Daniel Clewley

A Generalized Radar Backscattering Model Based on Wave Theory for Multilayer Multispecies Vegetation

A generalized radar scattering model based on wave theory is described. The model predicts polarimetric radar backscattering coefficients for structurally complex vegetation comprised of multiple species and layers. Compared to conventional two-layer crown-trunk models, modeling of actual forests has been improved substantially, allowing better understanding of microwave interaction with vegetation. The model generalizes an existing single-species discrete scatterer model and, by including scattering and propagation effects through judiciously defined vegetation layers, enables its application to an arbitrary number of species types. The scatterers within each layer are modeled as finite cylinders or disks having arbitrary size, density, and orientation, as in the predecessor model. The distorted Born approximation is used to represent the propagation through each layer, while scattering from each is modeled as a linear superposition of scattering from its respective random collection of scatterers. Interactions of waves within and between each layer and direct scattering from the ground are accounted for. Validation of the model is presented based on its application to 23 wooded savanna sites located in Queensland, Australia, and comparison with Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) Airborne Synthetic Aperture Radar (AIRSAR) data. Results indicate good agreement between simulated and actual backscattering coefficients, particularly at HH and VV polarizations. More discrepancies are found at HV polarizations and can be explained by uncertainties in the knowledge of input parameters, such as inaccuracies in the surface model, surface roughness parameterization, and soil moisture.

The Remote Sensing and GIS Software Library (RSGISLib)

Key to the successful application of remotely sensed data to real world problems is software that is capable of performing commonly used functions efficiently over large datasets, whilst being adaptable to new techniques. This paper presents an open source software library that was developed through research undertaken at Aberystwyth University for environmental remote sensing, particularly in relation to vegetation science. The software was designed to fill the gaps within existing software packages and to provide a platform to ease the implementation of new and innovative algorithms and data processing techniques. Users interact with the software through an XML script, where XML tags and attributes are used to parameterise the available commands, which have now grown to more than 300. A key feature of the XML interface is that command options are easily recognisable to the user because of their logical and descriptive names. Through the XML interface, processing chains and batch processing are supported. More recently a Python binding has been added to RSGISLib allowing individual XML commands to be called as Python functions. To date the Python binding has over 100 available functions, mainly concentrating on image utilities, segmentation, calibration and raster GIS. The software has been released under a GPL3 license and makes use of a number of other open source software libraries (e.g., GDAL/OGR), a user guide and the source code are available at http://www.rsgislib.org. HighlightsAn open source software platform for the processing of remotely sensed and GIS datasets.Support for large scale processing on HPC systems.Scalable segmentation and image-to-image registration algorithms.Close links with other software to be used in combination rather than replacement.A platform for research to be made available to the community into the future.

Sorted pulse data (SPD) library. Part I

The management and spatial-temporal integration of LiDAR data from different sensors and platforms has been impeded by a lack of generic open source tools and standards. This paper presents a new generic file format description (sorted pulse data; SPD) for the storage and processing of airborne and terrestrial LiDAR data. The format is designed specifically to support both traditional discrete return and waveform data, using a pulse (rather than point) based data model. The SPD format also supports 2D spatial indexing of the pulses, where pulses can be referenced using cartesian, spherical, polar or scan geometry coordinate systems and projections. These indexes can be used to significantly speed up data processing whilst allowing the data to be appropriately projected and are particularly useful when analysing and interpreting TLS data. The format is defined within a HDF5 file, which provides a number of benefits including broad support across a wide range of platforms and architectures and support for file compression. An implementation of the format is available within the open source sorted pulse data software library (SPDLib; http://www.spdlib.org). Highlights? A new open file format for the storage and processing of LiDAR. ? Specific support for the storage of waveform and discrete return data. ? Development of a pulsed based structure with simplifies many situations. ? Explicit inclusion of a spatial index which supports multiple projections.

Sorted pulse data (SPD) library—Part II: A processing framework for LiDAR data from pulsed laser systems in terrestrial environments

The management and spatial-temporal integration of LiDAR data from different sensors and platforms has been impeded by lack of generic open source tools and standards. This paper presents a new open source software system, the sorted pulse data software library (SPDLib), that provides a processing framework based on an implementation of a new file format for the storage of discrete-return and waveform LiDAR data from terrestrial, airborne and space borne platforms. A python binding and a visualisation tool (SPD Points Viewer), which build on top of the SPDLib and SPD file format have also been provided. The software and source code have recently been made freely available and can be accessed online through an open source code repository. Future developments will focus on the development of advanced waveform processing functionality and optimising IO performance. The software and documentation can be obtained from http://www.spdlib.org.

A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

A modular system for performing Geographic Object-Based Image Analysis (GEOBIA), using entirely open source (General Public License compatible) software, is presented based around representing objects as raster clumps and storing attributes as a raster attribute table (RAT). The system utilizes a number of libraries, developed by the authors: The Remote Sensing and GIS Library (RSGISLib), the Raster I/O Simplification (RIOS) Python Library, the KEA image format and TuiView image viewer. All libraries are accessed through Python, providing a common interface on which to build processing chains. Three examples are presented, to demonstrate the capabilities of the system: (1) classification of mangrove extent and change in French Guiana; (2) a generic scheme for the classification of the UN-FAO land cover classification system (LCCS) and their subsequent translation to habitat categories; and (3) a national-scale segmentation for Australia. The system presented provides similar functionality to existing GEOBIA packages, but is more flexible, due to its modular environment, capable of handling complex classification processes and applying them to larger datasets.

Evaluation of ALOS PALSAR Data for High-Resolution Mapping of Vegetated Wetlands in Alaska

As the largest natural source of methane, wetlands play an important role in the carbon cycle. High-resolution maps of wetland type and extent are required to quantify wetland responses to climate change. Mapping northern wetlands is particularly important because of a disproportionate increase in temperatures at higher latitudes. Synthetic aperture radar data from a spaceborne platform can be used to map wetland types and dynamics over large areas. Following from earlier work by Whitcomb et al. (2009) using Japanese Earth Resources Satellite (JERS-1) data, we applied the “random forests” classification algorithm to variables from L-band ALOS PALSAR data for 2007, topographic data (e.g., slope, elevation) and locational information (latitude, longitude) to derive a map of vegetated wetlands in Alaska, with a spatial resolution of 50 m. We used the National Wetlands Inventory and National Land Cover Database (for upland areas) to select training and validation data and further validated classification results with an independent dataset that we created. A number of improvements were made to the method of Whitcomb et al. (2009): (1) more consistent training data in upland areas; (2) better distribution of training data across all classes by taking a stratified random sample of all available training pixels; and (3) a more efficient implementation, which allowed classification of the entire state as a single entity (rather than in separate tiles), which eliminated discontinuities at tile boundaries. The overall accuracy for discriminating wetland from upland was 95%, and the accuracy at the level of wetland classes was 85%. The total area of wetlands mapped was 0.59 million km2, or 36% of the total land area of the state of Alaska. The map will be made available to download from NASA’s wetland monitoring website.

Managing uncertainty when aggregating from pixels to objects: habitats, context-sensitive mapping and possibility theory

Object-oriented remote sensing software provides the user with flexibility in the way that remotely sensed data are classified through segmentation routines and user-specified fuzzy rules. This paper explores the classification and uncertainty issues associated with aggregating detailed ‘sub-objects’ to spatially coarser ‘super-objects’ in object-oriented classifications. We show possibility theory to be an appropriate formalism for managing the uncertainty commonly associated with moving from ‘pixels to parcels’ in remote sensing. A worked example with habitats demonstrates how possibility theory and its associated necessity function provide measures of certainty and uncertainty and support alternative realizations of the same remotely sensed data that are increasingly required to support different applications.

Atmospheric Correction Performance of Hyperspectral Airborne Imagery over a Small Eutrophic Lake under Changing Cloud Cover

Atmospheric correction of remotely sensed imagery of inland water bodies is essential to interpret water-leaving radiance signals and for the accurate retrieval of water quality variables. Atmospheric correction is particularly challenging over inhomogeneous water bodies surrounded by comparatively bright land surface. We present results of AisaFENIX airborne hyperspectral imagery collected over a small inland water body under changing cloud cover, presenting challenging but common conditions for atmospheric correction. This is the first evaluation of the performance of the FENIX sensor over water bodies. ATCOR4, which is not specifically designed for atmospheric correction over water and does not make any assumptions on water type, was used to obtain atmospherically corrected reflectance values, which were compared to in situ water-leaving reflectance collected at six stations. Three different atmospheric correction strategies in ATCOR4 was tested. The strategy using fully image-derived and spatially varying atmospheric parameters produced a reflectance accuracy of ±0.002, i.e., a difference of less than 15% compared to the in situ reference reflectance. Amplitude and shape of the remotely sensed reflectance spectra were in general accordance with the in situ data. The spectral angle was better than 4.1° for the best cases, in the spectral range of 450–750 nm. The retrieval of chlorophyll-a (Chl-a) concentration using a popular semi-analytical band ratio algorithm for turbid inland waters gave an accuracy of ~16% or 4.4 mg/m3 compared to retrieval of Chl-a from reflectance measured in situ. Using fixed ATCOR4 processing parameters for whole images improved Chl-a retrieval results from ~6 mg/m3 difference to reference to approximately 2 mg/m3. We conclude that the AisaFENIX sensor, in combination with ATCOR4 in image-driven parametrization, can be successfully used for inland water quality observations. This implies that the need for in situ reference measurements is not as strict as has been assumed and a high degree of automation in processing is possible.

Soil Moisture Profiles and Temperature Data from SoilSCAPE Sites, USA

This data set contains in-situ soil moisture profile and soil temperature data collected at 20-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites in four states (California, Arizona, Oklahoma, and Michigan) in the United States. SoilSCAPE used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data at up to 12 sites over varying durations since August 2011. At its maximum, the network consisted of over 200 wireless sensor installations (nodes), with a range of 6 to 27 nodes per site. The soil moisture sensors (EC-5 and 5-TM from Decagon Devices) were installed at three to four depths, nominally at 5, 20, and 50 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. Temperature sensors were installed at 5 cm depth at six of the sites. Data collection started in August 2011 and continues at eight sites through late 2016. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional (airborne, e.g. NASAs Airborne Microwave Observation of Subcanopy and Subsurface Mission - AirMOSS) and national (spaceborne, e.g. NASAs Soil Moisture Active Passive - SMAP) scales.

The effects of noise on model inversion for the retrieval of forest structure from SAR data

The inversion of physics based models presents an alternative to empirical relationships for the retrieval of forest structure from Synthetic Aperture Radar (SAR) data. A major disadvantage of such techniques is instability in the presence of moderate levels of noise. The effects of noise on the accuracy with which parameters can be retrieved is evaluated in this study under a number of conditions.