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Mapping InvasiveLupinus polyphyllusLindl. in Semi-natural Grasslands Using object-based image analysis of UAV-borne images

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PFG-JOURNAL OF PHOTOGRAMMETRY REMOTE SENSING AND GEOINFORMATION SCIENCE 2020年第5期88卷 391-406页

作者： Wijesingha, Jayan Astor, Thomas Schulze-Brueninghoff, Damian Wachendorf, Michael Univ Kassel Grassland Sci & Renewable Plant Resources Steinstr 19 D-37213 Witzenhausen Germany

Knowledge on the spatio-temporal distribution of invasive plant species is vital to maintain biodiversity in grasslands which are threatened by the invasion of such plants and to evaluate the effect of control activities conducted. Manual digitising of aerial images with field verification is the standard method to create maps of the invasiveLupinus polyphyllusLindl. (Lupine) in semi-natural grasslands of the UNESCO biosphere reserve "Rhon". As the standard method is labour-intensive, a workflow was developed to map lupine coverage using an unmanned aerial vehicle (UAV)-borne remote sensing (RS) along with object-based image analysis (OBIA). UAV-borne red, green, blue and thermal imaging, as well as photogrammetric canopy height modelling (CHM) were applied. images were segmented by unsupervised parameter optimisation into image objects representing lupine plants and grass vegetation. image objects obtained were classified using random forest classification modelling based on objects' attributes. The classification model was employed to create lupine distribution maps of test areas, and predicted data were compared with manually digitised lupine coverage maps. The classification models yielded a mean prediction accuracy of 89%. The maximum difference in lupine area between classified and digitised lupine maps was 5%. Moreover, the pixel-wise map comparison showed that 88% of all pixels matched between classified and digitised maps. Our results indicated that lupine coverage mapping using UAV-borne RS data and OBIA provides similar results as the standard manual digitising method and, thus, offers a valuable tool to map invasive lupine on grasslands.

关键词： Invasive plant species Lupinus polyphylluslindl Unmanned aerial vehicles object-based image analysis Spatial coverage mapping Grassland

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High-Resolution Mapping of Forest Carbon Stock Using object-based image analysis (OBIA) Technique

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2020年第6期48卷 865-875页

作者： Pandey, Sanjay Kumar Chand, Narendra Nandy, Subrata Muminov, Abulqosim Sharma, Anchit Ghosh, Surajit Srinet, Ritika DAV Postgrad Coll Dept Bot Gorakhpur 273001 Uttar Pradesh India Nepal Foresters Assoc Kathmandu 44600 Nepal Govt India Indian Space Res Org Indian Inst Remote Sensing Dept Space Dehra Dun 248001 Uttarakhand India Agcy Hydrometeorol Dept Agrometeorol Dushanbe 734 Tajikistan PT Riau Andalan Pulp & Paper Pangkalan Kerinci 28381 Indonesia Int Water Management Inst Colombo Sri Lanka

This study assessed and mapped the aboveground tree carbon stock using very high-resolution satellite imagery (VHRS)-WorldView-2 in Barkot forest of Uttarakhand, India. The image was pan-sharpened to get the spectrally and spatially good-quality image. High-pass filter technique of pan-sharpening was found to be the best in this study. object-based image analysis (OBIA) was carried out for image segmentation and classification. Multi-resolution image segmentation yielded 74% accuracy. The segmented image was classified into sal (Shorea robusta), teak (Tectona grandis) and shadow. The classification accuracy was found to be 83%. The relationship between crown projection area (CPA) and carbon was established in the field for both sal and teak trees. Using the relationship between CPA and carbon, the classified CPA map was converted to carbon stock of individual trees. Mean value of carbon stock per tree for sal was found to be 621 kg, whereas for teak it was 703 kg per tree. The study highlighted the utility of OBIA and VHRS imagery for mapping high-resolution carbon stock of forest.

关键词： WorldView-2 Segmentation object-based image analysis Crown projection area Carbon stock

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Offshore benthic habitat mapping based on object-based image analysis and geomorphometric approach. A case study from the Slupsk Bank, Southern Baltic Sea

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 801卷 149712-149712页

作者： Janowski, Lukasz Wroblewski, Radoslaw Dworniczak, Janusz Kolakowski, Mateusz Rogowska, Karolina Wojcik, Michal Gajewski, Juliusz Gdynia Maritime Univ Maritime Inst Dlugi Targ 41-42 PL-80830 Gdansk Poland Univ Gdansk Inst Geog Bazynskiego 4 PL-480309 Gdansk Poland MEWO SA Starogardzka 16 PL-83010 Straszyn Poland Gdansk Univ Technol Fac Elect Telecommun & Informat Dept Comp Architecture Gabriela Narutowicza 11-12 PL-80233 Gdansk Poland

Benthic habitat mapping is a rapidly growing field of underwater remote sensing studies. This study provides the first insight for high-resolution hydroacoustic surveys in the Slupsk Bank Natura 2000 site, one of the most valuable sites in the Polish Exclusive Zone of the Southern Baltic. This study developed a quick and transparent, automatic classification workflow based on multibeam echosounder and side-scan sonar surveys to classify benthic habitats in eight study sites within the Slupsk Bank. Different predictor variables, four supervised classifiers, and the generalisation approach, improving the accuracy of the developed model were evaluated. The results suggested a very high significance for the classification performance of specific geomorphometric features that were not used in benthic habitat mapping before. These include, e.g., Fuzzy Landform Element Classification, Multiresolution Index of the Valley Bottom Flatness, and Multiresolution Index of the Ridge Top Flatness. Comparison of classification results with manual maps demonstrated that Random Forest had the highest performance of four tested supervised classifiers. Because the current needs include benthic habitat mapping for the whole area of the Polish Exclusive Economic Zone, the key findings of this study may be further applied to extensive areas in the Polish waters and other vast areas worldwide. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// ***/licenses/by/4.0/).

关键词： Benthic habitat mapping Multibeam echosounder Side-scan sonar object-based image analysis Feature selection Geomorphometry

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Advances of Local Climate Zone Mapping and Its Practice Using object-based image analysis

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ATMOSPHERE 2021年第9期12卷 1146页

作者： Ma, Lei Zhu, Xiaoxiang Qiu, Chunping Blaschke, Thomas Li, Manchun Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing Applicat Minist Nat ResourcesSch Geog & Ocean Sci Nanjing 210023 Peoples R China Tech Univ Munich TUM Signal Proc Earth Observat D-80333 Munich Germany German Aerosp Ctr DLR Remote Sensing Technol Inst D-82234 Wessling Germany Univ Salzburg Dept Geoinformat Z GIS A-5020 Salzburg Austria

In the context of climate change and urban heat islands, the concept of local climate zones (LCZ) aims for consistent and comparable mapping of urban surface structure and cover across cities. This study provides a timely survey of remote sensing-based applications of LCZ mapping considering the recent increase in publications. We analyze and evaluate several aspects that affect the performance of LCZ mapping, including mapping units/scale, transferability, sample dataset, low accuracy, and classification schemes. Since current LCZ analysis and mapping are based on per-pixel approaches, this study implements an object-based image analysis (OBIA) method and tests it for two cities in Germany using Sentinel 2 data. A comparison with a per-pixel method yields promising results. This study shall serve as a blueprint for future object-based remotely sensed LCZ mapping approaches.

关键词： local climate zones remote sensing mapping unit transferability object-based image analysis

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Landslide mapping using object-based image analysis and open source tools

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ENGINEERING GEOLOGY 2021年 282卷 106000-106000页

作者： Amatya, Pukar Kirschbaum, Dalia Stanley, Thomas Tanyas, Hakan Univ Space Res Assoc Columbia MD 21046 USA Goddard Earth Sci Technol & Res Columbia MD USA NASA Goddard Space Flight Ctr Hydrol Sci Lab Greenbelt MD USA Univ Twente Fac Geoinformat Sci & Earth Observat ITC Enschede Netherlands

Availability of high-resolution optical imagery and advances in image processing technologies have significantly improved our ability to map landslides. In recent years object-based image analysis (OBIA) has been gaining in popularity for landslide mapping due to its ability to incorporate spectral, textural, morphological and topographical properties. Many studies have been conducted based on commercial software. In this study, we create an open source Semi-Automatic Landslide Detection (SALaD) system utilizing OBIA and machine learning. Configured to run in Linux environment, it uses various open source Python packages and modules. This system was tested in 575 km(2) area along the Pasang Lhamu Highway, Nepal where large numbers of landslides were triggered by the 2015 Gorkha earthquake. Comparison with a manual inventory highlighted that this system was able to detect 70% of the landslide area. The speed and efficiency with which this system was able to detect landslides makes it a viable alternative to manual techniques for landslide mapping over large areas, when establishing approximate landslide locations is of prime importance.

关键词： Open source object-based image analysis Landslides RapidEye Python Nepal

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An open-source approach to characterizing Chihuahuan Desert vegetation communities using object-based image analysis

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JOURNAL OF ARID ENVIRONMENTS 2021年 188卷 104383-104383页

作者： Bankert, Andrew R. Strasser, Erin H. Burch, Cristy G. Correll, Maureen D. Bird Conservancy Rockies Ft Collins CO 80521 USA Texas Pk & Wildlife Medard TX USA

Methods: for quantifying vegetative cover across landscapes have, until recently, been limited to ground-based surveys or remote sensing via satellites or aircraft, both of which can limit the spatial scale of resulting data. Unmanned Aircraft Systems (UAS) can efficiently collect high-resolution sub-decimeter imagery of landscapes;geographic, object-based image analysis (GEOBIA) of the collected imagery can then be used to estimate vegetation cover. To date, few researchers have utilized open-source programs for GEOBIA. We developed GEOBIA methods in the open-source Program R to analyze visible spectrum UAS imagery from four sites in the Chihuahuan Desert of North America. These desert grasslands are difficult to quantify due to the patchiness of ground cover at small scales (e.g. <1 m) and the rarity of shrubs on the landscape. We used site-specific training data and multiple segmentation parameters to create vegetative and shrub cover data layers at a 15 cm resolution. We report overall accuracies of 77.2%-88.8% for vegetation classification and 95.7%-99.2% for shrub classification. Our work is some of the first to use open-source GEOBIA in grasslands and provides objective, reproducible data layers of desert vegetation, particularly shrubs, at the spatial scale necessary to inform management and conservation of Chihuahuan Desert grassland communities.

关键词： object-based image analysis Vegetation mapping Unmanned aerial systems Grassland vegetation Automated image analysis Habitat quantification

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AN object based image analysis OF MULTISPECTRAL SATELLITE AND DRONE imageS FOR PRECISION AGRICULTURE MONITORING

AN OBJECT BASED IMAGE ANALYSIS OF MULTISPECTRAL SATELLITE AN...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Dwivedi, Arun Kant Singh, Arun Kumar Singh, Dharmendra Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee Uttar Pradesh India Indian Inst Technol Roorkee Dept Elect & Commun Engn Roorkee Uttar Pradesh India

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Accurate information on spatial distribution of crop and vegetation indices for crop health monitoring is important for precision agriculture monitoring. However, freely available multispectral satellite images and unmanned aerial vehicle based multispectral images provides great opportunities for crop area estimation and extraction of vegetation indices. An object-based image analysis is better than pixel-based analysis because it is used statistical, geometrical, and topographic feature of the objects. Therefore, this paper presents an object-based image analysis of multispectral satellite and drone images for crop area estimation and extraction of vegetation indices for precision agriculture monitoring. object segmentation, feature extraction, and classification of multispectral satellite and drone images was done. The experimental results show that the high-resolution drone imagery provides better crop area estimation and vegetation indices compared to freely available coarse resolution satellite imagery due to mixed pixels especially boundary of the crop classes.

关键词： Precision agriculture monitoring area estimation object-based image analysis mixed pixels

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Integrated object-based image analysis for semi-automated geological lineament detection in southwest England

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COMPUTERS & GEOSCIENCES 2019年 123卷 137-148页

作者： Yeomans, Christopher M. Middleton, Maarit Shail, Robin K. Grebby, Stephen Lusty, Paul A. J. Univ Exeter CEMPS Camborne Sch Mines Penryn Campus Penryn TR10 9FE Cornwall England British Geol Survey Ctr Environm Sci Keyworth NG12 5GG Notts England Geol Survey Finland POB 77 FI-96101 Rovaniemi Finland Univ Nottingham Nottingham Geospatial Inst Innovat Pk Nottingham NG7 2TU England

Regional lineament detection for mapping of geological structure can provide crucial information for mineral exploration. Manual methods of lineament detection are time consuming, subjective and unreliable. The use of semi-automated methods reduces the subjectivity through applying a standardised method of searching. object based image analysis (OBIA) has become a mainstream technique for landcover classification, however, the use of OBIA methods for lineament detection is still relatively under-utilised. The Southwest England region is covered by high-resolution airborne geophysics and LiDAR data that provide an excellent opportunity to demonstrate the power of OBIA methods for lineament detection. Herein, two complementary but stand-alone OBIA methods for lineament detection are presented which both enable semi-automatic regional lineament mapping. Furthermore, these methods have been developed to integrate multiple datasets to create a composite lineament network. The top-down method uses threshold segmentation and sub-levels to create objects, whereas the bottom-up method segments the whole image before merging objects and refining these through a border assessment. Overall lineament lengths are longest when using the top-down method which also provides detailed metadata on the source dataset of the lineament. The bottom-up method is more objective and computationally efficient and only requires user knowledge to classify lineaments into major and minor groups. Both OBIA methods create a similar network of lineaments indicating that semi-automatic techniques are robust and consistent. The integration of multiple datasets from different types of spatial data to create a comprehensive, composite lineament network is an important development and demonstrates the suitability of OBIA methods for enhancing lineament detection.

关键词： object-based image analysis Airborne geophysics Lineament detection Semi-automated Southwest England

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Geographic object-based image analysis: A Primer and Future Directions

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REMOTE SENSING 2020年第12期12卷 2012-2012页

作者： Kucharczyk, Maja Hay, Geoffrey J. Ghaffarian, Salar Hugenholtz, Chris H. Univ Calgary Dept Geog Calgary AB T2N 1N4 Canada

Geographic object-based image analysis (GEOBIA) is a remote sensing image analysis paradigm that defines and examines image-objects: groups of neighboring pixels that represent real-world geographic objects. Recent reviews have examined methodological considerations and highlighted how GEOBIA improves upon the 30+ year pixel-based approach, particularly for H-resolution imagery. However, the literature also exposes an opportunity to improve guidance on the application of GEOBIA for novice practitioners. In this paper, we describe the theoretical foundations of GEOBIA and provide a comprehensive overview of the methodological workflow, including: (i) software-specific approaches (open-source and commercial);(ii) best practices informed by research;and (iii) the current status of methodological research. Building on this foundation, we then review recent research on the convergence of GEOBIA with deep convolutional neural networks, which we suggest is a new form of GEOBIA. Specifically, we discuss general integrative approaches and offer recommendations for future research. Overall, this paper describes the past, present, and anticipated future of GEOBIA in a novice-accessible format, while providing innovation and depth to experienced practitioners.

关键词： geographic object-based image analysis GEOBIA object-based image analysis OBIA machine learning deep learning convolutional neural network CNN GEOCNN

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Validating the use of object-based image analysis to map commonly recognized landform features in the United States

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CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE 2019年第5期46卷 441-455页

作者： Arundel, Samantha T. Sinha, Gaurav US Geol Survey Ctr Excellence Geospatial Informat Sci Rolla MO 65401 USA Ohio Univ Dept Geog Athens OH 45701 USA

The U.S. Geological Survey (USGS) National Geospatial Program (NGP) seeks to i) create semantically accessible terrain features from the pixel-based 3D Elevation Program (3DEP) data, and ii) enhance the usability of the USGS Geographic Names Information System (GNIS) by associating boundaries with GNIS features whose spatial representation is currently limited to 2D point locations. Geographic object-based image analysis (GEOBIA) was determined to be a promising method to approach both goals. An existing GEOBIA workflow was modified and the resulting segmented objects and terrain categories tested for a strategically chosen physiographic province in the mid-western US, the Ozark Plateaus. The chi-squared test of independence confirmed that there is significant overall spatial association between terrain categories of the GEOBIA and GNIS feature classes. Contingency table analysis also suggests strong category-specific associations between select GNIS and GEOBIA classes. However, 3D visual analysis revealed that GEOBIA objects resembled segmented regions more than they did individual landform objects, with their boundaries often failing to correspond to match what people would likely perceive as landforms. Still, objects derived through GEOBIA can provide initial baseline landscape divisions that can improve the efficiency of more specialized feature extraction methods.

关键词： GNIS object-based image analysis GEOBIA landform terrain feature automated mapping

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