黄河流域多源遥感土地覆被数据精度评价与一致性分析

Accuracy evaluation and consistency analysis of multi-source remote sensing land cover data in the Yellow River Basin

开源、多分辨率、及时的土地覆盖产品为了解全球地表覆盖状况、陆面过程模型模拟以及社会经济发展决策等提供了丰富的数据支撑,但多源的数据存在不同程度的不确定性,在区域尺度如何选择合适的土地覆被产品成为应用中的难题。本研究以黄河流域为例,对分辨率从30 m到1000 m的CLCD_v01_2020、GLOBELAND30、GLC_FCS30_2020、LANDCOVER(300 m)、MCD12Q1(500 m)和CNLUCC1000(1000 m)等6种2020年土地覆被产品进行区域尺度精度评价和一致性分析。基于Google Earth采集的1540个样本点分析6种数据在黄河流域的总体精度,以最高精度的数据为参考对其他数据进行面积一致性分析,并对6种数据进行类别混淆分析和混淆图谱分析。结果表明,6种数据中分类精度最高的为CLCD_v01_2020,总体精度(overall accuracy,OA)达88.12%;其次是GLOBELAND30(OA=85.32%)、GLC_FCS30_2020(OA=84.09%)、LANDCOVER300(OA=77.79%)、MCD12Q1(OA=73.38%)、CNLUCC1000(OA=71.82%),30 m土地覆被产品的KAPPA系数均在0.8以上,随着空间分辨率的下降,分类精度下降。6种数据的土地覆被类别组成的相对比例总体上趋于一致,但在耕地和草地两类土地覆被类别上仍存在较大差异,GLC_FCS30_2020与参考数据CLCD_v01_2020的相关性最高,R2达到0.9976。通过类别混淆分析可知6种数据普遍对耕地、林地和草地的混淆较为严重。类别混淆空间分析表明,验证数据与参考数据在黄河上游的草地、中下游部分耕地和建设用地等类型较为单一的区域一致性较高,而在陕西北部、山西西部的一致性较差,主要表现为草地和林地的混淆。针对黄河流域土地覆被数据一级分类,本研究建议,30 m分辨率的数据中选择CLCD_v01_2020,百米级分辨率数据中选择LANDCOVER300,二级分类则可以根据所需的分类体系选择合适的数据。

With the development of multi-source remote-sensing platforms and technologies,various land cover datasets have been developed that provide a wealth of data to support the understanding of global land cover conditions,land surface process model simulations,and socioeconomic development decisions.However,selecting appropriate data for different regions from nationally or globally available land cover datasets is challenging.In this study,six land cover products in 2020 over the Yellow River Basin,including CLCD_v01_2020,GLOBELAND30,GLC_FCS30_2020,LANDCOVER(300 m),MCD12Q1(500 m),and CNLUCC1000(1000 m),with resolutions ranging from 30 to 1000 m,were evaluated for regional-scale accuracy and consistency analysis.Accuracy analyses were performed on six products based on 1540 samples for seven land cover types collected by Google Earth.Data with the highest overall accuracy(OA)were used as a reference for the area consistency analysis of the other five products.Category confusion and confusion mapping analyses were performed on six types of data.Hopefully,this study will provide a scientific reference for users to select appropriate land cover data for the Yellow River Basin.The results showed that the highest classification accuracy was for CLCD_v01_2020,with an OA of 88.12%,followed by GLOBELAND30(OA=85.32%),GLC_FCS30_2020(OA=84.09%),LANDCOVER300(OA=77.79%),MCD12Q1(OA=73.38%),and CNLUCC1000(OA=71.82%).The KAPPA coefficients of the land cover products with a resolution of 30 m were all above 0.8,and the classification accuracy decreased as the spatial resolution decreased.CLCD_v01_2020,with the highest OA,was used as the reference dataset,and the area correlations and confusion mapping were calculated separately for the remaining five validation product datasets.The relative proportions of different land cover types were generally consistent across the six products;however,there were still large differences between croplands and grasslands.GLC_FCS30_2020 had the highest correlation with the reference data CLCD_v01_2020,with an R2 value of 0.9976.Category confusion analysis showed that the six data types were generally confused between croplands,forests,and grasslands.There was good consistency in the grasslands of eastern Qinghai in the upper reaches of the Yellow River and the cropland and construction land of the middle and lower reaches.The areas of poor consistency were mainly in the middle reaches of the Yellow River in northern Shaanxi and western Shanxi,which were mainly confused grasslands with forests.For the primary classification of land cover data in the Yellow River Basin,it is recommended that CLCD_v01_2020 data be selected for 30 m resolution and LANDCOVER300 for 100-m scale resolution data.In contrast,secondary classification can be chosen according to the desired classification system.