Algorithms of Expert Classification Applied in Quickbird Satellite Images for Land Use Mapping

Perea, Alberto J.; Larriva, José Emilio Meroño de; Ureña, María Jesús Aguilera

doi:10.4067/s0718-58392009000300013

Cited by 5 publications

(2 citation statements)

References 6 publications

(5 reference statements)

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“…Classifi cation is then carried out on individual objects using a combination of spatial and spectral information. Object-based techniques combined with high-resolution imagery have not only been shown to outperform pixel-based methods in highly heterogeneous landscapes (e.g., Moreno and De Larriva 2012 ;Perea et al 2009 ) but also require extensive technical expertise, time, and specialized GIS software.…”

Section: Lulc Change Detectionmentioning

confidence: 99%

Determining Greenhouse Gas Emissions and Removals Associated with Land-Use and Land-Cover Change

Kearney

Smukler

2016

Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture

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This chapter reviews methods and considerations for quantifying greenhouse gas (GHG) emissions and removals associated with changes in land-use and land-cover (LULC) in the context of smallholder agriculture. LULC change contributes a sizeable portion of global anthropogenic GHG emissions, accounting for 12.5 % of carbon emissions from 1990 to 2010 (Biogeosciences 9:5125-5142, 2012). Yet quantifying emissions from LULC change remains one of the most uncertain components in carbon budgeting, particularly in landscapes dominated by smallholder agriculture (Mitig Adapt Strateg Glob Chang 12:1001-1026 Biogeosciences 9:5125-5142, 2012; Glob Chang Biol 18:2089-2101. Current LULC monitoring methodologies are not well-suited for the size of land holdings and the rapid transformations from one land-use to another typically found in smallholder landscapes. In this chapter we propose a suite of methods for estimating the net changes in GHG emissions that specifi cally address the conditions of smallholder agriculture. We present methods encompassing a range of resource requirements and accuracy, and the trade-offs between cost and accuracy are specifi cally discussed. The chapter begins with an introduction to existing protocols, standards, and international reporting guidelines and how they relate to quantifying, analyzing, and reporting GHG emissions and removals from LULC change. We introduce general considerations and methodologies specifi c to smallholder agricultural landscapes for generating activity data, linking it with GHG emission factors and assessing uncertainty. We then provide methodological options, additional considerations, and minimum datasets required to meet the varying levels of reporting accuracy, ranging from low-cost high-uncertainty to high-cost low-uncertainty approaches. Technical step-by-step details for suggested approaches can be found in the associated website.

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Section: Lulc Change Detectionmentioning

confidence: 99%

Determining Greenhouse Gas Emissions and Removals Associated with Land-Use and Land-Cover Change

Kearney

Smukler

2016

Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture

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“…Perea1 J. A. et al [9] developed a methodology for the classification of digital aerial images, which, with the aid of object-based classification and the Normalized Difference Vegetation Index (NDVI), can quantify agricultural areas, by using algorithms of expert classification, with the aim of improving the final results of thematic classifications. It has been shown in [10] by Besag et al that Bayesian models often result in maps of far superior quality.…”

Section: Introductionmentioning

confidence: 99%

Land Cover Classification of Remotely Sensed Satellite Data using Bayesian and Hybrid classifier

Pradhan¹,

Ghose²,

Jeyaram³

2010

IJCA

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In this paper an attempt has been made to develop classification algorithm for remotely sensed satellite data using Bayesian and hybrid classification approach. Bayesian classification is a probabilistic technique which is capable of classifying every pattern until no pattern remains unclassified. Hybrid classification involves developing training patterns using unsupervised classification followed by classifying the pixels using supervised classification. It is observed that the overall accuracy was found to be 90.53% using the Bayesian classifier and 91.57% using the Hybrid classifier.

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Estimating upper soil horizon carbon stocks in a permafrost watershed of Northeast Siberia by integrating field measurements with Landsat-5 TM and WorldView-2 satellite data

Broderick

Frey

Rogan

et al. 2015

GIScience & Remote Sensing

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Algorithms of Expert Classification Applied in Quickbird Satellite Images for Land Use Mapping

Cited by 5 publications

References 6 publications

Determining Greenhouse Gas Emissions and Removals Associated with Land-Use and Land-Cover Change

Determining Greenhouse Gas Emissions and Removals Associated with Land-Use and Land-Cover Change

Land Cover Classification of Remotely Sensed Satellite Data using Bayesian and Hybrid classifier

Estimating upper soil horizon carbon stocks in a permafrost watershed of Northeast Siberia by integrating field measurements with Landsat-5 TM and WorldView-2 satellite data

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