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ID: 60112
Title: Using airborne hyperspectral data to characterize the surface pH and mineralogy of pyrite mine tailings.
Author: N. Zabcic, B. Rivard, C. Ong, A. Mueller.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 152-162 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Mine tailings, Hyperspectral, Mineral maps, pH, Sulfates, Oxides.
Abstract: Acid mine drainage (AMD) is a key concern of the mining industry due to its impact on the quality of water and soils surrounding mine waste deposits. Acid mine drainage derives from the oxidation of metal sulphides, e.g. pyrite (FeS2), exposed to oxygen and water. The leachate acidity is capable of releasing heavy metals contained in the mining waste rock, which can affect water quality and lead to metal enrichment in sediments and potentially resulting in ecosystem degradation. Predicting tailings leachate pH is key to the management of sulfide-bearing mine wastes and is an emerging remote sensing application with limited studies having been realized. Such a capability would supplement traditional methods (i.e. ground surveys) that are challenging to implement due to the extent and large volume of mine waste. This study reports regional scale tailings mineral maps generated from airborne hyperspectral information of the Sotiel-Migollas complex in Spain and pinpoints sources of AMD. The extraction of spectral endmembers from imagery revealed twenty six endmembers for tailings material that represent mostly mineral mixtures. From these, eleven spectral groups were defined, each encompassing minor variations in mineral mixtures. The mineral maps resulting from the use of these endmembers for the detailed investigation of four tailings serve as indicators of the metal, sulphate, and ph levels of the AMD solution at the time of mineral precipitation. Predicted mineralogy was assessed using spectra from samples collected in the field and associated X-ray diffraction measurements. We also discuss the relative merits of the minerals maps of this study and soil leachate pH maps that predictions consistent with the mineralogy predicted from the mineral maps and field and laboratory evidence. The pH maps offer information on the pH conditions of the tailings thus giving an insight on the different types of oxidation reactions that may occur.
Location: TE 15 New Biology Building
Literature cited 1: Bachmann, M., (PhD dissertation thesis) 2007. Automated MESMA Unmixing for Fractional Cover Estimates. University of Wurzburg, Wurzburg, Germany. Bigham, J.M., 1994. Mineralogy of ochre deposits formed by sulfide oxidation. In: Blowes, J.L., J.A.D.W. (Ed), Environmental Geochemistry of Sulfide Mine-Wastes, Short Course Handbook. Mineral Association of Canada, pp. 103-132.
Literature cited 2: Briard, J., 1976. L ' age de bronze en Europe Barbare. Hesperides Publications, Paris, pp. 81-86. Cloutis, E.A., Hawthrone, F., Mertzman, S., Krenn, K. Craig, M., Marcino, D., Methot, M., Strong, J., Mustard, J., Blaney, D., Bell III, J., Vilas, F., 2006. Detection and discrimination of sulfate minerals using reflectance spectroscopy. Icarus 184 (1), 121-157.

ID: 60111
Title: Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis.
Author: S.Padma, S.Sanjeevi.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 138-151 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Spectral matching, Hyperspectral image, Jeffries-Matusita, Spectral Angle Mapper, Classification.
Abstract: This paper proposes a novel hyperspectral matching technique by integrating the Jeffries-Matusita measure (JM) and the Spectral Angle Mapper (SAM) algorithm. The deterministic Spectral Angle Mapper and stochastic Jeffries-Matusita measure are orthogonally projected using the sine and tangent functions to increase their spectral ability. The developed JM-SAM algorithm is implemented in effectively discriminating the landcover classes and cover types in the hyperspectral images acquired by PROBA/CHRIS and EO-1 Hyperion sensors. The reference spectra for different land-cover classes were derived from each of these images. The performance of the proposed measure is compared with the performance of the individual SAM and JM approaches. From the values of the relative spectral discriminatory probability (RSDPB) and relative discriminatory entropy value (RSDE), it is inferred that the hybrid JM-SAM approach results in a high spectral discriminability than the SAM and JM measures. Besides, the use of the improved JM-SAM algorithm for supervised classification of the images results in 92.9 % and 91.47% accuracy compared to 73.13 %, 79.41 %, and 85.69 % of minimum-distance, SAM and JM measures. It is also inferred that the increased spectral discriminability of JM-SAM measure is contributed by the JM distance. Further, it is seen that the proposed JM-SAM measure is compatible with varying spectral resolutions of PROBA/CHRIS (62 bands) and Hyperion (242 bands).
Location: TE 15 New Biology Building
Literature cited 1: Andreoli, G., Bulgarelli, B., Hosgood, B., Tarchi, D., 2007. Hyperspectral Analysis of Oil and Oil-impacted Soils for Remote Sensing Purposes. EUR 22739 EN-DF Joint Research Centre. Office for Official Publications of the European Communities, Scientific and Technical Research series, Luxembourg, 34 pp. Ajithkumar, T.T., Thangaradjou, T., Kannan, L., 2008. Spectral reflectance properties of mangrove species of the Muthupettai mangrove environment, Tamil Nadu. J. Environ. Biol. 29, 785-788.
Literature cited 2: Bruzzone, L., Roli, F., Serpico, S.B., 1995. An extension of the jeffreys-Matusita distance to multiclass cases for feature selection. IEEE Trans. Geosci. Remote Sens. 33, 1318-1321. Cantero, M.C., Perez, R., Martinez, P., Aguilar, P.L., Plaza, J., Plaza, A., 2004. Analysis of the behavior of a neural network model in the identification and quantification of hyperspectral signatures applied to the determination of water quality. In: SPIE Optics East Conference, Chemical and Biological Standoff Detection, Philadelphia, PA.

ID: 60110
Title: Automated road markings extraction from mobile laser scanning data.
Author: Pankaj Kumar, Conor P. McElhinney, Paul Lewis, Timothy McCarthy,
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 125-137 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Road markings, Mobile laser scanning, LiDAR, Automation, Extraction.
Abstract: Road markings are used to provide guidance and instruction to road users for safe and comfortable driving. Enabling rapid, cost effective and comprehensive approaches to the maintenance of route networks can be greatly improved with detailed information about location, dimension and condition of road markings. Mobile Laser Scanning (MLS) systems provide new opportunities in terms of collecting and processing this information. Laser scanning systems enable multiple attributes of the illuminated targeted to be recorded including intensity data. The recorded intensity data can be used to distinguish the road markings from other road surface elements due to their higher retro-reflective property. In this paper, we present an automated algorithm for extracting road markings from MLS data. We describe a robust and automated way of applying a range dependent thresholding function to the intensity values to extract road markings. We make novel use of binary morphological operations and generic knowledge of the dimensions of road markings to complete their shapes and remove other road surface elements introduced through the use of thresholding. We present a detailed analysis of the most applicable values required for the input parameters involved in our algorithm. We tested our algorithm on different road sections consisting of multiple distinct types of road markings. The successful extraction of these road markings demonstrates the effectiveness of our algorithms.
Location: TE 15 New Biology Building
Literature cited 1: Barber, D., Mills, J., Smithvoysey, S., 2008. Geometric validation of a ground-based mobile laser scanning system. ISPRS J. Photogram. Rem. Sens, 63 (1), 128-141. Butler, D.A., 2011. Using GIS to automate the extraction of road markings for route corridor analysis. National University of Ireland Maynooth, pp. 1-78 (M.Sc. Dissertation).
Literature cited 2: Cahalane, C., Mccarthy, T., McElhinney, C.P., 2012. MIMIC: mobile mapping point density calculator. In: Proceedings of 3rd International Conference on Computing for Geospatial Research and Appications, Washington, 1-3 July, pp. 15: 1- 15: 9. Cahalane, C., McElhinney, C.P., McCarthy, T., 2011. Calculating the effect of dual-axis scanner rotations and surface orientation on scan profiles. In: Proceedings of 7th International Symposium on Mobile Mapping Technology. Krakow, 13-16 June.

ID: 60109
Title: Detecting leaf nitrogen content in wheat with canopy hyperspetctrum under different soil backgrounds.
Author: X.Yao, H.Ren, Z.Cao, Y. Tian, W. Cao, Y.Zhu, T.Cheng.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 114-124 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Wheat canopy, Leaf nitrogen content, Vegetation coverage, Soil background, Spectral index, Detecting model.
Abstract: Hyperspectral sensing techniques can be effective for rapid, non-destructive detecting of the nitrogen (N) status in crop plants; however, their accuracy is often affected by the soil background. Under different fractions of soil background, the canopy spectra and leaf nitrogen content (LNC) in winter wheat (Triticum aestivum L) were obtained from field experiments with different N rates and planting densities over 3 growing seasons. Five types of vegetation index (Vis: normalized difference vegetation index (NDVI), ratio vegetation index ( RVI), soil adjusted vegetation index (SAVI), optimize soil adjusted vegetation index (OSAVI), and perpendicular vegetation index (PVI) were constructed based on three types of spectral information: (1) the original and the first derivative (FD) spectrum, (2) the spectrum adjusted with the vegetation coverage (FVcover ) and (3) the pure spectrum extracted by linear mixed model. Comprehensive relationships of above five types of VI with LNC were quantified for LNC detecting under different soil backgrounds. The results indicated that all five types of VI were significantly affected by the soil background, with R2 values of around 0.55 for LNC detecting, with the OSAVI (R514, R469)L=0.04 producing the best performance of all five indices. However, based on the FVcover, the cover adjusted spectral index (CASI=NDVI(R513, R481) /(1+FVcover) produced the higher R2 value of 0.62 and the lower RRMSE of 13 %, and was less sensitive to the leaf area index (LAI), leaf dry weight (LDW), FVcover, and leaf nitrogen accumulation (LNA). The results demonstrate that the newly developed CASI could improve the performance of LNC estimation under different soil backgrounds.
Location: TE 15 New Biology Building
Literature cited 1: Adams, J., Smith, M., Gillespie, A., 1993. Imaging spectroscopy: interpretation based on spectral mixture analysis. In: Pieters, C.M., Englert, P. (Eds), Remote Geochemical Analysis: Elemental and Mineralogical Composition, 7. Cambridge University Press, New York, pp 145-166. Baret, F., Guyot, G., Major, D., 1989. TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation. In: Proc IGARRS ' 89. 12th Canadian Symposium on Remote Sensing vol. 3, No.1, Vancouver, Canada, pp. 1355-1358.
Literature cited 2: Blackmer, T., Schepers, J., Varvel, G., Walter -Shea, E., 1996. Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agron.J. 88 (1), 1-5 Chen, X., Vierling, L., 2006. Spectral mixture analyses of hyperspectral data acquired using a tethered balloon Remote Sens. Environ. 103 (3), 338-350.

ID: 60108
Title: Quantifying winter wheat residue biomass with a spectral angle index derived from China Environmental Satellite data.
Author: Miao Zhang, Bingfang Wu, Jihua Meng.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 105-113 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Crop residue biomass, Winter wheat, Spectral angle index, Field spectrometry, China Environmental Satellite (HJ-1B).
Abstract: Quantification of crop residue biomass on cultivated land is essential for studies of carbon cycling of agroecosystems, soil-atmospheric carbon exchange and Earth systems modeling. Previous focus on estimating crop residue cover (CRC) while limited research exists on quantifying crop residue biomass. This study takes advantage of the high temporal resolution of the China Environmental Satellite (HJ-1) data and utilizes the band configuration features of HJ-1B data to establish spectral angle indices to estimate crop residue biomass. Angles formed at the NIRIRS vertex by the three vertices at R, NIRIRS, and SWIR (ANIRIRS) of HJ-1B can effectively indicate winter wheat residue biomass. A coefficient of Determination (R2) of 0.811 was obtained between measured winter wheat residue biomass and ANIRIRS derived from simulated HJ-1B reflectance data. The ability of ANIRIRS for quantifying winter wheat residue biomass using HJ-1B satellite data was also validated and evaluated. Results indicate that ANIRIRS performed well in estimating winter wheat residue biomass with different residue treatments; the root mean square error (RMSE) between measured and estimated residue biomass was 0.038 kg/m2. ANIRIRS is a potential method for quantifying winter wheat residue biomass at a large scale due to wide swath width (350 km) and four-day revisit rate of the HJ-1 satellite. While ANIRIRS can adequately estimate winter wheat residue biomass at different residue moisture conditions, the feasibility of ANIRIRS for winter wheat residue biomass estimation at different fractional coverage of green vegetation and different environmental conditions (soil type, soil moisture content, and crop residue type) needs to be further explored.
Location: TE 15 New Biology Building
Literature cited 1: Aase, J.K., Tanaka, D.L., 1991. Reflectance from four wheat residues cover densities as influenced by three soil backgrounds. Agron.J.83, 753-757. Adams, J.B., Smith, M.O., Gillespie, A.R., 1989. Simple models for complex natural surfaces: a strategy for the hyperspectral era of remote sensing. In: IEEE International Geoscience Remote Sensing Symposium 89, IEEE Geoscience and Remote Sensing Society, New York, pp. 16-21.
Literature cited 2: Aguilar, J., Evans, R., Vigil, M., Daughtry, C.S.T., 2012. Spectral estimates of crop residue cover and density for standing and flat wheat stubble. Agron.J.104, 271-279. Atmospheric Correction Module, 2009. QUAC and FLAASH User ' s Guide, Atmospheric Correction Module, Version 4.7, August, 2009 Edition, Available online: http://www.exelisvis.com/portals/o/pdfs/envi/Flaash_Module.pdf.

ID: 60107
Title: Geospatial scenario based modeling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network.
Author: Chitrini Mozumder, Nitin K. Tripathi.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 92-104 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Wetland conservation, Urban growth, Land use modelling, Sensitivity analysis, Multi-layer perceptron neural network, Deepor Beel.
Abstract: In recent decades, the world has experienced unprecedented urban growth which endangers of the green environment in and around urban areas. In this work, an artificial neural network (ANN) based model is developed to predict future impacts of urban and agricultural expansion on the uplands of Deepor Beel, a Ramsar wetland in the city area Guwahati, Assam, India, by 2025 and 2035 respectively. Simulations were carried out for three different transition rates as determined from the changes during 2001-2011, namely simple exploration, Markov Chain (MC), and system dynamic (SD) modeling, using projected population growth, which were further investigated based on three different zoning policies. The first zoning policy employed no restriction while the second conversion restriction zoning policy restricted urban-agricultural expansion in the Guwahati Municipal Development Authority (GMDA) proposed green belt, extending to a third zoning policy providing wetland restoration in the proposed green belt. The prediction maps were found to be greatly influenced by the transition rates and the allowed transitions from one class to another within each sub-model. The model outputs were compared GMDA land demand as proposed for 2025 whereby the land demand as produced by MC was found to best match the projected demand. Regarding the conservation of Deepor Beel, the Landscape Development Intensity (LDI) Index revealed that wetland restoration zoning policies may reduce the impact of urban growth on a local scale, but none of the zoning policies was found to minimize the impact on a broader base. The results from this study may assist the planning and reviewing of land use allocation within Guwahati city to secure ecological sustainability of the wetlands.
Location: TE 15 New Biology Building
Literature cited 1: Agarwal, C., Green, G.M., Grove, J.M., Evans, T.P., Schweik, C.M., 2002. A Review and Assessment of Land-Use Change Models: Dynamics of Space, Time, AND Human Choice. Gen.Tech.Rep.NE-297. U.S. Department of Agriculture, Forest Service, Northeastern Research Station, Newtown Square, PA, 61 pp. Arsanjani, J.J., Helbich, M., Kainz, W., Darvishi Boloorani, A., 2013. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int. J. Appl. Earth Obs.Geoinf.21, 265-275.
Literature cited 2: Bell, E.J., 1974.Markov analysis of land use change-an application of stochastic processes to remotely sensed data. Socio-Econ.Plann.Lit.22 (4), 311-316. BenDor, T., Brozovic, N., Pallathucheril, V.G., 2008. The social impacts of wetland mitigation policies in the United States. J. Plann. Lit. 22 (4), 341-357.

ID: 60106
Title: Optimal attributes for the object based detection of giant reed in riparian habitats: A comparative study between Airborne High Spatial Resolution and WorldView-2 imagery.
Author: Maria Rosaria Fernandes, Francisca C. Aguiar, Joao M.N. Silva, Maria Teresa Ferreira, Jose M.C. Pereira.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 79-91 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Alien species, Riparian corridors, Arundo donax, OBIA, Geometric metrics, WorldView-2.
Abstract: Giant reed in an aggressive invasive plant of riparian ecosystems in many sub sub-tropical and warm-temperate regions, including Mediterranean Europe. In this study we tested a set of geometric, spectral and textural attributes in an object based image analysis (OBIA) approach to map giant reed invasions in riparian habitats. Bagging Classification and Regression Tree were used to select the optimal attributes and to build the classification rules sets. Mapping accuracy was performed using landscape metrics and the Kappa coefficient to compare the topographical and geometry similarity between the giant reed patches obtained with the OBIA map and with a validation map derived from on-screen digitizing. The methodology was applied in two high spatial resolution images: an airborne multispectral imagery and the newly WorldView-2 imagery. A temporal coverage of the airborne multispectral images was radiometrically calibrated with the IR-Mad transformation and used to assess the influence of the phonological variability of the invader. We found that optimal attributes for giant reed OBIA detection are a combination of spectral, geometric and textural information, with different scoring selection depending on the spectral and spatial characteristics of the imagery. WorldView-2 showed higher mapping accuracy (Kappa coefficient of 77% ) and spectral attributes, including the newly yellow band, were preferentially selected, although a tendency to overestimate the total invaded area, due to the low spatial resolution (2m of pixel size vs. 50 cm) was observed. When airborne images were used, geometric attributes were primarily selected and a higher spatial detail of the invasive patches was obtained, due to the higher spatial resolution. However, in highly heterogeneous landscapes, the low spectral resolution of the airborne images (4 bands instead of the 8 of WorldView-2) reduces the capability to detect giant reed patches. Giant reed displays peculiar spectral and geometric traits, at leaf, canopy and stand level, which makes the OBIA approach a very suitable technique for management purposes.
Location: TE 15 New Biology Building
Literature cited 1: Adam, E., Mutanga, O., 2009. Spectral discrimination of papyrus vegetation (Cyperus papyrus L) in swamp wetlands using field spectrometry. ISPRS J.Photogram.Rem.Sens.64 (6), 612-620 Aguir, F.C., Ferreira, M.T., 2013. Plant invasions in the rivers of the Iberian Peninsula, South-Western Europe- a review. Plant Biosyst. 147 (4), 1107-1119.
Literature cited 2: Aguiar, F.C., Moreira, I., Ferreira, M.T., 1996. Perception of aquatic weed problems by water resources managers. A Percepcao da Vegetacao Aquatica Infestante pelas Entidades Gestoras dos Recursos Hidricos.Rev.Cienc.Agr. 19 (4), 35-56. Aguiar, F.C., Ferreira, M.T., Albuquerque, A., Moreira, I., 2007. Alien and endemic flora on reference and non-reference sites from Mediterranean type-streams of Portugal. Aquat. Conserv. Mar. Freshwater Ecosyst. 17 (4), 335-347.

ID: 60105
Title: Assessing the effects of land use spatial structure on urban heat islands using HJ-IB remote sensing imagery in Wuhan, China.
Author: Hao Wu, Lu-Ping Ye, Wen-Zhong Shi, Keith C. Clarke.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 67-78 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Urban heat island, Land use spatial structure, Vegetation indexes, Landscape metrices, Fractal analysis, HJ-IB.
Abstract: Urban heat islands (UHIs) have attracted attention around the world because they profoundly affect biological diversity and human life. Assessing the effects of the spatial structure of land use on UHIs is essential to better understanding and improving the ecological consequences of urbanization. This paper presents the radius fractal dimension to quantify the spatial variation of different land use types around the hot centers. By integrating remote sensing images from the newly launched HJ-1B satellite system, Vegetation indexes, landscape metrics and fractal dimension, the effects of land use patterns on the urban thermal environment in Wuhan were comprehensively explored. The vegetation indexes and landscape metrics of the HJ-1B and the remote sensing satellites were compared and analyzed to validate the performance of the HJ-IB. The results have showed that land surface temperature (LST) is negatively related to only positive normalized difference vegetation index (NDVI) but to Fv across the entire range of values, which indicates that fractional vegetation (Fv) is an appropriate predictor of LST more than NDVI in forest areas. Furthermore, the mean LST is highly correlated with four class-based metrics and three land-scape-based metrics, which suggests that the landscape composition and the spatial configuration both influence UHIs. All of them demonstrate that the HJ-1B satellite has a comparable capacity for UHI studies as other commonly used remote sensing satellites. The results of the fractal analysis show that the density of built-up areas sharply decrease from the hot centers to the edges of these areas, while the densities of water, forest and cropland increase. These relationships reveal that water, like forest and cropland, has a significant effect in mitigating UHIs in Wuhan due to its large spatial extent and homogeneous spatial distribution. These findings not only confirm the applicability and effectiveness of the HJ-1B satellite system for studying UHIs but also reveal the impacts of the spatial structure of land use on UHIs, which is helpful for improving the planning and management of the urban environment.
Location: TE 15 New Biology Building
Literature cited 1: Aguiar, R., Oliveira, M., Gonccedilaves, H., 2002. Climate change impacts on the thermal performance of Portuguese buildings. Results of the SIAM study. Build.Serv.Eng.Res.Technol. 23 (4), 223-231. Arnfield, A.J., 2003. Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island.Int.J.Climatol. 23 (1), 1-26.
Literature cited 2: Backes, A.R., Bruno, OM., 2013. Texture analysis using volume-radius fractal dimension. Appl.Math.Comput. 219 (11), 5870-5875. Batty, M., Longley, P.A., 1987. Fractal-based description of urban form. Environ.Plan. B: Plan.Des 14 (2), 123-134.

ID: 60104
Title: Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing.
Author: Shengguo Gao, Zhongli Zhu, Shaomin Liu, Rui Jin, Guangchao Yang, Lei Tan.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 54-66 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Soil moisture, Remote sensing, Wireless sensor network (WSN), Bayesian maximum entropy (BME), Soft data.
Abstract: Soil moisture (SM) plays a fundamental role in the land-atmosphere exchange process. Spatial estimation based on multi in situ (network) data is critical way to understand the spatial structure and variation of land surface soil moisture. Theoretically, integrating densely sampled auxiliary data spatially correlated with soil moisture into the procedure of spatial estimation can improve its accuracy. In this study, we present a novel approach to estimate the spatial pattern of soil moisture by using the BME method based on wireless sensor network data and auxiliary information from ASTER (Terra) land surface temperature measurements. For comparison, three traditional geostatistic methods were also applied: ordinary kriging (OK), which used the wireless sensor network data only, regression kriging (RK) and ordinary co-kriging (Co-OK) which both integrated the ASTER land surface temperature as a covariate. In Co-OK, LST was linearly contained in the estimator, in RK, estimator is expressed as the sum of the regression estimate and the krigged estimate of the spatially correlated residual, but in BME, the ASTER land surface temperature was first retrieved as soil moisture based the linear regression, then the t-distributed prediction interval (PI) of soil moisture was estimated and used as soft data in probability form. The results indicate that all three methods provide reasonable estimations. Co-OK, RK and BME shows more obvious improvement compared to Co-OK, and even BME can perform slightly better than RK. The inherent issue of spatial estimation (overestimation in the range of low values and underestimation in the range of high values) can also further improved in both RK and BME. We can conclude that integrating auxiliary data into spatial estimation can indeed improve the accuracy, BME and RK take better advantage of the auxiliary information compared to Co-OK, and BME outperforms RK by integrating the auxiliary data in a probability form.
Location: TE 15 New Biology Building
Literature cited 1: Akylidiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E., 2002. Wireless sensor network: a survey.Comput.Netw.38, 393-422. Asli, M., Marcotte, D., 1995. Comparisoin of approaches to spatial estimation in a bivariate context.Math.Geol. 27, 641-658.
Literature cited 2: Bartlett, J.E., Kotrlik, J.W., Higgins, C.C., 2001. Organizational research: determining appropriate sample size in survey research appropriate sample size in survey research.Inf.Technol.Learn.Perform.J. 19 (1), 43-50. Bogaert, P., D ' Or, D., 2002. Estimating soil properties from thematic soil maps. Soil Sci.Soc.Am.J.66, 1492-1500.

ID: 60103
Title: Developing MODIS-based retrieval models of suspended particulate matter concentration in Dongting Lake, China.
Author: Guofeng Wu, Liangjie Liu, Fangyuan Chen, Teng Fei.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 46-53 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Lake management, Suspended particulate matter, Remote sensing, Empirical model.
Abstract: To case-II waters, suspended particulate matter (SPM) is one of the dominant water constituents, SPM concentration (CSPM) is a key parameter describing water quality, and developing remote sensing-based CSPM retrieval models is foundation for obtaining its spatiotemporal distributions. This study aimed to develop moderate resolution imaging spectroradiometer (MODIS)-based CSPM empirical retrieval models in Dongting Lake, China. The 95 CSPM measurements on 31 August 2012 and 14 June 2013and their corresponding MODIS Terra images were used to calibrate models, and the model calibration results showed that the 250 m MODIS red band obtained better fitting accuracies than the near infrared band; the quadratic and exponential models of single red band explained 75 % (estimated standard errors (SE) =6.19mg/l) and 71 % (SE=6.54 mg/l) of the variation of CSPM; and the quadratic and exponential models of red minus shortwave infrared (SWIR) band at 1240 and 1640 nm explained 72-73 % (SE=6.43-6.48 mg/l ) and 68-69 % (SE=6.83-6.96 mg/l) of the variations of CSPM, respectively. The quadratic and exponential models of red band and red minus SWIR band were applied to the MODIS Terra image on 16 September 2013 to estimate CSPM values. By comparing the estimated CSPM values on 16 September 2013 and the measured ones on 17 September 2013 at 40 sampling points for model validations, the results indicated that there existed significantly strong correlations between the measured and estimated CSPM values at a significance level of 0.05 for all models, and the exponential model of red minus SWIR band at 1240 nm achieved the best estimation result within all models. Such result provided foundation for obtaining the spatiotemporal distribution information of CSPM from MODIS images in Dongting Lake, which will be helpful for understanding, managing and protecting this ecosystem.
Location: TE 15 New Biology Building
Literature cited 1: Binding, C.E., Jerome, J.H., Bukata, R.P., Booty, W.G., 2010. Suspended particulate matter in Lake Erie derived from MODIS aquatic colour imagery.Int.J.Remote Sens, 31 (19), 5239-5255. Chen, S.S., Huang, W.R., Chen, X.Z., 2011a. An enhanced MODIS remote sensing model for detecting rainfall effects on sediment plume in the coastal waters of Apalachicola Bay.Mar.Environ.Res.72 (5), 265-272.
Literature cited 2: Chen, S.S., Huang, W.R., Chen, W.Q., Wang, H.Q., 2011b.Remote sensing analysis of rainstorm effects on sediment concentrations in Apalachicola Bay, USA. Ecol. Inform. 6 (2), 147-155. Chen, S.S., Huang, W.R., Wang, H.Q., Li, D., 2009.Remote sensing assessment of sediment re-suspension during Hurricane Frances in Apalachicola Bay, USA, E.col. Inform. 6 (2), 147-155.

ID: 60102
Title: Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale.
Author: M.M. Saberioon, M.S.M. Amin, A.R. Anuar, A. Gholizadeh, A. Wayayok, S. Khairunniza-Bejo.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 35-45 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Conventional digital camera, Image analysis, Rice, Nitrogen, Principal component analysis, Low altitude remote sensing.
Abstract: Nitrogen is n important variable farming management. The objectives of this study were to develop and test a new method to determine the status of nitrogen and chlorophyll content in rice leaf by analysing and considering all visible bands derived from images captured using a conventional digital camera. The images from the 6-pannel leaf colour chart were acquired using Basler Scout scA640-70fc under light-emitting diode lighting, in which principal component analysis was used to retain the lower order principal component to develop a new index. Digital photographs of the upper most collared leaf of rice (Oriza sativa L), grown over a range of soils with different nitrogen treatments, were processed into 11 indices and IPCA through six growth stages. Also a conventional digital camera mounted to an unmanned aerial vehicle was used to acquire images over the rice canopy for the purpose of verification. The result indicated that the conventional digital camera at the both leaf (r=-0.81) and the canopy (r=0.78) scale could be used as a sensor to determine the status of chlorophyll content in rice plants through different growth stages. This indicates that conventional low-cost digital cameras can be used for determining chlorophyll content and consequently for monitoring nitrogen content of the growing rice plants, thus offering a potentially inexpensive, fast, accurate and suitable tool for rice growers. Additionally, results confirmed that a low cost LARS system would be well suited for high spatial and temporal resolution images and data analysis for proper assessment of key nutrients in rice farming in a fast, inexpensive and non-destructive way.
Location: TE 15 New Biology Building
Literature cited 1: Aber, J.S., Aaviksoo, K. Karofeld, E., Aber, S.W., 2002. Patterns in Estonian bogs as depicted in colour kite aerial photographs.Suo 53, 1-15. Adamsen, F.J., Pinter, P.J., Barnes, E.M., LaMorte, R.L., Wall, G.W., Leavitt, S.W., Kimbau, B.A., 1999. Measuring wheat senescence with a digital camera. Crop Sci.39, 719-724.
Literature cited 2: Bayer, B.E., 1976. Color imaging array. US Patent 3,971,065.Eastman Kodak Company, Rochester, N.Y. Blackmer, T.M., Schepers, J.S., 1995. Use of a chlorophyll meter to monitor nitrogen status and schedule fertigation for corn.J. Prod. Agric. 8, 56-60.

ID: 60101
Title: Impact of the construction of a large dam on riparian vegetation cover at different elevation zones as observed from remotely sensed data.
Author: Christopher H.Kellogg, Xiaobing Zhou.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 19-34 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Vegetation cover, Three Gorges Dam, MODIS vegetation index, Time series, Elevation zone, Environmental impact.
Abstract: The impact of the construction of a large dam on riparian vegetation cover can be multifold. How the riparian vegetation cover changes at different elevation zones in response to the construction of a large dam and the subsequent impound of reservoir water is still an open question. In this study, we used satellite remote sensing data integrated with geographic information system (GIS) to monitor vegetation cover change at different riparian elevation zones on large spatial scale, taking the Three Gorges Dam in China as an example. Due to the large scale of this newly formed reservoir, it is expected to impact the riparian vegetation canopy both directly and indirectly. We chose to monitor vegetation cover changes along the 100 km riparian stretch of river directly upstream of the Three Gorges Dam site, over the construction period of eleven years (2000-2010), using MODIS vegetation indices products, digital elevation model (DEM) data from ASTER, and the time series water level data of the Three Gorges reservoir as the data sources. Results show that non-vegetated area increased within the elevation zone of 175-177 m and no change in vegetation cover was observed above 775 m in elevation. Regression analysis between the vegetation index data and the reservoir water level shows that increasing water levels have had a negative impact on vegetation cover below 175 m, a positive impact on vegetation cover is limited to the region between 175 and 775 m, and no significant impact was observed above 775 m. MODIS EVI product is less sensitive in mapping non-vegetated land cover change, but more sensitive in mapping vegetated land cover change, caused by the reservoir water level variation; both products are similar in effectively tracking a trend land cover change, caused by the reservoir water level variation, both products are similar in effectively tracking a trend between land cover change in each elevation zone with time or with reservoir water level.
Location: TE 15 New Biology Building
Literature cited 1: Baxter, R.M., 1977. Environmental effects of dams and impoundments. Annu.Rev.Ecol.Syst, 8, 255-283. Beck, P., Atzberger, C., Hogda, K., Johansen, B., Skidmore, A., 2006. Improved monitoring of vegetation dynamics of very high altitudes: a new method using MODIS NDVI, Remote Sens.Environ.100, 321-334.
Literature cited 2: Bellone, T., Boccardo, P., Perez, F., 2009. Investigation of vegetation dynamics using long-term normalized difference vegetation index time series.Am.J. Environ. Sci 5 (4), 460-466. Chen, G., 1993. Studies on Influences of Three Gorges Project on Ecological Environment. Science Press, Beijing, China.

ID: 60100
Title: Image-based correlation of Laser Scanning point cloud time series for landslide monitoring.
Author: Julien Travelleti, Jean-Philippe Malet, Christophe Delacourt.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 32. 1-18 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Terrestrial Laser Scanning, Point clouds, Image correlation, Landslide, Kinematics, Strain analysis.
Abstract: Very high resolution monitoring of landslide kinematics is an important aspect for a physical understanding of the failure mechanisms and for quantifying the associated hazard. In the last decade, the potential of Terrestrial Laser Scanning (TLS) to monitor slow-moving landslides has been largely demonstrated but accurate processing methods are still needed to extract useful information available in point cloud time series. This work presents an approach to measure the 3D deformation and displacement patterns from repeated TLS surveys. The method is based on the simplification of a 3D matching problem in 2D matching problem by using a 2D problem by using a 2D statistical normalized cross-correlation function. The computed displacement amplitudes are compared to displacements (1) calculated with the classical approach of iterative closest point and (2) measured from repeated dGPS observations. The performance of the method is tested on a 3 years dataset acquired at the Super-Sauze landslide (South French Alps) The observed landslide displacements are heterogeneous in time and space. Within the landslide, sub-areas presenting different deformation patterns (extension, compression) are detected by a strain analysis. It is demonstrated that pore water pressure changes within the landslide is the main controlling factor of the kinetics.
Location: TE 15 New Biology Building
Literature cited 1: Abellan, A., Jaboyedoff, M., Oppikoffer, T., Vilaplana, J.M., 2009. Detection of milli-metric deformation using terrestrial laser scanner. : Experiment and application to a rockfall event.Nat.Hazards Earth Syst.Sci 9, 365-372. Aryal, A., Brooks, A.B., Reid, M.E., Bawden, G.W., Pawlak, G., 2012. Displacement fields form point cloud data: application of particle imaging velocimetry to landslide geodesy.J.Geophys.Res.117 (F1), 1-15.
Literature cited 2: Avian,, M., Kellerre-Pirklbauer, A., Bauer, A., 2009. LiDAR for monitoring mass movements in permafrost environments at the cirque Hinteres Langtal, Austria, between 2000 and 2008.Nat.Hazards Earth Syst.Sci,9, 1087-1094. Bauer, A., Paar, G., Kaufmann, V., 2003. Terrestrial laser scanning for rock glacier monitoring. In: Phillips, M., Springman, S.M., Arenson, L.U. 9Eds). Proceedings of the eighth International Permafrost Conference, vol.1. Zurich, Balkema, pp. 55-60.

ID: 60099
Title: Differences between cropland and rangeland MODIS phenology (start-of-season) in Mali.
Author: Agnes Begue, Elodie Vintrou, Alexandre Saad, Pierre Hiernaux.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 31. 167-170 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Phenology, Cropland, Rangeland, MODIS, MCD 12Q2, Start of season.
Abstract: Start-of-season data are more and more used to qualify the land surface phenology trends in relation with climate variability and, more rarely, with human land management. In this paper, we compared the phenology product (MODIS MCD 12 Q2-Land Cover Dynamics Yearly), and an enhanced crop mask of Mali. The differences in terms of start-of -season (SOS) are spatially (north south gradient) and temporally (10 years, 2001-2009) analyzed in bioclimatic terms. Our results show that globally the MODIS MCD12Q2 SOS dates of croplands and rangeland differ, and that these differences depend on the bioclimatic zone. In Sahelian and Guinean regions, cropland vegetation begins to grow earlier than rangeland vegetation (8-day and 4-day advance, respectively). Between, in the Sudanian and Sudano-Sahelian parts of Mali, rangeland vegetation greens about one week earlier than croplands. These results are discussed in the context of the land surface heterogeneity at MODIS scale, and in the context of the natural vegetation ecology. These results could help interpreting phenological trends in climate change analysis.
Location: TE 15 New Biology Building
Literature cited 1: Archibald, S., Scholes, R.J., 2007. Leaf green-up in a semi-arid African savanna-separating tree and grass responses to environmental cues. J. Vegetat. Sci. 18 (4), 583-594. De Beurs, K.M., Henebry, G.M., 2004. Land surface phenology, climatic variation, and institutional change: analyzing agricultural land cover change in Kazakhstan. Remote Sens. Environ. 89 (4), 497-509.
Literature cited 2: Do, F.C., Goudiaby, V.A., Gimenez, O., Diagne, A.L., Diouf, M., Rocheteau, A., Akpo, I.E., 2005. Environmental influence on canopy phenology in the dry tropics. Forest Ecol.Manage.215 (1-3), 319-328. Ganguly, S., Friedl, M.A., Tan, B., Zhang, X., Verma, M., 2010. Land surface phenology from MODIS: characterization of the collection 5 global land cover dynamics product. Remote Sens.Environ.114 (8), 1805-1816.

ID: 60098
Title: Quantifying uncertainty in remote sensing-based urban land-use mapping.
Author: Kasper Cockx, Tim Van de Voorde, Frank Canters.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 31. 154-166 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Uncertainty, Land-use mapping, Urban remote sensing, Image classification, Spectral unmixing, Monte Carlo simulation.
Abstract: Land-use/and -cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land -use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface over at sub-pixel level through level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages Of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map-indicating absence of bias in the mapping process-it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map ' s uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modeling of urban growth.
Location: TE 15 New Biology Building
Literature cited 1: Agresti, A., Categorical Data Analysis, second ed. John Wiley & Sons, Hoboken, NJ. Baraldi, A., Parmiggiani, F., 1994. A Nagao-Matsuyama approach to high-resolution satellite image classification. IEEE Trans. Geosci.Remote Sens. 32 (4), 749-758.
Literature cited 2: Barnsley, M.J., Barr, S.L., 1996. Inferring urban land use from satellite sensor images using kernel-based spatial reclassification. Photogramm. Eng. Remote Sens. 62 (8), 949-958. Burnicki, A.C., Brown, D.G., Goovaerts, P., 2007. Simulating error propagation in land-cover change analysis: the implications of temporal dependence. Comput. Environ. Urban Syst. 31, 282-302.