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ID: 60060
Title: Distance metric -based forest cover change detection using MODIS time series.
Author: Xiaoman Huang, Mark A. Friedl.
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. 29. 78-92 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: MODIS, Change detection, Distance metrics, Time series, Forest disturbance.
Abstract: More than 12 years of global observations are now available from NASA ' s Moderate Resolution Imaging Spectroradiometer (MODIS). At this time series grows, the MODIS archive provides new opportunities for identification and characterization of land cover at regional to global spatial scales and interannual to decadal temporal scales. In particular, the high temporal frequency of MODIS provides a rich basis for monitoring land cover dynamics. At the same time, the relatively coarse spatial resolution of MODIS (250-500 m) presents significant challenges for land cover change studies. In this paper, we present a distance metric-based change detection method for identifying changed pixels at annual time steps using 500 m MODIS time series data. The approach we describe uses distance metrics to measure (1) the similarity between a pixel ' s annual time series for pixels of the same land cover class and (2) the similarity between annual time series from different years at the same pixel. Pre-processing, including gap-filling, smoothing and temporal subsetting of MODIS 500 m Nadir BRDF-adjusted Reflectance (NBAR) time series is essential to the success of our method. We evaluated our approach using three case studies. We first explored the ability of our method to detect change in temperate and boreal forest training sites in North America and Eurasia. We applied our method to map regional forest change in the Pacific Northwest region of the United States, and in tropical forests of the Xingu River Basin in Mato Grosso, Brazil. Results from these case studies show that the method successfully identified pixels affected by logging and fire disturbance in temperate and boreal forest sites. Change detection results in the Pacific Northwest compared well with a Landsat-based disturbance map, yielding a producer ' s accuracy of 85 %. Assessment of change detection results for the Xingu River Basin demonstrated that detection accuracy improves as the fraction of deforestation within a MODIS pixel increases, but that relatively small changes in forest cover were still detectable from MODIS. Annually, over 80% of pixels with > 20% deforested area were correctly identified and the timing of change showed good agreement with reference data. Errors of commission were largely associated with pixels located at the edges of disturbance events and inadequate characterization of land cover changes unrelated to deforestation in the reference data. Although our case studies focused on forests, this method is not specific to detection of forest cover change and has the potential to be applied to other types of land cover change including urban and agricultural expansion and intensification.
Location: TE 15 New Biology Building
Literature cited 1: Achard, F., Eva, H.D., Mayaux, P., Stibig, H.-J., Belward, A., 2004. Improved estimates of net carbon emissions from land cover change in the tropics for the 1990. Global Biogeochemical Cycles 18 (2), GB 2008. Angelici, G., Bryant, N., 1977. A land use change monitoring system based on LAND SAT. In: LARS Symposia.
Literature cited 2: Arvor, D., Meirelles, M., Vargas, R., Skorupa, L., Fidalgo, E., Dubreuil, V., Herlin, I., Berroir, J.-P., 2010. Monitoring land use changes around the indigenous lands of the Xingu Basin in Mato Grosso, Brazil. In: Proceedings of IGARSS ' 10. Baccini, A., Goetz, S.J., Walker, W.S., Laporte, N.T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P.S.A., Dubayah, R., Friedl, M.A., Samanta, S., Houghton, R.A., 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2 (3), 1182-185.

ID: 60059
Title: Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM.
Author: Otavio C. Montanher, Evlyn M.L.M. Novo, Claudio C.F. Barbosa, Camilo D. Renno, Thiago S.F. Silva.
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. 29. 67-77 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Top of atmosphere reflectance, Multiple regressions, Geology of the Amazon, Fluvial sediments, Spectral bands, Band ratios.
Abstract: Suspended sediment yield is a very important environmental indicator within Amazonian fluvial systems, especially for rivers dominated by inorganic particles, referred to as white water rivers. For vast portions of Amazonian rivers, suspended sediment concentration (SSC) is measured infrequently or not at all. However, remote sensing techniques have been used to estimate water quality parameters worldwide, from which data for suspended matter is the most successfully retrieved. This paper presents empirical models for SSC retrieval in Amazonian white water rivers using reflectance data derived from Landsat 5/TM. The models use multiple regression for both the entire dataset (global model, N=504) and for five segmented datasets (regional models) defined by general geological features of drainage basins. The models use VNIR bands, band ratios, and the SWIR band 5 as input. For the global model, the adjusted R2 is 0.76, while the adjusted R2 values for regional models vary from 0.77 to 0.89, all significant (p-value < 0.0001). The regional models are subjected to the leave- one-out cross validation technique, which presents robust results. The findings show that both the average error of estimation and the standard deviation increase as the SSC range increases. Regional models were more accurate when compared with the global model, suggesting changes in optical properties of water sampled at different sampling stations. Results confirm the potential for the estimation of SSC from Landsat /TM historical series data for the 1980s and 1990s, for which the in situ database is scarce. Such estimates supplement the SSC temporal series, providing a more comprehensive SSC temporal series which may show environmental dynamics yet unknown.
Location: TE 15 New Biology Building
Literature cited 1: Aalto, R., Dunne, T., Guyot, J.L., 2006. Geomorphic controls on Andean denudation dates. Journal of Geology 114, 85-99. Aranuvachapun, S., Walling, D.E., 1988. Landsat-MSS radiance as a measure of suspended sediment in the lower Yellow River (Hwang Ho). Remote Sensing of Environment 25, 145-165.
Literature cited 2: Baby, P., Guyot, J.L., Herail, G., 2009. Tectonic control of erosion and sedimentation in the Amazon Basin of Bolvia. Hydrological Processes 23, 3225-3229. Chander, G., Markham, B.L., Helder, D.L., 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 113, 893-903.

ID: 60058
Title: Environmental monitoring of EI Hierro Island Submarine volcano, by combining low and high resolution satellite imagery.
Author: F. Eugenio, J. Martin, J. Marcello, E. Fraile- Nuez.
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. 29. 53-66 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: El Hierro Island, Underwater volcanic eruption, Environmental impact, Low/high resolution satellite images, Chlorophyll-a, Diffuse attenuation coefficient.
Abstract: El Hierro Island, located at the Canary Islands Archipelago in the Atlantic coast of North Africa, has been rocked by thousands of tremors and earthquakes since July 2011. Finally, an underwater volcanic eruption started 300 m below sea level on October 10, 2011. Since then, regular multidisciplinary monitoring has been carried out in order to quantify the environmental impacts caused by the submarine eruption. Thanks to this natural tracer release, multisensorial satellite imagery obtained from MODIS and MERIS sensors have been processed to monitor the volcano activity and to provide information on the concentration of biological, chemical and physical marine parameters. Specifically, low resolution satellite estimations of optimal diffuse attenuation coefficient (K d) and chlorophyll -a (Chl-a) concentration under these abnormal conditions have been assessed. These remote sensing data have played a fundamental role during field campaigns guiding the oceanographic vessel to the appropriate sampling areas. In addition, to analyze El Hierro submarine volcano area, WorldView-2 high resolution satellite spectral bands were atmospherically and deglinted processed prior to obtain a high-resolution optimal diffuse attenuation coefficient model. This novel algorithm was developed using a matchup data set with MERIS and MODIS data, in situ transmittances measurements and a seawater radiative transfer model. Multisensor and multitemporal imagery processed from satellite remote sensing sensors have demonstrated to be a powerful tool for monitoring the submarine volcanic activities, such as discolored seawater, floating material and volcanic plume, having shown the capabilities to improve the understanding of submarine volcanic processes.
Location: TE 15 New Biology Building
Literature cited 1: Eugenio, F., Martin, J., Marcello, J., Bermejo, J.A., 2012. Atmospheric correction models for high resolution WorldView -2 multispectral imagery: a case study in Canary Islands, Spain. In: Proceedings SPIE Remote Sensing, Edimburgh, September. Fraile-Nuez, E., Gonzalez-Davila, M., Santana-Casiano, J.M., Aristegui, J., AlonsoGonzalez, I.J., Hernandez-Leon, S., Blanco, M.J., Rodriguez-santana, A., Hernandez-Guerra, A., Gelado-Caballero, M.D., Eugenio, F., Marcello, J., de Armas, D., Dominguez-Yanes, J.F., Montero, M.F., Laetsch, D.R., Vellez-Belchi, P., Ramos, A., Ariza, A.V., Comas-Rodriguez, I., Benitez-Barrios, V.M., 2012. The submarine volcano eruption at the Island of El Hierro: physical-chemical perturbation and biological response. Scientific Reports 2, 486.
Literature cited 2: Joyce, K., Belliss, S., Samsonov, S., McNeill, S., Glassey, P., 2009. A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Progress in Physical Geography 33 (2), 183-207. Kay, S., Hedley, J., Lavender, S., 2009. Sun glint correction of high and low spatial resolution images of aquatic scenes: a review of methods for visible and near-infrared wavelengths. Remote Sensing 1, 697-730.

ID: 60057
Title: A bootstrap method for assessing classification accuracy and confidence for agricultural land use mapping in Canada.
Author: Catherine Champagne, Heather Mc Nairn, Bahram Daneshfar, Jiali Shang.
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. 29. 44-52 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: None
Abstract: Land cover and land use classifications from remote sensing are increasingly becoming institutionalized framework data sets for monitoring environmental change. As such, the need for robust statements of classification accuracy is critical. This paper describes a method to estimate confidence in classification accuracy using a bootstrap approach. Using the method, it was found that classification accuracy and confidence, while closely related, can be used in complementary ways to provide additional information on map accuracy and define groups of classes and to inform the reference sampling strategies. Overall classification accuracy increases with an increase in the number of fields surveyed, where the width of classification confidence bounds decreases. Individual class accuracies and confidence were non-linearly related to the number of fields surveyed. Results indicate that some classes can be estimated accurately and confidently with fewer numbers of samples, where as others require larger reference data sets to achieve satisfactory results. This approach is an improvement over other approaches for estimating class accuracy and confidence as it uses repetitive sampling to produce a more realistic estimate of the range in classification accuracy and confidence that can be obtained with different reference data inputs.
Location: TE 15 New Biology Building
Literature cited 1: Carfagna, E., Gallego, F.J., 2005. Using remote sensing for agricultural statistics. International Statistical Review 73, 389-404. Chen, D.M., Stow, D., 2002. The effect of training strategies on supervised classification at different spatial resolutions. Photogrammetric Engineering & Remote Sensing 68, 1155-1161.
Literature cited 2: Congalton, R.G., Green, K., 1999. Assessing the accuracy of Remotely Sensed Data: Principles and Practices. Lewis, Boca Raton, F.L, 137 pp. DiCiccio, T.J., Efron, B., 1996. Bootstrap confidence intervals. Statistical Science 11, 189-212.

ID: 60056
Title: Quantification of anthropogenic and natural changes in oil sands mining infrastructure land based on RapidEYE and SPOT5.
Author: Ying Zhang, Bert Guindon, Nicholas Lantz, Todd Shipman, Dennis Chao, Don Raymond.
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. 29. 31-43 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Extraction of land changes, Change detection, Land disturbance, Mining land, Reclamation, Regrowth.
Abstract: Natural resources development, spanning exploration, production and transportation activities, alters local land surface at various spatial scales. Quantification of these anthropogenic changes, both permanent and reversible, is needed for compliance assessment and for development of effective sustainable management strategies. Multi-spectral high resolution imagery data from SPOT5 and RapidEye were used for extraction and quantification of the anthropogenic and natural changes for a case study of Alberta bitumen (oil sands) mining located in the Western Boreal Plains near Fort McMurray, Canada. Two test sites representative of the major Alberta bitumen production extraction processes, open pit and in situ extraction, were selected. A hybrid change detection approach, combining pixel -and object-based target detection and extraction, is proposed based on Change Vector Analysis (CVA). The extraction results indicate that the changed infrastructure landscapes of these two sites have different footprints linked with their differing oil sands production processes. Pixel-and object-based accuracy assessments have been applied for validation of the change detection results. For manmade disturbances, except for those fine linear features such as seismic lines, accuracies of about 80% have been achieved at the pixel level while, at the object level, these rise to 90-95%. Since many disturbance features are transient, a new landscape index, entitled the re-growth Index, has been formulated at single object level specifically to monitor restoration of these features to their natural state. It is found that the temporal behavior of the Re-growth Index in an individual patch varies depending on the type of natural land cover. In addition, the Re-growth Index is also useful for assessing the detectability of disturbed sites.
Location: TE 15 New Biology Building
Literature cited 1: Alberta Environment, 2009. State of the Environment: Oil Sands Reclamation. www3.gov.ab.ca Alberta Environment, 2012. Lower Athabasca Region Plan, 94 pp. http:// environment.alberta.ca.
Literature cited 2: Al-Khudhairy, D.H.A., Caravaggi, I., Giada, S., 2005. Structural damage assessments from Ikonos data using change detection, object-oriented segmentation, and classification techniques. Photogrammetric Engineering & Remote Sensing 71 (7), 825-837 Aronoff, S., Ross, W.A., 1982. Environmental monitoring of the Athabasca oil sands using landsat data. Photogrammetria 38 (3), 77-86.

ID: 60055
Title: Efficiency assessment of using satellite data for crop area estimation in Ukraine.
Author: Francisco Javier Gallego, Nataliia Kussul, Sergii Skakun, Oleksii Kravchenko, Andrii Shelestov, Olga Kussul.
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. 29. 22-30 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Remote sensing, Agriculture, Crop area, Classification, Ukraine.
Abstract: The knowledge of the crop area is a key element for the estimation of the total crop production of a country and, therefore, the management of agricultural commodities markets. Satellite data and derived products can be effectively used for stratification purposes and a-posteriori correction of area estimates from ground observations. This paper presents the main results and conclusions of the study conducted in 2010 to explore feasibility and efficiency of crop area estimation in Ukraine assisted by optical satellite remote sensing images. The study was carried out on three oblasts in Ukraine with a total area of 78, 500 km2. The efficiency of using images acquired by several satellite sensors (MODIS, Landsat-5/TM, AWiFS, LISS III, and Rapid Eye) combined with a field survey on a stratified sample of square segments for crop area estimation in Ukraine is assessed. The main criteria used for efficiency analysis are as follows: (i) relative efficiency that shows how much time the error of area estimates can be reduced with satellite images, and (ii) cost-efficiency that shows how much time the costs of ground surveys for crop area estimation can be reduced with satellite images. These criteria are applied to each satellite image type separately, i.e. no integration of images acquired by different sensors is made, to select the optimal dataset. The study found that only MODIS and Landsat-5/TM reached cost-efficiency thresholds while AWiFS, LISS-III, and RapidEye images, due to its high price, were not cost-efficient for crop area estimation in Ukraine at oblast level.
Location: TE 15 New Biology Building
Literature cited 1: Allen, J.D., 1990. A look at the remote sensing applications program of the national agricultural statistics service. J. Off. Stat. 6 (4), 393 -409. Arino, O., Gross, D., Ranera, F., Bourg, L., Leroy, M., Bicheron, P., et al., 2007. Glob-Cover: ESA service for global land cover from MERIS. In: IEEE International Geoscience and Remote Sensing Symposium Igarss, Barcelona, Spain, 23-27 July, pp. 2412-2415.
Literature cited 2: Ban, Y., 2003. Synergy of multitemporal ERS and Landsat TM data for classification of agricultural crops. Can.J. Remote Sens. 29 (4), 518-526. Bauer, M.E., Hixson, M.M., Davis, B.J., Etheridge, J.B., 1978. Area estimation of crops by digital analysis of Landsat data. Photogramm.Eng.Rem.Sens.44, 1033-1043.

ID: 60054
Title: Ecological site classification of semiarid rangelands: Synergistic use of Landsat and Hyperion imagery.
Author: Paula D. Blanco, Hector F. del Valle, Pablo J. Bouza, Graciela I. Metternicht, Leonardo A. Hardtke.
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. 29. 11-21 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Ecological site, Hyperion, Endmember selection, Mixture tuned matched filtering, logistic regression, networks, Land management.
Abstract: Ecological sites are the basic entity used in rangeland health assessment. This study evaluates the synergistic use of multi-and hyper-spectral satellite imagery for sub-pixel classification of ecological sites in semiarid rangelands. Hyperion and Landsat enhanced thematic mapper (ETM) data are included in a two-step procedure to mapping ecological sites in Patagonian rangelands of Argentina. Firstly, mixture tuned matched filtering and logistic regression analyses are used for Hyperion data processing to obtain ecological sit probability images in the area covered by hyperspectral imagery. Secondly, artificial neural networks are applied to model the relationships between the spectral response patterns of Landsat and the probability images from Hyperion, and used to map ecological sites over the entire study area. Overall classification accuracy was 81% (Kappa= 0.77) with relatively high accuracies for all ecological sites demonstrating that their spectral signatures are sufficiently distinct to be detectable. Better accuracies were obtained for shrub steppes with desert pavement (producer ' s and user ' s accuracies of 89% and 84 %, respectively), and shrub-grass steppes associated to tertiary calcareous outcrops (producer ' s and user ' s accuracies of 100% and 86%, respectively), while poorer accuracies resulted for shrub-grass steppes on old alluvial plains (producer ' s and user ' s accuracies of 75% and 56%, respectively). Fuzzy maps of ecological sites as presented in this research can provide rangeland managers with a tool to stratify the landscape and organize ecological information for rangeland health assessment and monitoring, prioritizing and selecting appropriate management actions, and promoting the recovery of areas degraded in these environments.
Location: TE 15 New Biology Building
Literature cited 1: Arsenault, E., Bonn, F., 2005. Evaluation of soil erosion protective cover by crop residues using vegetation indices and spectral mixture analysis of multispectral and hyperspectral data. Catena 62 (2-3), 157-172. Aspinall, R.J., 2002. Use of logistic regression for validation of maps of the spatial distribution of vegetation species derived from high spatial resolution hyper-spectral remotely sensed data. Ecol. Model. 157, 301-312.
Literature cited 2: Ballantine, J.A.C., Okin, G.S., Prentiss, D.E., Roberts, D.A., 2005. Mapping north African landforms using continental -scale unmixing of MODIS imagery. Remote Sens. Environ. 47, 470-483. Barros, V., Rivero, M.M., 1982. Mapas de Probabilidad de precipitation en la Provincia del Chubut, Contribucion N? 54. CENPAT-CONICET, Chubut, Argentina.

ID: 60053
Title: Remote estimation of grassland gross primary production during extreme meteorological seasons.
Author: Micol Rossini, Micro Migliavacca, Marta Galvagno, Michele Meroni, Sergio Cogliati, Edoardo Cremonese, Francesco Fava, Anatoly Gitelson, Tommaso Julitta, Umberto Morra di Cella, Consolata Siniscalco, Roberto Colombo.
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. 29. 1-10 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Gross primary production, Vegetation index, PRI, Grassland, Extreme events, Potential photosynthetically active, radiation.
Abstract: Different models driven by remotely sensed vegetation indexes (VIS) and incident photosynthetically active radiation (PAR) were developed to estimate gross primary production (GPP) in subalpine grassland equipped with an eddy covariance flux tower. Hyperspectral reflectance was collected using an automatic system designed for high temporal frequency acquisitions for three consecutive years, including one (2011) characterized by a strong reduction of the carbon sequestration rate during the vegetative season. Models based on remotely sensed and meteorological data were used to estimate GPP, and a cross-validation approach was used to compare the predictive capabilities of different model formulations. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterized by a strong seasonal dynamic. Model performances improved when including also PARpotential defined as the maximal value of incident PAR under clear sky conditions in model formulations. Best performing models are based entirely on remotely sensed data. This finding could contribute to the development of methods for quantifying the temporal variation of GPP also on a broader scale using current and future satellite sensors.
Location: TE 15 New Biology Building
Literature cited 1: Balzarolo, M., Anderson, K., Nichol, C., Rossini, M., Vescovo, L., Arriga, N., et al., 2011. Ground -based optical measurements at European flux sites: a review of methods, instruments and current controversies. Sensors 11, 7954-7981. Bates, D.M., Watts, D.G., 1988. Nonlinear regression analysis and its applications. John Wiley& Sons, New York.
Literature cited 2: Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., et al., 2010. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834-838. Beniston, M., 2005. Mountain climates and climatic change: an overview of processes focusing on the European Alps. Pure and Applied Geophysics 162, 1587-1606.

ID: 60052
Title: Early season monitoring of corn and soybeans with Terra SAR-X and RADARSAT-2
Author: H.McNairn, A. Kross, D.Lapen, R.Caves, J. Shang.
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. 28 252-259 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: TerraSAR-X, RADARSAT-2, Classification, Corn, Soybeans.
Abstract: Early and on-going crop production forecasts are important to facilitate food price stability for regions at risk, and for agriculture exporters, to set market value. Most regional and global efforts in forecasting rely on multiple sources of information from the field. With increased access to data from spaceborne Synthetic Aperture Radar (SAR), these sensors could contribute information on crop acreage. But these acreage estimates must be available early in the season to assist with production forecasts. This study acquired TerraSAR-X and RADARSAT-2 data over a region in eastern Canada dominated by economically important corn and soybean production. Using a supervised decision tree classifier, results determined that either sensor was capable of delivering highly accurate maps of corn and soybeans at the end of the growing season. Accuracies far exceeded 90%. Spatial and multi-temporal filtering approaches were compared and small improvements in accuracies were found by applying the multi-temporal filter to the RADARSAT-2 data. Of significant interest, this study determined that by using only three TerraSAR-X images corn could be accurately identified by the end of June, a mere six weeks after planting and at a vegetative growth stage (V6- sixth leaf collar developed ). However, soybeans required additional acquisitions given the variance in planting densities and planting dates in this region of Canada. In this case, accurate soybean classification required Terra SAR-X images until early August at the start of the reproductive stage (R5- seed development is beginning). Also important, by applying a multi-temporal filter accurate mapping (close to 90%) of corn and soybeans from RADARSAT-2 could occur five weeks earlier (by August 19) than if a spatial filter was used. Thus application of this filtering approach could accelerate delivery of crop inventory for this region of Canada. Corn and soybeans are important commodities both globally and within Canada. This study makes an important contribution as it demonstrates that TerraSAR-X can deliver acreage estimates of these two crops early enough to assist with in-season production forecasting.
Location: TE 15 New Biology Building
Literature cited 1: Agriculture and Agri-Food Canada, June 19, 2009. Corn: Situation and Outlook, Market Outlook Report, Vol. 1, number 2, Published on-line at http://www.agr.gc.ca/pol/mad-dam/index_e.php? s 1=pubs & s2=rmar&s3= php & page= rmar_01_02_2009-06-19 (last accessed 24.07.13). Agriculture and Agri-Food Canada, May 21, 2013. Canada: Outlook for Principal Field Crops, Published on-line at http://www.agr.gc.ca/pol/mad-dam/pubs/fco-ppc/pdf/fco-ppc_2013-05-21_eng.pdf (last accessed 24.07.13).
Literature cited 2: Baghdadi, N., Boyer, N., Todoroff, P., El Hajj, M., Begue, A., 2009. Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island. Remote Sensing of Environment 113, 1724-1738. Ban, Y., 2003. Synergy of multitemporal ERS-1 SAR and Landsat TM data for classification of agricultural crop. Canadian Journal of Remote Sensing 29, 518-526.

ID: 60051
Title: Historical extension of operational NDVI products for livestock insurance in Kenya.
Author: Anton Vrieling, Michele Meroni, Apurba Shee, Andrew G. Mude, Joshua Woodard, C.A.J.M. (Kees) de Bie, Felix Rembold.
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. 28 238-251 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: NDVI, AVHRR, SPOT, MODIS, Index insurance, Intercalibration.
Abstract: Droughts induce livestock losses that severely affect Kenyan pastoralists. Recent index insurance schemes have the potential of being a viable tool for insuring pastoralists against drought-related risk. Such schemes require as input a forage scarcity (or drought) index that can be reliably updated in near real-time, and that strongly relates to livestock mortality. Generally, a long record (>25 years) of the index is needed to correctly estimate mortality risk and calculate the related insurance premium. Data from current operational satellites used for large-scale vegetation monitoring span over a maximum of 15 years, a time period that is considered insufficient for accurate premium computation. This study examines how operational NDVI datasets compare to, and could be combined with the non-operational recently constructed 30-year GIMMS AVHRR record (1981-2011) to provide a near-real time drought index with a long term archive for the arid lands of Kenya. We compared six freely available, near -real time NDVI products: five from MODIS and one from SPOT -VEGETATION. Prior to comparison, all datasets were averaged in time for the two vegetative seasons in Kenya, and aggregated spatially at the administrative division level at which the insurance is offered. The feasibility of extending the resulting aggregated drought indices back in time was assessed using jackknifed R2 statistics (leave-one-year-out) for the overlapping period 2002-2011. We found that division -specific models were more effective than a global model for linking the division -level temporal variability of the index between NDVI products. Based on our results, good scope exists for historically extending the aggregated drought index, thus providing a longer operational record for insurance purposes. We showed that this extension may have large effects on the calculated insurance premium. Finally, we discuss several possible improvements to the drought index.
Location: TE 15 New Biology Building
Literature cited 1: Anyamba, A., Chretein, J. -P., Small, J., Tucker, C.J., Formenty, P.B., Richardson, J.H., Britch, S.C., Schnabelf, D.C., Erickson, R.L., Linthicum, K.J., 2009. Prediction of a Rift Valley fever outbreak. Proceedings of the National Academy of Sciences of the United States of America 106, 955-959. Atzberger, C, Eilers, P.H.C., 2011. A time series for monitoring vegetation activity and phenology at 10-daily time steps covering largeparts of South America. International Journal of Digital Earth 4, 365-386.
Literature cited 2: Atzberger, C., Klisch, A., Mattiuzzi, M., Vuolo, F., 2014. Phenological metrics derived over the European continent from NDVI3g data and MODIS time series. Remote Sensing 6, 257-284. Baltagi, B.H., 2008. Econometric Analysis of Panel Data, fourth ed. John Wiley & Sons Ltd., Chichester, West Sussex, UK.

ID: 60050
Title: Combined use of multi-seasonal high and medium resolution satellite imagery for parcel-related mapping of cropland and grassland.
Author: T.Esch, A. Metz, M. Marconcini, M. Keil.
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. 28 230-237 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Multi-seasonal analysis, High and medium resolution data, Object-oriented classification, Grassland, Crop types.
Abstract: A key factor in the implementation of productive and sustainable cultivation procedures is the frequent and area-wide monitoring of cropland and grassland. In particular, attention is focused on assessing the actual status, identifying basic trends and mitigating major threats with respect to land-use intensity and its changes in agricultural and semi-natural areas. Here, multi-seasonal analyses based on satellite Earth observation (EO) data can provide area-wide, spatially detailed and up-to-date geo-information on the distribution and intensity of land use in agricultural and grassland areas. This study introduces an operational, application-oriented approach towards the categorization of agricultural cropland and grassland based on a novel scheme combining multi-resolution EO data with ancillary geo-information available from currently existing databases. In this context, multi-seasonal high (HR) and medium resolution (MR) satellite imagery is used for both a land parcel -based determination of crop types as well as a cropland and grassland differentiation, respectively. In our experimental analysis, two HR IRS-P6 LISS-3 images are first employed to delineate the field parcels in potential agricultural and grassland areas (determined according to the German Official Topographic Cartographic Information System -ATKIS). Next, a stack of seasonality indices is generated based on 5 image acquisitions (i.e., the two LISS scenes and three additional IRS -P6 A WiFS scenes). Finally, a C5.0 tree classifier is applied to identify main crop types and grassland based on the input imagery and the derived seasonality indices. The classifier is trained using sample points provided by the European Land Use/ Cover Area Frame Survey (LUCAS). Experimental results for a test area in Germany assess the effectiveness of the proposed approach and demonstrate that a multi-scale and multi-temporal analysis of satellite data can provide spatially detailed and thematically accurate geo-information on crop types and the cropland -grassland distribution, respectively.
Location: TE 15 New Biology Building
Literature cited 1: Adv, 2012. Arbeitsgemeinschaft der Vermessungs-verwaltungen der Lander der Bundesrepublik Deutschland: Erlauterungen zum ATKIS-Objektartenkatalog der Arbeitsgemeinschaft der Vermessungsverwaltungen der Lander der Bundesrepublik Deutschland, Available URL: http://www.atkis.de (accessed 28.04.12). Bailey, J.T., Boryan, C., 2010. Remote sensing uses in agriculture at the national agricultural statistics service. In: Proceedings of 5th Conference on Agricultural Statistics, Integrating Agriculture into the National Statistical System (ICAS-V), 12-15 October 2010, Kampala, Uganda.
Literature cited 2: Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing 58, 239-258. Blaes, X., Vanhalle, L., Defourny, P., 2005. Efficiency of crop identification based on optical and SAR image time series. Remote Sensing of Environment 96, 352-365.

ID: 60049
Title: Continuous field mapping of Mediterranean wetlands using sub-pixel spectral signatures and multi-temporal Landsat data.
Author: Julia Reschke, Christian Huttich.
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. 28 220-229 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Wetland mapping, Wetland -dynamics, Land use/ land cover (LULC) Landsat ETM+, Multivariate reflectance analysis, Random forest.
Abstract: Wetlands rank among the most diverse ecosystems on earth and function as important ecosystem service providers. Pressures on wetland ecosystems caused by human activities, such as land use transformations or agricultural intensification, lead to strong wetland degradation. Satellite-based wetland mapping still bears the most uncertainties compared to other land cover types mapping. Image classification techniques have to better adapt to specific wetland characteristics, such as spatial heterogeneity, seasonal dynamics and fuzzy transitions between different land cover classes. For this purpose, a pixel-based method for wetland delineation based on multi-temporal Landsat data in West Turkey was developed and analyzed. In addition to common vegetation indices and texture measures, the usefulness of seasonal indices was tested. Multi-temporal Landsat imagery was combined with high resolution satellite data to extract sub-pixel information of coastal and inland wetland classes based on a random forest regression algorithm. The classification achieved an overall accuracy of 79.02%. In addition to the hard wetland classification the mapping framework provides a map of fractional cover information of different wetland classes including information about fuzzy spatial transitions of highly heterogeneous distribution patterns of wetland habitats and related intra-annual seasonal dynamics. Mapping spatio-temporal wetland dynamics at continuous field scales increases the applicability of Landsat-derived maps for local-scale ecosystem monitoring and environmental management on habitat level.
Location: TE 15 New Biology Building
Literature cited 1: Adam, E., Mutanga, O., Rugege, D., 2010. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wet-lands Ecology and Management 18 (3), 281-296. Archer, K., Kimes, R., 2008. Empirical characterization of random forest variable importance measures. Computational Statistics & Data Analysis 52 (4), 2249-2260.
Literature cited 2: Baker, C, Lawrence, R., Montagne, C., Patten, D., 2006. Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision -tree -based models. Wetlands 26, 465-474. Bartsch, A., Trofaier, A., Hayman, G., Sabel, D., Schlaffer, S., Clark, D., Blyth, E., 2012. Detection of open water dynamics with ENVISAT ASAR in support of land surface modeling at high latitudes, Biogeosciences 9, 703-714.

ID: 60048
Title: Multi-frequency, polarimetric SAR analysis for archaeological prospection.
Author: Christopher Stewart, Rosa Lasaponara, Giovanni Schiavon.
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. 28 211-219 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Archaeology, SAR, PALSAR, RADARSAT-2, Prospection, Polarimetry.
Abstract: The aim of this study is to assess the sensitivity to buried archaeological structures of C- and L-band Synthetic Aperture Radar (SAR) in various polarisations. In particular, single and dual polarised data from thephased Array type L-band SAR (PALSAR) sensor on-board the Advanced Land Observing Satellite (ALOS) is used, together with quadruple polarized (quad pol) data from the SAR sensor on Radarsat-2. The study region includes an isolated area of open fields in the eastern outskirts of Rome where buried structures are documented to exist. Processing of the SAR data involved multitemporal averaging, analysis of target decompositions, study of the polarimetric signatures over areas of suspected buried structures and changes of the polarimetric bases in an attempt to enhance their visibility. Various ancillary datasets were obtained for the analysis, including geological and lithological charts, meteorological data, Digital Elevation Models (DEMs) optical imagery and an archaeological chart. For the Radarsat-2 data analysis, results show that the technique of identifying the polarimetric bases that yield greatest backscatter over anomaly features, and subsequently changing the polarimetric bases of the time series, succeeded in highlighting features of interest in the study area. It appeared possible that some of the features could correspond with structures documented on the reference archaeological chart, but there was not a clear match between the chart and the results of the Radarsat-2 analysis. A similar conclusion was reached for the PALSAR data analysis. For the PALSAR data, the volcanic nature of the soil may have hindered the visibility of traces of buried features. Given the limitations of the accuracy of the archaeological chart and the spatial resolution of both the SAR datasets, further validation would be required to draw any precise conclusions on the sensitivity of the SAR data to buried structures. Such a validation could include geophysical prospection or excavation.
Location: TE 15 New Biology Building
Literature cited 1: Blom, R., Clapp, N., J. Hedges, G., 1997. Space technology and the discovery of the lost city of Ubar. In: Paper read at IEEE Aerospace Conf. February 1-8. Boerner, W.M., Mott, H., Luneburg, E., Livingston, C., Brisco, B., Brown, R.J., Paterson, J.S. with contributions by Cloude, S.R., Krogager, E., Lee, J.S. Schuler, D.L., Van Zyl, J.J., Randall, D., Budkewitsch, P., Pottier, E., 1998. Polarimetry in radar remote sensing: basic and applied concepts, Chapter 5 in Henderson, F.M. Lewis, A.J. (Eds), Principles and Applications of Imaging Radar, vol 2 of Manual of Remote Sensing Reyerson, R.A. (Ed), third ed., John Wiley & Sons, New York, 1998.
Literature cited 2: Brivio, P.A., Pepe, M., Tomason, R., 2000. Multispectral and multiscale remote sensing data for archaeological prospecting in an alpine alluvial plain. Journal of Cultural Heritage 1, 155-164. Cigna, F., Tapete, D., Lasaponara, R., Masini, N., 2013. Amplitude change detection with ENVISAT ASAR to image the cultural landscape of the Nasca Region, Peru. Archaeological Prospection 20, 117-131.

ID: 60047
Title: Prior-knowledge-based spectral mixture analysis for impervious surface mapping.
Author: Jinshui Zhang, Chunyang He, YuYu Zhou, Shuang Zhu, Guanyuan Shuai
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. 28 201-210 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Impervious surface, V-I-S, Spectral mixture analysis, Prior-knowledge.
Abstract: In this study, we developed a prior -knowledge-based spectral mixture analysis (PKSMA) to map impervious surfaces by using endmembers derived separately for high -and low-density urban regions. First, an urban area was categorized into high -and low-density urban areas, using a multi-step classification method. Next, in high-density urban areas that were assumed to have only vegetation and impervious surfaces (ISs), the vegetation-impervious model (V-I) was used in a spectral mixture analysis (SMA) with three endmembers: vegetation, high albedo, and low albedo. In low-density urban areas, the vegetation -impervious -soil model (V-I-S) was used in an SMA analysis with four endmembers: high albedo, low albedo, soil, and vegetation. The fraction of IS with high and low albedo in each pixel was combined to produce the final IS map. The root mean-square error (RMSE) of the IS map produced using PKSMA was about 11.0% compared to 14.52% only using four-endmember SMA. Particularly in high-density urban areas, PKSMA (RMSE=6.47%) showed better performance than four-endmember (15.91%). The results indicate that PKSMA can improve IS mapping compared to traditional SMA by using appropriately selected endmembers and is particularly strong in high -density urban areas.
Location: TE 15 New Biology Building
Literature cited 1: Adams, J.B., Sabol, D.E., Kapos, V., Almeida-Filho, R., Roberts, D.A., Smith, M.O., Gillespie, A.R., 1995, Classification of multiple images based on fractions of end-members: application to land-cover change in the Brazilian Amazon. Remote Sensing of Environment 52, 137-154. Al-Shalabi, M., Billa, L., Pradhan, B., Mansor, S., Al-Sharif, A.A., 2013. Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of sana ' s metropolitan city, Yemen. Environmental Earth Science 70, 425-437.
Literature cited 2: Anys, H., Bannari, A., He, D.C., Morin, D., 1994. Texture analysis for the mapping of urban areas using airborne MEIS-II images. In: Proc. First International Airborne Remote Sensing Conference and Exhibition, Strasbourg, France, pp. 231-245. Arnold Jr., C.L, Gibbons, C.J., 1996. Impervious surface coverage: the emergence of a key environmental indicator. Journal of the American Planning Association 62 (2), 243-258.

ID: 60046
Title: Spatialization of electricity consumption of China using saturation-corrected DMSP-OLS data.
Author: Xin Cao, Jianmin Wang, Jin Chen, Feng Shi.
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. 28 193-200 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Electricity consumption, DMSP-OLS, GDP, Saturation -correction.
Abstract: Electricity is one of the most important components in energy consumption, which is directly related to economic growth, CO2 emission and global warming. This research intends to estimate spatial distribution of electricity consumption in China, the largest developing country, and analyze the temporal and spatial change of electricity consumption during 1994-2009. The spatial modeling is based on the total electricity consumption of each province and DMSP (Defense Meteorological Satellite program) -Operational Line-scan System (OLS) data, the latter provides the nighttime light information corresponding to electricity consumption, GDP and population. A simple method was developed to correct the saturated pixels with digital number of 63 in non-radiance -corrected DMSP-OLS data, using cities ' GDP data. The spatial electricity consumption maps were produced during 1994-2009, and they were validated by the electricity consumption records of 101 cities. Finally, the spatial -temporal changes of electricity consumption were analyzed. The results of this research can help to understand the regional discrepancy, especially rural and urban areas of China, of electricity consumption and economic development.
Location: TE 15 New Biology Building
Literature cited 1: Amaral, S., Camara, G., Miguel, A., Monteiro, V., Quintanilha, J.A., Elvidge, C.D., 2005. Estimating population and energy consumption in Brazilian Amazonia using DMSP night-time satellite data. Computer, Environment and Urban Systems 29, 179-195. Chand, T.R.K., Badarinath, K.V.S., Elvidge, C.D., Tuttle, B.T., 2009. Spatial characterization of electrical power consumption patterns over India using temporal DMSP-OLS night-time satellite data. International Journal of Remote Sensing 30, 647-661.
Literature cited 2: De Souza Filho, C.R., Zullo Jr., Elvidge, C., 2004. Brazil ' s 2001 energy crisis monitored from space International Journal of Remote Sensing 25, 2475-2482. Elvidge, C.D., Baugh, K.E., Hobson, V.H., Kihn, E.A., Kroehl, H.W., Davis, E.R, et al., 1997a. Satellite inventory of human settlements using nocturnal radiation emissions: a contribution for the global toolchest. Global Change Biology 3, 387-395.