Yiming Feng, Kun Qiao, Shiang Feng, Lei Xi, Zhao Qi, Lan Lan
{"title":"1990 - 2021年塔里木环盆地植被覆盖度时空数据集","authors":"Yiming Feng, Kun Qiao, Shiang Feng, Lei Xi, Zhao Qi, Lan Lan","doi":"10.11922/11-6035.csd.2023.0010.zh","DOIUrl":null,"url":null,"abstract":"The Tarim Basin is an area with extremely fragile ecology and severe desertification subject to the ravages of human activities. As an important index of desertification monitoring, vegetation coverage can well reflect the luxuriant degree of surface vegetation. The monitoring of regional vegetation coverage is the basis of mastering the dynamic change of desertification and analyzing the causes of desertification. Using LANDSAT vegetation growth season (April-October) images from 1990 to 2021 as data sources, we obtained seven vegetation coverage data sets from 1990 to 2021 in the Ring Tarim Basin based on GEE remote sensing cloud platform. We intercepted the upper and lower thresholds of NDVI by adopting 0.5% confidence level to get the NDVI values of pure vegetation cover pixels and pure soil cover pixels, so as to remove the effect of the interannual climate differences on vegetation coverage calculation, and ensure the consistency in the calculation of vegetation coverage for each year. The observation work was carried out in 109 UAV orthorectified sample plots. In the following of data pre-processing, we obtained FVC values as validation samples by using a combined algorithm (vegetation index method and Otsu algorithm). The precision of the dataset is R2 = 0.79 and the linear expression is y = 0.8126x - 0.0267. This dataset can provide data support for the research of desertification change and driving mechanism.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A dataset of temporal-spatial FVC in the Ring Tarim Basin from 1990 to 2021\",\"authors\":\"Yiming Feng, Kun Qiao, Shiang Feng, Lei Xi, Zhao Qi, Lan Lan\",\"doi\":\"10.11922/11-6035.csd.2023.0010.zh\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Tarim Basin is an area with extremely fragile ecology and severe desertification subject to the ravages of human activities. As an important index of desertification monitoring, vegetation coverage can well reflect the luxuriant degree of surface vegetation. The monitoring of regional vegetation coverage is the basis of mastering the dynamic change of desertification and analyzing the causes of desertification. Using LANDSAT vegetation growth season (April-October) images from 1990 to 2021 as data sources, we obtained seven vegetation coverage data sets from 1990 to 2021 in the Ring Tarim Basin based on GEE remote sensing cloud platform. We intercepted the upper and lower thresholds of NDVI by adopting 0.5% confidence level to get the NDVI values of pure vegetation cover pixels and pure soil cover pixels, so as to remove the effect of the interannual climate differences on vegetation coverage calculation, and ensure the consistency in the calculation of vegetation coverage for each year. The observation work was carried out in 109 UAV orthorectified sample plots. In the following of data pre-processing, we obtained FVC values as validation samples by using a combined algorithm (vegetation index method and Otsu algorithm). The precision of the dataset is R2 = 0.79 and the linear expression is y = 0.8126x - 0.0267. This dataset can provide data support for the research of desertification change and driving mechanism.\",\"PeriodicalId\":57643,\"journal\":{\"name\":\"China Scientific Data\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"China Scientific Data\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.11922/11-6035.csd.2023.0010.zh\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"China Scientific Data","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.11922/11-6035.csd.2023.0010.zh","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A dataset of temporal-spatial FVC in the Ring Tarim Basin from 1990 to 2021
The Tarim Basin is an area with extremely fragile ecology and severe desertification subject to the ravages of human activities. As an important index of desertification monitoring, vegetation coverage can well reflect the luxuriant degree of surface vegetation. The monitoring of regional vegetation coverage is the basis of mastering the dynamic change of desertification and analyzing the causes of desertification. Using LANDSAT vegetation growth season (April-October) images from 1990 to 2021 as data sources, we obtained seven vegetation coverage data sets from 1990 to 2021 in the Ring Tarim Basin based on GEE remote sensing cloud platform. We intercepted the upper and lower thresholds of NDVI by adopting 0.5% confidence level to get the NDVI values of pure vegetation cover pixels and pure soil cover pixels, so as to remove the effect of the interannual climate differences on vegetation coverage calculation, and ensure the consistency in the calculation of vegetation coverage for each year. The observation work was carried out in 109 UAV orthorectified sample plots. In the following of data pre-processing, we obtained FVC values as validation samples by using a combined algorithm (vegetation index method and Otsu algorithm). The precision of the dataset is R2 = 0.79 and the linear expression is y = 0.8126x - 0.0267. This dataset can provide data support for the research of desertification change and driving mechanism.