Enter records

ID: 61035
Title: Stratified aboveground forest biomass estimation by remote sensing data.
Author: Hooman Latifi, Fabian E.Fassnacht, Florian Hartig, Christian Berger, Jaime Hernandez, Patricio Corvalan, Barbara Koch.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 229-241 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: LiDAR and hyperspectral remote sensing, Aboveground biomass, Statistical prediction, Post-stratification, model performance, factorial design.
Abstract: Remote-sensing-assisted estimates of aboveground forest biomass are essential for modeling carbon budgets. It has been suggested that estimates can be improved by building species-specific biomass models. However, few studies have attempted a systematic analysis of the benefits of such stratification, especially in combination with other factors such a sensor type, statistical prediction method and sampling design of the reference inventory data. We addressed this topic by analyzing the impact of stratifying forest data into three classes (broadleaved, coniferous an mixed forest).We compare predictive accuracy (a) between the strata (b) to a case without stratification for a set of preselected predictors from airborne LiDAR and hyperspectral data obtained in a managed mixed forest site in southwestern Germany. We used 5 commonly applied algorithms for biomass predictions on bootstrapped subsamples of the data to obtain cross validated RMSE and r2 diagnostics. Those values were analyzed in a factorial design by analysis of variance (ANOVA) to rank the relative importance of each factor. Selected models were used for wall-to-wall mapping of biomass estimates and their associated uncertainty. The results revealed marginal advantages for the strata-specific prediction models over the unstratified ones, which were more obvious on the wall-to-wall mapped area-based predictions. Yet further tests are necessary to establish the generality of these results. Input data type and statistical prediction method are concluded to remain the two most crucial factors for the quality of remote sensing -assisted biomass models.
Location: T E 15 New Biology Building.
Literature cited 1: Andersen,H.-E., Strunk, J.,Temesgen,H.,Atwood,D., Winterberger,K., 2011.Using multilevel remote sensing and ground data to estimate forest biomass resources in remote regions: a case study in the boreal forests of interior Alaska. Can.J.Remote Sens.37 (6), 1-16. Breidenbach, J.,Nothdurft,A., Kandler,G., 2010a.Comparison of nearest neighbor approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data.Eur.J.For.Res. 129 (5), 833-846.
Literature cited 2: Breidenbach, J., Naesset, E., Lien,V.,Gobakken,T., Solberg,S., 2010b.Prediction of species specific forest inventory attributes using a nonparametric semi-individual treecrown approach based on fused airborne laser scanning and multispectral data. Remote Sens.Environ.114, 911-924. Bright, B.C., Hudak,A.T., McGaughey,R., Andersen,H,-E,Negron, J., 2012.Predicting live and dead tree basal area of bark beetle affected forests from discrete-return lidar.Can.J.Remote Sens. 39 (s1), 99-111.

ID: 61034
Title: Analysis on spatio-temporal trends and drivers in vegetation growth during recent decades in Xinjiang, China.
Author: Jiaqing Du, Jianmin Shu, Junqi Yin, Xinjie Yuan, Ahati Jiaerheng, Shanshan Xiong, Ping He, Weiling Liu.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 216-228 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: AVHRR NDVI3g, Hydrothermal conditions, Planting structure, Correlation, Regional scale, Pixel scale.
Abstract: Vegetation plays an important role in regulating the terrestrial carbon balance and the climate system, and also overwhelmingly dominates the provisioning of ecosystem services. In this study, a non-stationary 1982-2012 AVHRR NDVI3g time series, the newest dataset, were used to evaluate spatio-temporal patterns of seasonal vegetation changes in Xinjiang province of China at regional, biome and pixel scales over progressively longer periods from 18 to 31 years, starting in 1982, and their linkages to climatic factors and human activities were analyzed. At regional scale, the increases were statistically significant for autumn NDVI during fourteen periods, for growing season and summer NDVI during the most periods, and for spring only during the first four periods. The rates of NDVI increase in growing season and all seasons significantly decreased over fourteen periods. At pixel scale, areas with significant browning rapidly increased over fourteen periods for growing season and all seasons, and these areas were mainly concentrated in northern desert of Xinjiang. Vegetation growth in Xinjiang was regulated by both moisture and thermal conditions: the response of NDVI in spring and autumn was more sensitive to thermal factors, such as temperature and potential evapotranspiration, and correlations between NDVI and precipitation and between NDVI and humidity index were stronger in summer and growing season. Extensive use of fertilizers and expanded farmland irrigated area increased vegetation growth for cropland. However, the rapid increase in the proportion of cotton cultivation and use of drip irrigation may reduce spring NDVI in the part of farmlands. Trend analysis during the multiple nested time series may contribute to a better and deep understanding of NDVI dynamic and foreseeing changes in the future. Accordingly, NDVI in Xinjiang will continuously increase at regional scale and the areas showing significant browning will also furthermore grow.
Location: T E 15 New Biology Building.
Literature cited 1: IPCC, 2007.Climate Change 2007.The Physical Science Basis. Cambridge University Press, Cambridge, UK. Alcaraz-Segura, D., Liras, E., Tabik, S., et al., 2010.1-1999 NDVI trends in the Iberian Peninsula across four time-series derived the AVHRR sensor: LTDR, GIMMS, FASIR, and PAL-II.Sensor 10, 1291-1314.
Literature cited 2: Bai, Z., Dent, D., 2009. Recent land degradation and improvement in China.AMBIO: J.Hum.Environ, 38, 150-156. Beck, H.E., McVicar, T.R, van Dijk, A.I.J.M., et al., 2011.Global evaluation of four AVHRR-NDVI data sets: Intercomparison and assessment against Landsat imagery. Remote Sens.Environ.115, 2547-2563.

ID: 61033
Title: Using remote sensing to monitor the influence of river discharge on watershed outlets and adjacent coral Reefs: Magdalena River and Rosario Islands, Colombia.
Author: Max J.Moreno-Madrinan, Douglas L. Rickman, Igor Ogashawara, Daniel E.Irwin, Jun Ye, Mohammad Z.Al-Hamdan.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 204-215 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Remote sensing, MODIS, TRMM, Water quality, Suspended sediments.
Abstract: Worldwide, coral reef ecosystems are being increasingly threatened by sediments loads from river discharges, which in turn are influenced by changing rainfall patterns due to climate change and by growing human activity in their watersheds. In this case study, we explored the applicability of using remote sensing (RS) technology to estimate and monitor the relationship between water quality at the coral reefs around the Rosario Islands, in the Caribbean Sea, and the rainfall patterns in the Magdalena River watershed. From the moderato Resolution Imaging Spectroradiometer (MODIS), this study used the water surface reflectance product (MOD09GQ) to estimate water surface reflectance as a proxy for sediment concentration and the landcover product (MCD12Q1 V51) to characterize landcover of the watershed. Rainfall was estimated by using the 3B43 V7 product from the Tropical Rainforest Measuring Mission (TRMM). For the first trimester of each year, we investigated the inter-annual temporal variation in water surface reflectance as the Rosario Islands and at the three main mouths of the Magdalena River water-shed. No increasing or decreasing trends of water surface reflectance were detected for any of the sites for the study period 2001-2014 (p>0.05) but significant correlations were detected among the trends of each site at the watershed mouths (r=0.57-0.90,p<0.05) and between them and the inter-annual variation in rainfall on the watershed (r=0.63-0.67,p<0.05). Those trimesters with above-normal water surface reflectance at the mouths and above-normal rainfall at the watershed coincided with La Nina conditions while the opposite was the case during El nino conditions. Although, a preliminary analysis of inter-annual land cover trends found only cropland cover in the watershed to be significantly correlated with water surface reflectance at two of the watershed mouths (r=0.58 and 0.63, p<0.05), the validation analysis draw only a 40.7 % of accuracy in this land cover classification. This requires further analysis to confirm the impact of the cropland on the water quality at the watershed outlets. Spatial analysis with MOD09GQ imagery detected the overpass of river plumes from Barbacoas Bay over the Rosario Islands waters.
Location: T E 15 New Biology Building.
Literature cited 1: Carricat-Ganivet, J.P., Merino, M., 2001.Growth responses of the reef-building coral Montastraea annularis along a gradient of continental influence in the southern Gulf of Mexico.Bull.Mar.Sci.68 (1), 133-146 (14). Cai,W.,Borlace,S.,Lengaigne,M.,VanRensch,P.,Collins,M.,Vecchi,G.,Timmermann,A.,Santoso,A.,McPhaden,M.J.,Wu,L.,England,M.H.,Wang,G.,Guilyardi,E.,Jin,F.F.,2014.Increasing frequency of extreme E1 Nino events due to greenhouse warming.Nat.Clim.Change 4, 111-116.
Literature cited 2: Cendales, M.H., Zea, S., Diaz, J.M., 2002. Geomorfologia y unidades ecologicas del complejo de arrecifes de las Islas del Rosario e Isla Baru (Mar Caribe, Colombia).Rev.Acad.Colom.Cienc.26 (101), 497-510, ISSN 03070-03908. Chen,Z.,Hu,C., Muller-Karger,F.E., 2007.Monitoring turbidity in Tampa Bay using MODIS/Aqua 25-m imagery. Remote Sens.Environ.109, 207-220, http://dx.doi.org/10.1016/j.rse.2006.12.019.

ID: 61032
Title: Synergistic use of RADARSAT-2 Ultra Fine and Fine Quad-Pol data to map oilsands infrastructure land: Object-based approach.
Author: Xianfeng Jiao, Ying Zhang, Bert Guindon.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 193-203 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Radarsat-2, PoISAR, Object-based classification, Mapping, Oilsands infrastructure land.
Abstract: The landscape of Alberta ' s oilsands region is undergoing extensive change due to the creation of infrastructure associated with the exploration for and extraction of this resource. Since most oil sands mining activities take place in remote forests or wetlands, one of the challenges is to collect up-to date and reliable information about the current state of land. Compared to optical sensors, SAR sensors have the advantage of being able to routinely collect imagery for timely monitoring by regulatory agencies. This paper explores the capability of high resolution RADARSAT-2 Ultra Fine and Fine Quad-Pol imagery for mapping oilsands infrastructure land using an object-based classification approach. Texture measurements extracted from Ultra Fine data are used to support an Ultra Fine based classification. Moreover, a radar vegetation index (RVI) calculated from PolSAR data is introduced for improved classification performance. The RVI is helpful in reducing confusion between infrastructure land and low vegetation covered surfaces. When Ultra Fine and polSAR data are used in combination, the kappa value of well pads and processing facilities detection reached 0.87.In this study, we also found that core hole sites can be identified from early spring Ultra Fine data. With single-date image, kappa value of core hole sites ranged from 0.61 to 0.69.
Location: T E 15 New Biology Building.
Literature cited 1: Arzandeh, S., Wang, J., 2002.Texture evaluation of RADARSAT imagery for wetland mapping.Can.J.Remote Sens.28 (5), 653-666, http://dx.doi.org/10.5589/m02-061. 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.ISPRSJ.Photogramm.Remote Sens.63 (6), 635-646.
Literature cited 2: Ainsworth, T.L., Kelly, J.P., Lee, J.-S., 2009.Classification comparisons between dual-pol, compact polarimetric and quad-pol SAR imagery.ISPRSJ. Photogramm.Remote Sens.64 (5), 464-471. http://dx.doi.org/10.1016/j.isprsjprs.2008.12.008. Ban, Y., Hu, H., Rangel, I.M., 2010.Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge approach.

ID: 61031
Title: Estimating above-ground biomass on mountain meadows and pastures through remote sensing.
Author: M.Barrachina, J.Cristobal, A.F.Tulla.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 184-192 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Aboveground biomass modeling, Vegetation and wetness indices, Mountain stock-breeding, Pyrenees, Landsat imagery, Multiple regression techniques.
Abstract: Extensive stock-breeding systems developed in mountain areas like the Pyrenees are crucial for local farming economies and depend largely on above-ground biomass (AGB) in the form of grass produced on meadows and pastureland. In this study, a multiple linear regression analysis technique based on in-situ biomass collection and vegetation and wetness indices derived from Landsat-5 TM data is successfully applied in a mountainous Pyrenees area to model AGB. Temporal thoroughness of the data is successfully applied in a mountainous Pyreness area to model AGB. Temporal thoroughness of the data is ensured by using a large series of images. Results of on-site AGB collection show the importance for AGB models to capture the high interannual and intraseasonal variability that results from both meteorological conditions and farming practices.AGB models yield best results at midsummer and end of summer before mowing operations by farmers, with a mean R2,RMSE and PE for 2008 and 2009 midsummer of 0.76, 95 gm-2 and 27%, respectively; and with a mean R2, RMSE and PE for 2008 and 2009 end of summer of 0.74, 128 gm-2 and 36 %, respectively. Although vegetation indices are a priori more related with biomass production, wetness indices play an important role in modeling AGB, being statistically selected more frequently (more than50 %) than other traditional vegetation indexes (around 27 %) such as NDVI. This suggests that middle infrared bands are crucial descriptors of AGB. The methodology applied in this work compares favorably with other works in the literature, yielding better results than those works in mountain areas, owing to the ability of the proposed methodology to capture natural and anthropogenic variations in AGB which are the key to increasing AGB modeling accuracy.
Location: T E 15 New Biology Building.
Literature cited 1: Al-Bakri, J.T., Taylor, J.C., 2003. Application of NOAA AVHRR for monitoring vegetation conditions and biomass in Jordan.J.Arid Environ.54, 579-593. Anderson, M., Neale, C., Li, F., Norman, J., Kustas, W., Jayanthi, H., Chavez, J., 2004.Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote Sens.Environ.92, 447-464.
Literature cited 2: Asner, G.P., 1998. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens.Environ.64, 234-253. Attarchi,S.,Gloaguen,R., 2014.Improving the estimation of above ground biomass using dual polarimetric PALSAR and ETM plus data in the Hyrcanian mountain forest (Iran).Remote Sens.6, 3693-3715.

ID: 61030
Title: Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa.
Author: Olena Dubovyk, Tobias Landmann, Barend F.N.Erasmus, Andreas Tewes.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 175-183 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Time-series data, Land surface phenology, Trend analysis, Vegetation patterns, Africa.
Abstract: Currently there is a lack of knowledge on spatio-temporal patterns of land surface dynamics at medium spatial scale in southern Africa, even though this information is essential for better understanding of ecosystem response to climatic variability and human-induced land transformations. In this study, we analysed vegetation dynamics across a large area in southern Africa using the 14-years (2000-2013) of medium spatial resolution (250 m) MODIS-EVI time series data. Specifically, we investigated temporal changes in the time series of key phenometrics including overall greenness, peak and timing of annual greenness over the monitoring period and study region. In order to specifically capture spatial and per pixel vegetation changes over time, we calculated trends in these phenometrics using a robust trend analysis method. The results showed that interannual vegetation dynamics followed precipitation patterns with clearly differentiated seasonality. The earliest peak greenness during 2000-2013 occurred at the end of January in the year 2000 and the latest peak greenness was observed at the mid of March in 2012.Specifically spatial patterns of long-term vegetation trends allowed mapping areas of (i) decrease or increase in overall greenness, (ii) decrease or increase of peak greenness, and (iii) shifts in timing of occurrence of peak greenness over the 14-year monitoring period. The observed vegetation decline in the study area was mainly attributed to human-induced factors. The obtained information is useful to guide selection of field sites for detailed vegetation studies and land rehabilitation interventions and serve as an input for a range of land surface models.
Location: T E 15 New Biology Building.
Literature cited 1: Adeyewa, Z.D., Nakamura, K., 2003. Validation of TRMM radar rainfall data over major climatic regions in Africa.J.Appl.Meterol.42, 331-347. Archibald, S., Scholes, R.J., 2007.Leaf green-up in a semi-arid African savanna-separating tree and grass responses to environmental cues.J.Veg.Sci.18, 583-594.
Literature cited 2: 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. Brown, J.C., Kastens, J.H., Coutinho, A.C., Victoria d, D.C., Bishop, C.RT, 2013. Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data. Remote Sens.Environ.130, 39-50.

ID: 61029
Title: Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images.
Author: Yingqing He, Bin Ai, Yao Yao, Fajun Zhong.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 164-174 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Artificial immune system, Cellular automata, Urban dynamic simulation, Self-adaptive, Multi-temporal remote sensing images.
Abstract: Cellular Automata (CA) have proven to be very effective for simulating and predicting the spatio-temporal evolution of complex geographical phenomena. Traditional methods generally pose problems in determining the structure and parameters of CA for a large, complex region or a long-term simulation. This study presents a self-adaptive CA model integrated with an artificial immune system to discover dynamic transition rules automatically. The model ' s parameters are allowed to be self-modified with the application of multi-temporal remote sensing images: that is, the CA can adapt itself to the changed and complex environment. Therefore, urban dynamic evolution rules over time can be efficiently retrieved by using this integrated model. The proposed AIS-based CA model was then used to simulate the rural-urban land conversion of Guangzhou from TM satellite image in the year 1990. Urban land in the years 1995, 2000, 2005, 2009 and 2012 was correspondingly used as the observed data to calibrate the model ' s parameters. With the quantitative index figure of merit (FoM) and pattern similarity, the comparison was further performed between the AIS-based model and a Logistic CA model. The results indicate that the AIS-based CA model can perform better and with higher precision in simulating urban evolution, and the simulated spatial pattern is closer to the actual development situation.
Location: T E 15 New Biology Building.
Literature cited 1: Batty, M., 1993.Using Geographical Information Systems in Urban Planning and policy Making. Geographical Information Systems: Spatial Modeling and Policy Evaluation.Springer-Verlag, Berlin, pp.51-69. Carter, J.H., 2000.The Immune system as a model for pattern recognition and classification.J.Am.Med.Inform.Assoc.7 (1), 28-41.
Literature cited 2: Chen, Y.M., Li, X., Liu, X.P., et al., 2014. Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy.Int.J.Geogr.Inform.Sci. 28 (2), 234-255. Chun, J.S., Kim, M.K., Jung, H.K, et al., 1997. Shape optimization of electromagnetic devices using immune algorithm.IEEE Trans.Magn. 33 (2), 1876-1879.

ID: 61028
Title: A lake detection algorithm (LDA) using Landsat 8 data: A comparative approach in glacial environment.
Author: Anshuman Bhardwaj, Mritunjay Kumar Singh, P.K.Joshi, Snehmani, Shaktiman Singh, Lydia Sam, R.D.Gupta, Rajesh Kumar.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 150-163 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: NDWI, GIS, Landsat-8, Glacial lakes, Remote sensing.
Abstract: Glacial lakes show a wide range of turbidity. Owing to this, the normalized difference water indices (NDWIs) as proposed by many researchers, do not give appropriate results in case of glacial lakes. In addition, the sub-pixel proportion of water and use of different optical band combinations are also reported to produce varying results. In the wake of the changing climate and increasing GLOFs (glacial lake outburst floods), there is a need to utilize wide optical and thermal capabilities of Landsat 8 data for the automated detection of glacial lakes. In the present study, the optical and thermal bandwidths of Landsat 8 data were explored along with the terrain slope parameter derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 2 (ASTER GDEM V2), for detecting and mapping glacial lakes. The validation of the algorithm was performed using manually digitized and subsequently field corrected lake boundaries. The pre-existing NDWIs were also evaluated to determine the supremacy and the stability of the proposed algorithm for glacial lake detection. Two new parameters, LDI (lake detection index) and LF (Lake Fraction) were proposed to comment on the performances of the indices. The lake detection algorithm (LDA) performed best in case of both, mixed lake pixels and pure lake pixels with no false detections (LDI=0.98) and very less areal underestimation (LF=0.73).The coefficient of determination (R2) between areal extents of lake pixels, extracted using the LDA and the actual lake area, was very high (0.99).With understanding of the terrain conditions and slight threshold adjustments, this work can be replicated for any mountainous region of the world.
Location: T E 15 New Biology Building.
Literature cited 1: Ackerman, T., Erickson, T., Williams, M.W., 2001. Combining GIS and GPS to improve our understanding of the spatial distribution of snow water equivalence (SWE).In proceedings of the 2001 ESRI User Conference, 10 July 2001, San Diego, CA (accessed 10.09.13). http://snobear.colorado.edu/Markw/Research/ESRI/ESRI.html. Bolch, T., Peters, J., Yegorov, A., Pradhan, B., Buchroithner, M., Blagoveshchensk, V., 2011.Identification of potentially dangerous glacial lakes in the Northern Tian Shan.Nat.Hazards 59, 1691-1714. http://dx.doi.org/10.1007/s11069-011-9860-2
Literature cited 2: Bolch, T., Buchroithner, M.F., Peters, J., Baessler, M., Bajracharya, S., 2008. Identification of glacier motion and potentially dangerous glacial lakes in the Mt.Everest region/Nepal using spaceborne imagery.Nat.Hazards Earth Syst.Sci.8, 1329-1340. Bryant, R.G., Rainey, M.P., 2002.Investigation of flood inundation on plays within the zone of chotts, using a time-series of AVHRR.Remote Sens.Environ.82 (2), 360-375.

ID: 61027
Title: Satellite monitoring of urbanization and environmental impacts-A comparison of Stockholm and Shanghai.
Author: Jan Hass, Dorothy Furberg, Yifang Ban.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 138-149 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Urbanization, Landuse/land cover (LULC), Ecosystem services, Landscape metrics, Environmental impact, SVM.
Abstract: This study investigates urbanization and its potential environmental consequences in Shanghai and Stockholm metropolitan areas over two decades. Changes in land use/land cover are estimated from support vector machine classifications of Landsat mosaics with grey-level co-occurrence matrix features. Landscape metrics are used to investigate changes in landscape composition and configuration and to draw preliminary conclusions about environmental impacts. Speed and magnitude of urbanization is calculated by urbanization indices and the resulting impacts on the environment are quantified by ecosystem services. Growth of urban areas and urban green spaces occurred at the expense of cropland in both regions. Alongside a decrease in natural land cover, urban areas increased by approximately 120% in Shanghai, nearly ten times as much as in Stockholm, where the most significant land cover change was a 12 % urban expansion that mostly replaced agricultural areas. From the landscape metrics results, it appears that fragmentation in both study regions occurred mainly due to the growth of high density built-up areas in previously more natural/agricultural environments, while the expansion of low density built-up areas was for the most part in conjunction with pre-existing patches. Urban growth resulted in ecosystem service value losses of approximately 445 million US dollars in Shanghai, mostly due to the decrease in natural coastal wetlands while in Stockholm the value of ecosystem services changed very little. Total urban growth in Shanghai was 1768 km2 and 100km2 in Stockholm. The developed methodology is considered a straight-forward low-cost globally applicable approach to quantitatively and qualitatively evaluate urban growth patterns that could help to address spatial, economic and ecological questions in urban and regional planning.
Location: T E 15 New Biology Building.
Literature cited 1: Alberti, M., 2005.The effects of urban patterns on ecosystemfunction.Int.Reg.Sci.Rev.28 (2), 168-192. Anderson, E., Ahrne, K., Pyykonen, M., Elmqvist, T., 2009.Patterns and scale relations among urbanization measures in Stockholm, Sweden.Lan.Ecol.24 (10), 1331-1339.
Literature cited 2: Ban,Y., Jacob,A.,2013.Object-based fusion of multitemporal multi-angle ENVISAT ASAR and HJ1-B multispectral data for urban land-cover mapping.IEEE Trans.Geosci.Remote Sens.51 (4), 1998-2006. Ban,Y.,Jacob,A.,Gamba,P.,2014a.Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor.ISPRS J.Photogramm.Remote Sens., In press.

ID: 61026
Title: Land cover changed object detection in remote sensing data with medium spatial resolution.
Author: Xiao tong Yang, Huiping Liu, Xiaofeng Gao.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 129-137 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Multi-temporal segmentation, Landcover changed object, Change indicators, Segmentation scale, Chi-square transformation.
Abstract: Landcover change information is crucial to analyse the process and the change patterns of environments and ecological systems. Recent studies have incorporated object-based image analysis for its ability to generate meaningful geographical objects into studies of change detection. In this research, we developed a systematic methodology to realize multi-type land cover changed object detection with medium spatial resolution remote sensing images in Beijing, China. Optimum index factor (OIF) was applied to determine the best change indicators and the chi-square transformation was carried out to determine the change threshold of the 4 classes of changed object. The clustering change vectors in the feature space were proposed to discriminate the change types. According to the accuracy assessment, the overall accuracy of changed/unchanged object detection was approximately 93.9 % with an overall kappa of 0.824, and the change type discrimination also achieved an overall accuracy of 81.67 %, indicating the effectiveness of the proposed method.
Location: T E 15 New Biology Building.
Literature cited 1: An, K., Zhang, J., Xiao, Y., 2007.Object-oriented urban dynamic monitoring-A case study of Haldian District of Beijing.Chin.Geog.Sci.17, 236-242. Benz, U.C., Hoffmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004.Multi-resoution: object-oriented fuzzy analysis of remote sensing data for GIS-ready information.ISPRS J.Photogramm.Remote Sens.58, 239-258.
Literature cited 2: Bontemps, S., Bogaert, P., Titeux, N., Defourny, P., 2008. An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution. Remote Sens.Environ.112, 3181-3191. Bryant, R.G., Gilvear, D.J., 1999.Quntifying geomorphic and riparian land cover changes either side of a large flood event using airborne remote sensing: River Tay Scotland. Geomorphology 29, 307-321.

ID: 61025
Title: Methods for improving accuracy and extending results beyond periods covered by traditional ground-truth in remote sensing classification of a complex landscape.
Author: George W.Mueller-Warrant, Gerald W.Whittaker, Gary M.Banowetz, Stephen M.Griffith, Bradley L.Barnhart.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 115-128 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Agriculture, Ground-truth data, Landuse, Landcover, Object-based, Pixel-based, Majority-rule.
Abstract: Successful development of approaches to quantify impacts of diverse landuse and associated agricultural management practices on ecosystem services is frequently limited by lack of historical and contemporary landuse data. We hypothesized that ground truth data from one year could be used to extrapolate previous or future landuse in a complex landscape where cropping systems do not generally change greatly from year to year because the majority of crops are established perennials or the same annual crops grown on the same fields over multiple years. Prior to testing this hypothesis, it was first necessary to classify 57 major landuses in the Willamette Valley of western Oregon from 2005 to 2011 using normal same year ground-truth, elaborating on previously published work and traditional sources such as Cropland Data Layers (CDL) to more fully include minor crops grown in the region .Available remote sensing data included Landsat, MODIS 16-day composites, and National Aerial Imagery Program (NAIP) imagery, all of which were resampled to a common 30 m resolution. The frequent presence of clouds and Landsat7 scan line gaps forced us to conduct of series of separate classifications in each year, which were then merged by choosing whichever classification used the highest number of cloud-and gap-free bands at any given pixel. Procedures adopted to improve accuracy beyond that achieved by maximum likelihood pixel classification included majority-rule reclassification of pixels within 91,442 Common Land Unit (CLU) polygons, smoothing and aggregation of areas outside the CLU polygons, and majority-rule reclassification over time of forest and urban development areas. Find classifications in all seven years separated annually disturbed agriculture, established perennial crops, forest, and urban development from each other at 90 to 95 % overall 4-class validation accuracy. In the most successful use of subsequent year ground-truth data to classify prior year landuse, an overall 57-class accuracy of 75 % was achieved despite the omission of 10 entire classes, most of which were annually disturbed or perennial crops grown on very few fields. Synthetic ground-truth data for the 2004 harvest year based on the most common landuse classes over the following 7 years classified 49 of 57 categories at an overall accuracy of 96 % in a final version that included CLU polygon majority rule, default smoothing and aggregation, and forcing of urban development and forest from multi-year majority-rule.
Location: T E 15 New Biology Building.
Literature cited 1: Anonymous, 2007, USDA, National Agricultural Statistics Service, 2007 Oregon Cropland Data Layer. Available online at: http://www.nass.usda.gov/research/Cropland/metadata/metadata.or07 htm (accessed 19.05.2009) Anonymous, 2009, USDA, Natural Resources Conservation Service, Conservation Effects Assessment Project (CEAP).Available online at: http://www.nrsc.usda.gov/technical/NRI/ceap/index.html (accessed 14.10.2009).
Literature cited 2: Aplin, P.,Atkinson,P.M.,Curran,P.J.,1999.Per-field Classification of Land Use Using the Forthcoming Very Fine Spatial Resolution Satellite Sensors: Problems and Potential Solutions.In:Atkinson, Tate (Eds), In Advances in Remote Sensing and GIS Analysis. John Wiley and Sons, West Sussex, UK, pp.219-239. Gitau, M.W.,Chaubey, I., Gbur,E.,Pennington,J.H.,Gorham,B., 2010.Impacts of land-use change and best management practice implementation in a conservation effects assessment project watershed:northwest Arkansas.J.Soil Water Conserv.65 (6), 353-368.

ID: 61024
Title: A spatiotemporal mining framework for abnormal association patterns in marine envoironments with a time series of remote sensing images.
Author: Cunjin Xue, Wanjiao Song, Lijuan Qin, Qing Dong, Xiaoyang Wen.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 105-114 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Marine association pattern, Spatiotemporal mining framework, Global change, Remote sensing, Pacific Ocean.
Abstract: A spatiotemporal mining framework is a novel tool for the analysis of marine association patterns using multiple remote sensing images. From data pretreatment, to algorithm design, to association rule mining and pattern visualization, this paper outlines a spatiotemporal mining framework for abnormal association patterns in marine environments, including pixel-based and object-based mining models. Within this framework, some key issues are also addressed. In the data pretreatment phase, we propose an algorithm for extracting abnormal objects or pixels over marine surfaces, and construct a mining transaction table with object-based and pixel-based and object-based mining models. Within this framework, some key issues are also addressed In this data pretreatment phase, we propose an algorithm for extracting abnormal objects or pixels over marine surfaces, and construct a mining transaction table with object-based and pixel-based strategies. In the mining algorithm phase, a recursion method to construct a direct association pattern tree is addressed with an asymmetric mutual information table, and a recursive mining algorithm to find frequent items. In the knowledge visualization phase, a ?Dimension-Attributes? visualization framework is used to display spatiotemporal association patterns. Finally, spatiotemporal association patterns for marine environmental parameters in the Pacific Ocean are identified, and the results prove the effectiveness and the efficiency of the proposed mining framework.
Location: T E 15 New Biology Building.
Literature cited 1: Agrawal, R., Srikant, R.1994.Fast algorithm for mining association rules. September 12-15, 1994, Santiago, Chile, Morgan Kaufmann In: Bocca, J.B., Jerke, M., Zaniolo, C. (Eds), Proceeding of the 20th International Conference on Very Large Databases, VLDB, 1215, pp.407-419, ISBN 1-55860-153-8. Bertolotto, M., DiMartino, S., Ferrucci, F., Kechadi, T., 2007.Towards a framework for mining and analysis spatio-temporal datasets.Int.J.Geog.Inf.Sci. 21 (8), 895-906.
Literature cited 2: Blanchard, J., Pinaud, B., Kuntz, P., Guilet, F., 2007.A 2D-3D visualization support for human-centered rule mining.Comput. Graphics 31, 350-360. Casey, K.S., Adamec, D., 2002.Sea surface temperature and sea surface height variability in the North Pacific Ocean from 1993 to 1999.J.Geophys.Res.107 (C8), 3099, http://dx.doi.org/10.1029/2001JC001060.

ID: 61023
Title: Fusion of TerraSAR-x and Landsat ETM+data for protected area mapping in Uganda.
Author: John Ricard Otukei, Thomas Blaschke, Michael Collins.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 99-104 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: TerraSAR-X, Landsat ETM+, Decision trees, Image fusion.
Abstract: TerraSAR-X satellite acquires very high spatial resolution data with potential for detailed land cover mapping.A known problem with synthetic aperture radar (SAR) data is the lack of spectral information. Fusion of SAR and multispectral data provides opportunities for better image interpretation and information extraction. The aim of this study was to investigate the fusion between TerraSAR-X and Landsat ETM+ for protected area mapping using high pass filtering (HPF), principal component analysis with band substitution (PCA) and principal component with wavelet transform (WPCA). A total of thirteen land cover classes were identified for classification using a non-parametric C 4.5 decision tree classifier. Overall classification accuracies of 74.99 %, 83.12 % and 85.38 % and kappa indices of 0.7220, 0.8100 and 0.8369 were obtained for HPF, PCA and WPCA fusion approaches respectively. These results indicate a high potential for a combined use of TerraSAR-X and Landsat ETM+ data for
Location: T E 15 New Biology Building.
Literature cited 1: Abd-Elrahman, A., Shaker, I.F., Abdel-Gawad, A.K., Abdel-Wahab, A., 2008.Enhancement of cloud-associated shadow areas in satellite images using wavelet image fusion. World Appl.Sci.J.4, 363-370. Al-Wasai, F.A., Kalyankar, N.V., Al-Zuky, A.A., 2011.The HIS transformations based image fusion.J.Global Res.Comput.Sci.2, 70-77.
Literature cited 2: Amolins, K., Zhang, Y., Dare, P., 2007.Wavelet based image fusion techniques-an introduction, review and comparison.ISPRS J.Photogramm.RTemote Sens.62, 249-263. Aplin, P., 2003.Remote sensing: base mapping.Prog.Phys.Geog.27, 275-283.

ID: 61022
Title: Fully constrained linear spectral unmixing based global shadow compensation for high resolution satellite imagery of urban areas.
Author: Jian Yang, Yuhong He, John Caspersen.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 88-98 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Global shadow compensation, object-based shadow detection, Linear spectral unmixing, Spectral mixing space, Spectral scatter plot, WorldView-2.
Abstract: Shadows commonly exist in high resolution satellite imagery, particularly in urban areas, which is a combined effect of low sun elevation, off-nadir viewing angle, and high-rise buildings. The presence of shadows can negatively affect image processing, including land cover classification, mapping and object recognition due to the reduction or even total loss of spectral information in shadows. The compensation of spectral information in shadows is thus one of the most important preprocessing steps for the interpretation and exploitation of high resolution satellite imagery in urban areas. In this study, we propose a new approach for global shadow compensation through the utilization of fully constrained linear spectral unmixing. The basic assumption of the proposed method is that the construction of the spectral mixing space. In order to ensure the continuity of land covers, a smooth operator is further used to refine the restored shadow pixels on the edge of non-shadow and shadow areas. The proposed method is validated using the WorldView -2 multisopectral imagery collected from downtown Toronto, Ontario, Canada. In comparison with the existing linear-correlation method, the proposed method produced the compensated shadows with higher quality.
Location: T E 15 New Biology Building.
Literature cited 1: Arbel, E., Hel-Or, H., 2011. Shadow removal using intensity surfaces and texture anchor points.IEEE Trans.Pattern Anal.Mach.Intell. 33, 1202-1216. Benz, U.C., Hoffmann, P., Willhuck, G., Lingenfelder, I., Heynen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRSJ.Photogramm.Remote Sens.58, 239-258.
Literature cited 2: Chen, Y., Wen, D., Jing, L., Shi, P., 2007. Shadow information recovery in urban areas from very high resolution satellite imagery.Int.J.Remote Sens.28, 3249-3254. Dare, P.M., 2005.Shadow analysis in high-resolution satellite imagery of urban areas from very high resolution satellite imagery.Int.J.Remote Sens.28, 3249-3254.

ID: 61021
Title: An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data.
Author: Yang Sao, James B.Campbell, Gregory N.Taff, Baojuan Zheng.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 78-87 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Cropland masks, MODIS, Ancillary data, Random forest, Corn yield forecasting.
Abstract: The Midwestern United States is one of the World ' s most important corn-producing regions. Monitoring and forecasting of corn yields in this intensive agricultural region are important activities to support food security, commodity markets, bioenergy industries, and formation of national policies. This study aims to develop forecasting models that have the capability to provide mid-season prediction of county-level corn yields for the entire Midwestern United States. We used multi-temporal MODIS NDVI (normalized difference vegetation index) 16-day composite data as the primary input, with digital elevation model (DEM) and parameter-elevation relationships on independent slopes model (PRISM) climate data as additional inputs. The DEM and PRISM data, along with three types of cropland masks were tested and compared to evaluate their impacts on model predictive accuracy. Our results suggested that the use of general cropland masks (e.g., summer crop or cultivated crops) generated similar results compared with use of an annual corn-specific mask. Leave-one year-out cross-validation resulted in an average R2 of 0.75 and RMSE value of 1.10t/ha. Using a DEM as an additional model input slightly improved performance, while inclusion of PRISM climate data appeared not to be important for our regional corn-yield model. Furthermore, our model has potential for real-time/yearly prediction. Our corn yield estimates are available as early as late July, which is an improvement upon previous corn-yield prediction models. In addition to annual corn yield forecasting, we examined model uncertainties through spatial and temporal analysis of the model ' s predictive error distribution .The magnitude of predictive error (by county) appears to be associated with the spatial patterns of corn fields in the study area.
Location: T E 15 New Biology Building.
Literature cited 1: Becker-Reshef, I., Vermote, E., Linderman, M., Justice, C., 2010.A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens.Environ.114 (6), 1312-1323. Boryan, C., Yang, Z., Mueller, R., Craig, M., 2011.Monitoring US agriculture: the US department of agriculture, national agricultural statistics service, cropland data layer program.Geocarto Int.26 (5), 341-358.
Literature cited 2: Breiman, L., 2001. Random forests.Mach.learn, 45 (1), 5-32. Chen, J., Jonsson, P., Tamura, M., GU, Z., Matsushita, B., Eklundh, L., 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensor Environ.91 (3), 332-344.