ASSESSING FOREST BIOMASS AND MONITORING CHANGES FROM DISTURBANCE AND RECOVERY WITH LIDAR AND SAR

This dissertation research investigated LiDAR and SAR remote sensing for assessing aboveground biomass and monitoring changes from anthropogenic forest disturbance and post-disturbance recovery. First, waveform LiDAR data were applied to map forest biomass and its changes at different key map scales for the two study sites: Howland Forest and Penobscot Experimental Forest. Results indicated that the prediction model at the scale of individual LVIS footprints is reliable when the geolocation errors are minimized. The evaluation showed that the predictions were improved markedly (20% R2 and 10% RMSE) with the increase of plot sizes from 0.25 ha to 1.0 ha. The effect of disturbance on the prediction model was strong at the footprint level but weak at the 1.0 ha plot-level. Errors reached minimum when footprint coverage approached about 50% of the area of 1.0 ha plots (16 footprints) with no improvement beyond that. Then, a sensitivity analysis was conducted for multi-source L-band SAR signatures, to change in forest biomass and related factors such as incidence angle, soil moisture, and disturbance type. The effect of incidence angle on SAR backscatter was reduced by an empirical model. A cross-image normalization was used to reduce the radiometric distortions due to changes in acquisition conditions such as soil moisture. Results demonstrated that the normalization ensured that the derived biomass of regrowth forests was cross-calibrated, and thus made the detection of biomass change possible. Further, the forest biomass was mapped for 1989, 1994 and 2009 using multi-source SAR data, and changes in biomass were derived for a 15- and a 20-year period. Results improved our understanding of issues concerning the mapping of biomass dynamic using L-ban SAR data. With the increase of plot sizes, the speckle noise and geolocations errors were reduced. Multivariable models were found to outperform the single-term models developed for biomass estimation. The main contribution of this research was an improved knowledge concerning waveform LiDAR and L-band SAR’s ability in monitoring the changes in biomass in a temperate forest. Results from this study provide calibration and validation methods as a foundation for improving the performance of current and future spaceborne systems

Electromagnetic Scattering Models for InSAR Correlation Measurements of Vegetation and Snow

Interferometric Synthetic Aperture Radar (InSAR) has proved successful and efficient in measuring the vertical structure of the distributed targets such as vegetation and snow, which are dominated by volume scattering. In particular, the InSAR correlation measurement has been utilized to retrieve the target vertical structural information. One existing and well-known electromagnetic scattering model of the InSAR correlation was first brought forward focusing on the single-pass InSAR observation of a sparse random medium like vegetation. However, the lack of the adaption of this InSAR scattering model for repeat-pass InSAR observation of vegetation as well as for single-pass InSAR observation of snow by considering its dense medium characteristics, essentially constrain fully exploiting InSAR\u27s capability of measuring sparse and dense medium characteristics. In this work, the well-known InSAR scattering model will be adapted to accommodate the two scenarios: 1) repeat-pass InSAR observation of vegetation and 2) single-pass InSAR observation of snow and considering its dense medium characteristics. Theoretical model derivations as well as parameter retrieval approaches are demonstrated for both of the applications, respectively. Both of the simulated and ground validation results are also presented. The InSAR scattering models along with the parameter retrieval analysis described in this work will expand InSAR\u27s capability as well as the range of vegetation and snow characteristics that can be retrieved by single-pass and/or repeat-pass InSAR systems

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

L-Band SAR Backscatter Related to Forest Cover, Height and Aboveground Biomass at Multiple Spatial Scales across Denmark

Mapping forest aboveground biomass (AGB) using satellite data is an important task, particularly for reporting of carbon stocks and changes under climate change legislation. It is known that AGB can be mapped using synthetic aperture radar (SAR), but relationships between AGB and radar backscatter may be confounded by variations in biophysical forest structure (density, height or cover fraction) and differences in the resolution of satellite and ground data. Here, we attempt to quantify the effect of these factors by relating L-band ALOS PALSAR HV backscatter and unique country-wide LiDAR-derived maps of vegetation penetrability, height and AGB over Denmark at different spatial scales (50 m to 500 m). Trends in the relations indicate that, first, AGB retrieval accuracy from SAR improves most in mapping at 100-m scale instead of 50 m, and improvements are negligible beyond 250 m. Relative errors (bias and root mean squared error) decrease particularly for high AGB values (>110 Mg ha$^{-1}$) at coarse scales, and hence, coarse-scale mapping ($\ge$150 m) may be most suited for areas with high AGB. Second, SAR backscatter and a LiDAR-derived measure of fractional forest cover were found to have a strong linear relation (R$^2$ = 0.79 at 250-m scale). In areas of high fractional forest cover, there is a slight decline in backscatter as AGB increases, indicating signal attenuation. The two results demonstrate that accounting for spatial scale and variations in forest structure, such as cover fraction, will greatly benefit establishing adequate plot-sizes for SAR calibration and the accuracy of derived AGB maps

THE UNCERTAINTY OF SPACEBORNE OBSERVATION OF VEGETATION STRUCTURE IN THE TAIGA-TUNDRA ECOTONE: A CASE STUDY IN NORTHERN SIBERIA

The ability to characterize vegetation structure in the taiga-tundra ecotone (TTE) at fine spatial scales is critical given its heterogeneity and the central role of its patterns on ecological processes in the high northern latitudes and global change scenarios. This research focuses on quantifying the uncertainty of TTE forest structure observations from remote sensing at fine spatial scales. I first quantify the uncertainty of forest biomass estimates from current airborne and spaceborne active remote sensing systems and a planned spaceborne LiDAR (ICESat-2) across sparse forest gradients. At plot-scales, current spaceborne models of biomass either explain less than a third of model variation or have biomass estimate uncertainties ranging from 50-100%. Simulations of returns from the planned ICESat-2 for a similar gradient show the uncertainty of near-term estimates vary according to the ground length along which returns are collected. The 50m length optimized the resolution of forest structure, for which there is a trade-off between horizontal precision of the measurement and vertical structure detail. At this scale biomass error ranges from 20-50%, which precludes identifying actual differences in aboveground live biomass density at 10 Mg•ha-1 intervals. These broad plot-scale uncertainties in structure from current and planned sensors provided the basis for examining a data integration technique with multiple sensors to measure the structure of sparse TTE forests. Spaceborne estimates of canopy height used complementary surface elevation measurements from passive optical and LiDAR to provide a means for directly measuring TTE forest height from spaceborne sensors. This spaceborne approach to estimating forest height was deployed to assess the spaceborne potential for examining the patterns of TTE forest structure explained with a conceptual biogeographic model linking TTE patterns and its dynamics. A patch-based analysis was used to scale estimates of TTE forest structure from multiple sensors and provided a means to simultaneously examine the horizontal and vertical structure of groups of TTE trees. The uncertainty of forest patch height estimates provides focus for improving spaceborne depictions of TTE structure patterns associated with recent change that may explain the variability of this change and the vulnerability of TTE forest structure

Retrieving secondary forest aboveground biomass from polarimetric ALOS-2 PALSAR-2 data in the Brazilian Amazon

Secondary forests (SF) are important carbon sinks, removing CO2 from the atmosphere through the photosynthesis process and storing photosynthates in their aboveground live biomass (AGB). This process occurring at large-scales partially counteracts C emissions from land-use change, playing, hence, an important role in the global carbon cycle. The absorption rates of carbon in these forests depend on forest physiology, controlled by environmental and climatic conditions, as well as on the past land use, which is rarely considered for retrieving AGB from remotely sensed data. In this context, the main goal of this study is to evaluate the potential of polarimetric (quad-pol) ALOS-2 PALSAR-2 data for estimating AGB in a SF area. Land-use was assessed through Landsat time-series to extract the SF age, period of active land-use (PALU), and frequency of clear cuts (FC) to randomly select the SF plots. A chronosequence of 42 SF plots ranging 3-28 years (20 ha) near the Tapajós National Forest in Pará state was surveyed to quantifying AGB growth. The quad-pol data was explored by testing two regression methods, including non-linear (NL) and multiple linear regression models (MLR).We also evaluated the influence of the past land-use in the retrieving AGB through correlation analysis. The results showed that the biophysical variables were positively correlated with the volumetric scattering, meaning that SF areas presented greater volumetric scattering contribution with increasing forest age. Mean diameter, mean tree height, basal area, species density, and AGB were significant and had the highest Pearson coefficients with the Cloude decomposition (λ3), which in turn, refers to the volumetric contribution backscattering from cross-polarization (HV) (ρ = 0.57-0.66, p-value < 0.001). On the other hand, the historical use (PALU and FC) showed the highest correlation with angular decompositions, being the Touzi target phase angle the highest correlation (Φs) (ρ = 0.37 and ρ = 0.38, respectively). The combination of multiple prediction variables with MLR improved the AGB estimation by 70% comparing to the NL model (R2 adj. = 0.51; RMSE = 38.7 Mg ha-1) bias = 2.1 ± 37.9 Mg ha-1 by incorporate the angular decompositions, related to historical use, and the contribution volumetric scattering, related to forest structure, in the model. The MLR uses six variables, whose selected polarimetric attributes were strongly related with different structural parameters such as the mean forest diameter, basal area, and the mean forest tree height, and not with the AGB as was expected. The uncertainty was estimated to be 18.6% considered all methodological steps of the MLR model. This approach helped us to better understand the relationship between parameters derived from SAR data and the forest structure and its relation to the growth of the secondary forest after deforestation events

Physics-based Modeling for High-fidelity Radar Retrievals.

Knowledge of soil moisture on a global scale is crucial for understanding the Earth's water, energy, and carbon cycles. This dissertation is motivated by the need for accurate soil moisture estimates and focuses on the improvement of soil moisture retrieval based on active remote sensing over vegetated areas. It addresses important, but often neglected, aspects in radar imaging: effects related to the ionosphere, multispecies vegetation (heterogeneity at pixel level), and heterogeneity at landscape level. The first contribution is the development of a generalized radar scattering model as an advancement of current radar modeling techniques for vegetated areas at fine-scale pixel level. It consists of realistic representations of multispecies and subsurface soil layer modeling, and includes terrain topography. This modeling improvement allows greater applicability to different land cover types and higher soil moisture retrieval accuracy. Most coarse-scale satellite pixels (km-scale or coarser) contain highly heterogeneous scenes with fine-scale (100 m or finer) variability of soil moisture, soil texture, topography, and vegetation cover. The second contribution is the development of spatial scaling techniques to investigate effects of landscape-level heterogeneity on radar scattering signatures. Using the above radar forward scattering model, which assumes homogeneity over fine scales, tailor-made models are derived for the contribution of fine-scale heterogeneity to the coarse-scale satellite pixel for effective soil moisture retrieval. Finally, the third contribution is the development of a self-contained calibration technique based on an end-to-end radar system model. The model includes ionospheric effects allowing the use of spaceborne radar signals for accurate soil moisture retrieval from lower frequencies, such as L- and P-band. These combined contributions will greatly increase the usability of low-frequency spaceborne radar data for soil moisture retrieval: ionospheric effects are mitigated, landscape level heterogeneity is resolved, and fine-scale scenes are better modeled. These contributions ultimately allow improved fidelity in soil moisture retrieval and are immediately applicable in current missions such as the ongoing AirMOSS mission that observes root-zone soil moisture with a P-band radar at fine-scale resolution (100 m), and NASA's upcoming SMAP spaceborne mission, which will assess surface soil moisture with an L-band radar and radiometer at km-scale resolution (3 km).PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107290/1/mburgin_1.pd

An intensity triplet for the prediction of systematic InSAR closure phases

Thesis (M.S.) University of Alaska Fairbanks, 2023Synthetic Aperture Radar (SAR), a microwave-based active remote sensing technique, has had a rich and contemporary history. Because such platforms can measure both the phase and intensity of the reflected signal, interferometric SAR (InSAR) has proliferated and allowed geodesists to measure topography and millimeter-to-centimeter scale deformations of the Earth's surface from space. Applications of InSAR range from measuring the inflation of volcanoes caused by magma movement to measuring the subsidence in permafrost environments caused by the thawing of ground ice. Advancements in InSAR time series algorithms and speckle models have allowed us to image such movements at increasingly high precision. However, analysis of closure phases (or phase triplets), a quantification of inconsistencies thought to be caused by speckle, reveal systematic behaviors across many environments. Systematic closure phases have been linked to changes in the dielectric constant of the soil (generally thought to be a result of soil moisture changes), but existing models require strong constraints on structure and sensitivity to moisture content. To overcome this obstacle and decompose the closure phase into a systematic and stochastic part, we present a data-driven approach based on the SAR intensities. Intensity observations are also sensitive to surface dielectric changes. Thus, we have constructed an intensity triplet that mimics the algebraic structure of the closure phase. A regression between such triplets allows us to predict the systematic part of the closure phase, which is associated with dielectric changes. We estimate the corresponding phase errors using a minimum-norm inversion of the systematic closure phases to inspect the impact of such systematic closure phases on deformation measurements. Correction of these systematic closure phases that correlate with our intensity triplet can account for millimeter-scale fluctuations of the deformation time series. In permafrost environments, they can also account for displacement rate biases up to a millimeter a month. In semi-arid environments, these differences are generally an order of magnitude smaller and are less likely to lead to displacement rate biases. From nearby meteorological stations, we attribute these errors to snowfall, freeze-thaw, as well as seasonal moisture trends. This kind of analysis shows great potential for correcting the temporal inconsistencies in InSAR phases related to dielectric changes and enabling even finer deformation measurements, particularly in permafrost tundra.Chapter 1. Introduction. Chapter 2. InSAR theory -- 2.1. Forming an interferogram -- 2.2. Time series estimation -- 2.3. Closure phases. Chapter 3. Predicting and removing systematic phase closures -- 3.1. An intensity triplet -- 3.2. Predicting systematic closure phases -- 3.2.1. Model -- 3.2.2. Parameter estimation -- 3.3. Significance testing -- 3.4. Inversion. Chapter 4. Data and preprocessing -- 4.1. Las Vegas, NV -- 4.2. Dalton Highway, AK -- 4.3. Ancillary processing. Chapter 5. Results -- 5.1. Overview -- 5.2. Coefficient of determination -- 5.3. Slope estimates -- 5.4. Intercept estimates -- 5.5. Impacts on deformation estimates. Chapter 6. Discussion -- 6.1. Variability in R2 and slope estimates -- 6.2. Implications for deformation estimates -- 6.3. Implications for observations of land surface properties -- 6.4. Unexplained systematic closure phases -- 6.5. Model improvements. Chapter 7. Conclusion -- References -- Appendices

The global tree carrying capacity (keynote)

editorial reviewe

Fourth Airborne Geoscience Workshop

The focus of the workshop was on how the airborne community can assist in achieving the goals of the Global Change Research Program. The many activities that employ airborne platforms and sensors were discussed: platforms and instrument development; airborne oceanography; lidar research; SAR measurements; Doppler radar; laser measurements; cloud physics; airborne experiments; airborne microwave measurements; and airborne data collection