The Status of Technologies to Measure Forest Biomass and Structural Properties: State of the Art in SAR Tomography of Tropical Forests
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Synthetic aperture radar (SAR) tomography (TomoSAR) is an emerging technology to image the 3D structure of the illuminated media. TomoSAR exploits the key feature of microwaves to penetrate into vegetation, snow, and ice, hence providing the possibility to see features that are hidden to optical and hyper-spectral systems. The research on the use of P-band waves, in particular, has been largely propelled since 2007 in experimental studies supporting the future spaceborne Mission BIOMASS, to be launched in 2022 with the aim of mapping forest aboveground biomass (AGB) accurately and globally. The results obtained in the frame of these studies demonstrated that TomoSAR can be used for accurate retrieval of geophysical variables such as forest height and terrain topography and, especially in the case of dense tropical forests, to provide a more direct link to AGB. This paper aims at providing the reader with a comprehensive understanding of TomoSAR and its application for remote sensing of forested areas, with special attention to the case of tropical forests. We will introduce the basic physical principles behind TomoSAR, present the most relevant experimental results of the last decade, and discuss the potentials of BIOMASS tomography.
KeywordsRemote sensing Forestry Synthetic aperture radar (SAR) Tomography Microwaves Aboveground biomass Forest height Terrain topography BIOMASS
Synthetic aperture radar (SAR) imagery is nowadays a most relevant technology for remote sensing of the natural environment, as witnessed by the increasing number of spaceborne SARs and their improving performance (Moreira 2014). Indeed, SAR systems provide a powerful and unique combination of features relevant to remote sensing, such as large spatial coverage, resolution of the order of few meters, and the possibility to operate largely independent of weather conditions and solar illumination (Curlander and McDonough 1991). Another most relevant feature, which is peculiar to microwave systems, is the capability to probe the interior part of the illuminated media. Indeed, microwaves, especially at long wavelengths, can penetrate for meters, or even tens of meters, into natural media that are non-transparent at optical frequencies, as it is the case for vegetation, snow, ice, and sand. This feature makes SAR data sensitive to the vertical structure of those media, hence providing access to features that are hidden to optical and hyper-spectral sensors, see for example Mariotti d’Alessandro et al. (2013), Tebaldini et al. (2016a, b), Rekioua et al. (2017), Paillou et al. (2003). The downside is that microwave scattering from distributed media may be quite complex, involving a number of different mechanisms through which the wave is backscattered. Considering forested areas, which are the focus of this paper, the radar signal is determined by direct scattering from elements within the vegetation canopy, from the underlying terrain, as well as from multiple scattering resulting from the waves bouncing off the ground in the direction of the radar after being scattered downward by the tree canopy and trunks (Treuhaft and Siqueira 2000; Mariotti d’Alessandro et al. 2013; Papathanassiou and Cloude 2001; Smith-Jonforsen et al. 2005; Lin and Sarabandi 1992). As a result, SAR data analysis has traditionally been carried out based on mathematical models that provide the best trade-off between the variety of phenomena captured by the model and the possibility to produce robust estimates of forest parameters through model inversion; see, for example, Freeman (2007), Treuhaft and Siqueira (2000).
The use of TomoSAR for investigating forest structure has been under analysis for more than a decade, based on theoretical studies and on the analysis of real data from airborne campaigns. Research on the use of P-band waves (wavelength ≈ 70 cm), in particular, has accelerated since 2007 in view of the future P-band spaceborne Mission BIOMASS, which will be launched by the European Space Agency (ESA) in 2022 with the aim of providing AGB accurately and globally (ESA 2012). Early research was generally aimed at retrieving information about the vertical structure of the vegetation, to complement traditional radiometric and interferometric measurements, (Tebaldini 2009; Mariotti d’Alessandro and Tebaldini 2012; Frey and Meyer 2011a, b). Boosted by the first encouraging results, successive studies demonstrated that P-band TomoSAR could effectively be used to derive an accurate characterization of forest structural properties and of the interaction with radar waves. The works in Frey and Meyer (2011a), Tebaldini and Rocca (2012), and Mariotti d’Alessandro et al. (2013), for example, first showed the variation in wave polarization inside the vegetation layer, which could then be used as a fingerprint to detect and quantify the arising of double-bounce scattering from ground–trunk interactions, see in particular (Mariotti d’Alessandro et al. 2013). TomoSAR was also demonstrated to be a most valuable tool to retrieve forest canopy height (Tebaldini and Rocca 2012; Ho Tong Minh et al. 2016) and, by virtue of the penetration capabilities of P-band waves, sub-canopy terrain topography (Tebaldini 2009; Gatti et al. 2011; Mariotti d’Alessandro and Tebaldini 2018a, b). A most important result concerning the application of P-band TomoSAR in tropical forests is the one published in Ho Tong Minh et al. (2014a). In that paper, it was first shown by analyzing a tropical site in French Guiana that tomographic intensity at the height of the ‘main canopy’ in a tropical forest provides a much higher correlation to forest AGB than traditional 2D SAR intensity. It was later shown that model parameterization at one site could be used to predict AGB based on TomoSAR at two sites in French Guiana in Ho Tong Minh et al. (2016), and below, we extend this to three more forest sites in Gabon.
Along with P-band, research on Tomography was carried out at L-band (wavelength ≈ 25 cm) as well, motivated by the proposal of the L-band bistatic SAR systems Tandem-L and SAOCOM-CS (Moreira et al. 2015; ESA 2015). Most noticeable results are those concerning separation of ground and volume scattering (Pardini and Papathanassiou 2017; Tebaldini and Rocca 2012), the impact of weather changes (Pardini et al. 2014), and a study on AGB retrieval from vertical structure parameters extracted using TomoSAR (Toraño Caicoya et al. 2015). Most recently, a study has showed that L-band TomoSAR provides greatly improved correlation to forest AGB in boreal forests (Blomberg et al. 2018). It is worth noting that most research at L-band has thus far been focused on temperate and boreal forests, under the general assumption that L-band waves can hardly penetrate to the ground in dense tropical forests. Yet, in two recent studies carried out in Gabon, it was clearly observed that L-band TomoSAR can actually characterize the full vertical structure of tropical forests (La Valle et al. 2017; Pardini et al. 2018).
The aim of this paper is to provide the reader with a comprehensive understanding of TomoSAR and its application for remote sensing of forested areas, by introducing the basic physical principles behind TomoSAR, the most relevant experimental results obtained in the last decade, and the potential for spaceborne applications. In the exposition, we will mostly focus on the case of P-band TomoSAR and tropical forests. This choice is due to two reasons. In the first place, it is in tropical environments that the use of Tomography appears today to make the most significant difference with respect to conventional SAR and interferometric SAR (InSAR) methods. The second reason is the upcoming BIOMASS Mission, which will implement tomographic imaging for the first 14 months of its lifetime, with optimized performance on equatorial areas (ESA 2012).
This paper is structured as follows. The basic principles required to understand tomographic imaging are introduced and discussed in Sect. 2. Section 3 is intended to provide a brief introduction to forest scattering from forested areas. The use of TomoSAR of the remote sensing of forested areas is discussed in Sects. 4, 5, and 6, which focuses on imaging of the forest structure, forest biomass, and the retrieval of forest height and terrain topography. A discussion of the potentials of tomographic imaging using the future spaceborne mission BIOMASS is provided in Sect. 7. Conclusions are drawn in Sect. 8.
2 Synthetic Aperture Radar Tomography
The expression synthetic aperture radar (SAR) tomography (TomoSAR) is generally used to indicate a microwave imaging technology to focus the illuminated scatterers in 3D space by processing data from multiple SAR acquisitions (Reigber and Moreira 2000). SAR tomographic imaging has been receiving increasing attention in the last years by different research groups, including application fields such as 3D urban scenes, snow, ice sheets, glaciers, and of course forested areas (Reigber and Moreira 2000; Tebaldini 2009; Frey and Meier 2011a; Mariotti d’Alessandro and Tebaldini 2012; Ho Tong Minh et al. 2014a; Banda et al. 2016; Frey et al. 2016; Rekioua et al. 2017; Tebaldini et al. 2016b; Yitayew et al. 2017).
2.1 Models and Algorithms for SAR Tomography
A large variety of approaches for tomographic processing is found in the literature. The most general and accurate approaches to tomographic imaging are obtained as 3D time domain back projection, which allow taking into account strongly irregular trajectories and very large baselines (Frey et al. 2009; Ponce et al. 2014; Tebaldini et al. 2016a, b). In most cases, however, tomographic imaging can be carried out by decoupling focusing in the range–azimuth plane from focusing in elevation. This approach allows casting tomographic processing in terms of a one-dimensional problem, as depicted in Eq. (6), resulting in a substantial advantage in terms of computational burden and enabling the employment of a large variety of techniques from spectral analysis (Gini et al. 2002; Budillon et al. 2011; Zhu and Bamler 2012; Aguilera et al. 2013; Huang et al. 2017). A most interesting aspect of these techniques is that they provide super-resolution capabilities, that is, the capability to resolve targets at a finer resolution than the one expressed in (4). Unfortunately, super-resolution is achieved at the expense of radiometric accuracy, which prevents the application of super-resolution techniques in a general context (Gini et al. 2002; Pardini and Papathanassiou 2017). Finally, a fundamental requirement to enable tomographic focusing is that the knowledge about the position of the radar along the trajectory in all flights is accurate enough to predict variations in the distance travelled by the wave to within an accuracy much better than the system wavelength. This accuracy is seldom met by current navigational systems concerning the location of one flight line with respect to another. As a result, SAR images are affected by space-varying phase disturbances, commonly referred to as phase screens, which produce blurring (Tebaldini and Monti Guarnieri 2010; Tebaldini et al. 2016a, b). For this reason, a preprocessing phase calibration step is quite often required before tomographic focusing (Tebaldini and Monti Guarnieri 2010; Tebaldini et al. 2016a, b; Azcueta and Tebaldini 2017; Mancon et al. 2017).
3 An Introduction to Forest Scattering in the Microwave Regime
Backscattering from the tree canopies. This SM is the result of direct backscattering from woody elements within the vegetation layer. Accordingly, it provides the most direct information about the vertical structure of the vegetation. The resulting signal is depolarized and hence presents with varying intensity in all polarimetric channels.
Backscattering from the terrain. This SM gives rise to a strongly polarized signal, for which the intensity is much larger in like-polarized returns than in cross-polarized ones. Especially at P-band, terrain scattering is weak compared to forest scattering and can usually be neglected.
Double-bounce scattering from trunk–ground interactions. This SM occurs as a result of the two specular reflections of the wave onto the tree trunks and the terrain, or vice versa. After the second reflection, the signal is conveyed back to the radar at varying intensity, depending on the tree characteristics and on topographic slope. Intensity is maximal when terrain topography is flat, whereas it tends to vanish on both positive and negative slopes (Smith-Jonforsen et al. 2005). It may be shown through geometrical arguments that this SM may be regarded as an equivalent point-like scatterer located at the tree trunk base. Hence, it appears at the terrain level in tomographic images. Another peculiar trait of trunk–ground scattering is found in the phase difference between HH and VV polarizations, which typically assumes values ranging from about 90° to 180°, depending on the Fresnel coefficients of the terrain and the tree trunks (Freeman and Durden 1998). Assuming flat topography, trunk–ground scattering is not expected to contribute at HV polarization (Freeman and Durden 1998).
Double-bounce scattering from canopy–ground interactions. This SM results from the waves bouncing off the ground in the direction of the radar after being scattered downward by vegetation elements within the canopy (woody elements at P-band), or vice versa. Canopy–ground scattering appears at the terrain level in tomographic images, as discussed in the case of trunk–ground scattering. The resulting signal is depolarized and hence presents with varying intensity in all polarimetric channels.
4 Sensitivity to Forest Structure
The first tomographic campaign carried out in the frame of BIOMASS studies is BIOSAR 2007, which took place at the Remningstorp forest site, in Southern Sweden (BIOSAR 2008). Prevailing tree species are Norway spruce, Scots pine and birch. The dominant soil type is till with a field layer, when present, of blueberry and narrow-thinned grass. Tree heights are on the order of 20 m, with emergent trees up to 30 m. The topography is fairly flat, terrain elevation above sea level ranging between 120 and 145 m. The acquisition campaign was carried out by DLR from March to May 2007 and comprises 14 fully polarimetric P-band SAR images. The horizontal baseline spacing is approximately 10 m, resulting in a maximum horizontal baseline of approximately 80 m and a vertical resolution ranging from approximately 10 m to 40 m from near to far range.
The subsequent tomographic campaign on boreal forest was BIOSAR (2008), flown in Northern Sweden at P-band and L-band (BIOSAR 2009). Tomographic data from BIOSAR (2008) confirmed the intuitions of BIOSAR 2007, revealing the presence of double-bounce scattering and allowing to study their dependence on topographic slope (Tebaldini and Rocca 2012).
The first tomographic campaign focused on tropical areas was TropiSAR, which was flown in summer 2009 at the two tropical sites of Paracou and Nouragues, French Guiana (TropiSAR 2011). The Paracou site was the first to be investigated using tomography. The forest at this site is classified as a lowland moist forest, with approximately 140–200 tree species per hectare. The tree top height reaches 45 m, with an average canopy height of about 30 m. The acquisition campaign was carried out by ONERA. The tomographic dataset at Paracou and comprises six fully polarimetric P-band SAR images. Vertical resolution is approximately 20 m.
In conclusion, although the results in this section have only been discussed qualitatively, they demonstrate a most important point: SAR tomography is highly sensitive to forest structure. An in-depth assessment of the role of SAR tomography in the remote sensing of forested areas will be presented in the next sections.
5 The Link to Forest Aboveground Biomass
Aboveground biomass (AGB, in Mg of dry matter per hectare or in t/ha) is the mass of vegetation standing aboveground. It is a key quantity as it constitutes an important ecosystem service, but is also a major store of carbon in the biosphere. The first works that studied the link between SAR tomography and AGB in a quantitative manner are those by Ho Tong Minh et al. (2014a, 2016). Both papers are based on an analysis of the correlation between forest AGB available from in situ surveys and the intensity of tomographic horizontal sections corresponding to different heights w.r.t. to the terrain. The analyzed datasets are the ones collected by ONERA during the TropiSAR campaign (TropiSAR 2011). The Paracou site was investigated in Ho Tong Minh et al. (2014a), whereas the Nouragues site was studied in Ho Tong Minh et al. (2016).
The Paracou site is located in a lowland tropical rain forest near Sinnamary. Terrain elevation is between 5 and 50 m, and mean annual temperature is 26 °C, with an annual range of 1–1.5 °C. The landscape is characterized by a relatively flat terrain, which is dissected by narrow streams. As mentioned in the last section, the forest in Paracou is classified as a lowland moist forest. The tree flora at Paracou exceeds 550 woody species attaining 2 cm diameter at breast height (DBH) have been described in Molino and Sabatier (2001), and a single hectare of forest may harbor 140–200 tree species. Top-of-canopy height reaches up to 45 m with the average value around 30 m. The Nouragues Ecological Research Station is located 120 km south of Cayenne, French Guiana, and was established in 1986. This area is a protected natural reserve characterized by a lowland moist tropical rainforest (Sabatier and Prévost 1988; Van Der Meer and Bongers 1996). Recent floristic censuses have recorded over 660 species of trees above 10 cm in trunk diameter (DBH) in a 12-ha plot. The landscape is a succession of small hills, between 60 and 120 m asl covered by a pristine forest. One prominent feature of the landscape is the presence of a granitic hill, called inselberg, with no vegetation at the top. Top-of-canopy height reaches up to 55 m with the average value around 35 m.
In situ forest AGB measurements were available in Paracou from 16 permanent plots established since 1984, and in Nouragues from two large- and long-term permanent plots established in 1992–1994 and regularly surveyed to the present (Ho Tong Minh et al. 2016). At both test sites, plots were subdivided in 100 × 100 m subplots (1 ha), resulting in 85 plots in Paracou and 22 in Nouragues (Ho Tong Minh et al. 2016).
Tomographic data consisted of fully polarimetric P-band SAR images acquired on 14 August 2009 in Nouragues (5 flight tracks) and 10 days later in Paracou (6 flight tracks). Importantly, the tomographic flight lines were displaced in a vertical plane rather than in a horizontal plane, which helped limiting spatial variations in vertical resolution across the scene swath (Dubois-Fernandez et al. 2012). This allowed both forest sites to be imaged at an approximately constant vertical resolution of 20 m without the need for super-resolution imaging techniques, thus preserving radiometric accuracy (Mariotti d’Alessandro et al. 2013; Ho Tong Minh et al. 2016).
2D SAR intensity is poorly correlated with AGB.
Tomographic intensity at 0 m is poorly and negatively correlated with AGB.
Tomographic intensity at 15 m is poorly correlated with AGB.
Tomographic intensity at 30 m is highly correlated with AGB. The observed sensitivity is ≈ 50 Mg/ha per dB.
Interestingly, the relation between TomoSAR intensity at 30 m and AGB becomes increasingly accurate by aggregating plots at a larger scale and produces a correlation coefficient of 0.97 for plot sizes of about 6 ha.
After TropiSAR, the next campaign focused on tropical forests was AfriSAR, which was carried out in Gabon in 2015 and 2016 (AfriSAR 2017). The campaign was shared between ONERA (dry season, July 2015) and DLR (wet season, February 2016) and included tomographic acquisitions at a vertical resolution of about 10 m to 15 m at the forest sites of Lopé, Mondah, Mabounié, and Rabi. These four forest sites are characterized by different physical forest structure types, biomass levels, growth stages, and different levels and kinds of disturbance. All sites contain ground data (permanent tree inventories) together with aerial Lidar scanning of the regions of interest from which reference biomass data have been generated (Labrière et al. 2018). Lopé is located 250 km east of the Libreville airport, and it is characterized by a mosaic between forests and savannas. Forest AGB ranges between a few tons per hectare (in the case of open woody savannas) and up 600t/ha. Two tomographic vertical sections of the Lopé forest sites are shown in Sect. 2, Fig. 3. Mondah is located 25 km north of the Libreville airport. It is a relatively young forest with high variability of density, including disturbed areas due to the proximity to the city. Tree height can also be higher than 50 m. Mabounié is located 180 km southeast of the Libreville airport. The landscape is mostly forested (including swamps and temporarily flooded areas), and most areas are rather hilly (altitude ranges between 25 and 230 m asl). Due to the presence of rare earths, mining exploration took place during the last decades, and many degraded areas are still visible. Rabi is located 260 km south from the Libreville airport. The area of interest contains a 25-ha permanent plot maintained by the Smithsonian Institute, for which extensive ground measurements are available. Next to this plot, an oil extraction area is present around which many degraded areas can be found.
Accordingly, all the results obtained so far at five tropical forest sites in South America and Equatorial Africa clearly indicate that tomographic intensity at 30 m is dramatically more correlated with AGB than 2D SAR intensity. The observed sensitivity was found to be about 50 Mg/ha per dB across the range of AGB values from about 200 to 500 tons/ha.
30 m is a biophysically relevant height in dense tropical forests.
Ground scattering acts as a noise factor on 2D SAR intensity, limiting its sensitivity to AGB. This disturbing factor is most efficiently canceled out in tomographic intensity at 30 m, which would explain the dramatic increase in sensitivity.
5.1 The ecological reason: the role of the 30 m layer
Considering the vertical resolution of the data considered in this paper, tomographic intensity at 30 m accounts approximately for scatterers in the layer from 20 to 40 m above the terrain. Accordingly, the question is whether there is any biophysical reason connecting this layer to total AGB.
To answer this question, we assume a simple structural model of tropical rain forests, which accounts for five layers: the overstorey, the main canopy, the understory, the shrub layer, and the forest floor. This structure is classically observed in aerial Lidar scanning, and even more precisely using terrestrial Lidar scanning. The overstorey refers to the crowns of emergent trees which are above the rest of the canopy (above 40 m). The canopy is the dense ceiling of closely packed trees and their branches centered at about 30 m, while the understory denotes more widely spaced, smaller tree species and young individuals that form a broken layer below the canopy (below 20 m). The shrub layer is characterized by shrubby species and young trees that grow only 2–6 m off the forest floor.
The canopy layer is the principal site for the interchange of heat, water vapor, and atmospheric gases. Under the canopy, there is little direct sunlight due to the extinction of the light through the canopy layer. For these reasons, it is expected that the layer from 20 to 40 m contains a major part of the leaves and a large proportion of woody elements, including trunks and most of the branches (primary, secondary, and higher order) that contribute to the total AGB. Still, the question remains whether the fraction of biomass contained in the 30-m layer is actually representative of the total AGB.
A first answer to this question was given in Ho Tong Minh et al. (2014a) by assuming a forest structure as derived from the TROLL model (Chave 1999; Maréchaux and Chave 2017), which is a spatially explicit forest growth simulator designed to study structural, successional and spatial patterns in natural tropical forests. The model includes competition for light, treefall gap formation and recruitment, which are the critical phenomena in the morphology of tropical forests. The parameters of the model for the species groups have been determined using field data in French Guiana. As a result, an area of 400 × 400 m2 was generated, from which biomass between 20 and 40 m was extracted and compared to total AGB. Simulations showed that biomass contained in the 20–40 m layer is about 40% of the total AGB, and that it is strongly correlated (rp = 0.92) to total AGB over the whole range of AGB from 250 to 700 t/ha (Ho Tong Minh et al. 2014a).
Most interestingly, this result is confirmed by the recent reanalysis of high-resolution discrete-return airborne Lidar data at nine tropical sites in South America (Meyer 2018), which analyzes the correlation between AGB and the area occupied at different heights by large trees. Correlation (R2) was found to be maximum at a height of 27–30 m at all the nine study sites, (Meyer 2018). In conclusion, both ecological modeling and empirical Lidar measurements support the idea that the 30-m layer do actually play a special role in tropical forests, as the fraction of biomass included in it provides a reliable proxy to total AGB.
5.2 The EM reason: the role of ground scattering
The behavior of ground scattering in a tropical forest was thoroughly investigated in Mariotti d’Alessandro et al. (2013) based on the tomographic P-band dataset acquired by ONERA at the forest site of Paracou, French Guiana.
Figure 10 shows that both the intensity and the copolar phase are modulated by ground range slope. In particular, in flat areas, intensity is seen to increase and the copolar phase approaches − 180°, which is a clear indication of the occurrence of double-bounce scattering from ground–trunk interactions (Mariotti d’Alessandro et al. 2013). With tomographic data, the variation in intensity associated with double-bounce scattering was accurately estimated as about 5 dB (see the bottom panels of Fig. 10).
Further analysis based on physical optics showed that the characteristic parameter that rules ground–trunk scattering is not only tree height, but also the length of the base available for ground reflections (Mariotti d’Alessandro et al. 2013), which indicates that this phenomenon is connected to the presence of nearby trees, understory, and undulating topography. The immediate conclusion that can be drawn from this analysis is that even in a dense tropical forest, double-bounce scattering from ground–trunk interactions is relevant on flat areas. Another conclusion is that ground–trunk scattering in tropical forests is strongly connected to local topography, and also to local forest features, such as tree density, and density of the understory. Moreover, although not considered in Mariotti d’Alessandro et al. (2013), soil moisture and canopy–ground scattering are expected to play a role, as predicted by EM models (Truong-Loi et al. 2015).
These conclusions are consistent with the results in Ho Tong Minh (2014a, 2016), showing that ground scattering is poorly and negatively correlated to AGB. Indeed, a slight decay of ground scattering with increasing AGB can be explained by assuming that total wave attenuation increases with AGB. However, as noted above, ground scattering is also determined by several other factors, which necessarily weakens the correlation to AGB.
6 Forest Height and Terrain Topography
7 BIOMASS Tomography
BIOMASS was selected in 2013 to be ESA’s 7th Earth Explorer Core Mission. The mission primary objective is to generate accurate maps of forest biomass and height at a global scale. BIOMASS will be implemented as a fully polarimetric SAR operating at P-band, taking advantage of the understorey penetration capabilities of P-band wavelengths (ESA 2012). Launch date is currently planned in 2022. The satellite will operate in two different observation phases, referred to as tomographic and interferometric phase, respectively. The tomographic phase will operate during the first 14 months of the mission lifetime. In this phase, the satellite will be orbited to provide seven consecutive acquisitions per site from slightly different points of view, hence enabling TomoSAR imaging. Geographical coverage during the tomographic phase will be global, and the seven passes will be acquired with a time lag of 3 days from one another. In the subsequent interferometric phase, the satellite orbits will be modified to achieve faster coverage. This phase will produce three consecutive acquisitions per site at a revisit time of 3 days, enabling AGB and forest height retrieval by SAR Polarimetry and Polarimetric Interferometry (ESA 2012).
The achievement of radiometrically and geometrically accurate tomographic products in the context of BIOMASS is a challenging task. Indeed, one has to account for several potentially damaging factors w.r.t. the airborne case, including degraded signal-to-noise ratio, coarser spatial resolution, propagation through the ionosphere, and temporal decorrelation effects due to the fact that the seven acquisitions needed to implement SAR tomography will be collected in 18 days.
The impact of temporal decorrelation was thoroughly studied in the frame of the long-term campaign TropiSCAT. The TropiSCAT campaign was implemented as a P-band fully polarimetric ground-based campaign, installed at the forest site of Paracou, French Guiana. The goal of the TropiSCAT campaign was to analyze the vertical distribution of temporal decorrelation in tropical forests. The system consists of 20 antennas installed on the 55-m high Guyaflux tower, each of which can be used as a transmitter or a receiver to form an equivalent monostatic vertical array of 15 elements for each polarization. The system was operated to collect tomographic data every 15 min for an overall period of about 1 year (Ho Tong Minh et al. 2013, 2014b). The results published in Ho Tong et al. (2014b, 2015b) indicated that the degradation of tomographic imaging due to temporal decorrelation is acceptable as long as the time lag between two consecutive acquisitions is 4 days or less. A recent study considered the emulation of BIOMASS tomography by mixing acquisitions from TropiSCAT gathered every 3 days, including sunny and rainy days. The results showed a total radiometric error due to temporal decorrelation of 1–1.5 dB, which would entail a biomass retrieval error around about 20% or better at spatial scales on the order of 6 ha (Bai et al. 2018).
The hypothesis that 30 m is a biophysically relevant height in dense tropical forests is strongly supported by ecological modeling (Chave 1999) and by a reanalysis of Lidar data across Amazonia as published in Meyer (2018).
Ground scattering (which includes terrain scattering and double scattering from trunk–ground and canopy–ground interactions) appears to be determined by a complex set of factors other than forest biomass, including local topography, tree density, understorey, and soil moisture. For this reason, it appears unlikely that ground scattering can be directly related to AGB in an operational context, at least in the absence of specific knowledge about local terrain and vegetation features.
In summary, tomography appears to bring the most complete information about AGB in tropical forests by virtue of its ability to single out the returns from different layers within the vegetation while rejecting ground scattering.
Besides forest biomass, SAR tomography was also demonstrated to be a powerful tool for mapping canopy height and sub-canopy terrain topography, as shown by studies conducted at both boreal and tropical sites. Based on results from the recent literature, forest height can be retrieved by SAR tomography to within an accuracy better than 3 m in tropical forests, as validated through a pixel-to-pixel comparison against Lidar data. Retrieval of terrain topography under forests has been shown to be possible in both boreal forests and dense tropical forests, supporting the idea that SAR tomography might be used in a recent future as an alternative to Lidar mapping.
Tomography analyses of forested areas will be implemented for the first time from space during the tomographic phase of the BIOMASS mission. The tomographic phase of BIOMASS will last for the first 14 months of mission lifetime, providing global geographical coverage and enabling tomographic imaging with seven passes acquired with a time lag of 3 days from one another. Notwithstanding many limitations w.r.t. airborne tomography, mostly arising from the coarser resolution and increased temporal decorrelation, studies based on simulated BIOMASS data derived from airborne campaign indicate that accurate tomographic imaging is feasible, and support the idea that forest AGB in tropical could be retrieved to within a 20% accuracy at spatial scales on the order of 6 ha.
All of the results presented within this paper were obtained in the frame of studies funded by the European Space Agency (ESA) in support of the BIOMASS mission, and we acknowledge ESA for the support it gave to the research on SAR Tomography over the last decade. This paper stemmed from the most fruitful Forest Properties Workshop organized in November 2017 in Bern (CH) by the International Space Science Institute (ISSI), which we wish to warmly acknowledge for this initiative. We also acknowledge various funding sources including CNES (France, TOSCA) and an “Investissement d’Avenir” program managed by Agence Nationale de la Recherche (CEBA, Ref. ANR-10-LABX-25-01).
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