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ID: 59545
Title: An adaptive approach for the segmentation and extraction of planar and linear/cylindrical features from laser scanning data.
Author: Zahra Lari, Ayman Habib.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 192-212 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Segmentation, Feature extraction, Principal Component Analysis, Spatial domain, Parameter domain, Clustering
Abstract: Laser scanning systems have been established as leading tools for the collection of high density three dimensional data over physical surfaces. The collected point cloud does not provide semantic information about the characteristics of the scanned surfaces. Therefore, different processing techniques have been developed for the extraction of useful information from this data which could be applied for diverse civil, industrial, and military applications. Planar and linear/cylindrical features are among the most important primitive information to be extracted from laser scanning data, especially those collected in urban areas. This paper introduces a new approach for the identification, parametrization, and segmentation of these features from laser scanning data while considering the internal characteristics of the utilized point cloud - i.e., local point density variation and noise level in the dataset. In the first step of this approach, a Principal Component Analysis of the local neighborhood of individual points is implemented to identify the points that belong to planar and linear/cylindrical features and select their appropriate representation model. For the detected planar features, the segmentation attributes are then computed through an adaptive cylinder neighborhood definition. Two clustering approaches are then introduced to segment and extract individual planar features in the reconstructed parameter domain. For the linear/cylindrical features, their directional and positional parameters are utilized as the segmentation attributes. A sequential clustering technique is proposed to isolate the points which belong to individual linear/cylindrical features through directional and positional attribute subspaces. Experimental results from simulated and real datasets demonstrate the feasibility of the proposed approach for the extraction of planar and linear/cylindrical features from laser scanning data.
Location: TE 12 New Biology Building
Literature cited 1: Al-Durgham, M., Habib, A., 2013. A framework for the registration and segmentation of heterogeneous LiDAR data. Photogramm. Eng.Remote Sens.79 (2). 135-145. Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A., 1994.An optimal algorithm for approximate nearest neighbor searching. In: proceedings of the 5th Annual ACM-SIAM Symposium on Discrete Algorithms, Philadelphia,PA, USA,pp.573-582.
Literature cited 2: Axelsson, P., 1999.Processing of laser scanner data-algorithms and applications.ISPRS J.Photogramm. Remote Sens.54 (2-3), 138-147. Belton., D., Lichti., D.D.,2006. Classification and segmentation of terrestrial laser scanner point clouds using local variance information. Int.Arch.Photogramm., Remote Sens. Spatial Inf.Sci.36 (part 5), 44-49.

ID: 59544
Title: Automatic representation and reconstruction of DBM from LiDAR data using Recursive Minimum Bounding Rectangle
Author: Eunju Kwak, Ayman Habib.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 171-191 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: LIDAR, Building, Modeling, Reconstruction, Automation, Generalization.
Abstract: Three -dimensional building models are important for various applications, such as disaster management and urban planning. The development of laser scanning sensor technologies has resulted in many different approaches for efficient building model generation using LiDAR data. Despite this effort, generation of these models lacks economical and reliable techniques that fully exploit the advantage of LiDAR data. Therefore, this research aims to develop a framework for fully-automated building model generation by integrating data-driven and model methods using LiDAR datasets. The building model generation starts by employing LiDAR data for building detection and approximate boundary determination. The generated building boundaries are then integrated into a model -based processing strategy because LiDAR derived planes show irregular boundaries due to the nature of LiDAR point acquisition. The focus of the research is generating models for the buildings with right-angled -corners, which can be described with a collection of rectangles under the assumption that the majority of the buildings in urban areas belong to this category. Therefore, by applying the Minimum Bounding Rectangle (MBR) algorithm recursively, the LiDAR boundaries are decomposed into sets of rectangles for further processing. At the same time, the quality of the MBRS is examined to verify that the buildings, from which the boundaries are generated, are buildings with right-angled-corners. The parameters that define the model primitives are adjusted through a model-based boundary fitting procedure using LiDAR boundaries. The level of details in the final Digital Building Model is based on the number of recursions during the MBR processing, which in turn are determined by the LiDAR point density. The model-based boundary fitting improves the quality of generated boundaries and as seen in experimental results, the quality depends on the average LiDAR point spacing. This research thus develops an approach which not only automates the building model generation, but also achieves the best accuracy of the model while utilizing only LiDAR data.
Location: TE 12 New Biology Building
Literature cited 1: Arefi, H., Engels J., Hahn, M., 2008. Levels of detail in 3D building reconstruction from LiDAR data. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Siences.Beijjing, pp 485-490 Awrangjeb, M., Ravanbakshsh, M., Fraser, C.S., 2010.Automatic detection of residential buildings using LiDAR data and multispectral imagery. ISPRS J. Photogramm. Remote Sens, 65,457-467.
Literature cited 2: Besi, P.J., Jain., R.C., 1988. Segmentation through variable -order surface fitting. IEEE Trans. Pattern Anal. Mach. Intell.10, 167-192. Brenner, C., 2005. Building reconstruction from images and laser scanning. Int.J. Appl.Earth Obs. Geoinform.6, 187-198.

ID: 59543
Title: Context-based automatic reconstruction and texturing of 3D urban terrain for quick-response tasks.
Author: Dimitri Bulatov, Gisela Haufel, Jochen Meidow, Melanie Pohl, Peter Solbrig, Peter Wernerus
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 157-170 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Building detection, Building reconstruction, Pose estimation, Simulation, Texturing, Urban terrain.
Abstract: Highly detailed 3D urban terrain models are the base for quick response tasks with indispensable human participation, e.g. disaster management. Thus, it is important to automate and accelerate the process of urban terrain modeling from sensor data such that the resulting 3D model is semantic, compact, recognizable, and easily usable for training and simulation purposes. To provide essential geometric attributes, buildings and trees must be identified among elevated objects in digital surface models. After building ground-plan estimation and roof details analysis, images from oblique airborne imagery are used to cover buildings faces with up to date texture thus achieving a better recognizably of the model. The three steps of the texturing procedure are sensor pose estimation, assessment of polygons projected into the images, and texture synthesis. Free geographic data, providing additional information about streets, forest areas, and other topographic object types, suppress false alarms and enrich the reconstruction results.
Location: TE 12 New Biology Building
Literature cited 1: Baillard, C., Zisserman, A., 2000. A plane-sweep strategy for the 3D reconstruction of buildings from multiple images. Int. Arch. Photogramm. Rem. Sensing Spatial Inf. Sci 33 (part B2), 56-62. Beder, C., Steffen, R., 2008. Incremental estimation without specifying a-priori covariance matrices for the novel parameters. In: Proc. Of VLMP Workshop on IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, pp.1-6
Literature cited 2: Bodensteiner, C., Arens, M., 2012. Real-time 2D video/3D LiDAR registration. In: IEEE 21 st International conference on Pattern Recognition, Tsukuba (Japan), pp 2206-2209 Bohm,J.,2004.Multi-imagefusionforocclusion-freefa?adetexturing.Int.Arch. Photogramm.Rem.Sensing Spatial Inf.Sci. 35(5), 867-872.

ID: 59542
Title: Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces.
Author: Domen Mongus, Niko Lukac, Borut Zalik.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 145-156 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: LiDAR, Ground extraction, Buildings detection, Mathematical morphology, DMP, LoFS.
Abstract: This paper proposes a new framework for ground extraction and building detection in LiDAR data. The proposed approach constructs the connectivity of a grid over the LiDAR point-cloud in order to perform multi-scale data decomposition. This is realised by forming a top-hat-scale-space using differential morphological profiles (DMPs) on the points ' residuals from the approximated surface. The geometric attributes of the contained features are estimated by mapping characteristic values from DMPs. Ground definition is achieved by using features ' geometry , whilst their surface and regional attributes are additionally considered for building detection. A new algorithm for local fitting surfaces (LoFS) is proposed for extracting planar points. Finally, transitions between planar ground and non-ground regions are observed in order to separate regions of similar geometrical and surface properties but different contexts (i.e. bridges and buildings). The methods were evaluated using ISPRS benchmark datasets and show superior results in comparison to the current state-of -the-art.
Location: TE 12 New Biology Building
Literature cited 1: Alharthy., A., Bethel, J., 2002. Heuristic filtering and 3D feature extraction from LiDAR data. Int: Arch. Photogram. Rem. Sens. Spatial Inform.Sci.34 (Part 3A), 29-34. Bevington, P., Robinson., D.K., 2002. Data Reduction and Error Analysis for the Physical Sciences, third ed. McGraw -Hill, New York.
Literature cited 2: Brovelli, M.A., Cannata, M., Longoni, U.M., 2004. LiDAR data filtering and DTM interpolation within GRASS.Trans. GIS 8 (2) 155-174. Chaplot, V., Darboux, F., Bourennane, H., Leguedois, S., Silvera, N., Phachomphon, K., 2006. Accuracy of interpolation techniques for the deviation of digital elevation models in relation to landform types and data density. Geomorphology 77(1-2), 126-141.

ID: 59541
Title: Discontinuous GBSAR deformation monitoring
Author: Michele Crosetto, Oriol Monserrat, Guido Luzi, Maria Cuevas-Gonzalez, Nuria Devanthery.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 136-141 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: SAR, GBSAR, Radar, Data analysis, Deformation monitoring, Landslides.
Abstract: This paper is focused on deformation monitoring using the Ground -Based SAR (GBSAR) technique and a particular data acquisition configuration, which is called discontinuous GBSAR (D-GBSAR). In the most commonly used GBSAR configuration, the radar is left installed in situ, acquiring data periodically, e.g. every few minutes. Deformations are estimated by processing sets of GBSAR images acquired during several weeks or months, without moving the system. By contrast, in the D-GBSAR the radar is installed and dismounted at each measurement campaign, revisiting a given site periodically. This configuration is useful to monitor slow deformation phenomena. This paper outlines the D-GBSAR data analysis procedure implemented by the authors. This is followed by a discussion of some specific aspects of D-GBSAR monitoring. Two successful examples of D-GBSAR monitoring are discussed: one concerns an urban area; while the second one involves a rural area where the monitoring requires the use of artificial corner reflectors.
Location: TE 12 New Biology Building
Literature cited 1: Barla. G., Antolini, F., Barla, M., Mensi, E., Piovano, G., 2010. Monitoring of the Beauregard landslide (Aosta Valley, Itly) using advanced and conventional techniques. Eng.Geol.116, 218-235. Casagli, N., Farina, P., Leva, D., Nico, G.,Tarchi, D., 2003. Ground-based SAR interferometry as a tool for landslide monitoring during emergencies. In: Proc. IGARSS 2003, vol. 4, pp.2924-2926.
Literature cited 2: Casagli, N., Catani, F., Del Ventisette, C., Luzi, G., 2010. Monitoring, prediction, and early warning using ground -based radar interferometry. Landslides 7(3), 291-301. Costantini, M., 1998. A novel phase unwrapping method based on network programming. IEEE Trans.Geosci. Remote Sens 36 (3), 813-821.

ID: 59540
Title: SAR change detection based on intensity and texture changes
Author: Maoguo Gong, Yu Li, Licheng Jiao, Meng Jia, Linzhi Su.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 123-135 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Change detection, Multivariate generalized Gaussian model, Robust principal component analysis, Graph cuts, Synthetic aperture radar.
Abstract: In this paper, a novel change detection approach is proposed for multitemporal synthetic aperture radar (SAR) images. The approach is based on two difference images, which are constructed through intensity and texture information, respectively. In the extraction of the texture differences, robust principal component analysis technique is used to separate irrelevant and noisy elements from Gabor responses. Then graph cuts are improved by a novel energy function based on multivariate generalized Gaussian model for more accurately fitting. The effectiveness of the proposed method is proved by the experiment results obtained on several real SAR images data sets.
Location: TE 12 New Biology Building
Literature cited 1: Bazi, Y., Bruzzone, L., Melgani, F., 2005. An unsupervised approach based on the generalized Gaussian model to automatic change detection in multi temporal SAR images. IEEE Trans. Geosci. Remote Sens.43, 874-887. Bazi, Y., Melgani., F., Al-Sharari, H.D., 2010. Unsupervised change detection in multispectral remotely sensed imagery with level set methods. IEEE Trans. Geosci. Remote Sens.48, 3178-3187.
Literature cited 2: Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens.65, 2-16 Bovolo, F., Bruzzone, L., 2005. A detail-preserving scale-driven approach to change detection in multitemporal SAR images. IEEE Trans. Geosci.Remote Sens.43, 2963-2972.

ID: 59539
Title: Novel Folded -PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing.
Author: Jaime Zabalza, Jinchang Ren, Mingqiang Yang, Yi Zhang, Jun Wang, Stephen Marshall, Junwei Han.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 112-122 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Folded Principal Component Analysis (F-PCA), Feature extraction, data reduction, Hyperspectral Imaging (HSI), Support Vector Machine (SVM), Remote sensing.
Abstract: As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral Imaging (HSI). Consequently, a novel Folded-PCA is proposed, where the spectral vector is folded into a matrix to allow the covariance matrix to be determined more efficiently. With this matrix-based representation, both global and local structures are extracted to provide additional information for data classification. Moreover, both the computational cost and the memory requirement have been significantly reduced. Using Support Vector Machine (SVM) for classification on two well-known HSI datasets and one Synthetic Aperture Radar (SAR) data set in remote sensing , quantitative results are generated for objective evaluations. Comprehensive results have indicated that the proposed Folded-PCA approach not only outperforms the conventional PCA but also the baseline approach where the whole feature sets are used.
Location: TE 12 New Biology Building
Literature cited 1: Abdi, H., Williams, L.J., 2010. Principal component analysis. Wiley Interdisciplinary Reviews: Computational statistics.http://dx.doi.org/10.1002/wics.101. Andric, M., Bondzulic, B., Zrnic, B., The database of radar echoes from various targets with spectral analysis.In: 10th Symposium on Neural Network Applications in Electrical Engineering (NEUREL), Serbia, September, pp.187-190, 2010. (doi:10.1109/NEUREL.2010.5644074).
Literature cited 2: Andric, M., Bondzulic., B., Zrnic, B., 2012.Feature extraction related to target classification for a radar Doppler echoes. In: 18 the telecommunications Forum TELFOR, Serbia, November, 2010. Archibald, R., Fann, G., 2007. Feature selection and classification of hyperspectral images with support vector machines. IEEE Geosci.Remote sens.Lett.4 (4), 674-677.http://dx.doi.org/10.1109/LGRS.2007.905116.

ID: 59538
Title: Detection of early plant stress responses in hyperspectral images.
Author: Jan Behmann, Jorg Steinrucken, Lutz Plumer
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 98-111 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Hyper Spectral, Learning, Modelling, Agriculture, Crop, Close range.
Abstract: Early stress detection in crop plants in crop plants is highly relevant, but hard to achieve. We hypothesize that close range hyperspectral imaging is able to uncover stress related processes non-destructively in the early stages which are invisible to the human eye. We propose an approach which combines unsupervised and supervised methods in order to identify several stages of progressive stress development from series of hyperspectral images. Stress of an entire plant is detected by stress response levels at pixel scale. The focus is on drought stress in barley (Hordeum Vulgare). Unsupervised learning is used to separate hyperspectral signatures into clusters related to different stages stress response and progressive senescence. Whereas all such signatures may be found in both, well watered and drought stressed plants, their respective distributions differ. Ordinal classification with Support Vector Machines (SVM) is used to quantify and visualize the distribution of progressive stages of senescence and to separate well watered from drought stressed plants. For each senescence stage a distinctive set of most relevant Vegetation Indices (VIS) is identified. The method has been applied on two experiments involving potted barley plants under well watered and drought stress conditions in a greenhouse. Drought stress is detected up to ten days earlier than using NDVI. Furthermore, it is shown that some VIs have overall relevance, while others are specific to particular senescence stages. The transferability of the method to the field is illustrated by an experiment on maize. (Zea Mays).
Location: TE 12 New Biology Building
Literature cited 1: Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua., P., Susstrunk,S., 2010. Slic Superpixels. Technical Report 149300, EPFL Technical Report No.149300. Agresti., A., 2002. Categorical Data Analysis, second ed. John Wiley & Sons, New Jersey, USA.
Literature cited 2: Blackburn, G.A., 2007. Hyperspectral remote sensing of plant pigments.J.Exp.Bot.58 (4), 855-867. Boyer, J.S., 1982.Plant productivity and productivity and environment. Science 218 (4571), 443-448.

ID: 59537
Title: Integration of intensity textures and local geometry descriptors from Terrestrial Laser Scanning to map chert in outcrops.
Author: Luca Penasa, Marco Franceschi, Nereo Preto, Giordano Teza, Vanessa Polito.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 88--97 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: TLS, Laser scanning, Classification, Geology, Data mining, Infrared, Texture, Point cloud.
Abstract: The potential of Terrestrial Laser Scanner imaging (TLS) as a tool to map chert, an amorphous variety of silica diffused in sedimentary rocks is here discussed together with an original method for its automatic detection. Reflectance measurements in the VIS-NIR band (400-2500 nm) show that chert displays low reflectance in the IR wavelengths that are operated by several commercial TLS. To develop and test a recognition method an outcrop of limestone with chert nodules was scanned with an IR (1541 nm) TLS. The intensity information, after proper distance correction, was coupled with geometric and intensity descriptors for training Support Vector Machines (SVM) to separate vegetation from rock and limestone from chert. Results, cross inspected in the field and with reference pictures, demonstrate that TLS data can be efficiently exploited to map chert when the monochromatic information of the intensity is integrated with feature descriptors and SVM classifiers.
Location: TE 12 New Biology Building
Literature cited 1: Abellan., A., Oppikofer., T., Jaboyedoff, M., Rosser, N., Lim, .M., Lato,M.,2014. Terrestrial laser scanning of rock slope instabilities .Earth Surf.Proc.Land.39, 80-97. Armesto -Gonzalez, J., Riveiro-Rodriguez, B., Gonzalez-Aguilera, D., Rivas-Brea, M.T., 2010. Terrestrial laser scanning intensity data applied to damage detection for historical buildings. J. Archaeol.sci 37, 3037-3047.
Literature cited 2: Batenburg, S.J., Montanari, A., Sprovieri, M., Hilgen, F.J., Coccioni, R., Gale, A.S., 2012. Astronomical tuning of black cherts in the Cenomanian Scaglia, Bianca as precursors of the Bonarelli level (0AE2) at Furlo, Italy, In: Abbasi, A., Giesen, N. (Eds), EGU General Assemly Conference Abstracts .p.6185. Bektas, F., Topal, T., Goncuoglu, M., Turanli, L., 2008.Evaluation of the alkali reactivity of cherts from Turkey. Constr. Build. Mater.22, 1183-1190.

ID: 59536
Title: A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images.
Author: Xiaodong Li, Feng Ling, Yun Du, Qi Feng, Yihang Zhang
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 76--87 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Land cover, Mapping, Change detection, Multitemporal, Super-resolution mapping, Hopfield neural network.
Abstract: The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse -resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial -temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Land sat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters.
Location: TE 12 New Biology Building
Literature cited 1: Ardila, J.P., Tolpekin., V.A., Bijker., W.,Stein, A., 2011. Markov-random-field -based super-resolution mapping for identification of urban trees in VHR images. ISPRS J. Photogramm. Remote Sens.66, 762-775. Atkinson, P.M., 1997. Mapping sub-pixel boundaries from remotely sensed images. Innovat.GIS IV, 166-180.
Literature cited 2: Atkinson, P.M., 2005. Sub-pixel target mapping from soft-classified, remotely sensed imagery. Photogramm. Eng. Remote Sens.71, 839-846. Atkinson, P.M., 2009. Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study. Int.J. Remote Sens, 30, 5293-5308.

ID: 59535
Title: Calibration of area based diameter distribution with individual tree based diameter estimates using airborne laser scanning.
Author: Qing Xu, Zhengyang Hou, Matti Maltamo, Timo Tokola.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 65--75 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Forestry, Laser scanning, Calibration, Combination, Estimation, Accuracy
Abstract: Diameter distribution is essential for calculating stem volume and timber assortments of forest stands. A new method was proposed in this study to improve the estimation of stem volume and timber assortments, by means of combining the Area -based approach (ABA) and individual tree detection (ITD), the two main approaches to deriving forest attributes from airborne laser scanning (ALS) data. Two methods, replacement, and histogram matching were employed to calibrate ABA-derived diameter distributions with ITD -derived diameter estimates at plot level. The results showed that more accurate estimates were obtained when calibrations were applied. In view of the highest accuracy between ABA and ITD, calibrated diameter distributions decreased its relative RMSE of the estimated entire growing stock, saw log and pulpwood fractions by 2.81%, 3.05% and 7.73% points at best, respectively. Calibration improved pulpwood fraction significantly, which contributed to the negligible bias of the estimated entire growing stock.
Location: TE 12 New Biology Building
Literature cited 1: Brandtberg, T., 1999.Automatic individual tree based analysis of high spatial resolution remotely sensed data. Doctoral Thesis. Acta universitatis Agriculturae sueciae, Silvestria 118.Swedish University of Agricultural Sciences, Centre for Image Analysis, Uppsala, Sweden. Breidenbach,J., Glaser, C., Schmidt, M., 2008.Estimation of diameter distributions by means of airborne laser scanner data.Can.J.For.Res.38 (6),1611-1620
Literature cited 2: Breidenbach, J., Naesset., E., Lien., V., Gobakken, T., Solberg., S., 2010.Prediction of species specific forest inventory attributes using a nonparametric semiindividual tree crown approach based on fused airborne laser scanning and multispectral data. Remote Sens.Environ114 (4), 911-924. Breidenbach,J., Naesset, E., Gobakken., T.,2012.Improving K-nearest neighbor predictions in forest inventories by combining high and low density airborne laser scanning data. Remote Sens.Environ.117, 358-365(special issue: Remote Sensing of Urban Environments)

ID: 59534
Title: Retrieval of leaf water content spanning the visible to thermal infrared spectra.
Author: Saleem Ullah, Andrew k. Skidmore, Abel Romoelo, Thomas A. Groen, Mohammad Naeem, Asad Ali
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 56-64 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Water stress, Remote sensing, Visible-near infrared and shortwave., Infrared (VNIR-SWIR),Mid infrared (MIR), Thermal infrared (TIR), Statistical models.
Abstract: The objective of this study was to investigate the entire spectra (from visible to the thermal infrared; 0.390-14.0?m) to retrieve leaf water content in a consistent manner. Narrow -band spectral indices (calculated from all possible two band combinations) and partial least square regression (PLSR) were used to assess the strength of each spectral region. The coefficient of determination (R?) and root mean square error (RMSE) were used to report the prediction accuracy of spectral indices and PLSR models. In the visible -near infrared and shortwave infrared (VNIR-SWIR) , the most accurate spectral index yielded R? of 0.89 and RMSE of 7.60%, whereas, in the mid infrared (MIR) the highest R? was 0.93 and RMSE of 5.97% Leaf water content was poorly predicted using two-band indices developed from the thermal infrared (R?=0.33). The most accurate PLSR model resulted from MIR reflectance spectra (R?=0.96, RMSE=4.74% and RMSE cross validation RMSEcv=6.17%) followed by VNIR-SWIR reflectance spectra (R?=0.91, RMSE=6.90% and RMSEcv =7.32%). Using thermal infrared (TIR) spectra, the PLSR model yielded a moderate retrieval accuracy (R?=0.67, RMSE=13.27% and RMSEcv=16.39%). This study demonstrated that the mid infrared (MIR) and shortwave infrared (SWIR) domains were the most sensitive spectral region for the retrieval of leaf water content.
Location: TE 12 New Biology Building
Literature cited 1: Aldakheel, Y.Y, Danson, F.M., 1997. Spectral reflectance of dehydrating leaves: measurements and modelling. Int. J. Remote sens.18, 3683-3690. Asner, G.P., Martin, R.E., 2008.Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote Sens. Environ.112, 3958-3970
Literature cited 2: Bauer, M.E., Daughtry, C.S.T, Beihl, L.L.Kanemasu, E.T., Hall, F.G.., 1986.Field spectroscopy of agricultural crops. IEEE Trans.Geosci.Remote Sens.24, 65-75. Bowman, W.D., 1989. The relationship between leaf water status, gas exchange, and spectral reflectance in cotton leaves. Remote Sens. Environ. 30, 249-255.

ID: 59533
Title: Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data.
Author: Kun Jia, Shunlin Liang, Ning Zhang, Xiangqin Wei, Xingfa GU, Xiang Zhao, Yunjun Yao, Xianhong Xie.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 49-55 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Land cover, Finer resolution, Temporal features, Classification, Landsat 8, Fusion
Abstract: Land cover classification of finer resolution remote sensing data is always difficult to acquire high frequency time series data which contains temporal features for improving classification accuracy. This paper proposed a method of land cover classification with finer resolution remote sensing data integrating temporal features extracted from time series coarser resolution data. The coarser resolution vegetation index data is first fused with finer resolution data obtain time series finer resolution data. Temporal features are extracted from the fused data and added to improve classification accuracy. The result indicates that temporal features extracted from coarser resolution data have significant effect on improving classification accuracy of finer resolution data, especially for vegetation types. The overall classification accuracy is significantly improved approximately 4% from 90.4% to 94.6% and 89.0% to 93.7% for using Landsat 8 and Landsat 5 data, respectively. The user and producer accuracies for all land cover types have been improved
Location: TE 12 New Biology Building
Literature cited 1: Bartholome, E., Belward, A.S., 2005. GLC2000: a new approach to global land cover mapping from Earth Observation Data. Int. J. Remote Sens.26, 1959-1977. Bounoua, L., DeFries R., Collatz, G.J., Sellers, P., Khan, H., 2002. Effects of land cover conversion on surface climate. Climatic change 52, 29-64.
Literature cited 2: Brown, J.C., Kastens, J.H., Coutinho, A.C., Victoria., D.D., Bishop., C.R., 2013. Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data. Remote Sens. Environ. 130, 39-50. Chen, J., Jonsson, P., Tamura, M., Gu, Z.H., Matsushita, B., Eklundh, L., 2004. A simple method for reconstructing, a high -quality NDVI time -series data set based on the Savitzky-Golay filter. Remote Sens. Environ. 91, 332-344.

ID: 59532
Title: A review of ground -based SAR interferometry for deformation measurement
Author: O. Monserrat, M. Crosetto, G. Luzi
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 40-48 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: SAR, Interferometry, Terrestrial, Deformation, Monitoring, Review
Abstract: This paper provides a review of ground-based SAR (GBSAR) interferometry for deformation measurement. In the first part of the paper the fundamentals of this technique are provided. Then the main data processing and analysis stages needed to estimate deformations starting from the GBSAR observations are described. This section introduces the two types of GBSAR acquisition modes, i.e., continuous and discontinuous GBSAR, and reviews the different GBSAR processing and analysis methods published in the literature. This is followed by discussion of the specific technical aspects of GBSAR deformation measurement. A section then summarizes the pros and cons of GBSAR for deformation monitoring. The last part of the paper includes two reviews: one concerning the GBSAR systems described in the literature, including non-strictly SAR systems and second one addresses the main GBSAR applications.
Location: TE 12 New Biology Building
Literature cited 1: Alba, M., Bernardini, G., A., Ricci., P.P., Roncoronia, F., Scaioni, , M., Valgoic., P., Zhangd, K., 2008. Measurement of dam deformations by terrestrial interferometric techniques. Int. Arch. Photogramm, Remote Sens. Spatial Inf. Sci.37 (part b1), 133-139. Barla, G., Antolini, F., Barla, M., Mensi, E., Piovono, G., 2010.Monitoring of the Beauregard landslide (Aosta valley, Italy) using advanced and conventional techniques. Eng.Geol.116 (3), 218-235.
Literature cited 2: Berardino, P., Fornaro., G., Lanari., R., Sansosti, E., 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR Interferograms. IEEE Trans. Geosci. Remote Sens. 40 (11), 2375-2383. Bernardini, G. Ricci, p., Coppi, F., 2007. Aground based microwave interferometer with imaging capabilities for remote measurements of displacements. In: proc. GALAHAD Workshop Within the 7th Geomatic Week and the 3rd International Geotelematics Fair (GlobalGeo) , Barcelona (Spain) ,20-23 February.

ID: 59531
Title: Assessment of NIR-red algorithms for observation of chlorophyll-a in highly turbid inland waters in China.
Author: Changchun Huang, Jun Zou, Yunmei Li, Hao Yang, Kun Shi, Junsheng Li, Yanhua Wang, Xia Chena , Fa Zheng.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 29-39 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Optimal spectral band, Optical properties, Chinese inland waters, MERIS, GOCI, Two-band and three -band algorithm
Abstract: It has been proven that empirical (two-band) and semi-analytical (three-band) algorithms based on near infrared and red (NIR-red) wavelengths can be used for estimating Cchl-a in a highly turbid productive waters with satisfactory performance. However, the optimal spectral bands and parameters of algorithms vary significantly because of the different optical properties of datasets. Using a comprehensive dataset, we validate and evaluate the applicability of empirical and semi-analytical algorithms for deriving Cchl-a for inland lakes in China. The comprehensive dataset contains 993 in situ samples collected from five inland lakes in China between 2006-2013. The optimal algorithms, Rrs (706)/Rrs (685) and [Rrs?? (685)- Rrs?? (707)] Rrs (722) , are calibrated using an in situ dataset, with root mean square mean square errors (RMSEs) of 10.66mg/m? and 8.47mg/ m? , respectively. The RMSE of the NIR-red two-and three-band algorithms for the validation data are 11.1mg/ m? and 8.82mg/ m?, respectively. The RMSE increase to 13.17mg/ m? and 12.58mg/ m? when the algorithms are applied to Medium Resolution Imaging Spectrometer (MERIS) and Geostationary ocean Color Imager (GOCI) centre wavelengths. The RMSEs for the validation data decrease to 8.80mg/ m? and 7.78mg/m? when the optimal spectral band (?1) shifts to 671 nm. The RMSE decrease to 10.03 mg/ m? and 9.09mg/ m? as a result of optimization of the model parameters when algorithms are applied to MERIS and GOCI centre wavelengths. The shifting of the optimal spectral band (the difference between 671 nm and 685 nm) increase the RMSEs from 8.80mg/ m? to 11.1mg/ m? for the two band algorithm. This indicates that the three -band algorithm is much more suitable for high-turbidity water than the two -band algorithm. Nevertheless, the two-band model can be used for extremely turbid and low Cchl-a waters for analysis of the retrieval results after cluster analysis of remote sensing reflectance. Meanwhile, shifting of the optimal spectral bands (?1) is highly correlated with the total suspended matter concentration (CTSM) (the Pearson correlation coefficient between ?1 and CTSM can reach 0.95). In conclusion, the results indicate that both the two-and three-band algorithms have high potential applicability for derivation of Cchl-a in high -turbidity inland waters in China.
Location: TE 12 New Biology Building
Literature cited 1: Abd-Elrahman, A., Croxton, M., Pande-Chettri, R., Toor, G.S., Smith, S., Hill., j., 2011.In situ estimation of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system. ISPRS J. Photogrammetry Remote Sens.66, 463-472. Astoreca, R., Doxaran, D., Ruddick, K., Rousseau, V., Lancelot, C., 2012. Influence of suspended particle concentration, composition and size on the variability of inherent optical properties of the Southern North Sea. Continental Shelf Res.35, 117-128.
Literature cited 2: Cleveland, J.S., Weidemann, A.D., 1993. Quantifying absorption by aquatic particles, a multiple scattering correction for glass-fiber filters. Limnol. Oceanorg. 38, 1321-1327. Chen, W.M., Chen, K.N., Hu., Y.H., 2006. Discussion on possible error for Phytoplankton chlorophyll -a concentration analysis using hot-ethanol extraction method .J., Lake Sci.18, 550-552.