ID: 54307
Title: Precise georefrencing of long strips of ALOS imagery
Author: C S Fraser and M Ravanbakhsh
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: High-resolution satellite imagery (HRSI), ground control points (GCPs), RPCs, ALOS PRSIM
Abstract: The main obstacle to achieving high precision in georeferencing from high-resolution satellite imagery (HRSI) remains the need for provision of good quality ground control points (GCPs), whether the GCPs are used to remove biases in RPC triangulation or to support physical sensor orientation models. The provision of GCPs can be very costly and is often not feasible in remote regions, the very areas where mapping from satellite imagery shows significant potential. In order to drastically reduce the number of GCPs required for georeferencing from HRSI, a generic sesor orientation model incorporating strip adjustment capacity has been adopted. Under this approach, the metadata for each separate scene is merged to produce a single, continuous set of orbit and attitude parameters, such that the entire strip of tens of images can be treated as a single image. The merging of orbit data results in a considerable reduction in both the number of unknown parameters and the number of required GCPs in the sensor orientation. RPCs are then generated from the adjusted orientation data for each image forming the strip or block. Application of the method to very long strips of ALOS PRSIM imagery is reported in this paper. The results of experimental testing indicate that one-pixel level accuracy can be achieved over strip lengths of more than 50 ALOS images, or 1,500km, with as few as four GCPs.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54306
Title: Delineation of impervious surface from multispectral imagery and lidar incorporating knowledge based expert system rules
Author: Kreh A Germaine and Ming-Chih Hung
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: ISODATA, Knowledge Based Expert System (KBES), Cover Height and Cover Slope,
Abstract: An attempt to delineate impervious surfaces in the City of Scottsbluff, Nebraska, was made using multispectral high spatial resolution imagery and lidar data. An ISODATA classification was performed and results aggregated into two parent classes, impervious and pervious. The ISODATA classification yielded an overall accuracy of 91.0 percent with a Kappa of 82.0 percent. A Knowledge Based Expert System (KBES) set of rules was designed incorporating the imagery classification with lidar data to derive two models. Cover Height and Cover Slope, to provide critical information not available from multispectral imagery. The rules were applied to the initial ISODATA classification to improve the classification accuracy to an overall accuracy of 94.0 percent with a Kappa of 87.9 percent. In this study, it was shown that lidar holds promise for improving the accuracy of impervious surface measurement, as well as the potential identification and measurement of other significant planimetric features such as buildings and trees.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54305
Title: Remote sensing classification using fractal dimensions over a subtropical hilly region
Author: Ji Zhu, Jiancheng Shi, Hanfang Chu, Jiawen Hu, Xiaozhou Li, and Wei Li
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: fractal dimension, ETM+ Enhanced Thematic Mapper, land-use, land-cover, vegetation
Abstract: Subtropical hilly regions in China have various and complex surface features, which are difficult to identify in remote sensing images. A new algorithm for calculating the fractal dimension of a single pixel of a remote-sensing image is presented. The fractal dimension due for a scene captured in an ETM+ (i.e., Enhanced Thematic Mapper Plus) image were used to compose a new image together with the second and fourth bands of the ETM+ image. Using the new image, the land-use/land-cover types and forest categories in the region were identified using a maximum likelihood classification. The accuracy assessment of the calssification gave an overall accuracy of 80.69 percent and a Khat value of 0.78. The proposed method is a little more accurate than the method that does not use fractal dimension data, especially for identification of different types of vegetation in the region.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54304
Title: Comparison of global-and local-scale pansharpening for rapid assessment of humanitarian emergencies
Author: Minho Kim, James B Holt, and Marguerite Madden
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: modified intensity-hue-saturation (MIHS), principal component analysis (PCA), multiplicative (MP), high pass filter (HPF), erreur relative globale adimensionnelle de synthese (ERGAS) , correlation coefficient (CC), relative difference to mean (RDM),
Abstract: We compared global-scale pansharpening of full-or large-scene QuickBird satellite imagery with local-scale pansharpening of a subset of these scenes by using modified intensity-hue-saturation (MIHS), principal component analysis (PCA),multiplicative (MP), and high pass filter (HPF) methods. The spectral properties of all pansharpened images were evaluated by using quantitative indices such as erreur relative globale adimensionnelle de synthese (ERGAS) correlation coefficient (CC), relative difference to mean (RDM), and relative difference to standard deviation (RDS). This study discovered that local-scale pansharpening was generally lower and higher than the global-scale approach in terms of ERGAS and CC, respectively. In particular, local-scale HPF produced pansharpening results very close to the original multispectral image with less than 0.18 land 0.07 percent of RDM and RDS, respectively. Local-scale fusion results with PCA and MP were similar to those of the global -scale approach. Local-scale pansharpening that uses very high spatial resolution imgery could lead to the rapid assessment of the magnitude and severity of humanitarian situations by producing a color image o fhih spatial resolution with reduced processing time.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54303
Title: Modeling the probability of misclassification in a map of land cover change
Author: Amy C Burnicki
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: post-classification change analysis, time-series maps, classification error
Abstract: An empirical model was developed to produce a spatially-explicit classification error probability surface for a map of land-cover change resulting from post-classification change analysis. The role of contextual information in predicting change classification error was assessed by testing the significance of variables identifying the absence or presence of classification error in the time-series maps and variables describing landscape composition and structure. A generalized additive model relating errors in classifying change to the series of predictor variables successfully explained over 90 percent of model residual deviance. Predictors capturing the location of classification error in the time-series were the primary determinants of change classification error. However, several predictors describing landscape characteristics were included in the final model. Their inclusion resulted in a sharper delineation between low-and high -error probability regions and a better understanding of the nature of change classification error.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54302
Title: Parameterizing support vector machines for land cover classification
Author: Xiaojun Yang
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: support vector machine, land-cover classification
Abstract: The support vector machine is a group of relatively novel statistical learning algorithms that have not been extensively exploited in the remote sensing community. In previous studies they have been found to generally outperform some popular classifiers. Several recent studies found that training samples and input data dimensionalities can affect image classification accuracies by those popular classifiers and support vector machines alike. The current study extends beyond these recent research framework and into another important inquiry area addressing the impacts of internal parameterization on the performance of support vector machines for land-cover classification. A set of support vector machines with different combinations of kernel types, parameters, and error penalty are carefully constructed to classify a Landsat Thematic Mapper image into eight major land-cover categories using identical training data. The accuracy of each classified map is further evaluated using identical reference data. The results reveal that kernel types and error penalty can substantially affect the classification accuracy, and that a careful selection of parameter settings can help improve the performance of the support vector classification. These findings reported here can help establish a practical guidance on the use of support vector machines for land-cover classification from remote sensor data.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54301
Title: Forest inventory, Lidar, and patents
Author: James W Flewelling
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 3, March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Airborne laser scanning (ALS), forest inventories
Abstract: The goal for this paper is to increase awareness of patents for analysis methods that use airborne laser scanning (ALS) data in estimating forest inventories. US patents that have been applied for or issued are identified and critically reviewed. They are discussed in the context of the current controversy on whether methods patents, particularly software patents, are being granted too liberally. The patent process should promote innovation, but may sometimes discourage innovation and hinder scientific research. Some patent claims may overlap prior art (earlier public information), or may be obvious extensions of known methods. Actions are identified by which researchers may be able to help limit the scope of pending patents or to identify prior art that could be useful in re-examinations of existing patents.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54300
Title: Full waveform-based analysis for forest type information derivation from large footprint spaceborne Lidar data
Author: Junjie Zhang, Alfred de Gier, Yanqiu Xing, and Gunho Sohn
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 3, March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Gaussian components, Support Vector Machine classification, broad-leaved forests, needle-leaved forests
Abstract: This study developed a new method to derive forest type information from large-footprint lidar data based on full waveform analysis. For this purpose, the raw waveform was decomposed into Gaussian components, and canopy return and ground return of the waveforms were separated. Two types of metrices hypothesized to have relationship with forest types were derived from the canopy return part of the waveform. The first type of metrics is quantile-based metrics reflecting the vertical distribution of canopy return energy, and the second type is statistical characteristics of the Gaussian components of canopy return part. Support Vector Machine classification was applied to different combinations of the metrics to find their relationship with different forest types. The results showed that the second type of metrices, indicating the canopy stratum characteristics, showed great promise in separating broad-leaved and needle-leaved forests with the accuracy ranging from 88.68 percent to 90.57 percent and Kappa statistic from 0.7406 to 0.7868.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54299
Title: Forestry applications for satellite Lidar remote sensing
Author: Jacqueline Rosette, Juan Suarez, Peter North, and Sietse Los
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 3, March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: NASA ' s Geosciences Laser Altimeter System (GLAS), wind-risk model , DESDynl sensor
Abstract: This paper presents a method to estimate forest parameters and surface topography from NASA ' s Geosciences Laser Altimeter System (GLAS). Their potential use as observational inputs to models is demonstrated using a wind-risk model for the UK, ForestGALES. Relative heights above ground were used as biophysical parameter estimators. Top Height was estimated with R2 = 0.73, RMSE = 4.5 m. Diameter at breast height estimates differed for conifer-dominated stands (R2 = 0.72, RMSE = 0.07 m) and for stands containing mostly broadleaves (R2 = 0.41, RMSE = 0.11 m). Ground elevation estimation produced R2 = 0.997, RMSE = 2.2 m.These three parameters were applied to ForestGALES for stand-level assessment of wind-throw risk. Stability is sensitive to small differences in tree dimensions, and therefore vegetation parameters require greater accuracy than those currently retrievable from GLAS to more reliably determine risk of wind-throw. Future satellite lidar missions such as NASA ' s DESDynl sensor aim to produce improved vegetation parameter estimation plus greater spatial coverage which would offer more inputs for forestry models.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54298
Title: Characterizing forest succession in Central Ontario using Lidar-derived indices
Author: Karin Y van Ewijk, Paul M Teitz and Neal A Scott
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 3, March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Light detection and ranging (lidar), mixed mature forest, canopy density indices
Abstract: This study investigates the potential of discrete return light detection and ranging (lidar) data to characterize forest succession in a mixed mature forest in Central Ontario using indices applied to the lidar point cloud. Derived indices include statistical indices, predicted Lorey ' s height (R2=0.86; RSME = 2.36 m) and quadratic mean diameter-at-breast-height (R2=0.68; RMSE = 1.21 cm), canopy density indices and an information theory based complexity index. To assess how well these indices are able to capture the vertical structure of forest stands, they are compared to Oliver and Larson ' s (1996) four stages of forest stand development. Best subsets regressions indicated that no single index is able to separate all four stages adequately. However, the predicted Lorey ' s height index is optimal for separating early from mid succession stages (p<0.0001) and a combination of height and complexity indices performed best to discriminate between mid-and late-succession stages (p<.0001)
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54297
Title: Dynamic range-based intensity normalization for airborne, discrete return Lidar data of forest canopies
Author: Demetrios Gatziolis
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 3, March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: aser energy, range-based normalization
Abstract: A novel approach for improving the consistency of intensity measurements using range-based normalization is introduced. The normalization is data-driven, can be fully automated, and involves scaling differences in observed instensity between returns collocated in space but registered to different laser scanning swaths. The scaling is proportional to the overall rate of attenuation f of laser energy. The utility of this approach for applications of lidar over forests was evaluated by examining classification results of broad cover types obtained using observed and normalized intensity measurements in an Oregon study area. The normalization was more effective for single returns, leading to a 53 percent reduction in intensity coefficient of variation between observed and range-normalized measurements. The poor cover type classification accuracy (44.4 percent; kappa 0.167) obtained by using observed intensities of first returns improved substantially to 75.6 percent (kappa 0.624) when using above-ground, single returns and f=2.04.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54296
Title: A mutli-level morphological active contour algorithm for delineating tree crowns in mountainous forest
Author: Chinsu Lin, Gavin Thomson, Chien-Shun Lo, and Ming-Shein Yang
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 3, March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Multi-level morphologica active contour (MMAC), tree crowns, bottom up erosion (BUE), top down dilation (TDD), active contour model (ACM)
Abstract: This paper introduces a multi-level morphologica active contour (MMAC) algorithm to identify and delineate tree crowns in mountainous forest based on rasterized airborne lidar data. MMAC is a generalized tree crown mapping algorithm which can accommodate multiple heads in a crown as well as overlapping crowns. The MMAC algorithm comprises three steps: bottom up erosion (BUE) which identifies stand candidates, top down dilation (TDD) which estimates the crown periphery and an active contour model (ACM) which delineates crown contours. Three sample plots were selected in Alishan National Scenic Area, Taiwan, (predominantly alder, sugi, and red cypress) for evaluation of the algorithm. When compared with ground survey data, the algorithm achieved an average detection accuracy of 24 percent omission error and 13 percent commission error in identifying individual trees in mountainous forest stands. Detection accuracy is potentially related to stand density.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54295
Title: Automated detection of branch dimensions in woody skeletons of fruit tree canopies
Author: Alexander Bucksch and Stefan Fleck
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 3, March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: SKELTRE, branch diameter, 3D canopy structure
Abstract: Modeling the 3D canopy structure of trees provides the structural mapping capability on which to assign distributed values of light-driven physiological processes in tree canopies. We evaluate the potential of automatically extracted skeletons from terrestrial lidar data as a basis for modeling canopy structure. The automatic and species independent evaluation method for lidar data of trees is based on the SKELTRE algorithm. The SKELTRE skeleton is a graphical representation of the branch hierarchy. The extraction of the branch hierarchy utilizes a graph splitting procedure to extract the branches from the skeleton. Analyzing the distance between the point cloud points and the skeleton is the key to the branch diameter. Frequency distributions of branch length and diameter were chosen to test the algorithm performance in comparison to manually measured data and resulted in a correlation of up to 0.78 for the branch length and up to 0.99 for the branch diameter.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54294
Title: Automated methods for measuring DBH and tree heights with a commercial scanning lidar
Author: Huabing Huang, Zhan Li, Peng Gong, Xiao Cheng, Nick Clinton, Chunxiang Cao, Wenjian Ni, and Lei Wang
Editor: Russell G Congalton
Year: 2011
Publisher: ASPRS, Vol 77, No 3, March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Lidar (Light detection and Ranging), diameter at breast height (DBH),Iterative Closet Point (ICP), Digital Elevation Model (DEM), Canopy Height Model (CHM)
Abstract: Accurate forest structural parameters are crucial to forest inventory, and modeling of the carbon cycle and wildlife habitat. Lidar (Light detection and Ranging) is particularly suitable to the measurement of forest structural parameters. In this paper, we describe a pilot study to extract forest structural parameters, such as tree height, diameter at breast height (DBH), and position of individual tree using a terrestrial lidar (LMS-Z360i; Riegel, Inc). The lidar was operated to acquire both vertical and horizontal scanning in the field in order to obtain a point cloud of the whole scene. An Iterative Closet Point (ICP) algorithm was introduced to obtain the transformation matrix of each range image and to mosaic multiple range images together. Based on the mosaiced data set, a variable scale and threshold filtering method was used to separate ground from the vegetation. Meanwhile, a Digital Elevation Model (DEM) and a Canopy Height Model (CHM) were generated from the classified point cloud. A stem detection algorithm was used to extract the location of individual trees. A slice above 1.3 m from the ground was extracted and rasterized. A circle fitting algorithm combined with the Hough transform was used to retrieve the DBH based on the rasterized grid. Tree heights were calculated using the height difference between the minimum and maximum Z values within the position of each individual tree with a 1 m buffer. All of the 26 trees were detected correctly, tree height and DBH were determined with a precision of 0.76 m and 3.4 cm, respectively, comparing with those visually measured in the lidar data. Our methods and results confirm that terrestrial lidar can provide nondestructive, high-resolution, and automatic determination of parameters required in forest inventory.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54293
Title: Making a photomap of the human eye based on the sperical shape of its sclera and the circular contour of its iris
Author: U Ethrog
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, issue 3, May 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Strip adjustment, Geometrical constraints, Photomap, Human eye, Iridology
Abstract: Iridology was defined as a method of diagnostic used in alternative medicine to analyze health status by studying colors, marks and signs in the sclera of the eye which according to iridologists can be demarcated into zones corresponding to parts of the human body. These are then made into "iridology charts" for comparison with photo images of a patient ' s eyes. Due to the large tilt angles of the images relative to the eye ' s geometrical axis, accurate comparison is not possible, and this paper suggests the use of photogrammetric methods for making a photomap for each of the patient ' s eyes which is then compared to its respective iridology chart. Because of the small overlapping area between any two successive images (about 20%) and the lack of control points, we were prevented from using conventional photogrammetric methods and instead had to develop an alternative photogrammetric method based on the spherical shape of the sclera and the circular shape of the iris contour. Accuracy tests showed that this method is relatively accurate and has small residuals following the process of bundle adjustment.
Location: 231
Literature cited 1: None
Literature cited 2: None