PE&RS Journals
In Press Peer Reviewed Articles
As a convenience to ASPRS members, in-press peer reviewed articles approved for publication in forthcoming issues of PE&RS have been made available for members of the society.
May 2013 Issue
Change Detection and Deformation Analysis in Point Clouds: Application to Rock Face Monitoring
Marco Scaioni, Riccardo Roncella, and Mario Ivan Alba
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The paper outlines a method to compare two digital surfaces of the same rock face to detect major changes resulting from detached rocks and deformations. A terrestrial laser scanning survey is used for data gathering. After georeferencing, if the cliff has a complex morphology, a 3D segmentation algorithm is applied to split the whole rock surface into more subregions with an almost planar structure. In each subregion the raw point cloud is resampled on a regular grid and multitemporal differences are analyzed. Anomalies in differences, which should be very close to zero if no geometric variations have occurred, are identified with the following purposes: (a) localizing gross changes due to rock detachments, (b) removing global rigid-body displacements, and (c) understanding local cliff deformations. In the case where the rock face is covered by vegetation, this has to be filtered out, e.g., by visual inspection of RGB images co-registered to the point cloud. This paper also describes a procedure to carry out vegetation filtering in automatic way from the analysis of near-infrared images captured by a camera integrated to laser scanner. The application of the full processing pipeline has been tested on a real case study located in the Italian pre-alpine area. Here, after filtering some vegetation, a total rock fall volume of 0.15 m3 was detected on a cliff of about 375 m2 and within a period of six months.
Classification of Coffee-Forest Landscapes Using Landsat TM Imagery and Spectral Mixture Analysis
Mikaela Schmitt-Harsh, Sean P. Sweeney, and Tom P. Evans
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This research applies linear spectral mixture analysis (LSMA) to a Landsat TM image, and assesses the value of fraction images (green vegetation, shade, soil) and the thermal band (TM-B6) in discriminating shade-grown coffee systems from forests. Four combinations of TM bands and fraction images were compared, and a maximum likelihood algorithm was used to classify five land cover classes: high-density woodlands, low-density woodlands, coffee agroforests, crop / pasturelands, and urban settlements. The classification accuracy of each model combination was assessed using both Kappa analyses and quality and allocation disagreement parameters. Results indicate improvements to classification accuracies following inclusion of TM-B6 and fraction images as inputs to the classification; however, only the use of TM-B6 led to significant improvements at the 95 percent confidence level. The highest classification accuracy achieved was 86 percent (Kstandard = 0.82), with producer’s and user’s accuracy of coffee agroforests reaching 89 percent and 90 percent, respectively, an improvement over previous research aimed at spectrally distinguishing coffee from other woody cover types.
The Influence of Multi-season Imagery on Models of Canopy Cover: A Case Study
John W. Coulston, Dennis M. Jacobs, Chris R. King, and Ivey C. Elmore
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Quantifying tree canopy cover in a spatially explicit fashion is important for broad-scale monitoring of ecosystems and for management of natural resources. Researchers have developed empirical models of tree canopy cover to produce geospatial products. For subpixel models, percent tree canopy cover estimates (derived from fine-scale imagery) serve as the response variable. The explanatory variables are developed from reflectance values and derivatives, elevation and derivatives, and other ancillary data. However, there is a lack of guidance in the literature regarding the use of leaf-on only imagery versus multi-season imagery for the explanatory variables. We compared models developed from leaf-on only Landsat imagery with models developed from multi-season imagery for a study area in Georgia. There was no statistical difference among models. We suggest that leaf-on imagery is adequate for the development of empirical models of percent tree canopy cover in the Piedmont of the Southeastern United States.
Combining Hyperspectral and Radar Imagery for Mangrove Leaf Area Index Modeling
Frankie K. K. Wong and Tung Fung
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This study acquired satellite-borne hyperspectral and radar data supplemented with in situ field survey to understand the current biophysical condition of mangrove in Mai Po Ramsar Site of Hong Kong through leaf area index (LAI) modeling by regressing field-measured LAI against vegetation indices (VIs), backscatter, and textural measures. Simple VI regression model revealed that triangular vegetation index (TVI) and modified chlorophyll absorption ratio index (MCARI) had the best relationship (r2 = 0.68) while the commonly-used NDVI had the poorest relationship (r2 = 0.017) with LAI. Poor correlation was found between measured LAI and radar parameters. Results from stepwise multiple regression suggested that TVI combined with GLCM-derived angular second moment formed the best model (r2 = 0.78) and reduced the estimation error (RMSE) to 0.2. This study has demonstrated that single use of radar parameters cannot effectively map mangrove LAI. Nonetheless, spectral and radar data are complementary to enhance the mapping.
Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use
Sory I. Toure, Douglas A. Stow, John R. Weeks, and Sunil Kumar
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The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution.
Influence of a Dense, Low-height Shrub Species on the Accuracy of a Lidar-derived DEM
Samuel B. Gould, Nancy F. Glenn, Temuulen T. Sankey, and James P. McNamara
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Airborne lidar provides an effective platform for collecting elevation data. However, the accuracy of lidar-derived digital elevation models (DEMs) can be adversely affected by natural conditions as well as methods used to process the data. Using a lidar dataset from a mountainous region of southwest Idaho, this study extends previous assessments of DEM accuracy with a focused investigation of a specific dense, low-height shrub species (Ceanothus velutinus). Bare-earth elevations were collected using survey-grade GPS and compared to lidar-derived elevations to assess DEM accuracy. Results suggest that the magnitude of elevation error varied depending on morphological characteristics of ceanothus, terrain slope, and filtering parameters used to process the lidar data. When using optimal filtering parameters, root mean square error (RMSEZ) was largest in areas of ceanothus cover, ranging from 0.17 to 0.26 m in slopes <25° and 0.28 to 0.37 m in slopes =25°. An examination of lidar returns found that ceanothus obstructed laser pulse penetration and few returns reached the ground surface. In areas of ceanothus cover, we conclude that the obstruction of the ground surface contributed to filtering errors, which resulted in mislabeled ground returns and decreased accuracy in bare-earth DEMs. These results have implications for the use of lidar-derived DEMs in areas of ceanothus throughout western North America, and in ecosystems with similar dense shrub cover.
