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ID: 60576
Title: Tracking seasonal changes of leaf and canopy light use efficiency in a Phlomis fruticosa Mediterranean ecosystem using field measurements and multi-angular satellite hyperspectral imagery.
Author: Stavros Stagakis, Nikos Markos, Olga Sykioti, Aris Kyparissis.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: PHOTOGRAMMETRY AND REMOTE SENSING Vol 97 138-151 (2014)
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords: Light use efficiency, PRI, Hyperspectral, CHRIS/PROBA, Viewing angle, Phlomis fruticosa.
Abstract: Numerous normalized difference spectral indices (NDSIs) derived from leaf measurements and CHRIS/PROBA hyperspectral and multi-angular satellite images were examined for their capacity to track seasonal variations of leaf (?leaf) and canopy (?can) light use efficiency of a Mediterranean phryganic ecosystem. A series of seasonal field ecophysiological measurements, i.e. leaf area index (LAI), leaf photosynthesis and leaf reflectance, were conducted on the Phlomis fruticosa shrubs at the days of CHRIS acquisitions over the study site. Leaf scale analysis confirmed background theory on the relationship of the photochemical reflectance index (PRI) with ?leaf and provided a detailed view of the wavelengths that can be used in PRI formulation for the specific species. In canopy scale analysis, PRI and some alternative formulations of this index based on CHRIS bands, presented the most significant relationships with ?can. Taking into account the functional relationship between ?can and chlorophyll content, a combination of the xanthophyll de-epoxidation band (531 nm) with 701 nm CHRIS band in a NDSI is suggested as an alternative to the original PRI formulation that could improve seasonal ?can estimations. The satellite observation geometry effects on the determination of ?can were not very intense for the studied ecosystem. However, the most effective viewing direction was proved to be the backward scattering, while zenith observations were the least efficient for the specific ecosystem, most probably due to increased background effects. Even though the sensitivity of the original PRI formulation to ?can was reduced in forward scattering viewing directions, when 531 nm xanthophyll de-epoxidation band was replaced with higher wavelength bands (540-550 nm), a strong PRI-?can relationship reappeared. These findings indicate possible shift of xanthophylls de-epoxidation signal according to viewing direction.
Location: T E 15 New Biology Building.
Literature cited 1: Adams, W.W., Demming-Adams, B., 1994. Carotenoid composition and down regulation of photosystem II in three conifer species during the winter. Physiol.Plant.92, 451-458. Asner, G.P., 1998. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 64, 234-253.
Literature cited 2: Barton, C.V.M., North, P.R.J., 2001. Remote sensing of canopy light use efficiency using the photochemical reflectance index-model and sensitivity analysis. Remote Sens. Environ. 78, 264-273. BEAM Project, 2014. BEAM Earth Observation Toolbox and Development Platform, European Space Agency (ESA), Brockmann Consult. http://www.brockmann-consult.de/beam (accessed 17.01.14).

ID: 60575
Title: Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation.
Author: Liguo Wang, Siyuan Hao, Qunming Wang, Ying Wang.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: PHOTOGRAMMETRY AND REMOTE SENSING Vol 97 123-137 (2014)
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords: Hyperspectral imagery, Semi-supervised classification, Spatial-spectral graph, Label Propagation, Adaptive method, Gabor filter
Abstract: Graph-based classification algorithms have gained increasing attention in semi-supervised classification. Nevertheless, the graph cannot fully represent the inherent spatial-spectral Label Propagation is proposed for semi-supervised classification of hyperspectral imagery. The spatial information was used in two aspects: on the one hand, the spatial features extracted by a 2-D Gabor filter were stacked with spectral features; on the other hand, the width of the Guassian function, which was used to construct graph, was determined with an adaptive method. Subsequently, the unlabeled samples from the spatial neighbors of the labeled samples were selected and the spatial graph was constructed based on spatial smoothness. Finally, labels were propagated from labeled samples to unlabeled samples with spatial-spectral graph to update the training set for a basic classifier (e.g., support Vector Machine, SVM). Experiments on four hypespectral datasets show that the proposed Spatial-Spectral Label Propagation based on the SVM (SS-LPSVM) can effectively represent the spatial information in the framework of semi-supervised learning and consistently produces greater classification accuracy than the standard SVM, the Laplacian Support Vector Machine (LapSVM), Transductive Support Vector Machine (TSVM) and Spatial-Contextual Semi-Supervised Support Vector Machine (SCS3VM).
Location: T E 15 New Biology Building.
Literature cited 1: Bau, T.C., S., Healey, G., 2010. Hyperspectral region classification using a three-dimensional Gabor filer bank. IEEE Trans. Geosci. Remote Sens. 48 (9), 3457-3464. Belkin, M., Niyogi, P., 2005. Semi-supervised learning on manifolds.Machine Learning J. 56, 209-239.
Literature cited 2: Belkin, M., Niyogi, P., Sindhwani, V., 2006. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples.J Machine Learn. Res 7, 2399-2434 Benediktsson, J.A., Palmason, J.A., Sveinsson, J., 2005. Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans. Geosci. Remote Sens. 43 (3), 480-491.

ID: 60574
Title: Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680 nm absorption feature with continuum.
Author: Ieda Del ' Arco Sanches, Carlos Roberto Souza Filho, Raymond Floyd Kokaly.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: PHOTOGRAMMETRY AND REMOTE SENSING Vol 97 111-122 (2014)
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords: Hyperspectral, Airborne sensor, Chlorophyll absorption feature, Continuum removal, Spectral feature analysis, Vegetation index.
Abstract: This paper explores the use of spectral feature analysis to detect plant stress in visible/near infrared wavelengths. A time series of close range leaf and canopy reflectance data of two plant species grown in hydrocarbon-contaminated soil was acquired with a portable spectrometer. The ProSpecTIR-VS air-borne imaging spectrometer was used to obtain far range hyperspectral remote sensing data over the field experiment. Parameters describing the chlorophyll 680 nm absorption feature (depth, width, and area) were derived using continuum removal applied to the spectra. A new index, the Plant Stress Detection Index (PSDI), was calculated using continuum -removed values near the chlorophyll feature centre (680 nm) and on the green-edge (560 and 575 nm). Chlorophyll feature ' s depth, width and area, the PSDI and a narrow-band normalized difference vegetation index were evaluated for their ability to detect stressed plants. The objective was to analyse how the parameters/indices were affected by increasing degrees of plant stress and to examine their utility as plant stress indicators at the remote sensing level (e.g. airborne sensor). For leaf data, PSDI and the chlorophyll feature area revealed the highest percentage (67-70%) of stressed plants. The PSDI also proved to be the best constraint for detecting the stress in hydrocarbon-impacted plants with field canopy spectra and airborne imaging spectroscopy data. This was particularly true using thresholds based on the ASD canopy data and considering the combination of higher percentage of stressed plants detected (across the thresholds) and fewer false-positives.
Location: T E 15 New Biology Building.
Literature cited 1: Analytical Spectral Devices (ASD), 2011a. FieldSpec Hi-Res portable spectroradiometer. http://www.asdi.com/products/fieldspec-3-hi-res-portable-spectroradiomerter (accessed 04.02.11). Analytical Spectral Devices (ASD), 2011b. Plant probe. http://www.asdi.com/acessories/plant-probe (accessed 08.02.11).
Literature cited 2: Carter, G.A., 1993. Responses of leaf spectral reflectance to plant stress. Am. J. Bot. 80 (3), 239-243. Carter, G.A., 1994. Ratios of leaf reflectance in narrow wavebands as indicators of plant stress. Int. J. Remote Sens. 15 (3), 697-703.

ID: 60573
Title: Applying object-based segmentation in the temporal domain to characterise snow seasonality.
Author: Jeffery A. Thompson, Brian G. Lees.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: PHOTOGRAMMETRY AND REMOTE SENSING Vol 97 98-110 (2014)
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords: MODIS, Object-based image analysis, Time-series, Alpine, Australia, Snow cover, Seasonality.
Abstract: In the context of a changing climate it is important to be able to monitor and map descriptors of snow seasonality. Because of its relatively low elevation range. Australia ' s alpine bioregion is a marginal area for seasonal snow-cover with high inter-annual variability. It has been predicted that snow-cover will become increasingly ephemeral within the alpine bioregion as warming continues. To assist the monitoring of snow seasonality and ephemeral snow-cover, a remote sensing method is proposed. The method adapted principles of object-based image analysis that have traditionally be used in the spatial domain and applied them in the temporal domain. The method allows for a more comprehensive characterisation of snow seasonality relative to other methods. Using high-temporal resolution (daily) MODIS image time-series, remotely sensed descriptors were derived and validated using in situ observations. Overall, moderate to strong relationships were observed between the remotely sensed descriptors of the persistent snow-covered period (start r = 0.70, p < 0.001; end r =0.88, p < 0.001 and duration r=0.88, p <0.001) and their in situ counterparts. Although only weak correspondence (r = 0.39, p < 0.05) was observed for the number of ephemeral events detected using remote sensing, this was thought to be related to differences in the sampling frequency of the in situ observations relative to the remotely sense observations. For 2009, the mapped results for the number of snow-cover events suggested that snow-cover between 1400 and 1799 m was characterized by a high numbers of ephemeral events.
Location: T E 15 New Biology Building.
Literature cited 1: Aitchison, C.W., 2001. The effect of snow cover on small animals. In: Jones, H.G., Pomeory, J.W., Walker, D.A., Hoham, R.W. (Eds), Snow Ecology: An Interdisciplinary Examination of Snow-covered Ecosystems. Cambridge University Press, Cambridge, pp. 229-265. Aplin, P., Smith, G.M., 2011.Introduction to object-based landscape analysis.Int.J.Geogr.Inf.Sci.25, 869-875.
Literature cited 2: Ault, T.W., Czajkowski, K.P., Benko, T., Coss, J., Struble, J., Spongberg, A., Templin, M., Gross, C., 2006. Validation of the MODIS snow product and cloud mask using student and NWS cooperative station observations in the Lower Great Lakes Region. Remote Sens. Environ. 105, 341-353. Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for Gis-ready information. ISPRS J. Photogr. Remote Sens. 58, 239-258.

ID: 60572
Title: Accuracy in estimation of timber assortments and stem distribution-A comparison of airborne and terrestrial laser scanning techniques.
Author: Ville Kankare, Jari Vauhkonen, Topi Tanhuanpaa, Markus Holopainen, Mikko Vastaranta, Mariana, Joensuu, Anssi Krooks, Juha Hyyppa, Hannu Hyyppa, Ptteri Alho, Risto Viitala.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: PHOTOGRAMMETRY AND REMOTE SENSING Vol 97 89-97 (2014)
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords: Stem distribution, Timber assortments, Timber quality, TLS, ALS, Individual tree.
Abstract: Detailed information about timber assortments and diameter distributions is required in forest management. Forest owners can make better decisions concerning the timing of timber sales and forest companies can utilize more detailed information to optimize their wood supply chain from forest to factory. The objective here was to compare the accuracies of high-density laser scanning techniques for the estimation of tree-level diameter distribution and timber assortments. We also introduce a method that utilizes a combination of airborne and terrestrial laser scanning in timber assortment estimation. The study was conducted in Evo, Finland. Harvester measurements were used as reference for 144 trees within a single clear-cut stand. The results showed that accurate tree-level timber assortments and diameter distributions can be obtained, using terrestrial laser scanning (TLS) or a combination of TLS and airborne laser scanning (ALS). Saw log volumes were estimate with higher accuracy than pulpwood volumes. The saw log volumes were estimated with relative root-mean -squared errors of 17.5 % and 16.8% with TLS and a combination of TLS and ALS, respectively. The respective accuracies for pulpwood were 60.1% and 59.3%. The differences in the bucking method used also caused some large errors. In addition, tree quality factors highly affected the bucking accuracy, especially with pulpwood volume.
Location: T E 15 New Biology Building.
Literature cited 1: Axelsson, P., 2000. DEM generation from laser scanner data using adaptive TIN models. Int. Arch. Photogram .Remote Sens. 33 (B4/1: Part 4), 111-118. Brandtberg, T., Warner, T., Landenberger, R., McGraw, J., 2003. Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America. Remote Sens. Environ. 85, 290-303.
Literature cited 2: Breidenbach, J., Naesset, E., Lien, V., Gobakken, T., Solberg, S., 2010. Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data. Remote Sens. Environ. 114, 911-924. Chandra, S., Sivaswamy, J., 2006. An analysis of curvature based ridge and valley detection. In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006. Toulouse, France, 14-19 May, 2006, pp. 737-740.

ID: 60571
Title: Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery.
Author: Qihao Weng, Peng Fu.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: PHOTOGRAMMETRY AND REMOTE SENSING Vol 97 78-88 (2014)
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords: Land surface temperature, Urban heat island, Diurnal temperature cycle, Thermal downscaling, Data fusion, Temporal resolution.
Abstract: Land surface temperature is a key parameter for monitoring urban heat islands, assessing heat related risks, and estimating building energy consumption. These environmental issues are characterized by high temporal variability. A possible solution from the remote sensing perspective is to utilize geostationary satellites images, for instance, images from Geostationary Operational Environmental System (GOES) and Meteosat Second Generation (MSG). These satellite systems, however with coarse spatial but high temporal resolution (sub-hourly imagery at 3-10 km resolution), often limit their usage to meteorological forecasting and global climate modeling. Therefore, how to develop efficient and effective methods to disaggregate these coarse resolution images to a proper scale suitable for regional and local studies need be explored. In this study, we propose a least square support vector machine (LSSVM) method to achieve the goal of downscaling of GOES image data to half-hourly 1-km LSTs by fusing it with MODIS data products and shuttle Radar topography Mission (SRTM) digital elevation data. The result of downscaling suggests that the proposed method successfully disaggregated GOES images to half-hourly 1-km LSTs with accuracy of approximately 2.5 k when when validated against with MODIS LSTs at the same over-passing time. The synthetic LST datasets were further explored for monitoring of surface urban heat island (UHI) in the Los Angeles region by extracting key diurnal temperature cycle (DTC) parameters. It is found that the datasets and DTC derived parameters were more suitable for monitoring of daytime- other than nighttime-UHI. With the downscaled GOES 1-km LSTs, the diurnal temperature variations can be well be characterized. An accuracy of about 2.5 k was achieved in terms of the fitted results at both 1 km and 5 km resolutions.
Location: T E 15 New Biology Building.
Literature cited 1: Anderson, M.C., Allen, R.G., Morse, A., Kustas, W.P., 2012. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sens. Environ.122, 50-65. Buscail, C., Upegui, E., Viel, J.F., 2012. Mapping heatwave health risk at the community level for public health action. Int. J. Health Geographics 11, 38. http://dx.doi.org/10.1186/1476-072X-11-38
Literature cited 2: Carlson, T., 2007. An overview of the ?triangle method? for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors 7 (8), 1612-1629 Dominguez, A., Kleissl, J., Luvall, J.C., Rickman, D.L., 2011. High-resolution urban thermal sharpener (HUTS). Remote Sens. Environ. 115 (7), 1772-1780.

ID: 60570
Title: Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects.
Author: Kang Yu, Victoria Lenz-Wiedemann, Xinping Chen, Georg Bareth.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: PHOTOGRAMMETRY AND REMOTE SENSING Vol 97 58-77 (2014)
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords: Leaf chlorophyll, Spectral indices, Partial least squares, Precision agriculture, Ratio of reflectance difference index (RRDI) Support vector machines, Spring barley, Lambda by-lambda band optimization.
Abstract: Monitoring in situ chlorophyll (Chl) content in agricultural crop leaves is of great importance for stress detection, nutritional state diagnosis, yield prediction and studying the mechanisms of plant and environment interaction. Numerous spectral indices have been developed for chlorophyll estimation from leaf-and canopy-level reflectance. However, in most cases, these indices are negatively affected by variations in canopy structure and soil background. The objective of this study was to develop spectral indices that can reduce the effects of varied canopy structure and growth stages for the estimation of leaf Chl. Hyperspectral reflectance data was obtained through simulation by a radiative transfer model, PROSAIL, and measurements from canopies of barley comprising different cultivars across growth stages using spectroradiometers. We applied a comprehensive band-optimization algorithm to explore five types of spectral indices: reflectance difference (RD), reflectance ratio (RR), normalized reflectance difference (NRD), difference of reflectance ratio (DRR) and ratio of reflectance difference (RRD). Indirectly using the multiple scatter correction (MSC) theory, we hypothesized that RRD can eliminate adverse effects of soil background, canopy structure and multiple scattering. Published indices and multivariate models such as optimum multiple band regression (OMBR), partial least squares regression (PLSR) and support vector machines for regression (SVR) were also employed. Results showed that the ratio of reflectance difference index (RRDI) optimized for simulated data significantly improved the correlation with Chl (R2= 0.98, p<0.0001) and was insensitive to LAI variations (1-8), compared to widely used indices such as MCARI/ OSAVI (R2=0.64, p <0.0001) and TCARI/OSAVI (R2=0.74, p< 0.0001). The RRDI optimized for barley explained 76% of the variation in Chl and outperformed multivariate models. However, the accuracy decreased when employing the indices for individual growth stages (R2<0.59). Accordingly, RRDIs optimized for open and closed canopies improved the estimations of Chl for individual stages before and after canopy closure, respectively, with R2 of 0.65 (p<0.0001) and 0.78 (p< 0.0001). This study shows that RRDI can efficiently eliminate the effects of structural properties on canopy reflectance response to canopy biochemistry. The results yet are limited to the datasets used in this study; therefore, transferability of the methods to large scales or other datasets should be further evaluated.
Location: T E 15 New Biology Building.
Literature cited 1: Asner, G.P., 1998. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 64 (3), 234-253. Asner, G.P., Martin, R.E., 2008. Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote Sens. Environ. 112 (10), 3958-3970.
Literature cited 2: Bajwa, S.G., Mishra, A.R., Norman, R.J., 2010. Canopy reflectance response response to plant nitrogen accumulation in rice. Precision Agric. 11 (5), 488-506. Bannari, A., Morin, D., Bonn, F., Huete, A.R., 1995. A review of vegetation indices. Remote Sens. Rev. 13 (1-2), 95-120.

ID: 60569
Title: Automatic histogram-based fuzzy C-means clustering for remote sensing imagery.
Author: Saman Ghaffarian, Salar Ghaffarian.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: PHOTOGRAMMETRY AND REMOTE SENSING Vol 97 46-57 (2014)
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords: Automatic clustering, Fuzzy C-means clustering, Histogram-based, Hyperspectral imagery, Image fusion, Remote sensing imagery.
Abstract: Fuzzy C-means (FCM) clustering has been widely used in analyzing and understanding remote sensing images. However, the conventional FCM algorithm is sensitive to initialization, and it requires estimations from expert users to determine the number of clusters. To overcome the limitations of the FCM algorithm, an automatic histogram-based fuzzy C-means (AHFCM) algorithm is presented in this paper. Our proposed algorithm has two primary steps: 1- clustering each band of a multispectral image by calculating the slope for each point of the histogram, in two directions, and executing the FCM clustering algorithm based on specific rules, and 2-automatic fusion of labeled images is used to initialize and determine the number of clusters in the FCM algorithm for automatic multispectral image clustering. The performance of our proposed algorithm is first tested on clustering a very high resolution aerial images, a high resolution Worldview2 satellite image, a Landsat 8 satellite image and an EO-1hyperspectral image, for a constant number of clusters. The superiority of the new method is demonstrated by comparing it with the well-known methods of FCM, K-means, fast global FCM (FGFCM) and kernelized fast global FCM (KFGFCM) clustering algorithms, both quantitatively by calculating the DB, XB and SC indices and qualitatively by visualizing the cluster results.
Location: T E 15 New Biology Building.
Literature cited 1: Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T., 2002. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imag. 21, 193-199. Bensaid, A., Hall, L.O., Bezdek, J., Clarke, L.P., Silbiger, M.L., Arrington, J.A., Murtagh, R.F., 1996. Validity-guided (Re) clustering for image segmentation. IEEE Trans. Fuzzy Syst. 4, 112-123.
Literature cited 2: Bezdek, J.C., 1981. Pattern Recognition With Fuzzy Objective Function Algorithms. Plenum, New York. Biosca, J.M., Lerma, J.L., 2008. Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based of fuzzy clustering methods. ISPRS J. Photogrammetry Remote Sens. 63, 84-98.

ID: 60568
Title: An efficient semi-supervised classification approach for hyperspectral imagery.
Author: Kun Tan, Erzhu Li, Qian Du, Peijun Du.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: PHOTOGRAMMETRY AND REMOTE SENSING Vol 97 36-45 (2014)
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords: Hyperspectral, Semi-supervised leaning, Classification, Segmentation, Spectral-spatial feature, SVM.
Abstract: In this paper, an efficient semi-supervised support vector machine (SVM) with segmentation-based ensemble (S2SVMSE) algorithm is proposed for hyperspectral image classification. The algorithm utilizes spatial information extracted by a segmentation algorithm for unlabeled sample selection. The unlabeled samples that are most similar to the labeled ones are found and the candidate set of unlabeled samples to be chosen is enlarged to the corresponding image segments. To ensure the finally selected unlabeled samples be spatially widely distributed and less correlated, random selection is conducted with the flexibility of the number of unlabeled samples actually participating in semi-supervised learning. Classification is also refined through a spectral-spatial feature ensemble technique. The proposed method with very limited labeled training samples is evaluated via experiments with two real hyperspectral images, where it outperforms the fully supervised SVM and semi-supervised version without spectral-spatial ensemble.
Location: T E 15 New Biology Building.
Literature cited 1: Bai, J., Xiang, S.M., Pan, C.H., 2013. A graph-based classification method for hyperspectral images. IEEE Trans. Geosci. Remote Sens. 51 (2), 803-817. Bioucas-Dias, J., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., Chanussot, J., 2013. Hyperspectral remote sensing data analysis and future challenges. Geosci. Remote Sens. Mag., IEEE 1 (2), 6-36.
Literature cited 2: Bioucas-Dias, J.M., Nascimento, J.M.P., 2008. Hyperspectral subspace identification. IEEE Trans. Geosci. Remote Sens. 46 (8), 2435-2445. Bruzzone, L., Chi, M.M., Marconcini, M., 2006. A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Trans. Geosci. Remote Sens. 44 (11), 3363-3373.

ID: 60567
Title: Fusion of imaging spectroscopy and airborne laser scanning data for characterization of forest ecosystems-A review.
Author: Hossein Torabzadeh, Felix Morsdorf, Michael E. Schaepman.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: PHOTOGRAMMETRY AND REMOTE SENSING Vol 97 25-35 (2014)
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords: Forest ecosystems, Data fusion, Airborne laser scanning, Imaging spectroscopy.
Abstract: Forest ecosystems play an important role in the global carbon cycle and it is largely unknown how this role might be altered by transients imposed by global change and deforestation. Remote sensing can provide information on ecosystem state and functioning and , among others, two remote sensing techniques, airborne laser scanning (ALS) and imaging spectroscopy (IS), have been used to characterize forest ecosystems, both independently and combined in fusion approaches. However, the fusion of these datasets should make the best use of the complementarity of both sensors and provide better and more robust vegetation products in forested ecosystems. Similar to other data fusion approaches, satisfying results depends on choosing appropriate fusion levels and methods. In this review paper, we summarize and classify relevant studies that focused on forest characterization using combined ALS and IS data, limited to the last decade. We classified the approaches by fusion level (data or product level) and by choice of methods (physical or empirical methods). Five different categories of products (landcover maps, aboveground biomass, biophysical paramerters, gross/net primary productivity and biochemical parameters), have been found as the main aspects of forest ecosystems studied so far. A qualitative accuracy analysis of the products exposed that currently land cover maps are profiting the most from ALS and IS data fusion, while there is room for improvements in respect to the other products, such as biophysical parameters. Only few studies using physical approaches were found, but we expect the use of such approaches will increase with the growing availability of physically based radiative transfer models that can simulate both, ALS and IS data.
Location: T E 15 New Biology Building.
Literature cited 1: Anderson, J., Plourde, L., M., Braswell, B., Smith, M., Dubayah, R., Hofton, M., Blair, J., 2008. Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest. Remote sens Environ. 112 (4), 1856-1870 Antonarakis, A.S., Munger, J.W., Moorcroft, P.R., 2014. Imaging spectroscopy-and lidar-derived estimates of canopy composition and structure to improve predictions of forest carbon fluxes and ecosystemdynamics.Geophys.Res.Lett.41 (7), 2535-2542.http://dx.doi.org/10.1002/2013GL058373.
Literature cited 2: Arroyo, L.A., Johansen, K., Armston, J., Phinn, S., 2010. Integration of LiDAR and QuickBird imagery for mapping riparian biophysical parameters and land cover types in Australian tropical savannas. For.Ecol. Manage. 259 (3), 598-606. Asner, G.P., Martin, R.E., 2008. Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote Sens. Environ. 112 (10), 3958-3970.

ID: 60566
Title: Adaptive non-local Euclidean medians sparse unmixing for hyperspectral imagery.
Author: Ruyi Feng, Yanfei Zhong, Liangpei Zhang.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: PHOTOGRAMMETRY AND REMOTE SENSING Vol 97 9-24 (2014)
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords: Hyperspectral imagery, Non-local Euclidean medians, Non-local means, Adaptive, Sparse unmixing.
Abstract: Sparse unmixing models based on sparse representation theory and a sparse regression model have been successfully applied to hyperspectral remote sensing image umixing. To better utilize the abundant spatial information and improve the unmixing accuracy, spatial sparse unmixing methods such as the non-local sparse unmixing (NLSU) approach have been proposed. Although the NLSU method utilizes non-local spatial information as the spatial regularization term and obtains a satisfactory unmixing accuracy, the final abundances are affected by the non-local neighborhoods and drift away from the true abundance values when the observed hyperspectral images have high noise levels. Furthermore, NLSU contains two regularization parameters which need to be appropriately set in real applications, which is a difficult task and often has a high computational cost. To solve these problems, an adaptive non-local Euclidean medians sparse unmixing (ANLEMSU) method is proposed to improve NLSU by replacing the non-local means total variation spatial consideration with the non-local Euclidean medians filtering approach. In addition, ANLEMSU utilizes a joint maximum a posteriori (JMAP) strategy to acquire the relationships between the regularization parameters and the estimated abundances, and achieves the fractional abundances adaptively, without the need to set the two regularization parameters manually. The experimental results using both simulated data and, hence, provides an effective option for the unmixing of hyperspectral remote sensing imagery.
Location: T E 15 New Biology Building.
Literature cited 1: 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 Observ. Rem. Sens. (JSTARS) 5 (2), 354 -379. Bioucas-Dias, J.M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N.M., Chanussot, J., 2013. Hyperspectral remote sensing data analysis and future challenges.IEEE Geosci.Rem.Sens.Mag. 1(2), 6-36.
Literature cited 2: Buades, A., Coll, B., Morel, J.M., 2005a. A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4 (2), 490-530. Buades, A., Coll, B., Morel, J.M., 2005. A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 60-65.

ID: 60565
Title: A very fast phase inversion approach for small baseline style interferogram stacks
Author: Kui Zhang, Zhenzhou Li, Guojie Meng, Yaqiong Dai.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: PHOTOGRAMMETRY AND REMOTE SENSING Vol 97 1-8 (2014)
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords: Derivative-based optimization, Weighted least squares, Phase inversion, Interferometric time-series analysis, Interferometric synthetic aperture radar (InSAR), Satellite remote sensing.
Abstract: The recently developed interferometric time-series analysis techniques have shown great potential in ground surface deformation monitoring applications. Such techniques overcome the drawbacks of the traditional differential radar interferometry (DInSAR) and can achieve millimeter-level measurement accuracy. One of the most important operations in interfereometric time-series analysis techniques-referred to as phase inversion-is to estimate relative deformation velocity and digital elevation model error from a double-differenced interferometric phase time-series. Unfortunately, current phase inversion methods generally exhibit a low computational efficiency due to their high non-linearity, especially in the case when the dimension of an interferogram stack is large. In this paper, a new approach is proposed to efficiently resolve phase inversion problems defined on stacks constructed by interferograms with small baselines. The approach separates an estimation procedure into two parts. First, preliminary estimates are obtained by weighted least squares. Then, the estimates are refined by optimizing the corresponding ensemble phase coherence function. The proposed approach was applied to simulated and real data. Experimental results demonstrate that it can accurat4ely address the phase inversion problem with a very high computational performance.
Location: T E 15 New Biology Building.
Literature cited 1: Berardino, P., Fornaro, G., Lanari, Sansosti, E., 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR intrferograms. IEEE Trans. Geosci. Remote Sens. 40, 2375-2383. Bhattacharya, A.., Voge, M., Arora, M.K., Sharma, M.L., Bhasin, R.K., 2013.Surface displacement estimation using multi-temporal SAR interferometry in a seismically active region of the Himalaya. Georisk: Assess.Manage.Risk Eng.Syst. Geohazards 7, 1-14.
Literature cited 2: Blanco-Sanchez, P., Mallorqui, J., Duque, S., Monells, D., 2008. The coherent pixels technique (CPT): an advanced DInSAR technique for nonlinear deformation monitoring. Pure Appl.Geophys.165, 1167-1193. Colesanti, C., Wasowski, J., 2006. Investigating landslides with space-borne synthetic aperture radar (SAR) interferometry. Eng.Geol. 88, 173-199.

ID: 60564
Title: Mangrove Sediment Metals from Southeast Coast of India.
Author: Kollimalai Sakthivel and Kandasamy Kathiresan.
Editor: Professor V. Subramanian
Year: 2014
Publisher: Capital Publishing Company.
Source: Centre for Ecological Sciences
Reference: Asian Journal of Water, Environment and Pollution.vol.11 (4) 89-96 (2014)
Subject: water, Environment and Pollution.
Keywords: Mangroves, sediment, heavy metals, iron, copper, cadmium, lead, nickel.
Abstract: Heavy metals such as iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), nickel (Ni), cobalt (Co), lead (Pb) and cadmium (Cd) concentration range were 13330-32500 ppm, 260-780 ppm, 32.7-158 ppm, 27.6-82.6 ppm, 15.6-46.8 ppm, 5.46-42.7 ppm, 3.48-14.7 ppm and 0.15-1.75 ppm reported respectively. At all the sampling sites, the mean concentrations were found to follow the decreasing orders: Fe>.Mn>Zn>Cu>Ni>Co>Pb>Cd. ANOVA of variation ratio and level of significance between study areas were 1 % significant except manganese (5%) and copper (not significant). Statistical reports also showed 1 % significant except cadmium (5%). Iron and zinc can be considered as high level of contamination and a serious threat, copper was a moderate to serious threat, nickel slight contamination and cadmium no hazards.
Location: T E 15 New Biology Building.
Literature cited 1: Alloway, B.J. (1990).Soil process and the behaviour of metals. In: Aloway, B.J. (ed.), Heavy metals in soils. Blackie, Wiley, New York. Alloway, B.J. (1995). Heavy metals in soil. 2nd Edition. Chapman and Hall, United Kingdom.
Literature cited 2: Ananthan, G., Ganesan, M., Sampathkumar, P., Mathevan Pillai, M.and L. Kannan (1992). Distribution of tract metals in water, sediment and plankton of the Vellar estury. Seaweed Research Utilization, 15: 69-75. Ananthan, G., Sampathkumar, P., Palpandi, C. and L. Kannan (2006). Distribution of heavy metals in Vellar estuary, Southeast coast of India. Journal of Ecotoxicology and Environmental Monitoring, 16: 185-191.

ID: 60563
Title: Physical Chemical and Biological Parameters of Water from Medical Waste Dumpsites in South-Western Niger Delta, Nigeria.
Author: Marian Isi Akinbo and Prekeyi Tawari-Fufeyin.
Editor: Professor V. Subramanian
Year: 2014
Publisher: Capital Publishing Company.
Source: Centre for Ecological Sciences
Reference: Asian Journal of Water, Environment and Pollution.vol.11 (4) 83-88 (2014)
Subject: water, Environment and Pollution.
Keywords: Biological, chemical, medical wastes, physical, water, Nigeria.
Abstract: Water pollution by effluent has become a question of considerable public and scientific concern in the light of evidence extreme toxicity to human health and to biological ecosystems. Hospital wastewater poses serious health hazard to healthworkers, the general public and the environment. This study was conducted to determine the physical, chemical and biological parameters of hospital wastewater in South-western Niger Delta, Nigeria. Water samples were collected from the Federal Medical Center, Owo, Ondo State, University of Benin Teaching Hospital, Irrua Teaching Hospital, Irrua, Stella Obasanjo Hospital, Benin City, Edo State and Central Hospital, Warri, Delta State and from general (non-medical) dumpsites in all the locations where the medical wastes were collected, and these served as controls. The water samples were collected were analyzed using standard techniques. There was no significant difference n physical, chemical and biological qualities of water from medical and non-medical waste dumpsites (P >0.05). The only organism isolated was Bacillus species while Aspergillus species was the only fungi found in this study with total counts within acceptable limits. Medical and non-medical wastes do not have significant impact on physical and chemical properties of surface water.
Location: T E 15 New Biology Building.
Literature cited 1: Abd El-Gawad, H.A. and M.A.Aly. (2011). Assessment of aquatic environmental for wastewater management quality in hospitals. A case study. Australian J Basic Appl Sci, 5 (7): 474-482. Abdel-Massih, R.M., Melki, P.N., Afif, C. and D. Ziad (2013). Detection of genotoxicity in hospital wastewater of a developing country using SOS chromotest and Ames fluctuation test. J. Enviro Engine Ecol Sci, doi: 10.7243/2013-1323-2-4.
Literature cited 2: Akter, N. (2000). Medical Waste Management: A Review. Asian Institute of Technology, Thailand. Alabi, O.A. and O.S .Shokunbi (2011). Toxicological effects of hospital wastewater using animal bioassays. Ann Biol Res, 2 (2): 265-275.

ID: 60562
Title: Mobilization of Arsenic in the Groundwater of some Char Lands in Meghna Basin, Bangladesh: A Mechanistic study.
Author: Md.Mahamud-Ul-Hoque, Md.Abdus Sabur, M.Emdadul Haque and Syed Safiullah.
Editor: Professor V. Subramanian
Year: 2014
Publisher: Capital Publishing Company.
Source: Centre for Ecological Sciences
Reference: Asian Journal of Water, Environment and Pollution.vol.11 (4) 75-81 (2014)
Subject: water, Environment and Pollution.
Keywords: Arsenic mobilization, char lands, adsorption/desorption, biogeochemical transformations, competitive ions.
Abstract: In this paper, we describe the mechanistic details for arsenic mobilization in some newly formed char lands in Meghna basin, Bangladesh. We measured the concentrations of arsenic along with some other associated parameters involved in the release mechanism of arsenic in sediment-water interface. Total 38 water samples were collected and analyzed from both shallow (60 to 110 feet) and deep (?200 feet ) aquifers and arsenic contaminated hot spots in the shallow aquifers where one spot ' s arsenic concentration is high as 2.5 mg/L which could be second highest arsenic contaminated spot in Bangladesh reported so far. Concentration of arsenic exhibits a clear positive correlation with iron and also with ammonium, bicarbonate and phosphate significantly. From the study, it reveals that reductive dissolution of arsenic-rich iron minerals is primarily responsible for the mobilization of arsenic in the ground water. The presence of other competitive ions such as phosphate and bicarbonate in the adsorption-desorption process were identified for facilitating the mobilization.
Location: T E 15 New Biology Building.
Literature cited 1: Ahmed, F., Bibi, M.H., Ishiga, H., Fukushima, T. and T. Maruoka (2010). Geochemical study of arsenic and other trace elements in groundwater and sediments of the Old Brahmaputra River Plain, Bangladesh. Environ Earth Sci, 60: 1303-1316. Al-Abadleh, H.A. and T. Hoang (2007). Surface speciation o organoarsenicals on iron (oxyhydr) oxides using As-XANES.Activity report, Canadian light source.
Literature cited 2: Anawar, H.M., Akai, J., Komaki, K., Terao, H., Yoshioka, T., Ishizuka, T., Safiullah, S. and K. Kato (2003). Geochemical occurrence of arsenic in groundwater of Bangladesh: Sources and mobilization processes. Journal of Geochemical Exploration, 77: 109-131. Anawar, H.M., Akai, J. and H. Sakugawa (2004). Mobilization of arsenic from subsurface sediments by effect of bicarbonate ions in groundwater. Chemosphere, 54: 753-762.