Enter records

ID: 60165
Title: Sequence-based mapping approach to spatio-temporal snow patterns from MODIS time-series applied to Scotland.
Author: Laura Poggio, Alessandro Gimona.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 122-135 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Snow cover, Hydrological modeling, Spatio-temporal analyses, Cloud filling.
Abstract: Snow cover and its monitoring are important because of the impact on important environmental variables, hydrological circulation and ecosystem services. For regional snow cover mapping and monitoring, the MODIS satellite sensors are particularly appealing. However cloud presence is an important limiting factor. This study addressed the problem of cloud cover for time-series in a boreal-Atlantic region where melting and re-covering of snow often do not follow the usual alpine-like patterns. A key requirement in this context was to apply improved methods to deal with the high cloud cover and the irregular spatio-temporal snow occurrence, through exploitation of space-time correlation of pixel values. The information contained in snow presence sequences was then used to derive summary indices to describe the time series patterns. Finally it was tested whether the derived indices can be considered an accurate summary of the snow presence data by establishing and evaluating their statistical relations with morphology and the landscape. The proposed cloud filling method had a good agreement (between 80 and 99 %) with validation data even with a large number of pixels missing. The sequence analysis algorithm proposed takes into account the position of the states to fully consider the temporal dimension, i.e. the order in which a certain state appears in an image sequence compared to its neighbourhoods. The indices that were derived from the sequence of snow presence proved useful for describing the general spatio-temporal patterns of snow in Scotland as they were well related (more than 60 % of explained deviance) with environmental information such as morphology supporting their uses as a summary of snow patterns over time. The use of the derived indices is an advantage because of data reduction, easier interpretability and capture of sequence position wise information (e.g. importance of short term fall /melt cycles). The derived seven clusters took into account the temporal patterns of the snow presence and they were well separated both spatially and according to the snow patterns and the environmental information. In conclusion, the use of sequences proved useful for analysing different spatio-temporal patterns of snow that could be related to other environmental information to characterize snow regimes regions in Scotland and to be integrated with ground measures for further hydrological and climatological analysis as baseline data for climate change models.
Location: TE 15 New Biology Building
Literature cited 1: Abbott, A., Tsay, A., 2000. Sequence analysis and optimal matching methods in sociology: review and prospect. Soc. Mehods Res. 29 (1), 3-33. Addink, E., Stein, A., 1999. A comparison of conventional and geostatistical methods to replace clouded pixels in NOAA-AVHRR images. Int J. Remote Sens. 20 (5), 961-977.
Literature cited 2: Akaike, H., 1973. Information theory and an extension of the maximum likelihood principle. In: Petrov, B., F. (Eds), Second International Symposium on Information Theory. Akademia Kiado, Budapest, Hungary, pp. 267-281. Andreadis, K.M., Lettenmaier, D., 2006. Trends in 20th century drought over the continental United States. Geophys. Res.Lett. 33 (10).

ID: 60164
Title: Woody vegetation and land cover changes in the Sahel of Mali (1967-2011)
Author: Raphael Spiekermann, Martin Brandt, Cyrus Samimi.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 1113-121 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Sahel, Object based classification, Degradation, Greening, RapidEye, Corona.
Abstract: In the past 50 years, the Sahel has experienced significant tree-and cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought Woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as increase of trees on farmer ' s fields. Mean woody cover decreased in the plains (-4%) but is stable on the plateau (+ !%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change.
Location: TE 15 New Biology Building
Literature cited 1: Allen, M., 2009. International Tree Foundation Narrative Report- MA295 Sahel ECO. Sahel ECO, Bamako. Andersen, G.L., 2006. How to detect desert trees using corona images: discovering historical ecological data. J. Arid Environ. 65, 491-511.
Literature cited 2: Anyamba, A., Tucker, C.J., 2005. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981 to 2003. J. Arid Environ. 63, 596-614. Brandt, M., Romankiewicz, C., Spikermann, R., Samimi, C., 2014a. Environmental change in time series -an interdisciplinary study in the Sahel of Mali and Senegal.J.Arid Environ. 105, 52-63, http://dx.doi.org/10.1016/j.jaridenv. 2014.02.019

ID: 60163
Title: A support vector machine to identify irrigated crop types using time-series Landsat NDVI data.
Author: Baojuan Zheng, Soe W. Myint, Prasad S. Thenkabail, Rimjhim M. Aggarwal.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 103-112 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Crop classification, Landsat, NDVI, Support vector machines, SVM.
Abstract: Site-specific information of crop types is required for many agro-environmental assessments. Te study investigated the potential of support vector machines (SVMs) in discriminating various crop types in a complex cropping system in the phoenix Active Management Area. We applied SVMs to Landsat time-series Normalized Difference Vegetation Index (NDVI) data using training datasets selected by two different approaches: stratified random approach and intelligent selection approach using local knowledge. The SVM models effectively classified nine major crop types with overall accuracies of > 86 % for both training datasets. Our results showed that the intelligent selection approach was able to reduce the training set size and achieved higher overall classification accuracy than the stratified random approach .The intelligent selection approach is particularly useful when the availability of reference data is limited and unbalanced among different classes. The study demonstrated the potential of utilizing multi-temporal Landsat imagery to systematically monitor crop types and cropping patterns over time in arid and semi-arid regions.
Location: TE 15 New Biology Building
Literature cited 1: ADWR, 2012a. Active Management Area Climate, Retrieved from http: //www. Azwater.gov/AzDWR/Statewide Planning/WaterAtlas/ActiveManagementAreas/PlanningAreaOverview/Climate.htm (accessed 28.08.13. ADWR, 2012 b. Phoenix AMA Cultural Water Demand, Retrieved from http://www.azwater.gov/AzDWR/StatewidePlanning/WaterAtlas/ActiveManagementAreas/Cultural/PhoenixAMA.htm (accessed 28.08.13)
Literature cited 2: Baatz, M., Schape, A., 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation.In: Strobl, J. (Ed.), Angewandte Geographische Informationsverarbeitung XIII. Beitrage zum AGIT-Symposium Salzburg 2000. Herbert Wichmann Verlag, Karlsruhe, pp. 12-23. Belousov, A.I, Verzakov, S.A., von Frese, J., 2002. A flexibl classification approach with optimal generalization performance: support vector machines. Chemom.Intell.Lab.Syst. 64, 15-25.

ID: 60162
Title: Very high resolution Earth observation features for testing the direct and indirect effects of landscape structure on local habitat quality.
Author: Paola Mairota, Barbara Cafarelli, Rocco Labadessa, Francesco P. Lovergine, Cristina Tarantino, Harini Nagendra, Raphael K. Didham.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 96-102 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Landscape structure, Habitat quality, Mixed effects models, Context-dependence.
Abstract: Modelling the empirical relationships between habitat quality and species distribution patterns is the first step to understanding human impacts on biodiversity. It is important to build on this understanding to develop a broader conceptual appreciation of the influence of surrounding landscape structure on local habitat quality, across multiple spatial scales. Traditional models which report that ' habitat amount ' in the landscape is sufficient to explain patterns of diversity, irrespective of habitat configuration or spatial variation in habitat quality at edges, implicitly treat each unit of habitat as interchangeable and ignore the high degree of interdependence between spatial components of land-use change. Here, we test the contrasting hypothesis, that local habitat units are not interchangeable in their habitat attributes, but are instead dependent on variation in surrounding habitat structure at both patch-and landscape levels. As the statistical approaches needed to implement such hierarchical casual models are observation-intensive, we utilise very high resolution (VHR) Earth Observation (EO) images to rapidly generate fine-grained measures of habitat patch internal heterogeneities over large spatial extents. We use linear mixed-effects models to test whether these remotely-sensed proxies for habitat quality were influenced by surrounding patch or landscape structure. The results demonstrate the significant influence of surrounding patch and landscape context on local habitat quality. They further indicate that such an influence can be direct, when a landscape variable alone influences the habitat structure variable, and/or indirect when the landscape and patch attributes have a conjoined effect on the response variable. We conclude that a substantial degree of interaction among spatial configuration effects is likely to be the norm in determining the ecological consequences of habitat fragmentation, thus corroborating the notion of the spatial context dependence of habitat quality.
Location: TE 15 New Biology Building
Literature cited 1: Adriaensen, F., Chardon, J.P., De Blust, G., Swinnen, E., Villalba, S., Gulinck, H., Matthy-sen, E., 2003. The application of least-cost modeling as a functional landscape model. Landsc. Urban Plan. 64, 233-247. Bender, D.J., Fahrig, L., 2005. Matrix spatial structure can obscure the relationship between inter-patch movement and patch size and isolation. Ecology 86, 1023-1033.
Literature cited 2: 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. Bounce, RG.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., Kovar, 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, 11-25.

ID: 60161
Title: A total variation model based on edge adaptive guiding function for remote sensing image de-noising.
Author: Xianghai Wang, Yingnan Liu, Hongwei Zhang, Lingling Fang.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 89-95 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Remote sensing image, De-noising, Total variation, Standard gradient, Edge adaptive guiding function.
Abstract: The unexpected noise generated during the process of remote sensing images formation and transmission process is a main factor undermining the images ' quality and usage. In recent years, thanks to its local self-adapting characteristics, formal normalization, and modeling flexibility, PDE has received wide attention for its image de-noising functions, thus pushing the realization of maintaining image details while successfully de-noising a new goal for remote sensing images filtering. Having firstly analyzed and discussed the TV model and M model, a modified variation-model (S model for short) based on edge adaptive guiding function is proposed in this paper. The model introduces edge adaptive guiding function based on the standard gradient into the non-linear diffusion term and re-constructed approaching term, which adaptively adjust the smooth intensity around edge and texture information-rich regions of remote sensing images. S-model does not only overcome staircase effect that is easily produced in the TV model, but also avoids losing details and texture information which is often seen in M model, it can efficiently eliminate noises, maintain a good image edge and keep texture details perfectly. The experimental results validate the effectiveness and stability of the proposed model.
Location: TE 15 New Biology Building
Literature cited 1: Alvarez, L., Morel, J.M., 1994. Formalization and Computational Aspects of Image Analysis. Cambridge University Press, United States of America, pp. 1-59. Aujol, J.F., 2009. Some first-order algorithms for total variation based image restoration. J. Math. Imaging Vis. 34 (3), 307-327.
Literature cited 2: Bo, Y., Wang, J., 2003. A wavelet-based filer for SAR speckle reduction and the comparative evaluation on its performance. J. Remote Sens. 7 (5), 393-499. Chen, Z., (PhD dissertation) 2005. Study on Testing Technology of DMC Moonlet On-Orbit. Inst. Of Remote Sensing Applications, Chinese Acad. Of Sci., China.

ID: 60160
Title: Waveform-based point cloud classification in land-cover identification.
Author: Yi-Hsing Tseng, Cheng-Kai Wang, Hone-Jay Chu, Yu-Chia Hung.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 78-88 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Airborne LiDAR, Full-waveform topographic LiDAR, Waveform feature, Optical image, Point cloud classification, Waveform-based classifiers.
Abstract: Full-waveform topographic LiDAR data provide more detailed information about objects along the path of a laser pulse than discrete-return (echo) topographic LiDAR data. Full-waveform topographic LiDAR data consist of a succession of cross-section profiles of landscapes and each waveform can be decomposed into a sum of echoes. The echo number reveals critical information in classifying land cover type. Most land covers contain one echo, whereas topographic LiDAR data in trees and roof edges contained multi-echo waveform features. To identify land-cover types, waveform-based classifier was integrated single-echo and multi-echo classifiers for point cloud classification. The experimental area was the Namasha district of Southern Taiwan, and the land-cover objects were categorized as roads, trees (canopy), grass (grass and crop), bare (bare ground), and buildings (buildings and roof edges). Waveform features were analyzed with respect to the single-and multi echo laser-path samples, and the critical waveform features were selected according to the Bhattacharyya distance. Next, waveform-based classifiers were performed using support vector machine (SVM) with the local, spatial features of waveform topographic LiDAR information, and optical image information. Results showed that by using fused waveform and optical information, the waveform-based classifiers achieved the highest overall accuracy in identifying land-cover point clouds among the models, especially when compared to an echo-based classifier.
Location: TE 15 New Biology Building
Literature cited 1: Abed, F.M., 2013. Potential of the incidence angle effect on the radiometric calibration of full-waveform airborne laser scanning in urban areas. Am. J. Remote Sens. 1, 77-87. Abed, F.M., Mills, J.P., Miller, P.E., 2012. Calibration of full-waveform ALS data based on robust incidence angle estimation. In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-5/W12, pp. 25-30.
Literature cited 2: Alexander, C., Tansey, K., Kaduk, J., Holland, D., Tate, N.J., 2010. Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas. ISPRS J. Photogramm. Remote Sens. 65, 423-432. Choi, E., Lee, C., 2003. Feature extraction based on the Bhattacharyya distance. Pattern Recognit. 36, 1703-1709.

ID: 60159
Title: Accounting for image uncertainty in SAR-based flood mapping.
Author: L. Giustarini, H. Vernieuwe, J. Verwaeren, M. Chini, R. Hostache, P.Matgen, N.E.C. Verhoest, B. De Baets.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 70-77 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Flood mapping, Speckle, Bootstrap, Synthetic aperture radar, Uncertainty.
Abstract: Operational flood mitigation and flood modeling activities benefit from a rapid and automated flood mapping procedure. A valuable information source for such a flood mapping procedure can be remote sensing synthetic aperture radar (SAR) data. In order to be reliable, an objective characterization of the uncertainty associated with flood maps is required. This work focuses on speckle uncertainty associated with the SAR data and introduces the use of a non-parametric bootstrap method to take into account this uncertainty on the resulting flood maps. From several synthetic images, constructed through bootstrapping the original image, flood maps are delineated. The accuracy of these flood maps is also evaluated w.r.t. an independent validation data set, obtaining, in the two test cases analyzed in this paper, F-values (i.e. values of the Jaccard coefficient) comprised between 0.50 and 0.65. This method is further compared to an image segmentation method for speckle analysis, with which similar results are obtained. The uncertainty analysis of the ensemble of bootstrapped synthetic images was found to be representative of image speckle, with the advantage that no segmentation and speckle estimations are required. Furthermore, this work assesses to what extent the bootstrap ensemble size can be reduced while remaining representative of the original ensemble, as operational applications would clearly benefit from such reduced ensemble sizes.
Location: TE 15 New Biology Building
Literature cited 1: Baraldi, A., Parmiggiani, F., An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters. IEEE Trans. Geosci. Remote Sens. 33, 293-304. Carreiras, J.M.B., Vasconcelos, M.J., Lucas, R.M., 2012.Understandingrelationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa). Remote Sens. Environ. 121, 426-444.
Literature cited 2: Caves, R., Quegan, S., White, R., 1998. Quantitive comparison of the performance of SAR segmentation algorithms. IEEE Trans. Image Process. 7, 1534-1546. Di Baldassarre, G., Schumann, G., Bates, P.D., 2009. A technique for the calibration of hydraulic models using uncertain satellite observations of flood extent. J. Hydrol. 367, 276-282.

ID: 60158
Title: Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping.
Author: Mustafa Turker, Dilek Koc-San.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 58-69 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Building extraction, SVM classification, Hough transformation, Perceptual grouping, High-resolution imagery.
Abstract: This paper presents an integrated approach for the automatic extraction of rectangular-and circular-shape buildings from high-resolution optical spaceborne images using the integration of support vector shape buildings from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping. The building patches are detected from the image using the binary SVM classification. The generated normalized digital surface model (nDSM) and the normalized difference vegetation index (NDVI) are incorporated in the classification process as additional bands. After detecting the building patches, the building boundaries are extracted through sequential processing of edge detection, Hough transformation and perceptual grouping. Those areas that are classified as building are masked and further processing operations are performed on the masked areas only. The edges of the buildings are detected through an edge detection algorithm that generates a binary edge image of the building patches. These edges are the n converted into vector form through Hough transform and the buildings are constructed by means of perceptual grouping. To validate the developed method, experiments were conducted on pan-sharpened and panchromatic Ikonos imagery, covering the selected test areas in Batikent district of Ankara, Turkey. For the test areas that contain industrial buildings, the average building detection percentage (BDP) and quality percentage (QP) values were computed to be 93.45 % and 79. 51 %, respectively. For the test areas that contain residential rectangular- shape buildings, the average BDP and QP values were computed to be 95.34 % and 79.05 %, respectively. For the test areas that contain residential circular-shape buildings, the average BDP and QP values were found to be 78.74 % and 66.81 %, respectively.
Location: TE 15 New Biology Building
Literature cited 1: Aguera, F., Liu, J.G., 2009. Automatic greenhouse delineation from QuickBird and Ikoonos satellite images. Comput.Electron. Agric. 66, 191-200. Ballard, D.H., 1981. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn. 13, 111-122.
Literature cited 2: Benediktson, J.A., Pesaresi, M., Arnason, K., 2003. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans. Geosci. Remote Sens. 41, 1940-1949. Canny, J., 1986. Computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679-698.

ID: 60157
Title: Remote sensing and object-based techniques for mapping fine-scale industrial disturbances.
Author: Ryan P. Powers, Txomin Hermosilla, Nicholas C. Coops, Gang Chen.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 51-57 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Feature extraction, Geographic object-based image analysis, (GEOBIA), Disturbance, Oil sands, Boreal.
Abstract: Remote sensing provides an important data source for the detection and monitoring of disturbances; however, using this data to recognize fine-spatial resolution industrial disturbances dispersed across extensive areas presents unique challenges (e.g., accurate delineation and identification) and deserves further investigation. In this study, we present and assess a geographic object-based image analysis (GEOBIA) approach with high-spatial resolution imagery (SPOT 5) to map industrial disturbances using the oil sands region of Alberta ' s northeastern boreal forest as a case study. Key components of this study were (i) the development of additional spectral, texture, and geometrical descriptors for characterizing image objects (groups of alike pixels) and their contextual properties, and (ii) the introduction of decision trees with boosting to perform the object-based land cover classification. Results indicate that the approach achieved an overall accuracy of 88 %, and that all descriptor groups provided relevant information for the classification. Despite challenges remaining (e.g., distinguishing between spectrally similar classes, or placing discrete boundaries), the approach was able to effectively delineate and classify fine-spatial resolution industrial disturbances.
Location: TE 15 New Biology Building
Literature cited 1: Archibald, W.R. Ellis, R., Hamilton, A.N., 1987. Responses of grizzly bears to logging truck traffic in the Kimsquit River Valley, British Columbia. Bears: Biol. Manage. 7, 251-257. Balaguer-Beser, A., Ruiz, L.A., Hermosilla, T., Recio, J.A., 2013. Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images. Comput. Geosci. 50, 115-127.
Literature cited 2: Blaschke, T., Hay, G.J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Feitosa, R., Van Der Meer, F., Van Der Werff, H., Van Coillie, F., Tiede, D., 2014. Geographic object-based image analysis: a new paradigm in remote sensing and geographic information science. ISPRS Int. J. Photogrammet. Remote Sens. 87, 180-191. Blaschke, T., Strobl, J., 2001. What ' s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GeoBIT/GIS 6, 12-17.

ID: 60156
Title: Developing a temporally land cover-based look-up table (TL-LUT) method for estimating land surface temperature based on AMSR-E data over the Chinese landmass
Author: Ji Zhou, Fengnan Dai, Xiaodong Zhang, Shaojie Zhao, Mingsong Li.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 35-50 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Land surface temperature, Passive microwave remote sensing, AMSR-E, MODIS, Land cover.
Abstract: The land surface temperature (LST) is an important parameter when studying the interface between the atmosphere and the Earth ' s surface. Comp0ared to satellite thermal infrared (TIR) remote sensing, passive microwave (PMW) remote sensing is better able to overcome atmospheric influences and to estimate the LST, especially in cloudy regions. However, methods for estimating PMW LSTs at the country and continental scales are still rare. The necessity of training such methods from a temporally dynamic perspective also needs further investigations. Here, a temporally land cover based look-up table (TL-LUT) method is proposed to estimate the LSTs from AMSR-E data over the Chinese landmass. In this method, the synergies between observations from MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer for EOS), which are onboard the same Aqua satellite, are explored. Validation with the synchronous MODIS LSTs demonstrates that uses a single brightness temperature in 36.5 GHz vertical polarization channel. The accuracy the TL-LUT method is better than 2.7 K for forest and 3.2 K for cropland. Its accuracy varies according to land cover type, time of day, and season. When compared with the in-situ measured LSTs at four sites without urban warming in the Tibet Plateau, the standard errors of estimation between the estimated AMSR-E LST and in-situ measured LST are from 5.1 K to 6.0 Kin the daytime and 3.1 K to 4.5 K in the nighttime. Further comparison with the in-situ measured air temperature at 24 meteorological stations confirms the good performance of the TL-LUT method. The feasibility of PMW remote sensing in estimating the LST for China can complement the TIR data and can, therefore, aid in the generation of daily LST maps for the entire country. Further study of the penetration of PMW radiation would benefit the LST estimation in barren and other sparsely vegetated environments.
Location: TE 15 New Biology Building
Literature cited 1: Basst, A., Grody, N.C., Peterson, T.C., 1998. Using the Special Sensor Microwave/Imager to monitor land surface temperatures, Wetness, and snow cover. J. Appl. Meteorol, 37, 888-911. Becker, F., Li, Z.L., 1990. Towards a local split window method over land surfaces. Int. J. Remote Sens. 11 (33), 369-393.
Literature cited 2: Chen, S.S., Chen, X.Z., Chen, W.Q., 2011. A simple retrieval method of land surface temperature from AMSR-E passive sensor data: theory and practice-current trends. Int. J. Appl. Earth Observ. Geoinf. 13 (1), 140-151. Dash, P., Gottsche, F.M., Olesen, F.S., Fischer, H., 2002. Land surface temperature and emissivity estimation from passive sensor data: theory and practice-current trends. Int. J. Remote Sens. 23 (13), 2563-2594.

ID: 60155
Title: Grassland habitat mapping by intra-annual time series analysis-Comparison of RapidEye and TerraSAR-X satellite data.
Author: Christian Schuster, Tobias Schmidt, Christopher Conrad, Birgit Kleinschmit, Michael Forster.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 25-34 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Natura 2000, Semi-natural grassland habitats, RapidEYE, TerraSAR-X, Time series, Feature selection.
Abstract: Remote sensing concepts are needed to monitor open landscape habitats for environmental change and biodiversity loss. However, existing operational approaches are limited to the monitoring of European dry heaths only. They need to be extended to further habitats. Thus far, reported studies lack the exploitation of intra-annual time series of high spatial resolution data to take advantage of the vegetations ' phonological differences. In this study, we investigated the usefulness of such data to classify grassland habitats in a nature reserve area in northeastern Germany. Intra-annual time series of 21 observations were used, acquired by a multi-spectral (RapidEye) and synthetic aperture radar (TerraSAR-X) satellite system, to differentiate seven grassland classes using a Support Vector Machine classifier. The classification accuracy was evaluated and compared with respect to the sensor type-multi-spectral or radar-and the number of acquisitions needed. Our results showed that very dense time series allowed for TerraSAR-X obtained similar accuracy as compared to RapidEye although distinctly more acquisitions were needed. This study introduces a new approach to enable the monitoring of small-scale grassland habitats and gives an estimate of the amount of data required for operational surveys.
Location: TE 15 New Biology Building
Literature cited 1: Alcantara, C., Kuemmerle, T., Prishchepov, A.V., Radeloff, V.C., 2012. Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sens Environ. 124, 334-347. Alexandridis, T.J., Lazaridou, E., Tsirika, A., Zalidis, G.C., 2009. Using Earth Observation to update a Natura 2000 habitat map for a wetland in Grece.J.Envion.Manage.90, 2243-2251.
Literature cited 2: Ali, I., Schuster, C., Zebisch, M., Forster, M., Kleinschmit, B., Notarnicola, C., 2013.First results of monitoring nature conservation sites in alpine region by using very high resolution, VHR X-Band SAR data. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens.6, 2265-2274. Atzberger, C., Eilers, PH.C., 2011. A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America. Int. J. Digit. Earth 4, 365-386.

ID: 60154
Title: Characterization and spatial modeling of urban sprawl in the Wuhan Metropolitan Area, China.
Author: Chen Zeng, Yaolin Liu, Alfred Stein, Limin Jiao.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34.10-24 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Urban sprawl, Monitoring, Remote sensing, GIS, Spatial statistics, Spatial modeling.
Abstract: Urban sprawl has led to environmental problems and large losses of arable land in China. In this study, we monitor and model urban sprawl by means of a combination of remote sensing, geographical information system and spatial statistics. We use time-series data to explore the potential socio-economic driving forces behind urban sprawl, and spatial models in different scenarios to explore the spatio-temporal interactions. The methodology is applied to the city of Wuhan, China, for the period from 1990 to 2013. The results reveal that the built-up land has expanded and has dispersed in urban clusters. Population growth, and economic and transportation development are still the main causes of urban sprawl; however, when they have developed to certain levels, the area affected by construction in urban areas (Jian Cheng Qu (JCQ) and the area of cultivated land (ACL) tend to be stable. Spatial regression models are shown to be superior to the traditional models. The interaction among districts with the same administrative status is stronger than if one of those neighbors is in the city center and the other in the suburban area. The expansion of urban built-up land is driven by the socio-economic development at the same period, and greatly influenced by its spatio-temporal neighbors. We conclude that the integration of remote sensing, a geographical information system, and spatial statistics offers an excellent opportunity to explore the spatio-temporal variation and interactions among the districts in the sprawling metropolitan areas. Relevant regulations to control the urban sprawl process are suggested accordingly.
Location: TE 15 New Biology Building
Literature cited 1: Ali, R., Zhao, H., 2008. Wuhan, China and Pittsburgh, USA: urban environmental health past, present and future. Ecohealth 5, 159-166. Alijoufie, M., Brussel, M., Zuidgeest, M., Van Maarseveen, M., 2013. Urban growth and transport infrastructure interaction in Jeddah between 1980 and 2007. Int. J. Appl .Earth Observ. Geoinform. 21, 493-505.
Literature cited 2: Angel, S., 2007. Urban Sprawl Metrics: An Analysis of Global Urban Expansion Using GIS, ASPRS, Tampa, FL. Anselin, I., Gallo, J.L., Jayet, H., 2008. Spatial Panel Econometrics Adv. Stud. Theor. Appl. Econometr. 46, 625-660.

ID: 60153
Title: Estimate of heavy metals in soil and streams using combined geochemistry and field spectroscopy in Wan-sheng mining area, Chongqinng, China.
Author: Song Lian, Jian Ji, Tan De-Jun, Xie Hong-Bing, Luo Zhen-Fu, Gao Bo.
Editor: F.D.van der Meer
Year: 2015
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 34. 1-9 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Heavy metals, Wang-sheng district, Spectral features, SMLR.
Abstract: Heavy metals contaminated soils and water will become a major environmental issue in the mining areas. This paper intends to use field hyper-spectra to estimate the heavy metals in the soil and water in Wan-sheng mining area in Chongqing. With analyzing the spectra of soil and water, the spectral features deriving from the spectral of the soils and water can be found to build the models between these features and contents of Al, Cu and Cr in the soil and water by using the Stepwise Multiple Linear Regression (SMLR). The spectral features of Al are: 480 nm, 500 nm, 565nm, 610 nm, 680 nm, 750 nm, 1000 nm, 1430 nm, 1755 nm, 1887 nm, 1920 nm, 1950 nm, 2210 nm, 2260 nm ; The spectral features of Cu are: 480 nm, 500 nm, 610 nm, 750 nm, 860 nm, 1300 nm, 1430 nm, 1755 nm, 1920 nm, 1950 nm, With these features, the best models to estimate the heavy metals in the study area were built according to the maximal R2. The R2 of the models of estimating Al, Cu and Cr in the soil and water are 0.813, 0.638, 0.604 and 0.742, 0.584, 0.513 respectively. And the gradient maps of these three types of heavy metals ' concentrations can be created by using the inverse distance weighted (IDW). The gradient maps indicate that the heavy metals in the soil have similar patterns, but in the North-west of the streams in the study area, the contents are of great differences. These results show that it is feasible to predict contaminated heavy metals in the soils and streams due to mining activities by using the rapid and cost-effective field spectroscopy.
Location: TE 15 New Biology Building
Literature cited 1: Aghaei, M., Lindner, H., Bogaerts, A., 2012. Optimization of operating parameters for inductively coupled plasma mass spectrometry: a computational study. Spectrochim. Acta B: At.Spectrosc. 76, 56-64. Ben Dor, E., Irons, J.R., Epema, G.F., 1999. Soil reflectance. In: Rencz, A.N. (Ed), Remote Sensing for the Earth Sciences: Manual of Remote Sensing. Wiley, Ney York, pp. 11-188.
Literature cited 2: Cai, Q.-Y., Mo, C. -H., -Q., Lu, H., Zeng, Q. -Y., -W., Wu, X, -L, 2013. Heavy metal contamination of urban soils and dusts in Guangzhou, South China. Environ. Monit. Assess. 185, 1095-1106. Chen, C., Liu, F., Quanjun, H., Heyin, S., 2010. The possibility on estimation of concentration of heavy metals in coastal waters from remote sensing data. In: Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, pp, 4216-4219.

ID: 60152
Title: Automated high resolution mapping of coffee in Rwanda using an expert Bayesian network.
Author: A. Mukashema , A. Veldkamp, A. Vrieling
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 33. 331-340 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Coffee, Expert knowledge, Bayesian network, Very high resolution imagery, Remote sensing, Rwanda.
Abstract: African highland agro-ecosystems are dominated by small-scale agricultural fields that often contain a mix of annual and perennial crops. This makes such systems difficult to map by remote sensing. We developed an expert Bayesian network model to extract the small-scale coffee fields of Rwanda from very high resolution data. The model was subsequently applied to aerial orthophotos covering more than 99% of Rwanda and on one QuickBird image for the remaining part. The method consists of a stepwise adjustment of pixel probabilities, which incorporates expert knowledge on size of coffee trees and fields, and on their location. The initial na?ve Bayesian network, which is a spectral-based classification, yielded a coffee map with an overall accuracy of around 50%. This confirms that standard spectral variables alone cannot accurately identify coffee fields from high resolution images. The combination of spectral and ancillary data (DEM and a forest map) allowed mapping of coffee fields and associated uncertaintities with an overall accuracy of 87 %. Aggregated to district units, the mapped coffee areas demonstrated a high correlation with the coffee areas reported in the detailed national coffee census of 2009 (R2 =0.92). Unlike the census data our map provides high spatial resolution of coffee area patterns of Rwanda. The proposed method has potential for mapping other perennial small scale cropping systems in the east African Highlands and elsewhere.
Location: TE 15 New Biology Building
Literature cited 1: Aguilera, P.A., Fernandez, A., Reche, F., Rumi, R. 2010. Hybrid Bayesian network classifiers: application to species distribution models. Environ. Model. Softw. 25, 1630-1639. Aguilera, P.A., Fernandez, A., Fernandez, R., Rumi, R., Salmeron, A., 2011. Bayesian networks in environmental modeling. Environ. Model.Sftw. 26, 1376-1388.
Literature cited 2: Brandtberg, T., 2007. Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar. ISPRS-J. Photogramm. Remote Sens. 61, 325-340 Brandtberg, T., Walter, F., 1998. Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis. Mach. Vis. Appl. 11, 64-73.

ID: 60151
Title: Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography.
Author: Xiaoxiao Li, Soe W. Myint, Yujia Zhang, Chritopher Galletti, Xiaoxiang Zhang, Billie Turner I I.
Editor: F.D.van der Meer
Year: 2014
Publisher: Centre for Ecological Sciences
Source: Centre for Ecological Sciences
Reference: Applied Earth Observation and Geoinformation. Vol. 33. 321-330 (2014).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Object-based image analysis, Land cover, Aerial photography, Urban, Phoenix, Classification system.
Abstract: Detailed land-cover mapping is essential for a range of research issues addressed by the sustainability and land system sciences and planning. This study uses an object-based approach to create a 1m land-cover classification map of the expansive Phoenix metropolitan area through the use of high spatial resolution aerial photography from National Agricultural Imagery Program. It employs an expert knowledge decision rule set and incorporates the cadastral GIS vector layer as auxiliary data. The classification rule was established on a hierarchical image object network, and the properties of parcels in the vector layer were used to establish land cover types. Image segmentations were initially utilized to separate the aerial photos into parcel sized objects, and were further used for detailed land type identification within the parcels. Characteristics of image objects from contextual and geometrical aspects were used in the decision rule set to reduce the spectral limitation of the four-band aerial photography. Classification results include 12 land- cover classes and subclasses that may be assessed from the sub-parcel to the landscape scales, facilitating examination of scale dynamics. The proposed object-based classification method provides robust results, uses minimal and readily available ancillary data, and reduces computational time.
Location: TE 15 New Biology Building
Literature cited 1: Anderson, J.R., Hardy, E.E., Roach, J.T., Witmer, R.E., 1976. A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey Professional Paper 964, 28 Baatz, M., Schape, A., 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale mage segmentation. J. Photogram. Remote Sens. 58, 12-23.
Literature cited 2: Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heyen, M., 2004. Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS.J. Photogram. Remote Sens. 58 (3), 239-258. Bhaskaran, S., Paramananda, S., Ramnarayan, M., 2010. Per-pixel and object-oriented classification methods for mapping urban features using Iknos satellite data. Appl. Geogr. 30 (4), 650-665.