Analysis of landuse change in periphery of Tokyo during last twenty years using the same seasonal landsat data

Hashiba, Hideki; Kameda, Kazuaki; Sugimura, Toshiro; Takasaki, Kenji

doi:10.1016/s0273-1177(97)01131-9

Cited by 11 publications

(2 citation statements)

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“…However, it is still difficult to quantify method-based errors. Uncertainty could come from the data used, perhaps as a mixed-pixel problem related to coarse spatial resolution [83], geographical distortions, atmospheric effects, or seasonal effects [84]. To prevent the effects of seasonal changes on the mangrove surface from impacting on the image classification results, we tried to collect the Landsat-X images in the same season of autumn (October-November).…”

Section: Discussionmentioning

confidence: 99%

Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

et al. 2020

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This research investigated the performance of four different machine learning supervised image classifiers: artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM) using SPOT-7 and Sentinel-1 images to classify mangrove age and species in 2019 in a Red River estuary, typical of others found in northern Viet Nam. The four classifiers were chosen because they are considered to have high accuracy, however, their use in mangrove age and species classifications has thus far been limited. A time-series of Landsat images from 1975 to 2019 was used to map mangrove extent changes using the unsupervised classification method of iterative self-organizing data analysis technique (ISODATA) and a comparison with accuracy of K-means classification, which found that mangrove extent has increased, despite a fall in the 1980s, indicating the success of mangrove plantation and forest protection efforts by local people in the study area. To evaluate the supervised image classifiers, 183 in situ training plots were assessed, 70% of them were used to train the supervised algorithms, with 30% of them employed to validate the results. In order to improve mangrove species separations, Gram–Schmidt and principal component analysis image fusion techniques were applied to generate better quality images. All supervised and unsupervised (2019) results of mangrove age, species, and extent were mapped and accuracy was evaluated. Confusion matrices were calculated showing that the classified layers agreed with the ground-truth data where most producer and user accuracies were greater than 80%. The overall accuracy and Kappa coefficients (around 0.9) indicated that the image classifications were very good. The test showed that SVM was the most accurate, followed by DT, ANN, and RF in this case study. The changes in mangrove extent identified in this study and the methods tested for using remotely sensed data will be valuable to monitoring and evaluation assessments of mangrove plantation projects.

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Section: Discussionmentioning

confidence: 99%

Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

et al. 2020

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“…In contrast, Tokyo city -having the highest income per capita -never has received any grants from the LAT redistribution mechanism. Second, the social security system, most of which is retirement benefits (83%) and medical care subsidies, has resumed the role of an indirect regional transfer system, because the periphery is ageing faster than the economic centers (Hashiba et al 1998). Well-educated adolescents tend to move to the more prosperous regions in the economic core of the country, 10 For instance, for the public sector "education" the first of the three factors is a variable measuring the number of pupils and teachers, multiplied by a prefecture-invariant unit cost factor for the respective service branch, and multiplied by a modification factor reflecting prefecture-specific conditions like climate and population density (see Aoki 2008).…”

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confidence: 99%

Regional heterogeneity, the rise of public debt and monetary policy in post-bubble Japan: lessons for the EMU

Fischer

Schnabl

2017

Int Econ Econ Policy

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Both Japan and parts of the European Monetary Union have experienced boom and bust in stock and real estate markets, which have been followed by a lasting crisis. The paper analyses the role of a high degree of regional heterogeneity for public debt and monetary policy in the context of crisis. It is shown for Japan that the attempts to maintain regional cohesion via a regional transfer mechanism has contributed to the unprecedented rise in public debt and persistent monetary expansion. Econometric estimations show that in Japan regional redistribution of funds has ensured homogeneous living conditions across Japanese regions preand post-crisis. The side condition is monetary expansion. A similar effect could emerge in Europe, if the crisis persists. Terms of use: Documents inJEL-Codes: H120, H770, E520.

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Analysis of surface temperature in an urban area using supervised spatial autocorrelation and Moran’s I

Song

Song²

2022

Earth Sci Inform

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Analysis of landuse change in periphery of Tokyo during last twenty years using the same seasonal landsat data

Cited by 11 publications

References 1 publication

Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

Regional heterogeneity, the rise of public debt and monetary policy in post-bubble Japan: lessons for the EMU

Analysis of surface temperature in an urban area using supervised spatial autocorrelation and Moran’s I

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