Continuous and operational monitoring of forest canopy structure plays an important role in assessing the global carbon budget, mapping forest disturbance, planning restoration activities, and informing decision-making. Several studies have taken advantage of synthetic aperture radar (SAR) for forest mapping and monitoring because of its regular reliable acquisitions and high sensitivity to the structural and dielectric properties of the forest. This work utilizes the Senitnel-1 C- and ALOS-2 PALSAR-2 L-band interferometric coherence for canopy height estimation in savanna woodlands. A simplified physics-based Random Volume over Ground (RVoG) model is used for the height estimation. This study uses datasets collected over two test sites, one in Injune, Australia, and the second in Kruger National Park (KNP), South Africa. The proposed method achieved an overall RMSE of 2.39m for canopy height with a Pearson coefficient, r = 0.83 by simultaneous use of both C- and L-band coherence.

The NISAR mission which in its most recent round of launch preparations was set to launch in the spring of 2024, and now delayed until later in the fall or early spring of 2025, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The two frequency, L- and S-band will full-polarimetric capability over a 250 km wide swath using the SweepSAR technique [1] will collect reliable set of observations (60 per year; 30 each for ascending and descending passes) on a continuing basis that will allow for the modeling and observation of time-varying processes that are prevalent in the living environment broadly described as Ecosystems. Among the prime science goals of the NISAR Ecosystems disciplines are in the characterization of agriculture, disturbance, biomass, forest structure and water dynamics seen in the world’s rivers, coasts, and permafrost regions. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

This study proposes an extended temporal correlation model for targets with a noticeable periodic seasonal trend. Several studies have explored the nature of decorrelation in synthetic aperture radar (SAR) interferograms. Specifically, providing a model the decay in interferometric correlation over time between two images remains a challenging task. Initially, it is necessary to assume that the contributions of distributed elements within the same pixel undergo a change, leading to a reduction in correlation with previous acquisitions. The exponential decay model is the simplest and most widely used in the scientific community by considering coherent and incoherent groups of scatterers within a resolution cell. However, the coherence over vegetation canopies with seasonal behavior does not exhibit a monotonic exponential decay with time. Hence, in this study, we introduce a periodic term to account for the nature of this seasonality. The performance of the proposed model is evaluated with a total of nearly 2000 Sentinel-1 interferometric SAR (InSAR) pairs acquired over two test sites located one in Nallamala, India, and the other in Injune, Australia. The proposed model performed significantly better than the exponential model with up to 83% improvement in RMSE in modeling the long-term coherence over vegetation with strong seasonal patterns.

Forest aboveground biomass is a key environmental variable needed for constraining models of the global carbon cycle, monitoring stocks and fluxes of carbon in forests, and optimizing forest management toward climate mitigation. To date, limited satellite data have been available that are sensitive to Aboveground Biomass Density (AGBD), and the availability of new satellite lidar data streams from NASA's Global Ecosystem Dynamics Investigation (GEDI) [1] and Ice Cloud and Elevation Satellite (ICESat-2) [2] enable a new generation of AGBD estimates representative of 2018–2022 conditions. Here we explore the transferability of GEDI's AGBD estimation framework to ICESat-2. We compare distribution of Relative Height (RH) metrics from both products between 50 and 52° N, and find that ICESat-2's RH metrics are biased high compared to GEDI. We reprocess ICESat-2 RH metrics to make them more comparable to GEDI height metrics, and present a comparison of biomass estimates based on the original and new ICESat-2 RH metrics in boreal forests.

The NISAR mission, an L-band and S-band 12-day repeat-pass InSAR, currently scheduled to be launched in January 2023, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The reliable set of 30 ascending and 30 descending 250 km swath of observations on a continuing basis will allow for the modeling and observation of hydrologic processes that serves as a forcing function and mode of energy transport for most living things, as well as changes in the landcover that are associated with agriculture, river and coastal dynamics, and disturbance. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

The forest structure parameters are important parameters for understanding of the global forest carbon storage and cycle, forest richness/biodiversity, as well as climate changes. Lidar waveform and (polarimetric) SAR interferometry have been widely and successfully used for extracting 3D forest structure profiles by means of both SAR and lidar airborne systems, but individually and on a local scale. To generate global-scale 3D forest structure information and to understand terrestrial carbon dynamics, fusing both spaceborne SAR and lidar data sets and developing new merging algorithms become critical. We have used (simulated) GEDI (Global Ecosystem Dynamics Investigator) data and interferometric SAR (InSAR) satellite data from DLR’s TanDEM-X at HH polarization over NASA AfriSAR campaign test sites, Gabon.

Three dimensional forest structure parameters are important components for understanding of the global forest carbon storage and cycle, as well as climate changes. Polarimetric SAR Interferometry (Pol-InSAR) techniques and waveform lidar have been widely and successfully used for extracting 3D forest structure profiles by means of both SAR and lidar airborne systems, but individually. Fusing both spaceborne SAR and lidar data sets and developing new merging algorithms are critical to measure global forest biomass and to understand terrestrial carbon dynamics. We have used GEDI (Global Ecosystem Dynamics Investigator) simulation data from NASA's LVIS (Land, Vegetation, and ICE sensor) and spaceborne SAR data from DLR's TanDEM-X at HH polarization over NASA AfriSAR campaign test sites, Gabon.

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

Terrestrial laser scanning (TLS) data provide 3-D measurements of vegetation structure and have the potential to support the calibration and validation of satellite and airborne sensors. The increasing range of different commercial and scientific TLS instruments holds challenges for data and instrument interoperability. Using data from various TLS sources will be critical to upscale study areas or compare data. In this paper, we provide a general framework to compare the interoperability of TLS instruments. We compare three TLS instruments that are the same make and model, the RIEGL VZ-400. We compare the range accuracy and evaluate the manufacturer's radiometric calibration for the uncalibrated return intensities. Our results show that the range accuracy between instruments is comparable and within the manufacturer's specifications. This means that the spatial XYZ data of different instruments can be combined into a single data set. Our findings demonstrate that radiometric calibration is instrument specific and needs to be carried out for each instrument individually before including reflectance information in TLS analysis. We show that the residuals between the calibrated reflectance panels and the apparent reflectance measured by the instrument are greatest for highest reflectance panels (residuals ranging from 0.058 to 0.312).

Research in Australia has focused on using the combination of moderate (25-30 m) spatial resolution ALOS PALSAR, Landsat-derived persistent green fractional cover and ICESAT GLAS data to generate maps of the structure of forests as an intermediate step to mapping above ground biomass (AGB) and AGB change using combinations of JERS-1 SAR, ALOS PALSAR and ALOS-2 PALSAR-2 data. The method involved a) segmentation of ALOS PALSAR and Landsat-derived persistent green fractional cover to produce objects across the landscape, b) subsequent assignment of objects to classes associated with different structural formations and c) combining cover and height metrics (derived from ICESAT data) to generate a classification of forest structural types. The approach was applied Australia-wide to produce a new forest structural classification. In future work, a series of algorithms are being compared to facilitate retrieval of AGB and detection of AGB change using combinations of these data, including the ALOS-2 PALSAR-2. Validation is being performed using a collation of field data and derived biomass estimates and airborne LiDAR acquired for a wide range of sites across Australia.

Raumonen et al.[1] have developed a new method for reconstructing topologically consistent tree architecture from TLS point clouds. This method generates a cylinder model of tree structure using a stepwise approach. Disney et al.[2] validated this method with a detailed 3D tree model where structure is known a priori, establishing a reconstruction relative error of less than 2%. Here we apply the same method to data acquired from Eucalyptus racemosa woodland, Banksia ameula low open woodland and Eucalyptus spp. open forest using a RIEGL VZ-400 instrument. Individual 3D tree models reconstructed from TLS point clouds are used to drive Monte Carlo ray tracing simulations of TLS with the same characteristics as those collected in the field. 3D reconstruction was carried out on the simulated point clouds so that errors and uncertainty arising from instrument sampling and reconstruction could be assessed directly. We find that total volume could be recreated to within a 10.8% underestimate. The greatest constraint to this approach is the accuracy to which individual scans can be globally registered. Inducing a 1cm registration error lead to a 8.8% total volumetric overestimation across the data set.

Calibration and validation of LAI products require accurate ground-based measurements. Many indirect ground-based sensors such as digital hemispherical photography (DHP), ceptometers, and terrestrial laser scanners (TLS) are used interchangeably to estimate reference values. However these sensors have biases in regards to the true LAI value, which can never be known in the field. Results from three representative woody ecosystems in Eastern Australia are presented from real field measurements. Significant differences were found between methods at the individual measurement and plot scale. Furthermore, one of the sites in South East Australia was measured and modeled in a 3D deterministic model. In this digital environment where the truth is known, sensors can be simulated to determine their bias.

Empirical relations are frequently used to derive leaf area index (LAI). Such relations often make assumptions that make it hard to link the derived LAI to realistic trees and forest canopies. In previous work we developed a set of analytical expressions to describe LiDAR waveforms with only a limited number of assumptions based on radiative transfer. These expressions were a function of crown macro-structure and LAI. The expressions were successfully tested when applied on crown archetypes, but showed significant error when applied to more realistic crowns. In this study, we analyse the effect of clumping on inferring LAI from realistic trees. Despite the potential of the expressions to detect subtle changes in LAI, absolute inferred LAI values can be significantly off. However, the strong correlation between true and inferred LAI (R2>; 0.97) for the two test cases in this study, allows for calibration of the inferred LAI values.

This paper presents the results of a study conducted to relate X and L band polarimetric SAR backscatter to pasture soil moisture and biomass as part of an environmental monitoring program. Extensive field data was collected concurrently with satellite SAR data acquisition - including dry/wet above ground biomass, soil moisture, surface roughness profiles and EM-38 electromagnetic sensor data. This data is used for both electromagnetically modeling the surface to work out the theoretical backscatter as well as empirical fitting regression models to the recorded SAR data and validation of existing inversion models.

Based on case studies undertaken in tropical forests in Brazil and Indonesia and subtropical woodlands in Australia, the paper highlights how data acquired by the Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (SAR) and Landsat sensors can be integrated to better quantify the extent, biophysical characteristics and/or dynamics of undisturbed, degraded and regenerating forests. The benefits of using time-series of Landsat sensor data to support the interpretation of ALOS PALSAR data and to identify areas with greatest potential for ecosystem recovery are conveyed.

With the successful launch of Japan's advanced land observing satellite (ALOS) phased array L-band SAR (PALSAR) and the recent provision of a Japanese earth resources satellite (JERS-1) synthetic aperture radar (SAR) mosaic for north Australia, significant opportunities for characterizing, mapping and monitoring the structural diversity and biomass of wooded savannas in Australia have been provided. This paper gives an overview of research undertaken and preparations being made to support such mapping in the state of Queensland. Preliminary observations on the integration of ALOS PALSAR and Landsat-derived foliage projective cover (FPC) for mapping woody regrowth and dead standing trees are presented.

This paper describes the development of a regression model for predicting foliage projective cover (FPC) using an extensive set of over 2000 field observations for Queensland, Australia. The model includes Landsat TM and ETM+ imagery and a climatological ancillary variable, vapour pressure deficit. The resulting model was validated using independent site data and preliminary validation against FPC estimates from airbourne laser scanner data is presented. Results suggest the model is robust and performing well over a range of soil types and vegetation communities. This regression-based methodology is currently included in the process of monitoring annual woody vegetation change over Queensland and will form the basis of new products for monitoring longer term trends in FPC