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Framework for susceptibility analysis of layered rock slopes considering the dimensions of the mapping units and geological data resolution at various map scales

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ENGINEERING GEOLOGY 2018年 246卷 310-325页

作者： Lin, Cheng-Han Lin, Ming-Lang Peng, Hou-Ren Lin, Hsi-Hung Natl Cheng Kung Univ Dept Civil Engn Tainan Taiwan Natl Taiwan Univ Dept Civil Engn Taipei Taiwan Cent Geol Survey MOEA Environm & Engn Geol Div Taipei Taiwan

In Taiwan, the failure of rock slopes has caused the catastrophic loss of lives and property. To understand the risks associated with rock slope failure, susceptibility analysis has been widely adopted as a fundamental task. However, currently, most susceptibility analyses have investigated only landslide occurrences and have been limited to the regional scale. In this case, susceptibility maps cannot provide detailed information about the rock slope stability as required by the engineers. To fill this gap, this study developed a methodological framework to analyze the susceptibility of layered rock slopes from regional to local scales. In each map scale, the appropriate size of the slope unit (SU) was produced as the mapping unit through the object-based image analysis (OBIA) method, and the susceptibility map was generated with an explicit purpose according to the resolution of geological data. Multiscale susceptibility analysis of layered rock slopes was performed in a metamorphic area in northern Taiwan. The performance of the susceptibility maps was validated by the field investigations and the data of the geologically sensitive area, which were released by the Central Geological Survey of Taiwan. The results show that different phases of the evaluation of rock slope stability were addressed, including the landslide activity and the rock slope characterization on the regional scale and the failure mechanisms on the local scale. In addition, suitable SUs can also be semiautomatically reproduced by the OBIA method with suggested strategies.

关键词： Layered rock slope Multiscale susceptibility analysis Slope unit Geological data resolution object-based image analysis

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Integration of historical map and aerial imagery to characterize long-term land-use change and landscape dynamics: An object-based analysis via Random Forests

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ECOLOGICAL INDICATORS 2018年第Part1期95卷 595-605页

作者： Liu, Dan Toman, Elizabeth Fuller, Zane Chen, Gang Londo, Alexis Zhang, Xuesong Zhao, Kaiguang Ohio State Univ Sch Environm & Nat Resources Environm Sci Grad Program Columbus OH 43210 USA Univ N Carolina Dept Geog & Earth Sci Lab Remote Sensing & Environm Change Charlotte NC 28223 USA Univ Maryland Pacific Northwest Natl Lab Joint Global Change Res Inst College Pk MD 20740 USA Univ Maryland Earth Syst Sci Interdisciplinary Ctr College Pk MD 20740 USA Ohio State Univ Sch Environm & Nat Resources Ohio Agr Res & Dev Ctr Wooster OH 44691 USA

Tracking Earth's past helps us to move from hindsight to foresight in seeking landscape sustainability, a pursuit aided by modern mapping capabilities but hindered by a dearth of historical landscape information. To fill the data gap and exemplify the use of old maps for land-use change sciences, we combined an old paper-based US civil war map and modem aerial photos to derive land-use history and landscape dynamics at fine scales for a region near Chancellorsville, USA, from 1867 to 2014. We also tested how advanced algorithms object-based image analysis and Random Forests (RF)-could aid in data processing. Automatic classification of the scanned 1867 paper map proved difficult, but its manual digitization could benefit from object-based image segmentation. Classifying digital aerial images was more accurate via the object-based than pixel-based method, but only if the images were segmented appropriately. In the object-based classification, spectral-based features were much more important and useful than shape/geometry features for land-cover discrimination, as ranked by RF. During the 147 years, 32% of the region changed in land type. Settlement and roads increased in extent by 1850% and 691%, respectively, and woodland decreased by 19%. These changes fragmented the landscape, altered the hydrological regime, and affected river morphology. The utility of old maps exemplified here provides an impetus for leveraging extant old maps or historical records to support land-use and global change research. Our study also connotes the importance of preserving and geotagging current non-traditional data, such as photos, videos, and citizen science data, that can serve as a baseline to document future landscape change.

关键词： Historical map Ancient map Military map Landscape ecology Landscape change Environmental management object-based image analysis Segmentation Random Forests Land-use change

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Modeling the impact of large-scale transportation infrastructure development on land cover

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2018年第1期10卷 26-42页

作者： Efthymiou, Dimitrios Antoniou, Constantinos Siora, Emmanouela Argialas, Demetre Natl Tech Univ Athens Sch Rural & Surveying Engn Zografos Greece

Transportation infrastructure and urban development are tightly connected and interact with each other. The objective of this paper is to measure the impact of large-scale transportation infrastructure locations on land cover. Spatial econometric models that link the impact of transportation infrastructure onto the urban evolution have been used with panel data (multiple time points). Land cover information was derived individually from two Landsat TM images (1984 and 2010) by applying a semi-automatic object-based image analysis (OBIA) approach, resulting in six generalized thematic categories. Data from multiple databases have been collected and cross-referenced. The study area is the Athens metropolitan area, Greece, where, during the last few decades, crucial transportation infrastructure has been developed and significant urbanization has been observed. Moreover, a fixed effects model has been developed for a study area, providing additional information for the differences across municipalities. The results of this research provide evidence toward the quantification of the impact of major road transportation infrastructure in urban development and can have applications in other areas that have not been yet fully developed.

关键词： Spatial econometric models object-based image analysis transportation infrastructure Athens

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Estimativa de biomassa e estoque de carbono de vegetação macrofítica a partir de dados espectrais e de campo, Banhado Grande, RS

Estimativa de biomassa e estoque de carbono de vegetação m...

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作者： Belloli, Tássia Fraga

Given the climate change scenario, the wetlands have stood out for acting substantially as atmospheric CO2 sinks from their fixation on plant biomass and peat bogs. However, the degradation of these ecosystems causes wetlands to move from CO2-fixing ecosystems to a source of greenhouse gases. Thus, quantifying and monitoring biomass is of great importance for preserving carbon stocks, and accurate carbon estimates can contribute to the preservation, loss prevention and restoration of these ecosystems. This study aimed to estimate plant biomass and organic carbon stocks of Scirpus giganteus species in Banhado Grande, from spectral data and field data. For this purpose, two methods were used: i) delimitation of the species area by Geographic object based image analysis (GEOBIA) and Ramdon Forest data mining, integrating Sentinel 1 and 2A sensor images; and ii) correlation and linear regression analysis from field and spectral data from the PlanetScope and Sentinel-2A sensors. Vegetation collections were performed in an experimental area in Banhado Grande, municipality of Glorinha-RS, during an annual cycle. In GEOBIA, the Random Forest (RF) data mining method was used to classify vegetation species in Banhado Grande, aiming to delimit the area occupied by the species *** and to estimate the stocks of biophysical variables for their total coverage area. The regression equations involved as dependent variables (y): oboveground biomass and organic carbon, obtained directly in the samples, and as independent variables (x) the spectral bands and the vegetation indices (VIs). The statistical treatment involved the analysis of the correlation matrix (r) between the variables x and y; the simple and multiple linear regression analysis, with the following statistics: R², R²aj., RMSE, CV%, SD and residual analysis. As a result, the GEOBIA classification reached an accuracy of 91.3% and 91% for the Emerging class, corresponding to the *** area (1,507 ha). Consider

关键词： Sensoriamento remoto Aboveground biomass blue carbon Áreas úmidas Biomassa remote sensing wetlands marshes Banhados object-based image analysis Carbono azul regression models Dissertação

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Generic rule-sets for automated detection of urban tree species from very high-resolution satellite data

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GEOCARTO INTERNATIONAL 2018年第4期33卷 357-374页

作者： Shojanoori, Razieh Shafri, Helmi Z. M. Mansor, Shattri Ismail, Mohd Hasmadi Univ Putra Malaysia Dept Civil Engn Fac Engn Serdang Malaysia Univ Putra Malaysia Geospatial Informat Sci Res Ctr Fac Engn Serdang Malaysia Univ Putra Malaysia Dept Forest Prod Fac Forestry Serdang Malaysia

The sustainable management and monitoring of urban forests is an important activity in the urbanized world, and operational approaches require information about the status of urban trees to determine the best strategy. One limitation in urban forest studies is the detection and discrimination of tree species using limited training data. Thus, this study focuses on developing generic rule sets from high-resolution WorldView-2 imagery in conjunction with spectral, spatial, colour and textural information for automated urban tree species detection. The object-based image analysis and its combination with statistical analysis of object features is utilized for this purpose. Results of attribute selection indicated that from 55 attributes, only 26 were useful to discriminate urban tree species, namely Messua ferrea L., Samanea saman and Casuarina sumatrana. Finally, the high overall accuracy, approximately 86.87% with kappa of 0.75 confirmed the transferability of the generic model.

关键词： WorldView-2 object-based image analysis CfsSubsetEval urban tree species

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Assessing the potential of multi-seasonal high resolution Pleiades satellite imagery for mapping urban tree species

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2018年 71卷 144-158页

作者： Pu, Ruiliang Landry, Shawn Yu, Qiuyan Univ S Florida Sch Geosci 4202 E Fowler AveNES 107 Tampa FL 33620 USA

This study evaluated the potential of five seasonal high resolution Pleiades satellite images for improving urban tree species classification in the City of Tampa, FL, USA. We assessed and compared the capabilities of individual and combined Pleiades images acquired during different seasons for classifying the urban tree species to understand the seasonal effect on tree species mapping accuracy. The seven species and groups included sand live oak (Quercus geminata), laurel oak (Q. laurifolia), live oak (Q. virginiana), pine (species group), palm (species group), camphor (Cinnamomum camphora), and magnolia (Magnolia grandifiora). A multi-level classification system was adopted to classify image objects of the tree species and groups. Species classification performance was compared between the five individual seasonal Pleiades images, between two combined dry-wet season images, and between the optimal single season and combined dry-wet season image data. Shade image objects were spectrally normalized to similar sunlit image objects. The tree species fraction features were extracted from the seasonal images using the Mixture Tuned Matching Filtering approach and used as additional features to a set of spectral and spatial/textural features. Random Forest, Support Vector Machine and Linear Discriminant analysis classifiers were used to classify the seven tree species and groups with image objects features. The experimental results indicate significantly improved tree species mapping accuracies using late spring season (April) image compared to all other seasonal images (p < 0.01), and combined dry-wet season images performed even better. Results suggest a significant seasonal effect on tree species classification. The results also demonstrate that the Random Forest outperformed the Support Vector Machine and Linear Discriminant analysis classifiers for tree species classification. Therefore, in practice, it is important to choose appropriate seasonal remote sensing data

关键词： Urban forest Pleiades imagery Shade spectral normalization object-based image analysis Multi-level classification system Random forest Support vector machine Linear discriminant analysis

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Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility analysis along the Karnali Highway, Nepal

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REMOTE SENSING 2019年第19期11卷 2284-2284页

作者： Amatya, Pukar Kirschbaum, Dalia Stanley, Thomas Univ Space Res Assoc Goddard Earth Sci Technol & Res Columbia MD 21046 USA NASA Hydrol Sci Lab Goddard Space Flight Ctr Greenbelt MD 20771 USA

The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 m(2) were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500-1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.

关键词： Karnali highway landslides very high-resolution optical imagery object-based image analysis DigitalGlobe PlanetScope landslide susceptibility logistic regression eCognition

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Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations

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REMOTE SENSING 2019年第12期11卷 1409-1409页

作者： Maxwell, Aaron E. Strager, Michael P. Warner, Timothy A. Ramezan, Christopher A. Morgan, Alice N. Pauley, Cameron E. West Virginia Univ Dept Geol & Geog Morgantown WV 26506 USA West Virginia Univ Davis Coll Agr Nat Resources & Design Morgantown WV 26506 USA

Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km(2). We obtained an overall accuracy of 96.7% and a Kappa statistic of 0.886 using a combination of NAIP orthophotography and ancillary data. Despite the high overall classification accuracy, some classes were difficult to differentiate, as highlight by the low user's and producer's accuracies for the barren, impervious, and mixed developed classes. In contrast, forest, low vegetation, and water were generally mapped with accuracy. The inclusion of ancillary data and first- and second-order textural measures generally improved classification accuracy whereas band indices and object geometric measures were less valuable. Including super-object attributes improved the classification slightly;however, this increased the computational time and complexity. From the findings of this research and previous studies, recommendations are provided for mapping large spatial extents.

关键词： land cover land cover mapping object-based image analysis GEOBIA machine learning random forests National Agriculture imagery Program NAIP

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Explorative Study on Mapping Surface Facies of Selected Glaciers from Chandra Basin, Himalaya Using WorldView-2 Data

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REMOTE SENSING 2019年第10期11卷 1207-1207页

作者： Jawak, Shridhar D. Wankhede, Sagar F. Luis, Alvarinho J. Govt India Minist Earth Sci ESSO NCPOR Vasco Da Gama 403804 Goa India Svalbard Sci Ctr SIOS Knowledge Ctr Svalbard Integrated Arctic Earth Observing Syst S POB 156 N-9171 Longyearbyen Svalbard Norway Mangalore Univ Dept Marine Geol Mangalagangothri 574199 Karnataka India

Mapping of surface glacier facies has been a part of several glaciological applications. The study of glacier facies in the Himalayas has gained momentum in the last decade owing to the implications imposed by these facies on the melt characteristics of the glaciers. Some of the most commonly reported surface facies in the Himalayas are snow, ice, ice mixed debris, and debris. The precision of the techniques used to extract glacier facies is of high importance, as the result of many cryospheric studies and economic reforms rely on it. An assessment of a customized semi-automated protocol against conventional and advanced mapping algorithms for mapping glacier surface facies is presented in this study. Customized spectral index ratios (SIRs) are developed for effective extraction of surface facies using thresholding in an object-based environment. This method was then tested on conventional and advanced classification algorithms for an evaluation of the mapping accuracy for five glaciers located in the Himalayas, using very high-resolution WorldView-2 imagery. The results indicate that the object-based image analysis (OBIA) based semi-automated SIR approach achieved a higher average overall accuracy of 87.33% (kappa = 0.85) than the pixel-based image analysis (PBIA) approach. Among the conventional methods, the Maximum Likelihood performed the best, with an overall accuracy of 78.71% (kappa = 0.75). The Constrained Energy Minimization, with an overall accuracy of 68.76% (kappa = 0.63), was the best performer of the advanced algorithms. The advanced methods greatly underperformed in this study. The proposed SIRs show a promise in the mapping of minor features such as crevasses and in the discrimination between ice-mixed debris and debris. We have efficiently mapped surface glacier facies independently of short-wave infrared bands (SWIR). There is a scope for the transferability of the proposed SIRs and their performance in varying scenarios.

关键词： glacier facies object-based image analysis pixel-based image analysis WorldView-2 Himalaya

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A Novel object-based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks

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REMOTE SENSING 2019年第6期11卷 684-684页

作者： Papadomanolaki, Maria Vakalopoulou, Maria Karantzalos, Konstantinos Natl Tech Univ Athens Remote Sensing Lab Zografos 15780 Greece Univ Paris Saclay INRIA Cent Supelec Ctr Vis Numer F-91190 Gif Sur Yvette France

Deep learning architectures have received much attention in recent years demonstrating state-of-the-art performance in several segmentation, classification and other computer vision tasks. Most of these deep networks are based on either convolutional or fully convolutional architectures. In this paper, we propose a novel object-based deep-learning framework for semantic segmentation in very high-resolution satellite data. In particular, we exploit object-based priors integrated into a fully convolutional neural network by incorporating an anisotropic diffusion data preprocessing step and an additional loss term during the training process. Under this constrained framework, the goal is to enforce pixels that belong to the same object to be classified at the same semantic category. We compared thoroughly the novel object-based framework with the currently dominating convolutional and fully convolutional deep networks. In particular, numerous experiments were conducted on the publicly available ISPRS WGII/4 benchmark datasets, namely Vaihingen and Potsdam, for validation and inter-comparison based on a variety of metrics. Quantitatively, experimental results indicate that, overall, the proposed object-based framework slightly outperformed the current state-of-the-art fully convolutional networks by more than 1% in terms of overall accuracy, while intersection over union results are improved for all semantic categories. Qualitatively, man-made classes with more strict geometry such as buildings were the ones that benefit most from our method, especially along object boundaries, highlighting the great potential of the developed approach.

关键词： earth observation satellite data machine learning convolutional neural networks GEOBIA object-based image analysis superpixels anisotropic diffusion

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