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Cover Estimations Using object-based image analysis Rule Sets Developed Across Multiple Scales in Pinyon-Juniper Woodlands

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RANGELAND ECOLOGY & MANAGEMENT 2014年第3期67卷 318-327页

作者： Hulet, April Roundy, Bruce A. Petersen, Steven L. Jensen, Ryan R. Bunting, Stephen C. USDA ARS Eastern Oregon Agr Res Ctr Burns OR 97720 USA Brigham Young Univ Dept Plant & Wildlife Sci Provo UT 84602 USA Brigham Young Univ Dept Geog Provo UT 84602 USA Univ Idaho Dept Rangeland Ecol & Management Moscow ID 83844 USA

Numerous studies have been conducted that evaluate the utility of remote sensing for monitoring and assessing vegetation and ground cover to support land management decisions and complement ground measurements. However, few comparisons have been made that evaluate the utility of object-based image analysis (OBIA) to accurately classify a landscape where rule sets (models) have been developed at various scales. In this study, OBIA rule sets used to estimate land cover from high spatial resolution imagery (0.06-m pixel) on Pinus L. (pinyon) and Juniperus L. (juniper) woodlands were developed using eCognition Developer at four scales with varying grains-1) individual plot, 2) individual sites, 3) regions (western juniper vs. Utah juniper sites), and 4) pinyon-juniper woodland network (all plots) that were within the same study extent. Color-infrared imagery was acquired over five sites in Oregon, California, Nevada, and Utah with a Vexcel UltraCamX digital camera in June 2009. Ground cover measurements were also collected at study sites in 2009 on 80 0.1-ha plots. Correlations between OBIA and ground measurements were relatively high for individual plot and site rule sets (ranging from r=0.52 to r=0.98). Correlations for regional and network rule sets were lower (ranging from r=0.24 to r=0.63), which was expected due to radiance differences between the images as well as vegetation differences found at each site. All site and plot OBIA average cover percentage estimates for live trees, shrubs, perennial herbaceous vegetation, litter, and bare ground were within 5% of the ground measurements, and all region and network OBIA average cover percentage estimates were within 10%. The trade-off for decreased accuracy over a larger area (region and network rule sets) may be useful to prioritize management strategies but will unlikely capture subtle shifts in understory plant communities that site and plot rule sets often capture.

关键词： eCognition Developer high spatial resolution imagery object-based image analysis rule sets SageSTEP

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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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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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Using object-based image analysis to detect laughing gull nests

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GISCIENCE & REMOTE SENSING 2021年第8期58卷 1497-1517页

作者： Martini, Benjamin F. Miller, Douglas A. USDA Wildlife Serv New York NY 10278 USA Penn State Univ Dept Ecosyst Sci & Management University Pk PA 16802 USA

Remote sensing has long been used to study wildlife;however, manual methods of detecting wildlife in aerial imagery are often time-consuming and prone to human error, and newer computer vision techniques have not yet been extensively applied to wildlife surveys. We used the object-based image analysis (OBIA) software eCognition to detect laughing gull (Leucophaeus atricilla) nests in Jamaica Bay as part of an ongoing monitoring effort at the John F. Kennedy International Airport. Our technique uses a combination of high resolution 4-band aerial imagery captured via manned aircraft with a multispectral UltraCam Falcon M2 camera, LiDAR point cloud data, and land cover data derived from a bathymetric LiDAR point cloud to classify and extract laughing gull nests. Our ruleset uses the site (topographic position of nest objects), tone (spectral characteristic of nest objects), shape, size, and association (nearby objects commonly found with the objects of interest that help identify them) elements of image interpretation, as well as NDVI and a sublevel object examination to classify and extract nests. The ruleset achieves a producer's accuracy of 98% as well as a user's accuracy of 65% and a kappa of 0.696, indicating that it extracts a majority of the nests in the imagery while reducing errors of commission to only 35% of the final results. The remaining errors of commission are difficult for the software to differentiate without also impacting the number of nests successfully extracted and are best addressed by a manual verification of output results as part of a semi-automated workflow in which the OBIA is used to complete the initial search of the imagery and the results are then systematically verified by the user to remove errors. This eliminates the need to manually search entire sets of imagery for nests, resulting in a much more efficient and less error prone methodology than previous unassisted image interpretation techniques. Because of the extensibility of OBI

关键词： Feature extraction laughing gull Leucophaeus atricilla object-based image analysis eCognition wildlife surveying

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A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 141卷 137-147页

作者： Ye, Su Pontius, Robert Gilmore, Jr. Rakshit, Rahul Clark Univ Grad Sch Geog Worcester MA 01610 USA

object-based image analysis (OBIA) has gained widespread popularity for creating maps from remotely sensed data. Researchers routinely claim that OBIA procedures outperform pixel-based procedures;however, it is not immediately obvious how to evaluate the degree to which an OBIA map compares to reference information in a manner that accounts for the fact that the OBIA map consists of objects that vary in size and shape. Our study reviews 209 journal articles concerning OBIA published between 2003 and 2017. We focus on the three stages of accuracy assessment: (1) sampling design, (2) response design and (3) accuracy analysis. First, we report the literature's overall characteristics concerning OBIA accuracy assessment. Simple random sampling was the most used method among probability sampling strategies, slightly more than stratified sampling. Office interpreted remotely sensed data was the dominant reference source. The literature reported accuracies ranging from 42% to 96%, with an average of 85%. A third of the articles failed to give sufficient information concerning accuracy methodology such as sampling scheme and sample size. We found few studies that focused specifically on the accuracy of the segmentation. Second, we identify a recent increase of OBIA articles in using per-polygon approaches compared to per-pixel approaches for accuracy assessment. We clarify the impacts of the per-pixel versus the per polygon approaches respectively on sampling, response design and accuracy analysis. Our review defines the technical and methodological needs in the current per-polygon approaches, such as polygon-based sampling, analysis of mixed polygons, matching of mapped with reference polygons and assessment of segmentation accuracy. Our review summarizes and discusses the current issues in object-based accuracy assessment to provide guidance for improved accuracy assessments for OBIA. Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Rem

关键词： Accuracy assessment object-based image analysis OBIA Remote sensing Per-pixel Per-polygon

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Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes

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GEOCARTO INTERNATIONAL 2019年第10期34卷 1144-1161页

作者： Zhang, Meng Zeng, Yongnian Huang, Wei Li, Songnian Cent South Univ Sch Geosci & Infophys Changsha Hunan Peoples R China Cent South Univ Spatial Informat Technol & Sustainable Dev Res Ct Changsha Hunan Peoples R China Heidelberg Univ Inst Geog Heidelberg Germany Ryerson Univ Dept Civil Engn Toronto ON Canada

Remote sensing has been proven promising in wetland mapping. However, conventional methods in a complex and heterogeneous urban landscape usually use mono temporal Landsat TM/ETM + images, which have great uncertainty due to the spectral similarity of different land covers, and pixel-based classifications may not meet the accuracy requirement. This paper proposes an approach that combines spatiotemporal fusion and object-based image analysis, using the spatial and temporal adaptive reflectance fusion model to generate a time series of Landsat 8 OLI images on critical dates of sedge swamp and paddy rice, and the time series of MODIS NDVI to calculate phenological parameters for identifying wetlands with an object-based method. The results of a case study indicate that different types of wetlands can be successfully identified, with 92.38%. The overall accuracy and 0.85 Kappa coefficient, and 85% and 90% for the user's accuracies of sedge swamp and paddy respectively.

关键词： Wetland mapping spatiotemporal fusion object-based image analysis complex heterogeneity urban landscape

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AN object-based image analysis SYSTEM based ON REGION-LINE PRIMITIVE ASSOCIATION FRAMEWORK 37

AN OBJECT-BASED IMAGE ANALYSIS SYSTEM BASED ON REGION-LINE P...

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IEEE International Geoscience & Remote Sensing Symposium

作者： Wang, Min Huang, Jiru Cui, Qi Wang, Jie Nanjing Normal Univ Minist Educ Key Lab Virtual Geog Environm Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9781509049516

We develop a new object-based image analysis (OBIA) software system, named remote-sensing knowledge finder (RSFinder), based on the region-line primitive association framework (RLPAF). In this system, straight-edge lines are promoted as line primitives for OBIA, which is fundamentally different with common OBIA systems, such as eCogntion and ENVI feature extraction module. based on region-line collaborative analyses, RsFinder can implement more powerful OBIAs than region-only-based systems.

关键词： Region-line primitive association framework object-based image analysis image segmentation high spatial resolution remote sensing

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Three-dimensional building change detection using object-based image analysis (case study: Tehran)

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APPLIED GEOMATICS 2021年第3期13卷 325-332页

作者： Tabib Mahmoudi, Fatemeh Hosseini, Sharareh Shahid Rajaee Teacher Training Univ Civil Engn Fac Dept Geomat Engn Tehran Iran

Natural disasters such as earthquakes and floods together with the urban sprawl conducted by increasing the population make multi-temporal changes in building areas. Destruction, buildings' renovation, and constructing new buildings are the main changes of the urban areas that should be detected to update three-dimensional city models. The results of performing three-dimensional changes detecting of high altitude objects such as buildings are more close to reality than the two-dimensional methods. In this study, a three-dimensional changes detection method is proposed based on digital elevation models (DEMs). In the first step of this proposed method, the normalized digital surface model (nDSM) is generated for timely datasets. Then, object-based image analysis is utilized by performing segmentation followed by the structural classification of DEMs. Differencing and comparing the multi-temporal classification maps as the third step of the proposed algorithm led to analyzing the occurred changes. The obtained results are evaluated in an urban area in Tehran, Iran, in a 9-year time interval. These results represent -9.7% decreasing rate in low-rise buildings and also -1.37% decreases in the ground. Moreover, the class of high-rise buildings increased for +16.4% which conforms to making new constructions in addition to the renovation of low-rise buildings. According to the area analyzing of the changes, 4.8% of the investigated study area has new constructions, 3.05% has buildings' renovation, and 3.89% has destruction in that 9-year period.

关键词： Three-dimensional change detection Normalized DSM object-based image analysis Segmentation

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Classification of Siachen Glacier Using object-based image analysis 2

Classification of Siachen Glacier Using Object-Based Image A...

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2nd International Conference on Intelligent Circuits and Systems (ICICS)

作者： Sharda, Shikha Srivastava, Mohit IK Gujral Punjab Tech Univ Kapurthala India Chandigarh Engn Coll Mohali India

ISBN: (纸本)9781538664834

With the advancement in computer technologies, many algorithms have been developed that mimic the human behavior of accurately identifying the objects based on patterns, colors, textures, sizes, shapes. based on the previous research, object-based image analysis (OBIA) approach has come out to be an emerging technology that can easily extract the objects like roads, buildings, rivers, lakes etc. from the remotely sensed image. In this paper, mapping of glacier ice/snow and the glacial lake has been performed. As a case study, Siachen glacier situated in the eastern Karakoram Range in the Himalayas has been mapped from Landsat 5 using OBIA approach. The rule base classification has been implemented by using eCognition Developer software.

关键词： Multi-resolution Segmentation Band Ratio NDWI NDVI object-based image analysis

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