Enter records

ID: 61792
Title: Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping.
Author: Sebastien Rapinel, Laurence Hubert-Moy, Bernard Clement.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 56-64 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Wetland function, Vegetation formations, CORINE biotopes, Very high spatial resolution, Object-based classification.
Abstract: Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, the inventory and characterization of wetland habitats are most often limited to small areas. This explains why the understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. While LiDAR data and multispectral Earth Observation (EO) images are often separately to map wetland habitats, their combined use is currently being assessed for different habitat types. The aim of this study is to evaluate the combination of multispectral and multiseasonal imagery and LiDAR data to precisely map the distribution of wetland habitats. The image classification was performed combining an object-based approach and decision-tree modeling. Four multispectral images with high (SPOT-5) and very high spatial resolution (Quickbird, KOMPSAT-2, aerial photographs) were classified separately. Another classification was then applied integrating summer and winter multispectral image data and three layers derived from LiDAR data: vegetation height, microtopography and intensity return. The comparison of classification results shows that some habitats are better identified on the winter image and others on the summer image (overall accuracies = 58.5 and 57.6 %).They also point out that classification accuracy is highly improved (overall accuracy = 86.5 %) when combining LiDAR data and multispectral images. Moreover, this study highlights the advantage of integrating vegetation height, microtopography and intensity parameters in the classification process. This article demonstrates that information provided by the synergetic use of multispectral images and LiDAR data can help in wetland functional assessment.
Location: T E 15 New Biology Building
Literature cited 1: Alber, A., Piegay, H., 2011. Spatial disaggregation and aggregation procedures for characterizing fluvial features at the network-scale: application to the Rhone basin (France).Geomorphology 125, 343-360. Alexandridis, T.K., Lazaridou, E., Tsirika, A., Zalidis, G.C., 2009.Using Earth Observation to update a Natura 2000 habitat map for a wetland in Greece.J.Environ.Manage.90, 2243-2251.
Literature cited 2: Amiaud, B., Bouzille, J.-B., Tournade, F., Bonis, A., 1998.Spatial patterns of soil salinities in old embanked marshlands in western France. Wetlands 18, 482-494. Axelsson, P., 1999.Processing of laser scanner data-algorithms and applications. ISPRS J.Photogramm. Remote Sens.54, 138-147.

ID: 61791
Title: Synergy of airborne LiDAR and Worldview-2 satellite imagery for land cover and habitat mapping: A Bio_SOS-EODHaM case study for the Netherlands.
Author: C.A.Mucher, L.Roupioz, H.Kramer, M.M.B.Bogers, R.H.G.Jongman, R.M.Lucas, V.E.Kosmidou, Z.Petrou, I.Manakos, E.Padoa-Schioppa, M.Adamo, P.Blonda.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 48-55 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: LiDAR, Optical remote sensing, Vegetation structure, Land cover, General habitat categories.
Abstract: A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EoDHaM (EO Data for Habitat Mapping) classification results for a Dutch case study. The EODHaM system was developed within the BIO_SOS (The BIOdiversity multi Source monitoring System: from Space TO Species) project and contains the decision rules for each land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall accuracy was lower (69.0 %) for the habitat map based on the General Habitat Category (GHC) system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, in preparation for new habitat classifications.
Location: T E 15 New Biology Building
Literature cited 1: Adamo, M., Tarantino, C., Tomaselli, V., Kosmidou, V., Petrou, Z., Manakos, I., Lucas, R.M., Mucher, C.A., Veronico, G., Marangi, C., De Pasquale, V.,Blonda, P., 2014. Expert knowledge for translating land cover/use maps to General Habitat Categories (GHCs).Landsc.Ecol., http:/dx.doi.org/10.1007/s10980-014-0028-9. Akay, A.E., Oguz, H., Karas, I.R., Aruga, K., 2008.Using LiDAR technology in forestry activities.Environ.Monit.Assess.151 (1-4), 117-125.
Literature cited 2: Bunce, R.G.H., Metzger, M.J.,Jongman, R.H.G., Brandt, J., De Blust,G., Elena-Rossello, R., Groom, G.B., Halada, L.,Hofer, G., Howard, D.C., Kova, P., Mucher, C.A., Padoa-Schioppa, E., Paelinx, D., Palo, A., Perez-Soba, M., Ramos, I.L.,Roche, P., Skanes, H., Wrbka, T., 2008.A standardized procedure for surveillance and monitoring European habitats and provision of spatial data.Landsc.Ecol.23 (1), 11-25. Bunce, R.H.G., Bogers, M.M.B., Roche, P., Walczak, M., Geijzendorffer, I.R., Jongman, R.H.G., 2011.Manual for Habitat and Vegetation Surveillance and Monitoring: Temperate, Mediterranean and Desert Biomes, 1st ed.Alterra report 2154, Wageningen.

ID: 61790
Title: A transferability study of the kernel-based reclassification algorithm for habitat delineation.
Author: Iphigenia Keramitsoglou, Dimitris Stratoulias, Eleni Fitoka, Charalampos Kontoes, Nicolas Sifakis.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 38-47 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Wetland mapping, Dual-data imagery, WorldView-2, ANAX.
Abstract: Wetland mapping using Earth Observation (EO) data has proved to be a challenging task for practitioners due to the complexity in the spatial structure and composition, the wide within-class spectral variability and the absence of easily distinguishable boundaries between habitat types. Furthermore, the inherent temporal water instability of these landscapes poses an obstacle to the integration of field data with remote sensing data, which also are not acquired simultaneously at all times. To cope with these limitations we tested the applicability of the Kernel-based reclassification (KRC) algorithm on very high spatial resolution (VHR) satellite imagery over a wetland. A composite multi temporal (i.e. dual-date) VHR WordView-2 image consisting of spectral bands and indices derived from two images acquired during flooded and dry water conditions were employed. This datasets stresses the seasonal variations of the habitat in response to environmental changes (i.e. flooding) occurring between the two acquisition dates. Multi-temporal imagery is an important information source for fine mapping of wetlands such are river deltas. A multi-temporal approach could reveal even more specific information during the phenology of these habitats. The methodology was applied firstly to Axios and then to Aliakmonas river deltas in Northern Greece. The results revealed an overall accuracy of 53 % in the forest and more complex site, and 86 % in the second site.
Location: T E 15 New Biology Building
Literature cited 1: Axios-Loudias-Aliakmonas National Park, 2013.Axios Loudias Aliakmonas Estuaries Management Authority.URL: http: //www.axiosdelta.gr/Default.aspx?tabid =459 & language =en-GB (accessed 15.02.13). Barnsley, M.J., Barr, S.L, 1996.Infring urban land use from satellite sensor images using kernel-based spatial reclassification.Photogramm.Eng.Remote Sens.62 (8), 949-958.
Literature cited 2: Chamber of Commerce and Industry of Thessaloniki and Gecon Consulting, 2007.Marketing plan study for the European project ?Terres d ' Eau?.INTERREG IIIB MEDOCC, Thessaloniki, Greece. Congalton, R.G., 1991.A review of assessing the accuracy of classification of remotely sensed data. Remote sens.Environ.37, 35-46.

ID: 61789
Title: Object-based class modeling for multi-scale riparian forest habitat mapping.
Author: Thomas Strasser, Stefan Lang.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 29-37 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Semi-automated habitat mapping, OBIA, Multi-level class modeling, Object hierarchy, WorldView-2, Natura 2000.
Abstract: Object-based class modeling allows for mapping complex, hierarchical habitat systems. The riparian zone, including forests, represents such a complex ecosystem. Forests within riparian zones are logically high productive and characterized by a rich biodiversity; thus considered of high community interest with an imperative to be protected and regularly monitored. Satellite earth observation (EO) provides tools for capturing the current state of forest habitats such as forest composition including intermixture of non-native tree species. Here we present, a semi-automated object based image analysis (OBIA) approach for the mapping of riparian forests by applying class modeling of habitats based on the European Nature Information System (EUNIS) habitat classifications and European Habitats Directive HabDir) Annex1.A very high resolution (VHR) WorldView-2 satellite image provided the required spatial and spectral details for a multi-scale image segmentation and rule-base composition to generate a six-level hierarchical representation of riparian forest habitats. Thereby habitats were hierarchically represented within an image object hierarchy as forest stands, stands of homogenous tree species and single trees represented by sunlit tree crowns.522 EUNIS level 3 (EUNIS-3) habitat patches with a mean patch size (MPS) of 12, 349.64 m2 were modeled from 938 forest stand patches (MPS =6868.20 m2 ) and 43, 742 tree stand patches (MPS=140.79 m2). The delineation quality of the modeled EUNIS-3 habitats (focal level) was quantitatively assessed to an expert-based visual interpretation showing a mean derivation of 11.71%.
Location: T E 15 New Biology Building
Literature cited 1: Allen, T.F.H., Starr, T.B., 1982.Hierarchy.University of Chicago Press, Chicago. Baatz, M., Schape, A., 2000.Multiresolution segmentation-an optimization approach for high quality multi-scale image segmentation. In: Strobl, J., Blaschke, T., Griesebner, G., (Eds.), Angewandte Geographische Informationsverarbeitung.Wichmann-Verlag, Heidelberg, pp.12-23.
Literature cited 2: Bunting, P., Lucas, R.M., Jones, K., Bean, A.R.2010.Characterisation and mapping of forest communities by clustering individual tree crowns. Remote Sens.Environ.114, 2536-2547. Burnett, C., Blaschke, T., 2003.A multi-scale segmentation/object relationship modeling methodology for landscape analysis.Ecol.Model.168, 233-249.

ID: 61788
Title: The Earth Observation Data for Habitat Monitoring (EODHaM) system.
Author: Richard Lucas, Palma Blonda, Peter Bunting, Gwawr Jones, Jordi Inglada, Marcela Arias, Vasiliki Kosmidou, Zisis I.Petrou, Ioannis Manakos, Maria Adamo, Rebecca Charnock, Christina Tarantino, Caspar A.Mucher, Rob H.G.Jongman, Henk Kramer, Damien Arvor
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 17-28 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Habitat, Land cover, Classification, Monitoring, Remote sensing.
Abstract: To support decision relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-Source monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for Habitat Monitoring 9EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitats maps (i.e., GHCs or annex 1). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India.
Location: T E 15 New Biology Building
Literature cited 1: Adamo, M., Tarantino, C., Tomaselli, V., Kosmido, V., Petrou, Z.I.,Manakos, M.,Lucas, R.M., Mucher, C.A., Veronico, G., Marangi, C., De Pasquale, V.,Blonda, P., 2014.Expert knowledge for translating land cover/use maps to general habitat categories 9GHC).Landsc.Ecol.29, 1045-1067, http:/dx.doi.org/10.1007/s 10980-014-0028-9. Arias, M., Inglada, J., Lucas, R.M., Blonda, P., 2013.Hedgerow segmentation on VHR optical satellite images for habitat monitoring. In: Proceedings, Geoscience and Remote Sensing Symposium (IGARSS), pp.3301-3304.
Literature cited 2: Bhandari, S., Phinn, S.R., Gill, T., 2012.Preparing Landsat image time series (LITS) for monitoring changes in vegetation phenology in Queensland, Australia. Remote Sens.4. 1856-1886. Blaschke, T., 2010.Object based image analysis for remote sensing.ISPRS J.Photogram.Rem.Sens.65, 2-16.

ID: 61787
Title: Remote sensing for mapping natural habitats and their conservation status-New opportunities and challenges.
Author: Christina Corbane, Stefan Lang, Kyle Pipkins, Samuel Alleaume, Michel Deshayes, Virginia Elena Garcia Millan, Thomas Strasser, Jeroen Vanden Borre, Spanhove Toon.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 7-16 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Satellite image analysis, Earth observation data, Natural and Semi-natural habitats, European Habitats Directive.
Abstract: Safeguarding the diversity of natural and semi-natural habitats in Europe is one of the aims set out by the Habitats Directive (Council Directive 92/43/EEC on conservation of natural habitats and of wild fauna and flora) and one of the targets of the European 2020 Biodiversity Strategy, and is to be accomplished distribution and conditions of these habitats is needed. Remote sensing can considerably contribute to habitat mapping and their observation over time. Several European projects and a large number of scientific studies have addressed the issue of mapping and monitoring natural habitats via remote sensing and the deriving of indicators on their conservation status. The multitude of utilized remote sensing sensors and applied methods used in these studies, however, impede a common understanding of what is achievable which current state-of-the-art technologies. The aim of this paper is to provide a synthesis on what is currently feasible in terms of detection and monitoring of natural and semi-natural habitats with remote sensing. To focus this endeavour, we concentrate on those studies aimed at direct mapping of individual habitat types or discriminating between different types of habitats occurring in relatively large, spatially contiguous units. By this we uncover the potential of remote sensing to better understand the distribution of habitats and the assessment of their conservation status in Europe.
Location: T E 15 New Biology Building
Literature cited 1: Achard, F., Estreguil, C., 1995.Forest classification of Southeast Asia using NOAA AVHRR data. Remote Sens.Environ.54, 198-208, http://dx.doi.org/10.1016/0034-4257 (95) 00153-0. Adam, E., Mutanga, O., Rugege, D., 2009.Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecol.Manage.18, 281-296, http://dx.doi.org/10.1007/s11273-009-9169-z.
Literature cited 2: Adamo, M., Tarantino, C., Tomaselli, V.,Kosmidou, V., Petrou, Z., Manakos, I., Lucas,R.M, Mucher, C.A, Vernico, G., Marangi, C., De Pasquale, V., Blonda, P.,2014.Expert knowledge for translating land cover/use maps to General Habitat Categories (GHC).Landsc.Ecol.29, 1045-1067, http://dx.doi.org/10.1007/s10980-014-0028-9. Ali, I., Schuster, C., Zebisch, M., Forster, M.,Kleinschmit,B., Notarnicola, C., 2013.First results of monitoring nature of conservation sites in alpine region by using very high resolution (VHR) X-band SAR data.IEEE J.Select.Top.Appl.Earth Observations Remote Sens.6, 2265-2274, http://dx.doi.org/10.1109/JSTARS.2013.2241735.

ID: 61786
Title: Earth Observation for habitat mapping and biodiversity monitoring.
Author: - (Editorial)
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 1-6 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Earth Observation, habitat, mapping, biodiversity, monitoring.
Abstract: Biodiversity-the variety of life forms and our ?natural capital and life-insurance? (European Commission, 2011) - is on decline (Isbell, 2010; Trocher and Schmeller, 2013), with consequences on ecosystem function and stability, and ultimately human well-being (Naeem et al., 2009). Since 1992, the International Convention on Biological Diversity, short CBD, has bundled the United Nations ' joint effort to halt or at least lower the accelerated loss of biodiversity, but indeed it remains one of the key global challenges that requires a concerted, effective use of latest technology. As by the end of 2010 (the ?International Year of Biodiversity?) the global society became aware that the ambitious goal of ?halting biodiversity? has not been reached, the importance of both observation and technology development became even more important.
Location: T E 15 New Biology Building
Literature cited 1: Adamczyk, J., Osberger, A., 2014.Red-edge vegetation indices for detecting and assessing disturbances in Norway spruce dominated mountain forests.Int.J.Appl.Earth Obs.Geoinform. (Special Issue ?Earth Observation for habitat mapping and biodiversity monitoring,? ed. By S.Lang et al. Adamo M., Tarantino C.,Tomaselli V., Kosmidou V., Petrou Z.Manakos I., Lucas R.M.,Mucher C.A., Vernico G., Marangi C., De Pasquale V., Blonda P., 2014.Expert knowledge for translating land cover/use maps to General Habitat Categories (GHC).Landscape ecology.DOI 10.1007/s 10980-014-0028-9.
Literature cited 2: Bjorgo, E., 2000b.Regfugee camp mapping using very high spatial resolution satellite sensor images. Geocarto Int.15, 79-88. Brown, D.Saito, K., Spence, R., Chenvidyakarn, T., Adams, B., Mcmillan, A., Platt, S., 2008.Indicators for measuring, monitoring and evaluating post-disaster recovery. In: Proceedings of the 6th International Workshop On Remote Sensing for Disaster Applications, Pavia. Addicot J.F., Aho, J.M., Antolini, M.F., Padilla, D.K., Richardson, J.S., Soluk, D.A., 1987.Ecological Neighbourhoods: Scaling Environmental Patterns.Oikos, pp.340-346. Arvor, D., Durieux, L., Andres, S., Marie-Angelique, L., 2013.Advances in geographic object-based image analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective.ISPRS Int.J.Photogramm. Remote Sens. 82, 125-137.

ID: 61785
Title: Satellite-based automated burned area detection: A performance assessment of the MODIS MC45A1 in the Brazilian Savanna.
Author: Fernanado Moreira De Araujo, Laerte G.Ferreira.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 36 94-103 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: MODIS MCD45A1, Burned area detection, Savannas.
Abstract: Burning, which cause major changes to the environment, can be effectively monitored via satellite data, regarding both the identification of active fires and the estimation of burned areas. Among the many orbital sensors suitable for mapping burned areas on global and regional scales, the moderate resolution imaging spectroradiometer (MODIS), on board the Terra and Aqua platforms, has been the most widely utilized. In this study, the performance of the MODIS MCD45A1 burned area product was thoroughly evaluated in the Brazillian savanna, the second largest biome in South America and global biodiversity hotspot, characterized by a conspicuous climatic seasonality and the systematic occurrence of natural and anthropogenic fires. Overall, September MCD45A1 polygons (2000-2012) compared well to the Landsat based reference mapping (r2=0.89), although large omissions errors, linked to landscape patterns, structures, and overall conditions depicted in each reference image, were observed. In spite of its spatial and temporal limitations, the MCD45A1 product proved instrumental for mapping and understanding fire behavior and impacts on the Cerrado landscapes.
Location: T E 15 New Biology Building
Literature cited 1: Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004.Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information.ISPRS J.Photogramm.Remote Sens.58, 239-258. Bjorgo, E., 2000a.Using very high spatial resolution multispectral satellite sensor imagery to monitor refugee camps.Int.J.Remote Sens.21, 611-616.
Literature cited 2: Bjorgo, E., 2000b.Regfugee camp mapping using very high spatial resolution satellite sensor images. Geocarto Int.15, 79-88. Brown, D.Saito, K., Spence, R., Chenvidyakarn, T., Adams, B., Mcmillan, A., Platt, S., 2008.Indicators for measuring, monitoring and evaluating post-disaster recovery. In: Proceedings of the 6th International Workshop On Remote Sensing for Disaster Applications, Pavia.

ID: 61784
Title: Detecting tenets to estimate the displaced populations for post-disaster relief using high resolution satellite imagery.
Author: Shifeng Wang, Emily So, Pete Smith.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 36 87-93 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Remote sensing, Disaster, Refugee, Automate, Panchromatic, Relief operation.
Abstract: Estimating the number of refugees and internally displaced persons is important for planning and managing an efficient relief operation following disasters and conflicts. Accurate estimates of refugee numbers can be inferred from the number of tents. Extracting t4ents from high-resolution satellite imagery has recently been suggested. However, it is still a significant challenge to extract tents automatically and reliably from remote sensing imagery. This paper describes a novel automated method, which is based on mathematical morphology, to generate a camp map to estimate the refugee numbers by counting tents on the camp map. The method is especially useful in detecting objects with a clear shape, size, and significant spectral contrast with their surroundings. Results for two study sites with different satellite sensors and different spatial resolutions demonstrate that the method achieves good performance in detecting tents. The overall accuracy can be up to 81 % in this study further improvements should be possible if over-identified isolated single pixel objects can be filtered. The performance of the method is impacted by spectral characteristics of satellite sensors and image scenes, such as extent of area of interest and the spatial arrangement of tents. It is expected that the image scene would have a much higher influence on the performance of the method than the sensor characteristics.
Location: T E 15 New Biology Building
Literature cited 1: Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004.Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information.ISPRS J.Photogramm.Remote Sens.58, 239-258. Bjorgo, E., 2000a.Using very high spatial resolution multispectral satellite sensor imagery to monitor refugee camps.Int.J.Remote Sens.21, 611-616.
Literature cited 2: Bjorgo, E., 2000b.Regfugee camp mapping using very high spatial resolution satellite sensor images. Geocarto Int.15, 79-88. Brown, D.Saito, K., Spence, R., Chenvidyakarn, T., Adams, B., Mcmillan, A., Platt, S., 2008.Indicators for measuring, monitoring and evaluating post-disaster recovery. In: Proceedings of the 6th International Workshop On Remote Sensing for Disaster Applications, Pavia.

ID: 61783
Title: Comparison of eMODIS and MOD/MYD13A2 NDVI products during 2012-2014 spring green-up periods in Alaska and northwest Canada.
Author: David Verbyla
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 36 83-86 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Vegetation phenology, MODIS, NDVI, Alaska
Abstract: Accurate monitoring of vegetation dynamics is required to understand the inter-annual variability and long term trends in terrestrial carbon exchange in tundra and boreal ecoregions. In western North America, two Normalized Vegetation Index (NDVI) products based on spectral reflectance data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are available. The MOD/MYD13A2 NDVI product is available as a 16-day composite geotif product in a sinusoidal projection as global hdf tiles. The eMODIS Alska NDVI product is available as a 7-day composite geotif product in a regional equal area conic projection covering Alaska and the entire Yukon River Basin. These two NDVI products were compared for the 2012-2014 late May-late June spring green-up periods in Alska and the Yukon Territory. Relative to the MOD/MYD13A2 NDVI product, it is likely that the eMODIS NDVI product contained more cloud-contaminated NDVI values. For example, the MOD/MYD13A2 product flagged substantially fewer pixels as ?good quality? in each 16-day composite period compared to the corresponding MODIS Alska NDVI product from a 7-day composite period. During the spring green-up period, when field-based NDVI increases, the eMODIS NDVI product averaged 43 percent of pixels that declined by at least 0.05 NDVI between 2 composite periods, consistent with cloud-contamination problems, while the MOD/MYD13A2 NDVI averaged only 6 percent of pixels. Based on a cloudy Landsat-8 scene, the e-MODIS compositing process selected 23 percent pixels, while the MOD/MYD13A2 composting process selected less than 0.003 percent pixels. Based on the results, it appears that the MOD/MYD13A2 NDVI product is superior for scientific applications based on NDVI phenology in the tundra and boreal regions of northwestern North America.
Location: T E 15 New Biology Building
Literature cited 1: Beck, P.S.A., Goetz, S.J., 2011.Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: ecological variability and regional differences.Environ.Res.Lett.6 (2011), 045501 (10 pp). Gamon, J.A., Huemmrich, K.F., Stone, R.S., Tweedie, C.E., 2013.Spatial and temporal vegetation growth following earlier snowmelt. Remote Sens.Environ.129, 144-153.
Literature cited 2: Goetz, S.J., Bunn, A.G., Fiske, G.J., Houghton, R.A., 2005.Satelite-observed photosynthetic trends across North America associated with climate and fire disturbance.PNAS 102 (38), 13521-13525. Jenkerson, C., Maiersperger, T., Schmidt, G., 2010 eMODIS: a user-friendly data source.

ID: 61782
Title: Monitoring land-use change by combining participatory land-use maps with standard remote sensing techniques: Showcase from a remote forest catchment on Mindanao, Philippines.
Author: Francois Mialhe, Yanni Gunnell, J.Andres F.Ignacio, Nicolas Delbart, Jenifer L.Ogania, Sabine Henry.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 36 69-82 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Land-use change, Remote sensing, Participatory land-use map, Methodology, Phillipines.
Abstract: This paper combines participatory activities (PA) with remote sensing analysis into an integrated methodology to describe an explain land-cover changes. A remote watershed on Mindanao (Philippines) is used to showcase the approach, which hypothesizes that the accuracy of expert knowledge gained from remote sensing techniques can be further enhanced by inputs from vernacular knowledge when attempting to understand complex land mosaics and past land-use changes. Six participatory sessions based on focus-group discussions were conducted. These were enhanced by community-based land-use mapping, resulting in a final total of 21 participatory land-use maps (PLUMs) co-produced by a sample of stakeholders with different sociocultural and ecological perspectives. In parallel, seven satellite images (Landsat MSS, Landsat TM, Landsat ETM+, and SPOT4) were classified following standard techniques and provided snapshots for the years 1976, 1996, and 2010Local knowledge and collective memory contributed to define and qualify relevant land-use classes. This also provided information about what had caused the land-use changes in the past. Results show that combining PA with remote sensing analysis provides a unique understanding of land-cover change because the two methods complement and validate one another. Substantive qualitative information regarding the chronology of land-cover change was obtained in a short amount of time across an area poorly covered by scientific literature. The remote sensing techniques contributed to test and to quantify verbal reports of land-use and land-cover change by stakeholders. We conclude that the method is particularly relevant to data-poor areas or conflict zones where rapid reconnaissance wok is the only available option. It provides a preliminary but accurate baseline for capturing land changes and for reporting their causes and consequences. A discussion of the main challenges encountered (i.e. how to combine different systems of knowledge), and options for further methodological improvements, are also provided.
Location: T E 15 New Biology Building
Literature cited 1: Abraao, M.B., Nelson, B.W., Baniwa, J.C., Yu, D.W., Shepard Jr, G.H., 2008.Ethnobotanical ground-truthing: indigenous knowledge, floristic inventories and satellite imagery in the upper Rio Negro, Brazil.J.Biogeogr.35, 2237-2248. Agrawal, A., 1995.Dismantling the divide between indigenous and scientific knowledge.Dev.Change 26 (3), 413-439.
Literature cited 2: Berkes, F., 2009.Indigenous ways of knowing and the study of environmentalchange.J.Roy.Soc.New Zeal.39 (4), 151-156. Berles. F., Folke, C., 2002.Back to the future: ecosystem dynamics and local knowledge. In: Gunderson, L.H., Holling, C.S. (Eds), Panarchy-Understanding Transformations in Human and Natural Systems. Island Press, pp.121-146.

ID: 61781
Title: Spectral characterization of coastal sediments using Field Spectral Libraries, Airborne Hyperspectral Images and Topographic LiDar Data. (FHyL).
Author: Ciro Manzo, Emiliana, Andrea Taramelli, Federico Filipponi, Leonardo Disperati
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 36 54-68 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Hyperspectral, Linear spectral mixing analysis.
Abstract: Beach dune systems are important for coastal zone ecosystems as they provide natural sea defences that dissipate wave energy. Geomorphological models of this near shore topography require site-specific sediment composition, grain size and moisture content as inputs. Hyperspectral, field radiometry and LiDAR remote sensing can be used as tools by providing synoptic maps of these properties. However, multi-remote sensing of near-shore beach images can only be interpreted if there are adequate bio-geophysical or empirical models for information extraction. Our aim was thus to model the effects of varying sediment properties on the reflectance in both field and laboratory condition within the FHyL (Field Spectral Libraries, Airborne Hyperspectral Images and Topographic LiDAR) procedure, using a multisource dataset (airborne Hyperspectral-MIVIS and topographic LiDAR) procedure, using a multisource dataset (airborne Hyperspectral)-MIVIS and topographic LiDAR-Hawk-eye II and field radiometry).The methodology consisted of (1) acquisition of simultaneous multi-source datasets (airborne Hyperspectral -MIVIS and topographic LiDAR-Hawk-eye) (ii) hyperspectral measurements of sediment mixtures with varying physical characteristics (moisture, grain size and minerals) field and laboratory conditions, (iii) determination and quantification of specific absorption features, and (iv) correlation between the absorption features and physical parameters cited above. Results showed the potential of hyperspectral signals to assess the effect of moisture, grain-size and mineral composition on sediment properties.
Location: T E 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. Wetlands ecology and Management, 18.Springer, Netherlands, pp.281-296. Adam, S., De Backer, A., De Wever, A.Sebbe, K., Toorman, E., Vincx, M., Mabaliu, J., 2011.Bio-physical characterization of sediment stability in mudflats using remote sensing: A laboratory Experiment. Cont. Shelf Res., http: dx.doi.org/10.1016/j.csr.2009.12.008.
Literature cited 2: Bauer, B.O., Davidson-Arnott, R.G.D., 2003.Ageneral framework for modeling sediment supply to coastal dunes including wind angle, beach geometry, and fetch effects. Geomorphology 49 (1) 89-108, http://dx.doi.org/10.1016/SO169-555X) 2) 00165-4.

ID: 61780
Title: A hyperspectral index sensitive to subtle changes in the canopy chlorophyll content under arsenic stress.
Author: Xuqing Li, Xiangnan Liu, Meiling Liu, Cuicui Wang, Xiaopeng Xia.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 36 41-53 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Canopy chlorophyll content, Random forest, Arsenic stress-sensitive index.
Abstract: Arsenic stress induces in subtle changes in the canopy chlorophyll content (CCC). Therefore, the establishment of a spectral index that is sensitive to subtle changes in the CCC is important for monitoring crop arsenic contamination in large areas by remote sensing. Experimental sites with three contamination levels were selected and were located in Chang Chun City, Jilin Province, China. Arsenic stress can induce small changes in the CCC, reflecting in the crop spectrum. This study created a new index to monitor the CCC. Then, the results from the index were compared with these from other indices and the random forest model, respectively. The final purpose of this study is to find an optimal index, which is sensitive to small changes in the CCC under arsenic stress for monitoring regional CCC in rice. The results indicate that the distribution of the CCC is aligned with the distribution of the arsenic stress level and that NVI (R640, R732, and R752) is the best index for monitoring CCC. The correlation coefficient R2 between the predicted values using NVI and the measured values of canopy chlorophyll content is 0.898, which performs better than the random forest model and other indices.
Location: T E 15 New Biology Building
Literature cited 1: Babar, M.A., Reynolds ,M.P.,VanGinkel, M.,Klatt, A.R.,Raun,W.R., Stone, M.L.,2006.Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat.CropSci.46 (3), 1046-1057. Breiman, L., 1996a.Bagging predictors.Mach.Learn.24 (2), 123-140.
Literature cited 2: Breiman, L., 1996b.Heuristics of instability and stabilization in model selection.Ann.Stat.24 (6), 2350-2383. Breiman, L., 1996c.Stacked regressions.Mach.Learn24 (1), 49-64.

ID: 61779
Title: Monitoring the impact of aerosol contamination on the drought-induced decline of gross primary productivity.
Author: Yao Zhang, Weizhong Li, Qiuan Zhu, Huai Chen, Xiuqin Fang, Tinglong Zhang, Pengxiang Zhao, Changhui Peng.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 36 30-40 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Aerosol, Cloud, Atmospheric contamination, Vegetation response, Fire disturbance, Quality flag.
Abstract: Southwestern China experienced a period of severe drought from September 2009 to May 2010.It led to widespread decline in the enhanced vegetation index (EVI) and gross primary productivity(GPP) in the springtime of 2010 (March to May).However, this study observed a spatial inconsistency between drought-impacted vegetation decline and the precipitation deficit. Significant aerosol loads that correspond to inconsistent areas were also observed during the drought period. After analyzing both MODIS GPP/NPP Collection 5 (C5) and the newer Collection 5.5 (C55) data, a large area observed to be experiencing GPP decline in the eastern part of the study area proved to be unreliable. Based on EVI data, after atmospherically contaminated data were screened from analysis, approximately 20 % of the study are exhibited browning whereas 33% displayed no change or greening and the remaining area (approximately 47 %) lacked sufficient data to document changing conditions. Correlation analysis showed that fire, occurrences, aerosol optical depth, and precipitation anomalies during the two drought periods (from September to February and from March to May) all contributed to a decrease in GPP.C55 data remains vulnerable to aerosol contamination due to a much higher correlation coefficient with aerosol optical depth compared to C5 data. In the future, users of remotely sensed data should be cautious of and take into account impacts related to atmospheric contamination, even during drought periods.
Location: T E 15 New Biology Building
Literature cited 1: Allen, C.D., Macalady, A.K., Chenchouni, H., Bachelet, D., McDowell, N., Vennetier, M., Kitzberger, T., Rigling ,A., Breshears, D.D.,Hogg ,E.H., Gonzalez, P., Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim, J.-H, Allard, G., Running, S.W., Semerci, A., Cobb, N., 2010.A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecol.Manage.259, 660-684. Aragao, L.E.O.C., Malhi, Y., Roman-Cuesta, R.M., Saatchi, S., Anderson, I.O., Shimabukuro, Y.E., 2007.Spatial patterns and fire response of recent Amazonian droughts.Geophys.Res.Lett, 34.
Literature cited 2: Bhuiyan, C., Singh, R., Kogan, F., 2006.Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data.Int.J.Appl.Earth Obs.Geoinf.8, 289-302. Bonan, G.B., 2008.Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444-1449.

ID: 61778
Title: Retrieval of tea polyphenol at leaf level using spectral transformation and multi-variate statistical approach.
Author: Dibyendu Dutta, Prabir Kumar Das, Uttam Kumar Bhunia, Upasana Singh, Shalini Singh, Jaswant Raj Sharma, Vinay Kumar Dadhwal.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 36 22-29 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Tea polyphenol, Hyperspectral, Discriminant analysis, Stepwise multiple linear regression, Partial least square regression, Principal component analysis.
Abstract: In the present study, field based hyperspectral data was used to estimate the tea (Camellia sinensis L).Polyphenol at Deha Tea garden of Assam state, India. Leaf reflectance spectra were first filtered for noise and then transformed into normalized and first derivative reflectance for further analysis. Stepwise discriminant analysis was carried out to select sensitive bands for a range of polyphenol concentration by minimizing the effects of other factors such as age of the bushes and management practices. The wave-lengths at 358, 369, 484, 845, 916, 1387, 1420, 1435, 1621 and 2294 nm were identified as sensitive to tea polyphenol, among which 2294 nm was found to be the most recurring band. The noise removed selected bands, their transformed derivatives and principal components were regressed with the tea polyphenol using univariate and milti-variate analysis. In univariate analysis the correlation was very poor with RMSE more than multi-varite analysis. In univariate analysis the correlation was very poor with RMSE more than 3.0.A significant improvement in R2 values were observed when multivariate analyses like stepwise multiple linear regression (SMLR) and partial least square regression (PLSR) was carried out. The PLSR of first derivative reflectance was most accurate (R2=0.81 and RMSE =1.39 mg g-1) among all the uni-and multivariate analysis for predicting the polyphenol of fresh tea leaves.
Location: T E 15 New Biology Building
Literature cited 1: An, H., Gu, L., 1989.Fast stepwise procedures of selection of variables by using AIC and BIC criteria.Acta Math.Appl.Sin.Engl.Ser. 5 (1), 60-67. Basu Majumder, A., Bera, B., Rajan, A., 2010.Tea statistics: global scenario.Inc.J.Tea Sci.8 (1), 121-124.
Literature cited 2: Bian, M., Skidmore, A.K., Schlerf,M., Wang, T., Liu, Y., Zeng, R., Fei, T., 2013.Predicting foliar biochemistry of tea (Camellia sinensis) using reflectance spectra measured at powder, leaf and canopy levels.ISPRS J.Photogram.Rem.Sens.78, 148-156. Bolster, K.L., Martin, M.E., Aber, J.D., 1996.Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectances: a comparison of statistical methods.Can.J.For.Res.26, 590-600.