Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure

Abrupt forest disturbances generating gaps \u3e0.001 km2 impact roughly 0.4–0.7 million km2a−1. Fire, windstorms, logging, and shifting cultivation are dominant disturbances; minor contributors are land conversion, flooding, landslides, and avalanches. All can have substantial impacts on canopy biomass and structure. Quantifying disturbance location, extent, severity, and the fate of disturbed biomass will improve carbon budget estimates and lead to better initialization, parameterization, and/or testing of forest carbon cycle models. Spaceborne remote sensing maps large-scale forest disturbance occurrence, location, and extent, particularly with moderate- and fine-scale resolution passive optical/near-infrared (NIR) instruments. High-resolution remote sensing (e.g., ∼1 m passive optical/NIR, or small footprint lidar) can map crown geometry and gaps, but has rarely been systematically applied to study small-scale disturbance and natural mortality gap dynamics over large regions. Reducing uncertainty in disturbance and recovery impacts on global forest carbon balance requires quantification of (1) predisturbance forest biomass; (2) disturbance impact on standing biomass and its fate; and (3) rate of biomass accumulation during recovery. Active remote sensing data (e.g., lidar, radar) are more directly indicative of canopy biomass and many structural properties than passive instrument data; a new generation of instruments designed to generate global coverage/sampling of canopy biomass and structure can improve our ability to quantify the carbon balance of Earth\u27s forests. Generating a high-quality quantitative assessment of disturbance impacts on canopy biomass and structure with spaceborne remote sensing requires comprehensive, well designed, and well coordinated field programs collecting high-quality ground-based data and linkages to dynamical models that can use this information

The Use of Multiple-Polarization Data in Foliage Penetrating (FOPEN) Synthetic Aperture Radar (SAR) Applications

Foliage penetrating (FOPEN) synthetic aperture radar (SAR) systems are capable of producing images of targets concealed under a foliage canopy. The quality and interpretability of these images, however, is generally limited by dense foliage clutter and by fundamental foliage-induced image degradation. Use of a polarimetric SAR to provide multiple polarization channels can mitigate these effects by offering target and scene information beyond that provided by a single-polarization SAR. This paper presents the results of a literature survey to investigate the use of multiple-polarization data in conjunction with FOPEN SAR applications. The effects of foliage propagation on SAR image quality are briefly summarized. Various approaches to multiple-polarization-based FOPEN target detection are described. Although literature concerning FOPEN target recognition is scarce, the use of multiple-polarization data for in-the-clear target recognition is described. The applicability of various target detection and recognition applications for use with concealed target SAR (CTSAR) imagery is considered

Using Shadows to Detect Targets in Synthetic Aperture Radar Imagery

Synthetic Aperture Radar (SAR) can generate high resolution imagery of re- mote scenes by combining the phase information of multiple radar pulses along a given path. SAR based Intelligence, Surveillance, and Reconnaissance (ISR) has the advantage over optical ISR that it can provide usable imagery in adverse weather or nighttime conditions. Certain radar frequencies can even result in foliage or limited soil penetration, enabling imagery to be created of objects of interest that would otherwise be hidden from optical surveillance systems. This thesis demonstrates the capability of locating stationary targets of interest based on the locations of their shadows and the characteristics of pixel intensity distributions within the SAR imagery. Shadows, in SAR imagery, represent the absence of a detectable signal reflection due to the physical obstruction of the transmitted radar energy. An object\u27s shadow indicates its true geospatial location. This thesis demonstrates target detection based on shadow location using three types of target vehicles, each located in urban and rural clutter scenes, from the publicly available Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. The proposed distribution characterization method for detecting shadows demonstrates the capability of isolating distinct regions within SAR imagery and using the junctions between shadow and non-shadow regions to locate individual shadow-casting objects. Targets of interest are then located within that collection of objects with an average detection accuracy rate of 93%. The shadow-based target detection algorithm results in a lower false alarm rate compared to previous research conducted with the same data set, with 71% fewer false alarms for the same clutter region. Utilizing the absence of signal, in conjunction with surrounding signal reflections, provides accurate stationary target detection. This capability could greatly assist in track initialization or the location of otherwise obscured targets of interest

A Structural Classification of Australian Vegetation Using ICESat/GLAS, ALOS PALSAR and Landsat Sensor Data

Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference map of Australian forest and woodland structure (height and cover), with this generated by integrating Landsat Thematic Mapper (TM) and Enhanced TM, Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and Ice, Cloud, and land Elevation (ICESat),and Geoscience Laser Altimeter System (GLAS) data. ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC) were used to segment and classify the Australian landscape. Then, from intersecting ICESat waveform data, vertical foliage profiles and height metrics (e.g., 95% percentile height, mean height and the height to maximum vegetation density) were extracted for each of the classes generated. Within each class, and for selected areas, the variability in ICESat profiles was found to be similar with differences between segments of the same class attributed largely to clearance or disturbance events. ICESat metrics and profiles were then assigned to all remaining segments across Australia with the same class allocation. Validation against airborne LiDAR for a range of forest structural types indicated a high degree of correspondence in estimated height measures. On this basis, a map of vegetation height was generated at a national level and was combined with estimates of cover to produce a revised structural classification based on the scheme of the Australian National Vegetation Information System (NVIS). The benefits of integrating the three datasets for segmenting and classifying the landscape and retrieving biophysical attributes was highlighted with this leading the way for future mapping using ALOS-2 PALSAR-2, Landsat/Sentinel-2, Global Ecosystem Dynamics Investigation (GEDI), and ICESat-2 LiDAR data. The ability to map across large areas provides considerable benefits for quantifying carbon dynamics and informing on biodiversity metrics

Investigating the effects of bistatic SAR phenomenology on feature extraction

Interest in bistatic radar has fluctuated since its first demonstration. Modern multistatic and MIMO radar systems have prompted a resurgence in the field, particularly where imaging radar and automatic target recognition are concerned. The lack of openly-available bistatic imagery and corresponding analysis of the unique artefacts which occur within it is a significant barrier to developing automatic target recognition methods for such systems. This paper looks to address these issues by presenting an appropriate simulation methodology for obtaining bistatic synthetic aperture radar imagery of ground vehicle targets and investigating the features that occur within this imagery. In this paper, a number of effects unique to the bistatic case are presented, and the performance degradation of a classifier at several bistatic angles is empirically demonstrated. A version of the final database will be publicly released to promote wider research into this challenge

New SAR Target Imaging Algorithm based on Oblique Projection for Clutter Reduction

International audienceWe have developed a new Synthetic Aperture Radar (SAR) algorithm based on physical models for the detection of a Man-Made Target (MMT) embedded in strong clutter (trunks in a forest). The physical models for the MMT and the clutter are represented by low-rank subspaces and are based on scattering and polarimetric properties. Our SAR algorithm applies the oblique projection of the received signal along the clutter subspace onto the target subspace. We compute its statistical performance in terms of probabilities of detection and false alarms. The performances of the proposed SAR algorithm are improved compared to those obtained with existing SAR algorithms: the MMT detection is greatly improved and the clutter is rejected. We also studied the robustness of our new SAR algorithm to interference modeling errors. Results on real FoPen (Foliage Penetration) data showed the usefulness of this approach

Lidar sampling for large-area forest characterization: A review

The ability to use digital remotely sensed data for forest inventory is often limited by the nature of the measures, which, with the exception of multi-angular or stereo observations, are largely insensitive to vertically distributed attributes. As a result, empirical estimates are typically made to characterize attributes such as height, volume, or biomass, with known asymptotic relationships as signal saturation occurs. Lidar (light detection and ranging) has emerged as a robust means to collect and subsequently characterize vertically distributed attributes. Lidar has been established as an appropriate data source for forest inventory purposes; however, large area monitoring and mapping activities with lidar remain challenging due to the logistics, costs, and data volumes involved.The use of lidar as a sampling tool for large-area estimation may mitigate some or all of these problems. A number of factors drive, and are common to, the use of airborne profiling, airborne scanning, and spaceborne lidar systems as sampling tools for measuring and monitoring forest resources across areas that range in size from tens of thousands to millions of square kilometers. In this communication, we present the case for lidar sampling as a means to enable timely and robust large-area characterizations. We briefly outline the nature of different lidar systems and data, followed by the theoretical and statistical underpinnings for lidar sampling. Current applications are presented and the future potential of using lidar in an integrated sampling framework for large area ecosystem characterization and monitoring is presented. We also include recommendations regarding statistics, lidar sampling schemes, applications (including data integration and stratification), and subsequent information generation. © 2012

Remote monitoring to predict bridge scour failure using Interferometric Synthetic Aperture Radar (InSAR) stacking techniques

Scour is the removal of ground material in water bodies due to environmental changes in water flow. It particularly occurs at bridge piers and the holes formed can make bridges susceptible to collapse. The most common cause of bridge collapse is due to scour occurring during flooding, some failures causing loss of life and most resulting in significant transport disruption and economic loss. Consequently, failure of bridges due to scour is of great concern to bridge asset owners, and is currently very difficult to predict since conventional assessment methods foresee very resource-demanding monitoring efforts in situ. This paper presents evidence of how InSAR techniques can be used to monitor bridges at risk of scour, using Tadcaster Bridge, England, as a case study. Tadcaster Bridge suffered a partial collapse due to river scour on the evening of December 29th, 2015 following a period of severe rainfall and flooding. 48 TerraSAR-X scenes over the bridge from the two-year period prior to the collapse are analysed using the small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) approach. The study highlights a distinct movement in the region of the bridge where the collapse occurred prior to the actual event. This precursor to failure observed in the data over a month before actual collapse suggests the possible use of InSAR as a means of an early warning system in structural health monitoring of bridges at risk of scour.This work was made possible by EPSRC (UK) Award 1636878, with iCase sponsorship from the National Physical Laboratory and additional funding from Laing O’Rourke