2018
DOI: 10.3390/rs10091334
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Land-Use Carbon Emissions Estimation for the Yangtze River Delta Urban Agglomeration Using 1994–2016 Landsat Image Data

Abstract: The amount and growth rate of carbon emissions have been accelerated on a global scale since the industrial revolution (1800), especially in recent decades. This has resulted in a significant influence on the natural environment and human societies. Therefore, carbon emission reduction receives continuously increasing public attention and has long been under debate. In this study, we made use of the land-use specific carbon emission coefficients from previous studies and estimated the land-use carbon emissions… Show more

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Cited by 64 publications
(43 citation statements)
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“…However, as all the images were Landsat data and processed with the same procedures, we assumed that similar accuracies were obtained for the LSMA results of 1995, 2003, and 2010. A similar assumption was also made by Cui et al (2018) when they assessed their multi-temporal image classification results obtained from a supervised classifier [52].…”
Section: Linear Spectral Mixture Analysissupporting
confidence: 53%
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“…However, as all the images were Landsat data and processed with the same procedures, we assumed that similar accuracies were obtained for the LSMA results of 1995, 2003, and 2010. A similar assumption was also made by Cui et al (2018) when they assessed their multi-temporal image classification results obtained from a supervised classifier [52].…”
Section: Linear Spectral Mixture Analysissupporting
confidence: 53%
“…It includes global and local spatial autocorrelation. Global spatial autocorrelation describes the average degree of association, spatial distribution patterns, and their significance among all geographic units in the study area, while local spatial autocorrelation can identify the aggregation and differentiation characteristics of local spatial features [52]. Moran's I and local Moran's I were used to describe the global and local spatial autocorrelation, given by [52]:…”
Section: Spatial Autocorrelation Analysesmentioning
confidence: 99%
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“…Using the maximum likelihood classifier, we classified the land Water 2019, 11, 1806 7 of 20 use/cover types in the CLB into six categories, namely farmland, forestland, grassland, water body, construction land, and unused land. The high-resolution satellite images in 2017 provided by Google Earth Pro were used to assess classification accuracy [68]. In total, 500 sample points were randomly generated in the classified image in ArcGIS 10.1 and then imported into Google Earth Pro to retrieve the ground-truthing data.…”
mentioning
confidence: 99%
“…As it can rank features in order of importance (a higher value implies a more important feature), random forest (RF) is often used for selecting essential features from a large number of features [58]. The number of decision trees in a random forest (mtree) and the number of features per node (ntry) are two key parameters in RF [59], and how they are combined impacts classification accuracy. Classification accuracy usually increases with the increase of mtree, and an optimal ntry is among √ p/2, √ p, and 2 √ p, where p is the number of features.…”
Section: Indicators Features Indicators Features Indicatorsmentioning
confidence: 99%