ID: 54276
Title: Modelling and analysing 3D buildings with a primal/dual data structure
Author: Pawel Boguslawski, Christopher M Gold, Hugo Ledoux
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, issue 2, March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: 3D modelling, Data structure, Building management, Disaster management, CAD
Abstract: While CityGML permits us to represent 3D city models, its use for applications where spatial analysis and/or real-time modifications are required is limited since at this moment the possibility to store topological relationships between the elements is rather limited and often not exploited. We present in this paper a new topological data structure, the dual half-edge (DHE), which permits us to represent the topology of 3D buildings (including their interiors) and of the surrounding terrain. It is based on the idea of simultaneously storing a graph in 3D space and its dual graph, and to link the two. We proposed Euler-type operators for incrementally constructing 3D models (for adding individual edges, faces and volumes to the model while updating the dual structure simultaneously), and we also propose navigation operators to move from a given point to all the connected planers or polyhedra for example. The DHE also permits us to store attributes to any element. We have implemented the DHE and have tested it with different CityGML models. Our technique allows us to handle important query types, for example finding the nearest exterior exit to a given room, as in disaster management planning. As the structure is locally modifiable the model may be adapted whenever a particular pathway is no longer available. The proposed DHE structure adds significant analytic value to the increasingly popular CityGML model.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54275
Title: Automatic reasoning for geometric constraints in 3D city models with uncertain observations
Author: Sandra Loch-Dehbi, Lutz Plumer
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, issue 2, March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Geometric reasoning, Statistical testing theory, Constraint-based modelling, 3D city models, Automatic theorem proving
Abstract: This paper presents a novel approach to automated geometric reasoning for 3D building models. Geometric constraints like orthogonality or parallelity play a prominent role in the man-made objects such as buildings. Thus, constraint based modelling, that specifies buildings by their individual components and the constraints between them, is a common approach in 3D city models. Since prototyped building models allow one to incorporate a priori knowledge they support the 3D reconstruction of buildings from point clouds and allow the construction of virtual cities. However, high level building models have a high degree of complexity and consequently are not easily manageable. Interactive tools are needed which facilitate the development of consistent models that, for instance, do not entail internal logical contradictions. Furthermore, there is often an interest in a compact, redundancy-free representation. We propose an approach that uses algebraic methods to prove that a constraint is deducible from a set of premises. while automated reasoning in 2D models is practical, a substantial increase in complexity can be observed in the transition to the three-dimensional space. Apart from that, algebraic theorem provers are restricted to crisp constraints so far. Thus, they are unable to handle quality issues, which are, however, an important aspect of GIS data and models. In this article we present an approach to automatic 3D reasoning which explicitly addresses uncertainty. Hereby, our aim is to support the interactive modelling of 3D city models and the automatic reconstruction of buildings. Geometric constraints are represented by multivariate polynomials whereas algebric reasoning is based on Wu ' s method of pseudodivision and characteristic sets. The reasoning process is further supported by logical inference rules. In order to cope with uncertainty and to address quality issues the reasoner integrates uncertain projective geometry and statistical hypothesis tests. Consequently, it allows one to derive uncertain from uncertain premises. The quality of such conclusions is quantified in a way which is sound both from a logical and a statistical perspective.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54274
Title: Learning grammar rules of building parts from precise models and noisy observations
Author: Y Dehbi, L Plumer
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, issue 2, March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Inductive logic programming, Attribute grammar, 3D model, uncertain projective geometry, Probability distributions
Abstract: The automatic interpretation of dense three-dimensional (3D) point clouds is still an open research problem. The quality and usability of the derived models depend to a large degree on the availability of highly structured models which represent sematics explicitly and provide a priori knowledge to the interpretation process. The usage of formal grammars for modelling man-made objects has gained increasing interest in the last few years. In order to cope with the variety and complexity of buildings, a large number of fairly sophisticated grammar rules are needed. As yet, such rules mostly have to be designed by human experts. This article describes a novel approach to machine learning of attribute grammar rules based on the Inductive Logic Programming paradigm. Apart from syntactic differences, logic programs and attribute grammars are basically the same language. Attribute grammars extend context-free grammars by attributes and sematic rules and provide a much larger expressive power. Our approach to derive attribute grammars is able to deal with two kinds of input data. On the one hand, we show how attribute grammars can be derived from precise descriptions in the form of example provided by a human user as the teacher. On the other hand, we present the acquisition of models from noisy observations such as 3D point clouds. This includes the learning of geometric and topological constraints by taking measurement errors into account. The feasibiltiy of our approach is proven exemplarily by stairs, and a generic framework for learning other building parts is discussed. Stairs aggregate an arbitrary number of steps in a manner which is specified by topological and geometric constraints and can be modelled in a recursive way. Due to this recursion, they pose a special challenge to machine learning. In order to learn the concept of stairs, only a small number of examples were required. Our approach represents and addresses the quality of the given observations and the derived constraints explicitly, using concepts from uncertain projective geometry for learning geometric relations and the Wakeby distribution together with decision trees for topological relations.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54273
Title: Quality analysis on 3D building models reconstructed from airborne laser scanning data
Author: Sander Oude Elberink, George Vosselman
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, issue 2, March 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Accuracy, Analysis, Three-dimensional, Building, Lidar
Abstract: This paper presents a method to assess the geometric quality of 3D building models. The quality depends on properties of the input data and the processing steps. Insight in the quality of 3D models is important for users to judge whether the models can be used in their specific applications. Without a proper quality description it is likely that the building models are either treated as correct or considered as useless because the quality is unknown. In our research we analyse how the quality parameters of the input data affect the quality of the 3D models. The 3D models have been reconstructed from dense airborne laser scanner data of about 20 pts/m2. A target based graph matching approach has been used to relate specific data features to general building knowledge. The paper presents a theoretical and an empirical approach to identify strong parts and shortcomings in 3D building models reconstructed from airborne laser scanning data without the use of reference measurements. Our method is tested on three different scenes to show that a proper quality description is essential to correctly judge the quality of the models.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54272
Title: An historical empirical line method for the retrieval of surface reflectance factor from multi-temporal SPOT HRV, HRVIR and HRG multispectral satellite imagery
Author: Barnaby Clark, Juha Suomalainen, Petri Pellikka
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: Atmospheric correction, Empirical line correction, Reflectance factor retrieval, SPOT
Abstract: SPOT satellite have been imaging Earth ' s surface SPOT 1 was launched in 1986. It is argued that absolute atmospheric correction is a prerequisite for quantitative remote sensing. Areas where land cover changes are occurring rapidly are also often areas most lacking in situ data which would allow full use of radiative transfer models for reflectance factor retrieval (RFR). Consequently, this study details the proposed historical empirical line method (HELM) for RFR from multi-temporal SPOT imagery. HELM is designed for use in landscape level studies in circumstances where no detailed overpass concurrent stmospheric or meterological data are available, but where there is field access to the research site(s)adn a goniometer or spectrometer is available. SPOT data are complicated by the + 270 off-nadir cross track viewing. Calibration to nadir only surface reflectance factor (ps) is denoted as HELM-1, whilst calibration to ps modelling imagery illumination and view geometries is termed HELM-2. Comparison of field measured ps with those derived from HELM corrected SPOT imagery, covering Helsinki, Finland, and Taita Hills, Kenya, indicated HELM-1 RFR absolute accuracy was +0.02ps in the visible and near infrared (VIS/NIR) bands and +0.03ps in the shortwave infrared (SWIR), whilst HELM-2 performance was +0.03ps in the VIS/NIR and +0.04ps in the SWIR. This represented band specific relative errors of 10-15%. HELM-1 and HELM-2 RFR were significantly better than at-satellite reflectance (pSAT), indicating HELM was effective in reducing atmospheric effects. However, neither HELM approach reduced variability in mean ps between multi-temporal images, compared to pSAT. HELM-1 calibration error is dependent on surface characteristics and scene illumination and view geometry. Based on multiangular ps measurements of vegetation-free ground targets, calibration error was negligible in the forward scattering direction, even at maximum off-nadir view. However, error exceeds 0.02 ps where off-nadir viewing was > 200 in the backscattering direction within + 550 azimuth of the principal plane. Overall, HELM-1 results were commensurate with an identified VIS/NIR 0.02 ps accuracy benchmark. HELM thus increases applicability of SPOT data to quantitative remote sensing studies.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54271
Title: Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and kalimantan, Indonesia
Author: Mark Broich, Matthew C Hansen, Peter Potapov, Bernard Adusei, Erik Lindquist, Stephen V Stehman
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: Landsat, MODIS, Composite, Forest cover loss, Humid tropics, Indonesia
Abstract: Monitoring loss of humid tropical forests via remotely sensed imagery is critical for a number of environemental objectives, including carbon accounting, biodiversity, and climate modelling science applications. Landsat imagery, provided free of charge by the US. Geological Survey Center for Earth Resources Observation and Science (USGS/EROS), enables consistent and timely forest cover loss updates from regional to biome scales. The Indonesia islands of Sumatra and Kalimantan are a centre of significant forest cover change within the humid tropics with implications for cabon dynamics, biodiversity maintenance and local livelihoods. Sumatra and Kalimantan feature poor observational coverage compared to other centers of humid tropical forest change, such as Mato Grosso, Brazil, due to the lack of ongoing acquisitions from nearby ground stations and the persistence of cloud cover obscuring the land surface. At the same time, forest change in Indonesia is transient and does not always result in deforestation, as cleared forests are rapidly replaced by timber plantations and oil palm estates. Epochal composites, where single best observations are selected over a given time interval and used to quatify change, are one option for monitoring forest change in cloudy regions. However, the frequency of forest cover change in Indonesia confounds the ability of image composite pairs to quantify all change. Transient change occurring between composite periods is often missed and the length of time required for creating a cloud-free composite often obscures change occcurring within the composite period itself. In this paper, we analyze all Landsat 7 imagery with <50% cloud cover and data and products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to qunatify forest cover loss for Sumatra and Kalimantan from 2000 to 2005. We demonstrated that time-series approaches examining all good land observations are more accurate in mapping forest cover change in Indonesia than change maps based on image composites. Unlike other time-series analyses employing observations with a consistent periodicity, our study area was characterized y highly unequal observation counts and frequencies due to persistent cloud cover, scan line corrector off (SLC-off) gaps, and the absence of a complete archive. Our method accounts for this variation by generating a generic variable space. We evaluated our results against an independent probability sample-based estimate of gross forest cover loss and expert mapped gross forest cover loss at 64 smaple sites. The mapped gross forest cover loss for Sumatra and Kalimantan was 2.86% of the land area, or 2.86 Mha from 2000 to 2005, with the highest concentration having occurred in Riau and Kalimantan Tengah provinces.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54270
Title: Estimating land-surface temperature under clouds using MSG/SEVIRI observations
Author: Lei LU, Valentijn Venus, Andrew Skidmore, Tiejun Wang, Geping Luo
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: Land-surface temperature (LST), LST under clouds, 4-channel algorithm
Abstract: The retrieval of land-surface temperature (LST) from thermal infrared satellite sensor observations is known to suffer from cloud contamination. Hence few studies focus on LST retrieval under cloudy conditions. In this paper a temporal neighboring-pixel approach is presented that reconstructs the diurnal cycle of LST by exploiting the temporal domain offered by geo-stationary satellite observations (i.e.MSG/SEVIRI), and yields LST estimates even for overcast moments when satellite sensor can only record cloud-top temperatures. Constrasting to the neighboring pixel appoach as presented by Jin and Dickinson (2002), our approach naturally satisfies all sorts of spatial homogeneity assumptions and is hence more suited for each surfaces characterized by scattered land-use practices. Validation is performed against in situ measurements of infrared land-surface temperature obtained at two validation sites in Africa. Results vary and show a bias of -3.68 K and a RMSE of 5.55 K for the validaton site in Kenya, while results obtained over the site in Burkina Faso are more encouraging with a bias of 0.37 K and RMSE of 5.11 K. Error analysis reveals that uncertainty of the estimation of cloudy sky LST is attributed to errors in estimation of the underlying clear sky LST, all-sky global radiation, and inaccuracies inherent to the ' neighboring pixel ' scheme itself. An error propagation model applied for the proposed temporal neighboring -pixel approach reveals that the absolute error of the obtained cloudy sky LST is less than 1.5 K in the best case scenario, and the uncertainty increases linearly with the absolute error of clear sky LST. Despite this uncertainty, the proposed method is practical for retrieving the LST under a cloudy sky condition, and it is promising to reconstruct diurnal LST cycles from geo-stationary satellite obsevations.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54269
Title: Surveying at the limits of local RTK networks: Test results from the perspective of high accuracy users
Author: M Selmira Garrido, Elena Gimenez, M Clara de Lacy, Antonio J Gil
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: GNSS, RTK positioning, Active network, Accuracy, Precision, SE Spain
Abstract: Precise GNSS-based differnetial positioning in real time is usually known as the real-time kinematics (RTK) technique. RTK reduces the effects of orbit errors and ionospheric and tropospheric refraction by forming differences between the observables (e.g. double-differences). These effects, however, grow with increasing baseline length, although the use of corrections generated in real-time from an active GNSS network allows these distance-dependent errors to be reduced. This technology increases the reliability of the system and thereby the accuracy of real-time positioning. In this study, the test results of RTK positioning at different test points located in the border area between the Autonomous Communities of the Region of Murcia and the Community of Valencia, in SE spain, are presented. The analysis is based on three GNSS local active networks present in this border area, namely MERISTEMUM, REGAM and ERVA network Test measurements with RTK rover were performed in this region in order to analyze the realtime services offered by these three networks. Moreover, precise coordinates for each test point were determined using Bernese 5.0. The results confirm that it is possible to achieve centimetre-scale accuracy with RTK positioning based on the networks studied, even in border regions.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54268
Title: Monitoring stress levels on rice with heavy metal pollution from hyperspectral reflectance data using wavelet-fractal analysis
Author: Meiling Liu, Xiangnan Liu, Weicui Ding, Ling Wu
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: Heavy metal pollution, Hyperspectral reflectance, Stress information, Wavelet transform, Fractal analysis
Abstract: Remote sensing allows monitoring heavy metal pollution in crops for agricultural production and foof security. This paper presents approach to wavelet-fractal analysis for exploring a set of sensitive spectral parameters to monitor the heavy metal pollution levels in rice crops from hyperspectral reflectance data. Hyperspectral and biochemical data were collected from three study farms in Changchun,Jilin Province, China. Our study explored the fractal dimension of reflectance with wavelet transform (FDWT) that demonstrated a better performance than other existing methods. Our results obtained in this study show that the red edge position (REP) was the most sensitive indicator for monitoring the heavy metal pollution levels in rice crops among common indices. As compared with REP, the FDWT is more sensitive to biochemical composition, namely with respect to chlorophyll concentrations, N, Cu and Cd. The established linear models showed a correlation coefficient (R2) above 0.70, model efficiency (ME) above 0.65 and a root mean square error (RMSE) below 3.5. Minimum FDWT values occurred in rice with Level II pollution followed by Level I pollution, and finally the safe level. This study suggests that wavelet transform is well suited as a spectral analysis method to eliminate noise and amplify the stress information from heavy metals. The wavelet transform in conjunction with fractal analysis is promising for detecting heavy metal-induced stress in rice crops.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54267
Title: Normalized algorithm for mapping and dating forest disturbances and regrowth for the United States
Author: Liming He, Jing M Chen, Shaoliang Zhang, Gustavo Gomez, Yude Pan, Kevin McCullough, Richard Birdsey, Jeffrey G Masek
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: Forest disturbance, The continental US, Fire, Logging, Disturbance index, Change detection algorithm
Abstract: Forest disturbances such as harvesting, wildfire and insect infestation are critical ecosystem processes affecting the carbon cycle. Because carbon dynamics are related to time since disturbance, forest standage that can be used as a surrogate for major clear-cut /fire disturbance information has recently been recognized as an important input to forest carbon cycle models for improving prediction accuracy. In this study, forest disturbances in the USA for the period of ~1990-2000 were mapped using 400+ pairs of re-sampled Landsat TM/ETM scenes in 500m resolution, which were provided by the Landsat Ecosystme Disturbance Adaptive Processing System project. The detection disturbances were then separated into two five-year age groups, facilitated by Forest Inventory and Analysis (FIA) data, which was used to calculate the area of forest regeneration for each country in the USA.
In this study, a disturbance index (DI) was defined as the ratio of the short wave infrared (SWIR, band 5) to near-infrared (NIR, band 4) reflectance. Forest disturbances were identified through the Normalized Difference of Disturbance Index (NDDI) between circa 2000 and 1990, where a positive NDDI means disturbance and a negative NDDI means regrowth. Axis rotation was performed on the plot between DIs of the two matched Landsat scenes in order to reduce any difference of DIs caused by non-disturbance factors. The threshold of NDDI for each TM/ETM pair was determined by analysis of FIA data. Minor disturbances affecting small areas may be omitted due to the coarse resolution of the aggregated Landsat data, but the major stand-clearing disturbances (clear-cut harvest, fire) are captured. The spatial distribution of the detected disturbed areas was validated by Monitoring Trends in Burn Severity fire data in four states of the western USA (Washington, Oregon, Idaho, and California). Results indicate omission errors of 66.9%.
An important application of this remote sensing-based disturbance map is to associate with FIA forest age data for developing a US forest age map. The US forest age map was also combined with the Canadian forest age map to produce a continent-wide forest map, which becomes a remarkable data layer for North American carbon cycle modeling.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54266
Title: Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation
Author: Dhaval Vyas, N S R Krishnavya, K R Manjunath, S S Ray, Sushma Panigrahy
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: Hyperion (EO-1) data, Tropical forest, Band selection, Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine
Abstract: There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectal remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1)data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54265
Title: A combined spectral and object-based approach to transparent cloud removal in an operational settign for Landsat ETM+
Author: Gary R Watmough, Peter M Atkinson, Craig W Hutton
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: Cloud, Landsat ETM+, Remote sensing, Object-based analysis, ACCA
Abstract: The automated cloud cover assessement (ACCA) algorithm has provided automated estimates of cloud cover for the Landsat ETM+ mission since 2001. However, due to the lack of a band around 1.375 ?m, cloud edges and transparent clouds such as cirrus cannot be detected. Use of Landsat ETM+ imagery for terrestrail land analysis is further hampered by the relatively long revisit period due to a nadir only viewing sensor. In this study, the ACCA threshold parameters were altered to minimise omission errors in the cloud masks. Object-based analysis was used to reduce the commission errors from the extended cloud filters. The method resulted in the removal of optically thin cirrus cloud and cloud edges which are often missed by other methods in sub-tropical area. Although not fully automated, the principles of the method developed here provide an opportunity for using otherwise sub-optimal or completely unusable Landsat ETM+ imagery for operational applications. Where specific images are required for particular research goals the method can be used to remove cloud and transparent cloud helpign to reduce bias in subsequent land cover classification.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54264
Title: Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale
Author: Armel Thibaut Kaptue Tchuente, Jean-Louis Roujean, Steven M De Jong
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: Land cover, Africa, Per-pixel comparison, Accuracy assessment
Abstract: Land cover dynamics at the African continental scale is of great importance for global change studies. Actually, four satellite-derived land cover maps of Africa now available, e.g., ECOCLIMAP, GLC2000, MODIS and GLOBCOVER, are based on images acquired in the 2000s. This study aims at stressing the compliances and the discrepancies between these four land cover classifications systems. Each of them used different mapping initiatives and relies on different mappin standards, which supports the present investigation. In order to do a relative comparison of the four maps, a preamble was to reconcile their thematic lengends into more aggregated categories after a projection into the same spatial resolution. Results show that the agreement between the four land cover products is between 56 adn 69%. While all these land cover datasets show a reasonable agreement in terms of surface types and spatial distribution patterns, mapping of heterogeneous landscapes in the four products is not very successful. Land cover products based on remote sensing imagery can indeed significantly be improved by using smarter algorithms, better timing of image acquisition, improved class definitions. Either will help to improve the accuracy of future land cover maps at the African continental scale. Data producers may use the areas of spatial agreement for training area selection while users might need to verify the information in the areas of disagreement using additional data sources.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54263
Title: Estimation of regional evapotranspiraton over the North China Plain using geostationary satellite data
Author: Yunqiao Shu, Simon Stisen, Karsten H Jensen, Inge Sandholt
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: Evapotranspiration, FY-2C, MODIS products, North China Plain (NCP), NDVI, Surface temperature, Triangle method
Abstract: Data from the first operational Chinese geostationary satellite Fengyun-2C (FY-2C) satellite are applied in combination with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products for the assessment of regional evapotranspiration over the North China Plain. The approach is based on the improved triangle method, where the temperature-vegetation index space includes thermal inertia.Two thermal infrared channels from FY-2C are used to estimate surface temperature (Ts) based on a split window algorithm originally proposed for the MSG-SEVIRI sensor. Subsequently the high temperoral resolution of FY-2C data is exploited to give the morning rise in Ts. Combined with the 16 days composite MODIS vegetation indices product (MOD13) at a spatial resolution of 5 km, evaporative fraction (EF) is estimated by interpolation in the ?Ts-NDVI traingular-shaped scatter space. Finally, regional actual evapotranspiration (ET) is derived from the evaporative fraction and available energy estimated from MODIS surface albedo products MCD43. Spatial variations of estimated surface variables (Ts, EF and ET) corresponded well to land cover patterns and farmland management practices. Estimated ET and EF also compared well to lysimeter data collected for the period June 2001-September 2007. The improved triangle method was also appleid to MODIS products for comparison. Estimates based on FY-2C products proved to provide slightly better results than those based on MODIS products. The consistency of the estimated spatial variation with other spatial data supports the use of FY-2C data for ET estimation using the improved triangle method. Of particular value is the high temporal frequency of image acquisitions from FY-2C which improves the likelihood of obtaining cloud free image acquisitions as compared to polar orbiting sensors like MODIS.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54262
Title: Evaluating a thermal image sharpening model over a mixed agricultural landscape in India
Author: C Jeganathan, N A S Hamm, S Mukherjee, P M Atkinson, P L N Raju, V K Dadhwal
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: Sharpening, Dis-aggregation, Land surface temperature, ASTER, MODIS
Abstract: Fine spatial resolution (e.g, <300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution ( >1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution -adjusted global model; the piece-wise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Radiometer (ASTER) thermal data (90m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990m to 90 m, root mean square error (RMSE) of < 2 K could, on average, be achieved only for 990-270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70 K respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data ( 1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. the RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and the local models at 250 m were 3..08, 2.92 and 1.98 K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants.
Location: 231
Literature cited 1: None
Literature cited 2: None