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Land cover changes assessment using object-based image analysis in the Binah River watershed (Togo and Benin)

EARTH AND SPACE SCIENCE 2015年第10期2卷 403-416页

作者： Badjana, Heou Maleki Helmschrot, Joerg Selsam, Peter Wala, Kperkouma Fluegel, Wolfgang-Albert Afouda, Abel Akpagana, Koffi Univ Abomey Calavi Grad Res Program Climate Change & Water Resources West African Sci Serv Ctr Climate Change & Adapte Cotonou Benin Univ Lome Fac Sci Lab Bot & Plant Ecol Lome Togo Univ Jena Dept Geoinformat Hydrol & Modelling Jena Germany Univ Hamburg Bioctr Klein Flottbek & Bot Garden Southern African Sci Serv Ctr Climate Change & Ad Hamburg Germany

In this study, land cover changes between 1972 and 2013 were investigated in the Binah River watershed (North of Togo and Benin) using remote sensing and geographic information system technologies. Multitemporal satellite images-Landsat MSS (1972), TM (1987), and OLI-TIRS (2013)-were processed using object-based image analysis and post-classification comparison methods including landscape metrics and changes trajectories analysis. Land cover maps referring to five main land cover classes, namely, agricultural land, forest land, savannah, settlements, and water bodies, were produced for each acquisition date. The overall accuracies were 76.64% (1972), 83.52% (1987), and 88.84% (2013) with respective Kappa statistics of 0.69, 0.78, and 0.86. The assessment of the spatiotemporal pattern of land cover changes indicates that savannah, the main vegetation type, has undergone the most dominant change, decreasing from 67% of the basin area in 1972 to 56% in 1987 and 33% in 2013. At the same time, agricultural land has significantly increased from 15% in 1972 to 24% in 1987 and 43% in 2013, while some proportions of agricultural land were converted to savannah relating to fallow agriculture. In total, more than 55% of the landscape experienced changes between 1972 and 2013. These changes are primarily due to human activities and population growth. In addition, agricultural activities significantly contributed to the increase in the number of patches, degree of division, and splitting index of forest and savannah vegetations and the decrease in their effective mesh sizes. These results indicate further fragmentation of forest and savannah vegetations between 1972 and 2013. Further research is needed to quantitatively evaluate the influences of individual factors of human activities and to separate these from the impacts of climate change-driven disturbances.

关键词： object-based image analysis land cover change land cover change trajectories landscape metrics the Binah River watershed

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High throughput object-based image analysis of β-amyloid plaques in human and transgenic mouse brain

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JOURNAL OF NEUROSCIENCE METHODS 2012年第1期204卷 179-188页

作者： Samaroo, Harry D. Opsahl, Alan C. Schreiber, Jan O'Neill, Sharon M. Marconi, Michael Qian, Jessie Carvajal-Gonzalez, Santos Tate, Barbara Milici, Anthony J. Bales, Kelly R. Stephenson, Diane T. Pfizer Global Res Dev Neurosci Biol Groton CT 06340 USA Definiens AG D-80339 Munich Germany

Advances in imaging technology have enabled automated approaches for quantitative image analysis. In this study, a high content object based image analysis method was developed for quantification of beta-amyloid (A beta) plaques in postmortem brains of Alzheimer's disease (AD) subjects and in transgenic mice over overexpressing A beta. Digital images acquired from immunohistochemically stained sections of the superior frontal gyrus were analyzed for A beta plaque burden using a Definiens object-based segmentation approach. Blinded evaluation of A beta stained sections from AD and aged matched human subjects accurately identified AD cases with one exception. Brains from transgenic mice overexpressing A beta (PS1APP mice) were also evaluated by our Definiens object based image analysis approach. We observed an age-dependent increase in the amount of A beta plaque load that we quantified in both the hippocampus and cortex. From the contralateral hemisphere, we measured the amount of A beta in brain homogenates biochemically and observed a significant correlation between our biochemical measurements and those that we measured by our object based Definiens system in the hippocampus. Assessment of A beta plaque load in PS1APP mice using a manual segmentation technique (image-Pro Plus) confirmed the results of our object-based image analysis approach. image acquisition and analysis of 32 stained human slides and 100 mouse slides were executed in 8 h and 22 h, respectively supporting the relatively high throughput features of the Definiens platform. The data show that digital imaging combined with object based image analysis is a reliable and efficient approach to quantifying A beta plaques in human and mouse brain. (C) 2011 Elsevier B.V. All rights reserved.

关键词： High-throughput PS1APP object-based image analysis Alzheimer's disease

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An object-based image analysis of WorldView-3 image for Urban Flood Vulnerability Assessment and Dissemination Through ESRI Story Maps

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2021年第11期49卷 2639-2654页

作者： Aahlaad, Musunuru Mozumder, Chitrini Tripathi, Nitin Pal, Indrajit Asian Inst Technol AIT Sch Engn & Technol SET Dept Informat & Commun Technol Remote Sensing & Geog Informat Syst Khlong Luang Thailand Asian Inst Technol AIT Disaster Preparedness Mitigat & Management DPMM Khlong Luang Thailand

The utilization of remote sensing and GIS is increasing in importance across the world for disaster preparedness, assessments, mitigation, and governance. A major focus is on extracting and disseminating useful information from satellite and airborne data sources for effective planning and decision making during and after disasters. This study presents a case study from Ko-Rian, a sub-district in Ayutthaya, Thailand which is frequently hit by monsoon flooding. First, an approach is demonstrated to extract buildings and roads from very-high-resolution satellite images. The results showed that use of selected multiple indices with object-based image analysis produced excellent results in mixed urban environment. Secondly, a procedure and framework are proposed for geodatabase modeling of urban flood vulnerability and disseminating information to a wider community through ESRI story maps. The proposed approach using virtual reality technologies such as Google Street View for assessing physical vulnerability and ESRI story maps to share the information can significantly reduce the time and costs of the traditional survey. The techniques and approach presented in this study can be useful to develop detailed vulnerability profile for effective decision making and dynamic planning for a flood resilient city.

关键词： ESRI story map GIS object-based image analysis Urban flood Vulnerability WorldView-3 image

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Identification of Forested Landslides Using LiDar Data, object-based image analysis, and Machine Learning Algorithms

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REMOTE SENSING 2015年第8期7卷 9705-9726页

作者： Li, Xianju Cheng, Xinwen Chen, Weitao Chen, Gang Liu, Shengwei China Univ Geosci Fac Informat Engn Wuhan 430074 Peoples R China China Univ Geosci Fac Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 Enschede Netherlands China Aero Geophys Survey & Remote Sensing Ctr La Inst Remote Sensing Method Beijing 100083 Peoples R China

For identification of forested landslides, most studies focus on knowledge-based and pixel-based analysis (PBA) of LiDar data, while few studies have examined (semi-) automated methods and object-based image analysis (OBIA). Moreover, most of them are focused on soil-covered areas with gentle hillslopes. In bedrock-covered mountains with steep and rugged terrain, it is so difficult to identify landslides that there is currently no research on whether combining semi-automated methods and OBIA with only LiDar derivatives could be more effective. In this study, a semi-automatic object-based landslide identification approach was developed and implemented in a forested area, the Three Gorges of China. Comparisons of OBIA and PBA, two different machine learning algorithms and their respective sensitivity to feature selection (FS), were first investigated. based on the classification result, the landslide inventory was finally obtained according to (1) inclusion of holes encircled by the landslide body;(2) removal of isolated segments, and (3) delineation of closed envelope curves for landslide objects by manual digitizing operation. The proposed method achieved the following: (1) the filter features of surface roughness were first applied for calculating object features, and proved useful;(2) FS improved classification accuracy and reduced features;(3) the random forest algorithm achieved higher accuracy and was less sensitive to FS than a support vector machine;(4) compared to PBA, OBIA was more sensitive to FS, remarkably reduced computing time, and depicted more contiguous terrain segments;(5) based on the classification result with an overall accuracy of 89.11% +/- 0.03%, the obtained inventory map was consistent with the referenced landslide inventory map, with a position mismatch value of 9%. The outlined approach would be helpful for forested landslide identification in steep and rugged terrain.

关键词： landslide inventory LiDar object-based image analysis machine learning the Three Gorges

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An object-based image analysis of Pinyon and Juniper Woodlands Treated to Reduce Fuels

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ENVIRONMENTAL MANAGEMENT 2014年第3期53卷 660-671页

作者： Hulet, April Roundy, Bruce A. Petersen, Steven L. Jensen, Ryan R. Bunting, Stephen C. ARS USDA Eastern Oregon Agr Res Ctr Burns OR 97720 USA Brigham Young Univ Provo UT 84602 USA Brigham Young Univ Dept Geog Provo UT 84602 USA Univ Idaho Dept Rangeland Ecol & Management Moscow ID 83843 USA

Mechanical and prescribed fire treatments are commonly used to reduce fuel loads and maintain or restore sagebrush steppe rangelands across the Great Basin where pinyon (Pinus) and juniper (Juniperus) trees are encroaching and infilling. Geospatial technologies, particularly remote sensing, could potentially be used in these ecosystems to (1) evaluate the longevity of fuel reduction treatments, (2) provide data for planning and designing future fuel-reduction treatments, and (3) assess the spatial distribution of horizontal fuel structure following fuel-reduction treatments. High-spatial resolution color-infrared imagery (0.06-m pixels) was acquired for pinyon and juniper woodland plots where fuels were reduced by either prescribed fire, tree cutting, or mastication at five sites in Oregon, California, Nevada, and Utah. imagery was taken with a Vexcel UltraCam X digital camera in June 2009. Within each treatment plot, ground cover was measured as part of the Sagebrush Steppe Treatment Evaluation Project. Trimble eCognition Developer was used to classify land cover classes using object-based image analysis (OBIA) techniques. Differences between cover estimates using OBIA and ground-measurements were not consistently higher or lower for any land cover class and when evaluated for individual sites, were within +/- 5 % of each other. The overall accuracy and the K (hat) statistic for classified thematic maps for each treatment were: prescribed burn 85 % and 0.81;cut and fell 82 % and 0.77, and mastication 84 % and 0.80. Although cover assessments from OBIA differed somewhat from ground measurements, they are sufficiently accurate to evaluate treatment success and for supporting a broad range of management concerns.

关键词： eCognition object-based image analysis Prescribed burn Mastication Pinyon-juniper woodlands Restoration

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Applying object-based image analysis and knowledge-based classification to ADS-40 digital aerial photographs to facilitate complex forest land cover classification

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JOURNAL OF APPLIED REMOTE SENSING 2017年第1期11卷 015001-1-015001-16页

作者： Hsieh, Yi-Ta Chen, Chaur-Tzuhn Chen, Jan-Chang Natl Pingtung Univ Sci & Technol Dept Forestry 1 Shuehfu Rd Pingtung 91201 Taiwan

In general, considerable human and material resources are required for performing a forest inventory survey. Using remote sensing technologies to save forest inventory costs has thus become an important topic in forest inventory-related studies. Leica ADS-40 digital aerial photographs feature advantages such as high spatial resolution, high radiometric resolution, and a wealth of spectral information. As a result, they have been widely used to perform forest inventories. We classified ADS-40 digital aerial photographs according to the complex forest land cover types listed in the Fourth Forest Resource Survey in an effort to establish a classification method for categorizing ADS-40 digital aerial photographs. Subsequently, we classified the images using the knowledge-based classification method in combination with object-based analysis techniques, decision tree classification techniques, classification parameters such as object texture, shape, and spectral characteristics, a class-based classification method, and geographic information system mapping information. Finally, the results were compared with manually interpreted aerial photographs. images were classified using a hierarchical classification method comprised of four classification levels (levels 1 to 4). The classification overall accuracy (OA) of levels 1 to 4 is within a range of 64.29% to 98.50%. The final result comparisons showed that the proposed classification method achieved an OA of 78.20% and a kappa coefficient of 0.7597. On the basis of the image classification results, classification errors occurred mostly in images of sunlit crowns because the image values for individual trees varied. Such a variance was caused by the crown structure and the incident angle of the sun. These errors lowered image classification accuracy and warrant further studies. This study corroborates the high feasibility for mapping complex forest land cover types using ADS-40 digital aerial photographs. (C) The Authors. Pu

关键词： ADS-40 object-based image analysis knowledge-based classification hierarchical-classification

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Mapping riparian condition indicators in a sub-tropical savanna environment from discrete return LiDAR data using object-based image analysis

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ECOLOGICAL INDICATORS 2010年第4期10卷 796-807页

作者： Johansen, Kasper Arroyo, Lara A. Armston, John Phinn, Stuart Witte, Christian Univ Queensland Ctr Remote Sensing & Spatial Informat Sci Sch Geog Planning & Environm Management Brisbane Qld 4072 Australia Queensland Govt Ctr Remote Sensing Dept Environm & Resource Management Indooroopilly Qld 4068 Australia

Mapping, monitoring and managing the environmental condition of riparian zones are major focus areas for local and state governments in Australia. New remotely sensed data techniques that can provide the required mapping accuracies, complete spatial coverage and processing and mapping transferability are currently being developed for use over large spatial extents. The research objective was to develop and apply an approach for mapping riparian condition indicators using object-based image analysis of airborne Light Detection and Ranging (LiDAR) data. The indicators assessed were: streambed width;riparian zone width;plant projective cover (PPC);longitudinal continuity;coverage of large trees;vegetation overhang;and stream bank stability. LiDAR data were captured on 15 July 2007 for two 5 km stretches along Mimosa Creek in Central Queensland, Australia. Field measurements of riparian vegetation structural and land form parameters were obtained between 28 May and 5 June 2007. object-based approaches were developed for mapping each riparian condition indicator from the LiDAR data. The validation and empirical modelling results showed that the object-based approach could be used to accurately map the riparian condition indicators (R-2 = 0.99 for streambed width, R-2 = 0.82 for riparian zone width, R-2 = 0.89 for PPC, R-2 = 0.40 for bank stability). These research findings will be used in a 26,000 km mapping project assessing riparian vegetation and physical form indicators from LiDAR data in Victoria, Australia. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Airborne LiDAR Riparian zone Environmental indicators object-based image analysis Remote sensing

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Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery

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GEOCARTO INTERNATIONAL 2018年第10期33卷 1064-1083页

作者： Stefanidou, A. Dragozi, E. Stavrakoudis, D. Gitas, I. Z. Aristotle Univ Thessaloniki Dept Forestry & Nat Environm Fac Agr Forestry & Nat Environm Thessaloniki Greece

Efficient forest fire management requires precise and up-to-date knowledge regarding the composition and spatial distribution of forest fuels at various spatial and temporal scales. Fuel-type maps are essential for effective fire prevention strategies planning, as well as the alleviation of the environmental impacts of potential wildfire events. The aim of this study was to assess and compare the potential of Disaster Monitoring Constellation and Landsat-8 OLI satellite images (Operational Land imager), combined with object-based image analysis (GEOBIA), in operational mapping of the Mediterranean fuel types at a regional scale. The results showcase that although the images of both sensors can be used with GEOBIA analysis for the generation of accurate fuel-type maps, only the OLI images can be considered as applicable for regional mapping of the Mediterranean fuel types on an operational basis.

关键词： Remote sensing fuel-type mapping DMC Landsat-8 OLI object-based image analysis

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High-resolution broad-scale mapping of soil parent material using object-based image analysis (OBIA) of LiDAR elevation data

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CATENA 2020年第0期188卷 104422-000页

作者： Prince, Antoine Franssen, Jan Lapierre, Jean-Francois Maranger, Roxane Univ Montreal Geog Dept 520 Chemin Cote St Catherine Montreal PQ H2V 2B8 Canada Univ Montreal Biol Sci Dept Pavillon Marie Victorin Montreal PQ H3C 3J7 Canada

Precise mapping of subsurface properties, especially soil parent material (SPM), is critical to understanding most geomorphic and hydrologic processes. Because spatial variability of the subsurface properties is highly correlated to the morphology of the earth's surface, DEMs and aerial/satellite imagery have been used successfully for lithological mapping. However, most of the studies using this approach have used either coarse-resolution DEMs or covered limited areas. LiDAR-derived topographic attributes may provide an opportunity for finer-resolution mapping of subsurface properties at much larger spatial scales. Here, we present an approach that uses object-based image analysis (OBIA) - the process of clustering pixels into homogeneous objects - paired with a classification tree (CT) algorithm to map SPM at 1 m resolution and at a regional spatial scale using LiDAR-derived topographic attributes. Combining OBIA of topographic attributes with the CT method proved to be an efficient approach for mapping of SPM at fine resolution and broad scales. Using field-acquired SPM samples, we found that this approach was able to explain 73.5% of the variance of three SPM classes (i.e., bedrock, glacial till, sands) using only four common topographic metrics (i.e., slope, topographic wetness index, terrain ruggedness index, multi-resolution topographic positioning index). When comparing our approach to other classification methods and to other studies that have mapped SPM, we obtain the highest overall accuracy with a value of 85%. This approach allowed us to produce a fine-resolution 185 km(2) SPM map in a heterogeneous post-glaciation Precambrian Shield setting.

关键词： Soil parent material object-based image analysis LiDAR Digital mapping Classification tree Terrain analysis

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An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography

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JOURNAL OF ARID ENVIRONMENTS 2007年第1期69卷 1-14页

作者： Laliberte, A. S. Rango, A. Herrick, J. E. Fredrickson, Ed L. Burkett, L. New Mexico State Univ USDA ARS Las Cruces NM 88003 USA

Research into automatic image processing of digital plot photography has increased in recent years. However, in most studies only overall vegetation cover is estimated. In and regions of the southwestern US, grass cover is typically a mixture of green and senescent plant material and it is important to be able to quantify both types of vegetation. Our objectives were to develop an image analysis approach for estimating fractional cover of green and senescent vegetation using very high-resolution ground photography, and to compare image-based estimates with line-point-intercept (LPI) measures. We acquired ground photography for 50 plots using an eight megapixel digital camera. The images were transformed from the RGB (red, green, blue) color space to the IHS (intensity, hue, saturation) color space. We used an object-based image analysis approach to classify the. images into soil, shadow, green vegetation, and senescent vegetation. Shadow and soil were effectively masked out by using the intensity and saturation bands, and a nearest neighbor classification was used to separate green and senescent vegetation using intensity, hue and saturation as well as visible bands. Correlation coefficients between LPI- and image-based estimates for green and senescent vegetation were 0.88 and 0.95 respectively. image analysis underestimated total and senescent vegetation by approximately 5%. The object-based image-processing approach is less labor and time intensive than the LPI method, is a viable alternative to ground-based methods, and has the potential to be incorporated into rangeland monitoring protocols. Published by Elsevier Ltd.

关键词： rangeland vegetation rangeland monitoring very high-resolution images object-based image analysis

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