Accuracy Assessment of Multi-source Land Use Products in the Loess Hilly and Gully Region

CHEN Le; WEI Wei

doi:10.13870/j.cnki.stbcxb.2024.03.024

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Accuracy Assessment of Multi-source Land Use Products in the Loess Hilly and Gully Region

更新时间：2025-03-12

- Accuracy Assessment of Multi-source Land Use Products in the Loess Hilly and Gully Region
- Vol. 38, Issue 3, Pages: 177-186(2024)
- 作者机构：
  
  1. 中国科学院生态环境研究中心城市与区域生态国家重点实验室,北京,100085
  2. 中国科学院大学,北京,100049
  3. 陕西黄土高原地球关键带国家野外科学观测研究站,西安,710061
- 作者简介：
- 基金信息：
- DOI：10.13870/j.cnki.stbcxb.2024.03.024
  CLC： TP79
- Published：2024
- 稿件说明：
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CHEN Le, WEI Wei. Accuracy Assessment of Multi-source Land Use Products in the Loess Hilly and Gully Region[J]. 2024, 38(3): 177-186.
DOI：

CHEN Le, WEI Wei. Accuracy Assessment of Multi-source Land Use Products in the Loess Hilly and Gully Region[J]. 2024, 38(3): 177-186. DOI： 10.13870/j.cnki.stbcxb.2024.03.024.

摘要

[目的] 为评估多源土地利用产品在黄土丘陵沟壑区的精度。[方法] 以黄河二级支流关川河流域为例

基于高精度GCLUCC土地利用数据

从空间分布、面积特征、样本点精度和空间一致性4个方面评估空间分辨率为10

30 m的6种土地利用产品(WorldCover 10 m、ESRI 10 m、GLC_FCS30-2020、GlobleLand30、CNLUCC和CLCD)的准确性。GCLUCC数据是基于GF-2(0.8 m)、DEM(5 m)、野外采样点(6 400个)和Google Earth样本点(2 320个)

采用面向对象法和人工目视解译

空间分辨率为1 m

整体精度超过95%。[结果] (1)分类特征方面

多数产品能提取主要土地类别

但提取效率和准确度存在明显差异

特别是在耕地、林地和建设用地等地类的空间分布上。(2)面积一致性上

各产品与GCLUCC在不同地类的面积上存在显著差异。部分产品的草地面积是GCLUCC的2倍以上

而林地和水域面积仅占GCLUCC的0.13%~12.11%和1.41%~11.27%。(3)整体精度方面

GlobleLand30和WorldCover 10 m展现较高精度

空间图谱精度分别达到58.21%和50.19%。而CLCD和CNLUCC的准确性相对较低

其中

林地与草地、耕地与草地及建设用地与耕地间的分类混淆严重。(4)所有产品与实际地表间的空间误差显著

尤其在对乔木、灌木、裸地和水体分类上面临挑战。[结论] 当前土地利用产品在黄土丘陵沟壑区精确表征地表覆盖特征方面存在挑战。未来的产品开发应更多地考虑地形和地理特征

并加强对特定土地利用类型的识别。研究结果可为黄土高原生态保护和地表信息获取提供理论参考。

Abstract

[Objective] The study was aimed to assess the accuracy of multi-source land use products in the loess hilly and gully regions. [Method] Using the Guanchuan River Basin

a secondary tributary of the Yellow River

as a case study

this research evaluated the accuracy of six land use products with spatial resolutions of 10 and 30 m (WorldCover 10 m

ESRI 10 m

GLC_FCS30-2020

GlobleLand30

CNLUCC

and CLCD). The evaluation was based on high-precision GCLUCC land use data and focused on four aspects: spatial distribution

area characteristics

sample point accuracy

and spatial consistency. The GCLUCC data

with a spatial resolution of 1 m and an overall accuracy exceeding 95%

was derived from GF-2 (0.8 m)

DEM (5 m)

6 400 field sampling points

and 2 320 Google Earth sample points

using an object-oriented approach and manual visual interpretation. [Results] (1) In terms of classification features

most products could extract major land categories

but significant differences in extraction efficiency and accuracy were evident

especially in the spatial distribution of arable land

forest land

and construction land. (2) Regarding area consistency

significant variations were found between each product and GCLUCC in terms of area of different land categories. For instance

the grassland area in some products was more than double that of GCLUCC

while forest and water areas were only 0.13%~12.11% and 1.41%~11.27% of GCLUCC

respectively. (3) In terms of overall accuracy

GlobleLand30 and WorldCover 10 m showed relatively higher accuracy

with spatial map accuracy reaching 58.21% and 50.19%

respectively. In contrast

CLCD and CNLUCC had relatively lower accuracy

with significant classification confusion between forest and grassland

arable and grassland

and construction and arable land. (4) Significant spatial discrepancies existed between all products and the actual ground cover

particularly in the classification of trees

shrubs

bare land

and water bodies. [Conclusion] Current land use products face challenges in accurately characterizing surface cover features in the loess hilly and gully regions. Future product development should consider topographical and geographical features more thoroughly and enhance the identification of specific land use types. These results can provide a theoretical reference for ecological conservation and land resource information acquisition in the Loess Plateau.

关键词

Keywords

references

YANG Y K, XIAO P F, FENG X Z, et al. Accuracy assessment of seven global land cover datasets over China[J].ISPRS Journal of Photogrammetry and Remote Sensing,2017,125:156-173.

GRIFFITHS P, NENDEL C, HOSTERT P. Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping[J].Remote Sensing of Environment,2019,220:135-151.

LIU H, GONG P, WANG J, et al. Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020-iMap World 1.0[J].Remote Sensing of Environment,2021,258:e112364.

YANG J, HUANG X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J].Earth System Science Data,2021,13(8):3907-3925.

CHEN B, HUANG B, XU B. Multi-source remotely sensed data fusion for improving land cover classification[J].ISPRS Journal of Photogrammetry and Remote Sensing,2017,124:27-39.

HUA T, ZHAO W, LIU Y, et al. Spatial consistency assessments for global land-cover datasets: A comparison among GLC2000,CCI LC, MCD12, GLOBCOVER and GLCNMO[J].Remote Sensing,2018,10(11):e1846.

李蓓蓓,方修琦,叶瑜,等.全球土地利用数据集精度的区域评估:以中国东北地区为例[J].中国科学:地球科学,2010,40(8):1048-1059. LI B B, FANG X Q, YE Y, et al. Regional evaluation of global land use data set accuracy: A case study of Northeast China[J].Scientia Sinica (Terrae),2010,40(8):1048-1059.

马京振,孙群,肖强,等.河南省GlobeLand30数据精度评价及对比分析[J].地球信息科学学报,2016,18(11):1563-1572. MA J Z, SUN Q, XIAO Q, et al. Accuracy assessment and comparative analysis of GlobeLand30 dataset in Henan province[J].Journal of Geo-information Science,2016,18(11):1563-1572.

STEHMAN S V,WICKHAM J D, WADE T G, et al. Designing a multi-objective, multi-support accuracy assessment of the 2001 National Land Cover Data (NLCD 2001) of the conterminous United States[J].Photogrammetric Engineering and Remote Sensing,2008,74(12):1561-1571.

STEHMAN S V, FOODY G M. Key issues in rigorous accuracy assessment of land cover products[J].Remote Sensing of Environment,2019,231:e111199.

史文中,秦昆,陈江平,等.可靠性地理国情动态监测的理论与关键技术探讨[J].科学通报,2012,57(24):2239-2248. SHI W Z, QIN K, CHEN J P,et al. Key theories and technologies on reliable dynamic monitoring for national geographical state[J].Chinese Science Bulletin,2012,57(24):2239-2248.

眭海刚,冯文卿,李文卓,等.多时相遥感影像变化检测方法综述[J].武汉大学学报(信息科学版),2018,43(12):1885-1898. SUI H G, FENG W Q, LI W Z, et al. Review of change detection methods for multi-temporal remote sensing imagery[J].Geomatics and Information Science of Wuhan University,2018,43(12):1885-1898.

赵安周,刘宪锋,朱秀芳,等.2000-2014年黄土高原植被覆盖时空变化特征及其归因[J].中国环境科学,2016,36(5):1568-1578. ZHAO A Z, LIU X F, ZHU X F, et al. Spatiotemporal analyses and associated driving forces of vegetation coverage change in the Loess Plateau[J].China Environmental Science,2016,36(5):1568-1578.

丁晓彤,孙文义,穆兴民,等.多源土地利用/覆被产品在黄土高原应用和精度评估[J].水土保持研究,2023,30(2):201-210. DING X T, SUN W Y, MU X M, et al. Application and accuracy evaluation of multi-source land use/cover products on the Loess Plateau[J].Research of Soil and Water Conservation,2023,30(2):201-210.

李海防,卫伟,邓居礼,等.关川河流域1995-2010年降雨特征及其水土流失效应[J].水土保持通报,2013,33(6):84-93. LI H F, WEI W, DENG J L, et al. Characteristics of precipitation and soil erosion from 1995 to 2010 in Guanchuan River Basin[J].Bulletin of Soil and Water Conservation,2013,33(6):84-87,93.

赵传普,徐学选,陈天林,等.关川河流域土地利用变化对径流的影响[J].水土保持研究,2015,22(3):83-87. ZHAO C P, XU X X, CHEN T L, et al. Impact ofland use changes on the streamflow in guanchuanhe watershe?剛楊癝攮牒??慥獡楲湣孨?嵯??桓楯湩敬猠敡??漠畗牡湴慥汲?潃景??捥潲??杴物楯据甬氲琰由爵攬?有〨社?????????????㈱?? 李海防,卫伟,陈瑾,等.定西关川河流域退耕还林还草对景观格局演变的影响[J].干旱区研究,2014,31(3):410-415. LI H F, WEI W, CHEN J,et al. Effect of grain-for-green project on evolution of landscape pattern in the Guanchuan River Basin, Dingxi[J].Arid Zone Research,2014,31(3):410-415.

李玉婷,张建军,田宁宁,等.黄土高原半干旱区退耕还林对土壤微量元素的影响[J].农业机械学报,2015,46(8):113-120. LI Y T, ZHANG J J, TIAN N N, et al. Effects of grain for green project on soil microelements in semi-arid area of Loess Plateau[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(8):113-120.

ZHANG X, LIU L Y, CHEN X D, et al. GLC/FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery[J].Earth System Science Data,2021,13(6):2753-2776.

CAO B W, YU L, NAIPAL V, et al. A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine[J].Earth System Science Data,2021,13(5):2437-2456.

ZHAO H Q, FANG X, DING H, et al. Extraction of terraces on the Loess Plateau from high-resolution DEMs and imagery utilizing object-based image analysis[J].ISPRS International Journal of Geo-Information,2017,6(6):e157.

LUO L H, LI FY, DAI Z Y, et al. Terrace extraction based on remote sensing images and digital elevation model in the Loess Plateau, China[J].Earth Science Informatics,2020,13(2):433-446.

HOPKINS A J, SNYDER N P. Performance evaluation of three DEM-based fluvial terrace mapping methods[J].Earth Surface Processes and Landforms,2016,41(8):1144-1152.

DAI W, NA J M, HUANG N, et al. Integrated edge detection and terrain analysis for agricultural terrace delineation from remote sensing images[J].International Journal of Geographical Information Science,2020,34(3):484-503.

吴宗洋,蔡卓雅,郭英,等.黄河流域多源遥感土地覆被数据精度评价与一致性分析[J].中国生态农业学报(中英文),2023,31(6):917-927. WU Z Y, CAI Z Y, GUO Y, et al. Accuracy evaluation and consistency analysis of multi-source remote sensing land cover data in the Yellow

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