基于伪纯像元的精度评价策略及其应用

doi:10.12082/dqxxkx.2022.220048

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (8): 1617-1630.doi: 10.12082/dqxxkx.2022.220048

基于伪纯像元的精度评价策略及其应用

徐肖¹^,²(), 李娅婷¹^,², 樊辉¹^,²^,^*()

1.云南大学国际河流与生态安全研究院,昆明 650091
2.云南省国际河流与跨境生态安全重点实验室,昆明 650091

收稿日期:2022-01-25 修回日期:2022-02-22 出版日期:2022-08-25 发布日期:2022-10-25
通讯作者: *樊辉（1972— ）,男,江西修水人,博士,研究员,主要从事地理大数据挖掘研究。E-mail: fanhui@ynu.edu.cn
作者简介:徐肖（1998— ）,女,四川南充人,硕士生,主要从事地理大数据挖掘研究。E-mail: xuxiao@mail.ynu.edu.cn
基金资助:
第二次青藏高原综合科学考察研究(2019QZKK0402);国家自然科学基金项目(41971239);国家自然科学基金项目(41461017);云南省教育厅科学研究基金项目(2022Y061)

Accuracy Assessment Strategy based on Pseudo-pure Pixels and its Application

XU Xiao¹^,²(), LI Yating¹^,², FAN Hui¹^,²^,^*()

1. Institute of International Rivers and Eco-Security, Yunnan University, Kunming, 650091, China
2. Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Kunming 650091, China

Received:2022-01-25 Revised:2022-02-22 Online:2022-08-25 Published:2022-10-25
Contact: FAN Hui
Supported by:
The Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK0402);National Natural Science Foundation of China(41971239);National Natural Science Foundation of China(41461017);Scientific Research Foundation of Yunnan Provincial Education Department(2022Y061)

摘要/Abstract

摘要：

土地覆被是地球科学研究中的重要参量,评价土地覆被数据的制图精度是保障数据合理使用的前提。本文提出了一种基于伪纯像元的精度评价策略（伪纯像元策略）,即当低空间分辨率栅格窗口内对应的高空间分辨率数据中优势类别（面积最大的地类）的占比高于伪纯像元纯度阈值（代表像元纯度,取值范围：35%~100%,步长为5%）时,以此栅格窗口为基准生成土地覆被类型为优势类别的伪纯像元用于精度评价。以澜沧江-湄公河（澜湄）流域为试验区,选择GlobeLand30为参考数据,并基于混淆矩阵精度评价方法对比分析了伪纯像元策略与重采样法（最近邻法和众数法）在CCI-LC（300 m）和MCD12Q1（500 m） 2套全球土地覆被数据精度评价中的差异。结果表明：① 伪纯像元策略在35%~100%纯度阈值下对CCI-LC和MCD12Q1在澜湄流域评价的精度分别为72.76%~55.26%和71.44%~45.41%,比重采样法评价的单一精度（众数法：71.21%和70.54%、最近邻法：71.48和69.87%）能更好地反映像元纯度对土地覆被数据精度的影响;② CCI-LC的总体精度高于MCD12Q1,且2套数据的精度差随纯度阈值的增大而增加,CCI-LC和MCD12Q1在35%、100%纯度阈值下的精度差分别为1.32%和9.85%;③ 2套数据中耕地、有林地、草地和水体的分类精度均相对较高,而灌木林地（精度接近0）和裸地的分类精度均较低;④ 2套数据与GlobeLand30的空间不一致区域多出现在土地覆被类型高度异质化的混合像元区域,且随纯度阈值的增大,评价样本栅格更趋均质,混合像元对评价精度的影响也会递减。伪纯像元精度评价策略适用于跨空间分辨率土地覆被数据的精度对比,为评价全球土地覆被产品在区域尺度的适用性及适用范围提供了新的检验策略。

关键词: 伪纯像元, 重采样, 精度评价, 土地覆被, 澜湄流域, GlobeLand30, CCI-LC, MCD12Q1

Abstract:

Land cover is an important parameter in geoscience research, and the accuracy assessment of land cover products is a prerequisite to ensure reasonable application of land cover products. In this study, an accuracy assessment strategy based on pseudo-pure pixels (i.e., pseudo-pure pixel strategy) is proposed. That is, calculating the area of land cover types of the high-resolution pixels within a pixel window of coarse spatial resolution data and defining the land cover type with the largest area as the advantage type, and then generating pseudo-pure pixels of advantage type based on the pixel window when the area proportion of the advantage type is higher than a pseudo-pure pixel purity threshold (ranging from 35% to 100%, with a step length of 5%). We take the Lancang-Mekong (Lanmei) basin as the study area and select the GlobeLand30 as the reference data. The confusion matrix accuracy assessment method was used to compare the difference in the accuracy of two sets of global land cover data, i.e., CCI-LC (300 m) and MCD12Q1 (500 m), using different assessment methods, i.e., the pseudo-pure pixel strategy and the resampling method (Nearest and Majority). Our results show that: (1) The accuracy using the pseudo-pure pixel strategy for CCI-LC and MCD12Q1 in the Lanmei Basin under the purity thresholds of 35%~100% are 72.76%~55.26% and 71.44%~45.41%, respectively, and it can better reflect the influence of pixel purity on the accuracy of land cover data than the single accuracy obtained by resampling method (Nearest: 71.21% and 70.54%; Majority: 71.48 and 69.87%); (2) The overall accuracy of CCI-LC is higher than that of MCD12Q1. The accuracy difference of the two datasets increases with the increase of purity threshold, and it is 1.32% and 9.85% respectively for purity threshold of 35% and 100%, respectively; (3) In both datasets, the classification accuracy of cropland, forest, grassland, and water is relatively high, and the classification accuracy of shrub land and bare land is relatively low; (4) The spatial inconsistency between the two datasets and the GlobeLand30 mainly occur in mixed pixel regions with highly heterogeneous land cover types. And the assessment sample grids are purer with the increase of pseudo-pure pixel threshold, which reduces the effects of mixed pixels on accuracy assessment. The pseudo-pure pixel strategy has the potential to compare the mapping accuracy of land cover data with different spatial resolutions, and provides a promising validation method for determining the applicability and application scope of global land cover products at regional scales.

Key words: pseudo-pure pixel, resampling, accuracy assessment, land cover, Lancang-Mekong River Basin, GlobeLand30, CCI-LC, MCD12Q1

徐肖, 李娅婷, 樊辉. 基于伪纯像元的精度评价策略及其应用[J]. 地球信息科学学报, 2022, 24(8): 1617-1630.DOI:10.12082/dqxxkx.2022.220048

XU Xiao, LI Yating, FAN Hui. Accuracy Assessment Strategy based on Pseudo-pure Pixels and its Application[J]. Journal of Geo-information Science, 2022, 24(8): 1617-1630.DOI:10.12082/dqxxkx.2022.220048

图/表 11

图1

图2

表1

表2

不同土地覆被分类系统间的类别对应关系

编号	类别	GlobleLand30	CCI-LC	MCD12Q1
1	耕地	10用于种植农作物的土地,包括水田、灌溉旱地、大棚用地,以及果园茶园等灌木类经济作物种植地	10/11/12农田旱地/草本覆被的耕地/树或灌丛覆被的耕地 20灌溉旱地或洪泛耕地 30耕地(>50%)与树、灌、草等自然植被(<50%)的混交地	12农用地 14农用地与自然植被混合：40%~60%为天然乔木、灌木林地或草本植被
2	有林地	20树冠覆被度>30%的土地,包括落叶阔叶林、常绿阔叶林、落叶针叶林、常绿针叶林、混交林,以及树冠覆被度为10%~30%的疏林地	50常绿阔叶林(>15%) 60/61/62落叶阔叶林(>15%)/(>40%)/(15%~40%) 70/71/72常绿针叶林(>15%)/(>40%)/(15%~40%) 80/81/82落叶针叶林(>15%)/(>40%)/(15%~40%) 90针阔混交林 100乔木和灌木林地(>50%)与草本植物(<50%)的混交地	1常绿针叶林：冠幅>4 m²,覆被度>60% 2常绿阔叶林：冠幅>4 m²,覆被度>60% 3落叶针叶林：冠幅>4 m²,覆被度>60% 4落叶阔叶林：冠幅>4 m²,覆被度>60% 5针阔混交林：落叶常绿为主,冠幅>4 m²,覆被度>60% 8木本稀树草原：冠幅>4 m²,覆被度30%~60%
3	草地	30天然草本覆被度>10%的土地,包括草原、草甸、稀疏草原、荒漠草原、人工草地	110草本植被(>50%)与树(<50%)的混交地 130草地	10草地：以年生草本植物为主,高度<2 m
4	灌木林地	40灌木覆被度>30%的土地,包括山地灌丛、落叶和常绿灌丛,以及荒漠地区覆被度>10%的荒漠灌丛	120/121/122灌木林地/常绿灌木林地/落叶灌木林地 40树、灌、草等自然植被(>50%)与耕地(<50%)的混交地	6稠密灌丛：以木本多年生植物为主,高度1~2 m,覆被度>60% 7稀疏灌丛：以木本多年生植物为主,高度1~2 m,覆被度10%~60% 9稀树草原：树木覆被度10-30%,冠幅>4 m²
5	湿地	50有浅层积水或土壤过湿的土地,包括内陆沼泽、湖泊沼泽、河流洪泛湿地、森林/灌木湿地、泥炭沼泽、盐沼等	160被水淹没的有林地(淡水或咸水) 170被水淹没的有林地(海水) 180被水淹没的灌木林地和草本植物(淡水、海水或咸水)	11永久湿地：永久淹水面积30%~60%,植被覆被度>10%的土地
6	水体	60 陆地范围液态水覆被的区域	210水体	17水体：至少60%的区域被永久水体覆被
7	建设用地	80由人工建造活动形成的地表	190城镇地区	13城市和建筑区：至少30%的不透水表面,包括建筑材料、沥青道路
8	裸地	90植被覆被度<10%的自然覆被土地,包括荒漠、沙地、砾石地、裸岩、盐碱地等	200/201/202裸地/坚固的裸地/松散的裸地 150/152/153树、灌、草等稀疏植被(<15%)	16裸地：至少60%的区域是没有植被覆被的裸露地区(沙、岩石、土壤),植被覆被<10%
9	永久冰雪	100由永久积雪、冰川和冰盖覆被的土地	220 永久冰雪	15雪和冰：全年至少有60%的地区被冰雪覆被10个月

表2

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图5

图6

图7

图8

图9

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数据集	GlobleLand30	CCI-LC	MCD12Q1
传感器	Landsat TM5/ETM+、HJ-1	ENVISAT MERIS、SPOT	Terra MODIS
分类方法	像元-对象-知识(POK方法)	非监督分类	监督分类/决策树/神经网络
分辨率/m	30	300	500
时相	2000年、2010年	1992—2020年	2001—2015年
分类体系	自分类体系 (10类)	LCCS (22类)	IGBP (17类)
总体精度/%	83.50	74.40	75.00
验证方法	交叉验证	样本验证	交叉验证

基于伪纯像元的精度评价策略及其应用

Accuracy Assessment Strategy based on Pseudo-pure Pixels and its Application

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