ISPRS Journal of Photogrammetry and Remote Sensing 92 (2014) 26–37
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ISPRS Journal of Photogrammetry and Remote Sensing
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Review Article Remote sensing of alpine lake water environment changes on the Tibetan Plateau and surroundings: A review ⇑ Chunqiao Song a, Bo Huang a,b, , Linghong Ke c, Keith S. Richards d a Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong b Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong c Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong d Department of Geography, University of Cambridge, Cambridge CB2 3EN, United Kingdom article info abstract
Article history: Alpine lakes on the Tibetan Plateau (TP) are key indicators of climate change and climate variability. The Received 16 September 2013 increasing availability of remote sensing techniques with appropriate spatiotemporal resolutions, broad Received in revised form 26 February 2014 coverage and low costs allows for effective monitoring lake changes on the TP and surroundings and Accepted 3 March 2014 understanding climate change impacts, particularly in remote and inaccessible areas where there are lack Available online 26 March 2014 of in situ observations. This paper firstly introduces characteristics of Tibetan lakes, and outlines available satellite observation platforms and different remote sensing water-body extraction algorithms. Then, this Keyword: paper reviews advances in applying remote sensing methods for various lake environment monitoring, Tibetan Plateau including lake surface extent and water level, glacial lake and potential outburst floods, lake ice phenol- Lake Remote sensing ogy, geological or geomorphologic evidences of lake basins, with a focus on the trends and magnitudes of Glacial lake lake area and water-level change and their spatially and temporally heterogeneous patterns. Finally we Climate change discuss current uncertainties or accuracy of detecting lake area and water-level changes from multi- source satellite data and on-going challenges in mapping characteristics of glacial lakes using remote sensing. Based on previous studies on the relationship between lake variation and climate change, it is inferred that the climate-driven mechanisms of lake variations on the TP still remain unclear and require further research. Ó 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
1. Introduction focusing on the alpine lake changes and their response to climate change. The Tibetan Plateau (TP, hereinafter), which is known as ‘‘the Traditionally, the lake water balances are measured by the ‘‘di- Roof of the World’’ and ‘‘the Third Pole’’ of Earth, consists of a large rect’’ meteorological or hydrological stationed observations (Chu number of alpine lakes, with the total area surpassing 44,990 km2 et al., 2012b; Meng et al., 2012; Qi and Zheng, 2006; Zhang et al., (Jiang and Huang, 2004). As the alpine hydrologic environment has 2011a), which detect changes in lake water level and shoreline at been minimally disturbed by human activities, such as agricultural local spatial and temporal scales. This method is limited to a few irrigations and settlements, it is deemed as an important indicator lakes due to the remoteness and unavailability of most Tibetan of climate change. During the past decades, climate changes, char- lakes. The increased availability of remote sensing platforms with acterized by rapidly rising temperature, as well as changing precip- adequate spatial and temporal resolutions, near global coverage itation and evaportranspiration, have substantial impacts on water and low financial costs provide the potential of observing lake storage of the inland lakes over the plateau. In particular, the rap- water environments at larger spatial extent and longer timescales. idly rising temperature has accelerated glacier retreating and per- This paper therefore reviews the potential of optical images and sa- mafrost thawing, which is considered to be tightly associated with tellite altimetry data for monitoring the spatiotemporal variability accelerated lake expansions (Yao et al., 2007; Ye et al., 2008, 2007; of alpine lakes on the TP. The distribution characteristics of the al- Zhu et al., 2010). Recently there are rapidly growing literatures pine lakes were firstly introduced; Then, we analyze merits and demerits of a variety of available satellite data and their applicabil-
⇑ Corresponding author. Address: Department of Geography and Resource ity for plateau lakes; Thirdly, the commonly used semi-automated Management, The Chinese University of Hong Kong, Shatin 999077, Hong Kong. or automated algorithms of determining lake water body from E-mail address: [email protected] (B. Huang). multi-spectral and topographic data are introduced and the http://dx.doi.org/10.1016/j.isprsjprs.2014.03.001 0924-2716/Ó 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved. C. Song et al. / ISPRS Journal of Photogrammetry and Remote Sensing 92 (2014) 26–37 27 uncertainties are discussed; Finally we summarized applications of in the northeastern TP (e.g. Gyaring Lake and Ngoring Lake in the remote sensing techniques on change detection on lake extent and Yellow River Source) and South Tibet (e.g. Yamzhog Yumco, Ma- water level, glacial lake detection and outburst modeling, monitor- pam Yumco) where the annual rainfall is relatively plentiful. ing lake ice phenology, and relations of lake and climate. Besides, There are a large number of small-size glacial lakes distributed we discussed the advantages and limitations of remote sensing in glaciated regions of high mountains. Two types of typical glacial methods for lake water environment detection at different spatial lakes are known on the TP and surroundings: lakes dammed by an and temporal scales, and concluded several aspects of challenges end moraine and lakes dammed by a glacier (Ding and Liu, 1991). and problems that need to be solved. The moraine-dammed lakes are mainly distributed in the Hima- laya, the middle and eastern Nyainqentanglha Mts. and sections of the Hengduan Mts. in the southeastern TP (Fig. 1), while the 2. Overview of the Tibetan lakes supraglacial lakes are mainly scattered over the Karakoram Mts., the Pamir and the western Tianshan mountains. Recently with The Tibetan Plateau, located in central Asia, is the highest and accelerated global warming, the glacial lakes and frequently oc- most extensive plateau on Earth with an average altitude more curred glacial lake hazards have attracted increasing attention. than 4000 m (Fig. 1). There are more than 1500 lakes on the pla- teau, 312, 104, 7 and 3 (statistics in 2011) of which cover surface areas larger than 10 km2, 100 km2, 500 km2 and 1000 km2, respec- 3. Characteristics of satellites or sensors available for tively. The total area of Tibetan lakes accounts for about 49% of the monitoring lake total lake area in China (Bian et al., 2006; Ma et al., 2010). This re- gion is featured by rather complex topography and climate pat- So far, there have been a number of remote sensing satellites terns. The highland is divided into many different broad basins being put into practice. This section presents a brief overview on or valleys by many high mountain ranges, such as the Himalayas, characteristics of space-borne sensors applied in lake studies with Kunlun Mts., Qilian Mts., Gangdise Mts., and Nyainqêntanglha a focus on the Tibetan Plateau. Table 1 summarizes characteristics Mts. In summer, the climate of the plateau is mostly influenced of some typical satellites and sensors. These satellites or sensors by some airflows of tropospheric tropical easterlies, subtropic can be divided into three groups according to their application westerlies, and southwestern monsoon from the Indian Ocean objectives: for monitoring lake surface extent, water level changes, (Liu et al., 2008), which bring abundant precipitation during and water properties, including lake ice, temperature, and organic June–September. In winter, the climate is dominated by cold and contents. dry westerlies. With diverse climatic and topographical patterns, Lake waterbody or basin inundation area can be retrieved from these alpine lakes over the TP show strong spatial and temporal optical remote sensing imagery. Medium-resolution satellite variability and different responses to climate change. images have become available for lake environmental studies since The most dominant feature for these high-altitude and closed the early-1970s, with the launches of sensors: Landsat Multispec- lakes is the cold-arid climate condition. These lakes are mainly tral Scanner (MSS), Landsat Thematic Mapper (Pitman et al.) and supplied by precipitation runoff, as well as meltwater from glacier Enhanced Thematic Mapper Plus (ETM+), System Pour l’ Observa- and snow cover within adjacent catchments; and lake water leaves toire de la Terre (SPOT), TERRA ASTER, the China – Brazil Earth Re- through evaporation and seepage. For a small proportion of lakes, sources Satellite (CBERS). Other optical sensors with meter and the groundwater runoff becomes an important supplement for sub-meter spatial resolution, such as Quickbird, IKONOS, and Geo- water balance. In the northern Changtang Plateau, with low precip- Eye-1 provide satellite imagery comparable to aerial photography, itation and strong evaporation, many small lakes largely depend on suitable for fine extraction of lake shorelines. However, the high seasonal snowpack meltwater, thus show stronger intra-annual costs, narrow swath size, and long revisit intervals of a few months and inter-annual variability, or even dry up in some years. There limit their applications on large-scale environment monitoring. are also a few freshwater or mildly saline lakes, mainly distributed For medium or high resolution optical imagery, the frequent
Fig. 1. Spatial distribution of alpine lakes and topographic characteristics of the Tibetan Plateau. 28 C. Song et al. / ISPRS Journal of Photogrammetry and Remote Sensing 92 (2014) 26–37
Table 1 Characteristics of satellites and sensors usually applied in lake studies.
Satellites Sensors Operation period Spatial resolution (footprint diameter) Temporal resolution For monitoring lake surface extent NOAA/TIROS AVHRR 1978–present 1001 m Daily TERRA MODIS 1999–present 250 m/1000 m Daily AQUA 2003–present Landsat (1–7) MSS 1972–1983 80 m 16 d TM 1982–1999 30 m 16 d ETM 1999–present 15/30/60 m 16 d CBERS-1/2 CCD 1999–present 19.5 m 16 d TERRA ASTER 1999–present 15/30/90 m 16 d SPOT- 1–5 Pan/MS/SWI Multispectral 1986–present 5/10/20 m 26 d IKONOS Pan/MS Multispectral 1999–present 1/4 m 3 d For monitoring lake water levels ERS-1/2 RA 1991–2003 7km 35d ENVISAT RA-2 2002–2008 2–10 km 35 d TOPEX Poseidon 1992–2006 5km 10d Jason-1 Poseidon2 2001–present 5km 10d ICESat GLAS 2003–2009 70 m 8 d For monitoring lake water properties (temperature, lake ice, contents, etc.) Nimbus-7 SMMR, SSM/I 1978–present 825 m 5–6 d ERS-1/2 SAR 1991–2003 10 m (l) Â 1 m (w) TERRA/AQUA MODIS 1999/2000–present 250 m/1000 m Daily ENVISAT MERIS 2002–2012 300 m 3 d cloud-covered condition over the plateau makes it difficult to tor influencing the primary productivity of lakes, and a sensitive obtain long-term and consecutive observations on alpine lake changes. indicator to climate change. The water temperature can be re- To avoid this problem, some studies proposed to use time-series trieved based on the thermal infrared bands of the optical imagery images provided by the National Oceanic and Atmospheric Admin- of MODIS or NOAA/AVHRR (Chavula et al., 2009; Politi et al., 2012), istration (NOAA) Advanced Very High Resolution Radiometer or microwave sensors such as the special sensor microwave/ima- (AVHRR) and Moderate-resolution Imaging Spectroradiometer ger (SSM/I), the scanning multichannel microwave radiometer (MODIS) to detect the seasonal and inter-annual changes of lakes (SMMR), and the Advanced Microwave Scanning Radiometer for by taking advantage of the high temporal resolution of these sen- EOS (AMSR-E). Besides, Chl-a concentration of lake water body sors, but only limited to several large lakes on the TP (Bian et al., can also be estimated from remote sensing data (Gitelson et al., 2006; Song et al., 2013a) due to low spatial resolution. Therefore, 2011; Gons et al., 2002; Han and Rundquist, 1997; Yacobi et al., how to integrate the fine spatial resolution and high-frequency re- 2011) by calculating the ratio of the reflectance peak near visit of sensors, such as by means of image fusion or unmixing 700 nm in the NIR region to the reflectance at 670 nm in the red mapping, is the key solution to provide more effective observations region (Chl-a absorption peak). For alpine lakes on the TP, the lake on Tibetan lake changes. ice phenology can also be monitored by high-frequency revisit sa- Water level change is another important indicator of lake water tellite observations (Chen et al., 1995; Kropácˇek et al., 2013), such balance. A number of satellite sensors have been developed to ob- as MODIS and NOAA/AVHRR. tain land surface elevation data. Radar altimeters, in spite of with the primary goal of studying the ocean surface (Berry, 2000; Coo- per and Hinton, 1996), have been also applied to detect water-level 4. Remote sensing methods for extracting lake water body changes in inland lakes (Kim et al., 2009; Kropácˇek et al., 2012; Song et al., 2013a). Currently, radar altimeter data are mainly ob- Accurate mapping of water bodies is the key procedure to de- tained from Jason-1, ERS-1/2 (European Remote Sensing Satellite), tect lake variations. The proposed algorithms of retrieving water TOPEX (Ocean Topography Experiment) and ENVISAT (Environ- bodies from remote sensing images involve digitizing through vi- mental Satellite) (Chambers, 2007; Klokocˇník et al., 2008; Legresy sual interpretation, thresholding, traditional image classification et al., 2005; Papa et al., 2003). However, the accuracy of radar techniques, edge detection using a single or a combination of mul- altimetry in the determination of surface elevation is limited by tiple bands and algebraic operations (e.g. band ratio, spectral water its large footprint, which has a diameter of several kilometres (de- indexes), spectral transformation, and texture analysis. tails shown in Table 1). Thus they are merely applied in some large The thresholding method is considered a common approach to lakes, such as Lake Qinghai, Namco. A better solution is to use data extract water bodies because it is easier to use and less computa- from laser altimeters because of the smaller footprint (70 m) and tionally time-consuming than alternatives approaches (Ryu et al., higher vertical measurement accuracy. The Ice, Cloud, and Land 2002). This method is based on thresholds of the band intensity, Elevation Satellite (ICESat), launched by the National Aeronautics which spatially corresponds to the land–water interface (Kallio and Space Administration (NASA) in January 2003 with the Geosci- et al., 2008; Manavalan et al., 1993; Smith, 1997; White and El As- ence Laser Altimeter System (GLAS), become an effective alterna- mar, 1999). However, separating water bodies from other land cov- tive to water-level monitoring (Song et al., 2013a,b; Wang et al., er types based on one standard cut-off value in a single band may 2013; Zhang et al., 2011a,b). Many applications suggested that be problematic (Frazier and Page, 2000; Jain et al., 2005), as the the ICESat data is reliable in horizontal and altimetric precisions spectral reflectance of water is highly spatiotemporal variability. (Abshire et al., 2005; Shuman et al., 2006; Zwally et al., 2002). Consequently, thresholding computations should use a range of Satellite imagery is not only applied to directly detect changes threshold values (Song and Civco, 2002) or employ various spectral in lake extent or water level, can also be adopted to monitor lake indexes based on multiple-band analysis, such as the normalized water properties based on various algorithms or remote sensing difference water index (NDWI) (McFeeters, 1996), the modified indices. For example, lake water temperature is an important fac- normalized difference water index (MNDWI) (Xu, 2005, 2006), C. Song et al. / ISPRS Journal of Photogrammetry and Remote Sensing 92 (2014) 26–37 29 the water index (WI) through combining tasseled cap transform 5.1. Change detection on Tibetan lake extent wetness index (TCW) and NDWI (Ouma and Tateishi, 2006), and land surface water index (LSWI) (Xiao et al., 2005). Nevertheless, During the past several decades, alpine inland lakes on the TP the threshold value for discriminating water body from other land are characterized by high spatiotemporal variability, thus receive covers is somewhat dependent on human experience, thus influ- increasing attention. Most studies focused on examining variations encing the accuracy of image classifications. of some typical lakes. Table 2 presents area change rates for some Both supervised and unsupervised classification procedures typical large lakes or lake groups on the TP. For example, the larg- are also widely used for mapping water body extent. A super- est lake in the plateau, Lake Qinghai, showed a decrease of vised maximum-likelihood classification was used by Kingsford 1.17–3.40% in lake area during the period the 1970s to the early- et al. (1997) to map wetlands on Landsat MSS imagery. Other 2000s (Feng and Li, 2006; Shao et al., 2008; Shen and Kuang, popular supervised classification techniques include minimum 2003) while began to expand since 2005 (Li et al., 2012; Liu distance (Richards, 2013), Mahalanobis distance (Richards, et al., 2013). Other large lakes in central Tibet, such as Namco 2013), Spectral Angle Mapper (Kruse et al., 1993), Spectral Infor- and Silingco, attract a lot of attention due to the possible relation- mation Divergence (Du et al., 2004), and Support Vector Machine ship between lake expansion and glacier melting. As shown in Ta- (Wu et al., 2004). Their merits and demerits were compared and ble 2, Silingco showed more rapid growth rate during the analyzed discussed by Nath and Deb (2010). The major problem of period compared to other large Tibetan lakes, with an increase of supervised algorithm is a priori knowledge about the number more than 30% from the 1970s to the 2000s (Bian et al., 2010; Liao of classes and the spectral signature attributed to each class in et al., 2012; Zhang et al., 2011c). Lu et al. (2005) compared different the scene. By contrast, unsupervised classification based on lake groups in the Namco basin and the Silingco basin (supplied by ‘‘IsoData’’ (Iterative Self-Organizing Data Analysis Technique) or both precipitation and glacial meltwater) and in the Yellow River ‘‘k-means’’ clustering provides an automatically spectral differ- Source (mainly depending on precipitation) and found the first entiation for each class (Kloiber et al., 2002; Olmanson et al., two groups expanded rapidly while the third group (e.g. Gyaring 2008). A general problem of unsupervised algorithms is that data Lake, Ngoring Lake) decreased by 3.77% between the late 1970s may comprise clusters of different shapes and sizes. In this context, and the early 2000s. Other lakes in the inner plateau, such as Zhar- the definition of clusters and the selection of an adequate iNamco, Tangra Yumco, UlanUla Lake, Duoersuodong Co, showed similarity measure are difficult. Another drawback for these shrinkage from the early 1970s and the late 1990s, but rapid algorithms is that the number of classes is usually unknown, as expansions in the 2000s. By contrast, most lakes in southern Tibet, the clustering process considers feature classes instead of final such as YamzhogYumco, MapamYumco, Lhanagtso, and Peiku Co, spectrum classes. showed shrinkage in recent decades (Liao et al., 2012; Niu et al., Other algorithms include morphological segmentation, spectral 2008; Shao et al., 2008; Ye et al., 2007). mixture analysis (SMA), etc. For example, Bagli et al. (2004) inte- The investigation of lake variations at regional or the whole pla- grated the morphological segmentation and spectral thresholding teau scales reveals that these inland lakes showed a strong spatio- to identify water bodies. To generate a water-body fraction at the temporally variability during the period 1970–present. Li et al. sub-pixel level, the SMA and multiple endmember spectral mix- (2011b) and Song et al. (2013b) examined all Tibetan lakes (be- ture analysis (MESMA) have been widely used (Gong et al., 1993; yond the size what can be accurately depicted from the images) Roberts et al., 1993, 1998), which can derive physically interpret- and found that lakes in different geographical and climatic sub-re- able information of each endmember (water, bare land, grassland, gions showed different patterns of spatial and temporal variability. and others) within each pixel. The number and total area of lakes (more than 10 km2) increased As indicated above, various water-body extracting algorithms from 261 and 35,638.11 km2 in the early-1970s, to 262 and have their own defects, such as the requirement of prior knowl- 35,803.50 km2 in 1990, to 298 and 371 37,206.32 km2 in 2000, edge, manual thresholding setting, or complicated manipulation. and to 313 and 41,938.66 km2 in 2011. Similarly, Liao et al. To overcome these problems, Luo et al. (2009) developed an auto- (2012) reported that the ratio of the number of expanding lakes matic water extraction method which gets improved result by a to the number of shrinking lakes in the 1990s and 2000s was much step-by-step iterative transformation mechanism, but the segmen- higher than that in the 1970s and 1980s. The comparison of the re- tation and buffering may be not so adequate; Qiao et al. (2012) sults from Li et al. (2011b) and Song et al. (2013b), suggests lakes in proposed an automatic approach of ‘‘whole-local’’ double scale the inner plateau expanded at a more rapid growth rate (27.03%) transformation, along with water index computation using spec- than other regions. tral feature fitting (SFF) method (Clark and Swayze, 1995) and iter- Due to the limited availability of mid-high spatial resolution ative algorithm, to precisely extract the range of modern lakes; images (such as Landsat, ASTER, CEBERS), most studies analyzed Verpoorter et al. (2012) combines multiple image classifiers de- above focus on the inter-annual or inter-decadal variations of scribed above and developed the GeoCoverTM Water Bodies lakes. Intra-annual variations of Tibetan lakes are seldom reported. Extraction Method (GWEM), to eliminate drawbacks of each tech- Based on time-series MODIS images, Liu et al. (2013) and Song nique in achieving a robust method that performs well under dif- et al. (2013a) investigated the seasonal variability (within unfreez- ferent circumstances. ing period) of lake extent (Lake Qinghai and Silingco) and found that both lakes showed a slight decrease in area from April to June and then begin to increase until the maximum extent in late Sep- 5. Major applications of the remote sensing technique for lakes tember or October. on the TP
This section introduces the progress of Tibetan lake studies with 5.2. Change detection on the water level of Tibetan lakes the assistance of remote sensing techniques, including five aspects: monitoring lake extent variations, detecting water-level changes of Lake water level is an important parameter for water storage Tibetan lakes, investigation of glacial lakes and their outburst estimation, as well as a sensitive indicator for lake water balance simulation and prediction, monitoring lake water properties, and regional climatic variability at timescales of years to decades investigating geological or geomorphologic environment within (Ponchaut and Cazenave, 1998). Especially for lakes with steep lake basins. lakeshore, such as lakes along the Gangdise Mts., lake extent 30 C. Song et al. / ISPRS Journal of Photogrammetry and Remote Sensing 92 (2014) 26–37
Table 2 Summary on area variations of major lakes on the TP.
Lake or region Periods Area variations Source km2 % The whole plateau (lake area > 10 km2) 1970s–2011 6300.55 17.68 Song et al. (2013b) Inner plateau (area > 0.1 km2) 1976–2009 6776.40 27.03 Li et al. (2011b) Northeastern TP 1970s–2004 À46.33 À0.69 Huang et al. (2011) 39 lakes along the Road S301 1970–2000 207.82 8.79 Wang et al. (2008) 22 lakes in the Southern Changtang Plateau 1975–2005 1162.19 18.90 Wan et al. (2010) 7 lakes in Yellow River Source 1969–2001 À47.60 À3.77 Lu et al. (2005) 5 lakes in Namco lake basin 1970–2000 42.37 2.13 8 lakes in Silingco lake basin 1969–1999 222.32 11.01 Wetlands in Qomolangma National Nature Reserve 2000–2009 / <0.10 Li et al. (2009)
Lake Qinghai 1975–2000 À149.60 À3.40 Shen and Kuang (2003) 1976–2000 À60.60 À1.41 Shao et al. (2008) 1986–2005 À51.56 À1.17 Feng and Li (2006) 1988–2004 À105.73 À2.50 Liu and Liu (2008) 2001–2010 58.13 1.36 Li et al. (2012) 2008–2011 44.48 1.02 Liu et al. (2013) Namco 1970–2000 38.15 1.96 Wu et al. (2007) 1970–2000 37.45 1.93 Wu and Zhu (2008) 1970–2007 72.16 3.73 Chen et al. (2009) 1970–2008 89.65 4.63 Liao et al. (2012) 1971–2004 95.38 4.90 Zhu et al. (2010) 1976–2001 24.37 1.25 Shao et al. (2008) 1976–2009 188.60 10.34 Zhang et al. (2011c) 1984–2004 87.40 4.55 Niu et al. (2008) 2002–2009 À52.17 À2.68 La et al. (2011) Silingco 1970–2008 553.36 33.68 Liao et al. (2012) 1972–1999 116.00 6.80 Yang et al. (2003) 1975–2008 574.46 35.4 Bian et al. (2010) 1976–1999 140.52 8.48 Shao et al. (2008) 1976–2009 724.20 44.79 Zhang et al. (2011c) 2002–2009 241.97 12.37 La et al. (2011) ZhariNamco 1970–2008 11.10 1.12 Liao et al. (2012) 1976–2009 71.60 7.80 Zhang et al. (2011c) 1977–1999 À22.59 À2.31% Shao et al. (2008) 2002–2009 11.69 1.21 La et al. (2011)
Tangra Yumco 1970–2008 À12.35 À4.77 Liao et al. (2012) 1977–2000 À3.35 À0.40 Shao et al. (2008) 2002–2009 12.8 1.61 La et al. (2011)
YamzhogYumco 1970–2008 À40.58 À6.47 Liao et al. (2012) 1972–2010 À78.16 À11.52 Chu et al. (2012a) 1976–2000 À1.46 À0.24 Shao et al. (2008) 1980–2000 À35.00 À5.82 Ye et al. (2007) 2002–2009 À96.61 À16.34 La et al. (2011)
MapamYumco 1970–2008 À3.43 À0.83 Liao et al. (2012) 1974–2003 À34.16 À4.4 Ye et al. (2008) 1984–2004 5.40 1.31 Niu et al. (2008)
Lhanagtso 1970–2008 À12.35 À4.57 Liao et al. (2012) Peiku Co 1970–2008 À10.47 À3.74 Liao et al. (2012) Gozha Co 1970–2008 À4.18 À1.67 Liao et al. (2012) PumaYumco 1970–2008 4.18 1.46 Liao et al. (2012) 1984–2004 11.40 4.01 Niu et al. (2008) Zigetangco 1970–2001 15.58 8.17 Lei et al. (2009) Yaggain-canco 1975–2008 68.13 195.00 Bian et al. (2010) Duoqingco 1989–2009 28.4 25.59 La and Laba (2011) Ngangla Ringco 1977–1999 À17.10 À3.32 Shao et al. (2008) Bangongco 1977–2000 11.85 1.90 Shao et al. (2008) Har Lake 1977–1999 Changeless Changeless Shao et al. (2008) Gyaring Lake 1976–2000 À4.70 À0.87 Shao et al. (2008) 1976–2006 À14.57 À2.74 Zhang et al. (2010) Ngoring Lake 1976–2000 Changeless Changeless Shao et al. (2008) 1976–2006 À1.25 À0.20 Zhang et al. (2010) Chibuzhang Co 1977–2000 Changeless Changeless Shao et al. (2008) Duoersuodong Co 1970–2008 99.79 26.75 Liao et al. (2012) Ayakkum Lake 1972–1999 À5.63 À0.88 Shao et al. (2008) UlanUla Lake 1976–2000 À59.8 À10.62 Shao et al. (2008)
Note: the underlined figures refer to shrinking lakes. C. Song et al. / ISPRS Journal of Photogrammetry and Remote Sensing 92 (2014) 26–37 31 displacements may be too slight to indicate the lake dynamics Glacial lake inventories with a prioritization of detected lakes (Li et al., 2011b; Shao et al., 2007). are important as they allow non-specialist local authorities to Lake surface elevations over one lake can be considered to be quickly identify lakes where more detailed and comprehensive the same at a specific time. Therefore, the altimetry data over spe- studies should be directed (Allen et al., 2009). In the earlier periods, cific areas over the lake in different campaigns can be used to de- the inventory of glacial lakes has been generally carried out based tect water-level changes (Urban et al., 2008). The method of on topographic maps (Mool et al., 2001). As not all the topographic deriving lake water-level time series from ICESat/GLAS data was maps for some countries are available and most of the topographic summarized by Phan et al. (2012) and Wang et al. (2013), including maps of the glaciated region are of poor quality, different types of saturation correction for footprints of laser echoes, removing satellite images have been used vigorously. Based on multi-source water-level slope, and filtering and calculating altimetry data of remote sensing data, field survey, and topographic maps, an inven- different campaigns. Wang et al. (2012b) proposed an automatic tory study was conducted by the International Centre for Inte- and robust algorithm for lake water footprint (LWF) identification grated Mountain Development (ICIMOD) jointly with other for time-series change detection of water level. The performance of relevant organisms of Bhutan, China, India, Nepal, and Pakistan. this algorithm was tested by applications on four lakes (including The inventory found there are 8790 glacial lakes in Hindu Kush PaikuCo in South Tibet). The producer’s accuracy for four tested Himalaya (HKH). In addition, glacial lakes have been mapped and lakes is all greater than 94%, and the user’s accuracy ranges from analyzed in specific regions of the HKH, such as in Bhutan (Fujita 97.9% to 90%. et al., 2009; Komori, 2008; Pitman et al., 2012), Nepal (Bolch Based on the ICESat altimetry data, water level changes for et al., 2008; Yamaba and Sharma, 1992), India (Gardelle et al., 74 lakes of the TP were firstly examined (Zhang et al., 2011b). 2011; Govindha Raj, 2010; Randhawa et al., 2005; Worni et al., Among the 74 lakes, 62 (84%) showed an upward tendency and 2012), and Tibet (Wang et al., 2011a,b). 12 (16%) of the lakes showed a downward tendency in water level. Currently, change detection on glacial lakes is mainly based on Available GLAS data over the Tibetan lakes was selected via the remote sensing data by using methods such as combining multi- MODIS lake mask, water level variations during 2003–2009 of spectral data (Kargel et al., 2005), water surface index (Huggel 154 lakes with areas more than 1 km2 could be observed (Phan et al., 2002), and reclassification remap tables (Wu and Zhu, et al., 2012). Most of observed lakes had significant rises in water 2008). To assess glacial lake variations in the Chinese Himalaya, level. The result indicates that 67.53% of the lake levels were trend- two phases of glacial lake inventory were conducted based on ing up by 0–0.4 m/year, while 18.18% were trending down by 278 aerial survey topography maps from the 1970s and 38 ASTER 0 À0.2 m/year. Overall, an area averaged rise in lake level of images in the 2000s (Wang et al., 2012a, 2011c). The results show 0.20 m/year was observed during the analyzed period. Song et al. that the number of glacial lakes has decreased, from 1750 to 1680 (2013b) combined satellite altimetry data and optical remote sens- (a shrinkage rate of 4.0%), while the total area of glacial lakes in- ing images to enable comprehensive investigation of three-dimen- creased from 166.48 km2 to 215.28 km2 (an expansive rate of sional geometric changes in lake water storage using statistical 29.7%). Similar situations have been observed in other places of models, and estimated an increase of 92.43 km3 in total lake water the Himalayas, including north Bhutan (Fujita et al., 2008; volume between the early-1970s and 2011. As shown in Fig. 2, the Komori, 2008), Nepal Himalaya (Fujita et al., 2009, 2008), the water level changes of different lakes over the plateau displayed source regions of the Yarlung Zangbo River (Liu and Xiao, strongly spatial heterogeneity. In accordance with prior studies re- 2011), the Poiqu River basin and the Boshula mountain range of viewed above, most of the lakes in the southern Tibetan plateau the southeastern TP (Chen et al., 2007; Wang et al., 2011b), and and along the Himalayas (in the Yarlung Zangbo River catchment), in the Everest region (Bolch et al., 2008; Wessels et al., 2002; showed a downward tendency in water level, while most lakes in Wu et al., 2012; Yamaba and Sharma, 1992). As reported by the inner plateau showed an upward tendency. Kargel et al. (2005), glacial lake evolution was likely to be incon- In comparison, the space-borne radar altimetry, such as the TO- sistent among the different parts of HKH where the large majority PEX-Poseidon satellite, ERS-2, and ENVISAT, provides elevation of glaciers are retreating in the eastern and middle parts but the measurements since 1990s, but the relatively large footprint size glacier changes were slower in the western part. Gardelle et al. hampers the applications in small lakes. Hence many small lakes (2011) compared glacier lake variations among seven selected on the TP cannot be sampled and detected. Kropácˇek et al. study sites spreading along the HKH range, from Bhutan to (2012) examined lake level changes of Namco in central Tibet by Afghanistan, which represented different local patterns of combining three altimeters (RA-2/ENVISAT and GFO radar altime- temperature and precipitation variability. In the East (India, Nepal ters, and GLAS/ICESat laser altimeter), and revealed a rising rate of and Bhutan), glacial lakes were bigger and more numerous than lake level by 0.31 m/year in the period 2000–2009. The compar- that in the West (Pakistan, Afghanistan), and grew continuously ison of ENVISAT radar and ICESat laser altimetry on water-level between 1990 and 2009 by 20% to 65%. By contrast, during the change of Silingco was also given by Phan et al. (2012) and Song same period, the glacial lake coverage shrank in the Hindu Kush et al. (2013a), which suggested both data sources could derive (À50%) and the Karakorum (À30%). This finding seems to be in coherent lake level changes. good agreement with the glacier mass balance observations currently available (Yao et al., 2012). 5.3. Monitoring glacial lakes Accelerated glacier recession in high mountains lead to the rapid expansion of glacial lakes, some of which are hazardous Glacial lake outburst floods (GLOFs) have received increasing to communities and infrastructure downstream because of their attention because of case histories, in the Himalayas and else- potential to burst catastrophically (Richardson and Reynolds, where, that resulted in catastrophic destruction and loss of life in 2000). Several approaches have been already developed to exam- mountain regions (Cenderelli and Wohl, 2003; Xu and Feng, ine glacial lake hazards, including remote sensing (Fujita et al., 1989). Remote sensing makes it possible to timely monitor glacial 2008; Govindha Raj, 2010; Huggel et al., 2002), geographic infor- lakes in inaccessible mountainous areas. Its effectiveness is mation techniques (Allen et al., 2009; Jain et al., 2012), and/or manifested in three aspects: investigation of distribution and statistical and hydrodynamic modelling methods (Cenderelli and characteristics of glacial lakes, detection on glacial lake variations, Wohl, 2003; Wang et al., 2011a). Among these methods, remote and evaluation and prediction on the potential GLOFs integrating sensing is regarded as the best way to extensively investigate field investigations and hydrodynamic models. glacial lakes and the impact of GLOFs in inaccessible glacier 32 C. Song et al. / ISPRS Journal of Photogrammetry and Remote Sensing 92 (2014) 26–37
Fig. 2. Water-level changing rates of 120 Tibetan lakes during 2003–2009. mountainous areas. Some critical remote sensing-derived vari- 5.4. Measurements of lake water properties ables which indicate the potential danger of a glacial lake are listed in Table 3. Remote sensing techniques cannot only be detect changes in Combining these variables derived from remote sensing tech- lake surface extent, water level, and glacial lake variation and their niques, some empirical formulas and hydrological models are em- potential dangers, but also be employed to monitor some other ployed to simulate and assess the possibility of glacier lake properties of lake water, such as lake water temperature, salty con- outburst floods. For example, in assessing potentially dangerous tent, water clarity, suspended sediment concentration, and lake glacial lakes in the southeastern TP, a fuzzy consistent matrix ice. Tian et al. (2005) and Wan et al. (2008)’s studies on lake water method was applied to determine the weight of five selected vari- salty content derived from Landsat images have proved the remote ables, and characteristic statistical values were used as thresholds sensing to be a potential tool to monitor and assess salty properties to classify each variable (Wang et al., 2011a). Wang et al. (2012a) and variations of Tibetan lakes. proposed a three-level approach to assess regional glacial lake haz- The lake ice phenology is considered as an important indicator ard, in which the remote sensing provides an evaluations for these for regional climate change and climate variability. Continuous re- glacial lakes and initial inputs for the one-dimensional (1-D) mote sensing observations provide an efficient approach to moni- hydrodynamic model HEC-RAS. For the two-dimensional dynamic tor the freeze-up and break-up dates of lake ice, especially in some BASEMENT model, which allows the simulation of complex pro- high-latitude or alpine regions without in situ records (Johnson and cesses of GLOFs for typical glacial lakes, some input parameters, Stefan, 2006; Magnuson et al., 2000; Robertson et al., 1992). As the such as lake area and water level, dam width/height, and topogra- spatial resolution is relatively coarse for these high-frequency phy, were generated by remote sensing methods (Worni et al., revisiting satellites or sensors, such as NOAA, MODIS, or AMSR-E, 2012). only some large lakes on the TP can be examined (Chen et al.,
Table 3 Some critical variables indicating the potential GLOFs and its investigations using remote sensing data.
Variables Remote sensing interpretation Related references or sources Glacial lakes characteristics Lake size and rapid growth in Multi-temporal optical images Chen et al. (2007), Gardelle et al. (2011), Komori (2008) and Wang et al. area (2012a,b) Increase in lake water level Satellite altimetry data or multi-temporal spaceborne Fujita et al. (2009) stereo-imagery Rates of lake volume growth Multi-temporal optical images & digital terrain models Bajracharya et al. (2007) and Wessels et al. (2002) (DTMs) Glacier conditions Morphometric features of Geomorphometric DTM analysis & gradient Bolch et al. (2008) and Bolch et al. (2007) glaciers classification Glacier retreating Area or length of glaciers derived from optical images Bolch et al. (2008), Mool et al. (2001) and Wang et al. (2011b) Glacial activities Multi-temporal optical and radar images derived glacier Luckman et al. (2007), Nakawo et al. (1999), Quincey et al. (2007) and velocity Scherler et al. (2008) Dam conditions Width and height of moraine Multi-temporal optical images and stereo-imagery Bolch et al. (2008) and Fujita et al. (2008) dam Steepness of slope of the Multi-temporal DTMs moraine wall Down-valley conditions Geomorphometric DTM and optical images analysis Bajracharya et al. (2007) and Bolch et al. (2008) Distance between glacier and Optical image and DTM Wang et al. (2011b) and Worni et al. (2012) lake C. Song et al. / ISPRS Journal of Photogrammetry and Remote Sensing 92 (2014) 26–37 33
1995; Yin and Yang, 2005). Che et al. (2009) applied passive micro- Liao et al. (2012) applied the manual visual interpretation method wave remote sensing data (SMMR and SSM/I) to monitor the to extract lake extent on the basis of enhanced color images, and freeze-up and break-up dates of Lake Qinghai during 1978–2006, the accuracy greatly depended on personnel experience. The and revealed the lake ice covered duration decreased by about semi-automatic method NDWI demands to empirically set a 14–15 days during the analyzed period and this tendency was threshold to segmentate water body and lakeshore (Li et al., tightly associated with rising temperature. The results are vali- 2011a), which could induce more or less mis-estimations. dated being in good agreement with the hydrological station re- Thirdly, the season or period of image acquisition that is used to cords and the MODIS derived information. Kropácˇek et al. (2013) analyze inter-annual or decadal changes of lakes are inconsistent. examined the ice phenology of 59 large lakes on the Tibetan Pla- To detect inter-annual variations of lake area, each of adopted im- teau based on MODIS 8-day composite data for the period from age should be selected within a reasonable and constant season 2001 to 2010, and found that ice phenology of these lakes is fea- window. In general, Tibetan lakes get to the maximum extent tured by a highly spatial-clustering pattern charactering in geo- and keep relatively stable in late September or October and are less graphical position (altitude and latitude) and local settings, e.g. cloud and ice covered. Hence, the satellite images observed in au- shape complexity and salinity. A general trend in ice phenology tumn are appropriate for detect inter-annual variability of Tibetan on the TP cannot be identified probably due to the relatively short lakes. study period of ten years, but displays a highly inter-annual vari- Fourthly, different spatial reference systems (or map projec- ability being correlated with the air temperature. tions) also cause inconsistent area computations. Some studies made the statistical computation on lake area directly through multiplying the number of lake pixels by approximately averaged 5.5. Investigating the geological and geomorphologic environment of pixel size (Shen and Kuang, 2003), such as 30 m or 1.1 km sizes lake basins for Landsat and NOAA images respectively. Whereas the pixel sizes are the rough estimates and there are different degrees of projec- Because different texture features and interpretation signals tion distortions in different latitude areas. Besides, some bias may be established in remotely sensing images corresponding to may be caused by different definitions of the lake extent. For exam- different lake sediments in historical periods, the lake shorelines ple, as shown in Fig. 3c and d, disparities of Lake Qinghai or Yamz- vicissitude collapses, or lake basins deposition can be detected hog Yumco areas among different studies are caused by whether and analyzed. Based on water index computed by spectral feature many small neighboring small ponds or lagoons are included. This fitting (SFF) method, Qiao et al. (2010) developed a ‘‘whole-local’’ is partly associated with the lack of refined lake inventory and spatial scale transformation mechanism to obtain high-precise clear definitions of many small lakes over the plateau. extraction of modern lakes and integrated images and SRTM data to further detect and recover paleo shorelines in the Tibetan Pla- 6.1.2. Uncertainties of detecting lake water-level variations teau. Results showed that the shrinkage of lakes exceeds Previous studies have demonstrated that lake water-level 40,000 km2 in area, as well as exceeds 3000 km3 in volume over changes obtained from the ICESat/GLAS altimetry are in good the whole plateau since the Great Lake Period. Luo et al. (2010) agreements with that from in situ gauge observation (Urban used the thresholding method of spectral bands to distinguish et al., 2008; Zhang et al., 2011a). The cross-validation of lake levels the lake bathymetry and basin terrain for all the lakes in Hoh Xil from ICESat laser altimetry was conducted by Phan et al. (2012) by region. comparison with radar altimetry data provided by LEGOS/GOHS (Laboratoire d’Etudes en Geodesie et Oceanographie Spatiales, 6. Discussion Equipe Geodesie, Oceanographie et Hydrologie Spatiales) (LEGOS, 2010). The change rates of lake level resulting from both ap- 6.1. Uncertainties and challenges of remote sensing of lake changes on proaches seem quite comparable. However, it should be noted that the TP two key procedures might directly affect the reliability of water le- vel data. The first is to select appropriate land–water mask to iden- 6.1.1. Uncertainties of detecting lake extent variations tify over-lake footprints of satellite altimetry. For example, With increasing concerns on the climate change impacts on Ti- compared to the average size of ICESat/GLAS footprints of 70 m, betan lakes, based on remote sensing techniques, different satellite the spatial resolution of the 250 m MODIS land–water mask (Phan data sources with inconsistent spatial, temporal, and spectral res- et al., 2012) and 500 m MODIS snow cover product (Zhang et al., olutions, water-body extraction algorithms, and analyzed time 2011b) are relatively low, the mask may contain errors of eleva- spans were adopted. These factors inevitably generate some incon- tions representing lakeshore or alluvial fan. Lake extent derived sistencies or uncertainties in lake extent detection. As presented in from Landsat TM data may acquire more accurate laser footprints Fig. 3, the comparison of lake area variations from multiple data over the lake surface (Song et al., 2013b). Secondly, on-lake foot- sources for four typical lakes with the most concerns suggests that prints of satellite altimetry are probably affected by clouds, fog, large disparities of the lake area extraction exist in different stud- saturated reflected signals, lake surface waves, or snow on top of ies, despite that general consistent change tendencies are ob- lake ice. Thus these external and commission errors should be mit- served. The inconsistences of lake area variability among igated by the use of the threshold removing approach. More de- different studies may be mainly caused by the four factors. tailed case study was given by Phan et al. (2012). Firstly, different data sources are the primary factor leading to inconsistent results, especially between medium–high resolution 6.1.3. Uncertainties of detecting glacial lake variations images (e.g. Landsat and SPOT) and coarse satellite images (e.g. From the summary in Section 5.3, it is evident that the remote MODIS and NOAA). For example, the changing trend in lake area sensing technique has the potential to provide information for of Namco based on MODIS data set (La et al., 2011) during 2002– mapping, monitoring and assessing glacial lakes and GLOFs. How- 2009 completely differs from other results. It seems that the lake ever, this application seems to set higher demands on the effective- extent derived from coarse resolution images shows stronger in- ness of remote sensing. First, glacial lake hazards develop and ter-annual variability due to the misclassification of mixed pixels. occur in high-mountain environments where cloud formation is Secondly, different water-body extraction methods were promoted and hence the availabilities of high-quality satellite adopted. For example, Lu et al. (2005), Ye et al. (2008, 2007) and observations are often limited. The combination of optical and 34 C. Song et al. / ISPRS Journal of Photogrammetry and Remote Sensing 92 (2014) 26–37
Fig. 3. Comparison of surface areas from different studies for four typical lakes: (a) Namco, (b) Silingco, (c) Lake Qinghai, and (d) YamzhogYumco. X-axis: 1970–2011 (time); Y-axis: lake area (km2). microwave satellite data may provide alternative source for Recent studies have suggested that the TP has been experiencing reliable and robust glacial lake mapping (Strozzi et al., 2012). Sec- evidently warming since the 1970s and even an accelerated ten- ond, for satellite data to be routinely employed for glacial lake dency after the late 1990s (Liu and Chen, 2000; You et al., 2012, observations, the re-visiting frequency and the spectral character- 2010), resulting in accelerated glacier mass loss on the TP during istics of the sensor should ideally be appropriate to the application; the past decades (Kang et al., 2009; Kehrwald et al., 2008; Pu in glaciated regions, often a number of sensors are put together et al., 2008; Yang et al., 2008; Ye et al., 2006). Thus the increase into practice to ensure comprehensive data coverage (Komori, in glacial meltwater was commonly deemed as the primary con- 2008; Quincey et al., 2007), therefore the difference of overpass tributor to the lake growth in recent studies (Lu et al., 2005; Wang time and spectrum of satellite data has to be identified and unified. et al., 2013; Yao et al., 2007; Ye et al., 2008, 2007; Zhang et al., Furthermore, most glacial lakes in mountainous regions is very 2011b). Whereas, a few studies also reported that the increased small, and distributed in some complex terrains, the spectral mix- precipitation and decreased evaporation are also linked with lake ture and reflectance feature of shadowed glacial lake bring more expansion (Huang et al., 2011; Meng et al., 2012; Zhang et al., challenges to glacial lake mapping and change detection. Although 2011a). Zhu et al. (2010) provided the first quantitative analyses photogrammetry has been shown to provide very fine-resolution on the proportions of different factors contributing the Namco lake data on microscale glacial processes, commissions are high-cost, expansion. It suggests that although the precipitation (including and other problems concerning GCPs collection, poor photograph direct on-lake precipitations and precipitation runoff) are the main reproduction and incomplete camera calibration parameters often source of lake water supply (approximately accounting for 60%) still need to be overcome, particularly in harsh natural conditions compared to the glacier meltwater supply (10%), the increase of and remote areas. glacial meltwater played a more important role in the expansion of NamCo than decreased precipitation. 6.2. The climate-driven mechanism of lake changes From the perspective of conceptual model for lake water bal- ance, Lei et al. (2013) quantitatively estimated that increased pre- There have been a number of empirical or quantitative studies cipitation and runoff, and decreased lake evaporation were the on lake sensitivity to climatic variables at inter-annual or decadal main factors causing the rapid lake growth in the central TP, with scales. The climatic causes of lake change involve the precipitation, a contribution of 70% to total increased lake storage, while the evaporation, and melting water from mountain glaciers and snow glacier mass loss was estimated to contribute to the lake level rise packs. Most researchers draw the conclusion that the water loss in- of the three glacial-fed lakes (Silingco, Namco, and Pungco), duced by low precipitation and high evaporation is the direct fac- respectively accounting for 11.4–28.7% of the total lake level rise tor of severe shrinkage for many Tibetan lakes from the 1970s to based on the glacier mass balance observations between 1999 the 1980s (Li et al., 2011b; Liao et al., 2012; Song et al., 2013b). and 2010. This analyses is supported by the finding that there is C. Song et al. / ISPRS Journal of Photogrammetry and Remote Sensing 92 (2014) 26–37 35 a decrease in potential evapotranspiration, and an increase in pre- Che, T., Li, X., Jin, R., 2009. Monitoring the frozen duration of Qinghai Lake using cipitation and runoff on the central TP (Duan and Wu, 2009; Yang satellite passive microwave remote sensing low frequency data. Chin. Sci. Bull. 54, 2294–2299. et al., 2011; Zhang et al., 2009, 2007). 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