ID: 54592
Title: Integration of ground and satellite data to model Mediterranean forest porcesses
Author: M Chiesi, L Fibbi, L Genesio, B. Gioli, R Magno, F Maselli, M Moriondo, F P Vaccari
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Mediterranean forest, GPP, NEE, ET, C-Fix, BIOME-BGC
Abstract: The current work presents the testing of a modeling strategy that has been recently developed to simulate the gross and net carbon fluxes of Mediterranean forest ecosystems. The strategy is based on the use of a NDVI-driven parametric model, C-Fix, and of a biogeochemical model, BIOME-BGC, whose out-puts are combined to simulate the behavior of forest ecosystems at different developmetn stages. The performances of the modeling strategy are evaluated in three Italian study sites (San Rossore, Lecceto and Pianosa), where carbon fluxes are being measured through the eddy correlation technique. These sites are characterized by variable Mediterranean climates and are covered by different types of forest vegetation (pine wood, Holm oak forest and Macchia, respectively). The results of the tests indicate that the modeling strategy is generally capable of reproducing monthly GPP and NEE patterns in all three study sites. The highest accuracy is obtained in the most mature, homogenous pine wood of San Rossore, while the worst results are found in the Lecceto forest, where there are the most heterogeneous terrain, soil and vegetation conditions. The main error sources are identified in the inaccurate definition of the model inputs, particularly those regulating the site water budgets, which exert a strong control on forest productivity during the Mediterranean summer dry season. In general, the incorporation of NDVI-derived fAPAR estimates corrects for most of these errors and renders the forest flux simulations more stable and accurate.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54591
Title: Integrating temperature vegetation dryness index (TVDI) and regional water stress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+images
Author: Zhigiang Gao,Wei Gao, Ni-Bin Chang
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Drought assessment, Remote sensing, Urbanization effect, Urban heat island, Coastal managdment
Abstract: This paper presents a new drought assessment method by spatially and temporally integrating temperature vegetation dryness index (TVDI) with regional water stress index (RWSI) based on a synergistic approach. With the aid of LANDSAT TM/ETM data, we were able to retrieve the land-use and land-cover (LULC), vegetation indeces (VIs) , and land surface temerature (LST), leading to the derivation of three types of modified TVDI, including TVDI_SAVI, TVDI_ANDVI and TVDI_MSAVI, for drought assessment in a fast growing coastal area, Northern China. The categorical classfication of four drought impact levels associated with the RWSI values enables us to refine the spatiotemporal relationship between the LST and the VIs. Holistic drought impact assessment between 1987 and 2000 was carried out by linking RWSI with TVDIs group wise. Research findings indicate that : (1) LST and VIs were negatively correlated in most cases of low, medium, and high vegetation cover except the case of high density vegetation cover in 2000 due to the effect of urban heat island (UHI) effect; 92) the shortage of water in 1987 was more salient than that in 2000 based on all indices of TVDI and RWSI; and (3) TVDIs are more suitable for monitoring mild drought, normal and wet conditions when RWSI is smaller than 0.752; but they are not suitable for monitoring moderate and severe drought conditions.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54590
Title: Integrating conventional classifiers with a GIS expert system to increase the accuracy of invasive species mapping
Author: Mhosisi Masocha, Andrew K Skidmore
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Image classification, Invasion, Lantana camara, Remote sensing, Savanna, Zimbabwe
Abstract: Mapping the cover of invasive species using remotely sensed data alone is challenging, because many invaders occur as mid-level canopy species or as subtle understorey species and therefore contribute little to the spectral signatures caputed by passive remote sensing devices. In this study, two common non-parametric classifiers namely, the nerual network and support vector machine were used to map four cover classes of the invasive shrub Lantana camara in a protected game reserve adn the adjacent area under communal land management in Zimbabwe. These classifiers were each combined with a geographic information system (GIS) expert system, in order to test whether the new hybrid classifiers yielded significantly more accurate invasive species cover maps than the single classifiers. The neural network, when used on its own, mapped the cover of L. camara with an overall accuracy of 71% and a Kappa index of agreement of 0.61. When the neural network was combined with an expert system, the overall accuracy and Kappa index of agreement significantly increased to 83% and 0.77, respectively. Similarly, the support vector machine achieved an overall accuracy of 64% with a Kappa index of agreement of 0.52, whereas the hybrid support vector machine and expert system classifier achieved a significantly higher overall accuracy of 76% and a kappa index of agreement of 0.67. These results suggest athat integrating conventional image classifiers with an expert system increases the accuracy of invasive species mapping.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54589
Title: Shrinkage and fragmentation of marshes in the West Songmen Plain, China, from 1954 to 2008 and its possible causes
Author: Zongming Wang, Ni Huang, Ling Luo, Xiaoyan Li, Chunying Ren, Kaishan Song, Jing Ming Chen
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Marsh shrinkage, Land use change, Climate changes, Remote sensing, The West Songnen Plain
Abstract: Agriculture development under climatic variations has resulted in substantial loss of marshes in the West Songnen Plain in the past decades. In this paper, the shrinkage and fragmentation process of marshes and its possible causes in the West Songnen Plain from 1954 to 2008 were explored using historical topographic maps and remote sensing data. Results indicated that the West Songnen Plain underwent considerable shrinkage and fragmentation of marshes in that same period. Marshes occupied 6404 km2in 1954, but this area has decreased by 74% in the past 54 years. The average annual decrease rate of marshes was 88 km2 per year. Meanwhile, the number of marsh patches decreased from 1411 to 514, and the mean patch size decreased from 454 to 320 ha. Cropland and salinized wasteland were the two main land use types into which marshes were converted. During the same period, grassland decreased by 54%, cropland increased by 22%, and salinized wasteland expanded by 612%. A significant increase in air temperature and index of dryness was found in the study region, along with decreased precipitation, thereby affecting the marsh systems through the changing hydrological regimes. On the other hand, population, gross domestic product, and livestock number increased considerably as marshes shrank and became fragmented. Govermental policy changes played a key role in land use transformations in the study region.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54588
Title: Use of multi-temporal Landsat images for analyzing forest transition in relation to socioeconomic factors and the environment
Author: Zhi-Hua Shi, Lu Li, Wei Yin, Lei Ai, Nu-Fang Fang, Yan-Tun Song
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Forest transition, Socioeconomic factors, Environmental conditions, Remote sensing (RS), Non-metric multidimensional scaling (NMDS), China
Abstract: Recently there have been reports of forest regrowth occurring in different regions across the world. There is also a growing recognition of the potential beneficial impact that secondary forests may have on the global environment: providing crucial ecosystem services such as soil conservation, stabilization of hydrological cycles, carbon sequestration, and support for forest dependent communities. Consequently, there is a growing awareness of the need to recognize that landscapes are complex shifting mosaics wherein forest clearing and reforestation take place. In this study, the rates of reforestation, deforestation, forest regrowth and degradation were measured using multi-temporal Landsat images of Danjiangkou, China. Landsat data from 1990, 1999 and 2007 were (1) classified as dense forest, open forest and non-forest areas and (2) compared between years to identify forest cutting, regeneration and degradation. The results showed that there was a net gain of 29,315 ha of forest area (including dense and open forest ) from 1990 to 2007, showing a clear trend of reforestation in the study area. Forest modification (degradation and regrowth) and change categories (deforestation and reforestation) occurred simultaneously during the observation time period. Socioeconomic data from public statistics and environmental attributes allowed the assessment of the socioeconomic factors and the environmental conditions that caused these changes using non-metric multidimensional scaling (NMDS). The research showed that the socioeconomic factors due to different policies were major driving forces of forest transition, whereas environmental attributes of the underlying landscape constrained forest cover changes. These findings have led to a better understanding of forest transition at a local scale in our study region. Comprehensive knowledge of these relationships may be useful to reconstruct past forest transitions and predict future changes, and may help to enhance sustainable management practices aimed at preserving essential ecological functions.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54587
Title: Application of evidential reasoning to improve the mapping of regenerating forest stands
Author: Brice Mora, Richard A Fournier, Samuel Foucher
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Evidential reasoning, Data fusion, Dempster-Shafer, Dezert-Smarandache, Regenerating forest, Mapping, Soil and topographic information, Heterogeneous information
Abstract: This study confirmed the ability of the Dempster-Shafer theory (DST) and the Dezert-Smarandache (Free DSm model) theory to significantly improve the quality of maps of regenerating forest stands in southern Quebec, Canada compared to a classical Maximum Likelihood Algorithm (MLA). The proposed approach uses data fusion methods that allow the integration of remotely sensed imagery with conventional maps of ecophysiographic features. While the MLA provided an overall accuracy of 82.75%, the DST and Free DSm models had overall accuracies of 90.14% and 91.13% respectively. In addition, this study showed that the data fusion methods can model the influence of biophysical parameters (e.g. surface deposits and drainage) on the growth potential of regenerating forest stands. This study illustrates the importance of the mass function allocation for each ancillary data source. We found that a Bayesian belief configuration provided results equivalent to those obtained when representing data uncertainty. This demonstrates the difficulty in modelling uncertainty associated with each ancillary source.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54586
Title: Monitoring global land surface drought based on a hybrid evapotranspiration model
Author: Yunjun Yao, Shunlin Liang, Qiming Qin, Kaicun Wang, Shaohua Zhao
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Global land surface drought, Hybrid ET Model, Potential ET, Evaporative Drought Index, Palmer Drought Severity Index
Abstract: The latent heat of evapotranspiration (ET) plays an important role in the assessment of drought severity as one of sensitive indicator of land drought status. A simple and accurate method of estimating global ET for the monitoring of global land surface droughts from remote sensing data is essential. The objective of this research is to develop a hybrid ET model by introducing empirical coefficients based on a simple linear two-source land ET model, and to then use this model to calculate the Evaporative Drought Index (EDI) based on the actual estimated ET and the potential ET in order to characterize global surface drought conditions. This is done using the Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) products. AVHRR-NDVI products from the Global Inventory Modeling and Mapping Studies (GIMMS) group, and National Centres for Environmental Prediction Reanalysis-2 (NCEP-2) datasets. We randomly divided 22 flux towers into two groups and performed a series of cross-validations using ground measurements collected from the corresponding flux towers. The validation results from the second group of flux towers using the data from the first group for calibration show that the daily bias varies from -6.72 W/m2 to 12.95 W/m2 and the average monthly bias is -1.73 W/m2. Similarly, the validation results of the first group of flux towers using data from second group for calibration show that the daily bias varies from -12.91 W/m2 to 10.26 W/m2 and the average monthly bias is -3.59 W/m2. To evaluate the reliability of the hybrid ET model on a global scale, we compared the estimated ET from teh GEWEX, AVHRR-GIMMS-NDVI, and NECP-2 datasets with the latent heat flux from the Global Soil Wetness Project-2 (GSWP-2) datasets. We found both of them to be in good agreement, which further supports the validity of our model ' s global ET estimation. Significantly, the patterns of monthly EDI anomalies have a good spatial and temporal correlation with the Palmer Drought Severity Index (PDSI) anomalies from January 1984 to December 2002, which indicates that the method can be used to accurately monitor long-term global land surface drought.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54585
Title: Dynamic modeling of forest conversion: Simulation of past and future scenarios of rural activities expansion in the fringes of the Xingu National Park
Author: Eduardo E Maeda, Claudia Maria de Almeida, Arimatea de Carvalho Ximenes, Antonio R Formaggio, Yosio E Shimabukuro, Petri Pellikka
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Land use change, Xingu National Park, Brazilian Amazon, Simulation model, Landscape dynamics
Abstract: The present work is committed to simulate the expansion of agricultural and cattle raising activities within a watershed located in the fringes of the Xingu National Park, Brazilian Amazon. A spatially explicit dynamic model of land cover and land use change was used to provide both past and future scenarios of forest conversion into such rural activities, aiming to identify the role of driving forces of change in the study area. The employed modeling platform-Dinamica EGO-consists in a cellular automata environment that embodies neighborhodd-based transition algorithms and spatial feedback approaches in a stochastic multi-step simulation framework. Biophysical variables and legal restrictions drove this simulation model, and statistical validation tests were then conducted for the generated past simulations (from 2000 to 2005), by means of multiple resolution fitting methods. Based on optimal calibration of past simulations, future scenarios were conceived, so as to figure out trends and spatial patterns of forest conversion in the study area for the year 2015. In all simulated scenarios, pasturelands remained nearly stable throughout the analyzed period, while a large expansion in croplands took place. The most optimistic scenario indicates that more than 50% of the natural forest will be replaced by either cropland or pastureland by 2015. This modeling experiment revealed the suitability of the adopted model to simulate processes of forest conversion. It also indicates its possible further appicability in generating simulations of deforestation for areas with expanding rural activities in the Amazon and in tropical forests worldwide.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54584
Title: Water body mapping method with HJ-1A/B satellite imagery
Author: Shanlong Lu, Bingfang Wu, Nana Yan, Hao Wang
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: HJ-1A/B imagery, NDVI-NDWI, Integrated mapping method, Water body, Remote sensing
Abstract: This paper proposes an integrated water body mapping method with HJ-1A/B satellite imagery, the CCD (charge coupled device) data of the Chinese environmental satellites that were launched on September 6th, 2008. It combines the difference between NDVI and NDWI (NDVI-NDWI) with SLOPE and near-infrared (NIR) band. The NDVI-NDWI index is used to enhance the contrast between water bodies and the surrounding surface features: the topographic SLOPE is used to eliminate the mountain shadow; and the NIR band is used to reduce the effects of artificial construction land. The objectives are evaluating the potential of the HJ-1A/B imagery on water body monitoring, and proposing ideally mapping method. The test study results indicated that the NDVI-NDWI index is superior to the single index of NDVI and NDWI to enhance the contrast between water bodies and the rest of the features. On the basis of the accurately mapped water bodies in the HJ-1A/B CCD images of the study area, we conclude that the HJ-1A/B multi-spectral satellite images is an ideal data source for high spatial and temporal resolution water bodies monitoring. And the integrated water body mapping method is suitable for the applications of HJ-1A/B mutli-spectral satellite images in this field.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54583
Title: Impact of DEM source and resolution on topographic seismic amplification
Author: Muhammad Shafique, Mark Van der Meijde, Norman Kerle, Freek van der Meer
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: DEM resolution, Topographic attributes, Shear wave velocity, TAF
Abstract: The impact of topographic attributes on the uneven distribution of seismic response and associated devastation has frequently been observed and documented during seismic events, but has rarely been investigated at a regional scale. Existing numerical and experimental techniques applied to explore the impact of topographic attributes in the aggravatin of seismic response, have been limited to isolated and /or synthetic hills and ridges. Predicting the realistic regional impact of topographic seismic response is strongly dependent on the resolution and accuracy of regional topographic information. This study evaluates the topographic attributes and seismic parameters computed from mutli-resolution and source DEMs, to investigate the impact of data source and resolution on the derived topographic seismic response. Methodologies are developed to readily derive the spatial distribution of relevant topographic attributes and seismic parameters, utilizing the mutli-resolution and source DEMs. The impact of DEM source and resolution on slope gradient, relative height of terrain and shear wave velocity (Vs30) are addressed. It is observed that, even though, relatively coarse resolution DEMs underestimate the critical sites of steep slope gradient and the lower Vs30 zones, this has limited impact on the derived normalized topographic aggravation factor. The free and easily accessible DEMs provide an opportunity for reasonable prediction of topographic seismic response, especially in near-real time. The slope gradient is observed to be the most sensitive topographic attribute to amplified seismic response, followed by the relative height.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54582
Title: A geostatistical approach to data harmonization-Application to radioactivity exposure data
Author: O Baume, J O Skoien, G B M Heuvelink, E J Pebesma, S J Melles
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Network bias, Measurement equation, Natural drifts, Least squares estimation, Universal kriging
Abstract: Environmental issues such as air, groundwater pollution and climate change are frequently studied at spatial scales that cross boundaries between political and administrative regions. It is common for different adminstrations to employ different data collection methods. If these differences are not taken into account in spatial interpolation procedures then biases may appear and cause unrealistic results. The resulting maps may show misleading patterns and lead to wrong interpretations. Also, errors will propagate when these maps are used as input to environmental process models. In this paper we present and apply a geostatistical model that generalizes the universal kriging model such that it can handle heterogeneous data sources. The associated best linear unbiased estimation and prediction (BLUE and BLUP) equations are presented and it is shown that these lead to harmonized maps from which estimated biases are removed. The methodology is illustrated with an example of country bias removal in a radioactivity exposure assessement for four European countries. The application also addresses multicollinearity problems in data harmonization, which arise when both artifiical bias facors and natural drifts are present and cannot easily be distinguished . Solutions for handling multicollinearity are suggested and directions for further investigations proposed.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54581
Title: Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete montly time series of precipitation for Sicily, Italy
Author: A Di Piazza, F Lo Conti, L V Noto, F Viola, G La Loggia
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Precipitation, DEM ,Geostatics, Interpolation methods
Abstract: The availability of good and reliable rainfall data is fundamental for most hydological analyses and for the design and management of water resources systems. However, in practice, precipitation records often suffer from missing data values mainly due to malfunctioning of rainguage for specific time periods. This is an important issue in practical hydrology because it affects the continuity of rainfall data and ultimately influences the results of hydrologic studies which use rainfall as input. Many methods to estimate missing rainfall data have been proposed in literature and among these, most are based on spatial interpolation algorithms.
In this paper different spatial interpolation algorithms have been evaluated to produce a reasonably good continuous dataset bridging the gaps in the historical series. The algorithms used are deteministic methods such as inverse distance weighting, simple linear regression, multiple regression, geographically weighted regression and artificial neural networks, and geostatistical models such as ordinary kriging and residual ordinary kriging. In some of these methods, the elevation information, provided by a Digital Elevation Model, has been added to improve estimation of missing data. These algorithms have been applied to the mean annual and monthly rainfall data of Sicily (Italy), measured at 247 rainguages.
Optimization of different settings of the various interpolation methods has been carried out using a subset of the available rainfall dataset (modeling set) while the remaining subset (validation set) has been used to compare the results obtained by the different algorithms.
Validation results indicate that the univariate methods, neglecting the information of elevation, are characterized by the largest errors, which decrease when the elevation is taken into account. The ordinary kriging of residuals from linear regression between precipitation and elevation, which has provided the best performance at annual and monthly scale, has been used to complete the precipitation monthly time series in Sicily.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54580
Title: A data mining based approach to predict spatiotemporal changes in satellite images
Author: W Boulila, I R Farah, K Saheb Ettabaa, B Solaiman, H Ben Ghezala
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Remote sensing, Knowledge discovery in satellite image, databases, Data mining, prediction, Land cover change, classification, Decision trees, Fuzzy logic
Abstract: The interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conculsions based on collected data a challenging task. Recently, data mining appears to be promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited.
This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54579
Title: A multi-level segmentation methodology for dual-polarized SAR data
Author: M Dabboor, V Karathanassi, A Braun
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Polarimetric SAR, Dual-polarized SAR, Histogram, Multi-level segmentation, TerraSAR-X
Abstract: An innovative methodology for dual-polarized Synthetic Aperture Radar (SAR) data segmentation is proposed. The methodology is based on the thresholding of the 1D-histograms of the two images produced by the dual polarimetric bands. Thresholding of the histograms is performed using a non parameteric algorithm. Histograms after thresholding are combined together in a two dimensional histogram-based space in order to define sub-spaces, which are used for image segmentation. Sub-spaces are further divided based on two criteria which lead to a multi-level segmentation approach. Dual-polarized TerraSAR-X data, both HH and VV, are used in a study area located in the southwestern United Kingdom.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54578
Title: Toward reduction of artifacts in fused images
Author: Mario Lillo-Saavedra, Consuelo Gonzalo, Octavio Lagos
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 3, June 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Fractal dimension, Fusion image, Wavelet transform, Texture
Abstract: Most fusion satellite image methodologies at pixel-level introduce false spatial details, i.e artifacts, in the resulting fused images. In many cases, these artifacts appears because image fusion methods do not consider the differences in roughness or textural characteristics between different land covers. They only consider the digital values associated with single pixels. This effect increases as the spatial resolution image increases. To minimize this problem, we propose a new paradigm based on local measurements of the fractal dimension (FD). Fractal dimension maps (FDMs) are generated for each of the source images (panchromatic and each band of the multi-spectral images) with the box-counting algorithm and by applying a windowing process. The average of source image FDMs, previously indexed between 0 and 1, has been used for discrimination of different land covers present in satellite images. This paradigm has been applied though the fusion methodology based on the discrete wavelet transform (DWT), using the a trous algorithm (WAT). Two different scenes registered by optical sensors on board FORMOSAT-2 and IKONOS satellites were used to study the behaviour of the proposed methodology. The implementation of this approach, using the WAT method, allows adapting the fusion process to teh roughness and shape of the regions present in the image to be fused. This improves the quality of the fused images and their classification results when compared with the original WAT method.
Location: 231
Literature cited 1: None
Literature cited 2: None