ID: 57292
Title: Estimation of grassland biomass and nitrogen using MERIS data
Author: Saleem Ullah, Yali Si, Martin Schlerf, Andrew K Skidmore, Muhammad Shafique, Irfan Akhtar Iqbal
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Quantifying biomass, nitrogen concentration, and nitrogen density, vegetation indices, band depth analysis parameters
Abstract: This study aimed to investigate the potential of MERIS in estimating the quantity and quality of a grassland using various vegetation indices (NDVI, SAVI, TSAVI, REIP, MTCI and band depth anaysis parameters) at a regional scale. Green biomass was best predicted by NBDI (normalised band depth index) and yielded a calibration R2 of 0.73 and a Root Mean Square Error (RMSE) fo 136.2 gm-2 (using an independent validation dataset, n=30) compared to a much higher RMSE obtained from soil adjusted vegetation index SAVI (444.6 gm-2). Nitrogen density was also best predicted by NBDI and yielded a calibration R2 of 0.51 and a RMSE of 4.2 gm-2 compared to a relatively higher RMSE obtained from MERIS terrestrial chlorophyll index MTCI (6.6 gm-2). For the estimation of nitrogen concentration (%), band depth analysis parameters showed poor R2 of 0.2 and the results of MTCI and REIP were statistically non-significant (P>0.05). It is concluded that band depth analysis parameters consistently showed higher accuracy than condition over time at regional scale.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57291
Title: Mapping invasive Fallopia japonica by combined spectral, spatial, and temporal analysis of digital orthophotos
Author: Wouter Dorigo, Arko Lucieer, Tomaz Podobnikar, Andraz Carni
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Random forest classifier, Alien speices, texture, multitemporal, NDVI
Abstract: Janpanese knotweed (Fallopia japonica) is listed among100 of the World ' s worst invasive alien species and poses increasing threat to ecosystems and agriculture in Northern America. Europe, and Oceania. This study proposes a remote sensing method to detect local occurrences of F. japonic from low-cost digital orthophotos taken in early spring and summer by concurrently exporing its temporal, spectral and spatial characteristics. Temporal characteristics of the species are quantified by a band ratio calculated from the green and red spectral channels of both images. The normalized difference vegetation index was used to capture the high near-infrared (NIR) reflectance of F. japonic in summer while the characteristic texture of F. japonica is quantified by calculating gray level co-occurrence matrix (GLCM) measures. After establishing the optimum kernel size to quantify texture, the different input features (spectral, spatial, and texture) were stacked and used as input to the random forest (RF) calssifier. The proposed method was tested for a built-up and semi-natural area in Slovenia.
The spectral, spatial and temporal provided an equally important contribution for differentiating F. japonica from other land cover types. The combination of all signatures resulted in a producer accuracy of 90.3% and a user accuracy of 98.% for F. japonica when validation was based on independent regions of interest. A producer accuracy of 61.4% was obtained for F. japonica when comparing the classification result with all occurrences of F.japonica identified during a field validation campaign. This is an encouraging result given the very small patches in which the species usually occur and the high degree of intermingling with other plants. All hot spots were identified and even likely infestations of F. japonica that had remained undiscovered during the field campaign were detected. The probability images resulting from the RF classifier can be used to reduce the relatively large number of false alarms and may assist in targeted eradication measures. Classificatin skill only slightly reduced when NIR information was not considered, which is an important recognition with regard to transferability of the method to the most basic type of digital color orthophotos. The possibility to use orthophotos, which at most municipalities are commonly available and easily accessible, facilitates an immediate implementation of the approach in situations where intervention is urgent.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57290
Title: Random forests as a tool for estimating uncertainty at pixel-level in SAR image classification
Author: Lien Loosvelt, Jan Peters, Henning Skriver, Hans Lievens, Frieke M B Van Coillie, Bernard De Baets, Niko E C Verhoest
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Synthetic aperture radar (SAR), Random forests, Multi-frequency, multi-date, land cover, crop classification, model uncertainty, prediction probability, data fusion, entropy
Abstract: It is widely acknowledged that model inputs can cause considerable errors in the model output. Since land cover maps obtained from the classification of remote sensing data are frequently used as input to spatially explicit environmental models, it is important to provide information regarding the classification quality of the produced map. Map quality is generally assessed at the global or class-specific level, using standard methods based on the confusion matrix. Unfortunately, these accuracy measures do not provide information regarding the spatial variability in classification quality. In this paper, we introduce Random-Forests for the probabilistic mapping of vegetation from high-dimensional remote sensing data and present a comprehensive methodology to assess and analyze classificatio uncertainty based on the local probabilities of class membership. We apply this method to SAR image data in order to investigate whether multi-cofiguratio in the dataset decreases the local uncertainty estimates. Polarimetric L- and C-band EMISAR data, acquired in April, May, June and July of 1998, and covering the agricultural Foulum test site in Denmark, are used. Results show that multi-configuration in the dataset decreases the classification uncertainty for the different agricultural crops as compared to the single-configuration alternative. Furthermore, the uncertainty assessment reveals lower confidence for the classification of (mixed) pixels at the field edges, and for some fields an uncertainty pattern is observed which is hypothesized to be caused by field preparation practices and cropping systems. This study demonstrates that uncertainty assessment provides valuable information on the performance of land cover classification models, both in space and time. Moreover, uncertainty estimates can be easily assessed when using the Random Forests algorithm.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57289
Title: Evaluation of the DisTrad thermal sharpening methodology for urban areas
Author: Wiesam Essa, Boud Verbeiren, Johannes van der Kwast, Tim Van de Voorde, Okke Batelaan
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Impervious surface percentage index, thermal remote sensing, thermal sharpening, DisTrad, spectral indexes, Urbanization mapping, Urban heat island UHI
Abstract: The goal of this paper is to evaluate the DisTrad sharpening technique for deriving land surface temperatures over urban areas. While the original DisTrad technique is based on the correlation between land surface temperature and NDVI, this study evaluates the performance of DisTrad over different land covers by analysing the correlation between land surface temperature and 15 different indices: BASVI, R, B, NDWI, NDBal, SVI, SAVI, NDBI, NDSI, VC, V, IBI, NDVI. In addition, we have analysed the correlation between land surface temperature and impervious percentage. These indices and land surface temperature were derived from a Landsate 7 ETM+ image of 2001 covering the city of Dublin. It is concluded that for most indices selecting 25% of the pixels with the lowest coefficient of variance increases the correlation between the index and the land surface temperature. Results show that the DisTrad technique in combination with impervious percentage sharpens urban areas at 30 m resolution most successfully. Although vegetation cover was high during acquisition of the image, the use of impervious percentage showed improved results compared to NDVI. This allows an improved estimation of spatial patterns of urban heat islands.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57288
Title: Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor
Author: A Ramoelo, A K Skidmore, M A Cho, M Schlerf, R Mathieu, IMA Heitkonig
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Grass nitrogen, Savanna ecosystem, Integrated modeling, Red-edge band, RapidEye, vegetation indices
Abstract: The regional mapping of grass nutrients is of interest in the sustainable planning and management of live-stock and wildlife grazing. The objective of this study was to estimate and map foliar and canopy nitrogen (N) at a regional scale using a recent high resolution spaceborne multispectral sensor (i.e RapidEye) in the Kruger National Park (KNP) and its surrounding areas, South Africa. The RapidEye sensor contains five spectral bands in the visible -to-near infrared (VNIR), including a red-edge band centered at 710nm. The importance of the red-edge band for estimating foliar chlorophyll and N concentrations has been demonstrated in many previous studies, mostly using field spectroscopy. The utility of the red-edge band of the RapidEye sensor for estimating grass N was investigated in this study. A two-step approach was adopted involving (i) vegetation indices and (ii) the integration of vegetation indices with environmental or ancillary variables using a stepwise multiple linear regression (SMLR) and a non-linear spatial least squares regression (PLSR). The model involving the simple ratio (SR) index (R805/R710) defined as SR54, altitude and the interaction between SR54 and altitude (Sr54* altitude) yielded the highest accuracy for canopy N estimation, while the non-linear PLSR yeilded the highest accuracy for foliar N estimation through the integration of remote sensing (SR54) and environmental variables. The study demonstrated the possibility to map grass nutrients at a regional scale provided there is a spaceborne sensor encompassing the red edge waveband with a high spatial resolution.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57287
Title: Polarimetric classification of borel forest using nonparametric feature selection and multiple classifiers
Author: Yasser Maghsoudi, Michael collins, Donald G. Leckie
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: PolSAR data, forest, classification, feature selection, leaf-on, leaf-off
Abstract: Polarimetric SAR data contains a large amount of potential information that may be used to characterize forested scenes. However, the large number of PolSAR parameters and discriminators cannot all be used in most classification problems. Some form of feature selection will improve classification results and improve the efficiency of the system. In addition, classification of PolSAR data may be improved with an ensemble of classifiers, each tuned to a different class. Our research is in the Petawawa experimental forest, in the boreal forest northwest of Ottawa, Ontario, Canada. We employ Radarsat-2 fine-quad image data acquired in August (leaf-on) and November (leaf-off) of 2009. We present two system designs in this paper. The first system consists of a feature selector based on a non-parametric evaluation function and a support vector machine for classification. We demonstrate that the feature selection step improves classification accuracy significantly over a baseline classifier. We then present a system consisting of an ensemble of SVM classifiers, each with its own feature selection component and trained on an individual class. The classifier likelihoods are combined in a final step. We demonstrate that this system improves classification accuracy significantly over a single -classifier system. Finally, we demonstrate that classification accuracies are significantly higher when leaf-on and leaf-off images are combined over a single season image.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57286
Title: Monitoring patterns of urban heat islands of the fast-growing Shanghai metorpolis, China: Using time-series of Landsat TM/ETM+ data
Author: Ying-ying Li, Hao Zhang, Wolfgang Kainz
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Land-use/land cover (LULC), Land surface temperature (LST), Urban heat island (UHI), Remote sensing, Shanghai, China
Abstract: In this paper, Shanghai city, the biggest city in China, was selected as the case for quantifying the impact of land-use/land cover (LULC) change on patterns of surface urban heat island (UHI). Time series of LULC maps of Shanghai were produced from Landsat TM/ETM+ images between 1997 and 2008, during which this city experienced unprecedented urban growth. The results show that dramatic changes in LULC have occurred, with loss of cropland, forest and shrub to urban use. The built-up land increased by 219.50%. In contrast, bare land, cropland, fallow land, forest and shrub decrease by 79.38%, 50.50%, 43.35% and20.90%, respectively. Consequently, these drastically altered the land surface characteristics and spatiotemporal patterns of UHI. According to temporal analysis on seasonal and inter-annual variations of intensity of the UHI (UHII), both the mean UHII between the city center (UHIIC-R) and UHII between the peri-urban and surrounding rural areas (UHIIP-R) reached maximum during summer period, followed by UHIIC-R and UHIIP-R during spring period. UHIIC-R and UHIIP-R were both relatively weak during winter period. In contrast, UHIIC-R and UHIIP-R were both slight during autumn period. Spatially, there were significant LSTs gradients from the city center to surrounding rural areas. furthermore, the overall relationships between surface UHI pattern and pixel-based biophysical features as well as population density and road density were quantitatively explored. Thus, based on temporal-spatial analysis of land use dynamics, patterns of LST, and socioeconomic driving forces, our findings highlight the urgent demands for planers and decision-makes to deliberately take urban expansion, UHI effects, and their impact on local climate change into account in future planning.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57285
Title: Comparison of regression and kriging techniques for mapping the average annual precipitation of Turkey
Author: P A Bostan, G B M Heuvelink, S Z Akyurek
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Regression, Kriging, precipitation, spatial interpolation, extrapolation, validation
Abstract: Accurate mapping of the spatial distribution of annual precipitation is important for many applications in hydrology, climatology, agronomy, ecology and other environmental sciences. In this study, we compared five different statistical methods to predict spatially the average annual precipitation of Turkey using point observations of annual precipitation at meterological stations and spatially exhaustice covariate data (i.e. elevation, aspect, surface roughness, distance to coast, land use and eco-region). The methods compared were multiple linear regression (MLR), ordinary krigning (OK), regresion krigning (RK), universal kriging (UK), and geographically weighted regression (GWR). Average annual precipitation of Turkey from 1970 to 2006 was measured at 225 meteorological stations that are fairly uniformly distributed across the country, with a somewhat higher spaital density along the coastline. The observed annual precipitation varied between 255 mm and 2209 mm with an average of 628 mm. The annual precipitation was highest along the southern and northern coasts and low in the centre of the country, except for the area near the Van Lake, Keban and Ataturk Dams. To compare the performance of the interpolation techniques the total dataset was first randomly spit in ten equally sized test datasets. Next, for each test data set the remaining 90% of the data comprised the training dataset. Each training dataset was then used to calibrate and apply the spatial prediction model. Predictions at the test dataset locations were compared with the observed test data. Validation was done by calculating the Root Mean Squared Error (RMSE), R-square and Standardized MSE (SMSE) values. According to these criteria, universal krigning is the most accurate with an RMSE of 178 mm, an R-square of 0.61 and an SMSE of 1.06, whilst mulitple linear regression performed worst (RMSE of 222 mm , R-square of 0.39, and SMSE of 1.44). Ordinary kriging, UK using only elevation and geographically weighted regression are intermediate with RMSE values of 201 mm, 212 mm and 211 mm , and an R-square of 0.50, 0.44 and 0.45, respectively. The RK results are close to those of UK with an RMSE of 186 mm and R-square of 0.57. The sptial extrapolation performance of each method was also evaluated. This was done by predicting the annual precipitation in the eastern part of Turkey using observations from the western part. Results showed that MLR, GWR and RK performed best with little differences between these methods. The large prediction error variances confirmed that extrapolation is more difficult than interpolation. Whilst spatial extrapolation benefits most from covariate information as shown by an RMSE reduction of about 60 mm, in this study covariate information was also valuable for spatial interpolation because it reduced the RMSE with on average 30 mm.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57284
Title: Design and implementation of an algorithm for automatic 3D reconstruction of building models using genetic algorithm
Author: Mostafa Kabolizade, Hamid Ebadi, Ali Mohammadzadeh
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Building reconstruction, 3D modeling, Gereralization, genetic algorithm, LiDAR
Abstract: Automatic extraction and reconstruction of objects from Light Detection and Ranging (LiDAR) data and images has been a topic of research for decades. In other words, laser scanner data are powerful data source for acquisition and updating of large scale topographic maps. With this information, topographic objects like buildings, trees and the relief can be determined. The goal of this research is to extract and delineate building ground plans from LiDAR data and reconstruction of buildings in 3D space. The focus of the research lies on the different possibilities to reconstruct the building models. In this paper, a reconstruction method based on genetic algorithms (GA) is presented by optimizing height and slopes of gable roof of building models. The proposed algorithm consists of three steps; initial building boundaries are detected in the first step. Then, in extraction step, in order to improve the accuarcy of detection step, initial building contours are generalized and buildings are extracted. Finally and in reconstruction step, a GA-based method is used for reconstructing the building models. Also, the method has proved to be computationally efficient, and the reconstructed models have an acceptable accuracy. Examination of the results shows that the reconstructed buildings from complex study areas that uses the proposed method have root mean square error (RMSE) of 0.1m.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57283
Title: Applying vegetation indices to detect high water table zones in humid warm-temperate regions using satellite remote sensing
Author: Kaoru Koide, Katsuaki Koike
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: AgbNDVI, Geobotany, Groundwater discharge, Reflectance spectra, Segmentation analysis, SPOT HRV
Abstract: This study developed a geobotanical remote sensing method for detecting high water table zones using differences in the conditions of forest trees induced by groundwater supply in a humid warm-temperate region. A new vegetation index (VI) termed added green band NDVI (AgbNDVI) was proposed to discriminate the differences. The AgbNDVI proved to be more sensitive to water stress on green vegetation than existing VIs, such as SAVI and EV12, and possessed a strong linear correlation with the vegetation fraction. To validate a proposed vegetation index method, a 23 km2 study area was selected in the Tono region of Gifu prefecture, central Japan. The AgbNDVI values were calculated from atmospheric corrected SPOT HRV data. To correctly extract high VI points, the influence factors on forest tree growth were identified using the AgbNDVI values, DEM and forest type data; the study area was then divided into 555 domains chosen from a combination of the influence factors and forest types. Thresholds for extracting high VI points were defined for each domain based on histograms of AgbNDVI values. By superimposing the high VI points on topographic and geologic maps, most high VI points are clearly located on either concave or convex slopes, and are found to be proximal to geologic boundaries - particularly the boundary between the Pliocene gravel layer and the Cretaceous granite, which should act as a groundwater flow path. In addition, field investigations support the correctness of the high VI points, because they are located around groundwater seeps and in high water table zones where the growth increments and biomass of trees are greater than at low VI points.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57282
Title: A methodology to generate a synergetic land-cover map by fusion of different land-cover products
Author: A Perez-Hoyos, F J Garcia-Haro, J San-Miguel-Ayanz
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Synergetic map, Land-cover classification, LCCS, CORINE, GLC2000, MODIS, GlobCover
Abstract: The main goal of this study is to develop a general framework for building a hybrid land-cover map by the synergistic combination of a number of land-cover classifications with different legends and spatial resolutions. The proposed approach assesses class-specific accuracies of datasets and establishes affinity between thematic legends using a common land-cover language such as the UN Land-Cover Classification System (LCCS). The approach is illustrated over a large region in Europe using four land-cover datasets (CORINE, GLC2000, MODIS and GlobCover), but it can be applied to any set of existing products. The multi-classification map is excepted to improve the performance of individual classifictions by reconciling their best characteristics while avoiding their main weaknesses. The intermap comparison reveals improved agreement of the hybrid map with all other land-cover products and therefore indicates the successful exploration of synergies between the different products. The approach offers also estimates for the classification confidence associated with the pixel label and flexibility to shift the balance between commission and omission errors, which are critical in order to obtain a desired reliable map.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57281
Title: Calibrating CORINE land cover 2000 on forest inventories and climatic data: An example for Italy
Author: Roberto Pilli
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: CORINE, forest inventory, precipitation, temperature
Abstract: National Forest Inventories (NFIs) provide quantitative information on forest area, volume, growing stock and composition at national level but in most cases these data cannot be directly linked with geographical or climatic data sources, such as temperature and precipitation. Assuming that NFIs represent the most useful source of data to evaluate area, volume, age class distribution and composition of forests, the objective of this work was to combine these data with the geo-referenced data provided by CORINE (Coordination of Information on the Environment) and with other data on local climatic conditions. This is a necessary prerequisite for a long-term project aimed at the application of a full carbon accounting approach at European level. The approach was applied to the Italian NFI, assumed as a representative example at European level.
Maps of temperature and precipitation classes were projected over a CORINE map and over the Italian administrative units defined at Nuts 2 level. The resulting combinations of precipitation and mean temperature values were used to define 24 climatic land units (CLUs). The results were validated against data provided by 111 weather stations in 3 different administrative regions.
The proportion of NFI forest area associated with each CLU at regional level, divided into broadleaved, coniferous and mixed forests, was estimated on the basis of CORINE data.
Because this approach maintained all the information reported by NFI linking them to climatic data, both the final forest area and the distribution of forest types at national level were also maintained, avoiding any inconsistency with data directly provided by CORINE. This also ensured full consistency with the forest threshold parameters applied at national level and with the application of these data to international reporting documents (i.e the UNFCCC report on LULUCF).
The approach can easily be applied to every other European country, linking data reported by NFI with other land use geo-referenced data and with climatic data. In the meantime, maintaining the supra-national classification system proposed by CORINE, the results could be more consistently compared across national borders.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57280
Title: Elevation change of Alamkouh glacier in Iran since 1955, based on remote sensing data
Author: Neamat Karimi, Ashkan Farokhnia, Sara Shishangosht, Mohammad Elmi, Morteza Eftekhari, Hossein Ghalkhani
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Alpine glaciers, Glacier changes, Remote sensing imagery, Digital elevation model, Elevation change
Abstract: To reconstruct morphometric changes in alpine glaciers, accurate and repeatable topographic surveys are required. In the present study, the elevation changes of Alamkouh glacier in Iran was evaluated by means of several multi-temporal remote sensing images with a range of high to medium nominal scales from 1955 (aerial photos), 1997 (topographic map), 2002 (Terra-ASTER) and 2010 (LiDAR). The procedures of Digital Elevation Models (DEM) extraction from aerial photos and ASTER imagery were employed by using several Ground Control Points (GCPs) which were measured by Differential Global Positioning System (DGPS) from non-glaciated areas. For assessing and correcting these DEMs to quantify Alamkouh elevation changes, first a 3-D co-registration was applied to remove the systematic shifts from the four DEMs. After the 3-D co-registration, significant biases related to elevation were found in DEMs and the existing linear relationship between the elevation differences and elevation was used to adjust the DEMs. Finally, the morphometric changes were assessed for different dates by subtracting these adjusted DEMs. The present study came across some interesting findings, including the maximum thinning rate (about -4.5 + 0.32 m/year) in high-elevated areas which fell down to about -0.5 + 0.06m/year toward the tongue of the glacier between 1955 and 2010. The total volume loss during this period (1955 - 2010) is about 0.29 + 0.03 km3 which the highest retreat (equal to 42% of total volumetric change) occurred during 1997-2002. The estimation of cross-sectional elevation changes confirm that the maximum glacier surface lowering has taken place in the middle of the glacier, and this rate has decreased toward the sides due to the thicker debris covers and large colluvial debris sources along the steeper and more unstable valley rocks, which could retard the melting rate.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57279
Title: Spectral responses to plant available soil moisture in a Californian grassland
Author: Shishi Liu, Dar A Roberts, Oliver A Chadwick, Chris J Still
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Soil moisture, Grassland, MODIS, AVIRIS, Vegetation indices
Abstract: This study established relationships among plant available soil moisture and reflectance and vegetation indices (VIs) derived from AVIRIS and MODIS data in grassland ecosystem in California. Strong correlations were observed between soil moisture and different forms of reflecatnce in the red-edge, near infrared and shortwave infrared bands. Both greenness -based and canopy-water-based indices were linearly related with soil moisture during the growing season, the wet and the dry season. The relationship was stronger with antecedent soil moisture, particularly in the dry season. Using plant available soil moisture, which is the difference between measured soil moisture and the wilting point, improved the relationship by reducing the soil property effect. Furthermore, results suggested that the difference in sensors had little impact on the relationships in grassland, but the parameters of relationship were influenced by the spatial resolution of sensors.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57278
Title: Development of a spatial data infrastructure for coastal management in the Amirante Islands, Seychelles
Author: Sarah M Hamylton, Justin Prosper
Editor: F. D. van der Meer
Year: 2012
Publisher: Elsevier, Vol 19, October 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Seychelles, Remote sensing, Spatial data infrastructure
Abstract: Spatial data infrastructures play a key role in coastal management decision making in the Seychelles. This paper describes four components of a web-based spatial data infrastructure that were developed to facilitate coastal management of the Amirante Islands in the Seychelles. The four components include: (i) the institutional arrangements for using spatial data effectively to address local management challenges, (ii) the production of island habitat maps from remotely sensed data, (iii) the tasks undertaken for promoting access to and use of this spatial data, and (iv) and example of how this data is used for a specific coastal management application in the Seychelles. By outlining these fuor components, the value of this spatial data infrastructure framework for tropical coastal management in the Seychelles is demonstrated.
Location: TE 15, Biology Sciences Building, IISc
Literature cited 1: None
Literature cited 2: None