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

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

Utilization of bistatic TanDEM-X data to derive land cover information

Forests have significance as carbon sink in climate change. Therefore, it is of high importance to track land use changes as well as to estimate the state as carbon sink. This is useful for sustainable forest management, land use planning, carbon modelling, and support to implement international initiatives like REDD+ (Reducing Emissions from Deforestation and Degradation). A combination of field measurements and remote sensing seems most suitable to monitor forests. Radar sensors are considered as high potential due to the weather and daytime independence. TanDEM-X is a interferometric SAR (synthetic aperture radar) mission in space and can be used for land use monitoring as well as estimation of biophysical parameters. TanDEM-X is a X-band system resulting in low penetration depth into the forest canopy. Interferometric information can be useful, whereas the low penetration can be considered as an advantage. The interferometric height is assumable as canopy height, which is correlated with forest biomass. Furthermore, the interferometric coherence is mainly governed by volume decorrelation, whereas temporal decorrelation is minimized. This information can be valuable for quantitative estimations and land use monitoring. The interferometric coherence improved results in comparison to land use classifications without coherence of about 10% (75% vs. 85%). Especially the differentiation between forest classes profited from coherence. The coherence correlated with aboveground biomass in a R² of about 0.5 and resulted in a root mean square error (RSME) of 14%. The interferometric height achieved an even higher correlation with the biomass (R²=0.68) resulting in cross-validated RMSE of 7.5%. These results indicated that TanDEM-X can be considered as valuable and consistent data source for forest monitoring. Especially interferometric information seemed suitable for biomass estimation

Modelling forest road trafficability with satellite-based soil moisture variables

Recent decades have seen increased temperatures and precipitation in the Nordic countries with long-term projections for reduced frost duration and depth. The consequence of these trends has been a gradual shift of delivery volumes to the frost-free season, requiring more agile management to exploit suitable weather conditions. Bearing capacity and trafficability are dependent on soil moisture state and in this context two satellite missions offer potenially useful information on soil moisture levels; NASA's SMAP (Soil Moisture Active Passive) and ESA's Sentinel-1. The goal of this pilot study was to quantify the performance of such satellite-based soil moisture variables for modeling forest road bearing capacity (e-module) during the frost-free season. The study was based on post-transport registrations of 103 forest road segments on the coastal and interior side of the Scandinavian mountain range. The analysis focused on roads of three types of surface deposits. Weekly SMAP soil moisture values better explained the variation in road e-module than soil water index (SWI) derived from Sentinel-1. Soil Water Index (SWI), however, reflected the weather conditions typical for operations on the respective surface deposit types. Regression analysis using (i) SMAP-based soil dryness index and (ii) its interaction with surface deposit types, together with (iii) the ratio between a combined SMAP_SWI dryness index and segment-specific depth to water (DTW) explained over 70% of the variation in road e-module. The results indicate a future potential to monitor road trafficability over large supply areas on a weekly level, given further refinement of study methods and variables for improved prediction

A 3-D Full-Wave Model to Study the Impact of Soybean Components and Structure on L-Band Backscatter

Microwave remote sensing offers a powerful tool for monitoring the growth of short, dense vegetation like soybean. As the plants mature, changes in their biomass and 3-D structure impact the electromagnetic (EM) backscatter signal. This backscatter information holds valuable insights into crop health and yield, prompting the need for a comprehensive understanding of how structural and biophysical properties of soybeans as well as soil characteristics contribute to the overall backscatter signature. In this study, a full-wave model is developed for simulating L-band backscatter from soybean fields. Leveraging the ANSYS High-Frequency Structure Simulator (HFSS) framework, the model solves for the scattering of EM waves from realistic 3-D structural models of soybean, explicitly incorporating the interplant scattering effects. The model estimates of backscatter match well with the field observations from the SMAPVEX16-MicroWEX and SMAPVEX12, with average differences of 1-2 dB for co-pol and less than 4 dB for cross-pol. Furthermore, the model effectively replicates the temporal dynamics of crop backscatter throughout the growing season. The HFSS analysis revealed that the stems and pods are the primary contributors to HH-pol backscatter, while the branches contribute to VV-pol, and leaves impact the cross-pol signatures. In addition, a sensitivity study with 3-D bare soil surface resulted in an average variation of 8 dB in co- and cross-pol, even when the root mean square height and correlation length were held constant

Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling

Forest biomass and carbon monitoring play a key role in climate change mitigation. Operational large area monitoring approaches are needed to enable forestry stakeholders to meet the increasing monitoring and reporting requirements. Here, we demonstrate the functionality of a cloud-based approach utilizing Sentinel-2 composite imagery and process-based ecosystem model to produce large area forest volume and primary production estimates. We describe the main components of the approach and implementation of the processing pipeline into the Forestry TEP cloud processing platform and produce four large area output maps: (1) Growing stock volume (GSV), (2) Gross primary productivity (GPP), (3) Net primary productivity (NPP) and (4) Stem volume increment (SVI), covering Finland and the Russian boreal forests until the Ural Mountains in 10 m spatial resolution. The accuracy of the forest structural variables evaluated in Finland reach pixel level relative Root Mean Square Error (RMSE) values comparable to earlier studies (basal area 39.4%, growing stock volume 58.5%, diameter 35.5% and height 33.5%), although most of the earlier studies have concentrated on smaller study areas. This can be considered a positive sign for the feasibility of the approach for large area primary production modelling, since forest structural variables are the main input for the process-based ecosystem model used in the study. The full coverage output maps show consistent quality throughout the target area, with major regional variations clearly visible, and with noticeable fine details when zoomed into full resolution. The demonstration conducted in this study lays foundation for further development of an operational large area forest monitoring system that allows annual reporting of forest biomass and carbon balance from forest stand level to regional analyses. The system is seamlessly aligned with process based ecosystem modelling, enabling forecasting and future scenario simulation.Peer reviewe

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

Global sensitivities of forest carbon changes to environmental conditions

40000125197/18/I‐NBThe responses of forest carbon dynamics to fluctuations in environmental conditions at a global scale remain elusive. Despite the understanding that favourable environmental conditions promote forest growth, these responses have been challenging to observe across different ecosystems and climate gradients. Based on a global annual time series of aboveground biomass (AGB) estimated from radar satellites between 1992 and 2018, we present forest carbon changes and provide insights on their sensitivities to environmental conditions across scales. Our findings indicate differences in forest carbon changes across AGB classes, with regions with carbon stocks of 50–125 MgC ha−1 depict the highest forest carbon gains and losses, while regions with 125–150 MgC ha−1 have the lowest forest carbon gains and losses in absolute terms. Net forest carbon change estimates show that the arc-of-deforestation and the Congo Basin were the main hotspots of forest carbon loss, while a substantial part of European forest gained carbon during the last three decades. Furthermore, we observe that changes in forest carbon stocks were systematically positively correlated with changes in forest cover fraction. At the same time, it was not necessarily the case with other environmental variables, such as air temperature and water availability at the bivariate level. We also used a model attribution method to demonstrate that atmospheric conditions were the dominant control of forest carbon changes (56% of the total study area) followed by water-related (29% of the total study area) and vegetation (15% of the total study area) conditions. Regionally, we find evidence that carbon gains from long-term forest growth covary with long-term carbon sinks inferred from atmospheric inversions. Our results describe the contributions from the atmosphere, water-related and vegetation conditions to forest carbon changes and provide new insights into the underlying mechanisms of the coupling between forest growth and the global carbon cycle.publishersversionpublishe

L-band synthetic aperture radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs

Woody canopy cover (CC) is the simplesttwo dimensional metric for assessing the presence ofthe woody component in savannahs, but detailed validated maps are not currently available in southern African savannahs. A number of international EO programs (including in savannah landscapes) advocate and use optical LandSAT imagery for regional to country-wide mapping of woody canopy cover. However, previous research has shown that L-band Synthetic Aperture Radar (SAR) provides good performance at retrieving woody canopy cover in southern African savannahs. This study’s objective was to evaluate, compare and use in combination L-band ALOS PALSAR and LandSAT-5 TM, in a Random Forest environment, to assess the benefits of using LandSAT compared to ALOS PALSAR. Additional objectives saw the testing of LandSAT-5 image seasonality, spectral vegetation indices and image textures for improved CC modelling. Results showed that LandSAT-5 imagery acquired in the summer and autumn seasons yielded the highest single season modelling accuracies (R2 between 0.47 and 0.65), depending on the year but the combination of multi-seasonal images yielded higher accuracies (R2 between 0.57 and 0.72). The derivation of spectral vegetation indices and image textures and their combinations with optical reflectance bands provided minimal improvement with no optical-only result exceeding the winter SAR L-band backscatter alone results (R2 of ∼0.8). The integration of seasonally appropriate LandSAT-5 image reflectance and L-band HH and HV backscatter data does provide a significant improvement for CC modelling at the higher end of the model performance (R2 between 0.83 and 0.88), but we conclude that L-band only based CC modelling be recommended for South African regionshttp://www.elsevier.com/locate/jag2017-10-31hb2016Geography, Geoinformatics and Meteorolog