{"title":"1982-2020年中国青藏高原高寒草地长期时空连续NDVI产品的开发","authors":"Xiali Yang, Xiaodong Huang, Ying Ma, Yuxin Li, Qisheng Feng, Tiangang Liang","doi":"10.1002/glr2.12076","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>The time-series data of the Normalized Difference Vegetation Index (NDVI) is a crucial indicator for global and regional vegetation monitoring. However, the current assessment of global and regional long-term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time-series data sets.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>In this study, a long time-series monthly NDVI data set with a spatial resolution of 250 m from 1982 to 2020 was developed by combining Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR (Advanced Very High-Resolution Radiometer) time-series NDVI products using the Random Forest (RF) downscaling model.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Compared to the MODIS NDVI product, the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05, respectively, with <i>R</i><sup>2</sup> values mostly above 0.7.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>The long time-series NDVI products generated in this study are reliable in terms of accuracy and have great potential for long-term dynamic monitoring of terrestrial ecosystems on the Qinghai–Tibet Plateau.</p>\n </section>\n </div>","PeriodicalId":100593,"journal":{"name":"Grassland Research","volume":"3 2","pages":"100-112"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/glr2.12076","citationCount":"0","resultStr":"{\"title\":\"Development of long-term spatiotemporal continuous NDVI products for alpine grassland from 1982 to 2020 in the Qinghai–Tibet Plateau, China\",\"authors\":\"Xiali Yang, Xiaodong Huang, Ying Ma, Yuxin Li, Qisheng Feng, Tiangang Liang\",\"doi\":\"10.1002/glr2.12076\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>The time-series data of the Normalized Difference Vegetation Index (NDVI) is a crucial indicator for global and regional vegetation monitoring. However, the current assessment of global and regional long-term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time-series data sets.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>In this study, a long time-series monthly NDVI data set with a spatial resolution of 250 m from 1982 to 2020 was developed by combining Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR (Advanced Very High-Resolution Radiometer) time-series NDVI products using the Random Forest (RF) downscaling model.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Compared to the MODIS NDVI product, the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05, respectively, with <i>R</i><sup>2</sup> values mostly above 0.7.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>The long time-series NDVI products generated in this study are reliable in terms of accuracy and have great potential for long-term dynamic monitoring of terrestrial ecosystems on the Qinghai–Tibet Plateau.</p>\\n </section>\\n </div>\",\"PeriodicalId\":100593,\"journal\":{\"name\":\"Grassland Research\",\"volume\":\"3 2\",\"pages\":\"100-112\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/glr2.12076\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Grassland Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/glr2.12076\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Grassland Research","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/glr2.12076","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of long-term spatiotemporal continuous NDVI products for alpine grassland from 1982 to 2020 in the Qinghai–Tibet Plateau, China
Background
The time-series data of the Normalized Difference Vegetation Index (NDVI) is a crucial indicator for global and regional vegetation monitoring. However, the current assessment of global and regional long-term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time-series data sets.
Methods
In this study, a long time-series monthly NDVI data set with a spatial resolution of 250 m from 1982 to 2020 was developed by combining Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR (Advanced Very High-Resolution Radiometer) time-series NDVI products using the Random Forest (RF) downscaling model.
Results
Compared to the MODIS NDVI product, the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05, respectively, with R2 values mostly above 0.7.
Conclusions
The long time-series NDVI products generated in this study are reliable in terms of accuracy and have great potential for long-term dynamic monitoring of terrestrial ecosystems on the Qinghai–Tibet Plateau.
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