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GIS and Remote Sensing advances in Land Change Science

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (15 February 2019) | Viewed by 79879

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Dr. Sotirios Koukoulas

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Department of Urban-Regional Planning and Regional Development, University of Thessaly, Pedion Areos, 38334 Volos, Greece
Interests: geographic information science; remote sensing; spatial statistics; land change science; environmental monitoring and modelling; spatial and environmental planning; climate change impacts
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Special Issue Information

Dear Colleagues,

In the face of the emergence of Land Change Science (LCS), Geographical Information and Remote Sensing sciences have claimed a central role in observing, quantifying, and monitoring changes in land surfaces. In this Special Issue, recent advances in Remote Sensing and GISc that are related to LCS will be presented.

Land changes studied at a variety of scales, both in space and time, will be presented in an attempt to explore the role of analytical tools and technologies in understanding changing landscapes. Priorities include novel techniques for quantifying and analyzing land change with the use of old and new remote sensors. Combining geographical data from multiple spatial, spectral and thematic scales to quantify changes and their spatial patterns are also among priorities. Issues related to spatial error distribution as well as the detection of false changes through time are of particular interest.

Papers incorporating novel and interesting techniques to study land change, as well as some interesting applications, will be considered. Well-prepared review papers are also welcomed.

Dr. Sotirios Koukoulas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Land cover and Environmental changes
Spatio-temporal analysis/modeling
Old and new remote sensors (combination, fusing, comparisons)
Downscaling techniques
Spatial accuracy

Published Papers (11 papers)

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Research

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20 pages, 24810 KiB

Open AccessArticle

Dramatic Loss of Agricultural Land Due to Urban Expansion Threatens Food Security in the Nile Delta, Egypt

by Taher M. Radwan, G. Alan Blackburn, J. Duncan Whyatt and Peter M. Atkinson

Remote Sens. 2019, 11(3), 332; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11030332 - 08 Feb 2019

Cited by 90 | Viewed by 12045

Abstract

Egypt has one of the largest and fastest growing populations in the world. However, nearly 96% of the total land area is uninhabited desert and 96% of the population is concentrated around the River Nile valley and the Delta. This unbalanced distribution and dramatically rising population have caused severe socio-economic problems. In this research, 24 land use/land cover (LULC) maps from 1992 to 2015 were used to monitor LULC changes in the Nile Delta and quantify the rates and types of LULC transitions. The results show that 74,600 hectares of fertile agricultural land in the Nile Delta (Old Lands) was lost to urban expansion over the 24 year period at an average rate of 3108 ha year⁻¹, whilst 206,100 hectares of bare land was converted to agricultural land (New Lands) at an average rate of 8588 ha year⁻¹. A Cellular Automata-Markov (CA-Markov) integrated model was used to simulate future alternative LULC change scenarios. Under a Business as Usual scenario, 87,000 hectares of land transitioned from agricultural land to urban areas by 2030, posing a threat to the agricultural sector sustainability and food security in Egypt. Three alternative future scenarios were developed to promote urban development elsewhere, hence, with potential to preserve the fertile soils of the Nile Delta. A scenario which permitted urban expansion into the desert only preserved the largest amount of agricultural land in the Nile Delta. However, a scenario that encouraged urban expansion into the desert and adjacent to areas of existing high population density resulted in almost the same area of agricultural land being preserved. The alternative future scenarios are valuable for supporting policy development and planning decisions in Egypt and demonstrating that continued urban development is possible while minimising the threats to environmental sustainability and national food security. Full article

(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

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22 pages, 11148 KiB

Open AccessArticle

Integrating Temporal Evolution with Cellular Automata for Simulating Land Cover Change

by Cangjiao Wang, Shaogang Lei, Andrew J. Elmore, Duo Jia and Shouguo Mu

Remote Sens. 2019, 11(3), 301; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11030301 - 01 Feb 2019

Cited by 22 | Viewed by 4170

Abstract

Simultaneously considering the spatial and temporal processes is essential for land cover simulation models. A cellular automaton (CA) usually simulates the spatial conversion of land cover through post-classification comparisons between the beginning and the end of the training period. However, such an approach does not consider the temporal evolution of land cover. As a result, a CA model fails to explain the realistic land cover change. This paper proposes a temporal-dimension-extension CA (TDE-CA) by integrating the temporal evolution of land cover with a CA. In the TDE-CA, the Breaks for Additive Season and Trend (BFAST) monitor algorithm was employed in the temporal evolution simulation module (TESM) to simulate the gradual evolution of land cover, and an optimized random forest CA (optimized RF-CA) was used to simulate the spatial conversion driven by many spatial variables. Subsequently, the Ensemble Kalman Filter (EnKF) was employed to integrate the TESM with the optimized RF-CA. The TDE-CA was then tested in the land cover simulation of Shendong mining area during the period 2005–2015. The TDE-CA was compared with a Null model, with its sub-models, and with the traditional CA models, including the Logistic-CA and the MLP-CA (Multilayer Perceptron CA) models. The results show that the TDE-CA is superior to the Null model. Furthermore, the overall accuracy and the Kappa coefficient of the TDE-CA were 79.84% and 71.61%, respectively; compared with the TESM and the optimized RF-CA, the values showed 17.14% and 4.48% improvements in the overall accuracies and 0.2167 and 0.0512 improvements in the Kappa coefficients, respectively. When compared with the Logistic-CA and the MLP-CA, we measured 8.41% and 8.25% improvements in the overall accuracies and 0.0985 and 0.0964 improvements in the Kappa coefficients. These experiments indicate that the TDE-CA not only provides an effective model for the spatiotemporal dynamical simulation of land cover, but also enhances the development of the existing simulation theory. Full article

(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

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17 pages, 5217 KiB

Open AccessArticle

Patterns and Determinants of Post-Soviet Cropland Abandonment in the Western Siberian Grain Belt

by Ho Nguyen, Norbert Hölzel, Andreas Völker and Johannes Kamp

Remote Sens. 2018, 10(12), 1973; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10121973 - 07 Dec 2018

Cited by 18 | Viewed by 4799

Abstract

The transition from a command to a market economy resulted in widespread cropland abandonment across the former Soviet Union during the 1990s. Spatial patterns and determinants of abandonment are comparatively well understood for European Russia, but have not yet been assessed for the vast grain belt of Western Siberia, situated in the Eurasian forest steppe. This is unfortunate, as land-use change in Western Siberia is of global significance: Fertile black earth soils and vast mires store large amounts of organic carbon, and both undisturbed and traditional cultural landscapes harbor threatened biodiversity. We compared Landsat images from ca. 1990 (before the break-up of the Soviet Union) and ca. 2015 (current situation) with a supervised classification to estimate the extent and spatial distribution of abandoned cropland. We used logistic regression models to reveal important determinants of cropland abandonment. Ca. 135,000 ha classified as cropland around 1990 were classified as grassland around 2015. This suggests that ca. 20% of all cropland remain abandoned ca. 25 years after the end of the Soviet Union. Abandonment occurred mostly at poorly drained sites. The likelihood of cropland abandonment increased with decreasing soil quality, and increasing distance to medium-sized settlements, roads and railroads. We conclude that soil suitability, access to transport infrastructure and availability of workforce are key determinants of cropland abandonment in Western Siberia. Full article

(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

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20 pages, 15842 KiB

Open AccessArticle

Detecting Vegetation Change in Response to Confining Elephants in Forests Using MODIS Time-Series and BFAST

by Jacqueline Morrison, Thomas P. Higginbottom, Elias Symeonakis, Martin J. Jones, Fred Omengo, Susan L. Walker and Bradley Cain

Remote Sens. 2018, 10(7), 1075; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10071075 - 06 Jul 2018

Cited by 19 | Viewed by 7284

Abstract

Afromontane forests are biodiversity hotspots and provide essential ecosystem services. However, they are under pressure as a result of an expanding human population and the impact of climate change. In many instances electric fencing has become a necessary management strategy to protect forest integrity and reduce human-wildlife conflict. The impact of confining hitherto migratory elephant populations within forests remains unknown, and monitoring largely inaccessible areas is challenging. We explore the application of remote sensing to monitor the impact of confinement, employing the Breaks For Additive Season and Trend (BFAST) time-series decomposition method over a 15-year period on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) datasets for two Kenyan forests. Results indicated that BFAST was able to identify disturbances from anthropogenic, fire and elephant damage. Sequential monitoring enabled the detection of gradual changes in the forest canopy, with degradation and regeneration being observed in both sites. Annual rates of forest loss in both areas were significantly lower than reported in other studies on Afromontane forests, suggesting that installing fences has reduced land-use conversion from human-related disturbances. Negative changes in EVI were predominantly gradual degradation rather than large-scale, abrupt clearings of the forest. Results presented here demonstrate that BFAST can be used to monitor biotic and abiotic drivers of change in Afromontane forests. Full article

(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

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16 pages, 11914 KiB

Open AccessArticle

Improvement of Moderate Resolution Land Use and Land Cover Classification by Introducing Adjacent Region Features

by Longlong Yu, Jinhe Su, Chun Li, Le Wang, Ze Luo and Baoping Yan

Remote Sens. 2018, 10(3), 414; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10030414 - 08 Mar 2018

Cited by 15 | Viewed by 4851

Abstract

Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (LULC) classification. Limited by coarser resolutions, most of the traditional LULC classifications that are based on moderate resolution remote sensing images focus on the spectral features of a single pixel. Inspired by the spatial evaluation methods in landscape ecology, this study proposed a new method to extract neighborhood characteristics around a pixel for moderate resolution images. 3 landscape-metric-like indexes, i.e., mean index, standard deviation index, and distance weighted value index, were defined as adjacent region features to include the surrounding environmental characteristics. The effects of the adjacent region features and the different feature set configurations on improving the LULC classification were evaluated by a series of well-controlled LULC classification experiments using K nearest neighbor (KNN) and support vector machine (SVM) classifiers on a Landsat 8 Operational Land Imager (OLI) image. When the adjacent region features were added, the overall accuracies of both the classifiers were higher than when only spectral features were used. For the KNN and SVM classifiers that used only spectral features, the overall accuracies of the LULC classification were 85.45% and 88.87%, respectively, and the accuracies were improved to 94.52% and 96.97%. The classification accuracies of all the LULC types improved. Highly heterogeneous LULC types that are easily misclassified achieved greater improvements. As comparisons, the grey-level co-occurrence matrix (GLCM) and convolutional neural network (CNN) approaches were also implemented on the same dataset. The results revealed that the new method outperformed GLCM and CNN approaches and can significantly improve the classification performance that is based on moderate resolution data. Full article

(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

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5663 KiB

Open AccessArticle

Assessing Different Feature Sets’ Effects on Land Cover Classification in Complex Surface-Mined Landscapes by ZiYuan-3 Satellite Imagery

by Weitao Chen, Xianju Li, Haixia He and Lizhe Wang

Remote Sens. 2018, 10(1), 23; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10010023 - 23 Dec 2017

Cited by 37 | Viewed by 4553

Abstract

Land cover classification (LCC) in complex surface-mined landscapes has become very important for understanding the influence of mining activities on the regional geo-environment. There are three characteristics of complex surface-mined areas limiting LCC: significant three-dimensional terrain, strong temporal-spatial variability of surface cover, and spectral-spatial homogeneity. Thus, determining effective feature sets are very important as input dataset to improve detailed extent of classification schemes and classification accuracy. In this study, data such as various feature sets derived from ZiYuan-3 stereo satellite imagery, a feature subset resulting from a feature selection (FS) procedure, training data polygons, and test sample sets were firstly obtained; then, feature sets’ effects on classification accuracy was assessed based on different feature set combination schemes, a FS procedure, and random forest algorithm. The following conclusions were drawn. (1) The importance of feature set could be divided into three grades: the vegetation index (VI), principal component bands (PCs), mean filters (Mean), standard deviation filters (StDev), texture measures (Textures), and topographic variables (TVs) were important; the Gaussian low-pass filters (GLP) was just positive; and none were useless. The descending order of their importance was TVs, StDev, Textures, Mean, PCs, VI, and GLP. (2) TVs and StDev both significantly outperformed VI, PCs, GLP, and Mean; Mean outperformed GLP; all other pairs of feature sets had no difference. In general, the study assessed different feature sets’ effects on LCC in complex surface-mined landscapes. Full article

(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

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6753 KiB

Open AccessArticle

Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images

by ZhiYong Lv, WenZhong Shi, XiaoCheng Zhou and Jón Atli Benediktsson

Remote Sens. 2017, 9(11), 1112; https://0-doi-org.brum.beds.ac.uk/10.3390/rs9111112 - 31 Oct 2017

Cited by 27 | Viewed by 5887

Abstract

Change detection is an increasingly important research topic in remote sensing application. Previous studies achieved land cover change detection (LCCD) using bi-temporal remote sensing images. However, many widely used methods detected change depending on a series of parameters, and determining parameters is time-consuming. Furthermore, numerous methods are data-dependent. Therefore, their degree of automation should be improved significantly. Three techniques, which consist of a semi-automatic change detection system, are proposed for LCCD to overcome the abovementioned drawbacks. The three techniques are as follows: (1) change magnitude image (CMI) noise reduction is based on Gaussian filter (GF), which is coupled with OTSU for reducing CMI noise automatically using an iterative optimization strategy; (2) a method based on histogram curve fitting is suggested to predict the threshold range for parameter determination; and (3) a modified region growing algorithm is built for iteratively constructing the final change detection map. The detection accuracies of the proposed system are investigated through four experiments with different bi-temporal image scenes. Compared with several widely used change detection methods, the proposed system can be applied to detect land cover change with high accuracy and flexibility. This work is an attempt to provide a change detection system that is compatible with remote sensing images with high and median-low spatial resolution. Full article

(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

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16711 KiB

Open AccessArticle

Circa 2010 Land Cover of Canada: Local Optimization Methodology and Product Development

by Rasim Latifovic, Darren Pouliot and Ian Olthof

Remote Sens. 2017, 9(11), 1098; https://0-doi-org.brum.beds.ac.uk/10.3390/rs9111098 - 27 Oct 2017

Cited by 77 | Viewed by 9504

Abstract

Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30 m spatial resolution format, with an update frequency of five years. In response to this need, the Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada for the base year 2010, as the first of a planned series of maps to be updated every five years, or more frequently. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2010 Land Cover Map of North America, which is produced by Mexican, American and Canadian government institutions under a collaboration called the North American Land Change Monitoring System (NALCMS). This paper describes the mapping approach used for generating this land cover dataset for Canada from Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) Landsat sensor observations. The innovative part of the mapping approach is the local optimization of the land cover classifier, which has resulted in increased spatial consistency and accuracy. Training and classifying with locally confined reference samples over a large number of partially overlapping areas (i.e., moving windows) ensures the optimization of the classifier to a local land cover distribution, and decreases the negative effect of signature extension. A weighted combination of labels, which is determined by the classifier in overlapping windows, defines the final label for each pixel. Since the approach requires extensive computation, it has been developed and deployed using the Government of Canada’s High-Performance Computing Center (HPC). An accuracy assessment based on 2811 randomly distributed samples shows that land cover data produced with this new approach has achieved 76.60% accuracy with no marked spatial disparities. Full article

(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

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19891 KiB

Open AccessArticle

Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region

by Celso M. Isidro, Neil McIntyre, Alex M. Lechner and Ian Callow

Remote Sens. 2017, 9(9), 945; https://0-doi-org.brum.beds.ac.uk/10.3390/rs9090945 - 12 Sep 2017

Cited by 22 | Viewed by 7842

Abstract

The unpredictable climate in wet tropical regions along with the spatial resolution limitations of some satellite imageries make detecting and mapping artisanal and small-scale mining (ASM) challenging. The objective of this study was to test the utility of Pleiades and SPOT imagery with an object-based support vector machine (OB-SVM) classifier for the multi-temporal remote sensing of ASM and other land cover including a large-scale mine in the Didipio catchment in the Philippines. Historical spatial data on location and type of ASM mines were collected from the field and were utilized as training data for the OB-SVM classifier. The classification had an overall accuracy between 87% and 89% for the three different images—Pleiades-1A for the 2013 and 2014 images and SPOT-6 for the 2016 image. The main land use features, particularly the Didipio large-scale mine, were well identified by the OB-SVM classifier, however there were greater commission errors for the mapping of small-scale mines. The lack of consistency in their shape and their small area relative to pixel sizes meant they were often not distinguished from other land clearance types (i.e., open land). To accurately estimate the total area of each land cover class, we calculated bias-adjusted surface areas based on misclassification values. The analysis showed an increase in small-scale mining areas from 91,000 m²—or 0.2% of the total catchment area—in March 2013 to 121,000 m²—or 0.3%—in May 2014, and then a decrease to 39,000 m²—or 0.1%—in January 2016. Full article

(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

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Review

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1943 KiB

Open AccessReview

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

by Weitao Chen, Xianju Li, Haixia He and Lizhe Wang

Remote Sens. 2018, 10(1), 15; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10010015 - 22 Dec 2017

Cited by 97 | Viewed by 9495

Abstract

Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines. Full article

(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

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2333 KiB

Open AccessTechnical Note

Usability Study to Assess the IGBP Land Cover Classification for Singapore

by Nanki Sidhu, Edzer Pebesma and Yi-Chen Wang

Remote Sens. 2017, 9(10), 1075; https://0-doi-org.brum.beds.ac.uk/10.3390/rs9101075 - 22 Oct 2017

Cited by 10 | Viewed by 6757

Abstract

Our research focuses on assessing the usability of the International Geosphere Biosphere Programme (IGBP) classification scheme provided in the MODIS MCD12Q1-1 dataset for assessing the land cover of the city-state, Singapore. We conducted a user study with responses from 33 users by providing them with Google Earth images from different parts of Singapore, asking survey-takers to classify these images according to their understanding by the IGBP definitions provided. We also conducted interviews with experts from major governmental agencies working with satellite imagery, which highlighted the need for a detailed land classification for Singapore. In addition to the qualitative analysis of the IGBP land classification scheme, we carried out a validation of the MCD12Q1-1 remote sensing product against SPOT-5 imagery for our study area. The user study revealed that survey-takers were able to correctly classify urban areas, as well as densely forested areas. Misclassifications between Cropland and Mixed Forest classes were highest and were attributed by users to the broad terminology of the IGBP of the two land cover class definitions. For the accuracy assessment, we obtained validation points using weighted and unweighted stratified sampling. The overall classification accuracy for all 17 IGBP land classes is 62%. Upon selecting only the four most occurring IGBP land classes in Singapore, the classification accuracy improved to 71%. Validation of the MCD12Q1-1 against ground truth for Singapore revealed less-common land classes that may be of importance in a global context but are sources of error when the same product is applied at a smaller scale. Combining the user study with the accuracy assessment gives a comprehensive overview of the challenges associated with using global-level land cover data to derive localized land cover information specifically for smaller land masses like Singapore. Full article

(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

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