ID: 59587
Title: CO adsorption on FeN (N=1-4) transition metal clusters: a density functional theory study.
Author: Vivek Sinha and Pradip Kr. Ghorai.
Editor: R. Srinivasan.
Year: 2014
Publisher: Current Science Association and Indian Academy of Sciences.
Source: Centre for Ecological Sciences
Reference: Current Science Vol. 106 (9) 1243-1248 (2014).
Subject: Current Science.
Keywords: Adsorption, density functional theory, metal cluster, vibrational frequency.
Abstract: The structures and stabilities of the FeN (CO) m (N=1-4, m=1-5) clusters have been theoretically studied at the density functional theory (DFT) and ab initio levels of theory. In particular, the energetic, structural and vibrational frequencies of carbon monoxide (CO) chemisorptions on iron clusters are studied and compared with those of nitric oxide (NO) and ammonia (NH3) adsorption. While the C-O and N-O bond lengths and vibrational frequencies strongly depend on cluster size and ligand population, the N-H bond length and vibrational frequency are independent on both cluster size and ligand population. For a particular FeN cluster, the vibrational frequency increases as the ligand population increases. On the other hand, keeping the number of ligands fixed, the vibrational frequency decreases significantly with the FeN cluster size. NO and CO being strong field ligands in the spectrochemical series, we have seen red shift of the vibrational frequencies with both cluster size and ligand population. As NH3 is a weak field ligand, we do not observe any variation. We have also observed that the HOMO-LUMO gap of both FeN (CO) m and FeN (NO)m clusters strongly depends on N and m. For FeN (NH3) m cluster, HOMO-LUMO gap decreases as more NH3 molecules are adsorbed on the metal centre.
Location: TE 12 New Biology Building
Literature cited 1: Heng-Feng, G., Gong-Ping, L. Yan-Hui, J., Isomers of the Cu5 cluster: a density function theory study. Chin. Phys. B, 2011, 20, 033105-033110.
Zaleski, C.M. et al., Synthesis, structure, and magnetic properties of a large lanthanide transition metal single molecule magnet. Angew. Chem. Int. Ed. Engl., 2004, 30, 3912-3914.
Literature cited 2: Zeinalipour-Yazdi, C.D., Cooksy, A.L. and Efstathiou, A.M., CO adsorption on transition metal clusters: trends from density functional theory. Surf. Sci., 2008, 602, 1858-1862.
Taylor, K.C., Nitric oxide catalysis in automotive exhaust systems. Catal. Rev. -Sci. Eng., 1993, 35, 475-481.
ID: 59586
Title: Forest area estimation and reporting: implications for conservation, management and REDD+.
Author: N.H. Ravindranath, I.K. Murthy, Joshi Priya, Sujata Upgupta, Swapan Mehra and Srivastava Nalin.
Editor: R. Srinivasan.
Year: 2014
Publisher: Current Science Association and Indian Academy of Sciences.
Source: Centre for Ecological Sciences
Reference: Current Science Vol. 106 (9) 1201 -1206 (2014).
Subject: Current Science.
Keywords: Afforestation, deforestation, forest cover, monitoring and reporting, REDD+
Abstract: Periodic estimation, monitoring and reporting on area under forest and plantation types and afforestation rates are critical to forest and biodiversity conservation, sustainable forest management and for meeting international commitments. This article is aimed at assessing the adequacy of the current monitoring and reporting approach adopted in India in the context of new challenges of conservation and reporting to international conventions and agencies. The analysis shows that the current mode of monitoring and reporting of forest area is inadequate to meet the national and international requirements. India could be potentially over-reporting the area under forest by including many non-forest tree categories such as commercial plantations of coconut, cashew, coffee and rubber, and fruit orchards. India may also be under-reporting deforestation by reporting only gross forest area at the state and national levels. There is a need for monitoring and reporting of forest cover, deforestation and afforestation rates according to categories such as (i) natural/primary forest, (ii) secondary/degraded forests, (iii) forest plantations (iv) commercial plantations, (v) fruit orchards and (vi) scattered trees.
Location: TE 12 New Biology Building
Literature cited 1: Ravindranath, N.H., Srivastava, N., Murthy, I.K., Malaviya, S., Munsi, M. and Sharma, N., Deforestation and forest degradation in India-implications for REDD+. Curr. Sci., 2012, 102, 1117-1125.
State of Forest Reports, 1987 to 2011. Forest Survey of India, Dehradun.
Literature cited 2: Puyravaud, J.P., Davidar, P. and Laurence, W.F., Cryptic loss of India ' s Forests. Conserv. Lett., 2010, 3, 390-394.
Gilbert, N., India ' s forest area in doubt. Nature, 2012, 489, 14-15.
ID: 59585
Title: An effective thin cloud removal procedure for visible remote sensing images.
Author: Huanfeng Shen, Huifang Li, Yan Qian, Liangpei Zhang, Qiangqiang Yuan.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 224-235 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Thin cloud removal, High fidelity, Visible images, Adaptive, Homomorphic filter
Abstract: Clouds are obstructions for land-surface observation, which result in the regional information being blurred or even lost. Thin clouds are transparent, and images of regions covered by thin clouds contain information about both the atmosphere and the ground. Therefore, thin cloud removal is a challenging task as the ground information is easily affected when the thin cloud removal is performed. An efficient and effective thin cloud method is proposed for visible remote sensing images in this paper, with the aim being to remove the thin clouds and also restore the ground information. Since thin cloud is considered as low-frequency information, the proposed method is based on the classic homomorphic filter and is executed in the frequency domain. The optimal cut-off frequency for each channel is determined semi-automatically. In order to preserve the clear pixels and ensure the high fidelity of the result, cloudy pixels are detected and handled separately. As a particular kind of low-frequency information, cloud-free water surfaces are specially treated and corrected. Since only cloudy pixels are involved in the calculation, the method is highly efficient and is suited for large remote sensing scenes. Scenes including different land-cover types were selected to validate the proposed method, and a comparison analysis with other methods was also performed. The experimental results confirm that the proposed method is effective in correcting thin cloud contaminated images while preserving the true spectral information.
Location: TE 12 New Biology Building
Literature cited 1: Ackerman, S.A., Moeller, C.C., Gumley, L.E., Strabala, K.I., Menzel, W.P., Frey, R.A., 1998. Discriminating clear sky from clouds with MODIS. J. Geophys. Res. D: Atmos.103, 32141-32157.
Chavez Jr., P.S., 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens. Environ. 24 (3), 459-479.
Literature cited 2: Daniel, L., Siong, O.C., Chay, L.S., Lee, K.H., White, J., 2004. A multiparameter moment-matching model-reduction approach for generating geometrically parameterized interconnect performance models. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 23 (5), 678-693.
Du, Y., Guindon, B., Cihlar, J., 2002. Haze detection and removal in high resolution satellite image with wavelet analysis. IEEE Trans. Geosci. Remote Sens. 40 (1), 210-217.
ID: 59584
Title: Roughness measurements over an agricultural soil surface with structure from Motion.
Author: B.Snapir, S. Hobbs, T.W. Waine.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 210-223 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Photogrammetry, Radar, Point cloud, Satellite, Camera, Surface, Close range, Multitemporal.
Abstract: This paper presents an accessible and reliable method to measure surface roughness of agricultural soils with a setup designed to tackle some of the challenges posed by roughness to SAR sensing. The method relies on Structure from Motion (SfM). From a large collection of unconstrained images (~700 images ) acquired with a commercial-grade camera, digital elevation models (DEMs) are generated for a SAR-pixel-size plot (2 x 11 m), with horizontal and vertical RMS errors of respectively 1.5 mm and 3.1 mm.
Example results highlight the need for individually detrending all sampled sub-DEMs when studying the converegence of the roughness parameters for increasing DEM length. This point appears to be missing in previous publications. The efficiency of the Fourier-based method used compute the roughness parameters allows investigating anisotropy at a 1? angular resolution. This could benefit investigations on the flashing fields phenomenon observed within narrow direction bands over tilled fields.
The inclusion of permanent reference targets into the soil makes multitemporal measurements over the same plot straightforward. Ten Acquisitions from April to July 2013 show noticeable natural changes in roughness with cracking during dry periods and smoothing during rainfalls. As expected, changes in RMS height and correlation length appear inversely correlated and can be related to in situ measurements of soil moisture, soil temperature, and rainfall. Analysis of changes in power spectral density indicates that the observed roughness changes only affect scales below 50 cm, i.e. scales relevant for microwave scattering. Even though it seems that millimetric changes for horizontal scales below 1 cm are not observable, measurement performance could be improved by adding more detailed pictures to the image set.
This SfM-based method appears to be well-suited to study the dynamics and characterization of roughness for SAR and more generally geosciences.
Location: TE 12 New Biology Building
Literature cited 1: Aguilar, M.A., Aguilar, F.J., Negreiros, J., 2009. Off-the-shelf laser scanning and close range digital photogrammetry for measuring agricultural soils microrelief. Biosyst. Eng. 103 (4), 504-517. < http://dx.doi.org/10.1016/j.biosystemseng.2009.02.010>
Alvarez-Mozos, J., Verhoest, N.E., Larraanaga, A., Casali, J., Gonzalez-Audicana, M., 2009. Influence of surface roughness spatial variability and temporal dynamics on the retrieval of soil moisture from SAR observations. Sensors 9 (1), 463-489.http://dx.doi.org/10.3390/s90100463, URL< http://dx.doi.org/10.3390/s90100463>
Literature cited 2: Astre, H., 2012. Sfmtoolkit, <http.dx.doi.org/10.3390/s90100463>
Baghdadi, N., Holah, N., Zribi, M., 2006. Soil moisture estimation using multi-incidence and multi-polarization asar data. Int. J. Remote Sens. 27 (10), 1907-1920. http://dx.doi.org/10.1080/01431160500239032,URL<http://dx.doi.org/10.1080/01431160500239032.
ID: 59583
Title: Detecting blind building facades from highly overlapping wide angle aerial imagery.
Author: Jean-Pascal Burochin, Bruno Vallet, Mathieu Bredif, Clement Mallet, Thomas Brosset, Nicolas Paparoditis.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 193-209 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Vertical imagery, Aerial, Urban, Building fa?ade, Opening detection, Feature, Classification, Learning.
Abstract: This paper deals with the identification of blind building, i.e. facades which have no openings, in wide angle aerial images with a decimeter pixel size, acquired by nadir looking cameras. This blindness characterization is in general crucial for real estate estimation and has, at least in France, a particular importance on the evaluation of legal permission of constructing on a parcel due to local urban planning schemes.
We assume that we have at our disposal an aerial survey with a relatively high stereo overlap along-track and across-track and a 3D city model of LoD 1, that can have been generated with the input images. The 3D model is textured with the aerial imagery by taking into account the 3D occlusions and by selecting for each fa?ade the best available resolution texture seeing the whole fa?ade. We then parse all 3D facades textures by looking for evidence of openings (windows or doors). This evidence is characterized by a comprehensive set of basic radiometric and geometrical features. The blindness prognostic is then elaborated through an (SVM) supervised classification.
Despite the relatively low resolution of the images, we reach a classification accuracy of around 85 % on decimeter resolution imagery with 60 X 40 % stereo overlap.
On the one hand, we show that the results are very sensitive to the texturing resampling process and to vegetation presence on fa?ade textures. On the other hand, the most relevant features for our classification framework are related to texture uniformity and horizontal aspect and to the maximal contrast of the opening detections.
We conclude that standard aerial imagery used to build 3D city models can also be exploited to some extent and at no additional cost for fa?ade blindness characterization.
Location: TE 12 New Biology Building
Literature cited 1: Abu-Mostafa, Y.S., Magdon-Ismail, M., Lin, H.-T., 2012. Learning From Data. AMLBook.com, USA.
Alegre, F., Dellaert, F., 2004. A probabilistic approach to the semantic interpretation of building facades. In: Proc. International Workshop on Vision Techniques Applied to the Rehabilitation of City Centres, CIPA, Lisbon, Portugal, October 25-27.
Literature cited 2: Bel-Hadj-Ali, A., 2001. Positional and shape quality of areal entities in geographic databases: quality information aggregation versus measures classification. In: ECSQARU Workshop on Spatio-Temporal Reasoning and Geographic Information Systems. Toulouse, France, September 19-21
Benedek, C., Descombes, X., Zerubia, J., 2013. Building development monitoring in multitemporal remotely sensed image pairs with stochastic birth-death dynamics. IEEE Trans. Pattern Anal.Mach.Intell.34 (1), 33-50.
ID: 59582
Title: Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery.
Author: Rongjun Qin.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 179-192 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Stereo Imagery, Semi-global matching, Building change detection, Self-organizing map. Markov random field, 3D building models.
Abstract: Due to the fast development of the urban environment, the need for efficient maintenance and updating of 3D building models is ever increasing. Change detection is an essential step to spot the changed area for data (mp/3D models) updating and urban monitoring. Traditional methods based on 2D images are no longer suitable for change detection n building scale, owing to the increased spectral variability of the building roofs and larger perspective distortion of the very high resolution (VHR) imagery. Change detection in 3D is increasingly being investigated using airborne laser scanning data or matched Digital Surface Models(DSM), but rare study has been conducted regarding to change detection on 3D city models with VHR images, which is more informative but meanwhile more complicated. This is due to the fact that the 3D models are abstracted geometric representation of the urban reality, while the VHR images record everything. In this paper, a novel method is proposed to detect changes directly on LOD (Level of Detail) 2 building models with VHR spaceborne stereo images from a different date, with particular focus on addressing the special characteristics of the 3D models. In the first step, the 3D building models are projected onto a raster grid, encoded with building object, terrain object, and planar faces. The DSM is extracted from the stereo imagery by hierarchical semi-global matching (SGM). In the second step, a multi-channel change indicator is extracted between the 3D models and stereo images, considering the inherent geometric consistency (IGC), height difference, and texture similarity for each planar face. Each channel of the indicator is the n clustered with the self-organizing Map (SOM), with ?change?, ?non-change?, and ?uncertain change? status labeled through a voting strategy. The ?uncertain changes? are then determined with a Markov Random Field (MRF) analysis considering the geometric relationship between faces. In the third step, buildings are extracted combining the multispectral images and the DSM by morphological operators, and the new buildings are determined by excluding the verified unchanged buildings from the second step. Both the synthetic experiment with Worldview-2 stereo imagery and the real experiment with IKONOS stereo imagery are carried out to demonstrate the effectiveness of the proposed method. It is shown that the proposed method can be applied as an effective way to monitoring the building changes, as well as updating 3D models from one epoch to the other.
Location: TE 12 New Biology Building
Literature cited 1: Akca, D., 2007. Matching of 3D surfaces and their intensities. ISPRS J. Photogr. Remote Sens. 62 (2). 112-121.
Akca, D., Freeman, M., Sargent, I., Gruen, A., 2010. Quality assessment of 3D building data. Photogram. Rec. 25 (132), 339-355.
Literature cited 2: Bazi, Y., Bruzzone, L., Melgani, F., 2005. An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Trans. Geosci. Remote Sens. 43 (4), 874-887.
Blake, A., Kohli, P., Rother, C., 2011. Markov Random Fields for Vision and Image Processing. The MIT Press.
ID: 59581
Title: Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery.
Author: Benoit Beguet, Dominique Guyon, Samia Boukir, Nesrine Chehata.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 164-178 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Forestry, Multiple regression, Feature selection, Texture, Multi-scale,Multi-resolution, Pleiades, Quickbird.
Abstract: The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick ' s texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure.
To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRif, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on quickbird and Pleiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure variables from VHR satellite images. Thus an average prediction error of ~1.1 m is expected on crown diameter, ~0.9 m on tree spacing, ~ 3 m on height and ~ 0.06 m on diameter at breast height.
Location: TE 12 New Biology Building
Literature cited 1: Allen, D.M., 1974. The relationship between variable selection and prediction. Technometrics 16 (125), 125-127.
Barbier, N., Couteron, P., Proisy, C., Malhi, Y., Gastelu-Etchegorry, J.P., 2010. The variation of apparent crown size and canopy heterogeneity across lowland Amazonian forests. Global Ecol.Biogeogr.19, 72-84.
Literature cited 2: Beguet, B., Chehata, N., Boukir, S., Guyon, D., 2012.Retrieving forest structure variables from Very High Resolution satellite images using an automatic method. ISPRS Ann. Photogramm. Remote Sens. Spatial Inform. Sci.1-7, 1-6.
Beguet, B., Boukir, S., Guyon, D., Chehata, N. 2013. Modelling-based feature selection for classification of forest structure using very high resolution multispectral imagery, SMC2013, IEEE Int. Conf. on systems, Man, and Cybernetics, Manchester, UK, pp, 4294-4299.
ID: 59580
Title: Keypoint-based 4-Points Congruent Sets-Automated marker-less registration of laser scans.
Author: Pascal Willy Theiler, Jan Dirk Wegner, Konrad Schindler
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 149-163 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Point cloud registration, Terrestrial laser scanning, 3D keypoint extraction, Congruent point sets, Geometric matching.
Abstract: We propose a method to automatically register two point clouds acquired with a terrestrial laser scanner without placing any markers in the scene. What makes this task challenging are the stongly varying point densities caused by the line-of sight measurement principle and the huge amount data. The first property leads to low point densities in potential overlap areas with scans taken from different viewpoints while the latter calls for highly efficient methods in terms of runtime and memory requirements.
A crucial yet largely unsolved step is the initial coarse alignment of two scans without any simplifying assumptions, that is, point clouds are given in arbitrary local coordinates and no knowledge about their relative orientation is available. Once coarse alignment has been solved, scans can easily be fine-registered with standard methods like least-squares surface or iterative closest point matching. In order to drastically thin out the original point clouds while retaining characteristic features, we resort to extracting 3D keypoints. Such clouds of keypoints, which can be viewed as a sparse but nevertheless discriminating native representation f the original scans, are then used as a sparse but nevertheless discriminative representation of the original scans, are then used as input to a very efficient matching method originally developed in computer graphics, called 4-points congruent sets (4PCS) algorithm. We adapt the 4PCS matching approach to better suit the characteristics of laser scans.
The resulting Keypoint-based 4-points congruent sets (K-4PCS) method is extensively evaluated on challenging indoor and outdoor scans. Beyond the evaluation on real terrestrial laser scans, we also perform experiments with simulated indoor scenes, paying particular attention to the sensitivity of the approach with respect to highly symmetric scenes.
Location: TE 12 New Biology Building
Literature cited 1: Aiger, D., Mitra, N.J., Chen-Or, D., 2008. 4-Points congruent sets for robust pairwise surface registration.ACM Trans.Graph.27 (3), 1-10.
Akca, D., 2003, Full automatic registration of laser scanner point clouds. In: Proc. Optical 3D measurement Techniques VI, pp.330-337.
Literature cited 2: Allaire, S., Kim, J., Breen, S., Jaffray, D., Pekar, V., 2008. Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1-8.
Bae, K.H., 2009. Evaluation of the convergence region of an automated registration method for 3D laser scanner point clouds. Sensors 9 (1), 355-375.
ID: 59579
Title: Adaptive MAP sub-pixel mapping model based on regularization curve for multiple shifted hyperspectral imagery.
Author: Yanfei Zhong, Yunyun Wu, Liangpei Zhang, Xiong Xu.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 134-148 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Hyperspectral image, Sub-pixel mapping, Multiple shifted images, Maximum a posteriori (MAP), L-curve, U-curve.
Abstract: Sub-pixel mapping is a promising technique for producing a spatial distribution map of different categories at the sub-pixel scale by using the fractional abundance image as the input. The traditional sub-pixel mapping algorithms based on single images often have uncertainty due to insufficient constraint of the sub-pixel land-cover patterns within the low-resolution pixels. To improve the sub-pixel mapping accuracy, sub-pixel mapping algorithms based on auxiliary datasets, e.g., multiple shifted images, have been designed, and the maximum a posteriori (MAP) model has been successfully applied to solve the ill-posed sub-pixel mapping problem. However, the regularization parameter is difficult to set properly. In this paper, to avoid a manually defined regularization parameter, and to utilize the complementary information, a novel adaptive MAP sub-pixel mapping model based on regularization curve, namely AMMSSM, is proposed for hyperspectral remote sensing imagery. In AMMSSM, a regularization curve which includes an L-curve or U-curve method is utilized to adaptively select the regularization parameter. In addition, to take the influence of the sub-pixel spatial information into account, three class determination strategies based on a spatial attraction model, a class determination strategy, and a winner-takes-all method are utilized to obtain the final sub-pixel mapping result. The proposed method was applied to three synthetic images and one real hyperspectral image. The experimental results confirm that the AMMSSM algorithm is an effective option for sub-pixel mapping, compared with the traditional sub-pixel mapping method based on a single image and the latest sub-pixel mapping methods based on multiple shifted images.
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. Rem. Sens. 66 (6), 762-775.
Atkinson, P.M., 1997. Mapping sub-pixel boundaries from remotely sensed images. In: Innovations in GIS IV, vol.4 Taylor & Francis, London, UK, pp. 166-180.
Literature cited 2: Atkinson, P.M., 2005. Sub-pixel target mapping from soft-classified, remotely sensed imagery. Photogramm. Eng. Rem. Sens. 71 (7), 839-846.
Bioucas-Dias, J.M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., Chanussot, J., 2012. Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens. (JSTARS) 5 (2), 354-379.
ID: 59578
Title: A new landscape metric for the identification of terraced sites: The Slope Local Length of Auto-Correlation (SLLAC)
Author: Giulia Sofia, Francesco Marinello, Paolo Tarolli.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 123-133 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Terraces, LIDAR, DTM, Slope, Autocorrelation, Anthropogenic landscape.
Abstract: This work presents the potential for high -resolution remote sensing data (LiDAR digital terrain models) to determine the spatial heterogeneity of terraced landscapes. The study objective is achieved through the identification of a new parameter that distinguishes this unique landscape form from more natural land formations. The morphological indicator proposed is called the Slope Local Length of Auto-Correlation (SLLAC), and it is derived from the local analysis of slope self-similarity. The SLACC is obtained over two steps: (i) calculating the correlation between a slope patch and a defined surrounding area and (ii) identifying the characteristic length of correlation for each neighbourhood. The SLLAC map texture can be measured using a surface metrology metric called the second derivative of peaks, or Spc. For the present study, we tested the algorithm for two types of landscapes: a Mediterranean and an Alpine one. The research method involved an examination of both real LiDAR DTMs and simulated ones, in which it was possible to control terrace shapes and the percentage of area covered by terraces. The results indicate that SLLAC maps exhibit a random aspect for natural surfaces. In contrast, terraced landscapes demonstrate a higher degree of order, and this behavior is independent of the morphological context and terracing system. The outcomes of this work also prove that Spc values decrease as the area of terraced surfaces increases within the investigated region: the Spc for terraced areas is significantly different from the Spc of a natural landscape. In areas of smooth natural morphology, the Spc identifies terraced areas with a 20 % minimum height range covered in terraces. In contrast, in areas of steep morphologies and vertical cliffs, the algorithm performs well when terraces cover at least 50 % of the investigated surface. Given the increasing importance of terraced landscapes, the proposed procedure offers a significant and promising tool for the exploration of spatial heterogeneity in terraced sites.
Location: TE 12 New Biology Building
Literature cited 1: Bailly, J.S., Levavasseur, F., 2012. Potential of linear features detection in a Mediterranean landscape from 3D VHR optical data: application to terrace walls. Geoscience and remote sensing symposium (IGARSS). IEEE Int. 7110-7113. http:// dx.doi.org/10.1109/IGARSS.2012.6352024.
Bazzoffi, P., Abbattista, F., Vanino, S., Pellegrini, S., 2006. Impact of land leveling for vineyard plantation on soil degradation in Italy. Bollettino della Societa Geologica Italiana. Volume speciale, pp. 191-199.
Literature cited 2: Brancucci, G., Paliaga, G., 2008. The problems with mapping: the case of Liguria, Terraced landscapes of the Alps. In: Scaramelli, G., Varotto, M. (Eds), Atlas, ALPTER Project, Marsilio, Venezia.
Burns, W.J., Coe, J.A., Kaya, B.S., Ma, L., 2010. Analysis of elevation changes detected from multi-temporal LiDAR surveys in forested landslide terrain in western Oregon.Environ.Eng.Geol. 16, 315-341.
ID: 59577
Title: Robust statistical approaches for local planar surface fitting in 3D laser scanning data.
Author: Abdul Nurunnabi, David Belton, Geoff West.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 106-122 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: 3D modelling, Feature extraction, Normal estimation, Outlier, Plane fitting, Point cloud, Robustness, Segmentation, Surface reconstruction.
Abstract: This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom Sample Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and /or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and purpose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks. Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA produce bias angles (angle between the fitted planes with and without outliers) of 0.20? and 0.24? respectively. Whereas LS, PCA and RANSAC produce worse bias angles 52.49?, 39.55? and 0.79? respectively. In terms of speed, DetRD-PCA takes 0.033 on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods.
Location: TE 12 New Biology Building
Literature cited 1: Alexa, M., Behr, J., Cohen-Or, D., Fleishman, S., Levin, D., Silva, C.T., 2001. Point set surfaces. In: Proceedings of the 12th IEEE conference on Visualization, San Diego, California, USA, 21-26 October, pp. 21-28.
Bae, K.-H, Belton, D., Lichti, D.D., 2005. A framework for position uncertainty of unorganised three-dimensional point clouds from near-monostatic laser scanners using covariance analysis. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. 36 (3/W19), pp. 7-12.
Literature cited 2: Belton, D., 2008. Classification and segmentation of 3D terrestrial laser scanner point cloud. PhD thesis, Department of Spatial Sciences, Curtin University of Technology, Australia.
Boulaassal, H., Landes, T., Grussenmeyer, P., Tarsha-Kurdi, F., 2007. Automatic segmentation of building facades using terrestrial laser data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 36 (3/W 52), pp. 12-14.
ID: 59576
Title: A sun-crown-sensor model and adapted C-correction logic for topographic correction of high resolution forest imagery.
Author: Yuanchao Fan, Tatjana Koukal, Peter J. Weisberg.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 94-105 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Diffuse radiation, Canopy shadowing, Radiometric correction, Topographic roughness, Moving window, IKONOS.
Abstract: Canopy shadowing mediated by topography is an important source of radiometric distortion on remote sensing images of rugged terrain. Topographic correction based on the sun-canopy -sensor (SCS) model significantly improved over those based on the sun-terrain-sensor (STS) model for surfaces with high forest canopy cover, because the SCS model considers and preserves the geotropic nature of trees. The SCS model accounts for sub-pixel canopy shadowing effects and normalizes the sunlit canopy area within a pixel. However, it does not account for mutual shadowing between the neighboring pixels. Pixel-to-pixel shadowing is especially apparent for fine resolution satellite images in which individual tree crowns are resolved. This paper proposes a new topographic correction model: the sun-crown-sensor (SCnS) model based on high-resolution satellite imagery (IKONOS) and high-precision LiDAR digital elevation model. An improvement on the C-correction logic with a radiance partitioning method to address the effects of diffuse irradiance is also introduced (SCnS+ C). In addition, we incorporate a weighting variable, based on pixel shadow fraction, on the direct and diffuse radiance portions to enhance the retrieval of at-sensor radiance and reflectance of highly shadowed tree pixels and form another variety of SCnS model (SCnS+W). Model evaluation with IKONOS test data showed that the new SCnS model outperformed the STS and SCS models in quantifying the correlation between terrain-regulated illumination factor and at-sensor radiance. Our adapted C-correction logic based on the sun-crown-sensor geometry and radiance partitioning better represented the general additive effects of diffuse radiation than C parameters derived from the STS or SCS models. The weighting factor Wt also significantly enhanced correction results by reducing within-class standard deviation and balancing the mean pixel radiance between sunlit and shaded slopes. We analyzed these improvements with model comparison on the red and near infrared bands. The advantage of SCnS+C and SCnS +W on both bands are expected to facilitate forest classification and change detection applications.
Location: TE 12 New Biology Building
Literature cited 1: Asner, G.P., Warner, A.S., 2003. Canopy shadow in IKONOS satellite observations of tropical forests and savannas. Remote Sens. Environ. 87, 521-533.
Civco, D.L., 1989. Topographic normalization of Landsat Thematic Mapper digital imagery. Photogram.Eng.Remote Sens. 55, 1303-1309.
Literature cited 2: Colby, J.D., 1991. Topographic normalization in rugged terrain.Photogram.Eng. Remote Sens. 57, 531-537.
Fan, Y., 2011. Tree crown mortality associated with roads in the Lake Tahoe Basin: a remote sensing approach (Master thesis). University of Nevada, Reno, NV. (UMI No. 1498683).
ID: 59575
Title: Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes.
Author: Anirudha Ghosh, P.K. Joshi.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 76-93 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Land surface temperature (LST), Hyperspectral imagery, Sharpening spatial resolution, Support vector machine (SVM), Gradient boosting machine (GBM), Partial least square (PLS).
Abstract: Land surface temperature (LST), a key parameter in understanding thermal behavior of various terrestrial processes, changes rapidly and hence mapping and modeling its spatio-temporal evolution requires measurements at frequent intervals and finer resolutions. We designed a series of experiments for disaggregation of LST (DLAST) derived from the Landsat ETM+ thermal band using narrowband reflectance information derived from the EO1-Hyperion hyperspectral sensor and selected regression algorithms over three geographic locations with different climate and land use cover (LULC) characteristics. The regression algorithms applied to this end were: partial least square regression (PLS), gradient boosting machine (GBM) and support vector machine (SVM). To understand the scale dependence of regression algorithms for predicting LST, we developed individual models (local models) at four spatial resolutions (480 m, 240 m , 120 m and 60 m) and tested the differences between these using RMSE derived from cross-validated samples. The sharpening capabilities of the models were assessed by predicting LST at finer resolutions using models developed at coarser spatial resolution. The results were also compared with LST produced by DisTrad sharpening model. It was found that scale dependence of the models is a function of the study area characteristics and regression algorithms. Considering the sharpening experiments, both GBM and SVM performed better than PLS which produced noisy LST at finer spatial resolutions. Based on the results, it can be concluded that GBM and SVM are more suitable algorithms for operational implementation of this application. These algorithms outperformed DisTrad model for heterogeneous landscapes with high variation in soil moisture content and photosynthetic activities. The variable importance measure derived from PLS and GBM provided insights about the characteristics of the relevant bands. The result indicate that wavelengths centered around 457, 671, 1488 and 2013-2083 nm are the most important in predicting LST. Nevertheless, further research is needed to improve the performance of regression algorithms when there is a large variability in LST and to examine the utility of narrowband vegetation indices to predict the LST. The benefits of this research may extend to applications such as monitoring urban heat island effect, volcanic activity and wildfire, estimating evapotranspiration and assessing drought severity.
Location: TE 12 New Biology Building
Literature cited 1: Agam, N., Kustas, W.P., Anderson, M.C., Li, F.Q., Neale, C.M.U., 2007a. A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sens. Environ. 107, 547-558.
Agam, N., Kustas, W.P., Anderson, M.C., Li, F.Q., Colaizzi, P.D., 2007b. Utility of thermal sharpening over Texas high plains irrigated agricultural fields. J. Geophys.Res.112, D19110.http://dx.doi.org/10.1029/2007JD008407.
Literature cited 2: Agam, N., Kustas, W.P., Anderson, M.C., Li, F.Q., Colaizzi, P.D., 2008. Utility of thermal image sharpening for monitoring field-scale evapotranspiration over rainfed and irrigated agricultural regions. Geophys.Res. Lett. 35, L02402. http://dx.doi.org/10.1029/2007GL032195.
Anderson, M.C., Norman, J.M., Kustas, W.P., Houborg, R., Starks, P.J., Agam, N., 2008. A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sens. Environ. 112, 4227-4241.
ID: 59574
Title: Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery.
Author: Mariana Belgiu, Lucian Dragut
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 67-75 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Supervised segmentation, Unsupervised segmentation, OBIA, Buildings, Random forest classifier, OpenStreetMap.
Abstract: Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies have suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches. Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. One supervised and two unsupervised segmentation methods were tested on three areas using QuickBird and WorldView-2 satellite imagery. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The two approaches yielded remarkably similar classification results, with overall accuracies ranging from 82% to 86%. The performance of one of the unsupervised methods was unexpectedly similar to that of the supervised method; they identified almost identical scale parameters as being optimal for segmenting buildings, resulting in very similar geometries for the resulting image objects. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications. The first is that object-based image analysis can be automated without sacrificing classification accuracy, and the second is that the previously accepted idea that classification is dependent on segmentation is challenged by our unexpected results, casting doubt on the value of pursuing ' optimal segmentation ' . Our results rather suggest that as long as under-segmentation remains at acceptable levels, imperfections in segmentation can be ruled out, so that a high level of classification accuracy can still be achieved.
Location: TE 12 New Biology Building
Literature cited 1: Anders, N.S., Seijmonsbergen, A.C., Bouten, W., 2011. Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping. Remote Sens. Environ. 115, 2976-2985.
Arvor, D., Durieux, L., Andres, S., Laporte, M.-A., 2013. Advances in geographic object-based image analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective. ISPRS J. Photogrammetry Remote Sens. 82, 125-137.
Literature cited 2: Baatz, M., Schape, A., 2000. Multiresolution segmentation -an optimization approach for high quality multi-scale image segmentation. In: Strobl, J., Blaschke, T., Griesebner, G., (Eds). Angewandte Geographische Informations-Verarbeitung XII, Wichmann Verlag.Karlsruhe, Germany.pp. 12-23.
Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensingdata for GIS-ready information.ISPRS J. Photogrammetry Remote Sens. 58, 239-258.
ID: 59573
Title: Deriving airborne laser scanning based computational canopy volume for forest biomass and allometry studies.
Author: Jari Vauhkonen, Erik Naesset, Terje Gobakken.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 57-66 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Light Detection and Ranging (LiDAR), Forest inventory, Tree allometry, Delaunay triangulation, Alpha shape, Simplicial homomorphism, Persistent homology.
Abstract: A computational canopy volume (CCV) based on airborne laser scanning(ALS) data is proposed to improve predictions of forest biomass and other related attributes like stem volume and basal area. An approach to derive the CCV based on computational geometry, topological connectivity and numerical optimization was tested with sparse-density, plot-level ALS data acquired from 40 field sample plots of 500-1000 m2 located in a boreal forest in Norway. The CCV had a high correspondence with the biomass attributes considered when derived from optimized filtrations, i.e. ordered sets of simplices belonging to the triangulations based on the point data. Coefficients of determination (R2) between the CCV and total above-ground biomass, canopy biomass, stem volume, and basal area were 0.88-0.89, 0.89, 0.83-0.97, and 0.88-0.92, respectively, depending on the applied filtration. The magnitude of the required filtration was found to increase according to an increasing basal area, which indicated a possibility to predict this magnitude by means of ALS-based height and density metrics. A simple prediction model provided CCVs which had R2 of 0.77-0.90 with the aforementioned forest attributes. The derived CCVs always produced complementary information and were mainly able to improve the predictions of forest biomass relative to models based on the height and density metrics, yet only by 0-1.9 percentage points in terms of relative root mean squared error. Possibilities to improve the CCVs by a further analysis of topological persistence are discussed.
Location: TE 12 New Biology Building
Literature cited 1: Anon, 1999. Pinnacle User ' s Manual. Javad Positioning Systems, San Jose, CA, p. 123.
Beland, M., Baldocchi, D.D., Widlowski, J.-L., Fournier, R.A., Verstrete, M.M., 2014. On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrtial LiDAR. Agric. Forest Meteorol. 184, 82-97
Literature cited 2: Braastad, H., 1966. Volume tables for birch. Meddeleser fra Det norske Skogforsoksvesen 21, 265-365 (In Norwegian with an English summary).
Brantseg, A., 1967. Volume functions and tables for scots pine. South Norway. Meddelelser fra Det norske Skogforsoksvesen 22, 689-739 (In Norwegian with an English summary).