基于2种夜间灯光影像亮度修正指数的城市建成区提取研究

doi:10.12082/dqxxkx.2020.200128

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (8): 1714-1724.doi: 10.12082/dqxxkx.2020.200128

基于2种夜间灯光影像亮度修正指数的城市建成区提取研究

闫庆武¹^,^*(), 厉飞¹, 李玲²

1.中国矿业大学环境与测绘学院,徐州 221116
2.徐州市规划设计院,徐州 221000

收稿日期:2020-03-20 修回日期:2020-05-12 出版日期:2020-08-25 发布日期:2020-10-25
通讯作者: 闫庆武 E-mail:yanqingwu@cumt.edu.cn
作者简介:闫庆武（1975— ）,男,山东邹城人,副教授,主要从事GIS应用、人口地理学、人口数据空间化研究。E-mail:yanqingwu@cumt.edu.cn
基金资助:
武汉大学测绘遥感信息工程国家重点实验室开放基金(18T03);内蒙古自治区科技计划项目(2060399-273)

Research on Built-up Area Extraction via Brightness Correction Indexes based on Two Kinds of Nighttime Light Images

YAN Qingwu¹^,^*(), LI Fei¹, LI Ling²

1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2. Urban Planning and Design Institute of Xuzhou, Xuzhou 221000, China

Received:2020-03-20 Revised:2020-05-12 Online:2020-08-25 Published:2020-10-25
Contact: YAN Qingwu E-mail:yanqingwu@cumt.edu.cn
Supported by:
Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University(18T03);Science and technology project of Inner Mongolia Autonomous Region(2060399-273)

摘要/Abstract

摘要：

夜间灯光影像容易受到道路灯光、水面散射等影响而产生背景噪声,这一定程度上影响了利用夜间灯光数据提取建成区的精度。本文基于夜间灯光影像的DN（Digital Number）值与路网密度正相关、与EVI指数（Enhanced Vegetation Index,增强型植被指数）呈负相关的规律,提出了2种可用于建成区提取的夜间灯光亮度修正指数：EVI夜间灯光亮度修正指数EANI (EVI Adjusted Nighttime Light(NTL) Index)和基于道路网密度与EVI指数的夜间灯光亮度修正指数REANI (Road Density & EVI Adjusted NTL Index),并利用珞珈1号卫星（LJ1-01,分辨率约130 m）影像和NPP-VIIRS影像（分辨率约 500 m） 2种不同空间分辨率夜间灯光遥感影像进行验证。以2018年徐州市建成区为研究对象,分区域（主城区、外围区）利用阈值法对2种原始夜间灯光影像、经EANI指数和REANI指数处理后的影像进行建成区提取,得到6种建成区提取的结果。研究表明： ① EANI指数和REANI指数能够有效抑制夜间灯光影像的背景噪声,建成区提取的结果均优于直接利用原始影像的结果,特别是对于城市化水平较低地区的建成区提取效果更佳;② 相较于NPP-VIIRS影像,利用LJ1-01影像提取建成区的效果提高6%左右,说明我国的LJ1-01夜间灯光影像在建成区提取方面有广阔的应用前景。EANI和REANI为建成区提取提供了有效工具,并可应用于城市规划和城市扩张等研究领域。

关键词: 夜间灯光影像, 珞珈01星影像, NPP-VIIRS影像, EANI, REANI, 去噪, 建成区提取, 徐州市

Abstract:

Nighttime light data are widely used to monitor human activities from space, such as urban development, population density simulation, energy gas emissions, power consumption, human activities and effects, economic development level, and ecological environment. However, due to road lights and light scattering from water surface, Nighttime Light (NTL) images always contain a lot of background noises. These background noises may greatly limit the application of nighttime light images in built-up area extraction. In our paper, based on high-resolution nighttime light images of Lujia1-01 from China and NPP-VIIRS from the United States in the second half of 2018, the Enhanced Vegetation Index (EVI) Adjusted NTL Index (EANI) and Road Density & EVI Adjusted NTL Index (REANI) were proposed to reduce background noises and applied to built-up area extraction. The EANI and REANI were developed based on the law that the Digital Number (DN) values of the nighttime light images are positively correlated with road density and negatively correlated with the EVI. In this paper, the Xuzhou city of China was selected as the research area. The threshold method was used to extract built-up areas in the main city and the peripheral area from the original LJ1-01 and NPP-VIIRS images, and the images processed by the EANI and the REANI, respectively. The results show that: (1) both EANI and REANI can effectively reduce background noises in nighttime light images. The results extracted from images processed by these two indexes were much better than that from the original NTL images, especially for the low urbanization areas; and (2) LJ1-01 performed better than NPP-VIIRS in built-up area extraction. Due to the higher spatial resolution of LJ1-01 data, the accuracy of extracted built-up areas from LJ1-01 was much higher than that from NPP-VIIRS in low-level urbanization areas, but was about the same with NPP-VIIRS in areas with high urbanization levels. Through error analysis, the relative error of extracted built-up areas from LJ1-01 decreased by about 6%, which indicates that LJ1-01 nighttime light image is promising for future built-up area extraction. Also, both EANI and REANI provide effective tools for the extraction of built-up areas and could be further applied to researches such as urban planning and urban expansion.

Key words: Nighttime Light Images, LJ1-01 Images, NPP-VIIRS Images, EANI, REANI, denoise, built-up area extraction, Xuzhou City

闫庆武, 厉飞, 李玲. 基于2种夜间灯光影像亮度修正指数的城市建成区提取研究[J]. 地球信息科学学报, 2020, 22(8): 1714-1724.DOI:10.12082/dqxxkx.2020.200128

YAN Qingwu, LI Fei, LI Ling. Research on Built-up Area Extraction via Brightness Correction Indexes based on Two Kinds of Nighttime Light Images[J]. Journal of Geo-information Science, 2020, 22(8): 1714-1724.DOI:10.12082/dqxxkx.2020.200128

图/表 8

图1

图2

图3

表1

LJ1-01和NPP-VIIRS主要参数"

参数	LJ1-01	NPP-VIIRS
空间分辨率/m	130	500
幅宽/km	250	3000
光谱范围/ $μm$	0.46~0.98	0.5~0.9
辐射分辨率/bits	14	14
可获取时间段	2018年6月至今	2012年至今

表1

图4

图5

表2

表3

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区域	参数	LJ1-01			NPP-VIIRS
区域	参数	原始影像	EANI	REANI	原始影像	EANI	REANI
研究区	阈值	8100	3900	2200	6.5	2.85	1.56
	提取的栅格数/个	24 663	24 640	24 644	2489	2491	2488
	提取面积/hm²	44 757.20	44 715.80	44 725.19	44 089.88	47 648.23	47 618.50
主城区	阈值	11 400	5950	3950	9	4.1	2.7
	提取的栅格数/个	17 355	17 355	17 320	1775	1784	1778
	提取面积/hm²	31 503.36	31 503.13	31 439.77	31 461.57	34 154.06	34 039.53
外围区	阈值	5780	2570	1315	4.73	2.07	1.03
	提取的栅格数/个	6963	6954	6970	713	716	716
	提取面积/hm²	12 619.05	12 602.21	12 634.82	12 610.09	13 649.56	13 684.48

	区域	LJ1-01			NPP-VIIRS
	区域	原始影像	EANI	REANI	原始影像	EANI	REANI
绝对误差/hm²	主城区+外围区	16 514	16 739	15 171	19 257	19 263	17 802
	主城区	6959	7441	6911	7032	7476	6929
	外围区	9555	9298	8260	12 225	11 787	10 873
相对误差/%	主城区+外围区	37.27	37.77	34.23	43.45	43.47	40.17
	主城区	22.01	23.53	21.86	22.24	23.64	21.91
	外围区	75.26	73.24	65.06	96.29	92.84	85.64

基于2种夜间灯光影像亮度修正指数的城市建成区提取研究

Research on Built-up Area Extraction via Brightness Correction Indexes based on Two Kinds of Nighttime Light Images

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