an airborne Ka-band synthetic aperture radar, capable of mapping water surface elevation (WSE) and water surface slope (WSS) using single-pass interferometry. AirSWOT participated in the NASA EVS-3 Delta-X campaign in 2021, which combined remote sensing from multiple instruments with an extensive coincident field data collection in the Mississippi River Delta, Louisiana, USA. As part of Delta-X, AirSWOT flew a greater number of flight lines than in previous AirSWOT campaigns, collecting a significant volume of data which can provide insight into the dynamics and quantity of water in the Atchafalaya and Terrebonne basins of the Mississippi River Delta. AirSWOT data has been processed into publicly available data products at a number of processing levels, depending on user needs and application, including a new Level-3 water surface product developed specifically for Delta-X. The Level-3 water surface product uses water masking and spatial averaging to produce a science-ready point data product, using the Level-2 GeoTIFF raster products as input. The Level-3 data allows profiles of WSE and WSS within designated channels to be easily calculated. AirSWOT estimates of WSE from Delta-X have been compared to in situ water level data with root mean square error (RMSE) of 9 cm, excluding data from two flights in September, 2021 which were adversely affected by poor weather conditions that affected the instrument hardware. Including all data, the RMSE increases to 12 cm. We have also used AirSWOT to help estimate the vertical datum for water level gauges without accurate vertical reference information. AirSWOT is capable of mapping WSE and WSS at high resolution in spatially complex coastal environments, making it a valuable instrument for studying these regions.

We designed a multisource approach for estimating forest above ground biomass (AGB) for the Laurentides Wildlife reserve in Canada, using airborne and spaceborne LiDAR data and radar imagery. Our workflow used detailed forestry inventory data as the starting point of the analysis, and remote sensing data to extend the spatial and temporal coverage of the biomass estimation. We estimated an average value of 35 Mg/ha of biomass for individual forest stands in the reserve, and an increase in average forest stand biomass between 2015 and 2022 of 3 Mg/ha, which indicates that the Laurentides reserve is overall a stable sink of Carbon. Our results show the benefits of using multisource remote sensing data for producing multitemporal and spatially explicit AGB maps.

In this work, we demonstrate how harmonized optical and SAR satellite imagery can be utilized for robust identification of open water surfaces at a global scale. We train an image segmentation architecture based convolutional neural network (CNN) to extract the most salient features from the input data and generate a per-pixel water/not-water classification. We find that combining optical and radar imagery helps reduce false positive and false negative inferences, illustrating the effectiveness of this harmonization. The resulting model is able to classify water surfaces at the resolution of the SAR sensor (12.5 meters) with a validation set precision and recall of 0.74 and 0.81 respectively. We also demonstrate that the trained model is capable of generating inferences beyond the geographic bounds of the training data.

Here, we present an enhanced algorithm to correct interferometric synthetic aperture radar (InSAR) phase unwrapping errors by incorporating iterative spatial bridging between islands and phase closure among interferograms. We use rapid repeat airborne synthetic aperture radar acquisitions from NASA’s airborne uninhabited aerial vehicle synthetic aperture radar (UAVSAR) instrument to estimate short-term changes in water level within coastal wetlands from a stack of consecutive interferograms acquired with very short temporal separation (~30 min). The algorithm is applied to six consecutive UAVSAR images collected in tidal wetlands of the Wax Lake Delta, Louisiana, USA. Validation of our water level change retrievals within situfield observations was conclusive with high correlation and an RMSE generally smaller than 3 cm. Comparison of our algorithm with other phase unwrapping error correction methods shows significant improvement (30%–35% increase in the number of correctly unwrapped pixels) when applied to rapid changes in water level. The set of corrections presented in this work enables measurement of water level change in deltas and other areas where tides drive highly dynamic flooding of inland vegetated areas. Although demonstrated for water level change, the method is applicable to other InSAR datasets with large spatial gradients or observed discontinuities between coherent but spatially isolated areas.

In the above article[1], Table I(b) cited an incorrect reference number. Reference [12] should have been given as [13], provided here as[2].

We map mangrove extents in Pongara National Park, Gabon using the Freeman-Durden Decomposition and InSAR Coherence derived from ALOS-2 imagery. Specifically, we obtain a land cover map derived from both this polarimetric decomposition and a 14-day repeat-pass coherence. Our classification model and results are highly interpretable based on a depth 2 decision tree. We further illustrate the correlation between InSAR coherence and height obtaining rough mangrove height estimates from TanDEM-X data. From our results, we observe that repeat-pass interferometric coherence provides invaluable information about mangrove extents and coastal forests. The clear identification of mangrove extents presents a significant opportunity for NISAR, which will provide 12-day repeat pass images over coastal areas globally.

We propose a framework to estimate high above ground biomass (AGB) from L-band SAR imagery leveraging spaceborne lidars such as GEDI or ICESat-2 and repeat-pass coherence. Our results indicate we are able to overcome model saturation typically associated with purely backscatter methodologies. We validate our approach using lidar-derived AGB maps from the AfriSAR datasets at Mondah, Ogooue, and Lope. We apply our framework to UAVSAR and ALOS-2 imagery to obtain 50 meter resolution biomass maps. We obtain <; 60% nRMSE (in some cases much better) with negligible relative bias using a multiscale random forest model. We illustrate that the inclusion of coherence can significantly improve high AGB estimation particularly at the coastal site Mondah.

High-resolution global-scale estimates of aboveground biomass density will soon be available from a suite of spaceborne LiDAR and radar missions. The 2016-2017 AfriSAR campaign was specifically designed to evaluate a suite of sensors for estimating biomass in a range of tropical forest environments. Here, we compare the calibration and biomass estimates from 5 different active sensors - ALOS Global Digital Surface Model (DSM), Shuttle Radar Topography Mission (SRTM), Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), NASA Land Vegetation Ice Sensor (LVIS), and TanDEM-X (TDX) - in the tallest known mangrove forest on Earth - Pongara National Park, Gabon. We leverage this comparison to evaluate the implications for future satellite missions that are aimed at improving global estimates of forest carbon storage. Our findings are directly relevant for space-borne missions estimating terrestrial carbon storage - GEDI, ICESat-2, NISAR, BIOMASS, Tandem-X, and Tandem-L - highlighting the specific uncertainty and bias that can be expected in several global biomass products in mangrove ecosystems.

The ecosystem science requirements for the NASA ISRO Synthetic Aperture Radar (NISAR) will need to be validated after its launch in 2021 [1]. Out of all disciplines that are encompassed by the NISAR mission, ecosystems are the one in most need of pre-launch proxy data, consisting of repeated L-band observations over an extended period of time. The solid earth, cryosphere, and hazards research communities have been able to use historical and contemporary spaceborne data available from ERS-1/2, Radarsat, TerraSAR, Sentinel-1, and others for developing and evaluating products similar to what NISAR would be able to provide. This has been possible, in part, because of the focus of these disciplines on sparsely vegetated surfaces and the fairly straight-forward correspondence of surface scattering properties at both L- and C-band (wavelength of 24 cm and 5 cm respectively). Time series data from L-band sensors of value for ecosystem science disciplines, in contrast, have been sporadic and irregular. Ecosystems targets are almost always vegetated, with the scattering components and volume scattering nature of the target giving different scattering responses at the different wavelength regimes. For this reason, the use of C-band observations as a proxy for NISAR's L-band, as is often done for other disciplines, is not possible for the development and testing of NISAR algorithms.In 2018, a plan was developed for a field/airborne/spaceborne campaign to acquire data in 2019 for NISAR pre-launch ecosystem algorithm development and for evaluation of NISAR ecosystem Cal/Val protocols. This plan includes the acquisition of not just L-band SAR data from the NASA/JPL UAVSAR airborne SAR, but also the acquisition of spaceborne data, airborne data, and field measurements to fully exercise the NISAR protocols for validation of its ecosystem science measurement requirements. Included in the plan is the processing of the field, airborne, and spaceborne data into validation products and the generation of NISAR-like level 3 science products.

We present a flexible methodology to identify forest loss in synthetic aperture radar (SAR) L-band ALOS/PALSAR images. Instead of single pixel analysis, we generate spatial segments (i.e., superpixels) based on local image statistics to track homogeneous patches of forest across a time-series of ALOS/PALSAR images. Forest loss detection is performed with Support Vector Machines (SVMs)trained on local radar backscatter features derived within superpixels. This method is applied to time-series of ALOS-1 and ALOS-2 radar images over a boreal forest within the Laurentides Wildlife Reserve in Québec. We evaluate four spatial arrangements including 1) single pixels, 2) square grid cells, 3) superpixels based on segmentation of the radar images, and 4) superixels derived from ancillary optical imagery (e.g. Landsat). Detection of forest loss with superpixels outperform single pixel and regular grid methods, especially when superpixels are generated from ancillary optical imagery. Results are validated with official Québec forestry data and Hansen forest loss products. Our results indicate that this approach may be applied operationally to monitor forests across large study areas with L-band radar instruments such as ALOS/PALSAR.

SAR tomographic methods have proven extremely adept at measuring vegetation vertical structure at a variety of wavelengths including L and P-bands [2]. Measuring the three dimensional structure of vegetation and its changes resulting from either natural or anthropogenic causes are key parameters in monitoring ecosystems. The NASA/JPL UAVSAR system has deployed to multiple sites including Alaska over the last several years to conduct tomographic SAR observations at L-band and P-band. This talk will provide a brief overview of a tomographic SAR experiment conducted in the boreal forests of Alaska in August and September of 2017 at both L and P-bands. This site consists mostly of relatively short vegetation with mean height less than 20 m and maximal height less than 25 m. It is sparse compared with previous temperate and tropical forest tomographic observations made by UAVSAR. These observations provides a unique data set to compare tomographic data at L and P-bands for this type of biome.

This paper presents the adaptive reflectance geometric correction (ARGC), a bidirectional reflectance distribution function (BRDF) correction algorithm to address intensity gradients across remotely sensed images. The ARGC is developed and tested on data from the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) collected over Louisiana's Atchafalaya River Delta, an area of complex wetland vegetation and waterbodies suited to AVIRIS-NG's fine spatial and spectral resolutions. Changing view and solar geometry, in conjunction with surfaces' anisotropic properties, impact a scene's observed reflectance. As traditional BRDF corrections may not be appropriate for wetland environments that have distinctive vegetation and hydrologic structures, more flexible functional corrections are shown to improve results. We compared two existing methods and the ARGC. The first method fits a quadratic function over image column averages, and the second is based on the inversion of the Ross Thick and Li Sparse kernels. Building upon the principles of these methods, the ARGC uses a multiple regression-based BRDF correction whereby the image's solar and view geometric descriptors form the independent variables. Each BRDF correction method was applied to the set of six partially overlapping AVIRIS-NG scenes. Assuming the actual surface reflectance of a given land cover type is independent of geometry, we used adjacent images' overlapping regions to quantitatively assess each correction method's efficacy. The ARGC produced the lowest overall root-mean-square difference and the lowest overlap mean absolute difference across the vast majority of bands. The ARGC is proposed as a practical new BRDF correction option for investigators using AVIRIS-NG data.

This paper presents the overall strategy of fusion of full waveform LIDAR and L-band Polarimetric and Interferometric radar (PolInSAR) images of forests in order to predict the forest vertical profile where there is no LIDAR information. The images considered are the radar dataset collected by the Uninhabited Vehicle Synthetic Aperture Radar (UAVSAR) and the Lidar vertical full waveforms acquired by LVIS over boreal forests in the Canadian province of Quebec. We propose dataset descriptors and fusion methods in order to predict lidar from radar. The first challenge is to find features that go beyond the difference of geometrical configurations between the two types of information, and also that compensate the effect of the incidence angle on radar observables. This has been studied in previous work [1] and will be used here in order to focus on the fusion methods. In this paper, we aim to predict the vertical structure of a forest canopy from PolInSAR images. We assume the Lidar waveforms are a good descriptor of structure and use 3 decomposition methods to qualitatively characterizes these waveforms: 1-Relative Height (RH) metrics, 2-Legendre decomposition and 3-spectral clustering. The prediction will be obtained by a neuronal network which requires an input vector representing the PolinSAR data. We extracted 7 parameters from the PolinSAR images: [θmean.hα, θ0.hα, γmean, λmeax, λmin, Rp, Ra]. Prediction of the RH produced a root mean square error (RMSE) of 3.6 m for the top height (I.e. RH100). On the other hand, the Legendre coefficients were predicted with an accuracy of 15%, and the spectral clustering classes were obtained with accuracies better than 80% with 2 classes, but rapidly decreasing as the number of classes (waveform shapes) is increased.

Kapok is a Python library created to estimate forest height using repeat-pass polarimetric synthetic aperture radar interferometry (PolInSAR). The library can import data collected by NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) sensor. The library includes functions for data visualization, coherence region plotting, coherence optimization, and inversion of the random volume over ground forest model. The estimated forest height maps or other output products can be exported in various GIS-ready raster formats for validation and analysis. The software is released in the hopes of growing the UAVSAR user community, as well as in the interests of education and outreach. Kapok has been released under the GNU GPL software license, and the full source code is available for download at: github.com/mdenbina/kapok.

Background The AfriSAR campaign was a joint NASA and European Space Agency airborne campaign conducted in Gabon in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The aim of the campaign was to collect ground, airborne SAR and airborne Lidar data for the development and evaluation of forest structure and biomass retrieval algorithms. The campaign consisted of two deployments, the first in 2015 with the ONERA SETHI SAR system and the second in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR and the DLR F-SAR. In addition, field teams from the Gabon ANPN (Agence Nationale des Parcs Nationaux), University College London and NASA were collecting ground data. Here we focus on the 2016 NASA contributions to campaign.

We have explored the effects of temporal baseline and terrain slope on forest height estimation using L-band repeat-pass polarimetric synthetic aperture radar interferometry. Data were collected using NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar instrument over a study area exhibiting high slope topography in the Laurentides Wildlife Reserve of Québec, Canada. We used lidar-derived canopy height and terrain slope maps to quantify the decorrelation effects that distort the observed coherences compared to the random volume over ground forest model. We derived forest height maps for a number of different temporal baselines using both fixed model parameters and model parameters that varied with slope, and compared the results. Use of a look-up table for the terrain slope effects improved the estimated forest heights, but further work is necessary to see if slope corrections derived from lidar data for this study area can be applied to other study areas, or generalized to a theoretical model.

This paper talks about retrieval of Snow Water Equivalent (SWE) using P-band Signals of Opportunity (SoOp). Modeling is done to show that the phase change in the observed signal is primarily due to change in SWE and is independent of snow density, soil moisture, snow grain size. In order to compare theory to experiment, experiment is conducted at Fraser, CO. Some preliminary data analysis from 1 week of data show that the phase changed when SWE changed.

A new version of the digital elevation model (DEM) generated from Shuttle Radar Topography Mission (SRTM) data is to begin release in 2016. The so-called NASADEM results from re-processing the raw radar echoes and telemetry, guided by global measurements of topography from the ICESat's Geoscience Laser Altimeter System (GLAS). Significant improvements in accuracy were obtained thanks to the removal of large-scale systematic biases due to a variety of arte-facts ranging from residual boom oscillations to the presence of vegetation.

Radiometric correction of radar images is essential to produce accurate estimates of biophysical parameters related to forest structure and biomass. We present a new algorithm to correct radiometry for 1) terrain topography and 2) variations of canopy reflectivity with viewing and tree-terrain geometry. This algorithm is applicable to radar images spanning a wide range of incidence angles over terrain with significant topography and can also take into account aircraft attitude, antenna steering angle, and target geometry. The approach includes elements of both homomorphic and heteromorphic terrain corrections to correct for topographic effects and is followed by an additional radiometric correction to compensate for variations of canopy reflectivity with viewing and tree-terrain geometry. The latter correction is based on lookup tables and enables derivation of biophysical parameters irrespective of viewing geometry and terrain topography. We evaluate the performance of the new algorithm with airborne radar data and show that it performs better than classical homomorphic methods followed by cosine-based corrections.

Mangroves are among the most-carbon rich forest in subtropics and tropics, containing on average 1,023 Mg carbon per hectare [1]. In order to better estimate mangrove biomass, carbon dynamics and land coverage changes, mangrove canopy height is a key parameter. However, there is a surprisingly absence of information needed for global-scale mangrove height mapping because of the lack of high spatial resolution data, available spaceborne data sets, and modeling techniques. In recent studies, the first single-pass TanDEM-X data showed a great possibility of mangrove canopy height estimate with accuracies comparable to airborne lidar canopy height model with single- and dual-Pol-InSAR techniques. Based on the method mentioned in [2], we here generated large-scale mangrove canopy height map with a 12-m spatial resolution over Sundarbans, the world largest mangrove forest, from existing global TDX acquisitions. The inversion result for mangrove canopy height was validated against field measurement data; a correlation coefficient of 0.852 and a RMSE of 0.774 m.

Mangrove Ecosystems thrive in the tropical transition zones between the land and the sea. These marine ecosystems contribute to the biodiversity of land and ocean habitats at various scales, acting as direct link to biogeochemical cycles of both upland and coastal regions. All of the positive and negative drivers of change of both natural and anthropogenic, within watershed and political boundaries play a role in the health and function of mangroves. As a result, they are among the most rapidly changing landscapes in the Americas; yet, the landscape scale dynamics are not well understood, difficult to measure in cloudy regions, and not operationally monitored at the global scale. This research presents a watershed scale monitoring approach of mangrove ecosystems using datasets that are freely available and techniques that are robust for application at the global scale.

This study assesses the potential of single-pass TanDEM-X interferometric SAR (InSAR) data to map forest canopy height, when a corresponding accurate digital terrain model (DTM) is available. In the proposed method, the forest canopy height model (CHM) is extracted by subtracting an airborne lidar DTM from the TanDEM-X digital surface model (DSM). We showed that the TanDEM-X coherence is influenced by local incidence angles and tree basal area. These factors should be taken into account when estimating forest canopy height using TanDEM-X combined to lidar data.

Estimating the amount of above ground biomass in forested areas and the measurement of carbon flux through the quantification of disturbance and regrowth are critical to develop a better understanding of ecosystem processes. Well-resolved and globally consistent inventories of forest carbon must rely on remote sensing measurements, particularly from polarimetric radars. While a wide variety of studies conducted over the past three decades have shown how radar polarimetric measurements can be used to estimate above ground carbon for regions with less than 100 Mg of biomass per hectare, there is no established methodology for assessing biomass estimation accuracy based on a priori instrument and mission parameters. In this paper, a framework for assessing biomass estimation accuracy is presented that is a blend of the basic imaging physics and empirically derived parameters that describe various relationships between biomass and radar polarimetric observable quantities. The implications of this error model on the design and performance of a polarimetric radar are explored using instrument, mission, and science parameters from a notional Earth observing mission.

This paper describes a physical model of the temporal changes that occur in vegetated land surfaces observed by a repeat-pass radar interferometer. We assume the temporal changes to be caused by a Gaussian-statistic motion of the vegetation elements, with motion variance changing along the vertical direction. We show that the temporal correlation between two interferometric radar signals is affected by the structural parameters of the vegetation, such as canopy height, and varies with the wave polarization. We validate the model using L-band data acquired by the Jet Propulsion Laboratory with the Uninhabited Aerial Vehicle Synthetic Aperture Radar airborne radar. This work provides new insights into the role of temporal decorrelation in interferometric radar applications.

We model the temporal decorrelation in volumetric media imaged by a repeat-pass SAR interferometer by using a temporal correlation function that varies with depth. An expression of this function is proposed and based on the Brownian motion of the canopy and soil elements. The spatial and temporal correlation terms are merged in a single coherence model that includes a large class of decorrelation effects, such as those induced by changes in the structure of the medium. We discuss the effects of the temporal correlation function and its implications on the parameters estimation using the POLINSAR random volume over ground model.

Remote sensing technologies have provided valuable data for landscape modeling, vegetation mapping and comprehensive studies of ecosystems. Airborne laser mapping or LIDAR (Light Detection and Ranging) can directly measure the three dimensional structure of plant canopies, as well as, provide accurate digital terrain models (DTM). While temperate and boreal pine forests have been studied using these methods, very limited work has been done in subtropical pine forests. In this study, airborne LIDAR was used to characterize the three dimensional structure of the forest on Long Pine Key at Everglades National Park. Analysis of the vertical distribution of airborne LIDAR data points has shown that distinctive patterns can be described which are characteristic of the vegetation communities and transition zones for pine forests , hammocks and marshes. This information is a valuable resource for forest managers by providing landscape structural data over large areas.

We describe a real-time radar processing algorithm for radar interferometry of Ocean surface with a Wide Swath Ocean Altimeter: a real aperture radar interferometer concept to measure Ocean surface topography on a 200km swath. The algorithm compresses the input data by a factor of over 7000 and improves interferometric correlation by approximating the viewing geometry. It takes into account continuous changes in the viewing geometry due to the Earth's Geoid modeled as a table of n th order polynomials called the viewing scenario table. Each polynomial models a segment of the orbit and approximates how the distance between the platform and the ground varies as a function of time. Various algorithm parameters are updated regularly to compensate for the changes in the viewing geometry. These updates are implemented as time shifts to coregister successive data rangelines and frequency shifts to correct effects related to geometric decorrelation.

In this paper we describe a methodology to map mangrove forests in 3D in the Caribbean region. We used shuttle radar topography mission (SRTM) elevation, lidar and field data to estimate mangrove mean tree height at the landscape scale. This paper emphasizes two regions which are undergoing ecosystem restoration activities: The Everglades National Park, USA and Cienaga Grande de Santa Marta, Colombia. In these regions we used, respectively, airborne and spaceborne (ICEsat (ice, cloud,and land elevation satellite )) lidar data to calibrate SRTM data and estimate mean tree height. Our results show the method is accurate if mangrove forest canopy vertical structure is well characterized.

An analysis technique is presented for quantifying statistically and under a unified framework non-stationarity in polarimetric SAR imagery. The unified framework is provided by a multi-resolution analysis (MRA) based on a particular wavelet frame that works like a differential operator. The wavelet MRA provides local estimates of a statistics called structure function, that in turn can characterize two types of non-stationary behavior: smoothed singularities (e.g. edges, point targets); self-similar processes with stationary increments (e.g. fractional Brownian motion). In the polarimetric case we are interested in the combined dependencies on scale and polarization state. To the purpose an extension of the wavelet MRA is introduced for deriving wavelet representations of an intensity image synthesized at any polarization state (pol-MRA). A novel formalism called the polarimetric structure signature condenses in graphical form the properties of points of discontinuity with respect to scale and polarization transformations. A test case illustrates the application of the pol-MRA technique to the analysis of weak but polarimetrically diverse linear features embedded in clutter.

The Global Rain Forest Mapping project (GRFM) is an initiative started by the National Agency for Space Development of Japan (NASDA) in 1996 with the main goal of creating a wall to wall radar map of the tropical belt with homogeneous and consistent characteristics. GRFM Africa-the part of the project related to tropical Africa-has evolved through several years to the stage where significant thematic products have been generated. It is maintained that these products bear relevance to global change studies and to the sustainable management of local resources in the tropics. The objective of this paper is to lend support to this proposition by illustrating through a few examples the results achieved so far. In particular two land cover maps are presented covering respectively the Central Congo basin, and the Gabon country. Validation of these large-scale high-resolution products poses a challenging problem. The method adopted in GRFM Africa is outlined. It is based on comparison with independent thematic information with known error budget, derived from a combination of optical remote sensing observations, national forestry maps and ground surveys.

The Global Rain Forest Mapping Project (GRFM) is an international collaborative effort initiated and managed by the National Space Development Agency of Japan (NASDA). The main goal of the project is to produce a high resolution wall-to-wall map of the entire tropical rain forest domain in four continents using the L-band SAR onboard the JERS-1 spacecraft. The processing phase, which entails the generation of wide area radar mosaics from the raw SAR data, was split according to the geographic area. In this paper, the focus is on the part related to Africa. The GRFM project's goal calls for the coverage of a continental scale area of several million km/sup 2/ using a sensor with the resolution of tens of meters. In the case of the African continent, this entails the assemblage of some 3900 high resolution SAR scenes into a bitemporal mosaic at 100 m pixel spacing and with known geometric accuracy. While this fact opens up an entire new perspective for vegetation mapping in the tropics, it presents a number of technical challenge. The authors report on the solutions adopted in the GRFM Africa mosaic development and discuss some quantitative and qualitative aspects related to the characterization and validation of the GRFM products. In particular, the mosaic geolocation and its validation are discussed in detail. Indeed, the internal geometric consistency (subpixel accuracy in the coregistration of the two dates), and the absolute geolocation (residual mean squared error of 240 m with respect to ground control points) are key features of the GRFM Africa mosaic.

The objective of this paper is to study the use of a decision tree classifier and multiscale texture measures to extract thematic information on the tropical vegetation cover from the Global Rain Forest Mapping (GRFM) JERS-1 SAR mosaics. The authors focus their study on a coastal region of Gabon, which has a variety of land cover types common to most tropical regions. A decision tree classifier does not assume a particular probability density distribution of the input data, and is thus well adapted for SAR image classification. A total of seven features, including wavelet-based multiscale texture measures (at scales of 200, 400, and 800 m) and multiscale multitemporal amplitude data (two dates at scales 100 and 400 m), are used to discriminate the land cover classes of interest. Among these layers, the best features for separating classes are found by constructing exploratory decision trees from various feature combinations. The decision tree structure stability is then investigated by interchanging the role of the training samples for decision tree growth and testing. They show that the construction of exploratory decision trees can improve the classification results. The analysis also proves that the radar backscatter amplitude is important for separating basic land cover categories such as savannas, forests, and flooded vegetation. Texture is found to be useful for refining flooded vegetation classes. Temporal information from SAR images of two different dates is explicitly used in the decision tree structure to identify swamps and temporarily flooded vegetation.

This paper describes the use of the wavelet transform for multiscale texture analysis. One of the basic problems is that texture measures have to adapt to the peculiarity of radar images that contain multiplicative speckle noise. In this paper, the focus is on the effect of speckle on the wavelet transform. The effect is first assessed analytically. It is shown that the wavelet coefficients are modulated by the multiplicative character of the speckle in a manner that is proportional to the target mean backscattering coefficient. The effect of speckle correlation is also demonstrated. Wavelet decomposition is then applied to a simulated radar image generated by a Monte Carlo approach and based on a statistical model. Modeling shows that the correlation properties of speckle have an effect up to a scale that corresponds to its granular size. The results also show that the main contribution to the wavelet transform for an homogeneous area is the first-order statistical distribution of speckle, which remains important even at large scales. The results are then compared to a ERS-1 synthetic aperture radar (SAR) image of a primary tropical forest region.