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ID: 61806
Title: Use of WorldView -2 time series to establish a wetland monitoring program for potential offsite impacts of mine sit rehabilitation.
Author: Timothy G.Whiteside, Renee E.Bartolo.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 42 24-37 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: WorldView-2, Time-series analysis, Vegetation change, Wetlands, Monitoring.
Abstract: The Ramsar-listed wetlands of the Magela Creek floodplain, situated in the World Heritage Kakadu National Park, in northern Australia are recognized for their biodiversity and cultural values. The flood-plain is also a downstream receiving environment for Ranger uranium mine, which is entering closure and rehabilitation phases. Vegetation on the floodplain is spatially and temporally variable which is related to the hydrology of the region, primarily the extent and level of inundation and available soil moisture. Time-series mapping of the floodplain vegetation will provide a contemporary baseline of annual vegetation dynamics to assist with determining whether change is natural or a result of the potential impacts of mine closure activities such as increased suspended sediment moving downstream. The research described here used geographic object-based image analysis (GEOBIA) to classify the upper Magela Creek floodplain vegetation from WorldView-2 imagery captured over four years (2010-2013) and ancillary data including a canopy height model. A step-wise rule set was used to implement a decision tree classification. The resulting maps showed the 12 major vegetation communities that exist on the Magela Creek floodplain and their distribution for May 2010, May 2011, June 2012 and June 2013with overall accuracies of over 80 % for each map. Most of the error appears to be associated with confusion between vegetation classes that are spectrally similar such as the classes dominated by grasses. Object-based change detection was then applied to the maps to analyse change between dates. Results indicate that change between dates was detected for large areas of the floodplain. Most of the change is associated with the amount of surface water present, indicating that although imagery was captured at the same time of year, the imagery represents different stages of the seasonal cycle of the floodplain.
Location: T E 15 New Biology Building
Literature cited 1: Adam, E., Mutanga, O., Rugege, D., 2010.Multispectral hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecol.Manage.1, 281-296. 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 XII.Wichmann-Verlag, Heidelberg, PP.12-23.
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. Bartolo, R., Paulka, S., Van Dam, R.A.Iles, M., Harford, A., 2013.Rehabilitation and closure ecological risk assessment for Range r uranium mine: Ranger uranium mine: documentation of initial problem formulation activities. Internal Report 624.Supervising Scientist; Darwin.

ID: 61805
Title: Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US.
Author: Ruiliang Pu, Jun Cheng.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 42 11-23 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Leaf area index (LAI) Vegetation index, Texture measure, WorldView-2, Landsat TM, Canonical correlation analysis.
Abstract: The leaf area index (LAI) of plant canopies is an important structural parameter that controls energy, water, and gas exchanges of plant ecosystems. Remote sensing techniques may offer an alternative for measuring and mapping forest LAI at a landscape scale. Given the characteristics of high spatial/spectral resolution of the WorldView-2 (WV2) sensor, it is of significance that the textural information extracted from WV2 multispectral (MS) bands will be first time used in estimating and mapping forest LAI. In this study, LAI mapping accuracies would be compared from (a) spatial resolutions between 2-m WV2 MS data and 30-m Landsat TM imagery, (b) the nature of variables between spectrum-based features and texture-based features, and (c) sensors between T M and WV2.Therefore spectral/textural features (SFs) were first selected and tested; then a canonical correlation analysis was performed with different data sets of SFs and LAI measurement; and finally linear regression models were used to predict and map forest LAI with canonical variables calculated from image data. The experimental results demonstrate that for estimating and mapping forest LAI (i) using high resolution data (WV2) is better than using relatively low resolution data ?; (ii) extracted from the same WV2 data, texture-based features have higher capability than that of spectrum-based features; (iii) a combination of spectrum-based features with texture-based features could lead to even higher accuracy of mapping forest LAI than their either one separately; and (iv) WV2 sensor outperforms T M sensor significantly. However, we need to address the possible overfitting phenomenon that might be brought in by using more input variables to develop models. In addition, the experimental results also indicate that the red-edge band in WV2 was the worst on estimating LAI among WV2 MS bands and WV2 MS bands in the visible range had a much higher correlation with ground measured LAI than that red-edge and NIR bands did.
Location: T E 15 New Biology Building
Literature cited 1: Anys, H., He, D., -C., 1995.Evaluation of textural and multipolarization radar features for crop classification. IEEE on geosciences and remote sensing. Transaction 33 (5), 1170-1181. Baret, F., Guyot, G., 1991.Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens.Environ.35, 161-173.
Literature cited 2: Belanger, M.J.Miller, J.R., Boyer, M.G., 1995.Comparative relationships between some red edge parameters and seasonal leaf chlorophyll concentrations.Can.J.Remote Sens.21 (1), 16-21. Brown, L., Chen, J.M., Leblanc, S.G., Cihlar, J., 2000.A shortwave infrared modification to the simple ratio for lai retrieval in boreal forests: an image and model analysis. Remote Sens.Environ.71, 16-25.

ID: 61804
Title: Comparison of the Landsat Surface Reflectance Climate Data Record (CDR) and manually atmospherically corrected data in a semi-arid European study area.
Author: Francesco Vuolo, Matteo Mattiuzzi, Clement Atzberger.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 42 1-10 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Landsat CDR, Surface reflectance, Atmospheric correction, ATCOR-2, Site-specific tuning.
Abstract: This study contributes to the quality assessment of atmospherically corrected Landsat surface reflectance data that are routinely generated by the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). This dataset, named Landsat Surface Reflectance Climate Data Record (Landsat CDR), is available at global scale and offers unprecedented opportunities to land monitoring and management services that require atmospherically corrected Earth observation (EO) data. Our assessment is based on the comparison of the Landsat CDR data against a set of Landsat and DEIMOS-1 images processed to a high degree of accuracy using an industry-standard atmospheric correction algorithm (ATCOR-2).The software package has been used for many years and its correction procedures can be considered consolidated and well-established. The dataset of Landsat and DEIMOS-1 images was acquired over a semi-arid agricultural area located in Lower Austria and was independently corrected by using a manual fine-tuning of ATCOR-2 parameters to reach the highest possible accuracy. Results show a very good correspondence of the surface reflectance in each of the six reflective spectral channels as well as for the NDVI (Normalized Difference Vegetation Index). An additional comparison against a NDVI time series from MODIS revealed also a good correspondence. Coefficients of determination (R2) between the two multi-year and multi-seasonal Landsat/DEIMOS datasets range between0.91 (blue band) and 0.98 (nIR, SWIR)-1 and SWIR-2).The results obtained for four semi-arid test site in Austria confirm previous findings an suggest that automatic atmospheric procedures, such as the one implemented by LEDAPS are accurate enough to be used in land monitoring services that require consistent multi-temporal surface reflectance data.
Location: T E 15 New Biology Building
Literature cited 1: Atkinson, P.M., Jeganathan, C., Dash, J., Atzberger, C., 2012.Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sens.Environ.123, 400-417. Atzberger, C., Eilers, P.H.C., 2011.Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements.Int.J.Remote Sens.32, 3689-3709.
Literature cited 2: Atzberger, C., Richter, K., 2012.Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery. Remote Sens.Environ.120, 208-218. Bhandari, S., Phinn, S., Gill, T., 2012.Preparing Landsat Image Time Series (LITS) for monitoring changes in vegetation phenology in Queensland.Aust.Remote Sens.4, 1856-1886.

ID: 61803
Title: Importance of landscape features and Earth observation derived habitat maps for modeling amphibian distribution in the Alta Murgia National Park.
Author: Gentile Francesco Ficetola, Maria Adamo, Anna Bonardi, Vito De Pasquale, Cristiano Liuzzi, Francesco Lovergine, Francesco Marcone, Fabio Mastropasqua, Cristina Tarantino, Palma Blonda, Emilio Pdoa-Schioppa.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 152-159 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Amphibians, General Habitat Category, Habitat map, Land cover map.
Abstract: Traditionally, analyses of relationships between amphibians and habitat focused on breeding environments (i.e., pond features) more than on the features of the surrounding environment. Nevertheless, for most amphibians the terrestrial phase is longer than the aquatic phase, and consequently landscape features (i.e., habitat mosaics) may have an important role for modeling amphibian distribution. There were different aims in this analysis. Firstly, we compared the effectiveness of the information provided by land cover/use (LC/LU) classes and habitat classes defined according to a new habitat taxonomy named General Habitat Category (GHC), which based on the concept of biological forms of dominant vegetation and class naturalness. The GHC map used was obtained from very resolution Earth observation data according to ecological expert rules involving concepts related to spatial and temporal relationships among LC/LU and habitat classes. Then, we investigated the importance for amphibians of the landscape surrounding ponds within the Italian Alta Murgia National Park. The work assessed whether LC/LU classes in pond surrounds are important for the presence/absence of amphibians in this area, and identified which classes are more important for amphibians. The results obtained can provide useful indications to management strategies ' aiming at conservation of amphibians within the study area.An information-theoretic approach was adopted to assess whether GHC maps allow to improve the performance of species distribution models. We used the Akaike ' s Information Criterion (AICc) to compare the effectiveness of GHC categories versus LC/LU categories in explaining the presence/absence of pool frogs. AICc weights suggest that GHC categories can better explain the distribution of frogs, compared to LC/LU classes.
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. Arntzen, J.W.,et al., 2009.Lissotriton italicus.In:IUCN Red List of Threatened Species. Version 2012.1.IUCN, Available from: www.iucnredlist.org.
Literature cited 2: Barton, K., 2011.MuMin: Multi-model Inference. R Package Version 1.0.0 http://CRAN.R-project.org/package=MuMin. Beebee, T.J.C., 1981.Habitats of the British amphibians (4): agricultural lowlands and a general discussion of requirements.Biol.Conserv.21, 127-139.

ID: 61802
Title: An ontological based on MODIS images to assess ecosystem functioning of Natura 2000 habitats: A case study for Quercus Pyrenaica forests.
Author: A.J.Perez-Luque, R.Perez-Perez, F.J.Bonet-Garcia, P.J.Magana.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 142-151 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Ontology, NDVI, Snow cover, (satellite) Earth observation, Sierra Nevada.
Abstract: The implementation of the Natura 2000 network requires methods to assess the conservation stats of habitats. This paper shows a methodological approach that combines the use of (satellite) Earth observation with ontologies to monitor Natura 2000 habitats and assess their functioning. We have created an ontological system called Savita that can describe both the ecosystem functioning and the behaviour of abiotic factors in a Natura 2000 habitat. This system is able to automatically download images from MODIS products, create indicators and compute temporal trends for them. We have developed an ontology that takes into account the different concepts and relations about indicators and temporal trends, and the spatio-temporal components of the datasets. All the information generated from datasets and MODIS images, is stored into a knowledge base according to the ontology. Users can formulate complex questions using a SPARQL end-point. This system has been tested and validated in a case study that uses Quercus Pyrenaica Wild. Forests as a target habitat in Sierra Nevada (Spain), a Natura 2000 site. We assess ecosystem functioning using NDVI. The selected abiotic factor is snow cover. Savia provides useful data regarding these two variables and reflects relationships between them.
Location: T E 15 New Biology Building
Literature cited 1: Alcaraz-Segura, D., Cabello, J., Paruelo, J., 2009.Baseline characterization of major Iberian vegetation types based on the NDVI dynamics. Plant Ecol.202, 13-29, http:dx.doi.org/10.1007/s11258-008-9555-2. Alcaraz-Sugura, D.,Calbello, J., Paruelo, J.M., Delibes, M., 2008.Trends in the surface vegetation dynamics of the national parks of Spain as observed by satellite sensors.Appl.Veg.Sci.11,431-440, http://dx.doi.org/10.3170/2008-7-18522.
Literature cited 2: Alcaraz-Segura, D., Liras, E.,Tabik, S., Paruelo, J., Cabello, J.,2010.Evaluating the consistency of the 1982-1999 NDVI trends in the Iberian Peninsula across four time-series derived from the AVHRR sensor: LTDR, GIMMS, FASIR, and PAL-II.Sensors 10, 1291-1314, http://dx.doi.org/10.3390/s100201291. Alcaraz-Segura, D., Paruelo, J., Cabello, J., 2006.Identification of current ecosystem functional types in the Iberian Peninsula.Glob.Ecol.Biogeogr.15, 200-212, http:/dx.doi.org/10.1111/j.1466-822X.2006.00215.x.

ID: 61801
Title: Using ontological inference and hierarchical matchmaking to overcome semantic heterogeneity in remote sensing-based biodiversity monitoring.
Author: Simon Nieland, Birgit Kleinschmit, Michael Forster.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 133-141 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Earth observation, Semantic reasoning, Interoperability, Nature conservation, Natura 2000.
Abstract: Ontology-based applications hold promise in improving spatial data interoperability. In this work we use remote sensing-based biodiversity information and apply semantic formalization and ontological inference to show improvements in data interoperability/comparability. The proposed methodology includes an observation-based, ?bottom-up? engineering approach for remote sensing applications and gives a practical example of semantic mediation of geospatial products. We apply the methodology to three different nomenclatures used for remote sensing-based classification of two heathland nature conservation areas in Belgium and Germany. We analysed sensor nomenclatures with respect to their semantic formalization and their bio-geographical differences. The results indicate that a hierarchical and transparent nomenclature is far more important for transferability than the sensor or study area. The inclusion of additional information, not necessarily belonging to a vegetation class description, is a key factor for the future success of using semantics for interoperability in remote sensing.
Location: T E 15 New Biology Building
Literature cited 1: Andres, S., Arvor, D., Pierkot, C., 2012.Towards an ontological approach for classifying remote sensing images. In: 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems, pp.825-832. Arvor, D., Durieux, L., Andres, S., Laporte, M.A., 2013.Advances in geographic object-based image analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective.ISPRS J.Photogramm.Remote Sens.82, 125-137.
Literature cited 2: Bader, F., Werner, N., 2003.Basic description logics. In: The description Logic Handbook. Theory, Implementation and Applications, pp.47-95. Bard, J.B.L., Rhee, S.Y., 2004.Ontologies in biology: design, applications and future challenges.Nat.Rev.Genet.5, 213-222.

ID: 61800
Title: Satellite Earth observation data to identify anthropogenic pressures in selected protected areas.
Author: Harini Nagendra, Paola Mairota, Carmela Marangi, Richard Lucas, Panayotis Dimopoulos, Joao Pradinho Honrado, Madhura Niphadkar, Caspar A.Mucher, Valeria Tomaselli, Maria Panitsa, Cristina Tarantino, Ioannis Manakos, Palma Blonda.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 124-132 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Biodiversity conservation, Earth observation, Changes in state, protected areas.
Abstract: Protected areas are experiencing increased levels of human pressure. To enable appropriate conservation action, it is critical to map and monitor changes in the type and extent of land cover/use and habitat classes, which can be related to human pressures over time. Satellite Earth observation (EO) data and techniques offer the opportunity to detect such changes. Yet association with field information and expert interpretation by ecologies is required to interpret, qualify and link these changes to human pressure. There is thus an urgent need to harmonize the technical background of experts in the field of EO data analysis with the terminology of ecologists, protected area management authorities and policy makers in order to provide meaningful, context-specific value-added EO products. This paper builds on the DPSIR framework, providing a terminology to relate the concepts of state, pressures, and drivers with the application of EO analysis. The type of pressure can be inferred through the detection of changes in state (i.e., changes inland cover/habitat conversion, land cover/habitat modification, habitat fragmentation and changes in landscape connectivity, and changes in plant community structure. These categories of change in landscape connectivity, and changes in plant community structure. These categories of changes in state to causal direct anthropogenic pressures. Drawing on expert knowledge, a set of protected areas located in diverse socio-ecological contexts and subject to a variety of pressures are analysed to (a) link the four categories of changes in state of land cover/habitats to the drivers (anthropogenic pressure), as relevant to specific target land cover and habitat classes; (b) identify (for pressure mapping) the most appropriate spatial and temporal EO data sources as well as interpretations from ecologists and field data useful in connection with EO data sources as well as interpretations from ecologists and field data useful in connection with EO data analysis. We provide detailed examples for two protected areas, demonstrating the use of EO data for detection of land cover/habitat change, coupled with expert interpretation to relate such change to specific anthropogenic pressures. We conclude with a discussion of the limitations and feasibility of using EO data and techniques to identify anthropogenic pressures, suggesting additional research efforts required in this direction.
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, Vernico, G., Marangi, C., De Pasquale, V., Blonda, P., 2014. Agarwal, S., Vailshery, L.S, Jaganmohan, M., Nagendra, H., 2013.Mapping urban tree species using very high resolution satellite imagery: comparing pixel-based and object-based approaches.ISPRS Int.J.Geo.Inf.2, 220-236.
Literature cited 2: Allard, A., 2003.Detection of vegetation degradation on Swedish mountainous heaths at an early stage by image interpretation.Ambio 32, 510-519. Buchanan, G.M., Butchart, S.H.M., Dutson, G., Pilgrim, J.D., Steininger, M.K., Bishop, K.D., Mayaux, P., 2008.Using remote sensing to inform conservation status assessment: estimates of recent deforestation rates on New Britain and the impacts upon endemic birds.Biol.Conserv.141, 56-66.

ID: 61799
Title: A composite indicator for assessing habitat quality of riparian forests derived from Earth observation data.
Author: Barbara Riedler, Lena Pernkopf, Thomas Strasser, Stefan Lang, Geoff Smith.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 114-123 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Habitat assessment, Conservation status, Forest structure, Spatial indicators, Flood plain, RF_S.
Abstract: Riparian forests are precious, complex habitats fostering high biodiversity where effective monitoring of habitat quality is particularly important. We present a composite indicator, referred to as Riparian Forest composite Indicator: focus on Structure (RFI_S) is composed of seven indicators derived from very high resolution (VHR) satellite imagery and LiDAR data, calculated on patch level. These indicators assess for important attributes of riparian forest quality: (1) tree species composition, (2) vertical forest structure, (3) horizontal forest structure and (4) water regime. For the aggregation of the RFI_S, two different weighting schemes, expert-based and statistical weighting, are applied. Forest patches with high cumulative RFI_S values represent patches of good habitat quality. These patches are primarily found along water bodies, reflecting the importance of water bodies for the structural complexity, an optimum water regime and tree species composition. For forest patches of low habitat quality the RFI_S helps to design suitable measures t o improve habitat quality status through its decomposability into the underlying indicators. A sensitivity analysis to test the robustness of the RFI_S shows that the indicator variance in terrain roughness has the strongest influence on the composite indicator. Finally, a comparison with an existing expert-based map on conservation status reveals the potential of a complementary quantitative assessment of habitat quality in the study site. We hence conclude that the RFI_S has a high capability to support sustainable forest management complementing regularly gathered in situ data.
Location: T E 15 New Biology Building
Literature cited 1: Andersson, L.H.H., 1991.Bryophytes and decaying wood a comparison between a managed and a natural forest. Holarctic.Ecol.14, 121-130. Arizpe, D., Mendes, A., Rabaca, J.E., 2008.Sustainable Riparian Zones: A Management Guide.Ripidurable, Portugal.
Literature cited 2: Azim, U.M., 2006.Structural and functional roles of riparian management areas in maintaining stream values in the Acadian Forest. In: Technical Bulletin 922, National Council for Air and Stream Improvement. Berg, A., Ehnstrom, B., Gustafsson, L., Hallingback, T., Jonsell, M., Weslien, J., 1994.Threatened plant, animal, and fungus species in Swedish forests: distribution and habitat associations.Conserv.Biol.8, 718-731.

ID: 61798
Title: Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system.
Author: Ana Sofia Vaz, Bruno Marcos, Joao Goncalves, Antonio Monteiro, Paulo Alves, Emilio Civantos, Richard Lucas, PaolaMairota, Javier Garcia-Robles, Joaquim Alonso,Palma Blonda, Angela Lomba, Joao Pradinho Honrado.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 106-113 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Land cover, Multi-model inference, Natura 2000, Very high resolution image, Woodland quality monitoring.
Abstract: There is an increasing need of effective monitoring systems for habitat quality assessment. Method based on remote sensing (RS) features, such as vegetation indices, have been proposed as promising approaches, complementing methods based on categorical data to support decision making. Here, we evaluate the ability of Earth observation (EO) data, based on a new automated, knowledge driven system, to predict several indicators for oak woodland habitat quality in Portuguese Natura 2000 site. We collected in-field data on five habitat quality indicators in vegetation plots from woodland habitats of a landscape undergoing agricultural abandonment. Forty -three predictors were calculated, and a multi-model inference framework was applied to evaluate the predictive strength of each data set for the several quality indicators. Three indicators were mainly explained by predictors related to landscape and neighborhood structure. Overall, competing models based on the products of the automated knowledge-driven system had the best performance to explain quality indicators, compared to models based on manually classified land cover data. The system outputs in terms of both land cover classes and spectral/landscape indices were considered in the study, which highlights the advantages of combining EO data with RS techniques and improved modeling based on sound ecological hypotheses. Our findings strongly suggest that some features of habitat quality, such as structure and habitat composition, can be effectively monitored from EO data combined with in-field campaigns as part of an integrative monitoring framework for habitat status assessment.
Location: T E 15 New Biology Building
Literature cited 1: Alessandro, P., Marta, C., 2012.Heterogeneity of linear forest formations: differing potential for diversity conservation. A case study in Italy.Agrofor.Syst.86, 83-93. Anderson, D.R., Link, W.A., Johnson, D.H., Burnham, K.P., 2001.Suggestions for presenting the results of data analyses.J.Wildl.Manag.65, 373-378.
Literature cited 2: Borne, J.V., Paelinckx, D., Mucher, C.A., Kooistra, L., Haest, B., De Blust, G., Schmidt, A.M., 2011.Integrating remote sensing in Natura 2000 habitat monitoring: prospects on the way forward.J.Nat.Conserv.19, 116-125. Brumelis, G., Jonsson, B.G., Kouki, J., Kuuluvainen, T., Shorohova, E., 2011.Forest naturalness in Northern Europe: Perspectives on process, structures and species diversity. Silva Fenn.45, 807-821.

ID: 61797
Title: Very high resolution Earth observation features for monitoring plant and animal community structure across multiple spatial scales in protected areas.
Author: Paola Mairota, Barbara Cafarelli, Rocco Labadessa, Francesco Lovergine, Cristina Tarantino, Richard M.Lucas, Harini Nagendra, Raphael K. Didham.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 100-105 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: VHR EO features, Biodiversity surrogates, Functional groups, Habitat quality, Monitoring.
Abstract: Monitoring the status and future trends in biodiversity can be prohibitively expensive using ground-based surveys. Consequently, significant effort is being invested in the use of satellite remote sensing to represent aspects of the proximate mechanisms (e.g., resource availability) that can be related to biodiversity surrogates (BS) such as species community descriptors. We explored the potential of very high resolution (VHR) satellite Earth observation (EO) features as proxies for habitat structural attributes that influence spatial variation in habitat quality and biodiversity change. In a semi-natural grassland mosaic of conservation concern in southern Italy, we employed a hierarchical nested sampling strategy to collect field and VHR-EO image features were derived from a Worldview-2 image. Three window sizes (grains) w3ere tested for analysis and computation of textural features, guided by the perception limits of different organisms. The modeled relationships between VHR-EO features and BS responses differed across scales, suggesting that landscape, patch and plot levels are respectively most appropriate when dealing with birds, plants and insects. This research demonstrates the potential of VHR-EO for biodiversity mapping and habitat modeling, and highlights the importance of identifying the appropriate scale of analysis for specific taxonomic groups of interest. Further, textural features are important in the modeling of functional group-specific indices which represent BS in high conservation value habitat types, and provide a more direct link to species interaction networks and ecosystem functioning than provided by traditional taxonomic diversity indices.
Location: T E 15 New Biology Building
Literature cited 1: Aman, A., Randriamanantena, H., Podaire, A., Frouin, R., 1992.Upscale integration of normalized difference vegetation index: the problem of spatial heterogeneity.IEEE Trans.Geosci.Remote Sens.30, 326-338. Barton, P.S., Cunningham, S.A., Manning, A.D., Gibb, H., Lindenmayer, D.B.Didham, R.K., 2013.The spatial scaling of beta diversity.Glob.Ecol.Biogeogr.22, 639-647.
Literature cited 2: Bibby, C.J.Burgess, N., Hill, D.A., 1992.Bird Census Techniques. Academic Press, London, 280 pp. Brotons, L., Wolff, A., Paulus, G., Martin, J.L., 2005.Effect of adjacent agricultural habitat on the distribution of passerines in natural grasslands, Biol.Conserv.124, 407-414.

ID: 61796
Title: Red-edge vegetation indices for detecting and assessing disturbances in Norway spruce dominated mountain forests.
Author: Joanna Adamczyk, Antonia Osberger.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 90-99 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Forest disturbance, Mountain regions, OBIA, Red-edge reflectance, Worldview-2, Rapideye.
Abstract: Here we propose an approach to enhance the detection and assessment of forest disturbances in mountain areas based on red-edge reflectance. The research addresses the need for improved monitoring of areas included in the European Natura 2000 network. Thirty-eight vegetation indices (VI) are assessed for sensitivity to topographic variations. A separability analysis is performed for the resulting set of ten VI whereby two VI (PSSRc2, SR 800/550) are found most suitable for threshold-based OBIA classification. With a correlation analysis (SRCC) between VI and the training samples we identify Datt4 as suitable to represent the magnitude of forest disturbance. The provided information layers illustrate two combined phenomena that were derived by (1) an OBIA delineation and (2) continuous representation of the magnitude of forest disturbance. The satisfactory accuracy assessment results confirm that the approach is useful for operational tasks in the long-term monitoring of Norway spruce dominated forests in mountainous areas, with regard to forest disturbance.
Location: T E 15 New Biology Building
Literature cited 1: Baatz, M., Schape, A., 2000.Multiresolution segmentation: an optimization approach for high quality-scale image segmentation. In: Proceedings of the 12th Symposium for Applied Geographic Information Processing (Angewandte Geographische Informationsverarbeitung XII.AGIT 2000).Salzburg, Austria, pp12-23. Balckburn, G.A., 1998.Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens.Environ.66 (3), 273-285.
Literature cited 2: CEC, 1993.Commission Regulation (EEC) No 926/93 of April 1993.OJL 100, 26.4.1993., pp.1-47. Cohen, W.B., Goward, S.N., 2004.Landsat ' s role in ecological applications of remote sensing. Bioscience 54 (6), 535-545.

ID: 61795
Title: Using information layers for mapping grassland habitat distribution at local to regional scales.
Author: Oliver Buck, Virginia E.Garcia Millan, Adrian Klink, Kian Pakzad.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 83-89 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Natura 2000, Biodiversity monitoring, Information layer, Grassland, Article 17 reporting, Habitat Directive.
Abstract: The Natura 2000 network of protected sites is one of the means to enable biodiversity conservation in Europe. EU member states have to undertake surveillance of habitats and species of community interest protected under the Habitat Directive. Remote sensing techniques have been applied successfully to monitor biodiversity aspects according to Natura 2000, but many challenges remain assessing dynamics and habitat changes outside protected sites. Grasslands are among the most threatened habitats in Europe. In this paper we the integration of expert knowledge into different standard classification approaches to map grassland habitats in Schleswig Holstein. Knowledge about habitat features is represented as raster information layers, and used in subsequent grassland classifications. Overall classification accuracies were highest for the maximum likelihood and support vector machine approaches using RapidEye time series, but results improved for specific grassland classes when information layers were included in the classification process.
Location: T E 15 New Biology Building
Literature cited 1: Asner, G.P., 1998.Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens.Environ.64, 234-253. 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.ISPRSJ.Photogram.Remote Sens.58, 239-258.
Literature cited 2: Blaschke, T., Johansen, K., Tiede, D., Weng, Q., 2011.Object-Based Image Analysis for Vegetation Mapping and Monitoring. Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications. Taylor 7Francis.London, pp.241-271. Bock, M., Xofis, P., Mitchley, J.,Rossner, G., Wissen, M., 2005.Object-Oriented methods for habitat mapping multi scales-case studies from Northern Germany and Wye Downs, UK.Nature Conserv.13, 75-89.

ID: 61794
Title: Object-based delineation of homogeneous landscape units at regional scale based on MODIS time series.
Author: Mar Bisquert, Agnes Begue, Michel Deshayes.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 72-82 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Segmentation, Enhanced Vegetation Index (EVI), Haralick texture evaluation, Earth observation.
Abstract: Landscapes can be described by seasonal and spatial patterns linked to vegetation type and phenology, environmental conditions, and human activities. The objective of this work is to propose and test an approach for delineating homogeneous landscape units at a regional scale by using only Earth observation data. We used MODIS (Moderate Imaging Spectroradiometer) images from 2007 to 2011, acquired over the whole continental French territory at 250 m spatial resolution. The data set includes time series of the Enhanced Vegetation Index (EVI) and time series of five Haralick texture indices. A principal components analysis (PCA) allowed us to choose the most representative indices (Spectral and textural) and dates to be used in the region-growing segmentation. Different combinations of input data, as well as different segmentation parameters, were tested and compared using unsupervised evaluation methods. These methods were used to analyze the radiometric homogeneity of the regions and the radiometric disparity between regions when changing the homogeneity criterion of the segmentation. The best segmentation results obtained included three EVI images, together with three images of the texture 2nd moment, corresponding to the average of the months of April, July and December from 2007 to 2011.The optimum homogeneity criterion for the region-growing segmentation using this combination of variables was 15.We believe this method is applicable at other scales and other data sets for vegetation and biodiversity studies, and for habitat mapping.
Location: T E 15 New Biology Building
Literature cited 1: Aguera, F., Aguilar, F.J., Aguilar, M.A., 2008.Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses.ISPRS J.Photogram.Remote Sens.63, 635-646, http://dx.doi.org/10.1016/j.isprsjprs.2008.03.003. Baatz, M., Schape, A., 2000.Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In: Angewandte Geographische Informationsverarbeitung XII.Presented at the Beitrage zum AGIT-Symposuium.Herbert Wichmann Verlag, Salzburg, pp.12-23.
Literature cited 2: Bisquert, M.M., Sanchez, J.M., Caselles, V., 2011.Fire danger estimation from MODIS Enhanced Vegetation Index data: application to Galicia region (north-west Spain.).Int.J.Wildland Fire 20, 465-473, http://dx.doi.org/10.1071/WF10002. Blaschke, T., 2010.Object based image analysis for remote sensing.ISPRS J.Photogram.RemoteSens.65, 2-16, http:dx.doi.org/10.1016/j.isprsjprs.2009.06.004.

ID: 61793
Title: Integrating RapidEye and ancillary data to map alpine habitats in South Tyrol, Italy.
Author: Anastasia Polychronaki, Nadine Spindler, Alexander Schmidt, Barbara Stoinschek, Marc Zebisch, Kathrin Renner, Ruth Sonnenschein, Claudia Notarnicola.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: EWRG, CES
Reference: Applied Earth Observation and Geoinformation. Vol. 37 65-71 (2015).
Subject: Applied Earth Observation and Geoinformation
Keywords: Alpine, Habitats, RapidEye, Support vector machines, Land-cover classification, Rule-based kernel.
Abstract: In this paper, we present a two-stage method for mapping habitats using Earth Observation (EO) data in three Alpine sites in South Tyroll, Italy. The first stage of the method was the classification of land cover types using multi-temporal RapidEye images and support vector machines (SVMs).The second stage involved reclassification of the land cover types to habitat types following a rule-based spatial kernel. The highest accuracies in land classification were 95.1% overall accuracy, 0.94 kappa coefficient and 4.9 % overall disagreement. These accuracies were obtained when the combination of images with topographic parameters and homogeneity texture was used. The habitat classification accuracies were rather moderate due to broadly defined rules and possible inaccuracies in the reference map. Overall, rather moderate due to the broadly defined rules and possible inaccuracies in the reference map. Overall, our proposed methodology could be implemented to map cost-effectively alpine habitats over large areas and could be easily adapted to map other types of habitats.
Location: T E 15 New Biology Building
Literature cited 1: ArcGIS Resource Center: http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#//009zooooowtoooooo.htm (accessed 23.08.14). Autonomous Province of Bolzano, 2014.http//www.provinz.bz.it.informatik/kartografie/landeskartografie-digitales-Gelaendemodell.asp (accessed 23.08.14).
Literature cited 2: Barnsley, M.J., Barr, S.L, 1992.Developing kernel-based spatial re-classification techniques for improved land-use monitoring using high resolution images.In: The XXIXth Conference of the International Society for Photogrammetry and Remote Sensing.ISPRS Archives, Washington, DC. Barnsley, M.J., Barr, S.L., 1996.Inferring urban land use from satellite sensor images using kernel-based spatialre-classification.Photogramm.Eng.Remote Sens.62 (8), 949-958.

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.