Detection of subpixel treefall gaps with Landsat imagery in Central Amazon forests

Treefall gaps play important roles in both forest dynamics and species diversity, but variability across the full range of gap sizes has not been reported at a regional scale due to the lack of a consistent methodology for their detection. Here we demonstrate the sensitivity of Landsat data for detecting gaps at the subpixel level in the Manaus region, Central Amazon. Spectral mixture analysis (SMA) on treefall gaps was used to map their occurrence across a 3.4 × 10⁴ km² landscape using the annual change in non-photosynthetic vegetation (ΔNPV) as the change metric. Thirty randomly selected pixels with a spectral signature of a treefall event (i.e. high ΔNPV) were surveyed in the field. The most frequent single-pixel gap size detected using Landsat was ~ 360 m², and the severity of the disturbance (ΔNPV) exhibited a significant (r² = 0.32, p = 0.001) correlation with the number of dead trees (> 10 cm diameter at breast height), enabling quantification of the number of downed trees in each gap. To place the importance of these single-pixel disturbances into a broader context, the cumulative disturbance of these gaps was equivalent to 40% of the calculated deforestation across the Manaus region in 2008. Most detected single-pixel gaps consisted of six to eight downed trees covering an estimated area of 250-900 m². These results highlight the quantitative importance of small blowdowns that have been overlooked in previous satellite remote sensing studies.     

Accurate subpixel edge location based on partial area effect

The estimation of edge features, such as subpixel position, orientation, curvature and change in intensity at both sides of the edge, from the computation of the gradient vector in each pixel is usually inexact, even in ideal images. In this paper, we present a new edge detector based on an edge and acquisition model derived from the partial area effect, which does not assume continuity in the image values. The main goal of this method consists in achieving a highly accurate extraction of the position, orientation, curvature and contrast of the edges, even in difficult conditions, such as noisy images, blurred edges, low contrast areas or very close contours. For this purpose, we first analyze the influence of perfectly straight or circular edges in the surrounding region, in such a way that, when these conditions are fulfilled, the features can exactly be determined. Afterward, we extend it to more realistic situations considering how adverse conditions can be tackled and presenting an iterative scheme for improving the results. We have tested this method in real as well as in sets of synthetic images with extremely difficult edges, and in both cases a highly accurate characterization has been achieved.     

The earth location of geostationary satellite imagery

Spin scan cloud pictures from NASA's Applications Technology Satellites are being computer processed as an operational experiment. Details of this near-real-time image manipulating activity are described. Processing involves the treatment of horizon and landmark features in support of the photogrammetric aspects and the subsequent transformation of images onto standard map projections.     

Separation of diffuse from sharp-edged features in digital imagery

A new classification procedure is proposed that removes sharp-edged features from digital imagery, which more clearly reveals any patterns more diffuse in nature that may be present. The procedure exploits the hypothesis that the sharp edges separate areas of uniform but differing optical density. These differences are frequently sufficiently large to obscure other patterns more diffuse (“fuzzy”) in nature. The technique is illustrated by application to a Landsat scene in which a suspected mosaic arising from soil variability is overlain by agricultural fields.     

基于方向可调小波变换的二次多项式插值图像亚像素边缘定位

提出了一种基于方向可调小波变换的二次多项式插值图像亚像素边缘定位的方法,不仅计算量小,其定位精度可以达到亚像素级,且具有较好的抗噪性能在机械零件尺寸测量中有很高的应用价值.     

Using aerial imagery and digital photography to monitor growth and yield in winter wheat

Monitoring wheat (Triticum aestivum L.) performance throughout the growing season provides information on productivity and yield potential. Remote sensing tools have provided easy and quick measurements without destructive sampling. The objective of this study was to evaluate genetic variability in growth and performance of 20 wheat genotypes under two water regimes (rainfed and irrigated), using spectral vegetation indices (SVI) estimated from aerial imagery and percentage ground cover (%GC) estimated from digital photos. Field experiments were conducted at Bushland, Texas in two growing seasons (2014–2015 and 2015–2016). Digital photographs were taken using a digital camera in each plot, while a manned aircraft collected images of the entire field using a 12-band multiple camera array Tetracam system at three growth stages (tillering, jointing and heading). Results showed that a significant variation exists in SVI, %GC, aboveground biomass and yield among the wheat genotypes mostly at tillering and jointing. Significant relationships for %GC from digital photo at jointing was recorded with Normalized Difference Vegetation Index (NDVI) at tillering (coefficient of determination, R² = 0.84, p< 0.0001) and with %GC estimated from Perpendicular Vegetation Index (PVI) at tillering (R² = 0.83, p< 0.0001). Among the indices, Ratio Vegetation Index (RVI), Green-Red VI, Green Leaf Index (GLI), Generalized DVI (squared), DVI, Enhanced VI, Enhanced NDVI, and NDVI explained 37–99% of the variability in aboveground biomass and yield. Results indicate that these indices could be used as an indirect selection tool for screening a large number of early-generation and advanced wheat lines.     

Cardiology education using hypermedia and digital imagery.

A computer-based educational system for the study of cardiovascular imaging is described. This system, based on HyperCard * and a standard Macintosh II, integrates hypertext retrieval, computer graphics, sound, and medical images into a single interactive environment stored on a standard hard disk. This 'hypermedia' approach allows arbitrary complexity coupled with direct, immediate, easy traversal of the images and related text, which provides the opportunity for students to move at their own pace, choose their own direction through the material and repeat as often as desired. Storage on magnetic medium allows for easy updating with new studies and material in order to keep pace with advances in medical imaging technology. The system could be mastered onto CD-ROM for ease of distribution if so desired. The system includes a tutorial on the basics of digital image representation and example studies from cineangiography, nuclear medicine, echocardiography and magnetic resonance imaging of the heart. Quantitative techniques for evaluation of left ventricular function are explained using computer graphics overlays on the original medical images. Color encoded functional images are also included as an aid to visualization of ventricular performance data. The system has proven useful as a primer for digital imaging in cardiology prior to specific case study in a traditional mentor relationship.     

Character location in scene images from digital camera

In this paper, a robust, connected-component-based character locating method is presented. It is an important part of an optical character recognition (OCR) system. Color clustering is used to separate the color image into homogeneous color layers. Next, for each color layer, every connected component in color layers is analyzed using black adjacency graph (BAG), and the component-bounding box is computed. Then, for coarse detection of characters, an aligning-and-merging-analysis (AMA) scheme is proposed to locate all the potential characters using the information about the bounding boxes of connected components in all color layers. Finally, to eliminate false characters, a four-step identification of characters is used. The experimental results in this paper have proven that the method is effective.     

Learning features from georeferenced seafloor imagery with location guided autoencoders

Although modern machine learning has the potential to greatly speed up the interpretation of imagery, the varied nature of the seabed and limited availability of expert annotations form barriers to its widespread use in seafloor mapping applications. This motivates research into unsupervised methods that function without large databases of human annotations. This paper develops an unsupervised feature learning method for georeferenced seafloor visual imagery that considers patterns both within the footprint of a single image frame and broader scale spatial characteristics. Features within images are learnt using an autoencoder developed based on the AlexNet deep convolutional neural network. Features larger than each image frame are learnt using a novel loss function that regularises autoencoder training using the Kullback–Leibler divergence function to loosely assume that images captured within a close distance of each other look more similar than those that are far away. The method is used to semantically interpret images taken by an autonomous underwater vehicle at the Southern Hydrates Ridge, an active gas hydrate field and site of a seafloor cabled observatory at a depth of 780 m. The method's performance when applied to clustering and content‐based image retrieval is assessed against a ground truth consisting of more than 18,000 human annotations. The study shows that the location based loss function increases the rate of information retrieval by a factor of two for seafloor mapping applications. The effects of physics‐based colour correction and image rescaling are also investigated, showing that the improved consistency of spatial information achieved by rescaling is beneficial for recognising artificial objects such as cables and infrastructures, but is less effective for natural objects that have greater dimensional variability.     

Edge and region analysis for digital image data

In this paper we provide a unified view of edge and region analysis. Our framework is based on the sloped-facet model which assumes that regions of image segments are maximal areas which are sloped planes. Edge strength between two adjacent pixels is measured by the F statistic used to test the significance of the difference between the parameters of the best-fitting sloped neighborhoods containing each of the given pixels. Edges are declared to exist at locations of local maxima in theF-statistic edge strength picture. We show that this statistically optimum procedure in essence scales the edge strength statistic of many popular edge operators by an estimate of the image noise. Such a scaling makes optimum detection possible by a fixed threshold procedure.     

Assessing the educational values of digital games

Abstract   In recent years, digital games have assumed an important place in the lives of children and adolescents. Effective content design is crucial to the success of digital game-based learning. Therefore, the tool for assessing the effectiveness of game design is accordingly very important for parents and teachers, so that they may encourage or discourage students to play. The purpose of this study is to develop an assessment tool to examine the educational values of digital games. In the first phase of this research, the research team developed the indices for assessing the educational values of digital games. An expert panel consisting of game scholars and professional game designers was established to construct the indices for evaluating digital games in three focus group discussions. Seventy-four game evaluation indices were sorted into seven categories: mentality change, emotional fulfilment, knowledge enhancement, thinking skill development, interpersonal skill development, spatial ability development and bodily coordination. In the second phase of the research, the game designers were asked to assess certain games by using the 74 indices. Meanwhile, the game scholars were also asked to evaluate the same pool of games by the same indices. The assessments by both the scholars and designers were then compared and the similarities were found. This research provided a preliminary framework for future game designers, parents and teachers in assessing educational values of digital games.     

Machine annotation and retrieval for digital imagery of historical materials

Annotating digital imagery of historical materials for the purpose of computer-based retrieval is a labor-intensive task for many historians and digital collection managers. We have explored the possibilities of automated annotation and retrieval of images from collections of art and cultural images. In this paper, we introduce the application of the ALIP (Automatic Linguistic Indexing of Pictures) system, developed at Penn State, to the problem of machine-assisted annotation of images of historical materials. The ALIP system learns the expertise of a human annotator on the basis of a small collection of annotated representative images. The learned knowledge about the domain-specific concepts is stored as a dictionary of statistical models in a computer-based knowledge base. When an un-annotated image is presented to ALIP, the system computes the statistical likelihood of the image resembling each of the learned statistical models and the best concept is selected to annotate the image. Experimental results, obtained using the Emperor image collection of the Chinese Memory Net project, are reported and discussed. The system has been trained using subsets of images and metadata from the Emperor collection. Finally, we introduce an integration of wavelet-based annotation and wavelet-based progressive displaying of very high resolution copyright-protected images. A preliminary version of this work has been presented at the DELOS-NSF Workshop on Multimedia in Digital Libraries, Crete, Greece, June 2003. The work was completed when Kurt Grieb and Ya Zhang were students of The Pennsylvania State University. James Z. Wang and Jia Li are also affiliated with Department of Computer Science and Engineering, The Pennsylvania State University. Yixin Chen is also with the Research Institute for Children, Children's Hospital, New Orleans.     

Extraction of bajadas from digital elevation models and satellite imagery

A methodology was designed for the extraction of bajadas from the 15 min US Geological Survey digital elevation models and Landsat Thematic Mapper imagery. The method was demonstrated for the Death Valley-California where progressive eastward tilting has enabled the west-side fans to coalesce and form bajadas. First, the drainage that crossed the uplands and the bajadas was extracted from the DEM. The drainage pixels were successively grown by checking the surrounding pixels on the basis of their gradient. It was concluded that for gradient in the interval [2°,11°] the upslope bajadas border was segmented. In order to eliminate the drainage pixels that belonged to the uplands, the drainage pixels were subtracted. Then, the isolated small 8-connected foreground pixels were identified and subtracted too. Finally, region growing was performed again to the remaining pixels with the same growing criterion. Isolated 8-connected background pixels, representing almost flat regions inside bajadas, were identified and merged to the segmented pixels. At the end, by taking into account the spectral response in the satellite image, the downslope border of bajadas was segmented. The extracted polygon was in agreement with the information depicted on (a) the US Geological Survey topographic map of scale 1:100,000 and (b) the satellite image and (c) the polygon classified manually by a photo-interpreter.     

Vineyard identification in an oak woodland landscape with airborne digital camera imagery

Using airborne multispectral digital camera imagery, we compared a number of feature combination techniques in image classification to distinguish vineyard from non-vineyard land-cover types in northern California. Image processing techniques were applied to raw images to generate feature images including grey level co-occurrence based texture measures, low pass and Laplacian filtering results, Gram-Schmidt orthogonalization, principal components, and normalized difference vegetation index (NDVI). We used the maximum likelihood classifier for image classification. Accuracy assessment is performed using digitized boundaries of the vineyard blocks. The most successful classification as determined by t-tests of the Kappa coefficients was achieved based on the use of a texture image of homogeneity obtained from the near infrared image band, NDVI and brightness generated through orthogonalization analysis. This method averaged an overall accuracy of 81 per cent for six frames of images tested. With post-classification morphological processing (clumping and sieving) the overall accuracy was significantly increased to 87 per cent (with a confidence level of 0.99).     

Automatic computation of pebble roundness using digital imagery and discrete geometry

The shape of sedimentary particles is an important property, from which geographical hypotheses related to abrasion, distance of transport, river behavior, etc. can be formulated. In this paper, we use digital image analysis, especially discrete geometry, to automatically compute some shape parameters such as roundness, i.e. a measure of how much the corners and edges of a particle have been worn away.In contrast to previous work in which traditional digital images analysis techniques, such as Fourier transform, are used, we opted for a discrete geometry approach that allowed us to implement Wadell's original index, which is known to be more accurate, but more time consuming to implement in the field.Our implementation of Wadell's original index is highly correlated (92%) with the roundness classes of Krumbein's chart, used as a ground-truth. In addition, we show that other geometrical parameters, which are easier to compute, can be used to provide good approximations of roundness.We also used our shape parameters to study a set of pebbles digital images taken from the Progo basin river network (Indonesia). The results we obtained are in agreement with previous work and open new possibilities for geomorphologists thanks to automatic computation.     

Automated tree recognition in old growth conifer stands with high resolution digital imagery

Automated individual tree isolation and species determination with high resolution multispectral imagery is becoming a viable forest survey tool. Application to old growth conifer forests offer unique technical issues including high variability in tree size and dominance, strong tree shading and obscuration, and varying ages and states of health. The capabilities of individual tree analysis are examined with two acquisitions of 70-cm resolution CASI imagery over a hemlock, amabilis fir, and cedar dominated old growth site on the west coast of Canada. Trees were delineated using the valley following approach of the Individual Tree Crown (ITC) software suite, classified according to species (hemlock, amabilis fir, and cedar) using object-based spectral classification and tested on a tree-for-tree basis against data derived from ground plots.Tree-for-tree isolation and species classification accuracy assessment, although often sobering, is important for portraying the overall effectiveness of species composition mapping using single tree approaches. This accuracy considers not only how well each tree is classified, but how well each automated isolation represents a true tree and its species. Omissions and commissions need to be included in overall species accuracy assessment. A structure of rules for defining isolation accuracy is developed and used. An example is given of a new approach to accuracy analysis incorporating both isolation and classification results (automated tree recognition) and the issues this presents.The automated tree isolation performed well on those trees that could be visually identified on the imagery using ground measured stem maps (approximately 50-60% of trees had a good match between manual and automated delineations). There were few omissions. Commission errors, i.e., automated isolations not associated with a delineated ground reference tree, were a problem (25%) usually associated with spurious higher intensity areas within shaded regions, which get confused in the process of trying to isolate shaded trees. Difficulty in classifying species was caused by: variability of the spectral signatures of the old growth trees within the same species, tree health, and trees partly or fully shaded by other trees. To accommodate this variability, several signatures were used to represent each species including shaded trees. Species could not be determined for the shaded cases or for the unhealthy trees and therefore two combined classes, a shaded class and unhealthy class with all species included, were used for further analysis. Species classification accuracy of the trees for which there was a good automated isolation match was 72%, 60%, and 40% for the non-shaded healthy hemlock, balsam, and cedar trees for the 1996 data. Equivalent accuracy for the 1998 imagery was 59% for hemlock, 80% for balsam, with only a few cedar trees being well isolated. If all other matches were considered an error in classification, species classification was poor (approximately 45% for balsam and hemlock, 25% for cedar). However, species classification accuracies incorporating the good isolation matches and trees for which there was a match of an isolations and reference tree but the match was not considered good were moderate (60%, 57%, and 38% for hemlock, balsam, and cedar from the 1996 data; 62%, 61%, and 89%, respectively, for the 1998 imagery).Automated tree isolation and species classification of old growth forests is difficult, but nevertheless in this example useful results were obtained.     

Use of digital terrain data in the interpretation of SPOT-1 HRV multispectral imagery

Abstract

This paper describes the complementary use of digital terrain information and SPOT-1 HRV multispectral imagery for the study and mapping of semi-natural upland vegetation. A digital terrain model was derived for a 10 by 10 km study area in southern Snowdonia, Wales, and was used to generate slope and aspect images. A model was developed as a first-order approximation of changes in radiance with local topography, assuming Lambertian behaviour of vegetation canopies. The model was implemented for the study area to assess its use in suppressing the effects of relief on scene radiance. Subsequently the model was refined to describe non-Lambertian reflectance.     

From location to location pattern privacy in location-based services

Location privacy is extensively studied in the context of location-based services (LBSs). Typically, users are assigned a location privacy profile and the precise locations are cloaked so that the privacy profile is not compromised. Though being well-defined for snapshot location privacy, these solutions require additional precautions and patches in case of consecutive LBS requests on the user trajectory. The attacker can exploit some background knowledge like maximum velocity to compromise the privacy profile. To protect against this kind of location privacy attacks, PROBE (Damiani et al. in Trans Data Priv 3(2):123–148, 2010)-like systems constantly check location privacy violations and alter requests as necessary. Clearly, the location privacy is defined in terms of snapshot locations. Observing that there are usually user-specific movement patterns existing in the shared LBS requests, this work extends location privacy to location pattern privacy. We present a framework where user-specific sensitive movement patterns are defined and sanitized in offline and online fashions, respectively. Our solution uses an efficient dynamic programming approach to decide on and to prevent sensitive pattern disclosure. An extensive experimental evaluation has been carried out too.