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

3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed

Spatio-temporal and structural analysis of vegetation dynamics of Lowveld Savanna in South Africa

Savanna vegetation structure parameters are important for assessing the biomes status under various disturbance scenarios. Despite free availability remote sensing data, the use of optical remote sensing data for savanna vegetation structure mapping is limited by sparse and heterogeneous distribution of vegetation canopy. Cloud and aerosol contamination lead to inconsistency in the availability of time series data necessary for continuous vegetation monitoring, especially in the tropics. Long- and medium wavelength microwave data such as synthetic aperture radar (SAR), with their low sensitivity to clouds and atmospheric aerosols, and high temporal and spatial resolution solves these problems. Studies utilising remote sensing data for vegetation monitoring on the other hand, lack quality reference data. This study explores the potential of high-resolution TLS-derived vegetation structure variables as reference to multi-temporal SAR datasets in savanna vegetation monitoring. The overall objectives of this study are: (i) to evaluate the potential of high-resolution TLS-data in extraction of savanna vegetation structure variables; (ii) to estimate landscape-wide aboveground biomass (AGB) and assess changes over four years using multi-temporal L-band SAR within a Lowveld savanna in Kruger National Park; and (iii) to assess interactions between C-band SAR with various savanna vegetation structure variables. Field inventories and TLS campaign were carried out in the wet and dry seasons of 2015 respectively, and provided reference data upon which AGB, CC and cover classes were modelled. L-band SAR modelled AGB was used for change analysis over 4 years, while multitemporal C-band SAR data was used to assess backscatter response to seasonal changes in CC and AGB abundant classes and cover classes. From the AGB change analysis, on average 36 ha of the study area (91 ha) experienced a loss in AGB above 5 t/ha over 4 years. A high backscatter intensity is observed on high abundance AGB, CC classes and large trees as opposed to low CC and AGB abundance classes and small trees. There is high response to all structure variables, with C-band VV showing best polarization in savanna vegetation mapping. Moisture availability in the wet season increases backscatter response from both canopy and background classes

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Advanced machine learning algorithms for Canadian wetland mapping using polarimetric synthetic aperture radar (PolSAR) and optical imagery

Wetlands are complex land cover ecosystems that represent a wide range of biophysical conditions. They are one of the most productive ecosystems and provide several important environmental functionalities. As such, wetland mapping and monitoring using cost- and time-efficient approaches are of great interest for sustainable management and resource assessment. In this regard, satellite remote sensing data are greatly beneficial, as they capture a synoptic and multi-temporal view of landscapes. The ability to extract useful information from satellite imagery greatly affects the accuracy and reliability of the final products. This is of particular concern for mapping complex land cover ecosystems, such as wetlands, where complex, heterogeneous, and fragmented landscape results in similar backscatter/spectral signatures of land cover classes in satellite images. Accordingly, the overarching purpose of this thesis is to contribute to existing methodologies of wetland classification by proposing and developing several new techniques based on advanced remote sensing tools and optical and Synthetic Aperture Radar (SAR) imagery. Specifically, the importance of employing an efficient speckle reduction method for polarimetric SAR (PolSAR) image processing is discussed and a new speckle reduction technique is proposed. Two novel techniques are also introduced for improving the accuracy of wetland classification. In particular, a new hierarchical classification algorithm using multi-frequency SAR data is proposed that discriminates wetland classes in three steps depending on their complexity and similarity. The experimental results reveal that the proposed method is advantageous for mapping complex land cover ecosystems compared to single stream classification approaches, which have been extensively used in the literature. Furthermore, a new feature weighting approach is proposed based on the statistical and physical characteristics of PolSAR data to improve the discrimination capability of input features prior to incorporating them into the classification scheme. This study also demonstrates the transferability of existing classification algorithms, which have been developed based on RADARSAT-2 imagery, to compact polarimetry SAR data that will be collected by the upcoming RADARSAT Constellation Mission (RCM). The capability of several well-known deep Convolutional Neural Network (CNN) architectures currently employed in computer vision is first introduced in this thesis for classification of wetland complexes using multispectral remote sensing data. Finally, this research results in the first provincial-scale wetland inventory maps of Newfoundland and Labrador using the Google Earth Engine (GEE) cloud computing resources and open access Earth Observation (EO) collected by the Copernicus Sentinel missions. Overall, the methodologies proposed in this thesis address fundamental limitations/challenges of wetland mapping using remote sensing data, which have been ignored in the literature. These challenges include the backscattering/spectrally similar signature of wetland classes, insufficient classification accuracy of wetland classes, and limitations of wetland mapping on large scales. In addition to the capabilities of the proposed methods for mapping wetland complexes, the use of these developed techniques for classifying other complex land cover types beyond wetlands, such as sea ice and crop ecosystems, offers a potential avenue for further research

Uncertainty quantification of a radiative transfer model and a machine learning technique for use as observation operators in the assimilation of microwave observations into a land surface model to improve soil moisture and terrestrial snow

Soil moisture and terrestrial snow mass are two important hydrological states needed to accurately quantify terrestrial water storage and streamflow. Soil moisture and terrestrial snow mass can be measured using ground-based instrument networks, estimated using advanced land surface models, and retrieved via satellite imagery. However, each method has its own inherent sources of error and uncertainty. This leads to the application of data assimilation to obtain optimal estimates of soil moisture and snow mass. Before conducting data assimilation (DA) experiments, this dissertation explored the use of two different observation operators within a DA framework: a L-band radiative transfer model (RTM) for soil moisture and support vector machine (SVM) regression for soil terrestrial snow mass. First, L-band brightness temperature (Tb) estimated from the RTM after being calibrated against multi-angular SMOS Tb's showed good performance in both ascending and descending overpasses across North America except in regions with sub-grid scale lakes and dense forest. Detailed analysis of RTM-derived L-band Tb in terms of soil hydraulic parameters and vegetation types suggests the need for further improvement of RTM-derived Tb in regions with relatively large porosity, large wilting point, or grassland type vegetation. Secondly, a SVM regression technique was developed with explicit consideration of the first-order physics of photon scattering as a function of different training target sets, training window lengths, and delineation of snow wetness over snow-covered terrain. The overall results revealed that prediction accuracy of the SVM was strongly linked with the first-order physics of electromagnetic responses of different snow conditions. After careful evaluation of the observation operators, C-band backscatter observations over Western Colorado collected by Sentinel-1 were merged into an advanced land surface model using a SVM and a one-dimensional ensemble Kalman filter. In general, updated snow mass estimates using the Sentinel-1 DA framework showed modest improvements in comparison to ground-based measurements of snow water equivalent (SWE) and snow depth. These results motivate further application of the outlined assimilation schemes over larger regions in order to improve the characterization of the terrestrial hydrological cycle

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

Empirical multi-band characterization of propagation with modelling aspects for communictions

Diese Arbeit präsentiert eine empirische Untersuchung der Wellenausbreitung für drahtlose Kommunikation im Millimeterwellen- und sub-THz-Band, wobei als Referenz das bereits bekannte und untersuchte sub-6-GHz-Band verwendet wird. Die großen verfügbaren Bandbreiten in diesen hohen Frequenzbändern erlauben die Verwendung hoher instantaner Bandbreiten zur Erfüllung der wesentlichen Anforderungen zukünftiger Mobilfunktechnologien (5G, “5G and beyond” und 6G). Aufgrund zunehmender Pfad- und Eindringverluste bei zunehmender Trägerfrequenz ist die resultierende Abdeckung dabei jedoch stark reduziert. Die entstehenden Pfadverluste können durch die Verwendung hochdirektiver Funkschnittstellen kompensiert werden, wodurch die resultierende Auflösung im Winkelbereich erhöht wird und die Notwendigkeit einer räumlichen Kenntnis der Systeme mit sich bringt: Woher kommt das Signal? Darüber hinaus erhöhen größere Anwendungsbandbreiten die Auflösung im Zeitbereich, reduzieren das small-scale Fading und ermöglichen die Untersuchung innerhalb von Clustern von Mehrwegekomponenten. Daraus ergibt sich für Kommunikationssysteme ein vorhersagbareres Bild im Winkel-, Zeit- und Polarisationsbereich, welches Eigenschaften sind, die in Kanalmodellen für diese Frequenzen widergespiegelt werden müssen. Aus diesem Grund wurde in der vorliegenden Arbeit eine umfassende Charakterisierung der Wellenausbreitung durch simultane Multibandmessungen in den sub-6 GHz-, Millimeterwellen- und sub-THz-Bändern vorgestellt. Zu Beginn wurde die Eignung des simultanen Multiband-Messverfahrens zur Charakterisierung der Ausbreitung von Grenzwert-Leistungsprofilen und large-scale Parametern bewertet. Anschließend wurden wichtige Wellenausbreitungsaspekte für die Ein- und Multibandkanalmodellierung innerhalb mehrerer Säulen der 5G-Technologie identifiziert und Erweiterungen zu verbreiteten räumlichen Kanalmodellen eingeführt und bewertet, welche die oben genannten Systemaspekte abdecken.This thesis presents an empirical characterization of propagation for wireless communications at mm-waves and sub-THz, taking as a reference the already well known and studied sub-6 GHz band. The large blocks of free spectrum available at these high frequency bands makes them particularly suitable to provide the necessary instantaneous bandwidths to meet the requirements of future wireless technologies (5G, 5G and beyond, and 6G). However, isotropic path-loss and penetration-loss are larger with increasing carrier frequency, hence, coverage is severely reduced. Path-loss can be compensated with the utilization of highly directive radio-interfaces, which increases the resolution in the angular domain. Nonetheless, this emphasizes the need of spatial awareness of systems, making more relevant the question “where does the signal come from?” In addition, larger application bandwidths increase the resolution in the time domain, reducing small-scale fading and allowing to observe inside of clusters of multi-path components (MPCs). Consequently, communication systems have a more deterministic picture of the environment in the angular, time, and polarization domain, characteristics that need to be reflected in channel models for these frequencies. Therefore, in the present work we introduce an extensive characterization of propagation by intensive simultaneous multi-band measurements in the sub-6 GHz, mm-waves, and sub-THz bands. Firstly, the suitability of the simultaneous multi-band measurement procedure to characterize propagation from marginal power profiles and large-scale parameters (LSPs) has been evaluated. Then, key propagation aspects for single and multi-band channel modelling in several verticals of 5G have been identified, and extensions to popular spatial channel models (SCMs) covering the aforementioned system aspects have been introduced and evaluated

DETERMINE: Novel Radar Techniques for Humanitarian Demining

Today the plague of landmines represent one of the greatest curses of modern time, killing and maiming innocent people every day. It is not easy to provide a global estimate of the problem dimension, however, reported casualties describe that the majority of the victims are civilians, with almost a half represented by children. Among all the technologies that are currently employed for landmine clearance, Ground Penetrating Radar (GPR) is one of those expected to increase the efficiency of operation, even if its high-resolution imaging capability and the possibility of detecting also non-metallic landmines are unfortunately balanced by the high sensor false alarm rate. Most landmines may be considered as multiple layered dielectric cylinders that interact with each other to produce multiple reflections, which will be not the case for other common clutter objects. Considering that each scattering component has its own angular radiation pattern, the research has evaluated the improvements that multistatic configurations could bring to the collected information content. Employing representative landmine models, a number of experimental campaigns have confirmed that GPR is capable of detecting the internal reflections and that the presence of such scattering components could be highlighted changing the antennas offset. In particular, results show that the information that can be extracted relevantly changes with the antenna separation, demonstrating that this approach can provide better confidence in the discrimination and recognition process. The proposed bistatic approach aims at exploiting possible presence of internal structure beneath the target, which for landmines means the activation or detonation assemblies and possible internal material diversity, maintaining a limited acquisition effort. Such bistatic configurations are then included in a conceptual design of a highly flexible GPR system capable of searching for landmines across a large variety of terrains, at reasonably low cost and targeting operators safety