ID: 56827
Title: Cartosat-1 stereo orthokit digital data: 546-335 30% of 19 -Jan-2012
Author: None
Editor: None
Year: 2012
Publisher: NRSC, Hyderabad
Source: Centre for Ecological Sciences
Reference: None
Subject: Cartosat-1 stereo orthokit digital data: 546-335 30% of 19 -Jan-2012
Keywords: None
Abstract: None
Location: 215
Literature cited 1: None
Literature cited 2: None
ID: 56826
Title: Cartosat-1 stereo orthokit digital data: 545-336 of 30-Jan-2012
Author: None
Editor: None
Year: 2012
Publisher: NRSC, Hyderabad
Source: Centre for Ecological Sciences
Reference: None
Subject: Cartosat-1 stereo orthokit digital data: 545-336 of 30-Jan-2012
Keywords: None
Abstract: None
Location: 215
Literature cited 1: None
Literature cited 2: None
ID: 56825
Title: Cartosat-1 stereo orthokit digital data: 545-335 of 30-Jan-2012
Author: None
Editor: None
Year: 2012
Publisher: NRSC, Hyderabad
Source: Centre for Ecological Sciences
Reference: None
Subject: Cartosat-1 stereo orthokit digital data: 545-335 of 30-Jan-2012
Keywords: None
Abstract: None
Location: 215
Literature cited 1: None
Literature cited 2: None
ID: 56824
Title: An in-depth simulation of EnMAP acquisition geometry
Author: P Schwind, R Muller, G Palubinskas, T Storch
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 70, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: EnMAP, Geometric simulation, Geometric correction, Attitude reconstruction, approximation, interpolation
Abstract: The future hyperspectral satellite EnMAP (Environmental Mapping and Analysis Program) uses two separate sensors for the acquisition of VNIR and SWIR imagery. Due to thier geometric configuration, the SWIR and VNIR instruments map the same positions on the ground with a time delay of 88 ms. Coupled with attitude controller inaccuracies this leads to an estimated co-registration error between SWIR and VNIR higher than the maximum 0.2 pixels designated in the specifications of EnMAP imagery. It is assumed that, by approximating or interpolating the real attitude and geometrically correcting the images, this co-registration error can be significantly reduced. To validate these assumptions, a geometric simulator was developed at the German Aerospace Center DLR which is responsible for the development of the ground segment of EnMAP. The implemented simulator, together with an evaluation of the absolute and relative accuracy, performed using this simulator, are presented in this article. The obtained results demonstrate that the desired co-registration accuracy between SWIR and VNIR imagery can be acheived by using Spline or Chebyshev approximation for the attitude reconstruction but not by using Lagrange interpolation.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56823
Title: An individual tree crown delineation method based on multi-scale segmentation of imagery
Author: Linhai Jing, Baoxin Hu, Thomas Noland, Jili Li
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 70, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Remote sensing, Forestry, extraction, imagery, segementation, multiresolution
Abstract: A forest consists of multi-scale branches, tree crowns, and tree clusters. Similar to small tree crowns in shape and scale, branches normally cause over-segmentation of imagery when a watershed segmentation approach is used to segment imagery for tree crown delineation. In order to eliminate such over-segmentation, a new method for individual tree crown delineation from optical imagery was proposed based on multi-scale filtering and segmentation in this study. In this method, the dominant sizes of tree crowns are first determined; Gaussian filters are designed to fit the three-dimensional radiometric shapes of multiscale tree crowns; the grayscale image is smoothed using the Gaussian filters and segmented using the watershed segmentation approach; and finally, the resulting multiple segmentation maps are integrated together to generate a tree crown map. In an experiment on aerial imagery of forests consisting of multiscale tree crowns, the proposed method yielded high-quality tree crown maps.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56822
Title: Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points
Author: Yang Shao, Ross S Junetta
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 70, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Land-cover mapping, Support vector machine, accuracy assessment
Abstract: Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two conventional nonparametric image classification algorithms: multilayer perceptron neural networks (NN) and classification and regression trees (CART). For 2001 MODIS time-series data, SVM generated overall accuracies ranging from 77% to 80% for training sample sizes from 20 to 800 pixels per class, compared to 67-76% and 62-73% for NN and CART, respectively. These results indicated that SVM ' s had superior generalization capability, particularly with respect to small training sample sizes. There was also less variability of SVM performance when classification trails were repeated using different training sets. Additionally, classification accuracies were directly related to sample homogeneity/ heterogeneity. The overall accuracies for the SVM algorithm were 91% (Kappa = 0.77) and 64% (Kappa = 0.34) for homogeneous and heterogenous pixels, respectively. The inclusion of heterogeneous pixesl in the training sample did not increase overall accuracies. Also, the SVM performance was examined for the classification of multiple year MODIS time-series data at annual intervals. Finally, using only the SVM output values, a method was developed to directly classify pixel purity. Approximately 65% of pixels within the Albemarle-Pamlico Basin study area were labeled as "functionally homogeneous" with an overall classification accuracy of 91% (Kappa = 0.79). The results indicated a high potential for regional scale operational land-cover characterization applications.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56821
Title: Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions
Author: M E J Cutler, D S Boyd, G M Foody, A Vetrivel
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 70, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Biomass, SAR, Artificial neural network, wavelets, Allometry
Abstract: Quantifying the above ground biomass of tropical forests is ciritical for understanding the dynamics of carbon fluxes between terrestrial ecosystems and the atmosphere, as well as monitoring ecosystem responses to environmental changes. Remote sensing remains an attractive tool for estimating tropical forest biomass but relationships and methods used at one site have not always proved applicable to other locations. This lack of a widely applicable general relationship limits the operational use of remote sensing as a method for biomass estimation, particularly in high biomass ecosystems. Here, multispectral Landsat TM and JERS-1 SAR data were used together to estimate tropical forest biomass at three separate geographical locations: Brazil, Malaysia and Thailand. Texture measures were derived from the JERS-1 SAR data using both wavelet analysis and Grey Level Co-occurrence Matrix methods, and coupled with multispectral data to provide inputs to artificial neural networks that were trained under four different training scenarios and validated using biomass measured from 144 field plots. When trained and tested with data collected from the same location, the addition of SAR texture to multispectral data showed strong correlations with above ground biomass (r = 0.79, 0.79 and 0.84 for Thailand, Malaysia and Brazil respectively). Also, when networks wee trained and tested with data from all three sites, the strength of correlation (r = 0.55) was stronger than previously reported results from the same sites that used multispectral data only. Uncertainty in estimating AGB from different allometric equations was also tested but found to have little effect on the strength of the relationships observed. The results suggest that the inclusion of SAR texture with multispectal data can go someway towards providing relationships that are transferable across time and space, but that further work is required if satellite remote sensing is to provide robust and reliable methodologies for initiatives such as Reducing Emissions from Deforestation and Degradation (REDD+).
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56820
Title: Discriminating indicator grass species for rangeland degradation assessment using hyperspectral data resampled to AISA Eagle resolution
Author: Khalid Mansour, Onisimo Mutanga, Terry Everson, Elhadi Adam
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 70, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Rangeland degradation, Random forest, Indicator species, Field spectrometer measurements, Variable selection
Abstract: The development of techniques to estimate and map increaser grass species is critical for better understanding the condition of the rangeland and levels of rangeland degradation. This paper investigates whether canopy reflectance spectra, resampled to AISA Eagle resolution can discriminate among four increaser species representing different levels of rangeland degradation. Canopy spectral measurements were taken from the four indicator species: Hyparrhenia hirta (HH), Eragrostis curvula (EC), Sporobolus africanus (SA), adn Aristida diffusa (AD). The random forest algorithm and a forward variable selection technique were used to identify optimal wavelengths for discriminating the species. Results revealed that the optimal number of wavelengths (n=8) that yielded the lowest OOB error (11.36%) in discriminating among the four increaser species are located in 966.7, 877.6, 691.9, 718.7, 902.7, 854.8, 674.1 and 703 nm. These wavelenghts are located in the visible, red-edge and near-infrared regions of the electromagnetic spectrum. The random forest algorithm can accurately discriminate species with an overall accuracy of 88.64% and a KHAT value of 0.85. The study demonstrated the possibility to upscale the method to airborne sensors such as AISA Eagle for mapping indicator species of rangeland degradation. A rotational grazing management plan should be considered as a way to create sutainable rangeland management in degraded area.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56819
Title: Correlation of multi-temporal ground-based optical images for landslide monitoring: Application, potential and limitations
Author: J Travelletti, C Delacourt, P Allemand, J P Malet, J Schmittbuhl, R Toussaint, M Bastard
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 70, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Image cross-correlation, Image matching, Landslide, Time-lapse photography, displacement monitoring
Abstract: The objective of this work is to present a low-cost methodology to monitor the displacement of continuously active landslides from ground-based optical images analyzed with a normalized image correlation technique. The performance of the method is evaluated on a series of images acquired on the Super-Sauze landslide (South French Alps) over the period 2008-2009. The image monitoring system consists of a high resolution optical camera installed on a concrete pillar located on a stable crest in front of the landslide and controlled by a datalogger. The data are processed with a cross-correlation algorithm applied to the full resolution images in the acquisition geometry. Then, the calculated 2D displacement field is orthorectified with a back projection technique using a high resolution DEM interpolated from Airborne Laser Scanning (ALS) data. The heterogeneous displacement field of the landslide is thus characterized in time and space. The performance of the technique is assessed using differential GPS surveys as reference. The sources of error affecting the results are then discussed. The strongest limitations for the application of the technique are related to the meteorological, illumination and ground surface conditions inducing partial or complete loss of coherence among the images. Small movements of the camera and the use of a mono-temporal DEM are the most important factors affecting the accuracy of the ortho-rectification of the displacement field. As the proposed methodology can be routinely and automatically applied, it offers promising perspectives for operational applications like, for instance, in early warning systems.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56818
Title: A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region
Author: Guiying Li, Dengsheng Lu, Emilio Moran, Luciano Dutra, Mateus Batistella
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 70, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: ALOS PALSAR, RADARSAT, Texture, Land-cover classification, Amazon
Abstract: This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms-maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural -network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agro-pasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56817
Title: The sensitivity based estimation of leaf area index from spectral vegetation indices
Author: Alemu Gonsamo, Petri Pellikka
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 70, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Leaf area index, sensitivity function, Spectral vegetation index
Abstract: The performance of seven spectral vegetation indices (SVIs) were investigated for their sensitivity ot a varying range of LAI. The evaluation was carried out for a dataset collected using SPOT 5 HRG 10 m imagery and simulated spectra using PROSPECT+SAIL reflectance models with varying soil reflectance back grounds. The aim was to evaluate the applicability of multiple SVIs for LAI mapping based on the sensitivity analysis. The main sensitivity function was the first derivative of the regression function divided by the standard errors of the SVIs. In addition, the sensitivity of individual band and SVI with LAI was carried out using the ordinary squares regressions. A new SVI, reduced infrared simple ratio (RISR) was developed based on an empirical red modification to infrared simple ratio (ISR) SVI. The new SVI was demonstrated which has significantly reduced the effect of soil background reflectance while maintaing high sensitivity to a wide range of LAI.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56816
Title: A geometry and texture coupled flexible generalization of urban building models
Author: Man Zhang, Liqiang Zhang, P Takis Mathiopoulos, Wenqing Xie, Yusi Ding, Hao Wang
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 70, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Urban buildings, FEdge, Clustering, Generalization, EBT
Abstract: In the past, numerous research efforts have focussed on generalization of city building models. However, a generic procedure for creaing flexible generalization results supporting the fast and efficient update of original building models with various complexities is still an open problem. Moreover, building clusters created in previously published generalization methods are not flexible enough to meet the various requirements for both legible and realistic visualization. Motivated by these observations, this paper proposes a new method for generating a flexible generalization outcome which enables convenient updating of original building models. It also proposes a flexible preprocessing of this generalized information to render a legible and realistic urban scene. This is accomplished by introducing a novel component structure, termed as FEdge, particularly designed for efficiently managing the geometry and texture information in building cluster instances (both original building models and building clusters) during the generalization, visualization and updating processes. Furthermore, a multiple representation structure, referred to as Evolved Buffer - Tree (EBT), is also introduced. The purpose of the EBT is to organize building cluster instances and to employ more flexible LODs for both legible and realistic visualization of urban scenes. FEdge has an intuitive planar shape which can be effectively used in representing rough 3D facade composed by detailed continuous meshes. Each FEdge is given a unique identifier, referred to as FEdge Index. In the proposed generalization scheme, firstly each original building model treated as a building cluster instance is abstracted and presented as FEdge Indices. These FEdge Indices are then used for producing generalized building cluster instances in the EBT portably, and to support convenient model updating and flexible preprocessing of the generalization results for renderable building cluster instances. Secondly, to acheive a legible and realistic visualization of urban scene, the EBT is flexibly assigned diverse LODs maintaining more important legible information than LODs defined in CityGML for 3D building models. To make the generalization more accurate by considering the city raods and districts, an algorithm for automatic road analysis is applied in our clustering and combination. Numerous experiments considering the geometrical and textual complexity of common urban building models, as well as a typical case study of complex city scene with a large number of building models, verify the effectiveness of our generalization method and the dynamic visualization of the generalized urban models.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56815
Title: Evaluation of biochemical indicators of iron and manganese contamination for sunflower plants: chlorophyll content, catalase and peroxidase
Author: Nandita Jena and Prasant Kumar Behera
Editor: Dr P K Wong, Dr R K Trivedy and Dr Sadhana Sharma
Year: 2012
Publisher: Global Science Publications, Vol 14, No 2, 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Asian Journal of Microbiology, Biotechnology & Environmental Sciences
Keywords: Catalase, peroxidase, chlorophyll, iron, manganese, micronutrient, Biological indicator, Stress
Abstract: Catalase and Peroxidase show grate response to the metal pollutants and increase in enzyme activity which may indicate a stress situation in plant. To find out the effect of various levels of Fe and Mn on chlorophyll contents and peroxidase enzyme activity was studied on sunflower plant. Concentration of chlorophyll "a" and "b" increased with rise of Fe concentration under the condition of Mn deficiency or excess. Catalase was positively correlated to chlorophyll "b" where as peroxidase is negatively correlated to chlorophyll "a" and "b". Activity of catalase increased with increase of Mn concentration up to 40 m mol after which it started declining. Peroxidase showed reverse effect which indicate that Mn and Fe nutrient level modified the total catalase and peroxidase activity in plant (sunflower).
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 56814
Title: Isolation of marine actinomycetes producting bioactives
Author: P V D Soujanya Kumari and V Ashok Kumar
Editor: Dr P K Wong, Dr R K Trivedy and Dr Sadhana Sharma
Year: 2012
Publisher: Global Science Publications, Vol 14, No 2, 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Asian Journal of Microbiology, Biotechnology & Environmental Sciences
Keywords: Actinomycetes, Marine actinomycete growth medium, Pathogenic bacteria. Zone of inhibition
Abstract: The antimicrobial properties of thirty isolates of Actinomycetes were tested on Kelbsiella pneumoniae, Pseudomonas aeruginosa, Salmonella typhimurium, Staphylococcus aureus, and Streptococcus pyogenes. The strains were isolated by using MAG medium and extracts were produced from the isolates by inoculating into seed and production media simultaneously. Pseudomonas aeruginosa was inhibited by maximum number (21) of extracts and Streptococcus pyogenes was inhibited by minimum number (8) of extracts. Salmonella typhimurium was not inhibited by any strain extract. The extracts can be employed as replacement or as an adjunct to chemotherapeutic agents.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 56813
Title: {seidp,pmas mad Trochoderma: The most effective bio-control agents against damping off pathogens of vegetable crops
Author: Nandita Jena
Editor: Dr P K Wong, Dr R K Trivedy and Dr Sadhana Sharma
Year: 2012
Publisher: Global Science Publications, Vol 14, No 2, 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Asian Journal of Microbiology, Biotechnology & Environmental Sciences
Keywords: Phytopathogens, Bio-control agent, Rhizosphere, damping off pathogens, Antagonestic, inhibition, plant protection, disease management
Abstract: Rhizoctonia solani, Fusarium oxisporum, Phytophthora colocasiae and Pithium sp are the most important phytopathogens responsible for damping off diseases of vegetable crops like Tomato, Cabbage, Brinjal and Sugar beet. The inhibitory effect of three species of Trichoderma and Pseudomonas on the population of the target damping off pathogens like R. solani, F. oxisporum, P. colocasiae and Pithium sp. in soil was studied in vitro. Uniform quantity of inoculums (1g/kg dry soil) of the above pathogens isolated from infected Tomato rhizosphere were added in sterilized soil and maintained in plastic pots. Three species of Trichoderma viz. T. viride, T. harzianum, T pseudokoningii and fluorescent pseudomonas, Pseudomonas fluorescens were inoculated as treatment in soil containing pathogens. The combined effect of all the bio-control agents was also evaluated. The population of R. solani, F. oxisporum, P. colocasiae, Pithium sp and Pseudomonas fluorescens was determined by soil dilution plate method. Reduction in population of R solani, F. oxisporum, P. colocasiae and Pithium sp by T. viride is 72.53, 89.83, 87.77 and 67.16%. The same value for T. harzianum and T. pseudokoningii is 88.83, 61.16, 79.38, 61.66 and 71.50, 63.81, 81.56, 90.66% respectively. Pseudomonas fluorescens can control the above pathogens inhibiting their population upto 68.50, 90.50, 92.62 and 88.83% in soil. The combined treatment of all the bio-control agents is more effective than that of individual after 45 days of treatment. But in initial stage of treatment the combined effect was less effective. This may be due to the antagonistic effect between P. fluorescens and Trichoderma species in the multiplying stages in soil.
Location: 241
Literature cited 1: None
Literature cited 2: None