陆表水体遥感监测研究进展

doi:10.12082/dqxxkx.2019.190518

摘要/Abstract

摘要：

江河湖泊等陆表水体在工农业生产、气候调节、生态系统维持等方面扮演着至关重要的角色。在气候变化与人类活动的作用下,陆表水体的空间分布始终在发生着变化,因而对其进行快速精准的时空变化监测对水资源管理与保护、未来气候变化预测等有着重要的意义。遥感技术为大范围水体动态监测提供了全新的技术手段,特别是在当前地球大数据背景下,水体提取算法不断改进,遥感数据源急剧增加,但缺乏对算法和数据演化过程的系统整理。鉴于此,本文对现有水体提取算法与遥感数据进行了综合梳理,归纳了单波段阈值法、多波段谱间关系法、水体指数与阈值法、支持向量机、随机森林、深度学习等常用算法的演变,以及遥感数据源由低（MODIS等）到中（Landsat等）和高（高分1/2号等）空间分辨率的发展过程,并在此基础上讨论了各算法与数据源在水体变化研究中的差异。此外,本文论述了数据处理平台由本地计算到高性能云计算平台（如谷歌地球引擎）的发展,云计算促进地表水变化研究由基于时间片段到基于时间序列连续过程分析的转变,以及云计算在大尺度范围的应用。最后,本文还对多源遥感数据融合与云计算平台的结合在地表水体连续变化监测中的应用进行了展望,并对不同类型水体提取的不确定性进行了讨论。

关键词: 水体提取, 遥感监测, 遥感数据, 算法, 云计算平台, 多源数据融合

Abstract:

Open surface water bodies play important roles in industrial and agricultural production, climate regulation, and ecosystem maintenance. The spatial distributions of surface water bodies are always changing due to climate change and anthropogenic activities. Therefore, rapid and accurate monitoring of the spatiotemporal dynamics of surface water bodies is of great significance for water resources management and protection, as well as prediction of climate change. Remote sensing technology with the advantages of broader perspective, stronger timely effectiveness, larger information, and the ability of not affected by geographical environment provides a new way to monitor the dynamics of open surface water bodies over large extents, especially in remote and inaccessible mountain regions. The approach of the era of big earth data leads to the continuous improvements of water body mapping algorithms and increasing amounts of remote sensing data. However, there still lacks systematic review and evaluation about the evolution of relevant algorithms and data sources. In this context, based on the relevant literature ranging from the 1980s to 2018, this paper reviewed and assessed the existing algorithms and remote sensing data sources used in open surface water body mapping, and concluded the evolution processes of the common algorithms, such as single-band threshold approach, multi-band spectral relationship approach, spectral- and index-based approach, Support Vector Machine (SVM), Random Forest (RF), and Deep Learning (DL). Besides, we summarized the evolution of remote sensing data from coarse spatial resolutions (e.g. MODIS) to medium (e.g. Landsat) and high (e.g. GF-1/2) spatial resolutions. Furthermore, the different performances between these algorithms and data used in the studies of water body changes were analyzed. Also, we demonstrated the development of computing platforms from local computer to high performance cloud computing platforms such as Google Earth Engine (GEE) and Amazon Web Service (AWS), and highlighted typical cases that conduct retrospective and continuous monitoring of land cover changes over the global or regional scales. Then, we discussed the progress of studies focusing on the monitoring of open surface water body changes, from epoch-based analyses to interannual change analyses. Finally, we discussed the significance of the combined use of multi-source remote sensing data fusion and cloud computing platforms in the continuous monitoring of surface water body changes, and the uncertainties in detecting the different types of surface water bodies.

Key words: open surface water body mapping, monitoring, remote sensing data, algorithm, cloud-computing platform, multi-source data fusion

周岩, 董金玮. 陆表水体遥感监测研究进展[J]. 地球信息科学学报, 2019, 21(11): 1768-1778.DOI:10.12082/dqxxkx.2019.190518

ZHOU Yan, DONG Jinwei. Review on Monitoring Open Surface Water Body Using Remote Sensing[J]. Journal of Geo-information Science, 2019, 21(11): 1768-1778.DOI:10.12082/dqxxkx.2019.190518

图/表 4

图1

表1

表2

常用遥感卫星用于水体提取的若干典型波段波长(μm)与空间分辨率(m)

	蓝光	绿光		红光	近红外	短波红外-1	短波红外-2
低空间分辨率传感器
MODIS	0.46~0.48/500	0.55~0.57/500		0.62~0.67/250	0.84~0.88/250	1.63~1.65/500	2.11~2.14/500
NOAA/AVHRR			0.55~0.68/1100		0.73~1.10/1100
Suomi NPP-VIIRS	0.49/750	0.56/750		0.64/370	0.87/370	1.61/750	2.25/750
MERIS	0.49/300	0.56/300		0.67/300	0.87/300
中等空间分辨率传感器
Landsat 1-5 MSS		0.50~0.60/80		0.60~0.70/80	0.70~0.80/80	0.80~1.10/80
Landsat 4-5 TM	0.45~0.52/30	0.52~0.60/30		0.63~0.69/30	0.76~0.90/30	1.55~1.75/30	2.08~2.35/30
Landsat 7 ETM+	0.45~0.52/30	0.52~0.60/30		0.63~0.69/30	0.76~0.90/30	1.55~1.75/30	2.08~2.35/30
Landsat 8 OLI	0.45~0.51/30	0.53~0.59/30		0.64~0.67/30	0.85~0.88/30	1.57~1.65/30	2.11~2.29/30
Sentinel-2 MSI	0.46~0.52/10	0.55~0.58/10		0.64~0.67/10	0.78~0.90/10	1.57~1.65/20	2.10~2.28/20
SPOT 1-3		0.50~0.59/20		0.61~0.68/20	0.79~0.89/20
SPOT 4		0.50~0.59/20		0.61~0.68/20	0.78~0.89/20	1.58~1.78/20
SPOT 5		0.49~0.61/10		0.61~0.68/10	0.78~0.89/10	1.58~1.78/20
SPOT 6-7	0.46~0.53/6	0.53~0.59/6		0.63~0.70/6	0.76~0.89/6
ASTER		0.52~0.60/15		0.63~0.69/15	0.76~0.86/15	1.60~1.70/30	2.15~2.43/30
HJ-1A/B	0.43~0.52/30	0.52~0.60/30		0.63~0.69/30	0.76~0.90/30	1.55~1.75/150
高空间分辨率传感器
RapidEye	0.44~0.51/5	0.52~0.59/5		0.63~0.69/5	0.76~0.85/5
IKONOS	0.45~0.53/4	0.52~0.61/4		0.64~0.72/4	0.77~0.88/4
Quickbird	0.45~0.52/2.44~2.88	0.52~0.60/2.44~2.88		0.63~0.69/2.44~2.88	0.76~0.90/2.44~2.88
Worldview 2	0.45~0.51/0.46	0.51~0.58/0.46		0.63~0.69/0.46	0.77~0.90/0.46
Worldview 3	0.45~0.51/0.31	0.51~0.58/0.31		0.63~0.69/0.31	0.77~0.90/0.31	1.64~1.68/0.31	2.15~2.29/0.31
Worldview 4	0.45~0.51/0.31	0.51~0.58/0.31		0.66~0.69/0.31	0.78~0.92/0.31	1.64~1.68/0.31	2.15~2.37/0.31
高分1号	0.45~0.52/8	0.52~0.59/8		0.63~0.69/8	0.77~0.89/8
高分2号	0.45~0.52/4	0.52~0.59/4		0.63~0.69/4	0.77~0.89/4
资源3号	0.45~0.52/6	0.52~0.59/6		0.63~0.69/6	0.77~0.89/6

表2

表3

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算法	水体指数
TCW_Crist^[19,20]	TCW_Crist = 0.1509 × B_blue + 0.1973 × B_green + 0.3279 × B_red + 0.3406 × B_Nir - 0.7112 × B_SWIR-1 - 0.4572 × B_SWIR-2
NDWI^[21]	NDWI=(B_green - B_Nir) / (B_green + B_Nir)
mNDWI^[22,23]	mNDWI=(B_green - B_SWIR-1) / (B_green + B_SWIR-1)
SNN^[25,26]	Sum457 = B_Nir + B_SWIR-1 + B_SWIR-2 ND5723 = [(B_SWIR-1 + B_SWIR-2) - (B_green + B_red)] / [(B_SWIR-1 + B_SWIR-2) + (B_green + B_red)] ND571 = [(B_SWIR-1 + B_SWIR-2) - B_blue] / [(B_SWIR-1 + B_SWIR-2) + B_blue]
AWEI_sh^[27]	AWEI_sh = B_blue + 2.5 × B_green - 1.5 × (B_Nir + B_SWIR-1) - 0.25 × B_SWIR-2
AWEI_nsh^[27]	AWEI_nsh = 4 × (B_green - B_SWIR-1) - (0.25 × B_Nir + 2.75 × B_SWIR-1)
NDWI+ VI^[28]	EVI = 2.5 × (B_Nir - B_red) / (B_Nir + 6.0 × B_red - 7.5 × B_blue +1) NDVI = (B_Nir - B_red) / (B_Nir + B_red) LSWI = (B_Nir - B_SWIR-1) / (B_Nir + B_SWIR-1) mNDWI=(B_green - B_SWIR-1) / (B_green + B_SWIR-1) NDWI=(B_green - B_Nir) / (B_green + B_Nir)
mNDWI+VI^[28]
LSWI + VI^[28]
算法	阈值选取	研究区	总体精度/%
TCW_Crist^[19,20]	TCW_Crist> 0	北卡罗来纳州
NDWI^[21]	NDWI > 0	内布拉斯加州
mNDWI^[22,23]	mNDWI > 0	厦门市	99.85
SNN^[25,26]	(Sum457 < 0.188)或(ND5723 < -0.457)或(ND571 < 0.04)或(Sum457 < 0.269且ND5723 < -0.234且ND571 < 0.40)	密苏里州	96.00
AWEI_sh^[27] AWEI_nsh^[27]	AWEI_sh > 0 AWEI_nsh > 0	丹麦、瑞士、新西兰、埃塞俄比亚、南非	93.00~98.00
NDWI+ VI^[28] mNDWI+VI^[28] LSWI + VI^[28]	EVI<0.1且(NDWI > NDVI或NDWI > EVI) EVI<0.1且(mNDWI > NDVI或mNDWI > EVI) EVI<0.1且(LSWI> NDVI或LSWI > EVI)	佛罗里达州、拉斯维加斯	92.23~99.1294.34~99.5893.60~99.52

数据产品	空间范围	空间分辨率	时间跨度
Global Inundation Extent from Multi-Satellites (GIEMS)^[51]	全球	0.25 °	1993-2007
Global Raster Water Mask at 250 meter Spatial Resolution^[52]	全球	250 m	2000-2002
GLOWABO^[53]	全球	30 m	2000
Global Land 30-water^[54]	全球	30 m	2000, 2010
Global 3 arc-second Water Body Map (G3WBM)^[55]	全球	90 m	2010
Global water cover map in 2013^[56]	全球	500 m	2013
GLCF GIW^[57]	全球	30 m	2000
Joint Research Centre (JRC)^[11]	全球	30 m	1984-2015
500 m 8-day Water Classification Maps^[58]	全球	500 m	2000-2015
500 m Resolution Daily Global Surface Water Change Database^[49]	全球	500 m	2001-2016