Estimation of change in forest variables using synthetic aperture radar

Large scale mapping of changes in forest variables is needed for both environmental monitoring, planning of climate actions and sustainable forest management. Remote sensing can be used in conjunction with field data to produce wall-to-wall estimates that are practically impossible to produce using traditional field surveys. Synthetic aperture radar (SAR) can observe the forest independent of sunlight, clouds, snow, or rain, providing reliable high frequency coverage. Its wavelength determines the interaction with the forest, where longer wavelengths interact with larger structures of the trees, and shorter wavelengths interact mainly with the top part of the canopy, meaning that it can be chosen to fit specific applications. This thesis contains five studies conducted on the Remningstorp test site in southern Sweden. Studies I – III predicted above ground biomass (AGB) change using long wavelength polarimetric P- (in I) and L-band (in I – III) SAR data. The differences between the bands were small in terms of prediction quality, and the HV polarization, just as for AGB state prediction, was the polarization channel most correlated with AGB change. A moisture correction for L-band data was proposed and evaluated, and it was found that certain polarimetric measures were better for predicting AGB change than all of the polarization channels together. Study IV assessed the detectability of silvicultural treatments in short wavelength TanDEM-X interferometric phase heights. In line with earlier studies, only clear cuts were unambiguously distinguishable. Study V predicted site index and stand age by fitting height development curves to time series of TanDEM-X data. Site index and age were unbiasedly predicted for untreated plots, and the RMSE would likely decrease with longer time series. When stand age was known, SI was predicted with an RMSE comparable to that of the field based measurements. In conclusion, this thesis underscores SAR data's potential for generalizable methods for estimation of forest variable changes

Prediction of Site Index and Age Using Time Series of TanDEM-X Phase Heights

Site index and stand age are important variables in forestry. Site index describes the growing potential at a given location, expressed as the height that trees can attain at a given age under favorable growing conditions. It is traditionally used to classify forests in terms of future timber yield potential. Stand age is used for the planning of management activities such as thinning and harvest. SI has previously been predicted using remote sensing, but usually relying on either very short time series or repeated ALS acquisitions. In this study, site index and forest stand age were predicted from time series of interferometric TanDEM-X data spanning seven growth seasons in a hemi-boreal forest in Remningstorp, a test site located in southern Sweden. The goal of the study was to see how satellite-based radar time series could be used to estimate site index and stand age. Compared to previous studies, we used a longer time series and applied a penetration depth correction to the phase heights, thereby avoiding the need for calibration using ancillary field or ALS data. The time series consisted of 30 TanDEM-X strip map scenes acquired between 2011 and 2018. Established height development curves were fitted to the time series of TanDEM-X-based top heights. This enabled simultaneous estimation of both age and site index on 91 field plots with a 10 m radius. The RMSE of predicted SI and age were 6.9 m and 38 years for untreated plots when both SI and age were predicted. When predicting SI and the age was known, the RMSE of the predicted SI was 4.0 m. No significant prediction bias was observed for untreated plots, while underestimation of SI and overestimation of age increased with the intensity of treatment

Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter

Above-ground biomass change accumulated during four growth seasons in a hemi-boreal forest was predicted using airborne L- and P-band synthetic aperture radar (SAR) backscatter. The radar data were collected in the BioSAR 2007 and BioSAR 2010 campaigns over the Remningstorp test site in southern Sweden. Regression models for biomass change were developed from biomass maps created using airborne LiDAR data and field measurements. To facilitate training and prediction on image pairs acquired at different dates, a backscatter offset correction method for L-band data was developed and evaluated. The correction, based on the HV/VV backscatter ratio, facilitated predictions across image pairs almost identical to those obtained using data from the same image pair for both training and prediction. For P-band, previous positive results using an offset correction based on the HH/VV ratio were validated. The best L-band model achieved a root mean square error (RMSE) of 21 t/ha, and the best P-band model achieved an RMSE of 19 t/ha. Those accuracies are similar to that of the LiDAR-based biomass change of 18 t/ha. The limitation of using LiDAR-based data for training was considered. The findings demonstrate potential for improved biomass change predictions from L-band backscatter despite varying environmental conditions and calibration uncertainties

Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter

Above-ground biomass change accumulated during four growth seasons in a hemi-boreal forest was predicted using airborne L- and P-band synthetic aperture radar (SAR) backscatter. The radar data were collected in the BioSAR 2007 and BioSAR 2010 campaigns over the Remningstorp test site in southern Sweden. Regression models for biomass change were developed from biomass maps created using airborne LiDAR data and field measurements. To facilitate training and prediction on image pairs acquired at different dates, a backscatter offset correction method for L-band data was developed and evaluated. The correction, based on the HV/VV backscatter ratio, facilitated predictions across image pairs almost identical to those obtained using data from the same image pair for both training and prediction. For P-band, previous positive results using an offset correction based on the HH/VV ratio were validated. The best L-band model achieved a root mean square error (RMSE) of 21 t/ha, and the best P-band model achieved an RMSE of 19 t/ha. Those accuracies are similar to that of the LiDAR-based biomass change of 18 t/ha. The limitation of using LiDAR-based data for training was considered. The findings demonstrate potential for improved biomass change predictions from L-band backscatter despite varying environmental conditions and calibration uncertainties

Polymer structures for photovoltaics using colloidal self-assembly, thermal nanoimprinting and electrohydrodynamic annealing

The efficiency of an organic photovoltaic cell depends mainly on its morphology where an exciton has to migrate to a p-n junction to create a photocurrent. Therefore the distance from the bulk of the cell to a junction interface should not exceed the diffusion length of the exciton. In this thesis, two novel lithographical methods, to produce specific polymer morphologies, were developed and evaluated. In the first method, called embedded annealing, self-assembled polystyrene colloids were embedded in a polydimethylsiloxane (PDMS) film and annealed under an electric field to produce a bi-polymer structure consisting of polymer columns in a thin film of PDMS. Polymer colloids were successfully assembled into two dimensional hexagonally close packed arrays. However, the annealing process was unsuccessful. The second method, imprint annealing, aimed to increase the aspect ratio (height/width) of thermally imprinted micrometer sized polystyrene features by annealing them in uniform electric fields. The results showed that the aspect ratio of imprinted features can be significantly increased, 21-fold, while maintaining the periodicity of the original imprint. This is in contrast to previous results where smooth polymer films annealed in uniform fields where the periodicity of the resulting structures cannot be independently controlled, and are highly sensitive to the electrode spacing. Feature sizes down to 1 µm and aspect ratios up to 4.5 were achieved using imprint annealing.Verkningsgraden hos en hos en solcell beror, för givna material, framförallt på dess uppbyggnad. För att bidra till fotoströmmen måste en genererad exciton vandra till en pn-övergång. På grund av detta bör det längsta avståndet till närmaste pn-övergång i solcellen inte vara längre än excitonens diffusionslängd. I detta examensarbete testas två olika litografiska metoder för att åstadkomma en specifik filmgeometri lämpad för organiska solceller. Den första metoden, kallad embedded annealing, går ut på att bädda in spontant ordnade sfäriska polystyrenkolloider i en polydimetylsiloxan (PDMS) -film för att sedan vid förhöjd temperatur applicera ett elektiskt fält över filmen. Förhoppningen var att på detta sätt töja ut kolloiderna till pelare genom PDMS-filmen. I det första steget ordnades kolloiderna sponant i tätpackade hexagonala tvådimensionella gitter på kiselsubstrat. Experimenten lyckades inte med hjälp av elektriska fält töja ut kolloiderna. Den andra metoden, imprint annealing, syftar till att öka höjd/bredd -förhållandet och minska diametern hos präglade polystyrenstrukturer. Dessa ursprungliga topografiska stukturer skapas med hjälp av en tryckpressmetod kallad nanoimprinting. Dessa strukturer värmdes upp, och ett uniformt elekrisk fält applicerades över dem. Mina resultat visar att man med elektriska fält avsevärt kan öka höjd-breddförhållandet hos polymerstrukturer och samtidigt bevara periodiciteten hos de ursprungliga strukturerna. Detta står i kontrast mot tidigare resultat på släta filmer, där periodiciteten inte kan kontrolleras oberonde av andra parametrar. Med imprint annealing ökades höjd-breddförhållandet hos enskilda strukturer upp till 21 gånger. Diametrar ner till 1 µm och höjd/breddförhållanden upp till 4,5 uppnåddes

Prediction of Site Index and Age Using Time Series of TanDEM-X Phase Heights

Site index and stand age are important variables in forestry. Site index describes the growing potential at a given location, expressed as the height that trees can attain at a given age under favorable growing conditions. It is traditionally used to classify forests in terms of future timber yield potential. Stand age is used for the planning of management activities such as thinning and harvest. SI has previously been predicted using remote sensing, but usually relying on either very short time series or repeated ALS acquisitions. In this study, site index and forest stand age were predicted from time series of interferometric TanDEM-X data spanning seven growth seasons in a hemi-boreal forest in Remningstorp, a test site located in southern Sweden. The goal of the study was to see how satellite-based radar time series could be used to estimate site index and stand age. Compared to previous studies, we used a longer time series and applied a penetration depth correction to the phase heights, thereby avoiding the need for calibration using ancillary field or ALS data. The time series consisted of 30 TanDEM-X strip map scenes acquired between 2011 and 2018. Established height development curves were fitted to the time series of TanDEM-X-based top heights. This enabled simultaneous estimation of both age and site index on 91 field plots with a 10 m radius. The RMSE of predicted SI and age were 6.9 m and 38 years for untreated plots when both SI and age were predicted. When predicting SI and the age was known, the RMSE of the predicted SI was 4.0 m. No significant prediction bias was observed for untreated plots, while underestimation of SI and overestimation of age increased with the intensity of treatment

Measurements of forest biomass change using L- and P-band SAR backscatter

Three-year forest above-ground biomass change were measured using L- and P-band Synthetic Aperture Radar (SAR) backscatter. The SAR data were collected in the airborne BioSAR 2007 and BioSAR 2010 campaigns over the hemiboreal Remningstorp test site in southern Sweden. Regression models for biomass were developed using reference biomass maps created using airborne laser scanning data and field measurements. The results from regression analysis show that using HV backscatter (or VH) in a model with above-ground biomass and backscatter change on either natural logarithmic or square root, and decibel scale, respectively, explained most of the variation in the biomass change, both for L- and P-band. In the case of L-band, the two best cases showed R2 values of 66%, when comparing two SAR images acquired 2007 and 2010. For P-band using the same models, the best cases showed R2 values of 62%. In summary, the results look promising using L- and P-band backscattering for mapping biomass change