Sub-canopy Topography Research Articles

InSAR-DEM Block Adjustment Model for Upcoming BIOMASS Mission: Considering Atmospheric Effects

The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m.

Automated Estimation of Sub-Canopy Topography Combined with Single-Baseline Single-Polarization TanDEM-X InSAR and ICESat-2 Data

TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data.

A Gradient-Constrained Morphological Operation for Retrieving Subcanopy Topography Over Densely Forested Areas From ICESat-2/ATL03 Data

A Gradient-Constrained Morphological Operation for Retrieving Subcanopy Topography Over Densely Forested Areas From ICESat-2/ATL03 Data

Simultaneous Estimation of Subcanopy Topography and Forest Height With Single-Baseline Single-Polarization TanDEM-X Interferometric Data Combined With ICESat-2 Data

Simultaneous Estimation of Subcanopy Topography and Forest Height With Single-Baseline Single-Polarization TanDEM-X Interferometric Data Combined With ICESat-2 Data

High-resolution sub-canopy topography mapping via TanDEM-X DEM combined with future P-band BIOMASS PolInSAR data

High-resolution sub-canopy topography mapping via TanDEM-X DEM combined with future P-band BIOMASS PolInSAR data

Parameterized Modeling and Calibration for Orbital Error in TanDEM-X Bistatic SAR Interferometry over Complex Terrain Areas

The TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) bistatic system provides high-resolution and high-quality interferometric data for global topographic measurement. Since the twin TanDEM-X satellites fly in a close helix formation, they can acquire approximately simultaneous synthetic aperture radar (SAR) images, so that temporal decorrelation and atmospheric delay can be ignored. Consequently, the orbital error becomes the most significant error limiting high-resolution SAR interferometry (InSAR) applications, such as the high-precision digital elevation model (DEM) reconstruction, subway and highway deformation monitoring, landslide monitoring and sub-canopy topography inversion. For rugged mountainous areas, in particular, it is difficult to estimate and correct the orbital phase error in TanDEM-X bistatic InSAR. Based on the rigorous InSAR geometric relationship, the orbital phase error can be attributed to the baseline errors (BEs) after fixing the positions of the master SAR sensor and the targets on the ground surface. For the constraint of the targets at a study scene, the freely released TanDEM-X DEM can be used, due to its consistency with the TanDEM-X bistatic InSAR-measured height. As a result, a parameterized model for the orbital phase error estimation is proposed in this paper. In high-resolution and high-precision TanDEM-X bistatic InSAR processing, due to the limited precision of the navigation systems and the uneven baseline changes caused by the helix formation, the BEs are time-varying in most cases. The parameterized model is thus built and estimated along each range line. To validate the proposed method, two mountainous test sites located in China (i.e., Fuping in Shanxi province and Hetang in Hunan province) were selected. The obtained results show that the orbital phase errors of the bistatic interferograms over the two test sites are well estimated. Compared with the widely applied polynomial model, the residual phase corrected by the proposed method contains little undesirable topography-dependent phase error, and avoids unexpected height errors ranging about from −6 m to 3 m for the Fuping test site and from −10 m to 8 m for the Hetang test site. Furthermore, some fine details, such as ridges and valleys, can be clearly identified after the correction. In addition, the two components of the orbital phase error, i.e., the residual flat-earth phase error and the topographic phase error caused by orbital error, are separated and quantified based on the parameterized expression. These demonstrate that the proposed method can be used to accurately estimate and mitigate the orbital phase error in TanDEM-X bistatic InSAR data, which increases the feasibility of reconstructing high-resolution and high-precision DEM. The rigorous geometric constraint, the refinement of the initial baseline parameters, and the assessment for height errors based on the estimated BEs are investigated in the discussion section of this paper.

Estimation of subcanopy topography based on single-baseline TanDEM-X InSAR data

Estimation of subcanopy topography based on single-baseline TanDEM-X InSAR data

Sub-Canopy Topography Estimation from TanDEM-X DEM by Fusing ALOS-2 PARSAR-2 InSAR Coherence and GEDI Data

This paper develops a framework for extracting sub-canopy topography from the TanDEM-X digital elevation model (DEM) by fusing ALOS-2 PARSAR-2 interferometric synthetic aperture radar (InSAR) coherence and Global Ecosystem Dynamics Investigation (GEDI) data. The main idea of this method is to estimate the forest height signals caused by the limited penetration of the X-band into the canopy from the TanDEM-X DEM. To achieve this goal, a spaceborne repeat-pass InSAR coherent scattering model is first used to estimate the forest height by the ALOS-2 PARSAR-2 InSAR coherence (APIC), taking the GEDI canopy height as the reference. Then, a linear regression model of the TanDEM-X DEM Vegetation Bias (TDVB) depending on the forest height and the fraction of vegetation cover (FVC) is established and used to estimate the sub-canopy topography. The proposed method was validated by the data of the Amazon rainforest and a boreal forest in Canada. The results showed that the proposed method extracted the sub-canopy topography at the study sites in the tropical forest and boreal forest with the root mean square error of 4.0 m and 6.33 m, respectively, and improved the TanDEM-X DEM accuracy by 75.7% and 39.7%, respectively.

The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography

Obtaining accurate and widespread measurements of the vertical structure of the Earth’s forests has been a long-sought goal for the ecological community. Such observations are critical for accurately assessing the existing biomass of forests, and how changes in this biomass caused by human activities or variations in climate may impact atmospheric CO2 concentrations. Additionally, the three-dimensional structure of forests is a key component of habitat quality and biodiversity at local to regional scales. The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6° N & S latitude. The GEDI instrument is a geodetic-class laser altimeter/waveform lidar comprised of 3 lasers that produce 8 transects of structural information. Over its two-year nominal lifetime GEDI is anticipated to provide over 10 billion waveforms at a footprint resolution of 25 ​m. These data will be used to derive a variety of footprint and gridded products, including canopy height, canopy foliar profiles, Leaf Area Index (LAI), sub-canopy topography and biomass. Additionally, data from GEDI are used to demonstrate the efficacy of its measurements for prognostic ecosystem modeling, habit and biodiversity studies, and for fusion using radar and other remote sensing instruments. GEDI science and technology are unique: no other space-based mission has been created that is specifically optimized for retrieving vegetation vertical structure. As such, GEDI promises to advance our understanding of the importance of canopy vertical variations within an ecological paradigm based on structure, composition and function.

Use of Airborne LiDAR To Estimate Forest Stand Characteristics

Small-Footprint Airborne LiDAR(light detection and ranging) remote sensing is a breakthrough technology for deriving forest canopy structural characteristics. Because the technique is relatively new as applied to canopy measurement in China, there is a tremendous need for experiments that integrate field work, LiDAR remote sensing and subsequent analyses for retrieving the full complement of structural measures critical for forestry applications. Data storage capacity and high processing speed available today have made it possible to digitally sample and store the entire reflected waveform, instead of only extracting the discrete coordinates which form the so-called point clouds. Return waveforms can give more detailed insights into the vertical structure of surface objects, surface slope, roughness and reflectivity than the conventional echoes. In this paper, an improved Expectation Maximum (EM) algorithm is adopted to decompose raw waveform data. Derived forest biophysical parameters, such as vegetation height, subcanopy topography, crown volume, ground reflectivity, vegetation reflectivity and canopy closure, are able to describe the horizontal and vertical forest canopy structure.

Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR

AbstractImproved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world's most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We evaluated LiDAR‐derived measures of three‐dimensional canopy structure and subcanopy topography using classification‐tree techniques to separate different dry forest types and successional stages in the Guánica Biosphere Reserve in Puerto Rico. We compared the LiDAR‐based results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar‐based topographic data. The accuracy of the LiDAR‐based forest type classification (including native‐ and exotic‐dominated forest classes) was substantially higher than those from previously available data (kappa = 0.90 and 0.63, respectively). The best result was obtained when combining LiDAR‐derived metrics of canopy structure and topography, and adding Landsat spectral data did not improve the classification. For the second objective, we observed that LiDAR‐derived variables of vegetation structure were better predictors of forest successional status (i.e., mid‐secondary, late‐secondary, and primary forests) than was spectral information from Landsat. Importantly, the key LiDAR predictors identified within each classification‐tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of LiDAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general.

Mud bank colonization by opportunistic mangroves: A case study from French Guiana using lidar data

Mud bank colonization by mangroves on the Amazon-influenced coast of French Guiana was studied using light detection and ranging (lidar) data which provide unique information on canopy geometry an sub-canopy topography. The role of topography was assessed through analysis of vegetation characteristics derived from these data. Measurements and analyses of mangrove expansion rates over space and time led to the identification of two distinct colonization processes. The first involves regular step-by-step mangrove expansion to the northwest of the experimental site. The second is qualified as ‘opportunistic’ since it involves a clear relationship between specific ecological characteristics of pioneer Avicennia and mud cracks affecting the mud bank surface and for which probabilities of occurrence were computed from terrain elevations. It is argued from an original analysis of the latter relationship that mud cracks cannot be solely viewed as water stress features that reflect desiccation potentially harmful to plant growth. Indeed, our results tend to demonstrate that they significantly enhance the propensity for mangroves to anchor and take root, thus leading to the colonization of tens of hectares in a few days. The limits and potential of lidar data are discussed with reference to the study of muddy coasts. Finally, the findings of the study are reconsidered within the context of a better understanding of both topography and vegetation characteristics on mangrove-fringed muddy coasts.

Validation of Vegetation Canopy Lidar sub-canopy topography measurements for a dense tropical forest

Large footprint (greater than 10 m wide) laser altimetry is a useful technique for mapping topography (including sub-canopy), canopy height and vertical structure in densely vegetated areas. In March 1998, the Laser Vegetation Imaging Sensor (LVIS), an airborne laser altimeter, mapped a ∼800 km 2 area of Costa Rica including the La Selva Biological Station using 25 m-diameter footprints as part of the pre-launch activities of the Vegetation Canopy Lidar (VCL) Mission. To investigate the utility of the lidar technique for making sub-canopy topography measurements, the precision and accuracy of the LVIS elevation measurements from this mission are assessed. Crossover analysis using laser shots whose recorded waveforms contained more than 50% of the total returned energy within their lowest reflections show the elevations have a precision of better than 1 m. Comparison of the LVIS elevations with coincident in situ ground elevation data reveals that the measurements are within ∼1.5 m of each other on less than 3° slopes. All measurements are within ∼5 m of each other (on slopes of up to 30°). These are very encouraging results given that the forests of this region are some of the densest, most complex on Earth, and that mapping their sub-canopy topography are near-impossible using any other remote sensing technique. Given the similarity of the measurement processes of the LVIS and VCL systems, these results suggest that the topographic measurements made by the VCL will meet stated accuracy goals under the majority of measurement conditions.

Modeling lidar returns from forest canopies

Remote sensing techniques that utilize light detection and ranging (lidar) provide unique data on canopy geometry and subcanopy topography. This type of information will lead to improved understanding of important structures and processes of Earth's vegetation cover. To understand the relation between canopy structure and the lidar return waveform, a three-dimensional (3D) model was developed and implemented. Detailed field measurements and forest growth model simulations of forest stands were used to parameterize this vegetation lidar waveform model. In the model, the crown shape of trees determines the vertical distribution of plant material and the corresponding lidar waveforms. Preliminary comparisons of averaged waveforms from an airborne lidar and model simulations shows that the shape of the measured waveform was more similar to simulations using an ellipsoid or hemi-ellipsoid shape. The observed slower decay of the airborne lidar waveforms than the simulated waveforms may indicate the existence of the understories and may also suggest that higher order scattering from the upper canopy may contribute to the lidar signals. The lidar waveforms from stands simulated from a forest growth model show the dependence of the waveform on stand structure.