Effect of Using Different Amounts of Multi-Temporal Data on the Accuracy: A Case of Land Cover Mapping of Parts of Africa Using FengYun-3C Data

Regional or continental-scale land cover mapping requires various amounts of months of multi-temporal satellite data to pick phenological variation in vegetation, enhancing differentiability among surface cover types and improving accuracy. However, little has been addressed about the number of mont...

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Autores principales:	Tesfaye Adugna, Wenbo Xu, Jinlong Fan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	land cover classification machine learning random forest feature selection/reduction FengYun-3C Africa Science Q
Acceso en línea:	https://doaj.org/article/3f0323f7e00743fda6c58fbcd9da89cc
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spelling	oai:doaj.org-article:3f0323f7e00743fda6c58fbcd9da89cc2021-11-11T18:58:00ZEffect of Using Different Amounts of Multi-Temporal Data on the Accuracy: A Case of Land Cover Mapping of Parts of Africa Using FengYun-3C Data10.3390/rs132144612072-4292https://doaj.org/article/3f0323f7e00743fda6c58fbcd9da89cc2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4461https://doaj.org/toc/2072-4292Regional or continental-scale land cover mapping requires various amounts of months of multi-temporal satellite data to pick phenological variation in vegetation, enhancing differentiability among surface cover types and improving accuracy. However, little has been addressed about the number of months/multi-temporal images needed to obtain the best result and the impact of using different amounts of these data on the accuracy of individual classes. This work aimed to analyze these effects by utilizing the various amounts of months of time series FengYun-3C (FY-3C) data within one year for land cover mapping of parts of Africa using a random forest classifier. The study area covers roughly one-third of Africa, including eastern, central, and northern parts of the continent. One-year FY-3C ten-day composite images consisting of eleven-band each with 1-km spatial resolution were divided into seven input datasets that comprise stacked images of 1-month, 3-month, 6-month, consecutive 9-month, 12-month, selected images from 12 months using band/feature importance, and selected 9-month. Comparisons of these datasets on independent test samples revealed that overall accuracy, kappa coefficient, and the accuracy of the individual classes generally increase significantly with increasing the number of data/months. However, the highest accuracy and kappa coefficient, 0.86 and 0.83, were obtained by processing selected 9-month imageries. The second maximum accuracy and kappa (0.85 and 0.82,) were found by manipulating 12-month scenes which are the same as the results obtained by applying feature reduction. Although 4% and 5% higher accuracy were achieved by manipulating 3-month and 6-month data relative to 1-month imageries, no variation of accuracy was observed between six- and nine-months of consecutive data, both yielding equal accuracy and kappa value (0.84 and 0.81) indicating redundancy of information. Overall, the high accuracy results show the feasibility of FY-3C data for land cover mapping of Africa.Tesfaye AdugnaWenbo XuJinlong FanMDPI AGarticleland cover classificationmachine learningrandom forestfeature selection/reductionFengYun-3CAfricaScienceQENRemote Sensing, Vol 13, Iss 4461, p 4461 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	land cover classification machine learning random forest feature selection/reduction FengYun-3C Africa Science Q
spellingShingle	land cover classification machine learning random forest feature selection/reduction FengYun-3C Africa Science Q Tesfaye Adugna Wenbo Xu Jinlong Fan Effect of Using Different Amounts of Multi-Temporal Data on the Accuracy: A Case of Land Cover Mapping of Parts of Africa Using FengYun-3C Data
description	Regional or continental-scale land cover mapping requires various amounts of months of multi-temporal satellite data to pick phenological variation in vegetation, enhancing differentiability among surface cover types and improving accuracy. However, little has been addressed about the number of months/multi-temporal images needed to obtain the best result and the impact of using different amounts of these data on the accuracy of individual classes. This work aimed to analyze these effects by utilizing the various amounts of months of time series FengYun-3C (FY-3C) data within one year for land cover mapping of parts of Africa using a random forest classifier. The study area covers roughly one-third of Africa, including eastern, central, and northern parts of the continent. One-year FY-3C ten-day composite images consisting of eleven-band each with 1-km spatial resolution were divided into seven input datasets that comprise stacked images of 1-month, 3-month, 6-month, consecutive 9-month, 12-month, selected images from 12 months using band/feature importance, and selected 9-month. Comparisons of these datasets on independent test samples revealed that overall accuracy, kappa coefficient, and the accuracy of the individual classes generally increase significantly with increasing the number of data/months. However, the highest accuracy and kappa coefficient, 0.86 and 0.83, were obtained by processing selected 9-month imageries. The second maximum accuracy and kappa (0.85 and 0.82,) were found by manipulating 12-month scenes which are the same as the results obtained by applying feature reduction. Although 4% and 5% higher accuracy were achieved by manipulating 3-month and 6-month data relative to 1-month imageries, no variation of accuracy was observed between six- and nine-months of consecutive data, both yielding equal accuracy and kappa value (0.84 and 0.81) indicating redundancy of information. Overall, the high accuracy results show the feasibility of FY-3C data for land cover mapping of Africa.
format	article
author	Tesfaye Adugna Wenbo Xu Jinlong Fan
author_facet	Tesfaye Adugna Wenbo Xu Jinlong Fan
author_sort	Tesfaye Adugna
title	Effect of Using Different Amounts of Multi-Temporal Data on the Accuracy: A Case of Land Cover Mapping of Parts of Africa Using FengYun-3C Data
title_short	Effect of Using Different Amounts of Multi-Temporal Data on the Accuracy: A Case of Land Cover Mapping of Parts of Africa Using FengYun-3C Data
title_full	Effect of Using Different Amounts of Multi-Temporal Data on the Accuracy: A Case of Land Cover Mapping of Parts of Africa Using FengYun-3C Data
title_fullStr	Effect of Using Different Amounts of Multi-Temporal Data on the Accuracy: A Case of Land Cover Mapping of Parts of Africa Using FengYun-3C Data
title_full_unstemmed	Effect of Using Different Amounts of Multi-Temporal Data on the Accuracy: A Case of Land Cover Mapping of Parts of Africa Using FengYun-3C Data
title_sort	effect of using different amounts of multi-temporal data on the accuracy: a case of land cover mapping of parts of africa using fengyun-3c data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3f0323f7e00743fda6c58fbcd9da89cc
work_keys_str_mv	AT tesfayeadugna effectofusingdifferentamountsofmultitemporaldataontheaccuracyacaseoflandcovermappingofpartsofafricausingfengyun3cdata AT wenboxu effectofusingdifferentamountsofmultitemporaldataontheaccuracyacaseoflandcovermappingofpartsofafricausingfengyun3cdata AT jinlongfan effectofusingdifferentamountsofmultitemporaldataontheaccuracyacaseoflandcovermappingofpartsofafricausingfengyun3cdata
_version_	1718431639018668032

Effect of Using Different Amounts of Multi-Temporal Data on the Accuracy: A Case of Land Cover Mapping of Parts of Africa Using FengYun-3C Data

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