Reference Imagery Research Articles

Delineation of the healthy rabbit tonsil by immunohistochemistry – A short communication

Situated in the oral cavity, the rabbit palatine tonsils are part of the mucosal immune system and help to defend the body against foreign pathogens. Expressed as two oval protrusions in the wall of the oropharynx, the rabbit palatine tonsils are characterized by excretory ducts and trabeculae. We here compare paraffin embedded and cryosections of the healthy rabbit tonsils. This analysis centers on evaluating the differential outcomes resulting from the application of these fixation methodologies in conjunction with immunohistochemical assays targeting collagen I, collagen III, fibronectin, α-smooth muscle actin (α-SMA), and ki67. Subsequent recommendations are provided based on our findings. Furthermore, we demonstrate the advantage of an antigen retrieval step in immunohistochemical labeling of paraffin sections. Basic classical histological stainings as HE, GT and elastin were also performed. Comparison of different stainings and labelings was furthermore performed in serial sections, showing that adjacent to the excretory ducts, the tonsillar tissue was particularly composed of collagen I and fibronectin, while the vessel walls were predominantly α-SMA positive. Moreover, PAR-2 immunohistochemical staining was performed, where a small fraction of the cells found in the tonsillar connective tissue were PAR-2 positive (probably a subpopulation of mast cells), as well as the lumen of some excretory ducts and trabeculae. Collagen III on the other hand was only weakly expressed in the tonsils. Proliferating ki67 positive cells were rare. This endeavor serves to furnish the scientific community with reference imagery pertinent to researchers opting for the rabbit palatine tonsil model. The diversity of staining techniques employed herein establishes a foundational repository of images, primed for comparative analysis against pathological conditions. Furthermore, these images hold the potential to illustrate inter-species variations. For instance, they can be juxtaposed against murine or rodent tonsils, or even offer insights into the human context.

Analysis of the Matchability of Reference Imagery for Aircraft Based on Regional Scene Perception

Scene matching plays a vital role in the visual positioning of aircraft. The position and orientation of aircraft can be determined by comparing acquired real-time imagery with reference imagery. To enhance precise scene matching during flight, it is imperative to conduct a comprehensive analysis of the reference imagery’s matchability beforehand. Conventional approaches to image matchability analysis rely heavily on features that are manually designed. However, these features are inadequate in terms of comprehensiveness, efficiency, and taking into account the scene matching process, ultimately leading to unsatisfactory results. This paper innovatively proposes a core approach to quantifying matchability by utilizing scene information from imagery. The first proposal for generating image matchability samples through a simulation of the matching process has been developed. The RSPNet network architecture is designed to effectively leverage regional scene perception in order to accurately predict the matchability of reference imagery. This network comprises two core modules: saliency analysis and uniqueness analysis. The attention mechanism employed by saliency analysis module extracts features at different levels and scales, guaranteeing an accurate and meticulous quantification of image saliency. The uniqueness analysis module quantifies image uniqueness by comparing neighborhood scene features. The proposed method is compared with traditional and deep learning methods for experiments based on simulated datasets, respectively. The results demonstrate that RSPNet exhibits significant advantages in terms of accuracy and reliability.

An automatic method for subpixel registration of KMSS-M imagery based on coarse-resolution actualized reference

The paper describes a method for automatic subpixel-accurate geographic referencing of imagery acquired by KMSS-M with 60 m spatial resolution, based on leveraging a coarse, reconstructed, cloud-free and daily updated MODIS surface reflectance reference image. The method is based on maximizing Pearson's correlation value when determining an optimal local displacement of the distorted image fragment by comparing with the reference image. To assess the effectiveness of the method when used over continental-scale and heterogeneous areas, three experiments were carried out providing quantitative estimates of imagery registration errors: an experiment with model datasets, an experiment to estimate the absolute registration error of MODIS reference imagery, and an experiment to estimate the registration error of geocorrected KMSS-M data. Experimental evaluation of the method based on model datasets of decameter-resolution Sentinel-2 (MSI) imagery demonstrated its robustness when used over a variety of environmental conditions over a one year-long observation period. The average georeferencing error of MODIS coarse-resolution reference was shown to be less than 20 meters in Red and Near-infrared bands. Corrected KMSS-M imagery evaluation over the Russian Grain Belt within 2020 has shown, on average, the subpixel referencing accuracy both in Red and Near-infrared bands, while the average absolute georeferencing error of the original uncorrected KMSS-M imagery was shown to be about 3 kilometers. Subpixel registration accuracy of KMSS-M imagery, corrected with MODIS-based coarse-resolution reference, opens new prospects for using multi-temporal analysis of this multispectral surface reflectance data in a variety of scientific and practical applications associated with vegetation cover satellite monitoring. The technological flexibility of the method ensures its applicability to data from other satellite systems for Earth optical remote sensing.

Landsat 9 Geometric Characteristics Using Underfly Data

The Landsat program has a long history of providing remotely sensed data to the user community. This history is being extended with the addition of the Landsat 9 satellite, which closely mimics the Landsat 8 satellite and its instruments. These satellites contain two instruments, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). OLI is a push-broom sensor that collects visible and near-infrared (VNIR) and short-wave infrared (SWIR) wavelengths at 30 m ground sample distance, along with a panchromatic 15 m band. The TIRS sensor contains two long-wave thermal spectral channels centered at 10.9 and 12 µm. The data from these two instruments, on both satellites, are combined into a single Landsat product. The Landsat 5–9 satellites follow a 16 day repeat cycle designated as the Worldwide Reference System (WRS-2), which provides a global notional gridded mapping for identifying individual Landsat scenes. The Landsat 8 and 9 satellites are flown such that their orbital tracks are separated by 8 days in this 16 day cycle. During the commissioning period of Landsat 9, and during its ascent to its operational WRS-2 orbit, the Landsat 9 satellite’s orbital track went under and crossed over the orbital track of the Landsat 8 satellite. This produced a unique situation where nearly time-coincident imagery could be obtained from the instruments of the two spacecrafts. From a radiometric standpoint, this allowed for near-time cross-calibration between the instruments to be performed. From a geometry perspective, calibration is achieved through high-resolution reference imagery over specific ground locations, thus ensuring calibration of the instruments and for the instruments to be well cross-calibrated geometrically. Although these underfly data do not provide calibration of the instruments between the platforms from a geometric perspective, they allow for the verification of the calibration steps involving the instruments and spacecraft. This paper discusses the co-registration of this unique set of data while also discussing other geometric aspects of these data by looking at and comparing the differences in sensor viewing and sun angles associated with the collections from the two platforms for imagery obtained over common geographic locations. The image-to-image comparisons between Landsat 8 and 9 coincident pairs, where both datasets are precision terrain products, are registered to within 2.2 m with respect to their root-mean-squared radial error (RMSEr). The 2.2 m represents less than 0.1 of a 30 m multispectral pixel in misregistration between the L9 and L8 underfly products that will be available to the user community. This unique dataset will provide well-registered, near-coincident image acquisitions between the two platforms that can be a key to any calibration or application comparisons. The paper also presents that, for images for which one of the image pairs failed precision corrections and became a terrain-corrected only product type, a range of 8–14 m RMSEr could be expected in co-registration, while, in cases where both image pairs failed the precision correction step and both images became a terrain-corrected only product type, a 14 m RMSEr could be expected for co-registration.

MACA: A Relative Radiometric Correction Method for Multiflight Unmanned Aerial Vehicle Images Based on Concurrent Satellite Imagery

Unmanned aerial vehicle (UAV) offers an unprecedented observing potential with ultrahigh spatial and temporal resolutions and high flexibility. However, it remains difficult to solve the radiometric inconsistency of multiflight UAV imagery. This study proposed a relative radiometric correction method for multiflight UAV images based on concurrent satellite imagery (MACA) consisting of two steps, i.e., cross-sensor spectral fitting (CSF) and fine-resolution spectral calibration (FSC). In CSF, relationships of multiflight UAV reflectance and the concurrent satellite reflectance were established for generating the fine-resolution reference imagery. Subsequently, a relative radiometric correction model was constructed in the FSC step to correct multiflight UAV imagery. The performance of MACA was evaluated using multiflight UAV datasets acquired on six cropland sites and a concurrent Sentinel-2 image. Compared with four typical or state-of-the-art relative correction methods, the correction using MACA yielded better consistency between UAV and Sentinel-2 data, regardless of individual spectral bands (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =0.79$ </tex-math></inline-formula>&#x2013;0.86, root mean square error (RMSE) <inline-formula> <tex-math notation="LaTeX">$=0.004$ </tex-math></inline-formula>&#x2013;0.019) or vegetation indices (VIs) (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =0.80$ </tex-math></inline-formula>&#x2013;0.86, RMSE <inline-formula> <tex-math notation="LaTeX">$=0.024$ </tex-math></inline-formula>&#x2013;0.054). Moreover, the prediction of plant nitrogen accumulation (PNA) based on the MACA-corrected UAV data had the highest accuracy and showed the spatial variation most significantly within and between fields for all sites. The results demonstrated that MACA was more robust in reducing spectral mismatch across sensors and eliminating the subjective error of pseudo-invariant features (PIFs) selection. MACA has the potential to be used to cross-calibrate multisensor data into a consistent standard, which will benefit multisensor synergies.

Pseudo Computed Tomography Image Generation from Brain Magnetic Resonance Image for Radiation Therapy Treatment Planning Using DCNN-UNET

Relative to Computed Tomography (CT), the increased soft tissue contrasts of magnetic resonance imaging (MRI) makes it a suitable imaging method to decide radiation therapy (RT). When MRI scans are used for therapy planning, a CT scan is still required for dosage calculation and x-ray-based patient placement. This raises workload, leads to incertitude owing to the requisite of image registration inter-modality and requires needless irradiation. Even though it would be advantageous to only use MR images, a way of estimating a pseudo-CT (pCT) must be used to generate electron density mapping and patient reference imagery. So, this paper brings an effective deep learning model to generate synthesized CT from MRI images using the following steps; a) data acquisition where CT and MRI scan images are collected, b) preprocessing of images to avoid the anomalies and noises using techniques like outlier elimination, data smoothening and data normalizing, c) feature extraction and selection using Principle Component Analysis (PCA) &amp; regression method, d) generating pCT from MRI using Deep Convolutional Neural Network and UNET (DCNN-UNET). Further, we assessed metrics such as DC, SSIM, MAE and MSE for this model. However, our suggested model outperforms with an accuracy of 95%.

Sub-pixel accuracy evaluation of FY-3D MERSI-2 geolocation based on OLI reference imagery

ABSTRACT The geolocation quality of satellite observation datasets is causally related to the credibility of operational products. The twice-daily global Earth observations provided by FY-3D/MERSI-2 (FengYun-3D/MEdium Resolution Spectral Imager II) have been used to generate various remote sensing application products, but their geolocation performance has not yet been documented, while the comprehensive analysis of geometric accuracy provides important guidance for improving geolocation accuracy and data application in the future. In this paper, a combination of phase-only correlation and anomaly isolation techniques was used to design a robust algorithm of sub-pixel geolocation displacement detection for MERSI-2 data with 1 km spatial resolution. Utilizing MERSI-2 data from three typical regions and monthly Landsat 8 OLI (Operational Land Imager) data synthesized by Google Earth Engine (GEE), an exhaustive presentation of how the developed method quantifies the geometric displacements of the Level 1 dataset to along-track and cross-track directions is described, ultimately revealing the deterioration of MERSI-2 on-orbit geolocation performance. During the 17 months of official operational products, the geolocation error increases by a factor of 1.13 in along-track direction and 1.56 in cross-track direction. But approximately 70% of the geometric displacements remain within ±300 m in both along-track and cross-track directions, while about 92% are within ±500 m. This method reliably ascertains that the geolocation accuracy of MERSI-2 matches within 1/2 Instantaneous Field of View (IFOV).

A Bi-Spectral Microbolometer Sensor for Wildfire Measurement.

This study describes the development of a prototype bi-spectral microbolometer sensor system designed explicitly for radiometric measurement and characterization of wildfire mid- and long-wave infrared radiances. The system is tested experimentally over moderate-scale experimental burns coincident with FLIR reference imagery. Statistical comparison of the fire radiative power (FRP; W) retrievals suggest that this novel system is highly reliable for use in collecting radiometric measurements of biomass burning. As such, this study provides clear experimental evidence that mid-wave infrared microbolometers are capable of collecting FRP measurements. Furthermore, given the low resource nature of this detector type, it presents a suitable option for monitoring wildfire behaviour from low resource platforms such as unmanned aerial vehicles (UAVs) or nanosats.

Automatically Monitoring Impervious Surfaces Using Spectral Generalization and Time Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta

Accurately monitoring the spatiotemporal dynamics of impervious surfaces is very important for understanding the process of urbanization. However, the complicated makeup and spectral heterogeneity of impervious surfaces create difficulties for impervious surface monitoring. In this study, we propose an automatic method to capture the spatiotemporal expansion of impervious surfaces using spectral generalization and time series Landsat imagery. First, the multitemporal compositing and relative radiometric normalization methods were used to extract phenological information and ensure spectral consistency between reference imagery and monitored imagery. Second, we automatically derived training samples from the prior MSMT_IS30-2020 impervious surface products and migrated the surface reflectance of impervious surfaces in the reference period of 2020 to other periods (1985–2015). Third, the random forest classification method, trained using the migrated surface reflectance of impervious surfaces and pervious surface training samples at each period, was employed to extract temporally independent impervious surfaces. Further, a temporal consistency check method was applied to ensure the consistency and reliability of the monitoring results. According to qualitative and quantitative validation results, the method achieved an overall accuracy of 90.9% and kappa coefficient of 0.859 in identifying the spatiotemporal expansion of impervious surfaces and performed better in capturing the impervious surface dynamics when compared with other impervious surface datasets. Lastly, our results indicate that a rapid increase of impervious surfaces was observed in the Yangtze River Delta, and the area of impervious surfaces in 2000 and 2020 was 1.86 times and 4.76 times that of 1985, respectively. Therefore, it could be concluded that the proposed method offered a novel perspective for providing timely and accurate impervious surface dynamics.

A customized framework for regional classification of conifers using automated feature extraction

Pinyon and juniper expansion into sagebrush ecosystems is one of the major challenges facing land managers in the Great Basin. Effective pinyon and juniper treatment requires maps that accurately and precisely depict tree location and degree of woodland development so managers can target restoration efforts for early stages of pinyon and juniper expansion. However, available remotely sensed layers that cover a regional spatial extent lack the spatial resolution or accuracy to meet this need. Accuracy can be improved using object-based image analysis methods such as automated feature extraction, which has proven successful in accurately classifying land cover at the site-level but to date has yet to be applied to regional extents due to time and computational limitations. Using Feature Analyst™, we implement our framework with 1-m2 reference imagery provided by National Agricultural Imagery Program to classify conifers across Nevada and northeastern California. Our resulting binary conifer map has an overall accuracy of 86%. We discuss the advantages to accuracy and precision our framework provides compared to other classification methods.● This framework allows automated feature extraction for large quantities of data and very high spatial resolution imagery● It leverages supervised learning● It results in high accuracy maps for regional spatial extents

Radio Pirata no meio da mata

Um texto performativo, que surge da escrita em sala de ensaio, a partir das pesquisas sobre ancestralidades travestis na América Latina, America do Sul. Dialogando o dilema da ordem e do progresso do pais como taticas de disciplinarização e controle bionecropolitico. O texto é uma experiencia sensorial a partir de uma imbricação de formas narrativas, jornalisticas, poéticas, miticas, publicitaria. No intuito de aproximar a escrita Travesti, como uma possibilidade de repensar uma nova epistemologia e novas maneiras de perceber e escrever o mundo. No caso de Radio Pirata no Meio da Mata o dialogo entre preservação e invasão aparece como um condutor narrativo para pensarmos que a propria identidade Travesti na Amazonia , mas tambem a identidade nacional de país terceiro mundista. Que mais consome , assassina e experimenta corpos existencias travestis.

Modular primitives for high-performance differentiable rendering

We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics pipeline: rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups, as well as user-programmable shading and geometry processing, all in high resolutions. Our modular primitives allow custom, high-performance graphics pipelines to be built directly within automatic differentiation frameworks such as PyTorch or TensorFlow. As a motivating application, we formulate facial performance capture as an inverse rendering problem and show that it can be solved efficiently using our tools. Our results indicate that this simple and straightforward approach achieves excellent geometric correspondence between rendered results and reference imagery.

Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies

Distributed systems of small satellites are generating a new type of remote sensing data: multi-spectral data with high spatial and temporal resolution and near-daily global coverage. This data is proving to be valuable for monitoring land cover change. However, in order to achieve widespread application for this purpose, all satellites in the distributed system, or constellation, must acquire imagery with accurate georeferencing and consistent radiometric properties. In this research, we developed a method to automatically co-register and radiometrically normalize a temporally dense series of smallsat images using geocorrected reference imagery. To demonstrate the approach, we normalized a smallsat image time series at both the pixel and the polygon level, smoothing spectral indices through time to detect both abrupt and gradual changes. Using PlanetScope imagery, we tested these methods in a forested region of British Columbia, Canada heavily impacted by forest fires in 2017. By examining the normalized difference vegetation index (NDVI) in the acquired imagery before and after the fires, we found that this method allowed simple identification of burned and unburned areas, which was not readily possible without applying the normalization method. Our result suggests that the developed approach can help fully exploit remote sensing datasets that have high spatial resolution and are acquired with high frequency but potentially contain radiometric inconsistencies in order to quickly identify land cover changes.

Exploratory Stage Lighting Design using Visual Objectives

AbstractLighting is a critical element of theater. A lighting designer is responsible for drawing the audience's attention to a specific part of the stage, setting time of day, creating a mood, and conveying emotions. Designers often begin the lighting design process by collecting reference visual imagery that captures different aspects of their artistic intent. Then, they experiment with various lighting options to determine which ideas work best on stage. However, modern stages contain tens to hundreds of lights, and setting each light source's parameters individually to realize an idea is both tedious and requires expert skill. In this paper, we describe an exploratory lighting design tool based on feedback from professional designers. The system extracts abstract visual objectives from reference imagery and applies them to target regions of the stage. Our system can rapidly generate plausible design candidates that embody the visual objectives through a Gibbs sampling method, and present them as a design gallery for rapid exploration and iterative refinement. We demonstrate that the resulting system allows lighting designers of all skill levels to quickly create and communicate complex designs, even for scenes containing many color‐changing lights.

Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests: A Test Case in Continental Southeast Asia

This study presents an approach to forest canopy disturbance monitoring in evergreen forests in continental Southeast Asia, based on temporal differences of a modified normalized burn ratio (NBR) vegetation index. We generate NBR values from each available Landsat 8 scene of a given period. A step of ‘self-referencing’ normalizes the NBR values, largely eliminating illumination/topography effects, thus maximizing inter-comparability. We then create yearly composites of these self-referenced NBR (rNBR) values, selecting per pixel the maximum rNBR value over each observation period, which reflects the most open canopy cover condition of that pixel. The ΔrNBR is generated as the difference between the composites of two reference periods. The methodology produces seamless and consistent maps, highlighting patterns of canopy disturbances (e.g., encroachment, selective logging), and keeping artifacts at minimum level. The monitoring approach was validated within four test sites with an overall accuracy of almost 78% using very high resolution satellite reference imagery. The methodology was implemented in a Google Earth Engine (GEE) script requiring no user interaction. A threshold is applied to the final output dataset in order to separate signal from noise. The approach, capable of detecting sub-pixel disturbance events as small as 0.005 ha, is transparent and reproducible, and can help to increase the credibility of monitoring, reporting and verification (MRV), as required in the context of reducing emissions from deforestation and forest degradation (REDD+).

Evaluating Landsat and RapidEye Data for Winter Wheat Mapping and Area Estimation in Punjab, Pakistan

While publicly available, cost-free coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems, commercial high spatial resolution satellite data are often preferred alternative for fine scale land tenure agricultural systems such as found in Pakistan. In this article, we integrated commercial 5 m spatial resolution RapidEye and free 30 m Landsat imagery in characterizing winter wheat in Punjab province, Pakistan. Specifically, we used 5 m spatial resolution RapidEye imagery from peak of the winter wheat growing season to derive training data for the characterization of time-series Landsat data. After co-registration, each RapidEye image was classified into wheat/no wheat labels at the 5 m resolution and then aggregated as percent cover to 30 m Landsat grid cells. We produced four maps, two using RapidEye derived continuous training data (of percent wheat cover) as input to a regression tree model, and two using RapidEye derived categorical training data as input to a classification tree model. From the RapidEye-derived 30 m continuous training data, we derived Map 1 as percent wheat per pixel, and Map 2 as binary wheat/no wheat classification derived using a 50% threshold applied to Map 1. To create the categorical wheat/no wheat training data, we first converted the continuous training data to a wheat/no wheat classification, and then used these categorical RapidEye training data to produce a categorical wheat map from the Landsat data. Two methods for categorizing the training data were used. The first method used a 50% wheat/no wheat threshold to produce Map 3, and the second method used only pure wheat (≥75% cover) and no wheat (≤25% cover) training pixels to produce Map 4. The approach of Map 4 is analogous to a standard method in which whole, pure, high-confidence training pixels are delineated. We validated the wheat maps with field data collected using a stratified, two-stage cluster design. Accuracy of the maps produced from the percent cover training data (Map 1 and Map 2) was not substantially better than the accuracy of the maps produced from the categorical training data as all methods yielded similar overall accuracies (±standard error): 88% (±4%) for Map 1, 90% (±4%) for Map 2, 90% (±4%) for Map 3, and 87% (±4%) for Map 4. Because the percent cover training data did not produce significantly higher accuracies, sub-pixel training data are not required for winter wheat mapping in Punjab. Given sufficient expertise in supervised classification model calibration, freely available Landsat data are sufficient for crop mapping in the fine-scale land tenure system of Punjab. For winter wheat mapping in Punjab and other like landscapes, training data for supervised classification may be collected directly from Landsat images without the need for high resolution reference imagery.

Il Malato e l’Immaginario. La malattia da segno di elezione a nuova forma di moralizzazione

Illness and Imagery. Desease as a sign of election to a new form of moralization. An analysis from the point of view of sociology of culture. The ways and forms in which evil is represented and perceived in a human community are the result of a complex social construction. Relevant meaning shifts can occur if only the reference imagery changes. E. g., the entire symbolic system of the never-ending struggle between good and evil becomes a mine of meanings where men of all time can find moral values that give sense to their existences (Alexander, 2006). Within this peculiar narrative, a prime role is played by the different universes of meaning that inform the relationship between man and disease (see Fleck 1983; Foucault, 1969 and 1992). The ways in which different social configurations contrast « eschatological anxiety » (Kermode, 1972: 113) caused by the illness are valid indicators of the broader changes in the imagery: disease as a sign of election (premodern society), or as an enemy to be defeated (modern society), to illness as a new form of moralization (contemporary society) (Camorrino, 2015a). The aim of this paper is to investigate this gradual and complex process.

Target geo-localization based on camera vision simulation of UAV

This paper presents a simulation study on estimating the Geo-Location of a target based on multiple image of the target taken from a gimbaled camera mounted on a unmanned aerial vehicle (UAV), which orbits around the target with a radius such that the target is always in the field of camera vision. The Camera Vision Simulation of the UAV is implemented by using an ortho Geo-TIFF (Geo-Spatial Tagged Information File Format) as imagery reference, positional and attitude attributes of UAV, Gimbal and Camera and internal characteristic of the simulated Camera. Target is localized using the simulation images taken from multiple bearing waypoints by applying the Geo-Location algorithm using the simulation parameters as reference. For improving the accuracy of the estimation, error reduction techniques like true average, moving average and recursive least square are also suggested and implemented.

Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

Tree cover maps are used for many purposes, such as vegetation mapping, habitat connectivity and fragmentation studies. Small remnant patches of native vegetation are recognised as ecologically important, yet they are underestimated in remote sensing products derived from Landsat. High spatial resolution sensors are capable of mapping small patches of trees, but their use in large-area mapping has been limited. In this study, multi-temporal Satellite pour l’Observation de la Terre 5 (SPOT5) High Resolution Geometrical data was pan-sharpened to 5 m resolution and used to map tree cover for the Australian state of New South Wales (NSW), an area of over 800,000 km2. Complete coverages of SPOT5 panchromatic and multispectral data over NSW were acquired during four consecutive summers (2008–2011) for a total of 1256 images. After pre-processing, the imagery was used to model foliage projective cover (FPC), a measure of tree canopy density commonly used in Australia. The multi-temporal imagery, FPC models and 26,579 training pixels were used in a binomial logistic regression model to estimate the probability of each pixel containing trees. The probability images were classified into a binary map of tree cover using local thresholds, and then visually edited to reduce errors. The final tree map was then attributed with the mean FPC value from the multi-temporal imagery. Validation of the binary map based on visually assessed high resolution reference imagery revealed an overall accuracy of 88% (±0.51% standard error), while comparison against airborne lidar derived data also resulted in an overall accuracy of 88%. A preliminary assessment of the FPC map by comparing against 76 field measurements showed a very good agreement (r2 = 0.90) with a root mean square error of 8.57%, although this may not be representative due to the opportunistic sampling design. The map represents a regionally consistent and locally relevant record of tree cover for NSW, and is already widely used for natural resource management in the state.

A Pattern-Based Definition of Urban Context Using Remote Sensing and GIS.

In Sub-Saharan Africa rapid urban growth combined with rising poverty is creating diverse urban environments, the nature of which are not adequately captured by a simple urban-rural dichotomy. This paper proposes an alternative classification scheme for urban mapping based on a gradient approach for the southern portion of the West African country of Ghana. Landsat Enhanced Thematic Mapper Plus (ETM+) and European Remote Sensing Satellite-2 (ERS-2) synthetic aperture radar (SAR) imagery are used to generate a pattern based definition of the urban context. Spectral mixture analysis (SMA) is used to classify a Landsat scene into Built, Vegetation and Other land covers. Landscape metrics are estimated for Built and Vegetation land covers for a 450 meter uniform grid covering the study area. A measure of texture is extracted from the SAR imagery and classified as Built/Non-built. SMA based measures of Built and Vegetation fragmentation are combined with SAR texture based Built/Non-built maps through a decision tree classifier to generate a nine class urban context map capturing the transition from unsettled land at one end of the gradient to the compact urban core at the other end. Training and testing of the decision tree classifier was done using very high spatial resolution reference imagery from Google Earth. An overall classification agreement of 77% was determined for the nine-class urban context map, with user's accuracy (commission errors) being lower than producer's accuracy (omission errors). Nine urban contexts were classified and then compared with data from the 2000 Census of Ghana. Results suggest that the urban classes appropriately differentiate areas along the urban gradient.