ID: 53867
Title: Analysis of user and producer risk when applying the ASPRS standards
Author: Francisco Javier Ariza Lopez, Alan D.J.Atkinson, Jose Luis Garcia Balboa, and Jose Rodriguez Avi
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 5, May 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Accuracy Standards for Large Scale Maps (ASLSM), root mean squared eror, Class1 maps, Class 2 maps
Abstract: Using a statistical simulation process, the behavior of the Accuracy Standards for Large Scale Maps (ASLSM) of the American Socity for Photogrammetry and Remote Sensing is analyzed according to the sample size, as well as the relation between the limiting errors (thresholds), stated by the standard as a root mean squared error, and the actual root mean squared error of the product. When the root mean squared error of the product equals the threshold of the standard the simulation results show the ASLSM is very restrictive, classifying 75 percent of products as Class 2 maps instead of Class 1 maps. If the variability of the product is greater or lesser than this threshold, results can be depicted by a family of acceptance curves. These curves can be employed by users to detemine the sample size needed to limit their acceptance risk, but alos by producers to analyze their rejection risk.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53866
Title: Generation of complex polyhedral building models by integrating Stereo-Aerial iamgery and Lidar data
Author: Ayman F. Habib, Ruifang Zhai, and Changiae Kim
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 5, May 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Rooftops, DBM
Abstract: The integration of lidar data and aerial imagery is a pormising approach for accurate building model generation. In this research, lidar data and stereo-aerial imagery are incorporated in the generation of complex polyhedral building models whose rooftops are bounded by straight lines. The process starts by utilizing lidar data for deriving building hypotheses and the initial boundaries of the planar patches consituting the buildings rooftops. The boundaries of these patches are then refined through the incorporation of stereo-aerial imagery while utilizing 3D geometric and spectral constraints. Precise building boundaries are derived through resolving feature matching problems and fully utilizing spectral information. Finally, an efficient manual monoplotting procedure is introduced to remove incorrect and add missing boundaries. The performance of the developed procedures is evaluated through experimental results from real data where the correctness, completeness, and accuracy of the derived building models are evaluated through comparison with a manually generated DBM.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53865
Title: Positional accuracy evaluation of declassified hexagon KH-9 mapping camera imagery
Author: Arzhan Surazakov and Vladimir Aizen
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 5, May 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Ground Control Points (GCPs), KH-9 Hexagon imagery, IceSAT laser, DEM RMSE
Abstract: This paper examines the positional accuracy of the declassified KH-9 Hexagon imagery and derived DEM. Aimed at geodesy and and mapmaking, the KH-9 program (1973 to 1980) resulted in an image archive with world wide stereo coverage at 6 to 9 m. We used six KH-9 images acquired in 1980 over two testfields in Central Asia. Using reseau marks on the scanned KH-9 frames, we found and corrected image distortions. In bundle orientation with Ground Control Points (GCPs) from QuickBird images, we achieved horizontal accuracies below 6 m for a flat terrain testfield and approximately 10 m for a mountainous terrain testfield. WIth three GCPs the image orientation horizontal accuracy degreaded by only 20 percent. We generated a DEM from the KH-9 images and estimated its vertical accuracy using IceSAT laser altimetry data and an additional DEM from 1:25 000 topographic maps. The DEM RMSE was 6.18 m over flat terrain and 20.0 m over mountainous terrain.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53864
Title: The forward propagation of integrated system component errors within airborne Lidar data
Author: Tristan Goulden and Chris Hopkinson
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 5, May 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: General Law of Propagation of Variances (GLOPOV),global positioning system
Abstract: Error estimates of lidar observations are obtained by applying the General Law of Propagation of Variances (GLOPOV) to the direct georeferencing equation. Within the formulation of variance propagation, the most important consideration is the values used to describe the error of the hardware component observations including the global positioning system, inertial measurement unit, laser ranger, and laser scanner (angular encoder noise and beam divergence). Data tested yielded in general, pessimistic predictions as 85 percent of residuals were within the predicted error level. Simulated errors for varying scan angles and altitudes produced horizontal errors largerly influenced by IMU subsystem error as well as angular encoder noise and beam divergence. GPS subsystem errors contribute the largest proportion of vertical error only at shallow scan angles and low altitudes. The transformation of the domination of GPS related error sources to total vertical error occurs at scan angles of 230, 130, and 80 at flying heights of 1,200 m, 2,000m and 3,000m AGL, respectively.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53863
Title: Detection of sinkhole hazards using airborne lase scanning data
Author: Sagi Filin and Amit Baruch
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 5, May 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Airborne laser scanning technology, geohazards, collapse sinkholes
Abstract: Airborne laser scanning technology is primarily perceived as a means for gathering detailed three-dimensional information about the surface and objects on it. The dense 3D data contain information about surface features adn geohazards, some of which are of subtle form. Geohazards are usually embedded within the terrain, and scarcely form distinct shape-transition to their surroundings; therefore their detection is challenging.We address in this paper detection of subtle terrain features and demonstrate it on collapse sinkholes. Collapse sinkholes are surface depressions whose formation has severe effect on the environment and on regional development. We present an autonomous model for their extraction and characterization. Sinkholes within the studied regions appear in various size, forms, from their embryonic to a well developed formation. The level of sinkhole detection is high, and as demonstrated, the model performs well under varying landforms and surface texture, with little influence on the correctness of the extracted sinkholes. As the results show, features of approximately 20 cm deep can be identified and separated from their surroundings in the data.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53862
Title: Comparing classification approaches for mapping cut-leaved teasel in highway environments
Author: Cuizhen Wang, Diego J. Bentivegna, Reid J. Smeda, and Randy E. Swanigan
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 5, May 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Cut-leaved teasel, Spectral angle mapping (SAM), maximum likelihood classifier (MLC),spectral information divergence (SID)
Abstract: Cut-leaved teasel is an invasive weed thriving in roadside environments and needs to be detected for implementation of management programs. This study tested several commonly applied classifiers to map teasel with an aerial hyperspectral image along the Interstate Highway 70 in central Missouri. A teasel/non-teasel mask was first built to exclude dominant land-covers that had distinct spectral differences from teasel. The spectral angle mapping (SAM) had the best results of delineating teasel from herbaceous background with its user ' s and producer ' s accuracies of 80 to 90 percent. Large commission errors of teasel were observed in the probability-based maximum likelihood classifier (MLC) and spectral information divergence (SID) methods. Compared with a regular land-use/land-cover classification in an unsupervised/supervised hybrid method, the post-masking SAM had much easier process of training data collection and achieved similar accuracies. It could be an optimal approach for mapping teasel and other weeds in highway environments.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53861
Title: Analysis of impervious surface and its impact on urban heat environment using the Normalized Difference Impervious Surface Index (NDISI)
Author: Hanqiu Xu
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 5, May 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Normalized difference impervious surface index (NDISI), Landsat ETM, Fuzhou City, ASTER, land surface temperature (LST)
Abstract: The fast urban expansion has led to replacement of natural vegetation-dominated land surfaces by various impervious materials. This has a significant impact on the environment due to modification of heat energy balance. Timely understanding of spatiotemporal information of impervious surface has become more urgent as conventional methods for estimating impervious surface are very limited. In response to this need, this paper proposes a new index, normalized difference impervious surface index (NDISI), for estimating impervious surface. The application of the index to the Landsat ETM + image of Fuzhou City and the ASTER image of Xiamen City in China has shown that the new index can efficiently enhance and extract impervious surfaces from satellite imagery, and the normalized NDISI can represent the real percentage of impervious surface. The index was further used as an indicator to investigate the impact of impervious surface on urban heat environment by examination of its quantitative relationship with land surface temperature (LST), vegetation, and water using multivariate statistical analysis. The result reveals that impervious surface has a positive exponential relationship with LST rather than a simple linear one. This suggests that the areas with high percent impervious surface will accelerate LST rise and urban heat island development.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53860
Title: Vegetation dynamics in Yellowstone ' s Northern range: 1985 to 1999
Author: Shannon L. Savage and Rick L. Lawrence
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 5, May 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Ranglands, Northern range (NR), Yellowstone National Park
Abstract: An inexpensive and reproducible method for monitoring rangelands over the northern range (NR) of Yellowstone National Park was developed utilizing Landsat imagery. A 1999 map of rangeland vegetation communities was created using boosted classification tree analysis. The 1999 map and a 1985 image were utilized in a change vector analysis resulting in a classified map for 1985. Classification accuracies ranged from 72.30 percent to 83.65 percent for the 1999 map and 72.60 percent to 88.73 percent for the 1985 map, depending upon level of class detail, demonstrating that Landsat-class data can be effectively used for efficient change analysis that maintains accuracy while reducing compound error. Spatial patterns of change were compared to theories from other studies related to change in the NR and were found to be consistent with effects from fire suppression, precipitation, and urban growth but not with trophic cascade from wolves or beaver effects.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53859
Title: Evaluation of the influence of local fuel homogeneity on fire hazard through Landsat-5 TM texture measures
Author: cristina Vega-Garcia, Jaime Tatay-Nieto, Ricardo Blanco, and Emilio Chuvieco
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 7, July 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Landscape homogeneity, Fire hazard,Logit models, Landsat-5
Abstract: This study analyzed the relationship between landscape homogeneity and fire hazard for a certain area and time period (1984 to 1995), by using logit models. Homogeneity was measured though eight texture measurements computed on visible and NIR bands of Landsat -5 TM data with vaying kernel sizes. Several significant models could be developed to predict future burning at the pixel level for the study period. The best spectral band for detecting proneness to burn was TM1, the blue band, and best results were achieved with large window sizes and the Homogeneity texture measure.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53858
Title: An innovative image space clustering technique for automatic road network vectorization
Author: M. Mokhtarzade, M.J.Valadan Zoej, H. Ebadi, and M.R.Sahebi
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 7, July 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Road vectorization, Ellipse Clustering technique, vectorization algorithm
Abstract: Road vectorization aims to delineate road centerlines from aerial and satellite images. In this paper, binary road image space clustering techniques, which are used to determine key points on the road, are expanded to a more accurate and reliable algorithm, the Increasing . Accurate noise cluster recognition and omission are two strengths of the proposed algorithm. In order to establish the true connections between predetermined key points on the road, a very fast, novel, and reliable fuzzy ellipse-shaped clustering methodology is introduced. Different accuracy assessment parameters are established and evaluated based on results obtained for simulated and real road binary images. The sub-pixel geometric accuracy of the extracted road network, with a completeness of more than 80 percent, demonstrates the promising results of the vectorization algorithm that is presented in this paper.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53857
Title: Improved land cover mapping using random forests combined with Landsat Thematic Mapper Imagery and Ancillary Geographic Data
Author: Xiaodong Na, Shuging Zhang, Xiaofeng Li, Huan Yu, and Chunyue Liu
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 7, July 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Machine learing alogirthms (MLAS), random forest (RF), classification and regression tree (CART), maximum likelihodd classification (MLC)
Abstract: Large area land-cover mapping involving large volumes of data is becoming more common in remote sensing applications. Thus, there is a pressing need for increased automation in the land-cover mapping process. The main objective of this research was to compare the performance of three machine learing alogirthms (MLAS) for mapping wetlands in the Sanjiang Plain combined Landsat TM imagery with ancillary geographical data. Three MLAS included random forest (RF), classification and regression tree (CART), and maximum likelihodd classification (MLC). Comparisons were based on several criteria: overall accuracy, sensitivity to data set size, and noise. Our results indicated that first, the random forest and CART approach can acieve substanial improvements in accuracy over the traditional MLC method. Random forest produced the highest overall accuracy (91.3 percent ) the kappa coefficient 0.8943, with marsh class accuracies ranging from 77.4 percent to 90.0 percent. Secondly, the random forest method was least sensitive to reduction in training sample size, and it was most resistant to the presence of noise compared to CART and MLC. The comparison between three MLAS revealed that the random forest approach was most resistant to training data deficiencies while improved land-cover map accuracy in marsh area.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53856
Title: A volumetric approach to change detection in satellite images
Author: Thomas B. Pollard, Ibrahim Eden, Joseph L. Mundy, and David B. Cooper
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 7, July 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Dense urban areas, volumetric appearance modeling (VAM), ground sampling distance (GSD)
Abstract: The increasing availability of very high resolution satellite imagery has spurred interest in automatically detecting very fine detailed changes in an area over time, a particularly useful tool for analyzing activity in dense urban areas. However, attempting automated change detection at this resolution is difficult due to the motion parallax of elevated structures. This paper presents a comprehensive solution to change detection in areas of significant 3D relief using a new framework called volumetric appearance modeling (VAM). This approach can manage the complications of unknown and changing world surfaces by maintaining a 3D voxel-based model, where probability distributions for surface occupancy and image appearance are stored in each voxel. These distributions are continuously updated as new images are received using an adaptive learning procedure. This representation is demonstrated to produce accurate change detection results under conditions of variable illumination and view point as well as haze conditions present in satellite imagery. The volumetric representation also supports automatic sensor model correction representation also supports automatic sensor model correction to align incoming imagery to a common geographic reference. This registration approach is demonstrated to achieve geo-positioning accuracy on the order of the ground sampling distance (GSD) or better.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53855
Title: Climate and land-use effects on Interannual fAPAR variability from MODIS 250 m data
Author: Marc Linderman, Yu Zeng, and Pedram Rowhani
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 7, July 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: fraction of Absorbed Photosynthetically Active Radiation (fAPAR), MODIS, Iowa, agriculture, prairies
Abstract: The goals of this research were to examine the effects of climate, land-use, and plot-level characteristics on interannual change of vegetation characteristics. Using mixed effects regression models, we examined the influences of climatic limiting factors and plot-level characteristics on annual fraction of Absorbed Photosynthetically Active Radiation (fAPAR) estimates from MODIS 250 m resolution data across agricultural and restored prairie plots in Iowa. Prairie plots had significantly higher annual fAPAR and less year-to-year variability than highly intensive agriculture. Comparisons between fixed and random effects models show that differential climate responses between land-use types explain approximately 85 percent of between pixel differences. Year-to-year climate differences and pixel scale land use factors explained approximately 48 percent of agriculture within-plot trends and interannual variability and 54 percent of prairie year-to-year variance. Agriculture responses were predominantly influenced by precipitation and prairies were primarily influenced by incident radiation and age since restoration.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53854
Title: Spectral modeling of population density: A study of Utah ' s Wasatch Front
Author: Ryan R. Jensen, Perry J. Hardin, and Mark W. Jackson
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 7, July 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Census Block Group (CBG), Landsat TM ,regression models, network models
Abstract: This study estimated the urban population of the Wasatch Front of Utah, USA by Census Block Group (CBG) using spectral variables derived from Landsat TM reflectance as predictors. Using 353 training CBGs and 446 validation CBGs, five regression models and twenty back-propagation neural network models were built and tested. The median percentage of error in the best and worst models was 25 percent and 50 percent, respectively. Neural network models generally provided better predictions than multiple regression models. The factros most highly related to population density included spectral variance in reflectance;contrast between urban vegetation and dark material (e.g.asphalt); constrast between vegetation and bare soil; total albedo, and thermal temperature.
Location: 231
Literature cited 1: None
Literature cited 2: None


ID: 53853
Title: Reconstruction and texturing of 3D urban terrain from uncalibrated monocular images using L1 splines
Author: Dimitri Bulatov and John E. Lavery
Editor: Russell G. Congalton
Year: 2010
Publisher: Asprs, Vol 76, No 4, April 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Optical images, monocular optical cameras,L1 spline
Abstract: We propose a five-step procedure for reconstruction and texturing of 3D urban terrain based on a novel approach for 3D surface reconstruction from optical images produced by monocular optical cameras on small, inexpensive vehicles without external referencing. The approach consists of generation of a nonparametric 2.5D L1-spline surface from a point cloud and iterative creation of parametric 3D L1-spline surfaces based on parametrizations using the previous surface. Computational results for a model house and for Gottesaue Palace in Karlsruhe, Germany are presented. The results presented here are proof of principle results that show that the L1-spline-based procedure is able to reconstruct and texture 3D urban terrain in spite of the large, abrupt changes in density of the point cloud, and that it produces results that are visually competitive with or superior to competing procedures. Future improvements (finer grids, adaptive grids and parameters, reduction of computing time) are outlined.
Location: 231
Literature cited 1: None
Literature cited 2: None