John Armston

Nondestructive estimates of above‐ground biomass using terrestrial laser scanning

Summary: Allometric equations are currently used to estimate above-ground biomass (AGB) based on the indirect relationship with tree parameters. Terrestrial laser scanning (TLS) can measure the canopy structure in 3D with high detail. In this study, we develop an approach to estimate AGB from TLS data, which does not need any prior information about allometry. We compare these estimates against destructively harvested AGB estimates and AGB derived from allometric equations. We also evaluate tree parameters, diameter at breast height (DBH) and tree height, estimated from traditional field inventory and TLS data. Tree height, DBH and AGB data are collected through traditional forest inventory, TLS and destructive sampling of 65 trees in a native Eucalypt Open Forest in Victoria, Australia. Single trees are extracted from the TLS data and quantitative structure models are used to estimate the tree volume directly from the point cloud data. AGB is inferred from these volumes and basic density information and is then compared with the estimates derived from allometric equations and destructive sampling. AGB estimates derived from TLS show a high agreement with the reference values from destructive sampling, with a concordance correlation coefficient (CCC) of 0·98. The agreement between AGB estimates from allometric equations and the reference is lower (CCC = 0·68-0·78). Our TLS approach shows a total AGB overestimation of 9·68% compared to an underestimation of 36·57-29·85% for the allometric equations. The error for AGB estimates using allometric equations increases exponentially with increasing DBH, whereas the error for AGB estimates from TLS is not dependent on DBH. The TLS method does not rely on indirect relationships with tree parameters or calibration data and shows better agreement with the reference data compared to estimates from allometric equations. Using 3D data also enables us to look at the height distributions of AGB, and we demonstrate that 80% of the AGB at plot level is located in the lower 60% of the trees for a Eucalypt Open Forest. This method can be applied in many forest types and can assist in the calibration and validation of broad-scale biomass maps.s

Relationship of MISR RPV parameters and MODIS BRDF shape indicators to surface vegetation patterns in an Australian tropical savanna

The global coverage of bidirectional reflectance distribution function (BRDF) products from the Multi-angle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) has the potential to provide quantitative information on surface vegetation structure for input to process modelling and model–data assimilation schemes for regional and biome-scale assessment of carbon dynamics. The relationship of MISR Rahman–Pinty–Verstraete (RPV) model parameters, derived from inversion of MISR 275 m fine mode data, and BRDF shape indicators calculated from the latest MODIS 500 m MCD43 BRDF product to vegetation patterns in an Australian tropical savanna was examined for a time series covering the dry season period from April to October 2005. The bidirectional reflectance products were compared with geographical information system (GIS) data coverage combining floristic polygons with Landsat thematic mapper (TM) based estimates of canopy cover and height classes. The analysis showed that both the MISR RPV asymmetry parameter Θ and several MODIS BRDF shape indicators constructed using the red band were sensitive to local-scale anisotropic scattering and thus vegetation structure. The MISR RPV asymmetry parameter Θ showed consistent variation between grasslands, forest (closed canopies), and more open tree–grass mixtures over time. The MODIS indicators such as NDHD-R and ANIF-R produced distinctly different temporal profiles for major vegetation types such as rainforest, Melaleuca woodland, and Dichanthium grassland. These indices also showed evidence of consistent discrimination between eucalypt savanna types that varied in canopy cover and tree height. A clumping index calculated from NDHD-R for a single period (day 177 in a time series) showed good correspondence with savanna vegetation canopy properties but was insensitive to dense canopy rainforest vegetation. These results indicate there is potential for both MISR and MODIS BRDF products to provide a quantitative description of vegetation types in global tree–grass systems. However, there is a pressing need for further study to calibrate the responses with fine-scale structural data derived from both field measurement and light detection and ranging (lidar).

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.

Estimating tree-cover change in Australia: challenges of using the MODIS vegetation index product

Time series of the vegetation index product MOD13Q1 from the Moderate Resolution Imagery Spectroradiometer (MODIS) were assessed for estimating tree foliage projective cover (FPC) and cover change from 2000 to 2006. The MOD13Q1 product consists of the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). There were four challenges in using the MOD13Q1 product to derive tree FPC: assessing the impact of the sensors varying view geometry on the vegetation index values; decoupling tree and grass cover contributions to the vegetation index signal; devising a method to relate the temporally composited vegetation index pixels to Lidar estimates of tree FPC for calibration; and estimating the accuracy of the FPC and FPC change measurements using independently derived Lidar, Landsat and MODIS cover estimates. The results show that, for complex canopies, the varying view geometry influenced the vegetation indices. The EVI was more sensitive to the view angle than the NDVI, indicating that it is sensitive to vegetation structure. An existing time series method successfully extracted the evergreen vegetation index signal while simultaneously minimizing the impact of varying view geometry. The vegetation indices were better suited to monitoring tree cover change than deriving accurate single‐date estimates of cover at regional to continental scales. The EVI was more suited to monitoring change in high‐biomass regions (cover >50%) where the NDVI begins to saturate.

Rapid characterisation of forest structure from TLS and 3D modelling

Raumonen et al.[1] have developed a new method for reconstructing topologically consistent tree architecture from TLS point clouds. This method generates a cylinder model of tree structure using a stepwise approach. Disney et al.[2] validated this method with a detailed 3D tree model where structure is known a priori, establishing a reconstruction relative error of less than 2%. Here we apply the same method to data acquired from Eucalyptus racemosa woodland, Banksia ameula low open woodland and Eucalyptus spp. open forest using a RIEGL VZ-400 instrument. Individual 3D tree models reconstructed from TLS point clouds are used to drive Monte Carlo ray tracing simulations of TLS with the same characteristics as those collected in the field. 3D reconstruction was carried out on the simulated point clouds so that errors and uncertainty arising from instrument sampling and reconstruction could be assessed directly. We find that total volume could be recreated to within a 10.8% underestimate. The greatest constraint to this approach is the accuracy to which individual scans can be globally registered. Inducing a 1cm registration error lead to a 8.8% total volumetric overestimation across the data set.

Evaluation of the Range Accuracy and the Radiometric Calibration of Multiple Terrestrial Laser Scanning Instruments for Data Interoperability

Terrestrial laser scanning (TLS) data provide 3-D measurements of vegetation structure and have the potential to support the calibration and validation of satellite and airborne sensors. The increasing range of different commercial and scientific TLS instruments holds challenges for data and instrument interoperability. Using data from various TLS sources will be critical to upscale study areas or compare data. In this paper, we provide a general framework to compare the interoperability of TLS instruments. We compare three TLS instruments that are the same make and model, the RIEGL VZ-400. We compare the range accuracy and evaluate the manufacturer’s radiometric calibration for the uncalibrated return intensities. Our results show that the range accuracy between instruments is comparable and within the manufacturer’s specifications. This means that the spatial XYZ data of different instruments can be combined into a single data set. Our findings demonstrate that radiometric calibration is instrument specific and needs to be carried out for each instrument individually before including reflectance information in TLS analysis. We show that the residuals between the calibrated reflectance panels and the apparent reflectance measured by the instrument are greatest for highest reflectance panels (residuals ranging from 0.058 to 0.312).

A regression model approach for mapping woody foliage projective cover using landsat imagery in Queensland, Australia

This paper describes the development of a regression model for predicting foliage projective cover (FPC) using an extensive set of over 2000 field observations for Queensland, Australia. The model includes Landsat TM and ETM+ imagery and a climatological ancillary variable, vapour pressure deficit. The resulting model was validated using independent site data and preliminary validation against FPC estimates from airbourne laser scanner data is presented. Results suggest the model is robust and performing well over a range of soil types and vegetation communities. This regression-based methodology is currently included in the process of monitoring annual woody vegetation change over Queensland and will form the basis of new products for monitoring longer term trends in FPC

Geometric correction and accuracy assessment of Landsat-7 ETM+ and Landsat-5 TM imagery used for vegetation cover monitoring in Queensland, Australia from 1988 to 2007

A range of programs exist globally that use satellite imagery to derive estimates of vegetation-cover for developing vegetation-management policy, monitoring policy compliance and making natural-resource assessments. Consequently, the satellite imagery must have a high degree of geometric accuracy. It is common for the accuracy assessment to be performed using the root mean square error (RMSE) only. However the RMSE is a non-spatial measure and more rigorous accuracy assessment methods are required. Currently there is a lack of spatially explicit accuracy assessment methods reported in the literature that have been demonstrated to work within operational monitoring programs. This paper reports on the method used by the Statewide Landcover and Trees Study (SLATS) to georegister and assess the registration accuracy of Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper (ETM+) imagery in Queensland, Australia. A geometric baseline with high accuracy (a statewide mean RMSE of 4.53 m) was derived by registering Landsat-7 ETM+ panchromatic imagery acquired in 2002 to a database of over 1600 control points, collected on the ground using a differential global positioning system. Landsat-5 TM and Landsat-7 ETM+ imagery for 12 selected years from 1988 to 2007 was registered to the baseline in an automated procedure that used linear geometric correction models. The reliability of the geometric correction for each image was determined using the RMSE, calculated using independent check points, as an indicator of model fit; by analysing the spatial trends in the model residuals; and through visual assessment of the corrected imagery. The mean RMSE of the statewide coverage of images for all years was less than 12.5 m (0.5 pixels). Less than 1 percent of images had non-linear spatial trends in the model residuals and some image misregistration after applying a linear correction-model; in those cases a quadratic model was deemed necessary for correction. Further research in the development of automated spatially explicit accuracy assessment methods is required.