Refined land-cover classification mapping using a multi-scale transformation method from remote sensing, unmanned aerial vehicle, and field surveys in Sanjiangyuan National Park, China,Journal of Applied Remote Sensing

当前位置： X-MOL 学术 › J. Appl. Remote Sens. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Refined land-cover classification mapping using a multi-scale transformation method from remote sensing, unmanned aerial vehicle, and field surveys in Sanjiangyuan National Park, China
Journal of Applied Remote Sensing ( IF 1.4 ) Pub Date : 2021-03-01 , DOI: 10.1117/1.jrs.15.014513
Shuai Han ₁ , Qingkai Meng ₁ , Haocheng Liu ₁ , Ying Peng ₂ , Jianping Han ₃ , Shenghong Jin ₃ , Shixiong Fan ₄ , Bingchang Xin ₃ , Lili He ₅ , Hao Li ₆

Affiliation

Mapping the refined land-cover classification mapping (RLCM) is a primary and essential strategy for evaluating the ecological change and understanding the ecosystem services. A common problem during the generation of RLCM is a scale mismatch between remote sensing (RS) data and field quadrat, which leads to inaccuracy of the classification result. A multi-scale transformation method was developed via integrating RS, unmanned aerial vehicle (UAV), and field surveys in Sanjiangyuan National Park (SNP). With the help of UAV, a large number of virtual biomass quadrats were resampled and interpolated, and the quantitative thresholds of different vegetation coverage in alpine meadow and steppe were determined to improve land-cover classification accuracy. Based on the spatial-temporal analysis of RLCM from 1990 to 2017, the whole ecological coverage was becoming better, and its driving factor was attributed to government policy and climate change. This study can provide a practical suggestion for the management and sustainable development in SNP.

更新日期：2021-03-03

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11