期刊
GEOCARTO INTERNATIONAL
卷 38, 期 1, 页码 -出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/10106049.2022.2152496
关键词
Landsat; land use land cover; surface biophysical parameters; machine learning; artificial intelligence
This study compares the accuracy of Landsat satellites' OLI and OLI-2 sensors in land use land cover (LULC) mapping. Image fusion techniques were applied to improve the spatial resolution of OLI and OLI-2 multispectral images, followed by LULC mapping using a support vector machine (SVM) classifier. The results demonstrate that OLI-2 provides more accurate LULC classification than OLI. Validation of the classified LULC maps reveals better performance of OLI-2 in distinguishing dense and sparse vegetation, as well as darker and lighter objects. The relationship between LULC maps and surface biophysical parameters using Local Moran's I also demonstrates the superiority of OLI-2 in LULC mapping compared to OLI.
Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor characteristics, which have significantly improved accuracy. In this study, a comparison of the accuracy of Landsat Operational Land Imager (OLI) and OLI-2 satellites in land use land cover (LULC) mapping has been made. For this, image fusion techniques have been applied to enhance the spatial resolution of both OLI and OLI-2 multispectral images, and then a support vector machine (SVM) classifier has been used for LULC mapping. The results show that LULC classification from OLI-2 has better accuracy than OLI. The validation of classified LULC maps shows that the OLI-2 data is more accurate in distinguishing dense and sparse vegetation as well as darker and lighter objects. The relationship between LULC maps and surface biophysical parameters using Local Moran's I also shows better performance of the OLI-2 sensor in LULC mapping than the OLI sensor.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据