Evaluation of the potential of ALOS PALSAR L-band quadpol radar data for the retrieval of growing stock volume in Siberia

Because of the massive wood trade, illegal logging and severe damages due to fires, insects and pollution, it is necessary to monitor Siberian forests on a large-scale, frequently and accurately. One possible solution is to use synthetic aperture radar (SAR) remote sensing technique, in particular by combining polarimetric technique. In order to evaluate the potentiality of ALOS PALSAR L-band full polarimetric radar for estimation of GSV, a number of polarimetric parameters are investigated to characterise the polarisation response of forest cover. Regardless of the weather conditions, a high correlation (R=-0.87) is achieved between polarimetric coherence and GSV. The coherence in sparse forest is always higher than in dense forest. The coherence level and the dynamic range strongly depends on the weather conditions. The four-component polarimetric decomposition method has been applied to the ALOS PALSAR L-band data to compare the decomposition powers with forest growing stock volume (GSV). Double-bounce and volume scattering powers show significant correlation with GSV. The correlation between polarimetric decomposition parameters and GSV is enhanced if the ratio of ground-to-volume scattering is used instead of considering polarimetric decomposition powers separately. Two empirical models have been developed that describe the ALOS PALSAR L-band polarimetric coherence and ground-to-volume scattering ratio as a function of GSV. The models are inverted to retrieve the GSV for Siberian forests. The best RMSE of 38 m³/ha and R²=0.73 is obtained based on polarimetric coherence. On the other hand, using the ratio of ground-to-volume scattering the best retrieval accuracy of 44 m³/ha and R²=0.62 is achieved. The best retrieval results for both cases are observed under unfrozen condition. Saturation effects for estimated GSV versus ground-truth GSV are not observed up to 250 m³/ha

Large area forest stem volume mapping using synergy of spaceborne interferometric radar and optical remote sensing: a case study of northeast chin

More than a decade of investigations on the use of the interferometric ERS-1/2 tandem coherence for forest applications have increased the understanding of the behaviour of C-band repeat-pass coherence over forested terrain. It has been shown that under optimal imaging conditions, ERS-1/2 tandem coherence can be used for stem volume retrieval with accuracies in the range of ground surveys. Large-area applications of ERS-1/2 tandem coherence are rare though. One of the main limitations concerning large-area exploitation of the existing ERS-1/2 tandem archives for forest stem volume retrieval is related to the considerable dependence of repeat-pass coherence upon the meteorological (rain, temperature, wind speed) and environmental (soil moisture variations, snow metamorphism) acquisition conditions. Conventional retrieval algorithms require accurate forest inventory data for a dense grid of forest sites to tune models that relate coherence to stem volume to the local conditions. Accurate forest inventory data is, however, a rare commodity that is often not freely available. In this thesis, a fully automated algorithm was developed, based on a synergetic use of the MODIS Vegetation Continuous Field product (Hansen et al., 2002), that allowed the training of the Interferometric Water Cloud Model IWCM (Askne et al., 1997) without further need for forest inventory data. With the new algorithm it was possible to train the IWCM on a frame-by-frame basis and thus to account for the spatial and temporal variability of the meteorological and environmental acquisition conditions. The new algorithm was applied to a multi-seasonal ERS-1/2 tandem dataset covering Northeast China that was acquired between 1995 and 1998 with baselines up to 400 m

Growing stock volume estimation in temperate forsted areas using a fusion approach with SAR Satellites Imagery

Forest monitoring plays a central role in the context of global warming mitigation and in the assessment of forest resources. To meet these challenges, significant efforts have been made by scientists to develop new feasible remote sensing techniques for the retrieval of forest parameters. However, much work remains to be done in this area, in particular in establishing global assessments of forest biomass. In this context, this Ph.D. Thesis presents a complete methodology for estimating Growing Stock Volume (GSV) in temperate forested areas using a fusion approach based on Synthetic-Aperture Radar (SAR) satellite imagery. The investigations which were performed focused on the Thuringian Forest, which is located in Central Germany. The satellite data used are composed of an extensive set of L-band (ALOS PALSAR) and X-band (TerraSAR-X, TanDEM-X, Cosmo-SkyMed) images, which were acquired in various sensor configurations (acquisition modes, polarisations, incidence angles). The available ground data consists of a forest inventory delivered by the local forest offices. Weather measurements and a LiDAR DEM complete the datasets. The research showed that together with the topography, the forest structure and weather conditions generally limited the sensitivity of the SAR signal to GSV. The best correlations were obtained with ALOS PALSAR (R2 = 0.61) and TanDEM-X (R2 = 0.72) interferometric coherences. These datasets were chosen for the retrieval of GSV in the Thuringian Forest and led with regressions to an root-mean-square error (RMSE) in the range of 100─200 m3ha-1. As a final achievement of this thesis, a methodology for combining the SAR information was developed. Assuming that there are sufficient and adequate remote sensing data, the proposed fusion approach may increase the biomass maps accuracy, their spatial extension and their updated frequency. These characteristics are essential for the future derivation of accurate, global and robust forest biomass maps

Estimation of biophysical parameters in boreal forests from ERS and JERS SAR interferometry

The thesis describes investigations concerning the evaluation of ERS and JERS SAR images and repeat-pass interferometric SAR images for the retrieval of biophysical parameters in boreal forests. The availability of extensive data sets of images over several test sites located in Sweden, Finland and Siberia has allowed analysis of temporal dynamics of ERS and JERS backscatter and coherence, and of ERS interferometric phase. Modelling of backscatter, coherence and InSAR phase has been performed by means of the Water Cloud Model (WCM) and the Interferometric Water Cloud Model (IWCM); sensitivity analysis and implications for the retrieval of forest biophysical parameters have been thoroughly discussed. Model inversion has been carried out for stem volume retrieval using ERS coherence, ERS backscatter and JERS backscatter, whereas for tree height estimation the ERS interferometric phase has been used. Multi-temporal combination of ERS coherence images, and to a lesser extent of JERS backscatter images, can provide stem volume estimates comparable to stand-wise ground-based measurements. Since the information content of the interferometric phase is strongly degraded by phase noise and uncorrected atmospheric artefacts, the retrieved tree height shows large errors

Hemiboreaalsete metsade kaardistamine interferomeetrilise tehisava-radari andmetelt

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Käesolev doktoritöö uurib tehisavaradari (SAR) kasutusvõimalusi metsa kõrguse hindamiseks hemiboreaalsete metsade vööndis. Uurimistöö viidi läbi Tartu Üli¬kooli, Tartu Observatooriumi, Aalto Ülikooli, Euroopa Kosmoseagentuuri (ESA) kaugseire keskuse ESRIN ja Reach-U koostöös. Uurimistöös kasutatud satelliidi¬andmed on pärit Saksa Kosmosekeskuse (DLR) kõrglahutusega bistaatilise X-laineala tehisavaradari TanDEM-X satelliidipaarilt. Sagedasti uuenevad satelliidiandmed, nende globaalne katvus ja kõrge ruumi¬line lahutus võimaldavad tehisavaradari abil kaardistada metsi ning nendes toimu¬vaid muutusi suurtel maa-aladel. Radari abil on võimalik saada kõrge lahutusvõimega pilte, mis on tundlikud taimestikule, maapinna karedusele ja dielektrilistele omadustele. Sünkroonis lendava radaripaari samaaegselt tehtud pildid elimineerivad võimalikud ajalised muutused taimestikus ning tänu sellele on radariandmetest võimalik tuletada metsade vertikaalset struktuuri ja kõrgust. Uurimistöös käsitletakse tehisavaradari interferomeetrilise koherentsuse tund¬likkust metsa kõrguse suhtes ning analüüsitakse, millised keskkonna ja klimaati¬lised tingimused ning satelliidi orbiidiga seotud parameetrid mõjutavad radari¬piltidelt erinevate puuliikide kõrguse hindamise täpsust. Lisaks keskendub väitekiri interferomeetrilisele koherentsusele tuginevate mudelite analüüsi¬misele ning nende täpsuse hindamisele operatiivse metsa kõrguse kaardistamise raken-duseks. Vaatluse alla on võetud kolm testala, mis asuvad Soomaa rahvuspargis, Võrtsjärve idakaldal Rannus ja Peipsiveere looduskaitsealal ning katavad kokku 2291 hektarit metsa. 23 TanDEM-X satelliidipildi koherentsuspilte võrreldakse samadel testaladel aerolaserskaneerimise (LiDAR) abil mõõdetud puistute kõrgu¬sega, mis on omakorda jagatud kolme rühma (kuused, männid ja laia¬lehised segametsad). RVoG (Random Volume over Ground) taimekatte mudel ning sellest tule¬tatud lihtsamad pooleempiirilised mudelid sobituvad olemasolevate TanDEM-X koherentsuse ning LiDARi metsa puistute kõrgusandmetega hästi. Töö tule¬mused kinnitavad, et tulevikus on suurte ja erinevatest metsatüüpidest koosne¬vate metsade kõrguse kosmosest kaardistamisel otstarbekas kasutusele võtta esmalt just soovitatud lihtsamad ja universaalsemad mudelid.This thesis presents research in the field of radar remote sensing and contributes to the forest monitoring application development using space-borne synthetic aperture radar (SAR). Satellite data is particularly useful for large-scale forestry applications making high revisit monitoring of the state of forests worldwide possible. The sensitivity of SAR to the dielectric and geometrical properties of the targets, penetration capacity and coherent imaging properties make it a unique tool for mapping and monitoring forest biomes. SAR satellites are also capable of retrieving additional information about the structure of the forest, tree height and biomass estimates as an essential input for monitoring the changes in the carbon stocks. Interferometric SAR (InSAR) is an advanced SAR imaging technique that allows the retrieval of forest parameters while working in nearly all weather conditions, independently of daylight and cloud cover. This research concen¬trates on assessing the impact of different variables affecting hemiboreal forest height estimation from space-borne X-band interferometric SAR coherence data. In particular, the research analyses the changes in coherence dynamics related to seasonal conditions, tree species and imaging properties using a large collection of interferometric SAR images from different seasons over a four-year period. The study is carried out over three test sites in Estonia using the extensive multi-temporal dataset of 23 TanDEM-X images, covering 2291 hectares of forests to describe the relation between the interferometric SAR coherence mag¬nitude and forest parameters. The work demonstrates how the correlation of interferometric coherence and Airborne LiDAR Scanning (ALS)-derived forest height varies for pine and deciduous tree species, for summer (leaf-on) and winter (leaf-off) conditions and for flooded forest floor. A simple semi-empirical modelling approach is proposed as being suitable for wide area forest mapping with limited a priori information under a range of seasonal and environ¬¬mental conditions. A Random Volume over Ground (RVoG) model and three semi-empirical models are compared and validated against a large dataset of coherence magnitude and ALS-measured data over hemiboreal forests in Estonia. The results show that all proposed models perform well in describing the relationship between hemiboreal forest height and interferometric coherence, allowing in future to derive forest stand height with an accuracy suitable for a wide range of applications

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

Exploring the potential of high temporal resolution X-band SAR time series for various permafrost applications with ground truth observations in the Lena River Delta, Siberia.

Permafrost is a subsurface phenomenon that cannot be directly monitored with satellite remote sensing. A variety of indirect approaches are currently being developed which aim to measure permafrost-related processes and environmental variables. Results of these studies aid the planning of future satellite missions which will allow large-scale permafrost monitoring. This thesis contributes to this ongoing effort by assessing the potential of repeat-pass TerraSAR-X (TSX) time series for permafrost-related applications. For the first time, multi-year Synthetic Aperture Radar (SAR) data with high temporal (11 days) and spatial (3 m) resolution was analysed for a region characterized by continuous permafrost in the Siberian Arctic. Extensive in situ data was collected during three summer and winter expeditions to validate and interpret remote sensing results. Three case studies were carried out: (i) the detection of land surface changes (e.g. ground freezing and thawing, surface wetness variations, snow cover onset and melt); (ii) monitoring bedfast lake ice and ice phenology (freeze-up, melt onset, break-up); and (iii) differential SAR interferometry (DInSAR) for thaw subsidence monitoring. For the first two case studies, time series of both backscatter intensity and 11-day interferometric coherence (i.e. a measure of phase stability between two SAR images) were investigated. Backscatter intensity was generally shown to be insensitive to the land surface changes but responded to events that occurred at the time of TSX acquisition (rain, snow shower, melt/freeze crust on snow). Interferometric coherence decreased dramatically across the entire image upon snow cover onset and melt, permitting the possible use of coherence for the monitoring of these events. Backscatter intensity was found to be an excellent tool for the detection and monitoring of bedfast lake ice due in part to improved temporal resolution compared to previously used SAR systems. Ice phenology was mostly well tracked with backscatter intensity. Interferometric coherence was found to be sensitive to the lake ice grounding and to the onset of surface melt on the lakes with bedfast ice. The investigation of coherence was a useful preparative step for the following DInSAR analysis. For the third case study, coherent 11-day and 22-day interferograms were available only for one summer of the two-year TSX time series. The cumulative DInSAR displacement strongly underestimated the subsidence observed on the ground. In situ observations revealed high variability of subsidence, which likely caused errors in phase unwrapping. Conventional DInSAR processing might therefore not be suitable for the accurate representation of permafrost thaw subsidence. This study highlights the importance of field measurements for the quantification of thaw subsidence with DInSAR, which were mostly omitted in the previous studies. All in all, this thesis shows the limitations and potential of TSX time series to spatially and temporally monitor permafrost. It thus provides an important contribution to the methodological development of a long-term permafrost monitoring scheme

Spatial variability of aircraft-measured surface energy fluxes in permafrost landscapes

Arctic ecosystems are undergoing a very rapid change due to global warming and their response to climate change has important implications for the global energy budget. Therefore, it is crucial to understand how energy fluxes in the Arctic will respond to any changes in climate related parameters. However, attribution of these responses is challenging because measured fluxes are the sum of multiple processes that respond differently to environmental factors. Here, we present the potential of environmental response functions for quantitatively linking energy flux observations over high latitude permafrost wetlands to environmental drivers in the flux footprints. We used the research aircraft POLAR 5 equipped with a turbulence probe and fast temperature and humidity sensors to measure turbulent energy fluxes along flight tracks across the Alaskan North Slope with the aim to extrapolate the airborne eddy covariance flux measurements from their specific footprint to the entire North Slope. After thorough data pre-processing, wavelet transforms are used to improve spatial discretization of flux observations in order to relate them to biophysically relevant surface properties in the flux footprint. Boosted regression trees are then employed to extract and quantify the functional relationships between the energy fluxes and environmental drivers. Finally, the resulting environmental response functions are used to extrapolate the sensible heat and water vapor exchange over spatio-temporally explicit grids of the Alaskan North Slope. Additionally, simulations from the Weather Research and Forecasting (WRF) model were used to explore the dynamics of the atmospheric boundary layer and to examine results of our extrapolation