Requirements for labelling forest polygons in an object-based image analysis classification

MacLean, Meghan Graham; Campbell, Michael J.; Maynard, Daniel S.; Ducey, Mark J.; Congalton, RussellG.

doi:10.1080/01431161.2012.747017

Cited by 15 publications

(14 citation statements)

References 20 publications

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“…For the six observed study areas a total of 31 tree species were observed (Table S1). Instead of a species specific classification, our analysis centered on the conventional Deciduous Forest, Mixed Forest, and Coniferous Forest partitioning defined by Justice et al, [5] and MacLean et al, [6]. Here we used the Anderson et al, [7] classification scheme definition for forests, being any area with 10 percent or greater aerial tree-crown density, which has the ability to produce lumber, and influences either the climate or hydrologic regime.…”

Section: Methodsmentioning

confidence: 99%

“…Similar to both PBC sampling methods, this and other OBC samples used 30 × 30 m effective areas for visually interpreting their classification. The second OBC reference data collection method (method four) used these previous 30 × 30 m classified areas as subsamples to represent the compositional heterogeneity at the image object (forest stand) level [5,10]. Forest stands which did not convey a clear majority, based on the subsamples, were classified based on a decision ruleset shown in Table 2.…”

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confidence: 99%

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Evaluating the Effectiveness of Unmanned Aerial Systems (UAS) for Collecting Thematic Map Accuracy Assessment Reference Data in New England Forests

Fraser¹,

Congalton²

2019

Forests

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Thematic mapping provides today’s analysts with an essential geospatial science tool for conveying spatial information. The advancement of remote sensing and computer science technologies has provided classification methods for mapping at both pixel-based and object-based analysis, for increasingly complex environments. These thematic maps then serve as vital resources for a variety of research and management needs. However, to properly use the resulting thematic map as a decision-making support tool, an assessment of map accuracy must be performed. The methods for assessing thematic accuracy have coalesced into a site-specific multivariate analysis of error, measuring uncertainty in relation to an established reality known as reference data. Ensuring statistical validity, access and time constraints, and immense costs limit the collection of reference data in many projects. Therefore, this research proposes evaluating the feasibility of adopting the low-cost, flexible, high-resolution sensor-capable Unmanned Aerial Systems (UAS, UAV, or Drone) platform for collecting reference data to use in thematic map accuracy assessments for complex environments. This pilot study analyzed 377.57 ha of New England forests, over six University of New Hampshire woodland properties, to compare the similarity between UAS-derived orthomosaic samples and ground-based continuous forest inventory (CFI) plot classifications of deciduous, mixed, and coniferous forest cover types. Using an eBee Plus fixed-wing UAS, 9173 images were acquired and used to create six comprehensive orthomosaics. Agreement between our UAS orthomosaics and ground-based sampling forest compositions reached 71.43% for pixel-based classification and 85.71% for object-based classification reference data methods. Despite several documented sources of uncertainty or error, this research demonstrated that UAS are capable of highly efficient and effective thematic map accuracy assessment reference data collection. As UAS hardware, software, and implementation policies continue to evolve, the potential to meet the challenges of accurate and timely reference data collection will only increase.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Evaluating the Effectiveness of Unmanned Aerial Systems (UAS) for Collecting Thematic Map Accuracy Assessment Reference Data in New England Forests

Fraser¹,

Congalton²

2019

Forests

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“…RS analysis for change detection is usually relying on digital satellite image classification by assigning image pixels to real-world LULC feature types (Paul et al, 2018). Pixel-based classification (PBC) is a conventional method and has been broadly applied as supervised and unsupervised classification based on characteristics of single pixel (MacLean et al, 2013;Rwanga and Ndambuki, 2017). However, when a pixel-by-pixel classification algorithm is applied to all available image signals, the pixels with similar spectral reflectance are grouped together, while some spatial and contextual information of image pixels are neglected.…”

Section: Introductionmentioning

confidence: 99%

“…However, when a pixel-by-pixel classification algorithm is applied to all available image signals, the pixels with similar spectral reflectance are grouped together, while some spatial and contextual information of image pixels are neglected. Thus, the pixels may not represent true geographical objects when using PBC method, and its accuracy would be affected (MacLean et al, 2013;Blaschke et al, 2014). Compared with PBC, the object-based image analysis (OBIA) method generates segments from pixel-based features to produce higher classification accuracy by incorporating more information and cre-ating more recognizable segments (Frohn, 2011;Chen et al, 2013;Ma et al, 2017;Paul et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Multi-Layer Perceptron Neural Network and Markov Chain Based Geospatial Analysis of Land Use and Land Cover Change

Shen¹,

Li²,

Wheate³

et al. 2020

J ENVIRON INFORM LET

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We combined multi-layer perceptron (MLP) neural network and Markov Chain (MC) modeling with object-based image analysis (OBIA) to map and predict land use and land cover (LULC) changes in Stoney Creek Watershed (SCW), British Columbia, Canada. Unsupervised classification was performed using Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images to produce LULC maps of years 1986, 1999 and 2016. The classification resulted in an overall accuracy of 91.50%. The results show that coniferous forest in SCW experienced a sharp loss while agriculture area increased (4.77% land gain) from 1986 to 2016. LULC scenarios were predicted through MLP neural network and MC modeling based on LULC change analysis data and transition potential. The results indicated that 'Coniferous Forest' LULC type had the highest (3.38% land loss) transition potential and 'Water' and 'Urban Area' LULC types had the lowest transition potential. Application of the proposed method provided valuable information of LULC patterns and dynamics for planners and researchers. The method also has the potential for improved management in other watersheds with similar LULC types.

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“…Nevertheless, the optimal sample size is dependent on the classification scenario and the sampling design. It has been found that the optimal sample size for object-based accuracy assessment is smaller than that of pixel-based accuracy assessment (Radoux et al 2011;MacLean et al 2013). …”

Section: Sample Sizementioning

confidence: 99%

A survey of methods incorporating spatial information in image classification and spectral unmixing

Wang

Shi

Diao

et al. 2016

International Journal of Remote Sensing

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Over the past decade, the incorporation of spatial information has drawn increasing attention in multispectral and hyperspectral data analysis. In particular, the property of spatial autocorrelation among pixels has shown great potential for improving understanding of remotely sensed imagery. In this paper, we provide a comprehensive review of the state-of-the-art techniques in incorporating spatial information in image classification and spectral unmixing. For image classification, spatial information is accounted for in the stages of pre-classification, sample selection, classifiers, post-classification, and accuracy assessment. With regards to spectral unmixing, spatial information is discussed in the context of endmember extraction, selection of endmember combinations, and abundance estimation. Finally, a perspective on future research directions for advancing spatial-spectral methods is offered.

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Requirements for labelling forest polygons in an object-based image analysis classification

Cited by 15 publications

References 20 publications

Evaluating the Effectiveness of Unmanned Aerial Systems (UAS) for Collecting Thematic Map Accuracy Assessment Reference Data in New England Forests

Evaluating the Effectiveness of Unmanned Aerial Systems (UAS) for Collecting Thematic Map Accuracy Assessment Reference Data in New England Forests

Multi-Layer Perceptron Neural Network and Markov Chain Based Geospatial Analysis of Land Use and Land Cover Change

A survey of methods incorporating spatial information in image classification and spectral unmixing

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