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Konstantinos Papathanassiou - One of the best experts on this subject based on the ideXlab platform.
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Multiscale Forest Structure Estimation from SAR Tomography
2018Co-Authors: Marivi Tello Alonso, Victor Cazcarra Bes, Matteo Pardini, Rico Fischer, Konstantinos PapathanassiouAbstract:Recently a framework for Forest Structure based on TomoSAR data has been defined. Based on the spatial distribution of the reflectivity peaks, two complementary descriptors are evaluated at a given scale. This paper high- lights the dependence of the Structure estimate on scale and shows, through analysis on real and simulated data that the restriction to one single scale does not always ensure a complete characterization of Structure. As a consequence, a multiscale framework, accounting for the evolution of the estimated Structure across scales is suggested for a better Forest Structure assessment. Furthermore, the effect of system resolution on the accuracy of the estimation is quantified and thus system requirements for Forest applications are determined.
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Assessment of Tomographic SAR Processing Techniques for Forest Structure Estimation
2018Co-Authors: Victor Cazcarra Bes, Matteo Pardini, Marivi Tello Alonso, Konstantinos PapathanassiouAbstract:The future SAR missions such as BIOMASS and Tandem-L will exploit the potential of Synthetic Aperture Radar Tomography to extract 3D Forest Structure information. Several algorithms can be applied for TomoSAR imaging. This paper analyses the performance of two non-parametric algorithms, Capon Beamforming and Compressive Sensing (CS), for Forest Structure applications, through a set of simulations reflecting different Forest scenarios (distribution of canopy layers and temporal decorrealtion) and system parameters (baseline distribution, multilook, and noise). Results show that CS is in general more stable than Capon in front of system and scene variability, but may be more affected by artefacts.
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Forest Structure monitoring by means of multi-baseline SAR configurations
2018Co-Authors: Konstantinos Papathanassiou, Victor Cazcarra Bes, Matteo Pardini, Marivi Tello Alonso, Jun Su KimAbstract:The penetration capability of microwaves into and through vegetation layers allows scattering interactions across the whole vertical vegetation extend. This, combined with the ability of multi-baseline Synthetic Aperture Radar (SAR) techniques to reconstruct the 3D radar reflectivity opens the door to the use of air- and space-borne SAR configurations to explore and map Forest Structure parameters on large (global) scales with high spatial and temporal resolution. In this paper we discuss the potential of multi-baseline SAR configurations to estimate (or map) physical Forest Structure parameters. For this, first the link between 3D radar reflectivity and 3D Forest Structure has to be established. Accordingly, a framework for qualitative and quantitative Forest Structure interpretation and estimation from 3D radar reflectivity reconstructed by multi-baseline SAR measurements is introduced, reviewed and validated against experimental data (including Lidar and inventory meassurements) acquired over/in different temperate and tropical Forest conditions. The role of system frequency, implementation and spatial resolution is discussed. The dependency of the obtained / possible Structure estimates on the spatial scales is addressed and through the analysis on real and simulated data it is demonstrated that a single (spatial) scale does not always ensure a complete characterization of Forest Structure. The potential of multi-baseline SAR configurations to monitor dynamic effects as the temporal variations of Forest Structure induced by natural or anthropogenic changes is considered. Results achieved in the framework of actual air-borne SAR (F-SAR) temperate and tropical Forest campaigns and/or experiments at different frequencies and/or configurations are critically discussed. Finally, the role of future multibaseline SAR spaceborne configurations / missions as Tandem-L in the context of global Forest Structure and Structure change mapping is discussed.
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Forest Structure Characterization From SAR Tomography at L-Band
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Marivi Tello, Victor Cazcarra Bes, Matteo Pardini, Konstantinos PapathanassiouAbstract:Synthetic aperture radar (SAR) remote sensing configurations are able to provide continuous measurements on global scales sensitive to the vertical Structure of Forests with a high spatial and temporal resolution. Furthermore, the development of tomographic SAR techniques allows the reconstruction of the three-dimensional (3-D) radar reflectivity opening the door for 3-D Forest monitoring. However, the link between 3-D radar reflectivity and 3-D Forest Structure is not yet established. In this sense, this paper introduced a framework that allows a qualitative and quantitative interpretation of physical Forest Structure from tomographic SAR data at L-band. For this, Forest Structure is parameterized into a set of a horizontal and a vertical Structure index. From inventory data, both indices can be derived from the spatial distribution and the dimensions of the trees. Similarly, two Structure indices are derived from the 3-D spatial distribution of the local maxima of the reconstructed 3-D radar reflectivity profiles at L-band. The proposed methodology is tested by means of experimental tomographic L-band data acquired over the temperate Forest site of Traunstein in Germany. The obtained horizontal and vertical Structure indices are validated against the corresponding estimates obtained from inventory measurements and against the same indices derived from the vertical profiles of airborne Lidar data. The high correlation between the Forest Structure indices obtained from these three different data sources (expressed by correlation coefficients between 0.75 and 0.87) indicates the potential of the proposed framework.
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Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography
Remote Sensing, 2017Co-Authors: Victor Cazcarra Bes, Rico Fischer, Maria Tello-alonso, Michael Heym, Konstantinos PapathanassiouAbstract:Synthetic Aperture Radar Tomography (TomoSAR) allows the reconstruction of the 3D reflectivity of natural volume scatterers such as Forests, thus providing an opportunity to infer Structure information in 3D. In this paper, the potential of TomoSAR data at L-band to monitor temporal variations of Forest Structure is addressed using simulated and experimental datasets. First, 3D reflectivity profiles were extracted by means of TomoSAR reconstruction based on a Compressive Sensing (CS) approach. Next, two complementary indices for the description of horizontal and vertical Forest Structure were defined and estimated by means of the distribution of local maxima of the reconstructed reflectivity profiles. To assess the sensitivity and consistency of the proposed methodology, variations of these indices for different types of Forest changes in simulated as well as in real scenarios were analyzed and assessed against different sources of reference data: airborne Lidar measurements, high resolution optical images, and Forest inventory data. The Forest Structure maps obtained indicated the potential to distinguish between different Forest stages and the identification of different types of Forest Structure changes induced by logging, natural disturbance, or Forest management.
Victor Cazcarra Bes - One of the best experts on this subject based on the ideXlab platform.
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Early Lessons on Combining Lidar and Multi-baseline SAR Measurements for Forest Structure Characterization
Surveys in Geophysics, 2019Co-Authors: Matteo Pardini, Wenlu Qi, Victor Cazcarra Bes, Ralph O. Dubayah, Marivi Tello, John Armston, Changhyun Choi, Konstantinos Panagiotis Papathanassiou, Seung-kuk Lee, Lola E. FatoyinboAbstract:The estimation and monitoring of 3D Forest Structure at large scales strongly rely on the use of remote sensing techniques. Today, two of them are able to provide 3D Forest Structure estimates: lidar and synthetic aperture radar (SAR) configurations. The differences in wavelength, imaging geometry, and technical implementation make the measurements provided by the two configurations different and, when it comes to the sensitivity to individual 3D Forest Structure components, complementary. Accordingly, the potential of combining lidar and SAR measurements toward an improved 3D Forest Structure estimation has been recognised from the very beginning. However, until today there is no established framework for this combination. This paper attempts to review differences, commonalities, and complementarities of lidar and SAR measurements. First, vertical lidar reflectance and SAR reflectivity profiles at different wavelengths are compared in different Forest types. Then, current perspectives on their combination for the generation of enhanced Structure products are discussed. Two promising frameworks for combining lidar and SAR measurements are reviewed. The first one is a model-based framework where lidar-derived parameters are used to initialize SAR scattering models, and relies on both the validity of the models and on the physical equivalence of the used lidar and SAR parameters. The second one is a Structure-based framework based on the ability of lidar and SAR measurements to express physical Forest Structure by means of appropriate indices. These indices can then be used to establish a link between the two kind of measurements. The review is supported by experimental results achieved using space- and airborne data acquired in recent relevant mission and campaigns.
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Multiscale Forest Structure Estimation from SAR Tomography
2018Co-Authors: Marivi Tello Alonso, Victor Cazcarra Bes, Matteo Pardini, Rico Fischer, Konstantinos PapathanassiouAbstract:Recently a framework for Forest Structure based on TomoSAR data has been defined. Based on the spatial distribution of the reflectivity peaks, two complementary descriptors are evaluated at a given scale. This paper high- lights the dependence of the Structure estimate on scale and shows, through analysis on real and simulated data that the restriction to one single scale does not always ensure a complete characterization of Structure. As a consequence, a multiscale framework, accounting for the evolution of the estimated Structure across scales is suggested for a better Forest Structure assessment. Furthermore, the effect of system resolution on the accuracy of the estimation is quantified and thus system requirements for Forest applications are determined.
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Assessment of Tomographic SAR Processing Techniques for Forest Structure Estimation
2018Co-Authors: Victor Cazcarra Bes, Matteo Pardini, Marivi Tello Alonso, Konstantinos PapathanassiouAbstract:The future SAR missions such as BIOMASS and Tandem-L will exploit the potential of Synthetic Aperture Radar Tomography to extract 3D Forest Structure information. Several algorithms can be applied for TomoSAR imaging. This paper analyses the performance of two non-parametric algorithms, Capon Beamforming and Compressive Sensing (CS), for Forest Structure applications, through a set of simulations reflecting different Forest scenarios (distribution of canopy layers and temporal decorrealtion) and system parameters (baseline distribution, multilook, and noise). Results show that CS is in general more stable than Capon in front of system and scene variability, but may be more affected by artefacts.
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Forest Structure monitoring by means of multi-baseline SAR configurations
2018Co-Authors: Konstantinos Papathanassiou, Victor Cazcarra Bes, Matteo Pardini, Marivi Tello Alonso, Jun Su KimAbstract:The penetration capability of microwaves into and through vegetation layers allows scattering interactions across the whole vertical vegetation extend. This, combined with the ability of multi-baseline Synthetic Aperture Radar (SAR) techniques to reconstruct the 3D radar reflectivity opens the door to the use of air- and space-borne SAR configurations to explore and map Forest Structure parameters on large (global) scales with high spatial and temporal resolution. In this paper we discuss the potential of multi-baseline SAR configurations to estimate (or map) physical Forest Structure parameters. For this, first the link between 3D radar reflectivity and 3D Forest Structure has to be established. Accordingly, a framework for qualitative and quantitative Forest Structure interpretation and estimation from 3D radar reflectivity reconstructed by multi-baseline SAR measurements is introduced, reviewed and validated against experimental data (including Lidar and inventory meassurements) acquired over/in different temperate and tropical Forest conditions. The role of system frequency, implementation and spatial resolution is discussed. The dependency of the obtained / possible Structure estimates on the spatial scales is addressed and through the analysis on real and simulated data it is demonstrated that a single (spatial) scale does not always ensure a complete characterization of Forest Structure. The potential of multi-baseline SAR configurations to monitor dynamic effects as the temporal variations of Forest Structure induced by natural or anthropogenic changes is considered. Results achieved in the framework of actual air-borne SAR (F-SAR) temperate and tropical Forest campaigns and/or experiments at different frequencies and/or configurations are critically discussed. Finally, the role of future multibaseline SAR spaceborne configurations / missions as Tandem-L in the context of global Forest Structure and Structure change mapping is discussed.
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Forest Structure Characterization From SAR Tomography at L-Band
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Marivi Tello, Victor Cazcarra Bes, Matteo Pardini, Konstantinos PapathanassiouAbstract:Synthetic aperture radar (SAR) remote sensing configurations are able to provide continuous measurements on global scales sensitive to the vertical Structure of Forests with a high spatial and temporal resolution. Furthermore, the development of tomographic SAR techniques allows the reconstruction of the three-dimensional (3-D) radar reflectivity opening the door for 3-D Forest monitoring. However, the link between 3-D radar reflectivity and 3-D Forest Structure is not yet established. In this sense, this paper introduced a framework that allows a qualitative and quantitative interpretation of physical Forest Structure from tomographic SAR data at L-band. For this, Forest Structure is parameterized into a set of a horizontal and a vertical Structure index. From inventory data, both indices can be derived from the spatial distribution and the dimensions of the trees. Similarly, two Structure indices are derived from the 3-D spatial distribution of the local maxima of the reconstructed 3-D radar reflectivity profiles at L-band. The proposed methodology is tested by means of experimental tomographic L-band data acquired over the temperate Forest site of Traunstein in Germany. The obtained horizontal and vertical Structure indices are validated against the corresponding estimates obtained from inventory measurements and against the same indices derived from the vertical profiles of airborne Lidar data. The high correlation between the Forest Structure indices obtained from these three different data sources (expressed by correlation coefficients between 0.75 and 0.87) indicates the potential of the proposed framework.
Matteo Pardini - One of the best experts on this subject based on the ideXlab platform.
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Early Lessons on Combining Lidar and Multi-baseline SAR Measurements for Forest Structure Characterization
Surveys in Geophysics, 2019Co-Authors: Matteo Pardini, Wenlu Qi, Victor Cazcarra Bes, Ralph O. Dubayah, Marivi Tello, John Armston, Changhyun Choi, Konstantinos Panagiotis Papathanassiou, Seung-kuk Lee, Lola E. FatoyinboAbstract:The estimation and monitoring of 3D Forest Structure at large scales strongly rely on the use of remote sensing techniques. Today, two of them are able to provide 3D Forest Structure estimates: lidar and synthetic aperture radar (SAR) configurations. The differences in wavelength, imaging geometry, and technical implementation make the measurements provided by the two configurations different and, when it comes to the sensitivity to individual 3D Forest Structure components, complementary. Accordingly, the potential of combining lidar and SAR measurements toward an improved 3D Forest Structure estimation has been recognised from the very beginning. However, until today there is no established framework for this combination. This paper attempts to review differences, commonalities, and complementarities of lidar and SAR measurements. First, vertical lidar reflectance and SAR reflectivity profiles at different wavelengths are compared in different Forest types. Then, current perspectives on their combination for the generation of enhanced Structure products are discussed. Two promising frameworks for combining lidar and SAR measurements are reviewed. The first one is a model-based framework where lidar-derived parameters are used to initialize SAR scattering models, and relies on both the validity of the models and on the physical equivalence of the used lidar and SAR parameters. The second one is a Structure-based framework based on the ability of lidar and SAR measurements to express physical Forest Structure by means of appropriate indices. These indices can then be used to establish a link between the two kind of measurements. The review is supported by experimental results achieved using space- and airborne data acquired in recent relevant mission and campaigns.
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Multiscale Forest Structure Estimation from SAR Tomography
2018Co-Authors: Marivi Tello Alonso, Victor Cazcarra Bes, Matteo Pardini, Rico Fischer, Konstantinos PapathanassiouAbstract:Recently a framework for Forest Structure based on TomoSAR data has been defined. Based on the spatial distribution of the reflectivity peaks, two complementary descriptors are evaluated at a given scale. This paper high- lights the dependence of the Structure estimate on scale and shows, through analysis on real and simulated data that the restriction to one single scale does not always ensure a complete characterization of Structure. As a consequence, a multiscale framework, accounting for the evolution of the estimated Structure across scales is suggested for a better Forest Structure assessment. Furthermore, the effect of system resolution on the accuracy of the estimation is quantified and thus system requirements for Forest applications are determined.
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Assessment of Tomographic SAR Processing Techniques for Forest Structure Estimation
2018Co-Authors: Victor Cazcarra Bes, Matteo Pardini, Marivi Tello Alonso, Konstantinos PapathanassiouAbstract:The future SAR missions such as BIOMASS and Tandem-L will exploit the potential of Synthetic Aperture Radar Tomography to extract 3D Forest Structure information. Several algorithms can be applied for TomoSAR imaging. This paper analyses the performance of two non-parametric algorithms, Capon Beamforming and Compressive Sensing (CS), for Forest Structure applications, through a set of simulations reflecting different Forest scenarios (distribution of canopy layers and temporal decorrealtion) and system parameters (baseline distribution, multilook, and noise). Results show that CS is in general more stable than Capon in front of system and scene variability, but may be more affected by artefacts.
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Forest Structure monitoring by means of multi-baseline SAR configurations
2018Co-Authors: Konstantinos Papathanassiou, Victor Cazcarra Bes, Matteo Pardini, Marivi Tello Alonso, Jun Su KimAbstract:The penetration capability of microwaves into and through vegetation layers allows scattering interactions across the whole vertical vegetation extend. This, combined with the ability of multi-baseline Synthetic Aperture Radar (SAR) techniques to reconstruct the 3D radar reflectivity opens the door to the use of air- and space-borne SAR configurations to explore and map Forest Structure parameters on large (global) scales with high spatial and temporal resolution. In this paper we discuss the potential of multi-baseline SAR configurations to estimate (or map) physical Forest Structure parameters. For this, first the link between 3D radar reflectivity and 3D Forest Structure has to be established. Accordingly, a framework for qualitative and quantitative Forest Structure interpretation and estimation from 3D radar reflectivity reconstructed by multi-baseline SAR measurements is introduced, reviewed and validated against experimental data (including Lidar and inventory meassurements) acquired over/in different temperate and tropical Forest conditions. The role of system frequency, implementation and spatial resolution is discussed. The dependency of the obtained / possible Structure estimates on the spatial scales is addressed and through the analysis on real and simulated data it is demonstrated that a single (spatial) scale does not always ensure a complete characterization of Forest Structure. The potential of multi-baseline SAR configurations to monitor dynamic effects as the temporal variations of Forest Structure induced by natural or anthropogenic changes is considered. Results achieved in the framework of actual air-borne SAR (F-SAR) temperate and tropical Forest campaigns and/or experiments at different frequencies and/or configurations are critically discussed. Finally, the role of future multibaseline SAR spaceborne configurations / missions as Tandem-L in the context of global Forest Structure and Structure change mapping is discussed.
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Forest Structure Characterization From SAR Tomography at L-Band
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Marivi Tello, Victor Cazcarra Bes, Matteo Pardini, Konstantinos PapathanassiouAbstract:Synthetic aperture radar (SAR) remote sensing configurations are able to provide continuous measurements on global scales sensitive to the vertical Structure of Forests with a high spatial and temporal resolution. Furthermore, the development of tomographic SAR techniques allows the reconstruction of the three-dimensional (3-D) radar reflectivity opening the door for 3-D Forest monitoring. However, the link between 3-D radar reflectivity and 3-D Forest Structure is not yet established. In this sense, this paper introduced a framework that allows a qualitative and quantitative interpretation of physical Forest Structure from tomographic SAR data at L-band. For this, Forest Structure is parameterized into a set of a horizontal and a vertical Structure index. From inventory data, both indices can be derived from the spatial distribution and the dimensions of the trees. Similarly, two Structure indices are derived from the 3-D spatial distribution of the local maxima of the reconstructed 3-D radar reflectivity profiles at L-band. The proposed methodology is tested by means of experimental tomographic L-band data acquired over the temperate Forest site of Traunstein in Germany. The obtained horizontal and vertical Structure indices are validated against the corresponding estimates obtained from inventory measurements and against the same indices derived from the vertical profiles of airborne Lidar data. The high correlation between the Forest Structure indices obtained from these three different data sources (expressed by correlation coefficients between 0.75 and 0.87) indicates the potential of the proposed framework.
Rico Fischer - One of the best experts on this subject based on the ideXlab platform.
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The Relevance of Forest Structure for Biomass and Productivity in Temperate Forests: New Perspectives for Remote Sensing
Surveys in Geophysics, 2019Co-Authors: Rico Fischer, Friedrich Bohn, Nikolai Knapp, Herman H. Shugart, Andreas HuthAbstract:Forests provide important ecosystem services such as carbon sequestration. Forest landscapes are intrinsically heterogeneous—a problem for biomass and productivity assessment using remote sensing. Forest Structure constitutes valuable additional information for the improved estimation of these variables. However, survey of Forest Structure by remote sensing remains a challenge which results mainly from the differences in Forest Structure metrics derived by using remote sensing compared to classical structural metrics from field data. To understand these differences, remote sensing measurements were linked with an individual-based Forest model. Forest Structure was analyzed by lidar remote sensing using metrics for the horizontal and vertical Structures. To investigate the role of Forest Structure for biomass and productivity estimations in temperate Forests, 25 lidar metrics of 375,000 simulated Forest stands were analyzed. For the lidar-based metrics, top-of-canopy height arose as the best predictor for describing horizontal Forest Structure. The standard deviation of the vertical foliage profile was the best predictor for the vertical heterogeneity of a Forest. Forest Structure was also an important factor for the determination of Forest biomass and aboveground wood productivity. In particular, horizontal Structure was essential for Forest biomass estimation. Predicting aboveground wood productivity must take into account both horizontal and vertical Structures. In a case study based on these findings, Forest Structure, biomass and aboveground wood productivity are mapped for whole of Germany. The dominant type of Forest in Germany is dense but less vertically Structured Forest stands. The total biomass of all German Forests is 2.3 Gt, and the total aboveground woody productivity is 43 Mt/year. Future remote sensing missions will have the capability to provide information on Forest Structure (e.g., from lidar or radar). This will lead to more accurate assessments of Forest biomass and productivity. These estimations can be used to evaluate Forest ecosystems related to climate regulation and biodiversity protection.
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Remote Sensing Measurements of Forest Structure Types for Ecosystem Service Mapping
Atlas of Ecosystem Services, 2019Co-Authors: Rico Fischer, Friedrich Bohn, Nikolai Knapp, Andreas HuthAbstract:Forests represent an important pool in the global carbon cycle. However, biomass stocks and carbon fluxes are variable due to the fact that Forest dynamics are driven by processes that act on different spatial and temporal scales. Estimating Forest biomass and productivity for larger regions is therefore a major challenge. In this study, horizontal and vertical Forest Structure is used to improve Forest ecosystem service mapping by remote sensing. By linking remote sensing techniques with vegetation modelling (here FORMIND) and Forest inventories, Forest Structure maps were derived for Germany (resolution 4 km). Using these maps, the role of Forest Structure for selected ecosystem services of Forests has been investigated. For Forest state estimations (like biomass) horizontal Forest Structure plays a key role while for productivity estimations both horizontal and vertical Structures are relevant. This concept of Forest Structure classification in combination with Forest modelling and remote sensing has high potential for applications at continental scales as future remote sensing missions will provide information on Forest Structure.
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Multiscale Forest Structure Estimation from SAR Tomography
2018Co-Authors: Marivi Tello Alonso, Victor Cazcarra Bes, Matteo Pardini, Rico Fischer, Konstantinos PapathanassiouAbstract:Recently a framework for Forest Structure based on TomoSAR data has been defined. Based on the spatial distribution of the reflectivity peaks, two complementary descriptors are evaluated at a given scale. This paper high- lights the dependence of the Structure estimate on scale and shows, through analysis on real and simulated data that the restriction to one single scale does not always ensure a complete characterization of Structure. As a consequence, a multiscale framework, accounting for the evolution of the estimated Structure across scales is suggested for a better Forest Structure assessment. Furthermore, the effect of system resolution on the accuracy of the estimation is quantified and thus system requirements for Forest applications are determined.
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Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography
Remote Sensing, 2017Co-Authors: Victor Cazcarra Bes, Rico Fischer, Maria Tello-alonso, Michael Heym, Konstantinos PapathanassiouAbstract:Synthetic Aperture Radar Tomography (TomoSAR) allows the reconstruction of the 3D reflectivity of natural volume scatterers such as Forests, thus providing an opportunity to infer Structure information in 3D. In this paper, the potential of TomoSAR data at L-band to monitor temporal variations of Forest Structure is addressed using simulated and experimental datasets. First, 3D reflectivity profiles were extracted by means of TomoSAR reconstruction based on a Compressive Sensing (CS) approach. Next, two complementary indices for the description of horizontal and vertical Forest Structure were defined and estimated by means of the distribution of local maxima of the reconstructed reflectivity profiles. To assess the sensitivity and consistency of the proposed methodology, variations of these indices for different types of Forest changes in simulated as well as in real scenarios were analyzed and assessed against different sources of reference data: airborne Lidar measurements, high resolution optical images, and Forest inventory data. The Forest Structure maps obtained indicated the potential to distinguish between different Forest stages and the identification of different types of Forest Structure changes induced by logging, natural disturbance, or Forest management.
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Towards Forest Structure Characteristics Retrieval from SAR Tomographic Profiles
2013Co-Authors: Marivi Tello, Matteo Pardini, Konstantinos Papathanassiou, Rico FischerAbstract:SAR Tomography has proven to be a unique tool for the retrieval of 3D Structure information from Forest scenarios: it can reveal different scattering mechanisms at different heights. However, the translation of these measurements into relevant Forest Structure information is not straightforward and research is still ongoing. In this direction, this paper suggest a framework for the estimation of Forest Structure from a SAR tomography scheme based on a low number of single pass coherences. Vertical reflectivity profiles are estimated by means of Compressive Sensing Imaging techniques. Two complementary descriptors are then suggested accounting for the simultaneous vertical and horizontal spatial variability of the scene. Their ability to reflect a characteristic Structure behavior for the different types of Forest considered is analyzed in simulated and real scenarios.
Marivi Tello - One of the best experts on this subject based on the ideXlab platform.
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Early Lessons on Combining Lidar and Multi-baseline SAR Measurements for Forest Structure Characterization
Surveys in Geophysics, 2019Co-Authors: Matteo Pardini, Wenlu Qi, Victor Cazcarra Bes, Ralph O. Dubayah, Marivi Tello, John Armston, Changhyun Choi, Konstantinos Panagiotis Papathanassiou, Seung-kuk Lee, Lola E. FatoyinboAbstract:The estimation and monitoring of 3D Forest Structure at large scales strongly rely on the use of remote sensing techniques. Today, two of them are able to provide 3D Forest Structure estimates: lidar and synthetic aperture radar (SAR) configurations. The differences in wavelength, imaging geometry, and technical implementation make the measurements provided by the two configurations different and, when it comes to the sensitivity to individual 3D Forest Structure components, complementary. Accordingly, the potential of combining lidar and SAR measurements toward an improved 3D Forest Structure estimation has been recognised from the very beginning. However, until today there is no established framework for this combination. This paper attempts to review differences, commonalities, and complementarities of lidar and SAR measurements. First, vertical lidar reflectance and SAR reflectivity profiles at different wavelengths are compared in different Forest types. Then, current perspectives on their combination for the generation of enhanced Structure products are discussed. Two promising frameworks for combining lidar and SAR measurements are reviewed. The first one is a model-based framework where lidar-derived parameters are used to initialize SAR scattering models, and relies on both the validity of the models and on the physical equivalence of the used lidar and SAR parameters. The second one is a Structure-based framework based on the ability of lidar and SAR measurements to express physical Forest Structure by means of appropriate indices. These indices can then be used to establish a link between the two kind of measurements. The review is supported by experimental results achieved using space- and airborne data acquired in recent relevant mission and campaigns.
-
Forest Structure Characterization From SAR Tomography at L-Band
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Marivi Tello, Victor Cazcarra Bes, Matteo Pardini, Konstantinos PapathanassiouAbstract:Synthetic aperture radar (SAR) remote sensing configurations are able to provide continuous measurements on global scales sensitive to the vertical Structure of Forests with a high spatial and temporal resolution. Furthermore, the development of tomographic SAR techniques allows the reconstruction of the three-dimensional (3-D) radar reflectivity opening the door for 3-D Forest monitoring. However, the link between 3-D radar reflectivity and 3-D Forest Structure is not yet established. In this sense, this paper introduced a framework that allows a qualitative and quantitative interpretation of physical Forest Structure from tomographic SAR data at L-band. For this, Forest Structure is parameterized into a set of a horizontal and a vertical Structure index. From inventory data, both indices can be derived from the spatial distribution and the dimensions of the trees. Similarly, two Structure indices are derived from the 3-D spatial distribution of the local maxima of the reconstructed 3-D radar reflectivity profiles at L-band. The proposed methodology is tested by means of experimental tomographic L-band data acquired over the temperate Forest site of Traunstein in Germany. The obtained horizontal and vertical Structure indices are validated against the corresponding estimates obtained from inventory measurements and against the same indices derived from the vertical profiles of airborne Lidar data. The high correlation between the Forest Structure indices obtained from these three different data sources (expressed by correlation coefficients between 0.75 and 0.87) indicates the potential of the proposed framework.
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IGARSS - Assessment of Forest Structure estimation by means of SAR Tomography: Potential and limitations
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Marivi Tello, Victor Cazcarra Bes, Matteo Pardini, Kostas PapathanassiouAbstract:Systems based on Synthetic Aperture Radar Tomography at low frequencies offer 3D imaging capabilities, appropriate for Forest monitoring. However the extraction of an ecologically meaningful measure of Forest Structure from the 3D reflectivity is not straightforward and several considerations need to be carefully taken into account, in order to avoid misinterpretations of the nature of the information reflected in the tomograms. Besides, it should be noted that the methodology employed in the TomoSAR inversion has a significant effect on the overall performance of the TomoSAR system to estimate Forest Structure. In this framework, this paper discusses the potential and limitations of TomoSAR systems for Forest Structure estimation.
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Towards Forest Structure Characteristics Retrieval from SAR Tomographic Profiles
2013Co-Authors: Marivi Tello, Matteo Pardini, Konstantinos Papathanassiou, Rico FischerAbstract:SAR Tomography has proven to be a unique tool for the retrieval of 3D Structure information from Forest scenarios: it can reveal different scattering mechanisms at different heights. However, the translation of these measurements into relevant Forest Structure information is not straightforward and research is still ongoing. In this direction, this paper suggest a framework for the estimation of Forest Structure from a SAR tomography scheme based on a low number of single pass coherences. Vertical reflectivity profiles are estimated by means of Compressive Sensing Imaging techniques. Two complementary descriptors are then suggested accounting for the simultaneous vertical and horizontal spatial variability of the scene. Their ability to reflect a characteristic Structure behavior for the different types of Forest considered is analyzed in simulated and real scenarios.