Combined object-based classification and manual interpretation–synergies for a quantitative assessment of parcels and biotopes

Lang, Stefan; Schöpfer, Elisabeth; Langanke, Tobias

doi:10.1080/10106040802121093

Cited by 28 publications

(24 citation statements)

References 3 publications

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“…Visual interpretation is superior to digital processing methods in delineating patches, even with the great recent advances made in object-based image analysis techniques [8,21,22]. In fact, objects generated from image segmentation are mostly image objects without realistic ecological meaning [19,22].…”

Section: Patch Delineation and Patch Classification: Visual Interpretmentioning

confidence: 99%

“…With the development of object-based image analysis, there is increasing interest in the automatic delineation of ecologically realistic objects [8,22,29]. The integration of future advances in image segmentation with knowledge-based classifications may allow us to incorporate ecological knowledge with ease, allowing for the automatic delineation of ecologically meaningful objects, or patches [8,22].…”

Section: Patch Delineation and Patch Classification: Visual Interpretmentioning

confidence: 99%

“…Our results show that an object-based approach provides an effective way for the classification of land cover features within a patch, and thus for patch classification. Therefore, an integration of the strength of visual interpretation for ecological patch delineation, and the effectiveness of an object-based approach for patch classification, provides a better way than using visual interpretation or an object-based approach alone, in quantification of fine-scale heterogeneous urban landscapes [8,30].…”

Section: Patch Delineation and Patch Classification: Visual Interpretmentioning

confidence: 99%

“…Visual interpretation is better suited for delimiting patches that incorporate built and non-built components of the landscape, compared to digital image processing approaches [4,[6][7][8]. Recently, there is increasing interest in delineating patches (sometimes referred to as land cover units), based on visual interpretation that incorporate both built and non-built components of the urban landscapes [4,[9][10][11], instead of digitizing individual landscape features [12].…”

Section: Introductionmentioning

confidence: 99%

“…These characteristics are crucial for identifying and delimiting patches that can represent important contrasts in ecological structure or process in the landscape, but are difficult to incorporate into conventional digital image processing techniques [6,7]. Therefore, in contrast to computer-based digital image processing approaches, visual interpretation integrates ecological knowledge into image analysis to define the boundaries of patches making the results more ecologically meaningful and relevant [4,7,8,14].…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification

et al. 2014

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Describing and quantifying the spatial heterogeneity of land cover in urban systems is crucial for developing an ecological understanding of cities. This paper presents a new approach to quantifying the fine-scale heterogeneity in urban landscapes that capitalizes on the strengths of two commonly used approaches-visual interpretation and object-based image analysis. This new approach integrates the ability of humans to detect pattern with an object-based image analysis that accurately and efficiently quantifies the components that give rise to that pattern. Patches that contain a mix of built and natural land cover features were first delineated through visual interpretation. These patches served as pre-defined boundaries for finer-scale segmentation and classification of within-patch land cover features which were classified using object-based image analysis. Patches were then classified based on the within-patch proportion cover of features. We applied this approach to the Gwynns Falls watershed in Baltimore, Maryland, USA. The object-based classification approach proved to be effective for classifying within-patch land cover features. The overall accuracy of the classification maps of 1999 and 2004 were 92.3% and 93.7%, respectively. This exercise demonstrates that by integrating visual interpretation OPEN ACCESSRemote Sens. 2014, 6 3370 with object-based classification, the fine-scale spatial heterogeneity in urban landscapes and land cover change can be described and quantified in a more efficient and ecologically meaningful way than either purely automated or visual methods alone. This new approach provides a tool that allows us to quantify the structure of the urban landscape including both built and non-built components that will better accommodate ecological research linking system structure to ecological processes.

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Section: Patch Delineation and Patch Classification: Visual Interpretmentioning

confidence: 99%

Section: Patch Delineation and Patch Classification: Visual Interpretmentioning

confidence: 99%

Section: Patch Delineation and Patch Classification: Visual Interpretmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification

et al. 2014

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Object‐Based Classification for Mountainous Vegetation Physiognomy Mapping

Thiérion¹,

Lang²

2018

QGIS and Applications in Agriculture and Forest

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Patterns of mortality in a montane mixed‐conifer forest in San Diego County, California

Freeman

Stow

2017

Ecological Applications

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We examine spatial patterns of conifer tree mortality and their changes over time for the montane mixed-conifer forests of San Diego County. These forest areas have recently experienced extensive tree mortality due to multiple factors. A spatial contextual image processing approach was utilized with high spatial resolution digital airborne imagery to map dead trees for the years 1997, 2000, 2002, and 2005 for three study areas: Palomar, Volcan, and Laguna mountains. Plot-based fieldwork was conducted to further assess mortality patterns. Mean mortality remained static from 1997 to 2002 (4, 2.2, and 4.2 trees/ha for Palomar, Volcan, and Laguna) and then increased by 2005 to 10.3, 9.7, and 5.2 trees/ha, respectively. The increase in mortality between 2002 and 2005 represents the temporal pattern of a discrete disturbance event, attributable to the 2002-2003 drought. Dead trees are significantly clustered for all dates, based on spatial cluster analysis, indicating that they form distinct groups, as opposed to spatially random single dead trees. Other tests indicate no directional shift or spread of mortality over time, but rather an increase in density. While general temporal and spatial mortality processes are uniform across all study areas, the plot-based species and quantity distribution of mortality, and diameter distributions of dead vs. living trees, vary by study area. The results of this study improve our understanding of stand- to landscape-level forest structure and dynamics, particularly by examining them from the multiple perspectives of field and remotely sensed data.

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Combined object-based classification and manual interpretation–synergies for a quantitative assessment of parcels and biotopes

Cited by 28 publications

References 3 publications

Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification

Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification

Object‐Based Classification for Mountainous Vegetation Physiognomy Mapping

Patterns of mortality in a montane mixed‐conifer forest in San Diego County, California

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