Stem volume estimation in boreal forests using ERS-1/2 coherence and SPOT XS optical data

The use of spaceborne synthetic aperture radar (SAR) systems to estimate stem volume and biomass in boreal forests has shown some promising results, but with saturation of the radar backscatter at relatively low stem volumes and limited accuracy of stem volume estimation. These limitations have motivated evaluation of more advanced methods, such as interferometry. The results presented in this study show that ERS interferometry, under favourable conditions, may be used to estimate stem volume at stand level with saturation level and accuracy useful for operational forestry management planning in boreal forests. Five interferograms were analysed, covering a test site located in the central part of Sweden with stem volume in the range of 0-305 m³ ha^-1. The best interferogram showed a linear relationship between stem volume and coherence with a root mean square error (RMSE) of approximately 26 m³ ha^-1, corresponding to 20% of the average stem volume, throughout the range of stem volume. No saturation was observed up to the maximum stem volume. However, the sensitivity of coherence to stem volume varied considerably between the interferograms. Finally, four SPOT XS images were evaluated and compared with the stem volume estimations obtained from the interferograms, resulting in a relative RMSE of about 24% of the stem volume, for the best case. The estimation of stem volume using coherence data was found to be better than optical data for stem volumes exceeding about 110 m³ ha^-1. The statistical analysis was performed using linear regression models with cross-validation.     

Land use classification of polarimetric SAR data by visual interpretation and comparison with an automatic procedure

A study is presented in which several representations of polarimetric SAR data were evaluated for the purpose of obtaining land use classification. Two methods comprising visual interpretation and an automatic procedure were used. For the study, fully polarimetric SAR data with a resolution of 3?m were obtained with the Dutch PHARUS sensor from a test area in the Netherlands showing various classes of land use. The land use classes consisted of bare soil, water, grass, urban areas, and forest. The visual inspection was performed by different groups of non-expert interpreters for each representation. It was found that people are quite successful by visual interpretation in performing land use classification using SAR data. Multi-polarized data are required for this purpose, although these data need not be fully polarimetric, since the best results were obtained with the hh- and hv-polarization combinations displayed in the red and green colour channels. The results show that land use classification by visual inspection is more effective than the automatic classification procedure.     

Compensation for subpixel roughness effects in thermal infrared images

Emissivity spectra recovered from spectral radiance images may have lowered spectral contrast due to irradiance from nearby surface elements (‘cavity effect’). For analysis based only on photointerpretation or Reststrahlen band identification, it is not always necessary to account for cavity effects, but for full spectral analysis it may be desirable. We present an approach to compensate thermal infrared (TIR) images for cavity radiation. This approach is based on optical estimates of subpixel surface roughness and estimation of cavity contribution for different natural surfaces using a TIR radiosity model. It was tested using tripod-mounted Hyper-Cam (Telops, Inc., Quebec City, Canada) hyperspectral TIR images of natural targets from the Mojave Desert, California, USA, along with centimetre-scale digital elevation models of similar targets measured by ground lidar. For remote subpixel roughness estimation, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) nadir- and aft-looking (27.6°) near-infrared (NIR) brightness ratios, as well as synthetic aperture radar (SAR) images calibrated to roughness root mean square (RMS), were used. The TIR compensation approach is adaptable for different spectral resolutions, including hyperspectral.     

A priori evaluation of two-stage cluster sampling for accuracy assessment of large-area land-cover maps

Two-stage cluster sampling reduces the cost of collecting accuracy assessment reference data by constraining sample elements to fall within a limited number of geographic domains (clusters). However, because classification error is typically positively spatially correlated, within-cluster correlation may reduce the precision of the accuracy estimates. The detailed population information to quantify a priori the effect of within-cluster correlation on precision is typically unavailable. Consequently, a convenient, practical approach to evaluate the likely performance of a two-stage cluster sample is needed. We describe such an a priori evaluation protocol focusing on the spatial distribution of the sample by land-cover class across different cluster sizes and costs of different sampling options, including options not imposing clustering. This protocol also assesses the two-stage design's adequacy for estimating the precision of accuracy estimates for rare land-cover classes. We illustrate the approach using two large-area, regional accuracy assessments from the National Land-Cover Data (NLCD), and describe how the a priori evaluation was used as a decision-making tool when implementing the NLCD design.     

An improved 3D imaging system for dimensional quality inspection of rolled products in the metal industry

Measurement, inspection and quality control in industry have benefited from 3D techniques for imaging and visualization in recent years. The development of machine vision devices at decreased costs, as well as their miniaturization and integration in industrial processes, have accelerated the use of 3D imaging systems in industry. In this paper we describe how to improve the performance of a 3D imaging system for inline dimensional quality inspection of long, flat-rolled metal products manufactured in rolling mills we designed and developed in previous works. Two dimensional characteristics of rolled products are measured by the system: width and flatness. The system is based on active triangulation using a single-line pattern projected onto the surface of the product under inspection for range image acquisition. Taking the system calibration into account the range images are transformed into a calibrated point cloud representing the 3D surface reconstruction of the product. Two approaches to improve the line detection and extraction method used in the original system are discussed, one intended for high-speed processing with lower accuracy, and the other providing high accuracy while incurring higher computational time expenses. A mechanism to remove, or at least reduce, the effects of product movements while manufacturing, such as bouncing and flapping, is also proposed to improve the performance of the system.     

Real-time recovery of moving 3D faces for emerging applications

Existing face imaging systems are not suitable to meet the face representation and recognition demands for emerging applications in areas such as interactive gaming, enhanced learning environments and directed advertising. This is mainly due to the poor capture and characterisation of facial data that compromises their spatial and temporal precision. For emerging applications it is not only necessary to have a high level of precision for the representation of facial data, but also to characterise dynamic faces as naturally as possible and in a timely manner. This study proposes a new framework for capturing and recovering dynamic facial information in real-time at significantly high order of spatial and temporal accuracy to capture and model subtle facial changes for enhanced realism in 3D face visualisation and higher precision for face recognition applications. We also present a novel, fast, and robust correspondence mapping approach for 3D registration of moving 3D faces.     

Robust 3D face capture using example-based photometric stereo

We show that using example-based photometric stereo, it is possible to achieve realistic reconstructions of the human face. The method can handle non-Lambertian reflectance and attached shadows after a simple calibration step. We use spherical harmonics to model and de-noise the illumination functions from images of a reference object with known shape, and a fast grid technique to invert those functions and recover the surface normal for each point of the target object. The depth coordinate is obtained by weighted multi-scale integration of these normals, using an integration weight mask obtained automatically from the images themselves. We have applied these techniques to improve the PhotoFace system of Hansen et al. (2010).     

Evaluation of linear spectral unmixing and ΔNBR for predicting post‐fire recovery in a North American ponderosa pine forest

The Differenced Normalized Burn Ratio (ΔNBR) is widely used to map post‐fire effects in North America from multispectral satellite imagery, but has not been rigorously validated across the great diversity in vegetation types. The importance of these maps to fire rehabilitation crews highlights the need for continued assessment of alternative remote sensing approaches. To meet this need, this study presents a first preliminary comparison of immediate post‐fire char (black ash) fraction, as measured by linear spectral unmixing, and ΔNBR, with two quantitative one‐year post‐fire field measures indicative of canopy and sub‐canopy conditions: % live tree and dry organic litter weight (gm^?2). Image analysis was applied to Landsat 7 Enhanced Thematic Mapper (ETM+) imagery acquired both before and immediately following the 2000 Jasper Fire, South Dakota. Post‐fire field analysis was conducted one‐year post‐fire. Although the immediate post‐fire char fraction (r ² = 0.56, SE = 28.03) and ΔNBR (r ² = 0.55, SE = 29.69) measures produced similarly good predictions of the % live tree, the standard error in the prediction of litter weight with the char fraction method (r ² = 0.55, SE = 4.78) was considerably lower than with ΔNBR (r ² = 0.52, SE = 8.01). Although further research is clearly warranted to evaluate more field measures, in more fires, and across more fire regimes, the char fraction may be a viable approach to predict longer‐term indicators of ecosystem recovery and may potentially act as a surrogate retrospective measure of the fire intensity.     

An integrated approach to land cover classification: An example in the Island of Jersey

A land cover map of Jersey was created using remotely sensed images recorded by satellite. This map brought together a range of disparate techniques, developed in isolation and mostly applied experimentally, integrating multisensor, multitemporal, enhanced spatial resolution data within an object-oriented integrated Geographical Information System (GIS) for an applications-driven, operational programme. It was developed under the Classification of Environment with Vector- and Raster-Mapping (CLEVER-Mapping) project: this improved approach to operational land cover mapping used information on the subdivision of the landscape into land parcels to help classify remotely sensed images on a per-parcel basis. The object-oriented approach allowed the use of remotely sensed information which relates directly to ground features, and the application of improved knowledge-based corrections using a range of external data. Unlike a conventional map, the parcel-based approach produced a GIS database containing classified land parcels which could also be used as a storage framework and analysis tool for other datasets in later analyses. The GIS recorded 21 land cover types. Validation against reference land parcel data gave a correspondence of between 85% and 95% depending on the level of class aggregation.