ID: 58788
Title: Temporal logic and operation relations based knowledge representation for land cover change web services.
Author: Jun Chen, Hao Wu, Songian Li, Anping Liao, Chaoying He, Shu Peng
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 140-150 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Land cover, Change information, Temporal Logic, Spatial operation, Knowledge representation.
Abstract: Providing land cover spatio-temporal information and geo-computing through web service is a new challenge for supporting global change research, earth system simulation and many other societal benefit areas. This requires an integrated knowledge representation and web implementation of static land cover and change information, as well as the related operations for geo-computing. The temporal logic relations among land cover snapshots and increments were examined with a matrix-based three-step analysis. Twelve temporal logic relations were identified and five basic spatial operations were formalized with set of operators, which were all used to develop algorithms for deriving implicit change information. A knowledge representation for land cover change information was then developed based on these temporal logic and operation relations. A prototype web service system was further implemented can be facilitated with such a web service system.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58787
Title: Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery.
Author: Oleksandr Kit, Matthias Ludeke.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 130-137 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Urban, Developing countries, Identification, High resolution, Multitemporal.
Abstract: This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very highr resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny-and LSD-based imagery pre-proccessing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate in considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58786
Title: A protocol for improving mapping and assessing of seagrass abundance along the West Central Coast of Florida using Landsat TM and EO-1 ALI/Hyperion images.
Author: Ruiliang Pu, Susan Bell.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 116-129 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Image optimization, Submerged aquatic vegetation (SAV), Fuzzy synthetic evaluation, Leaf area index, Biomass, Remote sensing.
Abstract: Seagrass habitats are characteristic features of shallow waters worldwide and provide a variety of ecosystem functions. Remote sensing techniques can help connect spatial and temporal information about seagrass resources. In this study, we evaluate a protocol that utilizes image optimization algorithms followed by atmospheric and sunlight connections to the three satellite sensors [Landsat 5 Thematic Mapper (TM), Earth Observing-1 (EO-1) Advanced Land Imager (ALI) and Hyperion (HYP)] and a fuzzy synthetic evaluation technique to map and assess seagrass abundance in Pinellas County, FL, USA. After image preprocessed with image optimization algorithms and atmosperic and sunlight correction approaches, the three sensors data were used to classify the submerged aquatic vegetation cover (%SAV cover) into 5 classes with a maximum likelihood classifier. Based on three biological metrics[%SAV, leaf area index(LAI), and Biomass] measured from the field, nine multiple regression models were developed for estimating the three biometrics with spectral variables derived from the three sensors data. Then, five membership maps were created with the three biometrics along with two environmental factors (water depth and distance-to-shortline). Finally, seagrass abundance maps were produced by using a fuzzy synthetic evaluation technique and five membership maps. The experimental results indicate that the HYP sensor produced the best results of the 5-class classification of %SAV cover (overall accuracy= 87% and Kappa=0.83 vs. 82% and 0.77 by ALI and 79% and 0.73 by TM) and better multiple regression models for estimating the three biometrics (R?=0.66, 0.62 and 0.61 for % SAV, LAI and Biomass vs 0.62, 0.61 and 0.55 by ALI and 0.58, 0.56 and 0.52 by TM) for creating seagrass abundance maps along with two evironmental factors. Combined our results demonstrate that the image optimization algorithms and the fuzzy synthetic evaluation technique were effective in mapping detailed seagrass habitats and assessing seagrass abundance with the 30-m resolution data collected by the three sensors.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58785
Title: Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables.
Author: Kimmo Nurminen, Mika Karjalainen, Xiaowei Yu, Juha Hyyppa, Eija Honkavaara.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 104-115 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Image matching, Dense point cloud, Surface model, Plot-level forest variables, Airborne laser scanning.
Abstract: Recent research results have shown that the performance of digital surface model extraction using novel high quality photogrammetric images and image matching is a highly competitive alternative to laser scanning. In this article, we proceed to compare the performance of these two methods in the estimation of plot-level forest variables. Dense point clouds extracted from aerial frame images were used to estimate the plot-level forest variables needed in a forest inventory covering 89 plots. We analyzed images with 60% and 80% forward overlaps and used test plots with off-nadir angles between 0? and 20?. When compared to reference ground measurements, the airborne laser scanning (ALS) data proved to be the most accurate: it yielded root mean square error (RMSE) values of 6.55% for mean height, 11.42% for mean diameter, and 20.72% for volume. when we applied a forward overlap of 80% , the corresponding results from aerial images were 6.77% for mean height, 12.00% for mean diameter, and 22.62% for volume. A forward overlap of 60% resulted in slightly deteriorated RMSE values of 7.55% for mean height, 12.20% for mean diameter, and 22.77% for volume. According to our results, the use of higher forward overlap produced only slightlly better results in the estimation of these forest variables. Additionally, we found that the estimation accuracy was not significantly impacted by the increase in the off-nadir angle. Our results confirmed that digital aerial photographs were about as accurate as ALS in forest resources estimation as long as terrain model was available.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58784
Title: Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies.
Author: Parinaz Rahimzadeh-Bajgiran, Aaron A Berg, Catherine Champagne, Kenji Omasa.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 94-103 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Soil moisture, Evaportive fraction, Land surface temperature, Air temperature, MODIS
Abstract: A new approach to estimate soil moisture (SM) based on evaporative fraction (EF) retrieved from optical thermal infrared MODIS data is presented for Canadian Prairies in parts of Saskatchewan and Alberta. An EF model using the remotely sensed land surface temperature (Ts)/vegetation index concept was modified by incorporating North American Regional Reanalysis (NAAR) Ta data and used for SM estimation. Two different combinations of temperature and vegetation fraction using the difference between Ts from MODIS Aqua and Terra images and Ta from NARR data (Ts-Ta Aqua-day and Ts-Ta Terra day, respectively) were proposed and the results were compared with those obtained from a previously improved model (?Ts Aqua-DayNight) as a reference. For the estimation of SM from EF, two empirical models were tested and discussed to find the most appropriate model for converting MODIS derived EF data to SM values. Estimated SM values were then correlated with in situ SM values (R?=0.42-0.77, p values < 0.04) exhibiting the possibility to estimate SM from remotely sensed EF models. The proposed Ts-Ta MODIS Aqua-day and Terra-day approaches resulted in better estimations of SM (on average higher R? values and similar RMSEs) as compared with the ?Ts reference approach indicating that the concept of incorporating NARR Ta data into Ts/Vegetation index model improved soil moisture estimation accuracy based on evaporative fraction. The accuracies of the predictions were found to be considerably better for intermediate SM values (from 12 to 22 vol/vol%) with square errors averaging below 11(vol/vol%)? . This indicates that the model needs further improvements to account for extreme soil moisture conditions. The findings of this research can be potentailly used to downscale SM estimations obtained from passive microwave remote sensing techniques.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58783
Title: Backscattering of individual LiDAR pulses from forest canopies explained by photogrammetrically derived vegetation structure.
Author: Ilkka Korpela, Aarne Hovi, Lauri Korhonen.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 81-93 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Close-range photogrammetry, Canopy imaging, Silhouette, Footprint, Echo Triggering, Waveforn LiDAR.
Abstract: In recent years, airborne LiDAR sensors have shown remarkable performance in the mapping of forest vegetation. This experimental study looks at LiDAR data at the scale of individual pulses to elucidate the sources behind interpulse variation in backscattering. Close-range Photogrammetry was used for obtaining the canopy reference measurements at the ratio scale. The experiments illustrated different orientation techniques in the field, LiDAR acquisitions and photogrammetry in both leaf-on and leaf-off conditions, and two-waveform recording LiDAR sensors. The intrafootprint branch silhouettes in zenith looking images, in which camera, footprint, and LiDAR sensor were collinear, were extracted and contrasted with LiDAR backscattering. An enhanced planimetric match (refinement of strip matching) was achieved by shifting pulses in a strip and searching for maximum correlation between the silhouette and LiDAR intensity. The relative silhouette explained upto 80-90% of the interpulse variation. We tested whether accounting for the Gaussian spread of intrafootprint irradiance would improve the correlations, but the effect was blurred by small-scale geometric noise. Accounting for reciever gain variations in the Leica ALS60 sensor data strengthened the dependences. The size of the vegetation objects required for triggering an echo constitute the complement to the actual canopy. We conclude that field photogrammetry is a useful tool for mapping forest canopies from below and that quantitative analysis is feasible even at the scale of single pulses for echanced understanding of LiDAR observations from vegetation.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58782
Title: Clustering based on eigenspace transformation-CBEST for efficient classification.
Author: Yaneli Chen, Peng Gong.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 64-80 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Land cover/use mapping, Large dataset, Landsat Thematic Mapper image, K-means, Remote sensing, Unsupervised classification.
Abstract: Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden of information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transforamtion to the dataset followed by compressing the data in the eigenspace transformation to the dataset followed by compresing the data in the eigenspace and storing them in coded matrices and vectors. The clustering process takes the advantage of the reduced size of the compressed data and thus reduces computational complexity. We name this approach Clustering Based on Eigen-space Transformation (CBEST). In our experiment a subscene of Landsat Thematic Mapper (TM) imagery, CBEST was found to be able to improve speed considerably over conventional K-means as the volume of data to be clustered increases. We assessed information loss and several other factors. In addition, we elvaluated the effectiveness of CBEST in mapping land cover/use with the same image that was acquired over Guangzhou City, South China and an AVIRIS hyperspectral image over Cappocanoe County, Indiana. Using reference data we assessed the accuracies for both CBEST and conventional K-means and we found that the CBEST was not negatively affected by information loss during compression in practice. We discussed potential applications of the fast clustering algorithm in dealing with large datasets in remote sensing studies.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58781
Title: Hyperspectral image noise reduction based on rank-1 tensor decomposition.
Author: Xian Guo, Xin Huang, Liangpei Zhang, Lefei Zhang.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 50-63 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Tensor decomposition, Rank-1 tensor, Hyperspectral image, Noise reduction, Rank estimation.
Abstract: In this study, a novel noise reduction algorithm for hyperspectral imagery (HSI) is proposed based on high-order rank-1 tensor decomposition. The hyperspectral data cube is considered as three-order tensor that is able to jointly treat both the spatial and spectral modes. Subsequently, the rank-1 tensor decomposition (R1TD) algorithm is applied to the sensor data, which takes into account both the spatial and spectral information of the hyperspectral data cube. A noise-reduced hyperspectral image is then obtained by combining the rank-1 tensors using an eigenvalue intensity sorting and reconstruction technique. Compared with the existing noise reduction methods such as the conventional channel-by-channel approaches and the recently developed multidimensional filter, the spatial-spectral adaptive total variation filter, experiments with both synthetic noisy data and real HSI data reveal that the proposed R1TD algorithm significantly improves the HSI data quality in terms of both visual inspection and image quality indices. The subsequent image classification results further validate the effectiveness of the posed HSI noise reduction algorithm.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58780
Title: Classifying a high resolution image of an urban area using super-object information.
Author: Brain Johnson, Zhixiao Xie.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 40-49 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Segmentation, Classification, Urban, High resolution, Land cover, Scale, Contextual.
Abstract: In this study, a multi-scale approach was used for classifying land cover in a high resolution image of an urban area. Pixels and image segments were assigned the spectral, texture, size, and shape information of their super-objects (i.e. the segments that are located within) from coarser segmentations of the same scene, and this set of super-object information was used as aditional input data for image classification. The accuracies of classifications that included super-object variables were compared with the classification accuracies of image segmentations that did not include super-object informtion was 78.11% and 0.727%, respectively. When single pixels or fine-scale image segments were assigned the statistics of the super-objects prior to classification, overall accuracy increased to 84.42% and the kappa coefficient increased to 0.804.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58779
Title: Amodified stochastic neighbor embedding for multi-feature dimension reduction of remote sensing images.
Author: Lefei Zhang, Liangpei Zhang, Dacheng Tao, Xin Huang.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 30-39 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Hyperspectral image, Multiple Features, Stochastic neighbor embedding, Dimension reduction, Classification.
Abstract: In autoamted remote sensing based on image analysis, it is important to consider the multiple features of certain pixel, such as the spectral signature, morphological property, and shape feature, in both the spatial and spectral domains, to improve the classification accuracy. Therfore, it is essential to consider the complementary properties of the different features and combine them in order to obtain an accurate classification rate. In this paper, we introduce a modified stochastic neighbor embedding (MSNE) algorithm for multiple features dimension reduction (DR) under probability preserving projection framework. For each feature, a probability distribution is constructed based on t-distributed stochastic neighbor embedding (t-SNE), and we then alternately solve t-SNE and learn the optimal combination co-efficients for different features in the proposed multiple features DR strategies, the suggested algorithm utilizes both the spatial and spectral features of a pixel to achieve a physically meaningful low-dimensional feature representation for the subsequent classification, by automatically learning a combination coefficient for each feature. The classification results using hyperspectral remote sensing images (HSI) show that MSNE can effectively improve RS image classification performance.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58778
Title: Error analysis of satellite attitude determination using a vision-based approach.
Author: Ludovico Carozza, Alessandro Bevilacqua.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 19-29 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Vision, Image registration, Error analysis, Accuracy analysis, Satellite, Feature tracking.
Abstract: Improvements in communication and processing technologies have opened the doors to exploit on-board cameras to compute objects spatial attitude using only the visual information from sequences of remote sensed images. The strategies and the algorithmic approach used to extract such information affect the estimation accuracy of the three-axis orientation of the object.
This work presents a method for analyzing the most relevant error sources, including numerical ones, possible drift effects and their influence on the overall accuracy, referring to vision-based approaches. The method in particular focuses on the analysis of the image registration algorithm, carried out through onpurpose simulations. The overall accuracy has been assessed on a challenging case study, for which accuracy represents the fundamental requirement. In particular, attitude determinatiion has been analysed for small satellites, by comparing theoretical findings to metric results from simulations on realistic groundtruth data. Significant laboratory experiments, using a numerical control unit, have further confirmed the outcome.
We believe that our analysis approach, as well as our findings in terms of errors characterization, can be useful at proof-of-concept design and planning levels, since they emphasize the main sources of error for visual based approaches employed for satellite attitude estimation. Nevertheless, the approach we present is also of general interest for all the affine applicative domains which require an accurate estimation of three-dimensional orientation parameters (i.e., robotics, airborne stabilization).
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58777
Title: Automatic extraction of building roofs using LIDAR data.
Author: Mohammad Awrangjeb, Chunsun Zhang, Clive S Fraser.
Editor: Derek Lichti.
Year: 2013
Publisher: Elsevier B V
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 83, pp. 1-18 (2013)
Subject: Photogrammetry and Remote Sensing
Keywords: Building, Feature, Extraction, Reconstruction, Automatiion, Integration, LIDAR, Orthoimage.
Abstract: Automatic 3D extraction of building roofs from remotely sensed data is important for many applications including city modelling. This paper proposes a new method for automatic 3D roof extraction through an effective integration of LIDAR (Light Detection And Ranging) data and multispectral orthoimagery. Using the ground height from a DEM (Digital Elevation Model), the raw LIDAR points are seperated into two groups. The first group contains the ground points that are exploited to constitute a ' ground mask ' . The second group contains the non-ground points which are segmented using an innovative image line guided segmentation technique to extract the roof planes. The image lines are extracted from the grey scale version of the orthoimage and then classified into several classes such as ' ground ' , ' tree ' , ' roof edge ' and ' roof ridge ' using the ground mask and colour and texture information from the orthoimagery. During segmentation of the non-ground LIDAR points, the lines from the latter two classes are used as baselines to locate the nearby LIDAR points of the neighbouring planes. For each plane a robust seed region is thereby defined using the nearby non-ground LIDAR points of a baseline and this region is iteratively grown to extract the complete roof plane. Finally, a newly proposed rule-based procedure is applied to remove planes constructed on trees. Experimental results show that the proposed method can successfully remove vegetation and so offers high extraction rates.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58776
Title: Volume Tables for Trees in Home Gardens of Kerala
Author: C N Krishnankutty.
Editor: Dr.P P Bhojvaid
Year: 2013
Publisher: Indian Forester
Source: Centre for Ecological Sciences
Reference: Indian Forester Vol. 139(no.7), 652-657 (2013)
Subject: The Indian Forester
Keywords: Home garden trees, Artocarpus heterophyllus, Ailanthus triphysa, Tectona grandis, Volume tables.
Abstract: Teak (Tectona grandis Linn.), jack (Artocarpus heterophyllus Lamk.), anjily (A. hirsutus Lamk.), matty (Ailanthus triphysa Dennst.) and mango (Mangifera indica Linn.) trees are commercially and economically important tree species in home gardens of Kerala. Different types of volume prediction equations were developed for each species through regression analysis, using data on diameter (m) at breast-height level (1.37 m from ground) of sample trees before felling and corresponding volume (m?) of commercial timber measured after felling. Volume of a tree refers to that under-bark of logs or billets with mid-girth (over-bark) 40cm and above, in case of teak, matty and mango trees. It refers to that under-sapwood (volume after removing sapwood) of logs with mid-girth (over-bark) 60 cm and above, in the case of jack and anjily trees. Using the best fitted equation selected from the set of 27 equations estimated for each species, volume estimates were predicted for those values of diameter corresponding to different values of girth at breast height from 60cm upwards with an interval of 5cm. Tabulating the girth in cm and volume in m?, volume tables were prepared for each species. The tables provide volume estimates corresponding to different values of girth at breast-height of trees which can easily be measured at site. The tables are useful to tree growers and purchasers, for obtaining an estimate of volume in a tree by referring the tables without felling the tree.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58775
Title: Effect of Pre-Treatments for Enhancing the Germination of ADANSONIA DIGITATA L. and COCHLOSPERMUM RELIGIOSUM L.
Author: R N Gahane, K K Kogje
Editor: Dr.P P Bhojvaid
Year: 2013
Publisher: Indian Forester
Source: Centre for Ecological Sciences
Reference: Indian Forester Vol. 139(no.7), 648-651 (2013)
Subject: The Indian Forester
Keywords: Adansonia, Cochlospermum, Germination, Survival rate, Agro-forestry, Seed dormacy.
Abstract: The present study deals with breaking the seed dormacy by various physical and chemical agents and establishment of seedlings of Adansonia digitata and Cochlospermum religiosum. In A. digitata, pre treatment with concentrated sulphuric acid for 12hrs exhibited 68% germination which was further enhanced up to 90.67% by post treatment soaking in 0.1M glucose solution. C. religiosum exhibited 32% germination with pre treatment of sulphuric acid for 25min and with post treatment soaking in 0.1M glucose solution showed 54.67% germination. Seedling survival was also found to be more in the combination treatment of sulphuric acid and 0.1M glucose solution (A.digitata-81.33%) than pure sulphuric acid treatment.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58774
Title: Life History of Chionaema Coccinae Recorded from Dehradun, Uttarakhand.
Author: Abesh Kumar Sanyal, V P Uniyal, Kailash Chandra.
Editor: Dr.P P Bhojvaid
Year: 2013
Publisher: Indian Forester
Source: Centre for Ecological Sciences
Reference: Indian Forester Vol. 139(no.7), 645-647 (2013)
Subject: The Indian Forester
Keywords: Cyana, Larva, Cocoon, Host plant, Moth, Life cycle.
Abstract: Life history stages of the Arctiid moth Chinaema coccinae, Moore, 1878 (Subfamily Lithosiinae) were recorded in Wildlife Institute of India campus, Dehradun. Larva, pupa and cocoon structure along with male female dimorphism were described. The cocoon along with the pupa were kept upto emergence of the adult to confirm species identification.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None