Enter records

ID: 60135
Title: Modelling urban growth in the Indo-Gangetic plain using nighttime OLS data and cellular automata.
Author: P.K.Roy Chowdhury, Sandeep Maithani.
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. 33. 155-165 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Operational Linescan System, Cellular automata, Modelling, Regional modeling, Urban growth, Indo Gangetic plain.
Abstract: The present study demonstrates the applicability of the Operational Linescan System (OLS) sensor in modelling urban growth at regional level. The nighttime OLS data provides an easy, inexpensive way to map urban areas at regional scale, requiring a very small volume of data. A cellular automata (CA) model was developed for simulating urban growth in the Indo-Gangetic plain; using OLS data derived maps as input. In the processed CA model, urban growth was expressed in terms of causative factors like economy, topography, accessibility and urban infrastructure. The model was calibrated and validated based on OLS data of year 2003 respectively using spatial metrics measures and subsequently the urban growth was predicted for the year 2020. The model predicted high urban growth in North Western part of the study area, in south eastern part growth would be concentrated around two cities, Kolkata and Howrah. While in the middle portion of the study area i.e. Jharkhand, Bihar and Eastern Uttar Pradesh, urban growth has been predicted in form of clusters, mostly around the present big cities. These results will not only provide an input to urban planning but can also be utilized in hydrological and ecological modeling which require an estimate of future built up areas especially at regional level.
Location: TE 15 New Biology Building
Literature cited 1: Almeida, C.M., Batty, M., Monteiro, A.M.V., Camara, G., Soares-Filho, B.S., Carqueira, G.C., Pennachin, C.L., 2003. Stochastic cellular automata modeling of urban land use dynamics: empirical development and estimation. Comput. Environ. Urban Syst. 27, 481-509. Barredo, J.I., Kasanko, M., McCormick, N., Lavalle, C., 2003. Modelling dynamic spatial process: simulation of urban future scenarios through cellular automata. Landsc. Urban Plann 64, 145-160
Literature cited 2: Barredo, J.I., Demicheli, L., Lavalle, C., Kasanko, M., McCormick, N., 2004. Modelling future urban scenarios in developing countries: an application case study in Lagos, Nigeria, Environ, Plann. B: Plann. Des. 32, 65-84. Batty, M., Xie, Y., 1997. Possible urban automata. Environ. Plann. B 24, 175-192.

ID: 60134
Title: Application of fuzzy AHP method to IOCG prospectivity mapping: A case study in Taherabad prospecting area, eastern Iran.
Author: Ali Najafi, Mohammad Hassan Karimpour, Majid Ghaderi.
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. 33. 142-154 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Fuzzy analytical hierarchy process, Mineral prospectivity mapping, Multi-criteria decision-making, Iron Oxide Copper-Gold deposits, Eastern Iran.
Abstract: Using fuzzy analytical hierarchy process (AHP) technique, we propose a method for mineral prospectivity mapping (MPM) which is commonly used for exploration of mineral deposits. The fuzzy AHP is a popular technique which has been applied for multi-criteria decision-making (MCDM) problems. In this paper was used fuzzy AHP and geospatial information system (GIS) to generate prospectivity model for Iron Oxide Copper-Gold (IOCG) mineralization on the basis of its conceptual model and geo-evidence layers derived from geological, geochemical and geophysical data in Taherabad area, eastern Iran. The FuzzyAHP was used to determine the weights belonging to each criterion. Three geoscientists knowledge on exploration of IOCG-type mineralization have been applied to assign weights to evidence layers in fuzzy AHP MPM approach. After assigning normalized weights to all evidential layers, fuzzy operator was applied to integrate weighted evidence layers. Finally for evaluating the ability of the applied approach to delineate reliable target areas, locations of known mineral deposits in the study area were used. The results demonstrate the acceptable outcomes for IOCG exploration.
Location: TE 15 New Biology Building
Literature cited 1: Abdi, M., Karimpour, 2013. Petrological characteristics of subduction-related magmatism in Kooh-Shah intrusive rocks, evidence for Eocene copper-gold porphyry systems in Lut Block, Eastern Iran. Acta Geol. Sin. 87, 1032-1044. Abedi, M., Torabi, S.A., Norouzi, G.H., 2013. Application of fuzzy AHP method to integrate geophysical data in prospect scale, a case study: Seridune copper deposit. Bill. Geofisica Teorica Appl. 54, 145-164.
Literature cited 2: Agterberg, F.P., Bonham-Carter, G.F., 1999. Logistic regression and weights of evidence modeling in mineral exploration. In: Proc. 28th Int.Symp. App. Comput. Mineral Ind. (APCOM), Golden, CO, USA, pp. 483-490. An, P., Moon, W.M., Rencz, A.N., 1991. Application of fuzzy theory for integration of geological, geophysical and remotely sensed data.Cn.J.Explor.Geophys. 27, 1-11.

ID: 60133
Title: Regional-scale mineral mapping using ASTER VNIRT/SWIR data and validation of reflectance and mineral map products using airborne hyperspectral CASI/SASI data.
Author: Cui Jing, Yan Bokun, Wang Runsheng, Tian Feng, Zhao Yingjun, LiuDechang, Yang Suming, Shen Wei.
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. 33. 127-141 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: ASTER, Atmospheric correction, Geological remote sensing, Seamless minerals mapping.
Abstract: ASTER data have been widely and successfully used in lithological mapping and mineral exploration for decades. The errors due to atmospheric water vapor and the characteristics of the photoelectric sensor could lead to the anomalous characteristics of band 5 and 9 in the ASTER standard reflectivity product. These anomalies could result in the spectroscopic misidentification of minerals. This study proposed a simple method of atmospheric correction for converting radiance-at-sensor to ground reflectance. The ASTER/VNIR/SWIR reflectance correction factor was derived to correct the spectral shape bias resulting from the radiometric calibration error using airborne hyperspectral CASI_SASI data. The ASTER VNIR/SWIR reflectance correction factor was derived to correct the spectral shape bias resulting from the radiometric calibration error. After applying the reflectance factor to the atmospheric-corrected ASTER L1B data, a band combination mapping method was proposed for identifying minerals for quickly and accurately. The results indicate that this method for atmospheric correction of ASTER data produces very good results in the arid and bare areas. It is still unknown whether the method is suitable for humid and rainy areas where atmospheric water vapor varies spatially more than in arid and bare areas. After applying the reflectance factor to the atmospheric-corrected ASTER L1B data, the mean error of all reflectance bands decreased from 0.0256 to 0.002, and the standard deviation decreased from 0.4251 to 0.0007. The errors of the 2/1, 5/6 and 9/8 band ratios decreased from 2.38 %, 4.102% and 4.28 % to 1.26 % -0.162%, and 0.31%, respectively. The radiometric calibration error of the ASTER band 1-9 data can lead to the overestimation of kaolinite. A band index of 2/1 for retrieving Fe3+ distribution map, and a new index should be developed.
Location: TE 15 New Biology Building
Literature cited 1: Aboelkhair, H., Ninomiya, Y., Watanabe, Y., Sato, I., 2010. Processing and interpretation of ASTER TIR data for mapping of rare-metal-enriched albite granitoids in the Central Eastern Desert of Egypt.J.Afr. Earth Sci. 58, 141-151. Abrams, M., Hook, S.J., 1995. Simulated Aster data for geologic studies. IEEE Trans. Geosci. Remote Sens. 33, 692-699.
Literature cited 2: Abrams, M., 2000. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER): data products for the high spatial resolution imager on NASA ' s Terra platform. Int. J. Remote Sens. 21, 847-859. Adler-Golden, S.M., Mattchew, M.W., Bemstein, S., Levine, R.Y., Berk, A., Richtsmeier, S.C., 1999. Atmospheric correction for short-wave spectral imagery based on MODTRAN 4In: Grenn, R.O., (Ed), Summaries of the Eighth JPL Airborne Earth Science Workshop JPL Publication, Bol. 99-17. Jet Propul. Lab. 1, Pasadena, C.A., pp. 21-29.

ID: 60132
Title: Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling.
Author: inci Guneralp, Anthony M.Filippi, Jarom Randall.
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. 33. 119-126 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Aboveground biomass, Remote sensing, Stochastic gradient boosting, Multivariate adaptive regression splines, Foodplain, River meander.
Abstract: Floodplain forests serve a critical function in the global carbon cycle because floodplains constitute an important carbon sink compared with other terrestrial ecosystems. Forests on dynamic floodplain land-scapes, such as those created by river meandering processes, are characterized by uneven-aged trees and exhibit high spatial variability, reflecting the influence of interacting fluvial, hydrological, and ecological processes. Detailed and accurate mapping of aboveground biomass (AGB) on floodplain landscapes characterized by uneven-aged forests is critical for improving estimates of floodplain-forest carbon pools. Which is useful for greenhouse gas (GHG) life cycle assessment. It would also help improve our process understanding of biomorphodynamics of river-floodplain systems, as well as planning and monitoring of conservation, restoration, and management of riverine ecosystems. Using stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), and Cubist, we remotely estimate AGB of a bottomland hardwood forest on a meander bend of a dynamic lowland river. As predictors, we use 30 -m and 10-m multispectral image bands (Landsat 7 ETM+ and SPOT 5, respectively) and ancillary data. Our findings show that SGB and MARS significantly outperform Cubist, which is used for U.S. national-scale forest biomass mapping. Across all data-experiments and algorithms, at 10-m spatial resolution, SGB yields the best estimates (RMSE= 22.49 tonnes/ha; coefficient of determination (R2) = 0.96) when geomorphometric data are also included. On the other hand, at 30-m spatial resolution, MARS yields the best estimates (RMSE =29.2 tonnes/ha; R2 = 0.94) when image-derived data are also included. By enabling more accurate AGB mapping of floodplains characterized by uneven-aged forests, SGB and MARS provide an avenue for improving operational estimates of AGB and carbon at local, regional/continental, and global scales.
Location: TE 15 New Biology Building
Literature cited 1: Baccini, A., Laporte, N., Goetz, S.J., Sun, M., Dong, H., 2008. A first map of tropical Africa ' s above-ground biomass derived from satellite imagery. Environ.Res.Lett. 3, 045011. Bendix, J., Hupp, C.R., 2000. Hydrological and geomorphological impacts on riparian plant communities. Hydrol.Process. 14, 2977-2990.
Literature cited 2: Beven, K.J., Kirkby, N.J., 1979. A physically based variable contributing area model of basin hydrology. Hydrol. Sci. B 24, 43-69. Blackard, J.A., Finco, M.V., Helmer, E.H., Holden, G.R., Hoppus, M.L., Jacobs, D.M., et al., 2008. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sens.Environ.112, 1658-1677.

ID: 60131
Title: Lacally adaptive unmixing method for lake-water area extraction based on MODIS 250 m bands
Author: Baodong Ma, Lixin Wu, Xuanxuan Zhang, Xingchun Li, Ying Liu, Shenglei Wang.
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. 33. 109-118 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Locally adaptive unmixing, 250 m MODIS data, Near infrared, Lake-water area, satellite image.
Abstract: Lakes in semi-arid regions serve an important function in maintaining regional ecological balance. Thus, the change in lake-water area should be monitored by using remotely sensed images. However, most high-spatial -resolution satellite sensors cannot provide frequent observation data because of the long revisiting cycle and cloud effects. The 250 m MODIS images, as well as red and near -infrared bands, are currently the best monitoring data because of their frequent revisit and medium spatial resolution. Spectral unmixing is commonly used to extract information from coarse-resolution images at subpixel level, however, in conventional unmixing, endmember selection is problematic, and a sufficient number of bands are necessary to solve the decomposition equations. In this study, we developed a locally adaptive unmixing (LAU) method to extract like-water area using 250 m MODIS images. In this method, pixels mixed with water and land are initially extracted. Then, two classes of endmembers of each mixed pixel are determined by referring to the reflectivity of the neighboring pixels with different weights. Water abundance in each mixed pixel could be calculated using a single-band image. Owing to the overestimation in the NIR band and the underestimation in the red band, the average of the results from the two bands was set as the ultimate lake-water area to minimize error. This method is not only locally adaptive for endmember selection, but is also independent of the number of bands. This approach would be useful for the frequent monitoring of lake area by using 250 m MODIS images.
Location: TE 15 New Biology Building
Literature cited 1: Canham, K., Schlamm, A., Ziemann, A., Basener, B., Messinger, D., 2011. Spatially adaptive hyperspectral unmixing. IEEE Trans. Geosc. Remote Sens. 49 (11), 4248-4262. Chipman, J.W., Lillesand, T.M., 2007. Satellite-based assessment of the dynamics of new lakes in southern Egypt. Int.J.Remote Sens. 28 (19), 4365-4379.
Literature cited 2: Dheeravath, V., Thenkabail, P.S., Chandrakantha, G., Noojipady, P., Reddy, G.P.O., Biradar, C.M., Gumma, M.K., Velpuri, M., 2010. Irrigated areas of India derived using MODIS 500 m time series for the years 2001-2003. ISPRS J.Photogram. Remote Sens. 65 (1), 42-59. Ding, X., Li, X., 2011. Monitoring of the water-area variations of Lake Dongting in China with ENVISAT ASAR images. Int.J. Appl.Earth Observ.Geoinform. 13 (6), 894-901.

ID: 60130
Title: PTrees: A point-based approach to forest tree extraction from lidar data.
Author: C.Vega, A. Hamrouni, S. EI Mokhtari, J. Morel, J. Bock, J.-P Renaud, M. Bouvier, S. Durrieu.
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. 33. 98-108 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Lidar, Forest inventory, Point cloud processing, Tree crown extraction, Dynamic segmentation, Point cloud normalization.
Abstract: This paper introduces PTrees, a multi-scale dynamic point cloud segmentation dedicated to forest tree extraction from lidar point clouds. The method process the point data using the raw elevation values (z) and compute height (H=Z-ground elevation) during post-processing using an innovative procedure allowing to preserve the geometry of crown points. Multiple segmentations are done at different scales. Segmentation criteria are then applied to dynamically select the best set of apices from the tree segments extracted the various scales. The selected set of apices is then used to generate a final segmentation. PTrees has been tested in 3 different forest types, allowing to detect 82% of the trees with under 10 % of false detection rate. Future development will integrate crown profile estimation during the segmentation process in order to both maximize the detection of suppressed trees and minimize false detections.
Location: TE 15 New Biology Building
Literature cited 1: Axelsson, P., 2000. DEM generation from laser scanner data using adaptive TIN models. In: Int Arch. Photogramm. Remote Sens., XXXIII, B4, Amsterdam, The Netherlands, pp. 111-118. Allouis, T., Durrieu, S., Vega, C., Couteron, P., 2013. Stem volume and above-ground biomass estimation of individual pine trees from LiDAR data: contribution of full-waveform signals. IEEE J. STARS 6 (2), 924-934.
Literature cited 2: Asner, G.P., Mascaro, J., Muller-Landau, H.C., Vieilledent, G., Vaudry, R., Rasamoelina, M., Hall, J., van Breugel, M., 2012. A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia 168 (4), 1147-1160. Avery, T.E., Burkhart, H.E., 2001. Forest Measurements 5 th ed. McGraw-Hill, Boston, pp. 456

ID: 60129
Title: Remote sensing image denoising application by generalized morphological component analysis.
Author: Chong Yu, Xiong Chen.
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. 33. 83-97 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Remote sensing image denoising, Generalized morphological component analysis, Blind source separation, Iterative thresholding strategy, Visual effect, Quantitative assessment.
Abstract: In this paper, we introduced a remote sensing image denoising method based on generalized morphological component analysis (GMCA). This novel algorithm is the further extension of morphological component analysis (MCA) algorithm to the blind source separation framework. The iterative thresholding strategy adopted by GMCA algorithm firstly works on the most significant features in the image, and then progressively incorporates smaller features to finely tune the parameters of whole model. Mathematical analysis of the computational complexity of GMCA algorithm is provided. Several comparisons experiments with state-of -the-art denoising algorithms are reported. In order to make quantitative assessment of algorithms in experiments, Peak Signal to Noise Ratio (PNSR) index and structural similarity (SSIM) index are calculated to assess the denoising effect from the gray-level fidelity aspect and the structure -level fidelity aspect, respectively. Quantitative analysis on experiment results, which is consistent with the visual effect illustrated by denoised images, has proven that the introduced GMCA algorithm possesses a marvelous remote sensing image denoising effectiveness and ability. I t is even hard to distinguish the original noiseless image from the recovered image by adopting GMCA algorithm through visual effect.
Location: TE 15 New Biology Building
Literature cited 1: Bobin, J., Starck, J.L., Fadili, J., Moudden, Y., 2007. Sparsity and morphological diversity in blind source separation. IEEE Trans. Image Process. 16 (November (11)), 2662-2674. Bruckstein, A., Elad, M., 2002. A generalized uncertainty principle and sparse representation in pairs of bases. IEEE Trans. Inform. Theory 48 (September (9)). 2558-2567.
Literature cited 2: Chen, S.S., Donoho, D.L., Sunders, M.A., 2001. Atomic decomposition by basis pursuit. SIAM Rev. 43 (1), 129-159 Chopra, A., Lian, H., 2010. Total variation, adaptive total variation and nonconvex smoothly clipped absolute deviation penalty for denoising blocky images. Pattern Recogn. 43 (August (8), 2609-2619.

ID: 60128
Title: Spatial analysis of human-induced vegetation productivity decline over eastern Africa using a decade (2001-2011) of medium resolution MODIS time-series data.
Author: Tobais Landmann, Olena Dubovyk.
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. 33. 76-82 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Land degradation, NDVI, RUE, Trend analysis, Land use change, Google Earth.
Abstract: Climate variation and land transformations related to exploitative land uses are among the main drivers of vegetation productivity decline and ongoing land degradation in East Africa. We combined analysis of vegetation trends and cumulative rain use efficiency differences (CRD), calculated from 250 -m MODIS NDVI time-series data, to map vegetation productivity loss over eastern Africa between 2001 and 2011. The CRD index values were furthermore used to discern areas of particular severe vegetation productivity loss over the observation period. Monthly 25 -km Tropical Rainfall Measuring Mission (TRMM) data metrics were used to mask areas of rainfall declines not released to human-induced land productivity loss. To provide insights on the productivity decline, we linked the MODIS-based vegetation productivity map to land transformation processes using very high resolution (VHR) imagery in Google Earth (GE) and a Landsat-based land-cover change map. In total, 3.8 million ha experienced significant vegetation loss over the monitoring period. An overall agreement of 68 % was found between the rainfall- corrected MODIS productivity decline map and all reference pixels discernable from GE and the Landsat map. The CRD index showed a good potential to discern areas with ' severe ' Vegetation productivity losses under high land-use intensities.
Location: TE 15 New Biology Building
Literature cited 1: Adewuyi, T., Baduku, A., 2012. Recent consequences of land degradation on farmland in the peri-urban area of Kaduna Metropolis, Nigeria.J.Sustain.Dev.Africa 14, 179-193. Adeyewa, Z.D., Nakamura, K., 2003. Validation of TRMM radar rainfall data over major climatic regions in Africa. J. Clim.Appl.Meteorol. 42, 331-347.
Literature cited 2: Atzberger, C., Eilers, PH.C., 2011. Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements .Int. J. Remote Sens. 32, 3689-3709. Bai, Z.G., Dent, D.L., Olsson, L., Schaepman, M.E., 2008. Proxy global assessment of land degradation. Soil Use Manage. 24, 223-234.

ID: 60127
Title: Leaf and canopy water content estimation in cotton using hyperspectral indices and radiative transfer models.
Author: Qiuxiang Yi, Fumin Wang, Anming Bao, Guli Jiapaer.
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. 33. 67-75 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: EWT, EWT canopy, PROSPECT-5 model, PROSPECT-5 +SAILH model, Hyperspectral vegetation indices, Cotton.
Abstract: In present study some vegetation indices for estimating leaf EWTand EWT canopy were investigated using simulations and field measurements. Leaf and canopy spectral reflectance as well as leaf EWT and EWT canopy were measured in cotton during the growing seasons of 2010 and 2011. The PROSPECT-5 model was coupled with the SAILH model to explore the performance of water -related vegetation indices for leaf EWT and EWT canopy estimation. The vegetation indices evaluated were published formulations and new simple ratio vegetation indices formulated with wavebands at 1060 nm and 1640 nm. The sensitivities of these indices to leaf internal structural N and LAI effects were assessed. Simulation results indicated that all of the water-related vegetation indices were insensitive to leaf internal structural N, with the highest coefficient of determination R2 <0.15 and the proposed index SR1640 (R1060 / R1640 ) and published index SR2 (R1070 /R 1340 ) showed the lowest relationships (R2 < 0.35) with LAI of all the vegetation indices. Furthermore, coefficients of determination between simulated leaf EWT (R2 >0.9; P<0.001) and EWT canopy (R2>0.8; P<0.001). Results obtained with field measurements were in agreement with simulation results, with the coefficient of determination R2=0.5 (P<0.001) for leaf EWT and R2 =0.57 (P<0.001) for EWT canopy by the new simple ratio indices. This study provides a new candidate for leaf EWT and EWT canopy estimation using hyperspectral vegetation indices.
Location: TE 15 New Biology Building
Literature cited 1: Aldakheel, Y.Y., Danson, F.M., 1997. Spectral reflectance of dehydrating leaves: measurements and modeling. Int.J. Remote Sens. 18, 3683-3690. Bacour, C., Jaccquemoud, S., T ourbier, Y., Dechambre, M., Frangi, J.P., 2002. Design and analysis of numerical experiments to compare four canopy reflectance models. Remote Sens. Environ. 79, 72-83.
Literature cited 2: Bowyer, P., Danson, F.M., 2004. Sensitivity of remotely sensed spectral reflectance to variation in live fuel moisture content. Remote Sens. Environ. 92, 297-308. Carlson, J.D., Burgan, R.E., 2003. Review of user needs in operational fire danger estimation: the Oklahoma example. Int. J. Remote Sens. 24, 1601-1620.

ID: 60126
Title: Mapping the alteration footprint and structural control of Taknar IOCG deposit in east of Iran, using ASTER satellite data.
Author: Khosrow Maroufi Naghadehi, Ardeshir Hezarkhani, Saeid Asadzadeh.
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. 33. 57-66 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: ASTER, Alteration, Mineral map, Match filtering, IOCG, Taknar.
Abstract: Taknar Fe + Cu ? Zn ? Pb ?Au ? Ag deposit in northeast of Iran is studied by Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) reflectance and emittance data. Structural and mineralogical evidence of IOCG mineralization is mapped by visual image interpretation and spectral processing techniques. The tectonic model is consistent with an extensional zone associated with a releasing bend of right-lateral regional faults, extending about 7 km2 and encompassing all the known orebodies of Taknar. A combination of band ratio logical operator and matched filtering were used for spectral mapping, which lead to a series of mineral content and crystallinity maps included ferric oxide, ferrous, white, mica, Chlorite, silica and opaque minerals. The channel way in which hydrothermal fluids were migrating is accurately defined by abundance of white mica and ferric iron oxide maps. Rhythmic sediments of Taknar formation which was characterized by chlorite mineral map is a ?reducing? environment that hosts the mineralization. This REDOX environment is also marked by a sudden change in white mica composition from acidic phases to neutral/alkaline. Subsequent field check and microscopic study indicated the accuracy of these remotely mapped minerals. Based on this finding, several new prospects for further exploration was proposed. These results indicate that ASTER data is capable of delineating alteration footprints of an IOCG mineral system in deposit scale exploration.
Location: TE 15 New Biology Building
Literature cited 1: Abrams, M., Hook, S., 2000. ASTER User Handbook, JetPropulsion Laboratory, ASTER User ' s guide. Part 1 General (ver. 3.0) 2001. Earth Remote Sensing Data Analysis Center, http://www.ersdac.org.jp/ Adler-Golden, S.M., Matthew, M.W., Bernstein, L.S., Levine, R.Y., Berk, A., Richtsmeier, S.C., Acharya, P.K., Anderson, G.P., Felde, G., Gardner, J., Hoke, M., Jeong, L.S., Pukall, B., Ratkowski, A., Burke, H.H., 1999. Atmospheric correction for short -wave spectral imagery based on MODTRAN4. IN: SPIE Proceedings on Imaging Spectrometry, v. 3753, pp. 61-69.
Literature cited 2: Barton, M.D., 2009. IOCG Deposits: A Cordilleran Perspective. University of Arizona, Tucson, AZ. Barton, M.D., Jhonson, D.A., 2004. Footprints of Fe-oxide (-Cu-Au) systems SEG 2004 Predictive Mineral Discovery Under Cover. Centre for Global Metallogeny, Spec. pub.33. The University of Western Australia, pp. 112-116.

ID: 60125
Title: Modeling soil parameters using hyperspectral image reflectance in subtropical coastal wetlands.
Author: Naveen J.P., Anne, Amr H.Abd-Elrahman, David B.Lewis, Nicole A. Hewitt.
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. 33. 47-56 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Hyperspectral remote sensing, Coastal wetlands, Soil properties, Particulate organic matter, Labile carbon, Labile nitrogen.
Abstract: Developing-spectral models of soils properties is an important frontier in remote sensing and soil science. Several studies have focused on modeling soil properties such as total pools of soil organic matter and carbon in bare soils. We extended this effort to model soil parameters in areas densely covered with coastal vegetation. Moreover, we investigated soil properties indicative of soil functions such as nutrient and organic matter turnover and storage. These properties include the portioning of mineral and organic soil between particulate (>53?m) and fine size classes, and the portioning of soil carbon and nitrogen pools between stable and labile fractions. Soil samples were obtained from Avicennia germinans mangrove forest and Juncus roemerianus salt marsh plots on the west coast of Central Florida. Spectra corresponding to field plot locations from Hyperion hyperspectral image were extracted and analyzed. The spectral information was regressed against the soil variables to determine the best single bands and optimal band combinations for the simple ratio (SR) and normalized difference index (NDI) indices. The regression analysis yielded levels of correlation for soil variables with R2 values ranging from 0.21 to 0.47 for best individual bands, 0.28 to 0.81 for two-band indices, and 0.53 to 0.96 for partial least-squares (PLS) regressions for the Hyperion image data. Spectral models using Hyperion data adequately (RPD> 1.4) predicted particulate organic matter (POM), silt +clay, labile carbon ?, and labile nitrogen (N) (where RPD= ratio of standard deviation to root mean square error of cross -validation [RMSECV].) The SR (0.53 ? m, 2.11 ? m) model of labile N with R2 =0.81, RMSECV=0.28, and RPD=1.94 produced the best results in this study. Our results provide optimism that remote-sensing spectral models can successfully predict soil properties indicative of ecosystem nutrient and organic matter turnover and storage, and do so in areas with dense canopy cover.
Location: TE 15 New Biology Building
Literature cited 1: Alongi, D., Trott, L., Wattayakorn, G., Clough, B., 2002. Below-ground nitrogen cycling in relation to net canopy production in mangrove forests of southern Thailand. Mar.Biol. 140 (4), 855-864. Anderson, I.C., Tobias, C.R., Neikirk, B.B., Wetzel, R.L., 1997. Development of a process-based nitrogen mass balance model for a Virginia (USA) Spartina alterniflora salt marsh: implications for net DIN flux. Mar. Ecol.Prog.Ser. 159, 13-27.
Literature cited 2: Baret, F., Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens.Environ. 35 (2-3), 161-173. Bartholomeus, H., Kooistra, L., Stevens, A., van Leeuwen, M., van Wesemael, B., Ben-Dor, E., Tychon, B., 2011. Soil organic carbon mapping of partially vegetated agricultural fields with imaging spectroscopy. Int. J. Appl. Earth Obs. Geoinf. 13 (1), 81-88.

ID: 60124
Title: Predicting maize yield in Zimbabwe using dry dekads derived from remotely sensed vegetation Condition Index.
Author: Farai Kuri, Amon Murwira, Karin S. Murwira, Mhosisi Masocha
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. 33. 39-46 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Vegetation Condition Index, Dry dekads, Maize yield, SPOT Normalized Difference Vegetation, Index.
Abstract: Maize is a key crop contributing to food security in Southern Africa yet accurate estimates of maize yield prior to harvesting are scarce. Timely and accurate estimates of maize production are essential for ensuring food security by enabling actionable mitigation strategies and policies for prevention of food shortages. In this study, we regressed the number of dry dekads derived from VCI against official ground-based maize yield estimates to generate simple linear regression models for predicting maize yield throughout Zimbabwe over four seasons (2009-10, 2010-11, 2011-12 and 2012-2013). The VCI was computed using Normalized Difference Vegetation Index (NDVI) time series data set from the SPOT VEGETATION sensor for the period 1998-2013. A significant negative linear relationship between number of dry dekads and maize yield was observed in each season. The variation in yield explained by the models ranged from 75 % to 90%. The models were evaluated with official ground-based yield data that was not used to generate the models. There is a close match between the predicted yield and the official yield statistics with an error of 33 %. The observed consistency in the negative relationship between number of dry dekads and ground-based estimates of maize yield as well as the high explanatory power of the regression models suggest that VCI-derived dry dekads could be used to predict maize yield before the end of the season thereby making it possible to plan strategies for dealing with food deficits or surpluses on time.
Location: TE 15 New Biology Building
Literature cited 1: Arawal, R., Meht, S.C., 2007. Weather based forecasting of crop yields, pests and diseases-IASRI Models J.Indian Soc. Agric.Stat. 61 (2), 255-263. Casley, D.J., Kumar, K., 1988. The collection, Analysis and Use of Monitoring and Evaluation Data. Johns Hopkins University Press for the World Bank, Baltimore, MD.
Literature cited 2: Chen, J., Jonsson, P., Tamura, M., Gu, Z., Matsushita, B., Eklundh, L., 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sens Environ. 91 (3-4), 332-344. Chenje, M., Sola, L., Paleczny, D., 1998. The State of Zimbabwe ' s Environment 1998. Government of the Republic of Zimbabwe, Ministry of Mines, Environment and Tourism, Harare, Zimbabwe.

ID: 60123
Title: Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data.
Author: Kun Jia, Shunlin Liang, Lei Zhang, Xiangqin Wei, Yunjun Yao, Xianhong Xie.
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. 33. 32-38 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Forest cover, Classification, Time series NDVI data, Remote sensing, Fusion.
Abstract: Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate -resolution Imaging-Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5 % from 88.99 % to 93.88 % compared to only using single Landsat ETM+ data.
Location: TE 15 New Biology Building
Literature cited 1: Barthlome, E., Belward, A.S., 2005. GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26, 1959-1977. Bonan, G.B., 2008.Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444-1449.
Literature cited 2: Brown, J.C., Kastens, J.H., Coutinho, A.C., Victoria, D.D., Bishop, C.R., 2013. Classifying multiyear agricultural land use data from Mato Grosso Using time-series MODIS vegetation index data. Remote Sens. Environ. 130, 39-50. Caplow, S., Jagger, P., Lawlor, K., Sills, E., 2011. Evaluating land use and livelihood impacts of early forest carbon projects: lessons for learning about REDD.Environ. Sci.Policy 14, 152-167.

ID: 60122
Title: Coupling potential of ICES at/GLAS and STRM for the discrimination of forest landscape types in French Guiana.
Author: I.Fayad, N.Baghdadi, V.Gond, J.S. Bailly, N.Barbier, M.El Hajj, F. Fabre.
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. 33. 21-31 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: SRTM DEM, ICESat/GLAS, Tropical forest, French Guiana.
Abstract: The Shuttle Radar Topography Mission (SRTM) has produced the most accurate nearly global elevation dataset to date. Over vegetated areas, the measured SRTM elevations are the result of a complex interaction between radar waves and tree crowns. In this study, waveforms acquired by the Geoscience Laser Altimeter System (GLAS) were combined with SRTM elevations to discriminate the five forest landscape types (LTs) in French Guiana. Two differences were calculated: (1) penetration depth, defined as the GLAS highest elevations minus the SRTM elevations and (2) the GLAS centroid elevations minus the SRTM elevations. The results show that these differences were similar for the five LTs, and they increased as a function of the GLAS canopy height and of the SRTM roughness index. Next, a Random Forest (RF) classification was used to analyze the coupling potential of GLAS and SRTM in the discrimination of forest landscape types in French Guiana. The parameters used in the RF classification were the GLAS canopy height, the SRTM roughness index, the difference between the GLAS centroid elevations and the SRTM elevations. Discrimination of the five forest landscape types in French Guiana was possible, with an overall classification accuracy of 81.3 % and a kappa coefficient of 0.75. All forest LTs were well classified with an accuracy varying from 78.4 % to 97.5%. Finally, differences of near coincident GLAS waveforms, one from the wet season and one from the dry season, were analyzed. The results showed that the open forest (LT (LT12), in some locations, contains trees that loose leaves during the dry season. These trees allow LT12 to be easily discriminated from the other LTs that retain their leaves using the following three criteria: (1) difference between the GLAS centroid elevations and the SRTM elevations, (2) ratio of top energy in the wet season to top energy in the dry season, or (3) ratio of ground energy in the wet season to ground energy in the dry season.
Location: TE 15 New Biology Building
Literature cited 1: Addo-Fordjour, P., Rahmad, Z.B., 2013. Mixed species allometric models for estimating above-ground liana biomass in tropical primary and secondary forests, Ghana.ISRN Forestry Vol. 2013, Article ID 153587. Ali, S.S., Dare, P., Jones, S.D., 2008. Fusion of remotely sensed multispectral imagery and Lidar data for forest structure assessment at the tree level. ISPRS Proceedings, Beijing XXXVII, B7.
Literature cited 2: Baghdadi, N., le Maire, G., Fayad, I., Bailly, J.S., Nouvellon, Y., Lemos, C., Hakamada, R., 2014. Testing different methods of forest height and aboveground biomass estimations from ICESat/GLAS data in Eucalyptus plantations in Brazil. IEEE-JSTARS 7, 290-299. Bartholome, E., Belward, A., Beuchle, R., Eva, H., Fritz, S., Hartley, A., Mayaux, P., Stibig, H.J., 2004. Global land cover for the year 2000, landcover classification produced with data acquired in 2000 from the VEGETATION instrument, onboard the SPOT-4 satellite, 1/25.500.000 scale map. In: European Commission, LB-55-03-099-ENC.

ID: 60121
Title: Atmospheric effects on the performance and threshold extrapolation of multi-temporal Landsat derived dNBR for burn severity assessment.
Author: Lei Fang, Jian Yang.
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. 33. 10-20 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Burn severity, Landsat, dNBR, Optimality, Atmospheric correction, Chinese boreal forest.
Abstract: The Landsat derived differenced Normalized Burn Ratio (dNBR) is widely used for burn severity assessments. Studies of regional wildfire trends in response to climate change require consistency in dNBR mapping across multiple image dates, which may vary in atmospheric condition. Conversion of continuous dNBR images into categorical burn severity maps often requires extrapolation of dNBR thresholds from present fires for which field severity measurements such as Composite Burn Index (CBI) data are available, to historical fires for which CBI data are typically unavailable. Although differential atmospheric effects between image collection dates could be lead to biased estimates of historical burn severity patterns, little is known concerning the influence of atmospheric effects on dNBR performance and threshold extrapolation. In this study, we compared the performance of dNBR calculated from six atmospheric correction methods using an optimality approach. The six correction methods included one partial (Top of atmosphere reflectance, TOA), two absolute, and three relative methods. We assessed how the correction methods affected the CBI-dNBR correlation and burn severity mapping in a Chinese boreal forest fire which occurred in 2010. The dNBR thresholds of the 2010 fire for each of the correction methods were then extrapolated to classify a historical fire from 2000. Classification accuracies of threshold extrapolations were assessed based on Cohen ' s Kappa analysis with 73 field-based validation plots. Our study found most correction methods improved mean dNBR optimality of the two fires. The relative correction methods generated 32 % higher optimality than both TOA and absolute correction methods. All the correction methods yielded high CBIdNBR correlations (mean R2 =0.847) but distinctly different dNBR thresholds for severity classification of 2010 fire. Absolute correction methods could substantially increase optimality score, but were insufficient to provide a consistent scale of radiometric condition between multi-temporal Landsat images, which resulted in lower severity classification accuracies (Kappa=0.53) than those relative correction methods (Kappa =0.72) for the 2000 fire. Consistent radiometric response in remote sensing datasets proved essential for accuracy in regional burn severity trends monitoring Extrapolation of empirical dNBR thresholds to historical conditions without relative normalization will likely lead to biased burn severity classifications.
Location: TE 15 New Biology Building
Literature cited 1: Allen, J.L., Sorbel, B., 2008. Assessing the differenced Normalized Burn Ratio ' s ability to map burn severity in the boreal forest and tundra ecosystems of Alaska ' s national parks. IJWF 17, 463-475. Bobbe, T., Finco, M.V., Quayle, B., Lannom, K., Sohlberg, R., Parsons, A., 2001. Field measurements for the training and validation of burn severity maps from space-borne, remotely sensed imagery. USDI Joint Fire Science Program Final Project Report JFSP RFP.
Literature cited 2: Boby, L.A., Schuur, E.A.G., Mack, M.C., Verbyla, D., Jhonstone, J.F., 2010. Quantifying fire severity, carbon, and nitrogen emissions in Alaska ' s boreal forest. Ecol.Appl.20, 1633-1647. Cai, W., Yang, J., Liu, Z., Hu, Y., Weisberg, P.J., 2013. Post-fire tree recruitment of a boreal larch forest in Northeast China.For.E col.Manage. 307, 20-29.