ID: 59463
Title: Geographic Object-Based Image Analysis - Towards a new paradigm.
Author: Thomas Blaschke, Geoffrey J. Hay, Maggi Kelly, Stefan Lang, Peter Hofmann, Elisabeth Addink, Raul Queiroz Feitosa, Freek van der Meer, Harald van der Werff, Frieke van Coillie, Dirk Tiede
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 180-191 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: GEOBIA; OBIA; GIScience; Remote sensing; Image segmentation; Image classification.
Abstract: The amount of scientific literature on (Geographic) Object-based Image Analysis - GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ' per-pixel paradigm ' and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59462
Title: Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping.
Author: Luca Demarchi, Frank Canters, , Claude Cariou, Giorgio Licciardi , Jonathan Cheung-Wai Chan,
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 166-179 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Airborne high-resolution hyperspectral imagery; APEX; Data dimensionality reduction; BandClust; Autoassociative Neural Network; Machine learning classifiers.
Abstract: Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method - the first a transformation based approach, the second a feature-selection based approach - for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method ' s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59461
Title: Contextual classification of lidar data and building object detection in urban areas.
Author: Joachim Niemeyer, Franz Rottensteiner, , Uwe Soergel,
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 152-165 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: LIDAR; Point cloud; Classification; Urban; Contextual; Building; Detection.
Abstract: In this work we address the task of the contextual classification of an airborne LiDAR point cloud. For that purpose, we integrate a Random Forest classifier into a Conditional Random Field (CRF) framework. It is a flexible approach for obtaining a reliable classification result even in complex urban scenes. In this way, we benefit from the consideration of context on the one hand and from the opportunity to use a large amount of features on the other hand. Considering the interactions in our experiments increases the overall accuracy by 2%, though a larger improvement becomes apparent in the completeness and correctness of some of the seven classes discerned in our experiments. We compare the Random Forest approach to linear models for the computation of unary and pairwise potentials of the CRF, and investigate the relevance of different features for the LiDAR points as well as for the interaction of neighbouring points. In a second step, building objects are detected based on the classified point cloud. For that purpose, the CRF probabilities for the classes are plugged into a Markov Random Field as unary potentials, in which the pairwise potentials are based on a Potts model. The 2D binary building object masks are extracted and evaluated by the benchmark ISPRS Test Project on Urban Classification and 3D Building Reconstruction. The evaluation shows that the main buildings (larger than 50 m2) can be detected very reliably with a correctness larger than 96% and a completeness of 100%.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59460
Title: Assessment of satellite ocean color products of MERIS, MODIS and SeaWiFS along the East China Coast (in the Yellow Sea and East China Sea).
Author: Tingwei Cui, Jie Zhang, Junwu Tang, Shubha Sathyendranath, Steve Groom, Yi Ma, Wei Zhao, Qingjun Song
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 137-151 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Oceanography; Comparison; Retrieval; Algorithm; Satellite; Accuracy; Optical.
Abstract: The validation of satellite ocean-color products is an important task of ocean-color missions. The uncertainties of these products are poorly quantified in the Yellow Sea (YS) and East China Sea (ECS), which are well known for their optical complexity and turbidity in terms of both oceanic and atmospheric optical properties. The objective of this paper is to evaluate the primary ocean-color products from three major ocean-color satellites, namely the Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Through match-up analysis with in situ data, it is found that satellite retrievals of the spectral remote sensing reflectance Rrs(Lamda) at the blue-green and green bands from MERIS, MODIS and SeaWiFS have the lowest uncertainties with a median of the absolute percentage of difference (APDm) of 15-27% and root-mean-square-error (RMS) of 0.0021-0.0039 sr?1, whereas the Rrs(Lamda) uncertainty at 412 nm is the highest (APDm 47-62%, RMS 0.0027-0.0041 sr?1). The uncertainties of the aerosol optical thickness (AOT) ?a, diffuse attenuation coefficient for downward irradiance at 490 nm Kd(490), concentrations of suspended particulate sediment concentration (SPM) and Chlorophyll a (Chl-a) were also quantified. It is demonstrated that with appropriate in-water algorithms specifically developed for turbid waters rather than the standard ones adopted in the operational satellite data processing chain, the uncertainties of satellite-derived properties of Kd(490), SPM, and Chl-a may decrease significantly to the level of 20-30%, which is true for the majority of the study area. This validation activity advocates for (1) the improvement of the atmosphere correction algorithms with the regional aerosol optical model, (2) switching to regional in-water algorithms over turbid coastal waters, and (3) continuous support of the dedicated in situ data collection effort for the validation task.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59459
Title: Hierarchical extraction of landslides from multiresolution remotely sensed optical images
Author: Camille Kurtz, Andr? Stumpf , Jean-Philippe Malet, Pierre Gan?arski, Anne Puissant, Nicolas Passat,
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 122-136 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Landslide mapping; VHR images; Multiresolution region-based analysis; Hierarchical approach; Binary partition tree; Domain adaptation.
Abstract: The automated detection and mapping of landslides from Very High Resolution (VHR) images present several challenges related to the heterogeneity of landslide sizes, shapes and soil surface characteristics. However, a common geomorphological characteristic of landslides is to be organized with a series of embedded and scaled features. These properties motivated the use of a multiresolution image analysis approach for their detection. In this work, we propose a hybrid segmentation/classification region-based method, devoted to this specific issue. The method, which uses images of the same area at various spatial resolutions (Medium to Very High Resolution), relies on a recently introduced top-down hierarchical framework. In the specific context of landslide analysis, two main novelties are introduced to enrich this framework. The first novelty consists of using non-spectral information, obtained from Digital Terrain Model (DTM), as a priori knowledge for the guidance of the segmentation/classification process. The second novelty consists of using a new domain adaptation strategy, that allows to reduce the expert ' s interaction when handling large image datasets. Experiments performed on satellite images acquired over terrains affected by landslides demonstrate the efficiency of the proposed method with different hierarchical levels of detail addressing various operational needs.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59458
Title: Optimizing multi-resolution segmentation scale using empirical methods: Exploring the sensitivity of the supervised discrepancy measure Euclidean distance 2 (ED2)
Author: Chandi Witharana, Daniel L. Civco.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 108-121 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Multiresolution segmentation, GEOBIA, Opmital scale, Image objects, Empirical methods.
Abstract: Multiresolution segmentation (MRS) has proven to be one of the most successful image segmentation algorithms in the geographic object-based image analysis (GEOBIA) framework. This algorithm is relatively complex and user-dependent; scale, shape, and compactness are the main parameters available to users for controlling the algorithm. Plurality of segmentation results is common because each parameter may take a range of values within its parameter space or different combinations of values among parameters. Finding optimal parameter values through a trial-and-error process is commonly practiced at the expense of time and labor, thus, several alternative supervised and unsupervised methods for supervised automatic parameter setting have been proposed and tested. In the case of supervised empirical assessments, discrepancy measures are employed for computing measures of dissimilarity between a reference polygon and an image object candidate. Evidently the reliability of the optimal-parameter prediction heavily relies on the sensitivity of the segmentation quality metric. The idea behind pursuing optimal parameter setting is that, for instance, a given scale setting provides image object candidates different from the other scale setting; thus, by design the supervised quality metric should capture this difference. In this exploratory study, we selected the Euclidean distance 2 (ED2) metric, a recently proposed supervised metric, whose main design goal is to optimize the geometrical discrepancy (potential segmentation error (PSE)) and arithmetic discrepancy between image objects and reference polygons (number-of segmentation ratio (NSR)) in two dimensional Euclidean space, as a candidate to investigate the validity and efficacy of empirical discrepancy measures for finding the optimal scale parameter setting of the MRS algorithm. We chose test image scenes from four different space-borne sensors with varying spatial resolutions and scene contents and systematically segmented them using the MRS algorithm at a series of parameter settings. The discriminative capacity of the ED2 metric across different scales groups was tested using non-parametric statistical methods. Our results showed that the ED2 metric significantly discriminates the quality of image object candidates at smaller scale values but it loses the sensitivity at larger scale values. This questions the meaningfulness of the ED2 metric in the MRS algorithm ' s parameter optimization. Our expense of time. In this respect, especially in operational-level image processing, it is worth to re-think the trade-off between execution time of the processor-intensive MRS algorithm at series of parameter settings targeting a less-sensitive quality metric and an expert-lead trial-and-error approach.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59457
Title: Landscape Dynamics.
Author: T V Ramachandra
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 93-107 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: MLS data, Curb, Road markings, Intensity, Multi-thershold segmentation, Morphological operation.
Abstract: A mobile laser scanning (MLS) system allows direct collection of accurate 3D point information in unprecedented detail at highway speeds and at less than traditional survey costs, which serves the fast growing demands of transportation-related road surveying including road surface geometry and road environment. As one type of road feature in traffic management systems, road markings on paved roadways have important functions in providing guidance and information to drivers and pedestrians. This paper presents a stepwise procedure to recognize road markings from MLS point clouds. To improve computational efficiency, we first propose a curb-based method for road surface extraction. This method first partitions the raw MLS data into a set of profiles according to vehicle trajectory data, and then extracts small height jumps caused by curbs in the profiles via slope and elevation-difference thresholds. Next, points belonging to the extracted road surface are interpolated into a geo-referenced intensity image using an extended inverse-distance-weighted (IDW) approach. Finally, we dynamically segment the geo-referenced intensity image into road-marking candidates with multiple thresholds that correspond to different ranges determined by point-density appropriate normality. A morphological closing operation with a linear structuring element is finally used to refine the road-marking candidates by removing noise and improving completeness. This road-marking extraction algorithm is comprehensively discussed in the analysis of parameter sensitivity and overall performance. An experimental study performed on a set of road markings with ground-truth shows that the proposed algorithm provides a promising solution to the road-marking extraction from MLS data.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59456
Title: Using mobile laser scanning data for automated extraction of road markings.
Author: Haiyan Guan, Jonathan Li, Yongtao Yu, Cheng Wang, Michael Chapman, Bisheng Yang.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 93-107 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: MLS data, Curb, Road markings, Intensity, Multi-thershold segmentation, Morphological operation.
Abstract: A mobile laser scanning (MLS) system allows direct collection of accurate 3D point information in unprecedented detail at highway speeds and at less than traditional survey costs, which serves the fast growing demands of transportation-related road surveying including road surface geometry and road environment. As one type of road feature in traffic management systems, road markings on paved roadways have important functions in providing guidance and information to drivers and pedestrians. This paper presents a stepwise procedure to recognize road markings from MLS point clouds. To improve computational efficiency, we first propose a curb-based method for road surface extraction. This method first partitions the raw MLS data into a set of profiles according to vehicle trajectory data, and then extracts small height jumps caused by curbs in the profiles via slope and elevation-difference thresholds. Next, points belonging to the extracted road surface are interpolated into a geo-referenced intensity image using an extended inverse-distance-weighted (IDW) approach. Finally, we dynamically segment the geo-referenced intensity image into road-marking candidates with multiple thresholds that correspond to different ranges determined by point-density appropriate normality. A morphological closing operation with a linear structuring element is finally used to refine the road-marking candidates by removing noise and improving completeness. This road-marking extraction algorithm is comprehensively discussed in the analysis of parameter sensitivity and overall performance. An experimental study performed on a set of road markings with ground-truth shows that the proposed algorithm provides a promising solution to the road-marking extraction from MLS data.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59455
Title: Fusion of airborne laserscanning point clouds and images for supervised and unsupervised classification scene classification
Author: Markus, Gerke, Jing Xiao.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 78-92 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Visibility, Segmentation, Supervised, Unsupervised, Classification.
Abstract: Automatic urban object detection from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Laserscanning (ALS) data available today. This paper combines ALS data and airborne imagery to exploit both: the good geometric quality of ALS and the spectral image information to detect the four classes buildings, trees, vegetated ground and sealed ground. A new segmentation approach is introduced which also makes use of geometric and spectral data during classification entity definition. Geometric, textural, low level and mid level image features are assigned to laser points which are quantified into voxels. The segment information is transferred to the voxels and those clusters of voxels form the entity to be classified. Two classification strategies are pursued: a supervised method, using Random Trees and an unsupervised approach, embedded in a Markov Random Field framework and using graph-cuts for energy optimization. A further contribution of the paper concerns the image-based point densification for building roofs which aims to mitigate the accuracy Problems related to large ALS point spacing. Results for the ISPRS benchmark test data show that to rely on color information to separate vegetation from non-vegetation areas does mostly lead to good results, but in particular in shadow areas a confusion between classes might occur. The unsupervised classification strategy is especially sensitive in this respect. As far as the point cloud densification is concerned, we observe similar sensitivity with respect to color which makes some planes to be missed out, or false detections still remain. For planes where the densification is successful we see the expected enhancement of the outline.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59454
Title: Maximum-likelihood estimation for multi-aspect multi-baseline SAR interferometry of urban areas.
Author: Michael Schmitt, Uwe Stilla.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 68-77 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Synthetic Aperture Radar (SAR); Multi-aspect; Multi-baseline; Airborne; SAR interferometry (InSAR); Maximum likelihood estimation.
Abstract: The reconstruction of digital surface models (DSMs) of urban areas from interferometric synthetic aperture radar (SAR) data is a challenging task. In particular the SAR inherent layover and shadowing effects need to be coped with by sophisticated processing strategies. In this paper, a maximum-likelihood estimation procedure for the reconstruction of DSMs from multi-aspect multi-baseline InSAR imagery is proposed. In this framework, redundant as well as contradicting observations are exploited in a statistically optimal way. The presented method, which is especially suited for single-pass SAR interferometers, is examined using test data consisting of experimental airborne millimeterwave SAR imagery. The achievable accuracy is evaluated by comparison to LiDAR-derived reference data. It is shown that the proposed estimation procedure performs better than a comparable non-statistical reconstruction method.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59453
Title: EarthEnv-DEM90: A nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data.
Author: Natalie Robinson, James Regetz, Robert P. Guralnick.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 57-67 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Global Digital Elevation Model (DEM); Void-filling; Multi-scale smoothing; SRTM; ASTER; DEM blending.
Abstract: A variety of DEM products are available to the public at no cost, though all are characterized by trade-offs in spatial coverage, data resolution, and quality. The absence of a high-resolution, high-quality, well-described and vetted, free, global consensus product was the impetus for the creation of a new DEM product described here, ' EarthEnv-DEM90 ' . This new DEM is a compilation dataset constructed via rigorous techniques by which ASTER GDEM2 and CGIAR-CSI v4.1 products were fused into a quality-enhanced, consistent grid of elevation estimates that spans ~91% of the globe. EarthEnv-DEM90 was assembled using methods for seamlessly merging input datasets, thoroughly filling voids, and smoothing data irregularities (e.g. those caused by DEM noise) from the approximated surface. The result is a DEM product in which elevational artifacts are strongly mitigated from the input data fusion zone, substantial voids are filled in the northern-most regions of the globe, and the entire DEM exhibits reduced terrain noise. As important as the final product is a well defined methodology, along with new processing techniques and careful attention to final outputs, that extends the value and usability of the work beyond just this single product. Finally, we outline EarthEnv-DEM90 acquisition instructions and metadata availability, so that researchers can obtain this high-resolution, high-quality, nearly-global new DEM product for the study of wide-ranging global phenomena.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59452
Title: An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds.
Author: C. Cabo, C. Ordo?ez, S. Garc?a-Cort?s, J. Mart?nez
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 47-56 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Urban; Simplification; Detection; Laser scanning; Algorithms; Mobile.
Abstract: An algorithm for automatic extraction of pole-like street furniture objects using Mobile Laser Scanner data was developed and tested. The method consists in an initial simplification of the point cloud based on the regular voxelization of the space. The original point cloud is spatially discretized and a version of the point cloud whose amount of data represents 20-30% of the total is created. All the processes are carried out with the reduced version of the data, but the original point cloud is always accessible without any information loss, as each point is linked to its voxel. All the horizontal sections of the voxelized point cloud are analyzed and segmented separately. The two-dimensional fragments compatible with a section of a target pole are selected and grouped. Finally, the three-dimensional voxel representation of the detected pole-like objects is identified and the points from the original point cloud belonging to each pole-like object are extracted. The algorithm can be used with data from any Mobile Laser Scanning system, as it transforms the original point cloud and fits it into a regular grid, thus avoiding irregularities produced due to point density differences within the point cloud. The algorithm was tested in four test sites with different slopes and street shapes and features. All the target pole-like objects were detected, with the only exception of those severely occluded by large objects and some others which were either attached or too close to certain features.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59451
Title: Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests.
Author: Zakariyyaa Qumar, Onisimo Mutanga.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 39-46 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Thaumastocoris peregrinus, Environmental variables, Vegetation indices, WorldView-2 imagery, Plantation health monitoring.
Abstract: This study integrated environmental variables together with high spectral resolution WorldView-2 imagery to detect and map Thaumastocoris peregrinus damage in Eucalypt plantation forests in KwaZulu-Natal, South Africa. The WorldView-2 bands, vegetation indices and environmental variables were entered separately into PLS regression models to predict T. peregrinus damage. The datasets were then integrated to test the collective strength in predicting T. peregrinus damage. Important variables were identified by variable importance (VIP) scores and were re-entered into a PLS regression model. The VIP model was then extrapolated to map the severity of damage and predicted T. peregrinus damage with an R2 value of 0.71 and a RMSE of 3.26% on an independent test dataset. The red edge and near-infrared bands of the WorldView-2 sensor together with the temperature dataset were identified as important variables in predicting T. peregrinus damage. The results indicate the potential of integrating WorldView-2 data and environmental variables to improve the mapping and monitoring of insect outbreaks in plantation forests. The result is critical for plantation health monitoring using a new sensor which contains important vegetation wavelengths.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59450
Title: Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis.
Author: Tao Cheng, Benoit Rivard, Arturo G. S?nchez-Azofeifa, Jean-Baptiste F?ret, St?phane Jacquemoud, Susan L. Ustin.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 28-38 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Leaf mass per area; Dry matter content; Specific leaf area; PROSPECT model; Remote sensing; Wavelet analysis.
Abstract: Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices,namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (NDLMA). Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R2: 0.51-0.82, p < 0.0001). The best robustness (R2 = 0.74, RMSE = 18.97 g/m2 and Bias = 0.12 g/m2) was obtained using a combination of two low-scale features (1639 nm, scale 4) and (2133 nm, scale 5), the first being predominantly important. The transferability of the wavelet-based predictive model to the whole measured database was either better than or comparable to those based on spectral indices. Additionally, only the wavelet-based model showed consistent predictive capabilities among the three measured data sets. In comparison, the models based on spectral indices were sensitive to site-specific data sets. Integrating the NDLMA spectral index and the two robust wavelet features improved the LMA prediction. One of the bands used by this spectral index, 1368 nm, was located in a strong atmospheric water absorption region and replacing it with the next available band (1340 nm) led to lower predictive accuracies. However, the two wavelet features were not affected by data quality in the atmospheric absorption regions and therefore showed potential for canopy-level investigations. The wavelet approach provides a different perspective into spectral responses to LMA variation than the traditional spectral indices and holds greater promise for implementation with airborne or spaceborne imaging spectroscopy data for mapping canopy foliar dry biomass.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None


ID: 59449
Title: Assessment of the image misregistration effects on object-based change detection
Author: Gang Chen, Kaiguang Zhao, Ryan Powers
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 19-27 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Change detection; Object-based; Pixel-based; Misregistration; High-spatial resolution; Accuracy assessment.
Abstract: High-spatial resolution remote sensing imagery provides unique opportunities for detailed characterization and monitoring of landscape dynamics. To better handle such data sets, change detection using the object-based paradigm, i.e., object-based change detection (OBCD), have demonstrated improved performances over the classic pixel-based paradigm. However, image registration remains a critical pre-process, with new challenges arising, because objects in OBCD are of various sizes and shapes. In this study, we quantified the effects of misregistration on OBCD using high-spatial resolution SPOT 5 imagery (5 m) for three types of landscapes dominated by urban, suburban and rural features, representing diverse geographic objects. The experiments were conducted in four steps: (i) Images were purposely shifted to simulate the misregistration effect. (ii) Image differencing change detection was employed to generate difference images with all the image-objects projected to a feature space consisting of both spectral and texture variables. (iii) The changes were extracted using the Mahalanobis distance and a change ratio. (iv) The results were compared to the ' real ' changes from the image pairs that contained no purposely introduced registration error. A pixel-based change detection method using similar steps was also developed for comparisons. Results indicate that misregistration had a relatively low impact on object size and shape for most areas. When the landscape is comprised of small mean object sizes (e.g., in urban and suburban areas), the mean size of ' change ' objects was smaller than the mean of all objects and their size discrepancy became larger with the decrease in object size. Compared to the results using the pixel-based paradigm, OBCD was less sensitive to the misregistration effect, and the sensitivity further decreased with an increase in local mean object size. However, high-spatial resolution images typically have higher spectral variability within neighboring pixels than the relatively low resolution datasets. As a result, accurate image registration remains crucial to change detection even if an object-based approach is used.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None