Enter records

ID: 61040
Title: Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed, Nepal.
Author: Yogendra K.Karna, Yousif Ali Hussain, Hammad Gilani, M.C.Bronsveld, M.S.R.Murthy, Faisal Mueen Qamer, Bhaskar Singh Karky, Thakur Bhattarai, Xu Aigong, Chitra Bahadur Baniya.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 280-291 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Airborne LiDAR, CHM, CPA, Image classification, Multiresolution segmentation, WorldView-2.
Abstract: Integration of WorldView-2 satellite image with small footprint airborne LiDAR data for estimation of tree carbon at species level has been investigated in tropical forests of Nepal. This research aims to quantify and map carbon stock for dominant tree species in Chitwan district of central Nepal. Object based image analysis and supervised nearest neighbor classification methods were deployed for tree canopy retrieval and species level classification respectively. Initially, six dominant tree species (Shorea robusta, Schima wallichii, Lagerstroemia parviflora, Terminalia tomentosa, Mallotus phillippinensis and Semecarpus anacardium) were able to identified and mapped through image classification. The result showed a 76% accuracy of segmentation and 1970.99 as best average separability. Tree canopy height model (CHM) was extracted based on LiDAR ' s first and last return from an entire study area. On average, a significant correlation coefficient (r) between canopy projection area (CPA) and carbon; height and carbon; and CPA and height were obtained as 0.73, 0.76 and 0.63, respectively for correctly detected trees. Carbon stock model validation results showed regression models being able to explain up to 94 %, 78 %, 76 %, 84 % and 78 % of variations in carbon estimation for the following tree species: S.robusa, L.parviflora, T.tomentosa,S.walichii and others (combination of rest tree species).
Location: T E 15 New Biology Building.
Literature cited 1: Ahmed, R., Siqueira, P., Hensley, S., 2003. A study of forest biomass estimates from LiDAR in the northern temperate forests of New England. Remote Sens.Environ.130, 121-135. Asner, G., Mascaro, J., Muller-Landau, H., Vieilledent, G., Vaudry, R., Rasamoelina, M., Hall, J., Breugel, M., 2012.A universal airborne LiDAR approach for tropical forest carbon mapping.Oecologia 168, 1147-1160.
Literature cited 2: Bartelink, H.H., 1996.Allometric relationships on biomass and needle area of Douglas-fir.for.Ecol.Manage.86, 193-203. Belgiu, M., Dragut, L., 2014.Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery.ISPRS J.Photogramm.Remote Sens. 96, 67-75.

ID: 61039
Title: Detecting understory plant invasion in urban forests using LiDAR.
Author: Kunwar K.Singh, Amy J. Davis, Ross K.Meentemeyer.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 267-279 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Biological invasion, Chinese privet, Data integration, IKONOS, LiDAR, Lingustrum sinense, Random forest.
Abstract: Light detection and ranging (LiDAR) data are increasingly used to measure structural characteristics of urban forests but are rarely used to detect the growing problem of exotic understory plant invaders. We explored the merits of using LiDAR-derived metrics alone and through integration with spectral data to detect the spatial distribution of the exotic understory plant Lingustrum sinense, a rapidly spreading invader in the urbanizing region of Charlotte, North Carolina, USA. We analyzed regional-scale L.sinense, occurrence data collected over the course of three years with LiDAR-derived metrics of forest structure that were categorized in to the following groups: overstory, topography, and overall vegetation characteristics, and IKONOS spectral features-optical. Using random forest (RF) and logistic regression (LR) classifiers, we assessed the relative contributions of LiDAR and IKONOS derived variables to the detection of L.sinense We compared the top performing models developed for a smaller, nested experimental extent using RF and LR classifiers, and used the best overall model to produce a predictive map of the spatial distribution of L.sinense across our country-wide study extent. RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming model from the RF classifier produced the highest kappa of 64.8 %, improving on the parsimonious LR model kappa by 31.1% with a moderate gain of 6.2 % over the county extent model. Our results demonstrate the superiority of LiDAR-derived metrics over spectral data and fusion of LiDAR and spectral data for accurately mapping the spatial distribution of the forest understory invader L.sinense.
Location: T E 15 New Biology Building.
Literature cited 1: Andrew, M.E., Ustin, S.L., 2009.Habitat suitability modeling of an invasive plant with advanced remote sensing data.Divers.Distrib.15, 627-640. Asner, G.P., Vitousek, P.M., 2005.Remote analysis of biological invasion and biogeochemical change.Proc.Natl.Acad.Sci.U.S.A.102, 4383-4386.
Literature cited 2: Asner, G.P., Knapp, D.E., Kennedy-Bowdoin, T., Jones, M.O., Martin, R.E., Boardman, J., Hughes, R.F., 2008.Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR.Remote Sens.Environ.112, 1942-1955. BCAL LiDAR Tools (2013).Idaho State University, Department of Geosciences.In.Boise, Idaho: Boise Center Aerospace Laboratory (BCAL).

ID: 61038
Title: Spectra and vegetation index variations in moss soil crust in different seasons, and in wet and dry conditions.
Author: Shibo Fang, Weiguo Yu, Yue Qi.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 261-266 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Biological soil crusts (BSCs) Normalized difference vegetation index (NDVI), Biological soil crust index (BSCI), CI (Crust index).
Abstract: Similar to vascular plants, non-vascular plant mosses have different periods of seasonal growth. There has been little research on the spectral variations of moss soil crust (MSC) over different growth periods. Few studies have paid attention to the difference in spectral characteristics between wet MSC that is photosynthesizing and dry MSC in suspended metabolism. The dissimilarity of MSC spectra in wet and dry conditions during different seasons needs further investigation. In this study, the spectral reflectance of wet MSC and the dominant vascular plant (Artemisia) were characterized in situ during the summer (July) and autumn (September).The variations in the normalized difference vegetation index (NDVI), biological soil crust index (BSCI) and CI (crust index) in different seasons and under different soil moisture conditions were also analyzed. It was found that (1) the spectral characteristics of both wet and dry MSCs varied seasonally; (2) the spectral features of wet MSC appear similar to those of the vascular plant, Artemisia, whether in summer or autumn; (3) both in summer and in autumn, much higher NDVI values were acquired for wet than for dry MSC (0.6 ~0.7vs 0.3~0.4 units), which may lead to misinterpretation of vegetation dynamics in the presence of MSC and with the variations in rainfall occurring in arid and semi-arid zones; and (4)the BSCI and CI values of wet MSC were close to that of Artemisia in both summer and autumn, indicating that BSCI and CI could barely differentiate between the wet MSC and Artemisia.
Location: T E 15 New Biology Building.
Literature cited 1: Belnap, J., 2001.Biological Soil crusts: Structure, Function, and Management.Springer-Verlag, Berlin. Belnap, J.2002.Nitrogen fixation in biological soil crusts from southern Utah, USA, Biol.Fert.Soils 35, 128-135.
Literature cited 2: Beringer,J.,Lynch,A.H.,Chapin,F.S.,Mack,M.,Bonan,G.B., 2001.The representation of arctic soils in the land surface model: the importance of mosses.J.Climate 14, 3324-3335. Chen, J., Zhang, M.Y., Wang, L., Shimazaki, H., Tamura, M., 2005.A new index for mapping lichen-dominated biological crusts in desert areas. Remote Sens.Environ.96, 165-175.

ID: 61037
Title: Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands.
Author: Oz Kira, Raphael Linker, Antoly Gitelson.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 251-260 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Carotenoids, Chlorophyll, Neural network, Non-destructive technique, Reflectance.
Abstract: Leaf pigment content provides valuable insight into the productivity, physiological and phonological status of vegetation. Measurement of spectral reflectance offers a fast, nondestructive method for pigment estimation. A number of methods were used previously for estimation of leaf pigment content, however, spectral bands employed varied widely among the models and data used. Our objective was to find informative spectral bands in three types of models, vegetation indices (VI), neural network (NN) and partial least squares (PLS) regression, for estimating leaf chlorophyll (Chl) and carotenoids (Car) contents of three unrelated tree species and to assess the accuracy of the models using a minimal number of bands. The bands selected by PLS, NN and VIs were in close agreement and did not depend on the data used. The results of the uninformative variable elimination PLS approach, where the reliability parameter was used as indicator of the information contained in the spectral bands, confirmed the bands selected by the VIs, NN and PLS models. All three types of models were able to accurately estimate Chl content with coefficient of variation below 12 % for all three species with VI showing the best performance. NN and PLS using reflectance in four spectral bands were able to estimate accurately Car content with coefficient of variation below 14 %.The quantitative framework presented here offers a new way of estimating foliar pigment content not requiring model-re-parameterization for different species. The approach was tested using the spectral bands of the future Sentinel-2 satellite and the results of these simulations showed that accurate pigment estimation from satellite would be possible.
Location: T E 15 New Biology Building.
Literature cited 1: Baret, F., Houles, V., Guerif, M., 2007.Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management.J.Exp.Bot.58, 869-880. Blackburn, G.A., 2007a.Hyperspectral remote sensing of plant pigments.J.Exp.Bot.58, 855-867.
Literature cited 2: Blackburn, G.A., 2007b.Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation.Int.J.Remote Sens.28, 2831-2855. Blackburn, G.A., 1998. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens.Environ.66, 273-285.

ID: 61036
Title: Integrating optical satellite data and airborne laser scanning in habitat classification for wildlife management.
Author: W.Nijland, N.C.Coops, S.E.Nielsen, G.Stenhouse.
Editor: F.D.van der Meer
Year: 2015
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: APPLIED EARTH OBSERVATION AND GEOINFORMATION. Vol. 38 242-250 (2015).
Subject: APPLIED EARTH OBSERVATION AND GEOINFORMATION.
Keywords: Lidar, Grizzly, Habitat, Landcover, Classification, Landsat.
Abstract: Wildlife habitat selection is determined by a wide range of factors including food availability, shelter, security and landscape heterogeneity all of which are closely related to the more readily mapped land-cover types and disturbance regimes. Regional wildlife habitat studies often used moderate resolution multispectral satellite imagery for wall to wall mapping, because it offers favourable mix of availability, cost and resolution. However, certain habitat characteristics such as canopy structure and topographic factors are not well discriminated with these passive, optical datasets. Airborne laser scanning (ALS) provides highly accurate three dimensional data on canopy structure and the underlying terrain, thereby offers significant enhancements to wildlife habitat mapping. In this paper, we introduce an approach to integrate ALS data and multispectral images to develop a new heuristic wildlife habitat, and cover with optical estimates of species (conifer vs. deciduous) composition in to a decision tree classifier for habitat-or landcover types. We believe this new approach is highly versatile and transferable, because class rules can be easily adapted for other species or functional groups. We discuss the implications of increased ALS availability for habitat mapping and wildlife management and provide recommendations for integrating multispectral and ALS data into wildlife management.
Location: T E 15 New Biology Building.
Literature cited 1: Allen,A.W.,Jordan,P.A.,Terrell,J.W., 1987.Habitat suitability index models:Moose,Lake Superior region.U.S.Dep.Inter.FishWildl.Serv.Biol.Rep.82,60. Baker,C.,Lawrence,R.,Montage,C.,Patten,D.,2006.Mapping Wetlands and riparian areas using Landsat ETM+imagery and decision-tree-based models. Wetlands 26, 465-474.
Literature cited 2: Barret, F., Guyot, G., 1991.Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens.Environ.35, 161-173. Bardshaw, C., Hebert, D., 1996.Woodland caribou population decline in Alberta: fact or fiction? Rangifer 9 (Special issue), 223-224.

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.