THE AMBIGUITY IN FOREST PROFILES AND XTINCTION ESTIMATED FROM MULTIBASELINE INTERFEROMETRIC SAR

This paper demonstrates by simulation that in the estimation of vegetation profiles from multibaseline interferometric synthetic aperture radar (InSAR), the peak extinction coefficient is poorly determined for typical interferometric coherence and phase accuracies. This coefficient determines overall density and affects the relative density profiles estimated from interferometry. This paper shows that a given radar power profile gives rise to a family of vegetation density profiles, depending on the peak extinction assumed. It is further  demonstrated that estimating the peak extinction requires coherence accuracies of better than 0.1% and phase accuracies of better than a few tenths of a degree, both of which exceed the performance of typical or envisioned SAR systems. Two recommended approaches to profile production with InSAR are 1) use the radar power profile instead of the vegetation density profile for biomass estimation and other ecosystem characterization (in analogy to LIDAR power which is most frequently used for lidar studies of biomass) or 2) apply external information to establish the extinction characteristics needed for vegetation density profiles.Esse artigo procura demonstrar, por simulação, que na estimativa de perfis de volume da vegetação por interferometria  com múltiplas linhas de base, o pico de extinção não é adequadamente determinado pela coerência interferométrica e fase, com acurácias típicas de InSAR. Esse pico determina a densidade global, afetando os perfis de densidade relativa da vegetação estimados por interferometria. Esse trabalho mostra que para um dado perfil de potência-radar há uma série de perfis de densidade da vegetação, dependendo do pico de extinção assumido. É ainda demonstrado que a estimativa do pico de  extinção requer exatidões de coerência melhores que 0,1%, bem como, de acurácias de fases que alguns décimos de graus, valores esses que atualmente excedem o desempenho de sistemas SAR em operação ou aqueles previstos. As duas abordagens recomendadas para a produção de perfis com InSAR são: (1) utilizar o perfil-radar, ao invés do perfil de densidade de vegetação, para estimação de biomassa e outras caracterizações de ecossistema (em nalogia à potência-lidar, a qual é mais  frequentemente utilizada nos estudos de biomassa baseados em LIDAR); ou (2) aplicar informação externa para estabelecer as características de extinção necessárias aos perfis de densidade de vegetação

The NASA AfriSAR campaign: Airborne SAR and lidar measurements of tropical forest structure and biomass in support of current and future space missions

International audienceIn 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30,000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1-hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community

4D Characterization of Short- and Long-term Height-varying Decorrelated Forest SAR Backscattering

Pol-InSAR and 3D multibaseline SAR Tomography (Tomo-SAR) can extract rich information on complex scenarios with multiple scatterers mapped in the SAR cell, in particular for forest remote sensing. However, forest scenarios are characterized by a temporal decorrelating volume canopy scatterer, and a set of related open problems exists, in particular for Tomo-SAR techniques to be applied to spaceborne monitoring of biomass. Multipass 4D Differential Tomography (Diff-Tomo) is a promising advancement, furnishing space (height)-time signatures of multiple scatterer dynamics in the SAR cell, originally with urban applications. In this paper, to better characterize forest decorrelation phenomena impacting Tomo-SAR/Pol-InSAR, experimental results are presented of the extension of Diff-Tomo methods for analyzing vegetated scenes, to extract jointly geometric and dynamic information of forest layers, at both the long and short time scale. The Diff-Tomo enabled functionality of separation in the height dimension of different temporal coherence levels (“coherence profilingâ€) that are mixed (undiscriminated) in classical total coherence analyses is extensively applied to airborne P-band multipolarimetric data, and results of this investigation are shown. Also, first ground-based radar results are presented of an innovative profiling along the height dimension of the short-term coherence, in particular aiming to characterize the magnitudes of short-term coherence times. Their expected variability along the tree structures is confirmed for the first time

First Demonstration of Space-Borne Polarization Coherence Tomography for Characterizing Hyrcanian Forest Structural Diversity

Structural diversity is recognized as a complementary aspect of biological diversity and plays a fundamental role in forest management, conservation, and restoration. Hence, the assessment of structural diversity has become a major effort in the primary international processes, dealing with biodiversity and sustainable forest management. Because of prohibitive costs associated with the ground measurements of forest structure, despite their high accuracy, space-borne polarization coherence tomography (PCT) can introduce an alternative approach given its ability to provide a vertical reflectivity profile and spatiotemporal resolutions related to detecting forest structural changes. In this study, for the first time ever, the potential of space-borne PCT was evaluated in a broad-leaved Hyrcanian forest of Iran over 308 circular sample plots with an area of 0.1 ha. Two aspects of horizontal structure diversity, including standard deviation of diameter at breast height (σdbh) and the number of trees (N), were predicted as important characteristics in wood production and biomass estimation. In addition, the performance of prediction algorithms, including multiple linear regression (MLR), k-nearest neighbors (k-NN), random forest (RF), and support vector regression (SVR) were compared. We addressed the issue of temporal decorrelation in space-borne PCT utilizing the single-pass TanDEM-X interferometer. The data were acquired in standard DEM mode with single polarization of HH. Consequently, airborne laser scanning (ALS) was used to estimate initial values of height hv and ground phase φ0. The Fourier–Legendre series was used to approximate the relative reflectivity profile of each pixel. To link the relative reflectivity profile averaged within each plot with corresponding ground measurements of σdbh and N, thirteen geometrical and physical parameters were defined (P1−P13). Leave-one-out cross validation (LOOCV) showed a better performance of k-NN than the other algorithms in predicting σdbh and N. It resulted in a relative root mean square error (rRMSE) of 32.80%, mean absolute error (MAE) of 4.69 cm, and R2* of 0.25 for σdbh, whereas only 22% of the variation in N was explained using the PCT algorithm with an rRMSE of 41.56%. This study revealed promising results utilizing TanDEM-X data even though the accuracy is still limited. Hence, an entire assessment of the used framework in characterizing the reflectivity profile and the possible effect of the scale is necessary for future studies

First Demonstration of Space-Borne Polarization Coherence Tomography for Characterizing Hyrcanian Forest Structural Diversity

Structural diversity is recognized as a complementary aspect of biological diversity and plays a fundamental role in forest management, conservation, and restoration. Hence, the assessment of structural diversity has become a major effort in the primary international processes, dealing with biodiversity and sustainable forest management. Because of prohibitive costs associated with the ground measurements of forest structure, despite their high accuracy, space-borne polarization coherence tomography (PCT) can introduce an alternative approach given its ability to provide a vertical reflectivity profile and spatiotemporal resolutions related to detecting forest structural changes. In this study, for the first time ever, the potential of space-borne PCT was evaluated in a broad-leaved Hyrcanian forest of Iran over 308 circular sample plots with an area of 0.1 ha. Two aspects of horizontal structure diversity, including standard deviation of diameter at breast height (σdbh) and the number of trees (N), were predicted as important characteristics in wood production and biomass estimation. In addition, the performance of prediction algorithms, including multiple linear regression (MLR), k-nearest neighbors (k-NN), random forest (RF), and support vector regression (SVR) were compared. We addressed the issue of temporal decorrelation in space-borne PCT utilizing the single-pass TanDEM-X interferometer. The data were acquired in standard DEM mode with single polarization of HH. Consequently, airborne laser scanning (ALS) was used to estimate initial values of height hv and ground phase φ0. The Fourier–Legendre series was used to approximate the relative reflectivity profile of each pixel. To link the relative reflectivity profile averaged within each plot with corresponding ground measurements of σdbh and N, thirteen geometrical and physical parameters were defined (P1−P13). Leave-one-out cross validation (LOOCV) showed a better performance of k-NN than the other algorithms in predicting σdbh and N. It resulted in a relative root mean square error (rRMSE) of 32.80%, mean absolute error (MAE) of 4.69 cm, and R2* of 0.25 for σdbh, whereas only 22% of the variation in N was explained using the PCT algorithm with an rRMSE of 41.56%. This study revealed promising results utilizing TanDEM-X data even though the accuracy is still limited. Hence, an entire assessment of the used framework in characterizing the reflectivity profile and the possible effect of the scale is necessary for future studies

Temporal Characteristics of Boreal Forest Radar Measurements

Radar observations of forests are sensitive to seasonal changes, meteorological variables and variations in soil and tree water content. These phenomena cause temporal variations in radar measurements, limiting the accuracy of tree height and biomass estimates using radar data. The temporal characteristics of radar measurements of forests, especially boreal forests, are not well understood. To fill this knowledge gap, a tower-based radar experiment was established for studying temporal variations in radar measurements of a boreal forest site in southern Sweden. The work in this thesis involves the design and implementation of the experiment and the analysis of data acquired. The instrument allowed radar signatures from the forest to be monitored over timescales ranging from less than a second to years. A purpose-built, 50 m high tower was equipped with 30 antennas for tomographic imaging at microwave frequencies of P-band (420-450 MHz), L-band (1240-1375 MHz) and C-band (5250-5570 MHz) for multiple polarisation combinations. Parallel measurements using a 20-port vector network analyser resulted in significantly shorter measurement times and better tomographic image quality than previous tower-based radars. A new method was developed for suppressing mutual antenna coupling without affecting the range resolution. Algorithms were developed for compensating for phase errors using an array radar and for correcting for pixel-variant impulse responses in tomographic images. Time series results showed large freeze/thaw backscatter variations due to freezing moisture in trees. P-band canopy backscatter variations of up to 10 dB occurred near instantaneously as the air temperature crossed 0⁰C, with ground backscatter responding over longer timescales. During nonfrozen conditions, the canopy backscatter was very stable with time. Evidence of backscatter variations due to tree water content were observed during hot summer periods only. A high vapour pressure deficit and strong winds increased the rate of transpiration fast enough to reduce the tree water content, which was visible as 0.5-2 dB backscatter drops during the day. Ground backscatter for cross-polarised observations increased during strong winds due to bending tree stems. Significant temporal decorrelation was only seen at P-band during freezing, thawing and strong winds. Suitable conditions for repeat-pass L-band interferometry were only seen during the summer. C-band temporal coherence was high over timescales of seconds and occasionally for several hours for night-time observations during the summer. Decorrelation coinciding with high transpiration rates was observed at L- and C-band, suggesting sensitivity to tree water dynamics.The observations from this experiment are important for understanding, modelling and mitigating temporal variations in radar observables in forest parameter estimation algorithms. The results also are also useful in the design of spaceborne synthetic aperture radar missions with interferometric and tomographic capabilities. The results motivate the implementation of single-pass interferometric synthetic aperture radars for forest applications at P-, L- and C-band

On the Sensitivity of TanDEM-X-Observations to Boreal Forest Structure

The structure of forests is important to observe for understanding coupling to global dynamics of ecosystems, biodiversity, and management aspects. In this paper, the sensitivity of X-band to boreal forest stem volume and to vertical and horizontal structure in the form of forest height and horizontal vegetation density is studied using TanDEM-X satellite observations from two study sites in Sweden: Remningstorp and Krycklan. The forest was analyzed with the Interferometric Water Cloud Model (IWCM), without the use of local data for model training, and compared with measurements by Airborne Lidar Scanning (ALS). On one hand, a large number of stands were studied, and in addition, plots with different types of changes between 2010 and 2014 were also studied. It is shown that the TanDEM-X phase height is, under certain conditions, equal to the product of the ALS quantities for height and density. Therefore, the sensitivity of phase height to relative changes in height and density is the same. For stands with a phase height >5 m we obtained an root-mean-square error, RMSE, of 8% and 10% for tree height in Remningstorp and Krycklan, respectively, and for vegetation density an RMSE of 13% for both. Furthermore, we obtained an RMSE of 17% for estimation of above ground biomass at stand level in Remningstorp and in Krycklan. The forest changes estimated with TanDEM-X/IWCM and ALS are small for all plots except clear cuts but show similar trends. Plots without forest management changes show a mean estimated height growth of 2.7% with TanDEM-X/IWCM versus 2.1% with ALS and a biomass growth of 4.3% versus 4.2% per year. The agreement between the estimates from TanDEM-X/IWCM and ALS is in general good, except for stands with low phase height

The NASA AfriSAR campaign: Airborne SAR and lidar measurements of tropical forest structure and biomass in support of current and future space missions

In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30,000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1-hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community.Additional co-authors: Bryan Blair, Christy Hansen, Yunling Lou, Ralph Dubayah, Scott Hensley, Carlos Silva, John R Poulsen, Nicolas Labrière, Nicolas Barbier, David Kenfack, Memiaghe Herve, Pulchérie Bissiengou, Alfonso Alonso, Ghislain Moussavou, Simon Lewis, Kathleen Hibbar