Peter Bunting

Radiative Transfer Modeling of a Coniferous Canopy Characterized by Airborne Remote Sensing

Abstract Solar radiation beneath a forest canopy can have large spatial variations, but this is frequently neglected in radiative transfer models for large-scale applications. To explicitly model spatial variations in subcanopy radiation, maps of canopy structure are required. Aerial photography and airborne laser scanning are used to map tree locations, heights, and crown diameters for a lodgepole pine forest in Colorado as inputs to a spatially explicit radiative transfer model. Statistics of subcanopy radiation simulated by the model are compared with measurements from radiometer arrays, and scaling of spatial statistics with temporal averaging and array size is discussed. Efficient parameterizations for spatial averages and standard deviations of subcanopy radiation are developed using parameters that can be obtained from the model or hemispherical photography.

The Earth Observation Data for Habitat Monitoring (EODHaM) System

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India.

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.

A multi-resolution area-based technique for automatic multi-modal image registration

To allow remotely sensed datasets to be used for data fusion, either to gain additional insight into the scene or for change detection, reliable spatial referencing is required. With modern remote sensing systems, reliable registration can be gained by applying an orbital model for spaceborne data or through the use of global positioning (GPS) and inertial navigation (INS) systems in the case of airborne data. Whilst, individually, these datasets appear well registered when compared to a second dataset from another source (e.g., optical to LiDAR or optical to radar) the resulting images may still be several pixels out of alignment. Manual registration techniques are often slow and labour intensive and although an improvement in registration is gained, there can still be some misalignment of the datasets. This paper outlines an approach for automatic image-to-image registration where a topologically regular grid of tie points was imposed within the overlapping region of the images. To ensure topological consistency, tie points were stored within a network structure inspired from Kohonens self-organising networks [24]. The network was used to constrain the motion of the tie points in a manner similar to Kohonens original method. Using multiple resolutions, through an image pyramid, the network structure was formed at each resolution level where connections between the resolution levels allowed tie point movements to be propagated within and to all levels. Experiments were carried out using a range of manually registered multi-modal remotely sensed datasets where known linear and non-linear transformations were introduced against which our algorithms performance was tested. For single modality tests with no introduced transformation a mean error of 0.011 pixels was identified increasing to 3.46 pixels using multi-modal image data. Following the introduction of a series of translations a mean error of 4.98 pixels was achieve across all image pairs while a mean error of 7.12 pixels was identified for a series of non-linear transformations. Experiments using optical reflectance and height data were also conducted to compare the manually and automatically produced results where it was found the automatic results out performed the manual results. Some limitations of the network data structure were identified when dealing with very large errors but overall the algorithm produced results similar to, and in some cases an improvement over, that of a manual operator. We have also positively compared our method to methods from two other software packages: ITK and ITT ENVI.

Distribution and drivers of global mangrove forest change, 1996–2010

For the period 1996-2010, we provide the first indication of the drivers behind mangrove land cover and land use change across the (pan-)tropics using time-series Japanese Earth Resources Satellite (JERS-1) Synthetic Aperture Radar (SAR) and Advanced Land Observing Satellite (ALOS) Phased Array-type L-band SAR (PALSAR) data. Multi-temporal radar mosaics were manually interpreted for evidence of loss and gain in forest extent and its associated driver. Mangrove loss as a consequence of human activities was observed across their entire range. Between 1996-2010 12% of the 1168 1°x1° radar mosaic tiles examined contained evidence of mangrove loss, as a consequence of anthropogenic degradation, with this increasing to 38% when combined with evidence of anthropogenic activity prior to 1996. The greatest proportion of loss was observed in Southeast Asia, whereby approximately 50% of the tiles in the region contained evidence of mangrove loss, corresponding to 18.4% of the global mangrove forest tiles. Southeast Asia contained the greatest proportion (33.8%) of global mangrove forest. The primary driver of anthropogenic mangrove loss was found to be the conversion of mangrove to aquaculture/agriculture, although substantial advance of mangroves was also evident in many regions.

Mangrove response to environmental change in Australia's Gulf of Carpentaria.

Abstract Across their range, mangroves are responding to coastal environmental change. However, separating the influence of human activities from natural events and processes (including that associated with climatic fluctuation) is often difficult. In the Gulf of Carpentaria, northern Australia (Leichhardt, Nicholson, Mornington Inlet, and Flinders River catchments), changes in mangroves are assumed to be the result of natural drivers as human impacts are minimal. By comparing classifications from time series of Landsat sensor data for the period 1987–2014, mangroves were observed to have extended seawards by up to 1.9 km (perpendicular to the coastline), with inland intrusion occurring along many of the rivers and rivulets in the tidal reaches. Seaward expansion was particularly evident near the mouth of the Leichhardt River, and was associated with peaks in river discharge with LiDAR data indicating distinct structural zones developing following each large rainfall and discharge event. However, along the Gulf coast, and particularly within the Mornington Inlet catchment, the expansion was more gradual and linked to inundation and regular sediment supply through freshwater input. Landward expansion along the Mornington Inlet catchment was attributed to the combined effects of sea level rise and prolonged periods of tidal and freshwater inundation on coastal lowlands. The study concluded that increased amounts of rainfall and associated flooding and sea level rise were responsible for recent seaward and landward extension of mangroves in this region.

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.