Comparison of variance estimation methods for use with two-dimensional systematic sampling of land use/land cover data

Aune-Lundberg, Linda; Strand, Geir-Harald

doi:10.1016/j.envsoft.2014.07.001

Cited by 38 publications

(29 citation statements)

References 27 publications

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“…Cochran ( 1946, see also Iachan 1982 analytically proved that a systematic design is more precise for linear populations whose spatial autocorrelation declines exponentially with separation distance. Examples of systematic designs bringing higher survey precision in spatially autocorrelated populations were reported in a range of applied ecological contexts (e.g., Dunn and Harrison 1993, Ambrosio et al 2004, Aune-Lundberg and Strand 2014 and in the application of Euclidean distance analysis to animal habitat selection analysis (Benson 2013 ) and in stereology (Gundersen et al 1999 ).…”

Section: True Precision Of Random and Systematic Survey Designsmentioning

confidence: 99%

“…More recent statistical investigation (D ' Orazio 2003, Wolter 2007 ) assumed systematic sampling to be more precise in autocorrelated populations and focused on the still unsolved problem of how to reliably estimate the variance of the estimate of the mean from a systematic survey for linear (Wolter 1984(Wolter , 2007 and two-dimensional (i.e., spatial) populations (D ' Orazio 2003 ). Published studies in the applied ecological literature have now shifted to general agreement that systematic designs are more precise in spatially autocorrelated populations (e.g., Dunn and Harrison 1993, Ambrosio et al 2004, Aune-Lundberg and Strand 2014.…”

Section: Introductionmentioning

confidence: 99%

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Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms

McGarvey¹,

Burch²,

Matthews³

2015

Ecological Applications

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Section: True Precision Of Random and Systematic Survey Designsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms

McGarvey¹,

Burch²,

Matthews³

2015

Ecological Applications

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“…AR18X18 uses a systematic sampling method, which has shown to be efficient in situations where autocorrelation is present (Aune-Lundberg & Strand 2014;McGarvey et al 2015). Primary Statistical Units (PSUs) covering 0.9 km 2 (1500 × 600 m) are located at the intersections of an 18 × 18 km grid mesh covering the entire Norwegian mainland, giving a total of 1081 PSUs in the survey.…”

Section: Vegetation Mapping Systemsmentioning

confidence: 99%

Composition and spatial structure of dwarf shrub heath in Norway

Aune-Lundberg

Strand

2017

Norsk Geografisk Tidsskrift - Norwegian Journal of Geography

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Detailed descriptions of individual vegetation types shown on vegetation maps can improve the ways in which the composition and spatial structure within the types are understood. The authors therefore examined dwarf shrub heath, a vegetation type covering large areas and found in many parts of the Norwegian mountains. They used data from point samples obtained in a wall-to-wall area frame survey. The point sampling method provided data that gave a good understanding of the composition and structure of the vegetation type, but also revealed a difference between variation within the vegetation type itself (intra-class variation) and variation resulting from the inclusion of other types of vegetation inside the map polygons (landscape variation). Intra-class variation reflected differences in the botanical composition of the vegetation type itself, whereas landscape variation represented differences in the land-cover composition of the broader landscape in which the vegetation type was found. Both types of variation were related to environmental gradients. The authors conclude that integrated point sampling method is an efficient way to achieve increased understanding of the content of a vegetation map and can be implemented as a supporting activity during a survey. ARTICLE HISTORY

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“…Land use and land cover (LULC) consists of fundamental characteristics of the Earth's system intimately connected with many human activities and the physical environment [1]. Information on LULC is of key importance in environmentally or ecologically protected ecosystems or native habitat mapping and restoration (Council Directive, 92/43/EEC, 1992).…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the study objectives are: (1) to analyze a number of secondary derivatives produced from S2 data with the SVMs to evaluate their added value in mapping LULC and specifically wetlands; and (2) to investigate the suitability of S2 data and their synergistic use with S1 data with contemporary LULC mapping techniques (SVMs and knowledge rules) for LULC mapping with emphasis on mapping wetlands.…”

Section: Introductionmentioning

confidence: 99%

Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

2017

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Abstract:This study aimed at evaluating the synergistic use of Sentinel-1 and Sentinel-2 data combined with the Support Vector Machines (SVMs) machine learning classifier for mapping land use and land cover (LULC) with emphasis on wetlands. In this context, the added value of spectral information derived from the Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Grey Level Co-occurrence Matrix (GLCM) to the classification accuracy was also evaluated. As a case study, the National Park of Koronia and Volvi Lakes (NPKV) located in Greece was selected. LULC accuracy assessment was based on the computation of the classification error statistics and kappa coefficient. Findings of our study exemplified the appropriateness of the spatial and spectral resolution of Sentinel data in obtaining a rapid and cost-effective LULC cartography, and for wetlands in particular. The most accurate classification results were obtained when the additional spectral information was included to assist the classification implementation, increasing overall accuracy from 90.83% to 93.85% and kappa from 0.894 to 0.928. A post-classification correction (PCC) using knowledge-based logic rules further improved the overall accuracy to 94.82% and kappa to 0.936. This study provides further supporting evidence on the suitability of the Sentinels 1 and 2 data for improving our ability to map a complex area containing wetland and non-wetland LULC classes.

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Comparison of variance estimation methods for use with two-dimensional systematic sampling of land use/land cover data

Cited by 38 publications

References 27 publications

Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms

Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms

Composition and spatial structure of dwarf shrub heath in Norway

Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

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