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Article: The importance of data type, laser spot density and modelling method for vegetation height mapping in continental China

TitleThe importance of data type, laser spot density and modelling method for vegetation height mapping in continental China
Authors
Issue Date2016
Citation
International Journal of Remote Sensing, 2016, v. 37, n. 24, p. 6127-6148 How to Cite?
Abstract© 2016 Informa UK Limited, trading as Taylor & Francis Group. Vegetation height not only has great significance in the field of ecology but also offers a useful contribution to detailed land cover classification. The first vegetation height map was acquired in this study using the ice, cloud, and land elevation satellite /geosciences laser altimeter system (ICESat/GLAS) and other multisource remote sensing data, such as moderate-resolution imaging spectroradiometer (MODIS) tree cover products, leaf area index (LAI) products, Nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR), climatic variables, and topographic indices. We mainly discuss the importance of data type, density of laser spot and modelling method in the generation of this vegetation height map in continental China. It was found that (1) a higher density of laser spot could improve the reliability of modelling in mountainous areas covered by a wide range of forest and shrub land; (2) in terms of the importance of input variables, in the random forest regression modelling, the most important ones are elevation, slope, mean air temperature, temperature variance, precipitation, precipitation variance, and NBAR; (3) when modelling using 50 ecozones covering the whole of continental China, the model showed a good performance with an accuracy of root mean square error (RMSE), correlation coefficient (r), index of agreement (d), and mean absolute error (MAE) at 5.7, 0.7, 0.8, and 3.8 m, respectively. A visual comparison suggests that the spatial pattern of vegetation height is consistent with that of land cover in China. It is very necessary in evaluating the importance of data type, laser spot density, and modelling method in vegetation height mapping in continental China.
Persistent Identifierhttp://hdl.handle.net/10722/296805
ISSN
2020 Impact Factor: 3.151
2020 SCImago Journal Rankings: 0.918
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Caixia-
dc.contributor.authorWang, Xiaoyi-
dc.contributor.authorHuang, Huabing-
dc.contributor.authorGong, Peng-
dc.contributor.authorWu, Di-
dc.contributor.authorJiang, Jinxiong-
dc.date.accessioned2021-02-25T15:16:43Z-
dc.date.available2021-02-25T15:16:43Z-
dc.date.issued2016-
dc.identifier.citationInternational Journal of Remote Sensing, 2016, v. 37, n. 24, p. 6127-6148-
dc.identifier.issn0143-1161-
dc.identifier.urihttp://hdl.handle.net/10722/296805-
dc.description.abstract© 2016 Informa UK Limited, trading as Taylor & Francis Group. Vegetation height not only has great significance in the field of ecology but also offers a useful contribution to detailed land cover classification. The first vegetation height map was acquired in this study using the ice, cloud, and land elevation satellite /geosciences laser altimeter system (ICESat/GLAS) and other multisource remote sensing data, such as moderate-resolution imaging spectroradiometer (MODIS) tree cover products, leaf area index (LAI) products, Nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR), climatic variables, and topographic indices. We mainly discuss the importance of data type, density of laser spot and modelling method in the generation of this vegetation height map in continental China. It was found that (1) a higher density of laser spot could improve the reliability of modelling in mountainous areas covered by a wide range of forest and shrub land; (2) in terms of the importance of input variables, in the random forest regression modelling, the most important ones are elevation, slope, mean air temperature, temperature variance, precipitation, precipitation variance, and NBAR; (3) when modelling using 50 ecozones covering the whole of continental China, the model showed a good performance with an accuracy of root mean square error (RMSE), correlation coefficient (r), index of agreement (d), and mean absolute error (MAE) at 5.7, 0.7, 0.8, and 3.8 m, respectively. A visual comparison suggests that the spatial pattern of vegetation height is consistent with that of land cover in China. It is very necessary in evaluating the importance of data type, laser spot density, and modelling method in vegetation height mapping in continental China.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Remote Sensing-
dc.titleThe importance of data type, laser spot density and modelling method for vegetation height mapping in continental China-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/01431161.2016.1252472-
dc.identifier.scopuseid_2-s2.0-84997427108-
dc.identifier.volume37-
dc.identifier.issue24-
dc.identifier.spage6127-
dc.identifier.epage6148-
dc.identifier.eissn1366-5901-
dc.identifier.isiWOS:000388599500023-
dc.identifier.issnl0143-1161-

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