ID: 55702
Title: Effect of isoflavones, genistein and diadzein on the ovaries of neonatal mice
Author: Rameshwar Jegathambigai, Ponnusamy Kumar, Iekhsan Othaman and Normadiah M Kassim
Editor: Prof Dr S Palanichamy
Year: 2011
Publisher: Palani Paramount Publications, Vol 28, Nos 1 & 2, Feb & March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Journal of Ecobiology - An International Journal for Scientific Research on Environmental biology, toxicology and inter relations
Keywords: Isoflavones, genistein, diadzein, poly-oocyte follicles, ovaries , mice
Abstract: Reproductive development toxicity of isoflavones has been demonstrated in several species of animals. The study was undertaken to determine the effects of the genistein and diadzein on the morphology of the ovaries of neonatal mice. Female pups at neonatal day 7 were treated with either genistein or diadzein at high or low doses (1 mg/kg or 10 mg/kg) by sub-cutaneous injections daily for 1 week. The individuals were sacrificed 24 h after the last treatment and the ovaries were dissected and processed for light microscopy. Histologically, the ovaries of the treated groups exhibited poly-oocyte follicles. In the high dose groups, 92% of the genistein and 75% of the diadzein-treated mice, while in the low-dose groups, 42% of the genistein and 33% of the diadzein-treated individuals exhibited poly-oocyte follicles. Hence, exposure of neonatal mice to phytoestrogens results in the development of poly-oocyte follicles in the ovaries, especially at higher doses. The presence of poly-oocyte follicles may result in infertility because they are far less viable than normal follicles.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55701
Title: Generation and evaluation of multitemporal digital terrain models of the Mt. Everest area from different optical sensors
Author: Tino Pieczonka, Tobias Bolch, Manfred Buchroithner
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: DTM, change assessment, Khumbu Himalaya, Cartosat-1, corona
Abstract: Mutlitemporal digital terrain models (DTM) are an important source for many purposes such as the detection of areas, which are susceptible to natural hazards such as landslides and glacial lake outburst floods, or for the examination of changes in glacier thickness. To exploit the potential of stereo satellite and aerial imagery for time series analysis, the employed methodology and software can be critical. A statistical analysis based on quartiles is presented to eliminate the influence of registration and elevation errors in DTMs. For our analysis, we used multi-temporal airborne and spaceborne stereoscopic images. The oldest images were recorded in the 1960s by the US American reconnaissance satellite Corona, while the most recent imagery are 2007 Cartosat-1 stereo scenes, along with one ASTER stereo pair. Complex panoramic distortion and limited spatial resolution resulted in the Corona and ASTER DTMs having the highest RMSEz. Due to differing acquisition techniques, applied software packages and temporal differences DTMs will never be identical. Therefore we propose a relative vertical accuracy assessment with a master DTM. We chose the Cartosat-1 DTM as it showed the highest absolute accuracy. Inaccuracies between the master and the slave DTMs were adjusted by means of trend surfaces and outliers were successfully eliminated applying the interquartile range.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55700
Title: A bundle adjustment approach with inner constraints for the scaled orthographic projection
Author: Keith F Blonquist, Robert T Pack
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Analytical photogrammetry, bundle adjustment, orthogonal projection, inner constraints, quaternions, narrow field-of-view
Abstract: Bundle adjustment is a method for simultaneously calculating both the interior and exterior orientation parameters of a set of images, and the object-space coordinates of the observed points. In the case of long focal length lenses and narrow field-of-view (FOV) imaging situations, collinearity based (perspective projection) algorithms may result in linear dependencies between parameters that cause solution instability. The use of a scaled orthographic projection model based on linear algebraic formulations was therefore adopted to reduce this risk. Using quaternions, a new mathematical model is derived that includes the partial derivatives as well as the inner constraint equations for a scaled orthographic bundle adjustment. The model was then tested using two images sets of a single, small vessel (about 6 m length) with a cube target of known dimensions at two distinct ranges; perspective solutions were also calcualted for comparison. RMS residual errors of 0.74 - 0.78 pixels associated with the new method compare favorably to a residual error ranged of 0.59 -0.74 pixels using a perspective bundle adjustment of the same target points. Relative precisions (as a ratio of target size) of between 1:1650 and 1:750 have been achieved at ranges of 375 m and 850 m, respectively, given comparisons with the known cube dimensions. A third image dataset consisting of a network of 16 images was solved with a 1:2200 relative precision showing the new method can successfully handle high redundancy. For the experiments that were conducted, the new method was found to produce less precise results than the perspective bundle solution for a FOV of 0.50 -0.650 where the object fills 5-8% of the image. However, it was found to match the precision of the perspective model (with an uncalibrated camera) for a FOV of 0.20-0.300 where the object of interest fills only 1-2% of the full image.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55699
Title: Multi-view dense matching supported by triangular meshes
Author: Dimitri Bulatov, Peter Wernerus, Christian Heipke
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Depth map, matching, multi-view, semi-global, triangle mesh
Abstract: We present a new procedure to compute dense 3D point clouds from a sequential set of images. This procedure is considered as a second step of a three-step algorithm for 3D reconstruction from image sequences, whose first step consists of image orientation and the last step is shape reconstruction. We assume that the camera matrices as well as a sparse set of 3D points are available and we strive for obtaining a dense and reliable 3D point cloud. Three novel ideas are presented: (1) for sparse tracking and traingulation, the search space for correspondences is reduced to a line segment by means of known camera matrices and disparity ranges are provided by triangular meshes from the already available points; (2) triangular meshes from extended sets of points are used for dense matching, because these meshes help to reconstrut points in weakly textured areas and present a natural way to obtain subpixel accuracy; (3) two non-local optimization methods, namely, 1D dynamic programming along horizontal lines and semi-global optimization were employed for refinement of local results obtained from an arbitrary number of images. All methods were extensively tested on a benchmark data set and an infrared video sequence. Both visual and quantitative results demonstrate the effectiveness of our algorithm.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55698
Title: Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models
Author: Roshanak Darvishzadeh, Clement Atzberger, Andrew Skidmore, Martin Schlerf
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Mediterranean grassland, Mapping LAI, Hyperspectral, Modeling, Partial least square regression, vegetation indices
Abstract: Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compaed inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R2 between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches (R2 = 0.89, nRMSE = 0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data (R2 = 0.91, nRMSE = 0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55697
Title: An approach to the radiometric aerotriangulation of photogrammetric images
Author: David Hernandez Lopez, Beatriz Felipe Garcia, Jose Gonzalez Piqueras, Guillermo Villa Alcazar
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Aerial images, radiometric aerotriangulation, calibration, atmospheric correction, bidirectional effects, kernel-driven models
Abstract: Harnessing the radiometric information provided by photogrammetric flights could be useful in increasing the thematic applications of aerial images. The aim of this paper is to improve relative and absolute homogenization in aerial images by applying atmospheric correction and treatment of bidirectional effects. We propose combining remote sensing methodologies based on radiative transfer models and photogrammetry models, taking into account the three-dimensional geometry of the images (external orientation and Digital Elevation Model). The photogrammetric flight was done with a Z/I Digital Mapping Camera (DMC) with a Ground Sample Distance (GSD) of 45 cm. Spectral field data were acquired by defining radiometric control points in order to apply atmospheric correction models, obtaining calibration parameters from the camera and surface reflectance images. Kernel- driven models were applied to correct the anisotropy caused by the bidirectional reflectance distribution function (BRDF) of surfaces viewed under large observation angles with constant illumination, using the overlapping area between images and the establishment of radiometric tie points. Two case studies were used: 8-bit images with applied Lookup Tables (LUTs) resulting from the conventional photogrammetric workflow for BRDF studies and original 12-bit images (Low Resolution Color, LRC) for the correction of atmospheric adn bidirectional effects. The proposed methodology shows promising results in the different phases of the process. The geometric kernel that shows the best performance is the Lidense kernel. The homogenization factor in 8-bit images ranged from 6% to 25% relative to the range of digital numbers (0-255), and from 18% to 35% relative to levels of reflectance (0-100) in the 12-bit images, representing a relative improvement of approximately 1-30%, depending on the band analyzed.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55696
Title: Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth
Author: Toshihiro Sakamoto, Michio Shibayama, Akihiko Kimura, Eiji Takada
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Crop phenology, Active sensing, flashlight
Abstract: A commercially available digital camera can be used in a low-cost automatic observation system for monitoring crop growth change in open-air fields. We developed a prototype Crop Phenology Recording System (CPRS) for monitoring rice growth, but the ready-made waterproof cases that we used produced shadows on the images. After modifying the waterproof cases, we repeated the fixed-point camera observations to clarify questions regarding digital camera-derived vegetation indices (VIs), namely, the visible atmospherically resistant index (VARI) based on daytime normal color images (RGB image) and the nighttime relative brightness index (NRBINIR) based on nighttime near infrared (NIR) images. We also took frequent measurements of agronomic data such as plant length, leaf area index (LAI), and aboveground dry matter weight to gain a detailed understanding of the temporal relationship between the VIs and the biophysical parameters of rice. In addition, we conducted another nighttime outdoor experiment to establish the link between NRBINIR and camera-to-object distance. The study produced the following findings. (1) The customized waterproof cases succeeded in preventing large shadows from being cast, especially on nighttime images, and it was confirmed that the brightness of the nighttime NIR images had spatial heterogeneity when a point light source (flashlight) was used, in contrast to the daytime RGB images. (2) The additional experiment using a forklift showed that both the ISO sensitivity and the calibrated digital number of the NIR (CDNNIR) had significant effects on the sensitivity of NRBINIR to the camera-to-object distance. (3) Detailed measurements of a reproductive stem were collected to investigate the connection between the morphological feature change caused by the panic sagging process and the downtrend in NRBINIR during the reproductie stages. However, these agronomic data were not completely in accord with NRBINIR in terms of the temporal pattern. (4) The time-series data for the LAI, plant length, and aboveground dry matter weight could be well approximated by a sigmoid curve based on NRBINIR and VARI. The results confirmed that NRBINIR was more sensitive to al of the agronomic data for overall season, including the early reproductive stages . VARI had an especially high correlation with LAI, unless yellow panicles appeared in the field of view.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55695
Title: Using snakes for the registration of topographic road database objects to ALS features
Author: Jens Gopfert, Franz Rottensteiner, Christian Heipke
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Network snakes, roads, registration, ALS, GIS vector data
Abstract: For historical reasons many national mapping agencies store their topographic data in a dual system consisting of a Digital Landscape Model (DLM) and a Digital Terrain Model (DTM). The DLM contains 2D vector data representing objects on the Earth ' s surface, such as roads and rivers, whereas the DTM is a 2.5 D representation of the related height information, often acquired by Airborne Laser Scanning (ALS). Today, many applications require reliable 3D topographic data. Therefore, it is advantageous to convert the dual system into a 3D DLM. However, as a result of different methods of acquisition, processing, and modelling, the registration of the two data sets often presents difficulties. Thus, a strightforward integration of the DTM and DLM might lead to inaccurate adn semantically incorrect 3D objects.
In this paper we propose a new method for the fusion of the two data sets that exploits paramettric active contours (also called snakes), focusing on road networks. For that purpose, the roads from a DLM initialise the snakes, definign their topology and their internal energy, whereas ALS features exert external forces to the snake via the image energy. After the optimisation process the shape and position of the snakes should coincide with the ALS features. With respect to the robustness of the method several known modifications of snakes are combined in a consistent framework for DLM road network adaptation. One important modification redefines the standard internal energy and thus the geometrical adaptation. One important modification redefines the standard internal energy and thus the geometrical model of the snake in order to precent changes in shape or position not caused by significant features in the image energy. For this purpose, the initial shape is utilized creating template-like snakes with the ability of local adaptation. This is one crucial point towards the applicability of the entire method considering the strongly varying significance of the ALS features. Other concepts related to snakes are integrated which enable our method to model network and ribbon-like characteristics simultaneously. Additionally, besides ALS road features information about context objects, such as bridges and buildings, is introduced as part of the image energy to support the optimisation process. Meaningful examples are presented that emphasize and evaluate the applicability of the proposed method.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55694
Title: Improving teh Wishart synthetic aperture radar image classifications through deterministic simulaetd annealing
Author: Francisco J Sanchez-Llado, Gonzalo Pajares, Carlos Lopez-Martinez
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Synthetic Aperture Radar (SAR), polarization, classification, Deterministic Simulated Annealing, Wishart classifier
Abstract: This paper proposes the use of Deterministic Simulated Annealing (DSA) for Synthetic Aperture Radar (SAR) image classification for cluster refinement. We use the initial classification provided by the maximum-likelihood calssifier based on the complex Wishart distribution that is then supplied to the DSA optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The improvement is verified by computing a cluster separability coefficient. During the DSA optimization process, for each iteration and for each pixel, two consistency coefficients are computed taking into account two kinds of relations between the pixel under consideration and its neighbors. Based on these coefficients and on the information coming from the pixel itself, it is re-classified. Several experiments are carried out to verify that the proposed approach outperforms the Wishart strategy. We try to improve the classification results by considering the spatial influences received by a pixel through its neighbors. Finally, a link about the contribution of DSA to thematic mapping is also established.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55693
Title: Improving the assessmetn of ICESat water altimetry accuracy accounting for autocorrelation
Author: HaniAbdallah, Jean-Stephane Bailly, Nicolas Baghdadi, Nicolas Lemarquand
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: GLAS, LIDAR, Accuracy, Temporal correlation, Block kriging, Great Lakes
Abstract: Given that water resources are scare and are strained by competing demands, it has become crucial to develop and improve techniques to observe the temporal and spatial variations in the inland water volume. Due to the lack of data and the heterogeneity of water level stations, remote sensing, and especially altimetry from space, appear as complementary techniques for water level monitoring. In addition to spatial resolution adn sampling rates in sapce or time, one of the most relevant criteria for satellite altimetry on inland water is the accuracy of the elevation data. Here, the accuracy of ICESat LIDAR altimetry product is assessed over the Great Lakes in North America. The accuracy assessment method used in this paper emaphsizes on autocorrelation in high temporal frequency ICESat measurements. It also considers uncertainties resulting from both in situ lake level reference data. A probabilistic upscaling process was developed. This processis based on several successive ICESat shots averaged in a spatial transect accounting for autocorrelation between successive shots. The method also applies pre-processing of the ICESat data with saturation correction of ICESat waveforms, spatial filtering to avoid measurement disturbance from the land-water transition effects on waveform saturation and data selection to avoid trends in water elevations across space. Initially this paper analyzes 237 collected ICESat transects, consistent with the available hydrometric ground stations for four of the Great Lakes. By adapting a geostatistical framework, a high frequency autocorrelation between successive shot elevation values was observed and then modeled for 45% of the 237 transects. The modeled autocorrelation was therefore used to estimate water elevations at the transect scale and the resulting uncertainty for the 117 transects without trend. This uncertainty was 8 times greater than the usual computed uncertainty, when no temporal correlation is taken into account. This temporal correlation, corresponding to approximately 11 consecutive ICESat shots, could be linked to low transmitted ICESat GLAS energy and to poor weaterh conditions. Assuming Gaussian uncertainties for both reference data and ICESat data upscaled at the transect scale, we derived GLAS deviations statistics by averaging the results at station and lake scales. An overall bias of -4.6 cm (underestimation) and an overall standard deviation of 11.6 cm were computed for all lakes. Results demonstrated the relevance of taking autocorrelation into account in satellite data uncertainty assessment.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55692
Title: Auto-detection and integration of tectonically significant lineaments from SRTMDEM and remotely-sensed geophysical data
Author: Alaa A Masoud, Katsuaki Koike
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Adaptive shading, Segment tracing, segment grouping, B-spline, tectonic model, Egypt
Abstract: A set of techniques was developed for automatically detecting tectonic lineaments from multi-souce remotely-sensed data at various scales. The tecniques include adaptive shading of grid data to enhance linear features, a segment-tracing alogrithm to extract line segments from the shaded grid data, grouping of the segments by concatenating short segments, and connecting them by proximity and co-linearity criteria to form a lineament that represents significant tectonic, B-spline smoothing was adopted for lineament representation. Finally, a technique for assessing the orientations and styles of faulting (normal, reverse, and strike-slip types) was developed for use in characterizing the extrapolated fracture planes. The applicability of the developed techniques was examined using 30 arc-second topography/bathymetry grids, 1-min gravity anomaly grids, and 2-min total field magnetic intensity grids covering Egypt and its surroundings. Lineaments derived from data types so diverse in composition and from various depths corresponded well with the referenced tectonic features over much of the region. Prominent ternds and faulting styles of lineaments provided important clues as to the timing of their development as well as strong support for a structural inheritance model. Results demonstrated the effectiveness of the developed techniques combined with intergration of remotely-sensed data in detecting regional fracture systems accurately and in characterizing geodynamics over a long timeframe.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55691
Title: Direct relative orientation with four independent constraints
Author: Yongjun Zhang, Xu Huang, Xiangyun Hu, Fangqi Wan, Liwen Lin
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Direct relative orientation, essential matrix, constraint, accuracy analysis, least squares adjustment
Abstract: Relative orientation based on the conplanarity condition is one of the most important procedures in photogrammetry and computer vision. The conventional relative orientation model has five independent parameters if interior orientation parameters are known. The model of direct relative orientation contains nine unknowns to establish the linear transformation geometry, so there must be four independent constraints among the nine unknowns. To eliminate the influence of over parameterization of the conventional direct relative orientation model, a new relative orientation model with four indenpendent constraints is proposed in this paper. The constraints are derived from the inherent orthogonal property of the rotation matrix of the right image of a stereo pair. These constraints are completely new as compared with the known literature. The proposed approach can find the optimal solution under least squares criteria. Experimental results show that the proposed approach is superior to the conventional model of direct relative orientation, especially at low altitude and close range photogrammetric applications.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55690
Title: Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: A case study in an urban-rural landscape in the Brazilian Amazon
Author: Dengsheng Lu, Guiying Li, Emilio Moran, Mateus Batistella, Corina C Freitas
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Landsat TM, ALOS PALSAR L-band, RADARSAT-2 C-band, Wavelet-merging technique, Spectral mixture analysis, impervious surface
Abstract: This research explored the integrated use of Landsat Thematic Mapper (TM) and radar (i.e. ALOS PALSAR L-band and RADARSAT-2 C-band) data for mapping impervious surface distribution to examine the roles of radar data with different spatial resolutions and wavelengths. The wavelet-merging technique was used to merge TM and radar data to generate a new dataset. A constrained least-squares solution was used to unmix TM Multispectral data and multisensor fusion images to four fraction images (high-albedo, low-albedo, vegetation, and soil). The impervious surface image was then extracted from the high-albedo and low-albedo fraction images. QuickBird imagery was used to develop an impervious surface image for use as reference data to evaluate the results from TM and fusion images. This research indicated that increasing spatial resolution by multisensor fusion imporved spatial patterns of impervious surface distribution, but cannot significantly improve the statistical area accuracy. This research also indicated that the fusion image with 10-m spatial resolution was suitable for mapping impervious surface spatial distribution, but TM multispectral image with 30 m was too coarse in a complex urban-rural landscape. On the other hand, this research showed that no significant difference in improving impervious surface mapping performance by using either PALSAR L-band or RADARSAT C-band data with the same spatial resolution when they were used for mutli-sensor fusion with the wavelet-based method.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55689
Title: The role of ground reference data collection in the prediction of stem volume with LiDAR data in mountain data
Author: Michele Dalponte, Cristina Martinez, Mirco Rodeghiero, Damiano Gianelle
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: LiDAR, Forestry, Reference data, Forest inventory design, Prediction
Abstract: Ground reference data collection represents an important element in the prediction of stem volume with LiDAR- derived variables, and at present it is the most expensive part of such analyses. In this paper two aspects of ground reference data collection were analyzed: (1) the positioning error of the ground plots: and (2) the optimal number of training plots. A system for the prediction of stem voluem at area- based level was adopted. LIDAR data were proprocessed and 13 variables describing both height and coverage were extracted. Models were defined using a stepwise ordinary least square (OLS) regression. Three experiments were conducted: (i) the role of the plots positioning error on prediction accuracy; (ii) the influence of random downsampling of plot numbers on prediction accuracy; and (iii) the influence of a stratified downsamling of plot numbers on prediction accuracy based on LiDAR-derived variables. A dataset comprising 799 ground plots was used. They were distributed throughout a mountainous area in the Southern Alps, where the presence of a complex landscape increases the uncertainty of the Global Positioning System (GPS) accuracy, and where a large variety of tree forest species and climatic environments make it necessary to have a large number of sample plots for accurate characterization of the study area. All the experiments provided important indications for LiDAR based forest inventories: the GPS error did not significantly influence the prediction accuracy and it was possible to reduce the number of training samples without compromising the generalizing ability of the prediction model. Leading on from these findings, a new ground sampling protocol based on genetic algorithms was proposed. The new protocol allowed us to obtain promising results for the considered dataset: using only 53 training plots, instead of 534 in the original dataset, we obtained the same results for the validation set. These results, obtained in a complex mountainous area, are representative of Alpine environments and allow us to infer that similar (or better) results could also be obtained within non mountainous areas.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 55688
Title: Use of ALS, Airborne CIR and ALOS AVNIR-2 data for estimating tropical forest attributes in Lao PDR
Author: Zhengyang Hou, Qing Xu, Timo Tokola
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 6, November 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: ALS, Airborne CIR, ALOS AVNIR-2, Tropical forest, Forest monitoring
Abstract: In this study, the potential of remote sensing in tropical forests is examined in relation to the diversification of sensors. We report here on the comparison of alternative methods that use multisource data from Airborne Laser Scanning (ALS), Airborne CIR and ALOS AVNIR-2 to estimate stem volume and basal aera, in Laos. Multivariate linear regressin analyses with stepwise selection of predictors were implemented for modelling. The predictors of ALS metrics were calculated by means of the canopy height distribution approach, while predictors from both spectral and textual features were respectively generated for Airborne CIR and ALOS AVNIR-2 data. With respect to the estimation capacity from individual data sources after leave-one-out cross-validation, the ALS data proved superior, with the lowest RMSE of 36.92% for stem volume and 47.35% for basal area, whereas Airborne CIR and ALOS AVNIR-2 remained at similar accuracy levels, but fell well behind the ALS data. By integrating ALS metrics with other predictors from Airborne CIR or ALOS AVNIR-2, hybrid modelling was further tested respectively. The results showed that only the hybrid model for stem volume involving ALS and Airborne CIR improved the accuracy of 1.9% in terms of relative RMSE than that of using ALS alone.
Location: 231
Literature cited 1: None
Literature cited 2: None