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ID: 60180
Title: GOCE data, models, and applications: A review
Author: M.van der Meijde, R. Pail, R. Bingham, R. Floberghagen.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 35 Part A. 4-15 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Gravity, GOCE, Applications, Data, Models.
Abstract: With the launch of the Gravity field and Ocean Circulation Explorer (GOCE) in 2009 the science in gravity got another boost. After the time-lapse and long-wavelength studies from Gravity Recovery and Climate Experiment (GRACE) a new sensor was available for determination of the Earth ' s gravity field and geoid with high accuracy and spatial resolution. Equipped with a 6-component gradiometer and flying at an altitude of 260 km and less GOCE provides the most detailed measurements of Earth ' s gravity from space ever. On top, GOCE also provides gravity gradients, i.e., the three-dimensional second derivatives of the gravitational potential. This paper provides a review of the results presented at the ' GOCE solid Earth workshop ' at the University of Twente, The Netherlands (2012), where an overview was given of the present status of the data models, and applications with GOCE which form the basis for this special issue and the review in this paper. An introduction will be given to the GOCE related research in geodesy, oceanography and solid Earth sciences indicates the first steps taken to integrate GOCE in the different application fields. For all three fields an overview is given on the most recent scientific results and developments, and first results specifically focusing on these studies where GOCE data has made a unique contribution and provides insights that would not have been possible without GOCE.
Location: TE 15 New Biology Building
Literature cited 1: Aitken, A.R.A., 2010. Moho geometry gravity inversion experiment (moggie): A refined model of the Australian moho, and its tectonic and isostatic implications. Earth and Planetary Science Letters 297 (1-2), 71-83. Albertella, A., Savcenko, R., Janjic, T., Rummel, R., Bosch, W., Schrter, J., 2012. High resolution dynamic ocean topography in the southern ocean from GOCE. Geo-physical Journal International 190 (2), 922-930.
Literature cited 2: Andreis, D., Canuto, E., Nov 2005. Drag-free and attitude control for the GOCE satellite. In: 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference, CDC-ECC, p. 40414046. Arabelos, D.N.,Tsoulis, D., 2013.The exploitation of state of the art digital terrain databases and combined or satellite-only Herat gravity models for the estimation of the crust-mantle interface over oceanic regions.Geophysical Journal International 193 (3), 1343-1352

ID: 60179
Title: Introduction to the special issue on GOCE Earth science applications and models
Author: M.Van der Meijde, R.Pail, R.Bingham
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 35 Part A. 1-3 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Gravity, GOCE, Applications, Solid Earth.
Abstract: With the launch of the Gravity field an Ocean Circulation Explorer (GOCE) in 2009 the study of Earth ' s gravity field received another boost. After the time-dependent and long-wavelength information from the Gravity Recovery and Climate Experiment (GRACE) mission a new sensor with high accuracy and spatial resolution was available for determination of the Earth ' s gravity field and geoid. Equipped with a 6-component gradiometer and flying at an altitude of 260 km and less, GOCE provides the most detailed measurements of Earth ' s gravity from space to date. Additionally, GOCE provides gravity gradients, i.e. ., the three-dimensional second derivatives of the gravitational potential. This special issue provides a review of the results presented at the ?GOCE solid Earth workshop ' at the University of Twente, The Netherlands (2012). The goal of this 2-day workshop was to provide training on the usage of GOCE data as well as to present the latest scientific results. The main workshop components were: to show the latest results on GOCE data in relation to solid Earth, provide new users with tips and tricks on which models and software to use, discuss quality and reliability of gravity data and models, and how to integrate GOCE data with own (local) gravity data. The workshop specifically focused on where GOCE data has made a unique contribution and provides insights that would not have been possible without GOCE.
Location: TE 15 New Biology Building
Literature cited 1: Bingham, R., Haines, K., Lea, D., 2015. A comparison of GOCE and drifter-based estimates of the north Atlantic steady-state surface circulation.Int.J.Appl.Earth Obs.Geoinform.35, 140-150. Bouman, J., Ebbing, J., Meekes, S., S., Fattah, R., Fuchs, M., Gradmann, S., Haggmans, R., Lieb, V., Schmidt, M., Dettmering, D., Bosch, W., 2015. GOCE gravity gradient data for lithospeheric modeling Int.J.Appl.Earth Obs.Geoinform.35, 16-30.
Literature cited 2: Braitenberg, C., 2015. Exploration of tectonic structures with GOCE in Africa and across-continents.Int.J.Appl.Earth Obs.Geoinform.35, 88-95. Drinkwater, M.R., Floberghagen, R., Haagmans, R., Muzi, D., Popescu, A., 2003. GOCE: Esa ' s first earth explorer core mission Space Sci.Rev. 108 (1-2), 419-432.

ID: 60178
Title: Quantifying determinants of cash crop expansion and their relatve effects using logistic regression modeling and variance partitioning.
Author: Rui Xiao, Shiliang Su, Gengchen Mai, Zhonghao Zhang, Chenxue Yang.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 258-263 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Cash crop expansion, Land use change, Logistic regression, Spatial determination.
Abstract: Cash crop expansion has been a major land use change in tropical and subtropical regions worldwide. Quantifying the determinants of cash crop expansion. This paper investigated the process of cash crop expansion n Hangzhou region (China) from 1985 to 2009 using remotely sensed data. The corresponding determinants (neighborhood, physical, and proximity) and their relative effects during three periods (1985-1994, 1994-2003, and 2003-2009) were quantified by logistic regression modeling and variance portioning. Results showed that the total area of cash crops increased from 58,874.1 ha in 1985 to 90, 375.1 ha in 2009, with a net growth of 53.5 %. Cash crops were more likely to grow in loam soils. Steep areas with higher elevation would experience less likelihood of cash crop expansion. A consistently higher probability of cash crop expansion was found on places with abundant farmland and forest cover in the three periods. Besides, distance to river and lake, distance to country center, and distance to provincial road were decisive determinants for farmer ' s choice of cash crop plantation. Different categories of determinants and their combinations exerted different influences on cash crop expansion. The joint effects of neighbourhood and proximity determinants were the strongest, and the unique effect of physical determinants decreased with time. Our study contributed to understanding of the proximate drivers of cash crop expansion in subtropical regions.
Location: TE 15 New Biology Building
Literature cited 1: Ansderson, M., Gribble, N., 1998. Partitioning the variation among spatial, temporal and environmental components in a multivariate data set. Aust. J. Ecol. 23, 158-167. Arsanjani, J.J., Helbich, M., Kainz, W., Boloorani, A.D., 2013. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int. J., Appl. Earth Obs. Geoinform. 21, 265-275.
Literature cited 2: Castiblanco, C., Etter, A., Aide, T.M., 2013. Oil palm plantations in Colombia: a model of future expansion. Environ. Sci. Policy 27, 172-183. Cheng, H.Q., Masser, I., 2003. Urban growth pattern modeling: a case study of Wuhan city, PR China.Landsc. Urban Plan. 62, 199-217.

ID: 60177
Title: The application of the Intermittent SBAS (ISBAS) InSAR method to the South Wales Coalfield, UK.
Author: Luke Bateson, Francesca Cigna, David Boon, Andrew Sowter.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 249-257 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: InSAR, Intermittent SBAS, ISBAS, Coal mining, Ground motion, Mineral rebound.
Abstract: Satellite radar interferometry is a well-documented technique for the characterisation of ground motions over large spatial areas. However, the measurement density is often constrained by the land use, with best results obtained over urban and semi urban areas. We use an implementation of the Small Baseline Subset (SBAS) methodology, whereby areas exhibiting intermittent coherence are considered alongside those displaying full coherence, in the final result, to characterize the ground motion over the South Wales Coalfield, United Kingdom, 55 ERS-1/2 Synthetic Aperture Radar (SAR) C-band images for the period between 1992 and 1999 are processed using the ISBAS (Intermittent Small Baseline Subset) technique, which provides 3.4 times more targets, with associated measurements than a standard SBAS implementation. The dominant feature of the observed motions is a relatively large spatial area of uplift. Uplift rates are as much as 1cm/yr. and are centered on the part of the coalfield which was most recently exploited. Geological interpretation reveals that this uplift is most likely a result of mine water rebound. Collieries in this part of the coalfield required a ground water to be pumped to enable safe coal extraction; following their closure pumping activity ceased allowing the water levels to return to equilibrium. Te ISBAS technique offers significant improvements in measurement density ensuring an increase in detection of surface motions and enabling easier interpretation.
Location: TE 15 New Biology Building
Literature cited 1: Banton, C., Bateson, L., McCormack, H., Holley, R., Watson, I., Burren, R., Lawrence, D., Cigna, F., 2013.Monitoring post-closure large scale surface deformation in mining areas. In: Fourie, A.B., Tibbett, M. (Eds). Mine closure 2013. Australian Centre for Geomechanics,Perth (in press) Berardino, P.,Fornaro, G., Lanari, R., Sansosti, E., 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans.Geosci. Remote Sens. 40 (11), 2375-2383.
Literature cited 2: Brabham, P., 2001. The rise and fall of the South Wales Coalfield. In: Engineering Geology of Classic Formations: The Coal Measures, A South Wales Perspective. Geological Society South Wales Regional Group and Engineering Group Seminar, 10th October,p 2,. Cigna, F., Bateson, L., Jordan, C., Dashwood, C., 2012. Fesibility of InSAR technologies for nationwide monitoring of geohazards in Great Britain. In: Remote Sensing and Photogrammetry Society (RSPSoc)-Annual Conference 2012, London, UK, pp. 1-4.

ID: 60176
Title: Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops.
Author: Angela Kross, Heather McNairn , David Lapen, Mark Sunohara, Catherine Champagne.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 235-248 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: RapidEye, Leaf area index, Above-ground dry biomass, Vegetation indices, Corn, Soybean.
Abstract: Leaf area index (LAI) and biomass are important indicators of crop development and the availability of this information during the growing season can support farmer decision making processes. This study demonstrates the applicability of RapidEye multi-spectral data for estimation of LAI and biomass of two crop types (corn and soyabean) with different canopy structure, leaf structure and photosynthetic pathways. The advantages of Rapid Eye in terms of increased temporal resolution (~daily), high spatial resolution (~5m) and enhanced spectral information (includes red-edge band) are explored as an individual sensor and as part of a multi-sensor constellation. Seven vegetation indices based on combinations of reflectance in green, red, red-edge and near infrared bands were derived from RapidEye imagery between 2011 and 2013. LAI and biomass data were collected during the same period for calibration and validation of the relationships between vegetation indices and LAI and dry above-ground biomass. Most indices showed sensitivity to LAI from emergence to 8 m2/m2. The normalized difference vegetation index (NDVI), the red-edge NDVI and the green NDVI were insensitive to crop type and had coefficients of variations (CV) ranging between 19 and 27 %; and coefficients of determination ranging between 86 and 88 %. The NDVI performed best for the estimation of dry leaf biomass (CV = 27 % and r2 =090) and was also insensitive to crop type. The red-edge indices did not show any significant improvement in LAI and biomass estimation over traditional multispectral indices. Cumulative vegetation indices showed strong performance for estimation of total dry above-ground biomass, especially for corn (CV ? 20 %). This study demonstrated that continuous crop LAI monitoring over time and space at the field level can be achieved using a combination of RapidEye, Landsat and SPOT data and sensor-dependent best-fit functions. This approach eliminates/ reduces the need for reflectance resampling, Vis inter-calibration and spatial resampling.
Location: TE 15 New Biology Building
Literature cited 1: Bala, S.K., Islam, A.S., 2009. Correlation between potato yield and MODIS-derived vegetation indices. Int. J. Remote Sens. 30, 2491-2507. Bastiaanssen, W.G.M., Molden, D.J., Makin, I.W., 2000. Remote sensing for irrigated agriculture: examples from research and possible applications. Agric. Water Manage. 46, 137-155.
Literature cited 2: Beckschafer, P., Fehrmann, L., Harrison, R.D., Xu, J., Kleinn, C., 2014. Mapping leaf area index in subtropical upland ecosystems using rapideye imagery and the randomforest algorithm.iForest 7, 1-11. Bolton, D.K., Friedl, M.A., 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric. For. Meteorol. 173, 74-84.

ID: 60175
Title: A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques.
Author: Komeil Rokni, Anuar Ahmad, Karim Solaiman, Sharifeh Hazini.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 226-234 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Surface water, Change detection, Image fusion, Classification.
Abstract: Normally, to detect surface water changes, water features are extracted individually using multi-temporal satellite data, and then analyzed and compared to detect their changes. This study introduced a new approach for surface water change detection, which is based on integration of pixel level image fusion and image classification techniques. The proposed approach has the advantages of producing a pansharpened multispectral image, simultaneously highlighting the changed areas, as well as providing a high accuracy result. In doing so, various fusion techniques including Modified IHS, High Pass Filter, Gram Schmidt, and Wavelet-PC were investigated to merge the multi-temporal Landsat ETM+ 2000 and TM 2010 images to highlight the changes. The suitability of the resulting fused images for change detection was evaluated using edge detection, visual interpretation, and quantitative analysis methods. Subsequently, artificial neutral network (ANN), support vector machine (SVM), and maximum likelihood (ML) classification techniques were applied to extract and map the highlighted changes. Furthermore, the applicability of the proposed approach for surface water change detection was evaluated in comparison with some common change detection methods including image differencing, principal components analysis, and post classification comparison. The results indicate that Lake Urmia lost about one third of its surface area in the period 2000-2010. The results illustrate the effectiveness of the proposed approach, especially Gram Schmidt-ANN and Gram Schmidt-SVM for surface water change detection.
Location: TE 15 New Biology Building
Literature cited 1: Aiazzi, B., Alparone, L., Baronti, S., Garzelli, A., 2002. Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis. IEEE Trans. Geosci. Remote Sens. 40, 2300-2312. Alesheikh, A.A., Ghorbanali, A., Nouri, N., 2007. Castline change detection using remote sensing. Int. J. Environ. Sci. Technol. 4, 61-66.
Literature cited 2: Bovolo, F., Bruzzone, L., Capobianco, L., Garzelli, A., Marchesi, S., Nencini, F., 2010. Analysis of the effects of pansharpening in change detection on VHR imges. IEEE Geosci. Remote Sens. Lett. 7, 53-57. Carper, W.J., Lillesand, T.M., Kiefer, R.W., 1990. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogramm. Eng. Remote Sens. 56, 459-467.

ID: 60174
Title: Evaluating the feasibility of multitemporal hyperspectral remote sensing for monitoring bioremediation.
Author: Marleen Noomen, Annika Hakkarainen, Mark van der Meijde, Harald van der Werff.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 217-225 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Multitemporal, Hyperspectral remote sensing, Benzene, Bioremediation, Red edge position, Pollution.
Abstract: In recent years, several studies focused on the detection of hydrocarbon pollution in the environment using hyperspectral remote sensing. Particularly the indirect detection of hydrocarbon pollution, using vegetation reflectance in the red edge region, has been studied extensively. Bioremediation is one of the methods that can be applied to clean up polluted sites. So far, there have been no studies on monitoring of bioremediation using (hyperspectral) remote sensing. This study evaluates the feasibility of hyperspectral remote sensing for monitoring the effect of bioremediation over time. Benzene leakage at connection points along a pipeline was monitored by comparing the red edge position (REP) in 2005 and 2008 using HyMap airborne hyperspectral images. REP values were normalized in order to enhance local variations caused by a change in benzene concentrations. 11 out of 17 locations were classified correctly as remediated, still polluted, or still clean, with a total accuracy of 645 %. When only polluted locations that were remediated were taken into account, the (user ' s) accuracy was 71%.
Location: TE 15 New Biology Building
Literature cited 1: Bammel, B.H., Birnie, R.W., 1994.Spectral reflectance response of big sagebrush to hydrocarbon-induced stress in the Bighorn Basin, Wyoming. Photogramm. Eng. Remote. Sens. 60, 87-96. Boochs, F., Kupfer, G., Dockter, K., Kuhbauch, W., 1990. Shape of the red edge as vitality indicator for plants. Int .J. Remote Sens. 11, 1741-1753.
Literature cited 2: Clevers, J.G.P.W., De Jong, S.M., Epema, G.F., Van Der Meer, F.D., Bakker, W.H., Skidmore, A.K., Scholte, K.H., 2002. Derivation of the red edge index using the MERIS standard band setting. Int. J. Remote Sens. 23, 3169-3184. Cocks, T., Jenssen, R., Stewart, A., Wilson, I., Shields, T., 1998. The HyMap airborne hyperspectral sensor: the system, calibration and performance. In: Schaepman, M., Schlapfer, D., Itten, K. (Eds), Proceeding of 1st EARSeL Workshop on Imaging Spectroscopy. Zurich, Switzerland, 6-8 October 1998, pp. 37-42.

ID: 60173
Title: Analysis and simulation of land use spatial pattern in Harbin prefecture based on trajectories and cellular automata-Markov modelling.
Author: Wenfeng Gong, Li Yuan, Wenyi Fan, Philip Stott.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 207-216 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Land use change, CA-Markov, Balance states transition matrix, Spatio-temporal evolution.
Abstract: There have been rapid population and accelerating urban growth with associated changes in land use and soil degradation in northeast China, an important grain-producing region. The development of integrated use of remote sensing, geographic information systems, and combined cellular automata-Markov models has provided new means f assessing changes in land use and land cover, and has enabled projection of trajectories in to the future. We applied such techniques to the prefecture-level city of Harbin, the tenth largest city in China. We found that there had been significant losses of the land uses termed ?cropland?, ? grassland?, ? wetland?, and ?floodplain? in favor of ?built-up land? and lesser transformations from ? floodplain? to ?forestland? and ?water body? over the 18-year period. However, the transition was not a simple process but a complex network of changes, interchanges, and multiple transitions. In the absence of effective land use policies, projection of past trajectories into a balance state in the future would result in the decline of cropland from 65.6% to 46.9% and the increase of built-up area from 7.7 % to 23.0 % relative to the total area of the prefecture in 1989. It also led to the virtual elimination of land use types such as unused wetland and floodplain.
Location: TE 15 New Biology Building
Literature cited 1: Anon, 1962. An outlook of studies on population problems in Japan.V. Retrospect and prospect. Japanese National Commission for UNESCO, Tokyo. Bell, E.J., 1974. Markov analysis of land use change-application of stochastic processes to remotely sensed data. Socioecon.Plann.Sci.8, 311-316.
Literature cited 2: Bormann, H., Breuer, L., Graeff, T., Huisman, J.A., 2007. Analysing the effects of soil properties changes associated with land use changes on the simulated water balance: a comparison of three hydrological catchment models for scenario analysis. Ecol. Model. 209, 29-40. Brath, A., Montanari, A., Moretti, G., 2006. Assessing the effect on food frequency of land use change via hydrological simulation (with uncertainty). J. Hydrol. 324, 141-153.

ID: 60172
Title: Robust methods for assessing the accuracy of linear interpolated DEM.
Author: Bin Wang, Wenzhong Shi, Eryong Liu.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 198-206 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: DEM accuracy, Interpolation residuals, Robust estimation, Confidence interval, Monte Carlo simulation.
Abstract: Methods for assessing the accuracy of a digital elevation model (DEM) with emphasis on robust methods have been studied in this paper. Based on the squared DEM residual population generated by the bi-linear interpolation method, three average-error statistics including (a) mean, (b) median, and (c) M-estimator are thoroughly investigated for measuring the interpolated DEM accuracy. Correspondingly, their confidence intervals are also constructed for each average error statistic to further evaluate the DEM quality. The first method mainly utilizes the student distribution while the second and third are derived from the robust theories. These innovative robust methods possess the capability of counteracting the outlier effects or even the skew distributed residuals in DEM accuracy assessment. Experimental studies using Monte Carlo simulation have commendably investigated the asymptotic convergence behavior of confidence intervals constructed by these three methods with the increase of sample size. It is demonstrated that the robust methods can produce more reliable DEM accuracy assessment results compared with those by the classical t-distribution-based method. Consequently, these proposed robust methods are strongly recommended for assessing DEM accuracy, particularly for those cases where the DEM residual population is evidently non-normal or heavily contaminated with outliers.
Location: TE 15 New Biology Building
Literature cited 1: Aguilar, F.J., Aguilar, M.A., Aguera, F., 2007. Accuracy assessment of digital elevation models using a non-parametric approach. Int. J. Geogr. Inf. Sci. 21, 667-686. Chaplot, V, Darboux, F., Bourennane, H., Leguedois, S., Silvera, N., Phachomphon, K., 2006. Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density. Geomorphology 77, 126-141.
Literature cited 2: Chrysoulakis, N., Abrams, M., Kamarianakis, Y., Stanislawski, M., 2011. Validation of ASTER GDEM for the area of Greece. Photogramm. Eng. Remote Sens. 77, 157-165. de Oliveira, C.G., Paradella, W.R., 2009. Evaluating the quality of the Digital Elevation Models produced from ASTER stereoscopy for topographic mapping in the Brazilian Amazon Region. An. Acad. Bras. Cienc. 81, 217-225.

ID: 60171
Title: Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data.
Author: Zhuokun Pan,Jingfeng Huang, Qingbo Zhou, Limin Wang, Yongxiang Cheng, Hankui Zhang, George Alan Blackburn, Jiing Yan, Jianhong Liu.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 188-197 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: HJ-1 A/B, NDVI time-series, S-G filter, Interpolation, Phenology parameters.
Abstract: With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-lime) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then he NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researchers will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data.
Location: TE 15 New Biology Building
Literature cited 1: Baisch, S., Bokelmann, G.t.HR., 1999. Spectral analysis with incomplete time-series: an example from seismology. Comput. Geosci. 25, 739-750. Begue, A., Vintrou, E., Saad, A., Hiernaux, P., 2014. Differences between cropland and rangeland MODIS phenology (start-of-season) in Mali. Int. J. Appl. Earth Observ. Geoinf, 31, 167-170.
Literature cited 2: Bradley, B.A., Jacob, R.W., Hermance, J.F., Mustard, J.F., 2007. A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sens. Environ. 106, 137-145. Brown, M.E., de Beurs, K.M., 2008. Evaluation of multi-sensor semi-arid crop season parameters based on NDVI and rainfall. Remote Sens. Environ. 112, 2261-2271.

ID: 60170
Title: Evaluating the robustness of models developed from field spectral data in predicting African grass foliar nitrogen concentration using WorldView-2 image as an independent test dataset.
Author: Onisimo Mutanga, Elhadi Adam, Clement Adjorlolo, Elfatih M. Abdel-Rahman.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 178-187 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Grassland nitrogen, Field spectral data, Spectral resampling, WorldView-2
Abstract: In this paper, we evaluate the extent to which the resampled field spectra compare with the actual image spectra of the new generation multispectral Worldview-2 (WV-2) satellite. This was achieved by developing models from resampled field spectra data and testing them on an actual WV-2 image of the study area. We evaluated the performance of reflectance ratios (RI), normalized difference indices (NDI) and random forest (RF) regression model in predicting foliar nitrogen concentration in a grassland environment. The field measured spectra were used to calibrate the RF model using a randomly selected training (n = 70 %) nitrogen data set. The model developed from the field spectra resampled to WV-2 wavebands was validated on an independent field spectral test dataset as well as on the actual WV-2 image of the same area (n = 30 %, bootstrapped a 100 times.) . The results show that the model developed using RI could predict nitrogen with a mean R2 of 0.74 and 0.65 on an independent field spectral test dataset and on the actual WV-2 image, respectively. The root mean square error of prediction (RMSE%) was 0.17 and 0.22 for the field test data set and the WV-2 image, respectively. Results provide an insight on the magnitude of errors that are expected when up-scaling field spectral models to airborne or satellite image data. The prediction also indicates the unceasing relevance of field spectroscopy studies to better understand the spectral models critical for vegetation quality assessment.
Location: TE 15 New Biology Building
Literature cited 1: Abdel-Rahman, E.M., Ahmed, F.B., Ismail, R., 2013. Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using E0-1 Hyperion hyperspectral data. Int. J. Remote Sens. 34, 712-728. Abdel-Rahman, E.M., Ahmed, F.B., Van den Berg, M., 2010. Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy. Int. J. Appl. Earth Obs.Geoinf. 12, S52-S57.
Literature cited 2: Adam, E., Mutanga, O., 2009. Spectral discrimination of papyrus vegetation (Cyperus Papyrus L) in swamp wetlands using field spectrometry. ISPRS J. Photogramm. Remote Sens. 64, 612-620. Adjorlolo, C., Cho, M.A., Mutanga, O., Ismail, R., 2012. Optimizing spectral resolutions for the classification of C3 and C4 grass species, using wavelengths of known absorption features. J.Appl.Remote Sens. 6, http: dx.doi.org/10.1117/1.JRS.6.063560.

ID: 60169
Title: Reducing background effects in orchards through spectral vegetation index correction.
Author: Jonathan Van Beek, Laurent Tits, Ben Somers, Tom Deckers, Pieter Janssens, Pol Coppin.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 167-177 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Biophysical variables, Mixture problem, Orchards, Canopy cover fraction, Vegetation indices, Signal unmixing.
Abstract: Satellite remote sensing provides an alternative to time-consuming and lobor intensive in situ measurements of biophysical variables in agricultural crops required for precision agriculture applications. In orchards, however, the spatial resolution causes mixtures of canopies and background (i.e. soil, grass and shadow), hampering the estimation of these biophysical variables. Furthermore, variable background mixtures obstruct meaningful comparisons between different orchard blocks, rows or within each row. Current correction methodologies use spectral differences between canopies and background, but struggle with a vegetated orchard floor. This background influence and the lack of a generic solution are addressed in this study. Firstly, the problem was demonstrated in a controlled environment for vegetation indices sensitive to chlorophyll content, water content and leaf area index. Afterwards, traditional background correction methods (i.e., soil-adjusted vegetation indices and signal unmixing) were compared to the proposed vegetation index correction. This correction was based on the mixing degree of each pixel (i.e. tree cover fraction) to rescale the vegetation indices accordingly and was applied to synthetic and Worldview-2 satellite imagery. Through the correction, the effect of background admixture for vegetation indices was reduced, and the estimation of biophysical variables was improved (? R2 =0.2-0.31).
Location: TE 15 New Biology Building
Literature cited 1: Acevedo-Opazo, C., Tisseyre, B., Guillaume, S., Ojeda, H., 2008. The potential of high spatial resolution information to define within-vineyard zones related to vine water status. Precis. Agric, 9 (5), 285-302. Adler-Golden, S.M., Berk, A., Bernstein, L.S., Richtsmeir, S., Acharya, P.K., Matthew, M.W., Anderson, G.P., et al., 1998. FLAASH, MODTRAN4 atmospheric correction package from hyperspectral data retrievals and simulation, In: Green, R.O. (Ed), Summaries of the Seventh JPL Airborne Earth Science Workshop. January 12-16, 1998, p. 442.
Literature cited 2: Aksoy, S., Yalniz, I.Z., Tasmedir, K., 2012. Automatic detection and segmentation of orchards using very high resolution imagery. IEEE Trans. Geosci. Remote Sens. 50 (8), 3117-3131. Delalieux, S., Somers, B., Hereijgers, S., Verstraeten, W., Keulemans, W., Coppin, P., 2008. A near-infrared narrow-waveband ratio to determine leaf area index in orchards. Remote Sens. Environ. 112 (10), 3762-3772.

ID: 60168
Title: Wheat lodging monitoring using polarimetric index from RADARSAT-2 data.
Author: Hao Yang, Erxue Chen, Zengyuan Li, Chunjiang Zhao, Guijun Yang, Stefano Pignatti, Raffaele Casa, Lei Zhao.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 157-166 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Wheat lodging, Radarsat-2, Polarimetric feature, Multitemporal, Monitoring, Disaster.
Abstract: The feasibility of monitoring lodging of wheat fields by exploiting fully polarimetric C-band radar images has been investigated in this paper. A set of backscattering intensity features and polarimetric features, derived by target decomposition techniques, was extracted from 5 consecutive Radarsat-2 images. The temporal evolutions of these features of lodging wheat fields were investigated as a function of DAS (day after sowing) during the entire growing season. The temporal behavior was compared between typical lodging fields and normal fields in different growing stages. It was found that polarimetric feature from synthetic aperture radar (SAR) data was very sensitive to wheat lodging. Then a method called polarimetric index, availing the sensitivity of polarimetry to the structure, was put forward to monitor wheat lodging. The method was validated by two sets of in situ data collected in Shangkuli Farmland area, Inner Mongolia, China, at heading and ripe stages of spring wheat. Almost all the lodging fields were successfully distinguished from normal fields. Furthermore, the result revealed that the polarimetric index can reflect the intrinsic feature of lodging wheat with god anti-inference ability such as wheat growth difference. While optical sensors relied on its spectral features to monitor crop lodging, the proposed method based on radar data utilized polarimetric features to monitor crop lodging.
Location: TE 15 New Biology Building
Literature cited 1: Baker, C.J., Berry, P.M., Spink, J.H., Sylvester-Bradley, R., Griffin, J.M., Scott, R.K., Clarke, R.W., 1998. A method for the assessment of the risk of wheat lodging. J. Theor. Biol. 194 (4), 587-603. Bao, Y., Zhang, J., Liu, X., Wang, Y., Ma, D., Sun, Z., 2013. Measurement and analysis of reflected information from crops canopy suffering from wind disaster influence. Spectrosc.Spectral Anal. 33 (4), 1057-1060.
Literature cited 2: Berry, P.M., Spink, J., 2012. Predicting yield loses caused by lodging in wheat. Field Crops Res. 137, 19-26. Berry, P.M., Sterling, M., Baker, C.J., Spink, J., Sparkes, D.L., 2003. A calibrated model of wheat of lodging compared with field measurements. Agric. Forest Meteorol. 119, 167-180.

ID: 60167
Title: Environmental assessment and land change analysis in seminatural land covers applicable to land management.
Author: Teresa Bullon.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 147-156 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Remote sensing, NDVI time series, Change detection, Landuse-land cover, Iberian Peninsula.
Abstract: The present research is based on the hypertemporal analysis of a set of 2012 images from the NDVI index from January 2003 to March 2012 provided by the medium-resolution sensor MODIS TERRA. The study area is located in the center of the Iberian Peninsula (Spain). The specific objectives of the study area to investigate the rhythms of the annual development of the NDVI of each of the classes, determine the classes that are the most sensitive to climatic variability and define the interannual sequences of variation in NDVI with an associated trend analysis. The classes situated in lower-altitude areas are strongly dependent on autumn rainfall and present negative temporal tendencies, and those situated at mountaintops and on upper slopes are correlated with spring-summer temperatures and exhibit stable or positive tendencies
Location: TE 15 New Biology Building
Literature cited 1: AEMET, 2010. Informe Mensual Climatologico. Enero 2010. Ministerio de Medio Ambiente Y Medio Rural Y Marino, Madrid http:// www.aemet.es/es/serviciosclimaticos/vigilancia.clima/resumenes. Ali, A. de Bie,C.A.J.M., Skidmore, A.K., Scarrott,R.G., Lymberakis,P., 2014. Mapping the heterogeneity of natural and semi-natural landscapes.Int.J.Appl.Earth Obs.Geoinf.26 (176), http://dx.doi.org/10.1016/j.jag.2013.06.007.
Literature cited 2: de Bie, C.A. J.M., Khan, M.R., Smakhtin,V.U., Venus, Weir, M.J.C., Smaling,E.M.A., 2011. Analysis of multitemporal SPOT NDVI images for small-scale land use mapping. Int.J.Remote Sens. 32 (21), 6673-6693, http://dx.doi.org/10.1080/01431161.2010.512939. De Bie, C.A.J.M., Nguyen, T.T.H., Ali,A., Scarrot, R., Skidmore, A.K., 2012. LaHMa: a landscape heterogeneity mapping method using hypertemporal datasets.Int.J.Geor.Inf.Sci.26, 1-16, http://dx.doi.org/10.1080/13658816.2012.712126.

ID: 60166
Title: Temporal optimization of image acquisition for land cover classification with Random Forest and MODIS time-series.
Author: Ingmar Nitze, Brian Barrett, Fiona Cawkwell.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 136-146 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Land cover classification, Random Forest, MODIS, Machine learning.
Abstract: The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types an land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimization of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Fortest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD 13 Q1) Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13 % using NDVI and 5 % using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8-10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless, anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use o high-temporal, moderate solution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results.
Location: TE 15 New Biology Building
Literature cited 1: Atzberger, C., Eilers, P.H., 2011. A time series for monitoring vegetation acivity and phenology at 10-daily time steps covering large parts of South America. Int. J. Digit.Earth 4 (5), 365-386. Beck, P.S., Atzberger, C., Hogda, K.A., Johansen, B., Skidmore, A.K., 2006. Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sens. Environ. 100(3), 321-334.
Literature cited 2: Breiman, L., Friedman,J.H., Olshen, R.A., Stone, C.J., 1984. Classification and Regression Trees. Wardsworth & Brooks, Monterey, CA. Breiman, L. , 2001. Random Forests. Mach. Learn. 45, 5-32.