scholarly journals Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas

2020 ◽  
Vol 12 (11) ◽  
pp. 1721 ◽  
Author(s):  
Fernanda F. Ribeiro ◽  
Dar A. Roberts ◽  
Laura L. Hess ◽  
Frank W. Davis ◽  
Kelly K. Caylor ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Forest Land ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Fine Spatial Resolution ◽  
Neotropical Savannas ◽  
Geographic Object ◽  
Forest Land Cover ◽  
Map Accuracy

Regional maps of vegetation structure are necessary for delineating species habitats and for supporting conservation and ecological analyses. A systematic approach that can discriminate a wide range of meaningful and detailed vegetation classes is still lacking for neotropical savannas. Detailed vegetation mapping of savannas is challenged by seasonal vegetation dynamics and substantial heterogeneity in vegetation structure and composition, but fine spatial resolution imagery (<10 m) can improve map accuracy in these heterogeneous landscapes. Traditional pixel-based classification methods have proven problematic for fine spatial resolution data due to increased within-class spectral variability. Geographic Object-Based Image Analysis (GEOBIA) is a robust alternative method to overcome these issues. We developed a systematic GEOBIA framework accounting for both spectral and spatial features to map Cerrado structural types at 5-m resolution. This two-step framework begins with image segmentation and a Random Forest land cover classification based on spectral information, followed by spatial contextual and topological rules developed in a systematic manner in a GEOBIA knowledge-based approach. Spatial rules were defined a priori based on descriptions of environmental characteristics of 11 different physiognomic types and their relationships to edaphic conditions represented by stream networks (hydrography), topography, and substrate. The Random Forest land cover classification resulted in 10 land cover classes with 84.4% overall map accuracy and was able to map 7 of the 11 vegetation classes. The second step resulted in mapping 13 classes with 87.6% overall accuracy, of which all 11 vegetation classes were identified. Our results demonstrate that 5-m spatial resolution imagery is adequate for mapping land cover types of savanna structural elements. The GEOBIA framework, however, is essential for refining land cover categories to ecological classes (physiognomic types), leading to a higher number of vegetation classes while improving overall accuracy.

scholarly journals Geographic Object Based Image Analysis of WorldView-3 Imagery for Urban Hydrologic Modelling at the Catchment Scale

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1133 ◽  
Author(s):  
Mark Randall ◽  
Rasmus Fensholt ◽  
Yongyong Zhang ◽  
Marina Bergen Jensen
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Land Cover Classification ◽  
Supplementary Data ◽  
Catchment Scale ◽  
Hydrologic Modelling ◽  
Sponge City ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object

China’s Sponge City initiative will involve widespread installation of new stormwater infrastructure including green roofs, permeable pavements and rain gardens in at least 30 cities. Hydrologic modelling can support the planning of Sponge Cities at the catchment scale, however, highly detailed spatial data for model input can be challenging to compile from the various authorities, or, if available, may not be sufficiently detailed or updated. Remote sensing methods show great promise for mitigating this challenge due to their ability to efficiently classify satellite images into categories relevant to a specific application. In this study Geographic Object Based Image Analysis (GEOBIA) was applied to WorldView-3 satellite imagery (2017) to create a detailed land cover map of an urban catchment area in Beijing. While land cover classification results based on a Bayesian machine learning classifier alone provided an overall land cover classification accuracy of 63%, the subsequent inclusion of a series of refining rules in combination with supplementary data (including elevation and parcel delineations), yielded the significantly improved overall accuracy of 76%. Results of the land cover classification highlight the limitations of automated classification based on satellite imagery alone and the value of supplementary data and additional rules to refine classification results. Catchment scale hydrologic modelling based on the generated land cover results indicated that 61 to 82% of rainfall volume could be captured for a range of 24 h design storms under varying degrees of Sponge City implementation.

Spatial and Temporal Patterns of Wetland Cover Changes in East Kolkata Wetlands, India From 1972 to 2011

2019 ◽  
pp. 1163-1177
Author(s):  
Xia Li ◽  
Chandana Mitra ◽  
Luke Marzen ◽  
Qichun Yang
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Population Pressure ◽  
Spatial And Temporal Patterns ◽  
Landsat Images ◽  
Object Based Image Analysis ◽  
East Kolkata Wetlands ◽  
Object Based ◽  
Comparison Results ◽  
Geographic Object

Land use and land cover change has a slow but prolonged impact on various aspects of environment on local, regional and global scales. In developing countries especially population pressure and food demand have compelled conversion of wetlands to built-up and agricultural lands. One such unique example is the East Kolkata Wetlands (EKWs) located on the eastern fringes of Kolkata City in India where such land cover change is very intense and rapid. In this study, wetland conversions in EKWs from 1972 to 2011 were analyzed with four Landsat images using the Geographic Object-Based Image Analysis (GeOBIA) and a post-classification comparison. Results suggested that wetland areas decreased by 17.9 percent during the study period. The western part of the wetlands saw the maximum conversion of wetlands to built-up areas with time, whereas the east and south experienced more of wetlands to agricultural and other land conversions

scholarly journals OBJECT-BASED IMAGE ANALYSIS OF DIFFERENT SPATIAL RESOLUTION SATELLITE IMAGERIES IN URBAN AND SUBURBAN ENVIRONMENT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 105-112
Author(s):  
I. Kotaridis ◽  
M. Lazaridou
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Land Cover ◽  
Spatial Resolution ◽  
Support Vector ◽  
Landsat 8 ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Very High Spatial Resolution ◽  
Sentinel 2A

Abstract. Monitoring urban and suburban land cover has become a particularly researched investigation field in remote sensing community, since there is a large amount of professionals interested in gathering useful information, regarding urban sprawl and its side effects in natural vegetation, urban parks and water bodies. This paper focuses on studying the implementation of an object-based image analysis methodological framework, in Orfeo ToolBox. Moderate, high and very high spatial resolution satellite images were utilized in order to generate thematic land cover maps of the study area located in Thessaloniki, Greece. Taking into consideration that there is not a relevant research in literature concerning the selection of segmentation parameters values, the optimal values are presented for MeanShift segmentation algorithm. Classifications were conducted with the use of Support Vector Machines algorithm and the final outputs are presented, accompanied by the evaluation of accuracy assessments which is a mandatory step in every remote sensing project. The analysis showed that OBIA, in this case, works well with Landsat-8 and QuickBird data and exceptionally well with Sentinel-2A data with over 90% overall accuracy. Critical considerations on the aforementioned are also included.

Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis

2018 ◽  
Vol 210 ◽  
pp. 259-268 ◽  
Author(s):  
Sory I. Toure ◽  
Douglas A. Stow ◽  
Hsiao-chien Shih ◽  
John Weeks ◽  
David Lopez-Carr
Keyword(s):  
Land Use ◽  
Image Analysis ◽  
Land Use Change ◽  
Land Cover ◽  
Spatial Resolution ◽  
Change Analysis ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Resolution Data

Spatial and Temporal Patterns of Wetland Cover Changes in East Kolkata Wetlands, India from 1972 to 2011

2016 ◽  
Vol 7 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Xia Li ◽  
Chandana Mitra ◽  
Luke Marzen ◽  
Qichun Yang
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Population Pressure ◽  
Spatial And Temporal Patterns ◽  
Landsat Images ◽  
Object Based Image Analysis ◽  
East Kolkata Wetlands ◽  
Object Based ◽  
Comparison Results ◽  
Geographic Object

Land use and land cover change has a slow but prolonged impact on various aspects of environment on local, regional and global scales. In developing countries especially population pressure and food demand have compelled conversion of wetlands to built-up and agricultural lands. One such unique example is the East Kolkata Wetlands (EKWs) located on the eastern fringes of Kolkata City in India where such land cover change is very intense and rapid. In this study, wetland conversions in EKWs from 1972 to 2011 were analyzed with four Landsat images using the Geographic Object-Based Image Analysis (GeOBIA) and a post-classification comparison. Results suggested that wetland areas decreased by 17.9 percent during the study period. The western part of the wetlands saw the maximum conversion of wetlands to built-up areas with time, whereas the east and south experienced more of wetlands to agricultural and other land conversions

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