Monitoring Dynamic Evolution of the Glacial Lakes by Using Time Series of Sentinel-1A SAR Images

remote sensing

Article Monitoring Dynamic Evolution of the Glacial Lakes by Using Time Series of Sentinel-1A SAR Images

Bo Zhang 1, Guoxiang Liu 1,2, Rui Zhang 1,2,* , Yin Fu 1, Qiao Liu 3 , Jialun Cai 1 , Xiaowen Wang 1,2 and Zhilin Li 1,2

1 Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China; [email protected] (B.Z.); [email protected] (G.L.); [email protected] (Y.F.); [email protected] (J.C.); [email protected] (X.W.); [email protected] (Z.L.) 2 State-Province Joint Engineering Laboratory of Spatial Information Technology of High-Speed Rail Safety, Chengdu 611756, China 3 Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China; [email protected] * Correspondence: [email protected]

Abstract: As an approach with great potential, the interpretation of space-borne synthetic aperture radar (SAR) images has been applied for monitoring the dynamic evolution of the glacial lakes in recent years. Considering unfavorable factors, such as inherent topography-induced effects and speckle noise in SAR images, it is challenging to accurately map and track the dynamic evolution of the glacial lakes by using multi-temporal SAR images. This paper presents an improved neighborhood-based ratio method utilizing a time series of SAR images to identify the boundaries of the glacial lakes and detect their spatiotemporal changes. The proposed method was applied to monitor the dynamic evolution of the two glacial lakes with periodic water discharge at the terminus of the Gongba Glacier Citation: Zhang, B.; Liu, G.; in the southeastern Tibetan Plateau by utilizing 144 Sentinel-1A SAR images collected between Zhang, R.; Fu, Y.; Liu, Q.; Cai, J.; October of 2014 and November of 2020. We first generated the reference intensity image (RII) by Wang, X.; Li, Z. Monitoring Dynamic averaging all the SAR images collected when the water in the glacial lakes was wholly discharged, Evolution of the Glacial Lakes by then calculated the neighborhood-based ratio between RII and each SAR intensity image, and finally Using Time Series of Sentinel-1A SAR identified the boundaries of the glacial lakes by a ratio threshold determined statistically. The time Images. Remote Sens. 2021, 13, 1313. https://doi.org/10.3390/rs13071313 series of areas of the glacial lakes were estimated in this way, and the dates for water recharging and discharging were accordingly determined. The testing results showed that the water of the two Academic Editor: Timo Balz glacial lakes began to be recharged in April and reached their peak in August and then remained stable dynamically until they began to shrink in October and were discharged entirely in February Received: 16 February 2021 of the following year. We observed the expansion process with annual growth rates of 3.19% and Accepted: 26 March 2021 12.63% for these two glacial lakes, respectively, and monitored a glacial lake outburst flood event in Published: 30 March 2021 July 2018. The validation by comparing with the results derived from Sentinel-2A/B optical images indicates that the accuracy for identifying the boundaries of the glacial lakes with Sentinel-1A SAR Publisher’s Note: MDPI stays neutral images can reach up to 96.49%. Generally, this contribution demonstrates the reliability and precision with regard to jurisdictional claims in of SAR images to provide regular updates for the dynamic monitoring of glacial lakes. published maps and institutional affil- iations. Keywords: Gongba Glacier; SAR image; glacial lake; dynamic evolution; neighborhood-based ratio

Copyright: © 2021 by the authors. 1. Introduction Licensee MDPI, Basel, Switzerland. Over the past decades, climate warming has exacerbated the melting of glaciers in This article is an open access article most of High Mountain Asia [1–4]. The glaciers in the southeastern Tibetan Plateau and its distributed under the terms and conditions of the Creative Commons vicinity have been observed with the most negative mass balances [3,4]. The continuous Attribution (CC BY) license (https:// mass losses of glaciers are generally accompanied by the increase in the amount and the creativecommons.org/licenses/by/ expansion in the area of glacial lakes [5,6], resulting in an increased risk of glacial lake 4.0/). outburst ﬂoods (GLOF) [7–9]. Monitoring the dynamic evolution of glacial lakes is essential

Remote Sens. 2021, 13, 1313. https://doi.org/10.3390/rs13071313 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 1313 2 of 21

for understanding the interactions between their spatiotemporal changes under climate warming and the risk assessment of related hazards. Current and accurate mapping of the glacial lakes is the basis for monitoring dynamic evolution and further research. With the emergence of many earth observation satellites, such as the Landsat series and Sentinel-2 A/B constellation, remote sensing technology has been widely used for the inventory and dynamic monitoring of glacial lakes [6–8,10–13]. Some conventional methods, such as spectral classification [14] and spectral transformation methods [15,16], are widely applied to identify the water surface. To enhance water bodies from the adjacent objects, McFeeters (1996) proposed a normalized difference water index (NDWI) algorithm to monitor changes related to water content using green and near-infrared (NIR) wavelengths [17]. Many studies about NDWI have been developed and applied to investigate and inventory glacial lakes [11,18–21]. However, mapping glacial lakes based on optical remote sensing is susceptible to shadow effects because they are mostly situated in high mountain areas with complex terrains. Also, for areas frequently covered with heavy clouds in the warm season, like the southeast Tibetan Plateau dominated by the Indian and Bengal monsoons, the acquisition of available cloud- free optical images is severely restricted. Synthetic aperture radar (SAR) represents an alternative to optical satellites for feature classification [22,23] and change detection [24,25] with the capability to collect data under all weather conditions without the restriction of clouds [26]. Since the smooth water surface will cause the incident SAR signal to be specularly reflected, the backscattered intensity in the SAR image corresponding to the water bodies is generally lower than their surroundings. Previous studies mostly used manual digitization or threshold segmentation to delineate the boundaries of glacial lakes [25,27–30]. However, due to the unique Range– Doppler imaging geometry of SAR images and the physical characteristics of radar waves, several issues may cause low-level amplitude in SAR images, such as incident angle, look direction, moisture on the surface, and the physical composition of the ground feature [26,31–33]. In other words, the pixels with low-level backscattered intensity in SAR images are not all matched to the water bodies, with the result that identification of glacial lakes directly based on the intensity values of SAR image may cause misclassification. Some methods have been reported on change detection for SAR images. Most of them are designed for bitemporal SAR images, such as the neighborhood-based ratio (NR) approach [34,35] and logarithmic transformation ratio (Log-Ratio) method [36]. The main objective of these methods is to detect the changed targets from stable areas between two acquisitions instead of focusing on the mapping of ground objects. Additionally, SAR images are susceptible to speckle noise and physical characteristics of ground objects [26,37], resulting in redundancy and random discriminations. Although these methods have been widely used as indicators of change detection, there are rare studies on the application to extract the boundary and monitor the dynamic evolution for glacial lakes. In this study, we developed an improved method based on the neighborhood-based ratio method to semi-automatically identify the boundaries of glacial lakes and detect their spatiotemporal changes. We selected two glacial lakes with periodic water discharge at the terminus of Gongba Glacier, southeast Tibetan Plateau, as the study site demonstrated the technique we developed, based on 144 Sentinel-1 C-band SAR images acquired from October 2014 to November 2020.

2. Study Area and Data Sources 2.1. General Information about the Study Area The Gongba Glacier is located on the west slope of the Mount Gongga (29◦35045”N, 101◦52045”E, 7556 m), southeastern Tibetan Plateau [38–40], as shown in Figure1a. Between the 17th and 19th centuries, the Gongba Glacier was divided into the Dagongba Glacier on the south side and the Xiaogongba Glacier on the north side [39]. According to the second glacial inventory data set of China [41], the Dagongba Glacier has a length of 10.6 km and an area of 19.1 km2 in 2009, and those for Xiaogongba Glacier are 6.46 km Remote Sens. 2021, 13, x FOR PEER REVIEW 3 of 22

the second glacial inventory data set of China [41], the Dagongba Glacier has a length of 10.6 km and an area of 19.1 km2 in 2009, and those for Xiaogongba Glacier are 6.46 km and 5.73 km2, respectively. The two glaciers are the typical debris-covered type, with the moraine extended from the terminus at 3900 m elevation to the foot of icefall at 4700 m elevation [42,43]. In recent years, a large number of supraglacial ponds and moraine-dammed lakes have been formed on the glacier tongue, of which the largest two are located at the terminus of Dagongba Glacier and Xiaogongba Glacier, labeled the Dagongba Glacial Lake Remote Sens. 2021, 13, 1313 (DGL) at 4100 m elevation and the Xiaogongba Glacial Lake (XGL) at 4130 m 3elevation, of 21 respectively, with a total area of more than 120,000 m2 (see Figure 1b). By the interpretation of satellite images, the water in both XGL and DGL is characterized by periodic re- chargingand 5.73 in km the2, respectively.warm season The and two discharging glaciers are thein the typical cold debris-covered season. According type, with to our the field surveymoraine in June extended and October from the terminus2018, the atXGL 3900 and m elevation DGL are to formed the foot when of icefall the at runoff 4700 m at the glacierelevation surface [42 ,43is ].obstructed and gathered by the terminal moraine deposits.

FigureFigure 1. (a) 1.The(a) topography The topography and and location location of ofthe the study study area, area, the the blue blue dotteddotted polygons polygons show show outlines outlines of the of the Gongba Gongba Glacier. Glacier. (b) The(b )Google The Google Earth Earth image image of ofthe the Xioagongba Xioagongba GlacialGlacial Lake Lake (XGL) (XGL) and and Dagongba Dagongba Glacial Glacial Lake (DGL) Lake acquired(DGL) acquired on 5 October on 5 Oc- tober 2017,2017, corresponding corresponding to to the the red red rectangular rectangular box inbox (a). in (a).

2.2. DataIn Sources recent years, a large number of supraglacial ponds and moraine-dammed lakes have been formed on the glacier tongue, of which the largest two are located at the terminus ofIn Dagongba this study, Glacier a total and Xiaogongbaof 144 Sentinel-1A Glacier, labeled satellite the ascending Dagongba GlacialC band Lake SAR (DGL) images at ac- quired4100 between m elevation October and the 2014 Xiaogongba to November Glacial Lake2020 (XGL) were atused 4130 for m elevation,mapping respectively, the boundaries of glacialwith a totallakes area and of monitoring more than 120,000 their dynamic m2 (see Figure evolution.1b). By All the the interpretation downloaded of satellite SAR images areimages, of the theinterferometric water in both wide XGL andswath DGL mode is characterized and the Level-1 by periodic single recharging look complex in the data productswarm seasonwith the and spatial discharging resolution in the of cold 13. season.96 m in According azimuth todirection our ﬁeld and survey 2.33 in m June in range directionand October [44]. The 2018, Sentinel-1 the XGL and A satellite DGL are acquired formed when data thecovering runoff atthe the study glacier area surface only isin single-polarizationobstructed and (VV) gathered mode by between the terminal October moraine 2014 deposits. and January 2017 (a total of 30 frames in this2.2. Datacase) Sources and acquired data using dual-polarization (VV+VH) mode since February 2017. Thus,In this for study, all the a total selected of 144 Sentinel-1ASAR images, satellite the following ascending Cprocessing band SAR imagesusing acquiredVV-polariza- tionbetween mode Octoberwas uniformly 2014 to November performed. 2020 In were addi usedtion for to mappingthe SAR the images, boundaries the Shuttle of glacial Radar Topographylakes and monitoring Mission (SRTM) their dynamic digital evolution. elevation All model the downloaded (DEM) with SAR a imagesspatial are resolution of the of approximatelyinterferometric 30 wide m (1 swath arc-sec) mode was and used the Level-1 for the single co-registered look complex processing data products of SAR with images andthe subsequent spatial resolution projection of 13.96 conversion. m in azimuth direction and 2.33 m in range direction [44]. TheFor Sentinel-1 validation A satellite purposes, acquired 40 optical data covering images the acquired study area by only the inSentinel-2 single-polarization satellite from November(VV) mode 2016 between to November October 20142020 and were January used 2017for comparison (a total of 30 and frames accuracy in this validation case) and acquired data using dual-polarization (VV+VH) mode since February 2017. Thus, with the derived boundaries of glacial lakes based on SAR images. The Sentinel-2 is com- for all the selected SAR images, the following processing using VV-polarization mode prisedwas uniformlyof a constellation performed. of two In addition polar-orbiting to the SAR satellites images, theplaced Shuttle in the Radar same Topography sun-synchro- nousMission orbit (SRTM)that has digital the capacity elevation of model a high (DEM) revi withsit period a spatial of resolution5 days to ofobtain approximately multispectral remote30 m (1sensing arc-sec) images was used with for the co-registeredresolution of processing 10 m [45]. of SARHowever, images considering and subsequent that the acquisitionprojection of conversion. space-borne optical satellite images covering the Gongba Glacier is vulnerable to Forobstruction validation by purposes, heavy clouds, 40 optical especially images acquiredin summer, by the all Sentinel-2selected optical satellite images from are filteredNovember manually 2016 toto November ensure that 2020 the were area used of interest for comparison is under and cloud-free accuracy validation conditions. with the derived boundaries of glacial lakes based on SAR images. The Sentinel-2 is comprised of a constellation of two polar-orbiting satellites placed in the same sun-synchronous orbit that has the capacity of a high revisit period of 5 days to obtain multispectral remote sensing images with the resolution of 10 m [45]. However, considering that the acquisition of space- borne optical satellite images covering the Gongba Glacier is vulnerable to obstruction by heavy clouds, especially in summer, all selected optical images are ﬁltered manually to ensure that the area of interest is under cloud-free conditions. Remote Sens. 2021, 13, 1313 4 of 21 Remote Sens. 2021, 13, x FOR PEER REVIEW 4 of 22

3. Methodology Methodology Figure 22 showsshows thethe datadata processingprocessing flowflow chartchart ofof thethe proposed proposed method. method. TheThe mainmain stepssteps of the proposed approach include the the ge generationneration of of reference intensity image image (RII), (RII), calculation of the neighborhood-basedneighborhood-based ratio,ratio, andand thethe selection selection of of a a threshold threshold for for classifica- classifi- cation.tion. In In the the following following text, text, we we will will first first describe describe the definitionthe definition of the of neighborhood-based the neighborhood- basedratio methodratio method and generation and generation of RII of and RII then and discussthen discuss the effectiveness the effectiveness of the of proposed the pro- method in the classification of glacial lakes (Section 3.1). Second, statistical analysis will posed method in the classification of glacial lakes (Section 3.1). Second, statistical analysis be used to compute the threshold for automatic classification of time series of ratio maps will be used to compute the threshold for automatic classification of time series of ratio (Section 3.2). Additionally, the boundaries extracted based on Sentinel-2 A/B satellite im- maps (Section 3.2). Additionally, the boundaries extracted based on Sentinel-2 A/B satel- ages are assumed to be the ground truth to validate the accuracy of the results derived lite images are assumed to be the ground truth to validate the accuracy of the results de- from the proposed method (Section 3.3). rived from the proposed method (Section 3.3).

Figure 2. The flow flow chart of data processing for identifying the boundaries of of glacial lakes based on proposed method. f f SARf N and ratiof N denote the smoothed filtered synthetic aperture radar (SAR) image and the binarized classified ratio SARN and ratioN denote the smoothed filtered synthetic aperture radar (SAR) image and the binarized classified ratio map, respectively.

3.1. The The Improved Improved Neighborhood-based Ratio Method For bitemporal bitemporal SAR images images of of the the same same geographical geographical area area acquired acquired at at different different dates, dates, thethe normalized normalized neighborhood neighborhood-based-based ratio (NR) method can be deﬁneddefined asas [[34]:34]:

µμ((,)rrc, c) µμ ( (,)rrc, c) NR(NR(I /III) / = )1=−− 1min min112, , 2 (1) 1 212 ( ) ( ) µμ221(,)rrc, c µμ1 (,)rcr, c

where I1 andand II22 representrepresent the the two two co-registered co-registered SAR SAR images images acquired acquired on on different different dates; r,c r,c μµ1(r,c)and)andµ μ22((r,c) denote) denote the the local local mean mean intensity intensity values values in in thethe deﬁneddefined windowwindow sizesize and defineddeﬁned weighting function (e.g., linear weighting and Gaussian weighting) for the pixel position (r,c) in I1 and I2, respectively; and min denotes the minimum operation. position (r,c) in I1 and I2, respectively; and min denotes the minimum operation. NR represents the similarity of involved SAR images. The larger it is, the greater NR represents the similarity of involved SAR images. The larger it is, the greater the the resemblance is. The intensity of SAR images is strongly related to topography [26]. resemblanceThe ratio operation is. The canintensity considerably of SAR images offset the is signalstrongly differences related to caused topography by topography [26]. The ratiovariations operation [26,34 can–36 considerably], and the neighborhood-based offset the signal differences averaging caused around by each topography pixel reduces variations [26,34–36], and the neighborhood-based averaging around each pixel reduces the

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the influence of speckle noise. However, in this case of identifying the boundaries of glacial lakes based on multi-temporal SAR images, three issues are difficult to apply directly: (1) NR is designed to detect changes for bitemporal SAR images instead of multi- temporal SAR images. (2) The classified targets that are stable will be neutralized in the NR calculation owing to their similar backscattering intensity. (3) Considering the scene of NR that contains various changes with both increasing and decreasing radiometry, the normalization operation will cause redundant change information and result in a negative impact on the mapping and tracking dynamic evolution of the glacial lakes. To apply the NR method for monitoring the dynamic evolution of the glacial lakes by using multi-temporal SAR images, we propose to use a fixed reference intensity image (RII) to participate in the neighborhood-based ratio operation for time series of SAR images. Ideally, there should be no distribution of water bodies and no disturbance from speckle noise in the RII while retaining the topography-related backscattered intensity discrepancy. Thus, for the calculated time series of ratio maps between RII and each SAR intensity image, the pixel values corresponding to the glacial lakes would be effectively enhanced, while the topography-related intensity changes would also be validly offset. During the cold season in the Tibetan Plateau, the surface of most glacial lakes would be ice-frozen as they are mainly formed at high altitudes with low temperatures. Further- more, some glacial lakes formed when the moraines, which block meltwater, are distributed at the terminus of the glacier [46], and the dead ice at the basin of these glacial lakes is frequently interconnected to the subglacial drainage system under the action of water erosion in the warm season, generating periodic water discharged phenomenon. The intensity value corresponding to the coverage area of the glacial lakes would be similar to the surroundings when the water is completely frozen or released. Thus, we propose to generate the RII by averaging all the SAR images collected when the water in the glacial lakes is wholly frozen or discharged. The RII representing the area of glacial lakes is zero and preserves the topographic effect caused by the difference in the local incidence angle of radar signals. Supposing that M images are selected when the glacial lakes are wholly frozen or discharged from a set of N SAR images, the RII can be generated by averaging the intensity of M images (shown as Equation (2)). For simplifying the operation of NG, each of the N images was filtered with the given window size and defined weighting function for two-dimensional linear filtering (shown as Equation (3)). The N ratio maps were calculated by dividing each pixel of the RII with the corresponding pixel in the N filtered SAR images, image by image (as shown in Equation (4)).

1 M RII(r, c) = ∑ SARi(r, c) (2) M i=1

f SARi (r, c) = im f ilter(SARi(r, c)), (i = 1, 2, 3, . . . , n) (3) RII(r, c) (r c) = (i = n) ratioi , f , 1, 2, 3, . . . , (4) SARi (r, c) f where SARi(r, c) and SARi (r, c) denote the pixel position (r, c) of the ith SAR images before and after filtering, respectively; imfilter denotes the linear filtering operation. We took the SAR image acquired on 9 August 2016 as an example to illustrate the effectiveness of the proposed method. We used the commercial GAMMA software [47] to achieve the pre-processing of SAR images, including co-registration and cropping to the region of interest. The intensity images were multi-looked with five looks in range direction, and one look in the azimuth direction. Since the water of DGL and XGL was discharged entirely in March and April each year by visual interpretation, the RII was generated by averaging 24 SAR images acquired during this period using Equation (2), shown in Figure3a . Then, we filtered all the SAR images with the window size of 3 × 3 pixels and Remote Sens. 2021, 13, x FOR PEER REVIEW 6 of 22

Gaussian weighting function using Equation 3 and calculated the time series of ratio maps following Equation 4, of which the ratio map for 9 August 2016 is shown in Figure 3d. Figure 3b,c show the generated 8-bit greyscale intensity map and the color relief map

Remote Sens. 2021, 13, 1313 of the selected SAR image, respectively. The high contrast between the covered6 of areas 21 of glacial lakes and the surroundings is because of the lower intensity values of water surface. However, as previously analyzed, there were two issues with the negative impact on tracking the boundaries of glacial lakes, speckle noise caused by the superposition of echo Gaussian weighting function using Equation (3) and calculated the time series of ratio maps signals from small elementary scatters [26] and topography-induced effects by local inci- following Equation (4), of which the ratio map for 9 August 2016 is shown in Figure3d. dence angle variations [31].

FigureFigure 3. (a)3. The(a) Thegenerated generated reference reference intensity intensity image, image, the the blue blue dotted lines lines indicate indicate the the shapes shapes of XGLof XGL and and DGL. DGL. (b) The (b) The intensityintensity map mapin the in 8-bit the 8-bit format format acquired acquired on on 9 August 9 August 2016, 2016, th thee yellow lineslines indicate indicate the the position position of proﬁlesof profiles corresponding corresponding to Figureto Figure 4. (c)4 .(Thec) Theintensity intensity color color relief relief map map corresponding corresponding to ((bb).). ((dd)) The The ratio ratio color color relief relief map map for 9for August 9 August 2016. 2016.

ComparingFigure3b,c Figure show the 3c,d, generated the contrast 8-bit greyscale between intensity the pixel map values and the corresponding color relief map to the glacialof the lakes selected and SARthe surroundings image, respectively. was signific The highantly contrast enhanced between in the the ratio covered map, areas and the speckleof glacial noise lakes andand topography-induced the surroundings is effects because were of the in parallel lower intensity successfully values decreased. of water To quantitativelysurface. However, compare as previously the difference analyzed, between there wereratio twomap issues and withintensity the negative map, we impact extracted on tracking the boundaries of glacial lakes, speckle noise caused by the superposition of their profile lines along the yellow lines “V” and “H”, shown in Figure 3b. By visual inter- echo signals from small elementary scatters [26] and topography-induced effects by local pretationincidence of anglethe covered variations area [31 ].of glacial lakes, we used 0.08 and 2 as the thresholds for classifyingComparing the intensity Figure map3c,d, theand contrast ratio map, between respectively, the pixel valuesand then corresponding evaluated the to the classifi- cationglacial results lakes empirically. and the surroundings The blue, wasgreen, significantly and red dotted enhanced lines in represent the ratio map, glacial and lake, the non- glacialspeckle lake, noise and and misclassification, topography-induced respectively, effects were as inshown parallel in successfullyFigure 4. decreased. To quantitativelyFigure 4a,b compare shows thethat difference speckle betweennoise and ratio the map topography-induced and intensity map, we effects extracted have a stronglytheir profile negative lines impact along thewhen yellow the intensity lines “V” image and “H is”, directly shown in applied Figure3 forb. Byclassifying visual the glacialinterpretation lakes, leading of the to covered multiple area apparent of glacial misclassifications. lakes, we used 0.08 In and contrast, 2 as the these thresholds errors are for classifying the intensity map and ratio map, respectively, and then evaluated the significantly neutralized in the ratio map derived by the proposed method (see Figure classification results empirically. The blue, green, and red dotted lines represent glacial 4c,d.lake, Of non-glacial the 200-pixel lake, values and misclassification, captured along respectively, the profile as lines, shown the in Figuremisclassification4. rates based on intensity image and ratio map are 37% and 0.5%, respectively. This way shows the excellent facilitation of the proposed method in the identification of glacial lake boundaries.

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Remote Sens. 2021, 13, 1313 based on intensity image and ratio map are 37% and 0.5%, respectively. This way shows7 of 21 the excellent facilitation of the proposed method in the identification of glacial lake boundaries.

FigureFigure 4.4. ((a,,cc)) show show the the distributions distributions of of intensity intensity values values along along the the yellow yellow profile profile lines lines “H” “H” and and “V”“V” in in Figure Figure3 b3b based based on on the the intensity intensity map, map, respectively. respectively. ( (bb,,dd)) showshow thethe profilesprofiles ofof thethe samesame lineslines correspondingcorresponding toto (a,,c) in the ratio ratio map, map, respectively. respectively. The The green, green, blue, blue, and and red red dotted dotted lines lines repre- represent non-glacial lakes, glacial lakes, and misclassification under the condition of using a sent non-glacial lakes, glacial lakes, and misclassification under the condition of using a threshold threshold by manual. by manual.

3.2. DeterminationFigure4a,b shows of A thatutomatic speckle Classification noise and the Threshold topography-induced for Glacial Lakes effects have a stronglyThe negative RII we generated impact when in the the previous intensity section image is is directly based on applied the averaging for classifying of multi the- glacialtemporal lakes, intensity leading images to multiple selected apparent when the misclassifications. basins of glacial In lakes contrast, are entirely these errors exposed, are significantly neutralized in the ratio map derived by the proposed method (see Figure4c,d). Of the 200-pixel values captured along the profile lines, the misclassification rates based on

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intensity image and ratio map are 37% and 0.5%, respectively. This way shows the excellent Remote Sens. 2021, 13, x FOR PEER REVIEWfacilitation of the proposed method in the identiﬁcation of glacial lake boundaries. 8 of 21

3.2. Determination of Automatic Classification Threshold for Glacial Lakes The RII we generated in the previous section is based on the averaging of multi- whichtemporal means intensity that imagesthe coverage selected areas when of the glacial basins lakes of glacial and the lakes surroundings are entirely exposed, in the RII have similarwhich means intensity that the values. coverage When areas meltwater of glacial lakes converged and the insurroundings the glacial in lakes, the RII the have values of pixelssimilar corresponding intensity values. to When the glacial meltwater lakes converged in the ratio in themaps glacial were lakes, exponentially the values ofamplified duepixels to corresponding the specular toreflection the glacial of lakes the inwater the ratiosurface, maps while were exponentiallythose for theamplified non-glacial lake areasdue to remained the specular unchanged. reflection ofThus, the waterwe suggest surface, applying while those the for upper the non-glacial confidence lake interval of theareas stacked remained ratio unchanged. maps corresponding Thus, we suggest to the applying non-glacial the upper lake confidencearea samples. interval of the stackedIn this ratio case, maps we correspondingused the identified to the non-glacialnon-glacial lake lake area area samples. in the zone “T” (16 by 21 In this case, we used the identified non-glacial lake area in the zone “T” (16 by pixels) shown in Figure 3a as the samples for threshold calculation. The histogram of the 21 pixels) shown in Figure3a as the samples for threshold calculation. The histogram samplesof the samples with 48384 with 48384-pixel-pixel values values and andthe fitted the fitted normal normal density density function function curve curve is isshown in Figureshown in5. FigureWe calculated5. We calculated the mean the and mean standard and standard deviation deviation using using the themaximum maximum likelihood estimatorlikelihood estimatorand then and computed then computed the inverse the inverse of cumulative of cumulative distribution distribution function values evaluatedvalues evaluated at the atprobabil the probabilityity values values of 0.997 of 0.997 and and its its95% 95% confidence confidence interval interval for the normal distribution.normal distribution. Finally, Finally, 2.14 and 2.14 2.15 and were 2.15 were solved solved as the as thelower lower and and upper upper confidence confidence bounds, respectively,bounds, respectively, and 2.15 and was 2.15 wasused used as asthe the fixed fixed threshold threshold for for classifying classifying the the time time series series of ratioof ratio ma maps.ps.

μ 1.13 δ  0.37

Figure 5.5.The The histogram histogram (blue (blue bars) bars) and and the ﬁtted the fitted normal normal density density function function curve (red curve line) of(red samples line) of samplesselected inselected the zone in “ Tthe” (16 zone by 21“T pixels)” (16 by are 21 shown pixels) in are Figure shown3. Note: in Figureµ and 3).δ represent Note: μ theand mean δ represent theand mean standard and deviation, standard respectively. deviation, respectively. 3.3. Accuracy Evaluation 3.3. Accuracy Evaluation We present to suppose the boundaries of the glacial lakes derived from Sentinel-2A/B opticalWe images present as theto suppose ground truth the boundaries to validate the of the accuracy glacial of lakes the proposed derived method. from Sentinel The -2A/B opticalnormalized images difference as the water ground index truth (NDWI) to validate was employed the accuracy as an indicatorof the proposed for classifying method. The ntheormalized glacial lakes. difference NDWI, w deﬁnedater index by McFeeters (NDWI) [17wa],s is employed a normalized as an ratio indicat indexor between for classifying the glacial lakes. NDWI, defined by McFeeters [17], is a normalized ratio index between the green band and near-infrared (NIR) band that has been widely used in the classification of the water for glacial lakes [6,7,8,10,11,12,13], which is defined as ρ  ρ NDWI  Green NIR (5) ρGreen ρ NIR

where ρGreen and ρNIR denote the green band and NIR band, respectively; for Sentinel- 2 A/B, they are corresponding to the band 3 and band 8 [48].

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the green band and near-infrared (NIR) band that has been widely used in the classiﬁcation of the water for glacial lakes [6–8,10–13], which is deﬁned as

ρ − ρ NDWI = Green NIR (5) ρGreen + ρNIR

where ρGreen and ρNIR denote the green band and NIR band, respectively; for Sentinel-2 A/B, they are corresponding to the band 3 and band 8 [48]. With the characteristics of the strong absorption in the NIR band and the high reflec- tivity in the green band of water, the contrast between the water bodies and their vicinities was enhanced in the NDWI map [17,49]. However, the threshold for further classification of glacial lakes based on NDWI varies depending on the local geomorphic conditions. Furthermore, considering that some of the images were acquired without water cover in the basins of glacial lakes, we suggest classifying the time series of NDWI maps using a uniform fixed threshold. In this case, to ensure the accuracy for classifying the glacial lakes based on the time series of NDWI maps, we tested and applied Otsu’s method for post-processing classification [50]. Otsu’s method is used to perform automatic image thresholding and returns a single intensity threshold that separates pixels into two classes determined by iteratively computing to minimize the intra-class intensity variance. We first selected the nine NDWI maps with the water storage areas by visual interpretation and automatically calculated their thresholds utilizing Otsu’s method image by image, then averaged these nine estimated values as the global threshold for classifying the time series of NDWI maps. Finally, we calculated the relative error of area between the results and the outlines of glacial lakes identified based on the proposed method as the accuracy for evaluation.

4. Experiment and Result 4.1. Classification Results and Dynamic Changes of XGL and DGL We calculated the time series of ratio maps based on the proposed method for the 144 co-registration SAR images shown in Figure6 and then binarized them using the threshold obtained in Section 3.2. To further eliminate the disturbance of speckle noise and small supraglacial ponds, we removed the objects with the area of connected components containing fewer than 16 pixels for the results after classification. Then, we used the DEM to complete the projection transformation of the Range–Doppler coordinate to the WGS-84 UTM zone 47N coordinate for the binarized ratio maps. Finally, we resampled the geocoded binarized ratio maps to the 10-m-grid resolution, the same as the Sentinel-2 satellite images. The projection conversion was performed after the classification was completed because the speckle noises were involved in the resampling process and exacerbated in the geocoded results. Figure7 shows the time series of classification results of DGL and XGL. The blue and green in each subgraph represent the areas covered by the glacial lake and the non-glacial lake, respectively. Moreover, the time series of boundaries of XGL and DGL, automatically delineated by the edge detection operator, were also mapped and shown as the white polygons superimposed in Figure7. We computed the areas of XGL and DGL derived from each period of SAR images, respectively. Furthermore, their annual dynamic changes between 2015 and 2020 are shown in Figure8. During this period, the observed largest area of XGL was 69,100 m2 on 24 August 2020, and that for DGL was 74,440 m2 on 14 August 2017. Remote Sens. 2021, 13, 1313 10 of 21 Remote Sens. 2021, 13, x FOR PEER REVIEW 10 of 22

Figure 6.6. TheThe 144144 ratioratio mapsmaps derived derived from from the the 144 144 Sentinel-1 Sentinel-1 A A SAR SAR images images based based on on the the proposed proposed method. method. The The upper upper left cornerleft corner of each of sub-ﬁgureeach sub-figure shows theshows date the when date the when corresponding the corresponding SAR image SAR was acquired,image was in theacquired, format in of “yyyymmdd”.the format of “yyyymmdd”. The color bar indicates the ratio level ranging from 0 to 5, noting that the values greater than 5 are also The color bar indicates the ratio level ranging from 0 to 5, noting that the values greater than 5 are also colored with the colored with the same exponent as 5. The region with a high ratio level represents the water surface. same exponent as 5. The region with a high ratio level represents the water surface.

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FigureFigure 7. 7.The The results results after after the the classification classification ofof glacialglacial lakes correspondingcorresponding to to the the 144 144 ratio ratio maps maps shown shown in in Figure Figure 6,6 ,noting noting that the extracted boundaries are superimposed in each subgraph as the white outlines. The region colored in green rep- that the extracted boundaries are superimposed in each subgraph as the white outlines. The region colored in green resents the non-glacial lake area, while blue represents the glacial lake area. The upper left corner of each sub-figure shows represents the non-glacial lake area, while blue represents the glacial lake area. The upper left corner of each sub-figure the date when the corresponding SAR image was acquired, in the format of “yyyymmdd”. The lower right corner of each showssub-figure the date is marked when the with corresponding its dynamic status SAR image in the was cycle. acquired, ↗ and in ↘ the indicate format ofthat “yyyymmdd”. it is filling and The discharging, lower right → corner andof each○ indicating sub-figure it isis markeddynamically with stable its dynamic and completely status in the drained. cycle. % and & indicate that it is filling and discharging, → and indicating it is dynamically stable and completely drained. #

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2015 2018 2016 2019 2017 2020

FigureFigure 8. 8.(a) (anda,b )(b show) show the the annual water water area area changes changes of theof XGL the and XGL DGL and between DGL 2015 between and 2015 and 2020, 2020, respectively. The curves corresponding to each year are superimposed for visual comparison.respectively. The curves corresponding to each year are superimposed for visual comparison.

Table 1.We Annual observed largest periodicstorage area water and corresponding recharging date and statistics discharging for DGL processesand XGL from within the dynamic 2015changes to 2020. of The XGL variation and is DGL calculated between by subtracting 2014 and the area 2020. of the The two recharging adjacent years. process of XGL began from late April to earlyXGL May each year. The water areaDGL increased rapidly in June and reachedYear Max its Area maximum Variation in August Date or SeptemberMax Area and Variation then remained Date stable for about two months2015 59 until,600 m October.2 / Subsequently,2015-06- the28 area59,400 of XGLm2 gradually/ shrank2015-09 until-08 the water body completely2016 64,900 disappearedm2 +5300 m in2 February2016-07-16 of the70 following,000 m2 year.+10,600 The pattern2016-08 of-09 dynamic change for2017 DGL 65, was500 m similar2 +600 to m that2 of2017 XGL-09 from-09 201474,400 to m 2017.2 The+4400 water of2017 DGL-08-04 was rapidly ﬁlled from2018 June68,800 to m July2 and+3300 wholly m2 2018 discharged-09-04 71 in,000 November. m2 −3400 However, 2018 the-07 pattern-18 of periodic water2019 66 recharge–maintain–discharge,000 m2 −2800 m2 2019-07-01 was 23 interrupted,700 m2 − from47,300 2018.2019 The-07- stage01 of dynamic stability2020 69, of100 the m2 water+3100 area m2 was2020 no- longer08-24 observed.36,600 m2 In 2019+12,900 and 2020,2020- the06-25 maximum water storage area of DGL was surveyed with only 36,600 m2 on 25 June 2020. We counted the annual maximum water storage area, and corresponding dates for XGL andWe DGL counted observed the from annual 2015 maximum to 2020, as shown water in storage Table 1. area,During and this corresponding period, the dates for largestXGL andannual DGL water observed area of XGL from showed 2015 a to remarkable 2020, as shownincreasing in trend Table with1. During an average this period, the annuallargest growth annual rate water of 3 area.19%. of Compared XGL showed with 2015, a remarkable the largest increasing water area trend of DGL with an average increasedannualgrowth by 25.25% rate inof 3.19%. 2017 (equivalent Compared to with 12.63% 2015, annually). the largest However, water area with of the DGL increased anomalousby 25.25% variation in 2017 (equivalent of DGL in toJuly 12.63% 2018, annually). the increasing However, trend of with DGL the was anomalous also variation interrupted.of DGL in July 2018, the increasing trend of DGL was also interrupted. 4.2. The Spatiotemporal Evolution Characteristics of DGL and XGL Table 1. Annual largest storage area and corresponding date statistics for DGL and XGL from 2015 To explore the phenomenon of periodic water recharge and discharge for XGL and DGL,to 2020. the Thedaily variation temperature is calculated and precipitation by subtracting data were the used area for of thethe twocorrelation adjacent analysis years. with changes in the glacial lakes. Corresponding to the study area and the period of SAR XGL DGL image acquisition, the in situ data recorded by the Jiulong County Meteorological Station (29°00′N,Year 101°30′E, Max ~3000 Area m) in Sichuan Variation Province were Date also acquired Max Areafrom October Variation 2014 to Date November 2020. Considering that the altitude of Jiulong Station is about 1000 m lower 2015 59,600 m2 / 2015-06-28 59,400 m2 / 2015-09-08 than the terminus of Gongba Glacier, linear correction was conducted for the recorded 2016 64,900 m2 +5300 m2 2016-07-16 70,000 m2 +10,600 2016-08-09 temperature data by using the standard temperature lapse rate of −6.49° per 1000 m. Since 2017 65,500 m2 +600 m2 2017-09-09 74,400 m2 +4400 2017-08-04 2018 68,800 m2 +3300 m2 2018-09-04 71,000 m2 −3400 2018-07-18 2019 66,000 m2 −2800 m2 2019-07-01 23,700 m2 −47,300 2019-07-01

2020 69,100 m2 +3100 m2 2020-08-24 36,600 m2 +12,900 2020-06-25

4.2. The Spatiotemporal Evolution Characteristics of DGL and XGL To explore the phenomenon of periodic water recharge and discharge for XGL and DGL, the daily temperature and precipitation data were used for the correlation analysis Remote Sens. 2021, 13, 1313 13 of 21

with changes in the glacial lakes. Corresponding to the study area and the period of SAR image acquisition, the in situ data recorded by the Jiulong County Meteorological Station (29◦000N, 101◦300E, ~3000 m) in Sichuan Province were also acquired from October 2014 to November 2020. Considering that the altitude of Jiulong Station is about 1000 m lower than the terminus of Gongba Glacier, linear correction was conducted for the recorded Remote Sens. 2021, 13, x FOR PEER REVIEW 13 of 21 temperature data by using the standard temperature lapse rate of −6.49◦ per 1000 m. Since the precipitation is only used as a reference for qualitative analysis, the variation in precipitation with altitude is assumed to be ignored in this study. the precipitation is only used as a reference for qualitative analysis, the variation in Figure9a,b show the dynamic area changes of XGL and DGL from October 2014 to precipitation with altitude is assumed to be ignored in this study. November 2020, respectively. The corrected monthly average temperature and monthly Figure 9a,b show the dynamic area changes of XGL and DGL from October 2014 to accumulatedNovember 2020, precipitation respectively. over The the corrected same period monthly are average both overlaid temperature in Figure and9 .monthly We calculated theaccumulated Pearson correlation precipitation coefficient over the between same perio thesed observations are both overlaid to quantitatively in Figure 9.analyze We the interrelationshipcalculated the Pearson between correlation the changes coefficient in the between glacial these lake areaobservations and the typicalto quantitatively climatic factors, i.e.,analyze temperature the interrelationship and precipitation. between the The changes correlation in the coefficients glacial lake ofarea XGL and with the typical temperature andclimatic precipitation factors, i.e., were temperature 0.65 and and 0.64, precipitation. respectively, The and correlation these values coefficients were of 0.39 XGL and 0.42 forwith DGL. temperature However, and if theprecipitation monitoring were period 0.65 and was 0.64, divided respectively, into two and segments these values on 1 January 2018,were 0.39 the correlationand 0.42 for coefficients DGL. However, of the if former the monitoring would be period 0.64 andwas 0.52,divided respectively, into two while thesegments values on of January the latter 1, 2018, would the correlation be 0.25 and coefficients 0.32. These of the suggestformer would that precipitationbe 0.64 and and meltwater0.52, respectively, are important while thenutrition values of the for latter glacial would lake be formation 0.25 and 0.32. and periodicThese suggest water that recharge. Furthermore,precipitation and considering meltwater that are precipitation important nutrition had a decreasing for glacial laketrend formation in recent and years, the spreadingperiodic water of XGL recharg ande. DGL Furthermore, also reflects considering an acceleration that precipitation of glacial meltwater, had a decreasing representing antrend aggravation in recent years, of Gongba the spreading Glacier ofablation. XGL and DGL also reflects an acceleration of glacial meltwater, representing an aggravation of Gongba Glacier ablation.

Sentinel-1 Sentinel-2 Temperature Precipitation

Figure 9. 9. (a(a,b,b) )show show the the XGL XGL and and DGL DGL area area changes changes from fromOctober October 2014 to 2014 November to November 2020, 2020, respec- respectively. The blue and green dotted lines represent the corrected monthly average tively. The blue and green dotted lines represent the corrected monthly average temperature and temperature and monthly cumulative precipitation changes, recorded by Jiulong Station. The pink monthlylines and the cumulative scattered triangles precipitation represent changes, the spatiotemporal recorded by changes Jiulong of Station. glacial lakes The calculated pink lines and the scatteredbased on the triangles Sentinel represent-1 SAR images the spatiotemporaland the Sentinel-2A/B changes optical of images. glacial lakes calculated based on the Sentinel-1 SAR images and the Sentinel-2A/B optical images. The melting of snow and ice on the Gongba Glacier began in March every year with the temperature rising above 0 °C. Since May, the temperature continued warming and was accompanied by enhanced precipitation. The glacier entered a strong ablation phase, part of the meltwater flowed into the downstream river through the subglacial drainage system and some flowed into XGL and DGL through runoff from the debris-covered

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The melting of snow and ice on the Gongba Glacier began in March every year with Remote Sens. 2021, 13, x FOR PEER REVIEW ◦ 14 of 22 the temperature rising above 0 C. Since May, the temperature continued warming and was accompanied by enhanced precipitation. The glacier entered a strong ablation phase, part of the meltwater flowed into the downstream river through the subglacial drainage systemsystem and and some some flowed flowed into into XGL XGL and and DGL DGL through through runoff runoff from from the the debris-covered debris-covered glacier glac- surface.ier surface. Subsequently, Subsequently, as the as temperature the temperature further further increased, increased, the snow the and snow ice atand the ice higher at the altitudeshigher altitudes were also were beginning also beginning to melt, and to melt, the rainfall and the intensity rainfall alsointensity increased also increased significantly. sig- Thus,nificantly. the replenishment Thus, the replenishment of abundant of meltwaterabundant meltwater induced the induced area of the XGL area and of DGLXGL and to expandDGL to rapidly. expand rapidly. ToTo inquire inquire about about the the cause cause of of the the periodic periodic water water discharge discharge of of glacial glacial lakes, lakes, we we made made twotwo field field trips trips to to the the Gongba Gongba Glacier Glacier on on June June 9 9 and and October October 17, 17, 2018. 2018. Numerous Numerous exposed exposed iceice cliffs cliffs and and entrances entrances of of the the subglacial subglacial drainage drainage system system were were identified identified at at the the bottom bottom of of thethe DGL DGL basin basin (see (see Figure Figure 10 10),), which which confirmed confirmed that that the the glacial glacial lake lake basin basin was was composed composed ofof buried buried dead dead ice ice and and thick thick moraines. moraines. After After the the meltwater meltwater gathered gathered in in the the glacial glacial lake lake basin,basin, the the drainagedrainage tunnels were were gradually gradually dredged dredged under under water water pressure pressure and and water water ero- erosion. During July and August, the recharge and discharge of glacial lakes were in a sion. During July and August, the recharge and discharge of glacial lakes were in a dy- dynamic balance. However, the temperature began to drop, accompanied by the decrease namic balance. However, the temperature began to drop, accompanied by the decrease in in precipitation. The reduction of meltwater and rainwater resulted in the rapid shrinkage precipitation. The reduction of meltwater and rainwater resulted in the rapid shrinkage of glacial lakes. Since October, the daily average temperature gradually dropped below of glacial lakes. Since October, the daily average temperature gradually dropped below 0 0 degrees, the melting of glaciers gradually weakened, and liquid precipitation almost degrees, the melting of glaciers gradually weakened, and liquid precipitation almost stag- stagnated. XGL and DGL entered the discharge period until they disappeared completely. nated. XGL and DGL entered the discharge period until they disappeared completely.

Figure 10. Photos of the DGL on the field survey on 17 October 2018. (a,b) show the two entrances Figure 10. Photos of the DGL on the ﬁeld survey on 17 October 2018. (a,b) show the two en- of the drainage tunnel at the basin of the DGL. The yellow dotted lines show the estimated entrances of the drainage tunnel at the basin of the DGL. The yellow dotted lines show the estimated trance radius. entrance radius.

4.3.4.3. Differentiation Differentiation Analysis Analysis of of XGL XGL and and DGL DGL Activity Activity Patterns Patterns AsAs mentioned mentioned above,above, thethe initiation of water water storage storage of of the the XGL XGL was was earlier earlier than than the theDGL, DGL, while while its itsdischarge discharge was was completed completed later later than than the DGL. the DGL. The TheXGL XGL was wasformed formed at the atterminal the terminal moraine moraine ridge of ridge Xiaogongba of Xiaogongba Glacier, Glacier, while the while DGL the was DGL sited was at the sited lateral atthe mo- lateralraine moraineridge of Dagongba ridge of Dagongba Glacier (see Glacier Figure (see 1) Figure. Furthermore,1). Furthermore, we found we apparent found apparent scouring scouringtraces of traces runoff of converging runoff converging to XGL to on XGL the onsurface the surface of Xiaogongba of Xiaogongba Glacier Glacier during during our in oursitu in fieldwork situ fieldwork and inferred and inferred that the that XGL the absorbed XGL absorbed almost all almost of the all meltwater of the meltwater and rain- andwater rainwater that flowed that over flowed the overglacier the surface glacier based surface on its based formation on its location formation and location topography. and topography.The DGL was The formed DGL wasat a formedbranch on at athe branch north onside the of north the Dagongba side of the Glacier Dagongba and absorbed Glacier andonly absorbed part of the only runoff, part of with the runoff,most of with the mostmeltwater of the and meltwater rainwater and flowing rainwater through flowing the throughmain body the mainof the body glacier of theto the glacier terminus. to the terminus. WeWe speculate speculate that that these these differences differences between between XGL XGL and and DGL DGL led led to to differences differences in in their their waterwater storage storage and and drainage drainage patterns. patterns. In In the the early early warm warm season season each each year, year, glacial glacial meltwater meltwa- waster thewas primary the primary component component of the rechargedof the recharged waterto water the glacial to thelakes. glacial During lakes. thisDuring period, this period, most of the meltwater flowing over the surface of Xiaogongba Glacier converged into the XGL, while that for the Dagongba Glacier was drained directly downstream, resulting in an earlier start of water storage of the XGL than the DGL. Considering that the

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most of the meltwater ﬂowing over the surface of Xiaogongba Glacier converged into the Remote Sens. 2021, 13, x FOR PEER REVIEW 15 of 21 XGL, while that for the Dagongba Glacier was drained directly downstream, resulting in an earlier start of water storage of the XGL than the DGL. Considering that the Dagongba Glacier is three times larger than the Xiaogongba Glacier, with increased melting of the rechargedglaciers and to intensivethe DGL, precipitation,facilitating the more rapid water expansion was recharged of its area. to The the water DGL, replenishment facilitating the inrapid the expansion drainage phase of its area. of the The glacial water lakes replenishment was similar in tothe the drainage storage phase phase, of thewhere glacial the weakenedlakes was similar melting to theof the storage glacier phase, and where the decreased the weakened precipitation melting of more the glacier significantly and the affecteddecreased the precipitation DGL. more signiﬁcantly affected the DGL.

4.4. The Glacial Lake Lake Outburst Outburst Floods Floods of DGL of DGL in 2018 in 2018 From 24 24 June June to to 18 18 July July 2018, 2018, DGL DGL was was quickly quickly almost almost filled ﬁlled within within 24 days 24 daysand then and quithenckly quickly drained drained in the in nextthe next 12 days 12 days before before July July 30 30 (see (see Figure Figuress 88 and 9 9).). This This was was significantlysigniﬁcantly different from the pattern observed from 2015 to 2017.2017. Furthermore,Furthermore, in the following warm seasons of 2019 2019 and and 2020, 2020, the the apparent apparent cyclical cyclical activity activity had had not not recover recovered.ed. We compared the average temperature and precipitation on a month month-by-month-by-month basis from 2015 to 2020 cross cross-sectionally,-sectionally, as shown in Figure 1111.. During this period, there were nono temperature anomalies observed. In In terms terms of precipitation, although it varied when the peak precipitationprecipitation waswas recorded recorded in in the the past past six six years, years, there there were were no anomalies no anomalies during during June Juneand Julyand inJuly 2018 in 2018 compared compared to other to other years. years.

Figure 11. ((a,,b)) show show the the comparison comparison of of monthly monthly mean mean temperature temperature and and cumulative cumulative precipitation precipitation from 2015 to 2020, respectively.

Compared to June 2018, we found a huge new drainage entrance with an area of about 190 m 2 atat the the bottom bottom of of the the DGL DGL basin basin with with the the benefit beneﬁt of drone drone imagery during our fieldworkﬁeldwork in in October October 2018 2018 (see (see Figure Figure 12d,e). 12 Additionally,d,e). Additionally we found, we an found inverted antriangular inverted triangularwashed-out washed gap that-out was gap 47 that m wide, was 2047 m m deep, wide, 75 20 m m long, deep, and 75 16000 m long, m3 inand volume 16000 onm3 the in volumelateral moraine on the lateral ridge of moraine the outer ridge west of side the ofouter the DGLwest (seeside Figureof the DGL12b,c). (see Considering Figure 12b that,c). Consideringthe downstream that riverthe downstream channel exhibited river channel evident exhibited traces of evident being washed traces of and be wideneding washed by and widened by flood, we speculate that a glacial lake outburst flood occurred in the DGL between the 18th and 30th of July 2018. We inferred the water flow path at the time of outburst of DGL based on the orthophoto acquired by the drone on 17 October 2018, as shown in the blue lines in Figure 12a. Under years of water erosion and water pressure, the dead ice at the bottom of the

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Under years of water erosion and water pressure, the dead ice at the bottom of the DGL basin was unblocked to form a huge subglacial entrance, along with an outlet about 70 m under an ice cliff about 300 m to the west (see Figure 12f). The rushing flood washed away the lateral moraine ridge composed of loose debris deposits. Following that, the debris- laden floodwater advancing downstream along the valley resulted in a visible widening of the approximately three km-long river channels, while the debris and rocks deposited in the channel significantly altered the path of the original river flow. Additionally, a small hydroelectric installation supplying power to the Gongga Monastery and a simple bridge were destroyed in the disaster. The XGL and DGL served as essential regulators in the water cycle of the Gongba Remote Sens. 2021, 13, 1313 Glacier catchment to buffer the rapid downstream dumping of meltwater. This16 glacial of 21 lake outburst event severely changed the activity cycle of DGL. Both in 2019 and 2020, the DGL burst again before it was filled. The area expansion of XGL and DGL responded to the accelerated melting of the Gongba Glacier in the context of climate warming and re- ﬂood, we speculate that a glacial lake outburst ﬂood occurred in the DGL between the 18th flected the potential disasters of the expanding glacial lakes in the southeast Tibetan Plat- and 30th of July 2018. eau.

Figure 12. (a) The drainage path of the glacial lake outburst flood in July 2018 for the DGL, with Figure 12. (a) The drainage path of the glacial lake outburst flood in July 2018 for the DGL, with the the dashed blue lines being the conjectured subglacial channel, the solid blue lines being drawn dashed blue lines being the conjectured subglacial channel, the solid blue lines being drawn based based on flood scour traces, and the cyan dotted line showing the outline of the DGL. (b,c) are onorthophotos flood scour based traces, on and images the cyan acqu dottedired by line drones showing on June the outline 9 and October of the DGL. 17, 2018, (b,c) arerespectively, orthophotos basedwith onthe imagesred dotted acquired line showing by drones the onflood-damaged June 9 and Octobersurface area. 17, 2018, (d,e) respectively,are orthophotos with ofthe the red dottednew, linelarge showing drainage the entrance flood-damaged at the bottom surface of area.the DGL (d,e) basin are orthophotos on 9 June and of the 17 new,October large 2018, drainage re- entrancespectively, at the with bottom the yellow of the DGLline showing basin on th 9e June outline and of 17 this October drainage 2018, entrance. respectively, (f) shows with the the yellow outlet lineof the showing DGL outburst the outline flood. of this drainage entrance. (f) shows the outlet of the DGL outburst flood.

4.5.We Comparison inferred theof Classification water flow path Results at the with time Optical of outburst Images of and DGL Accuracy based onAssessment the orthophoto acquiredFor by the the data drone used on in 17 this October study, 2018, since as the shown Sentinel-1 in the and blue Sentinel-2 lines in Figure satellites 12a. had Under dif- yearsferent of revisit water intervals, erosion and only water threepressure, times were the available dead ice where at the these bottom two of satellites the DGL collected basin wasdata unblocked on the same to form day, a 23 huge August subglacial 2018, 22 entrance, October along 2018, with and an11 outletOctober about 2020. 70 We m under gener- anated ice their cliff aboutcolor 300images, m to NDWI the west maps, (see classifi Figurecation 12f). Theresults rushing based flood on NDWI, washed SAR away images, the lateral moraine ridge composed of loose debris deposits. Following that, the debris-laden floodwater advancing downstream along the valley resulted in a visible widening of the approximately three km-long river channels, while the debris and rocks deposited in the channel significantly altered the path of the original river flow. Additionally, a small hydroelectric installation supplying power to the Gongga Monastery and a simple bridge were destroyed in the disaster. The XGL and DGL served as essential regulators in the water cycle of the Gongba Glacier catchment to buffer the rapid downstream dumping of meltwater. This glacial lake outburst event severely changed the activity cycle of DGL. Both in 2019 and 2020, the DGL burst again before it was filled. The area expansion of XGL and DGL responded to the accelerated melting of the Gongba Glacier in the context of climate warming and reflected the potential disasters of the expanding glacial lakes in the southeast Tibetan Plateau. Remote Sens. 2021, 13, 1313 17 of 21

4.5. Comparison of Classiﬁcation Results with Optical Images and Accuracy Assessment Remote Sens. 2021, 13, x FOR PEER REVIEW For the data used in this study, since the Sentinel-1 and Sentinel-217 of satellites22 had

different revisit intervals, only three times were available where these two satellites collected data on the same day, 23 August 2018, 22 October 2018, and 11 October 2020. We generated their color images, NDWI maps, classification results based on NDWI, SAR ratio maps, and classification results based on the proposed method and overlaid bound- images, ratio maps, and classification results based on the proposed method and overlaid aries derived from the different methods for visual comparison, shown in Figure 13. The boundaries derived from the different methods for visual comparison, shown in Figure 13. overlaid boundariesThe overlaid shown in boundaries the last column shown of in Figure the last 13 column indicate of Figurethe consistency 13 indicate of the the consistency results derived offrom the these results two derived different from method these twos. For different an example methods. of the For images an example acquired of the images from 22 Octoberacquired 2018 (second from 22 row October of Figure 2018 (second 13), an rowisolated of Figure island 13 ),was an isolatedexposed island in the was exposed XGL during thein discharged the XGL during phase the and discharged observed phase on both and optical observed and on SAR both images optical andwith SAR images similar outlines.with The similar area changes outlines. of The XGL area and changes DGL are of XGL superimposed and DGL are in superimposed Figure 9a,b, re- in Figure9a,b, spectively. Comparisonrespectively. between Comparison the areas between of the the glacial areas lake of the extracted glacial lake by extractedSentinel-1 by and Sentinel-1 and Sentinel-2 showedSentinel-2 remarkable showed similarity, remarkable and similarity, the time series and the of time these series two ofclassified these two results classified results also showed a consistentalso showed trend. a consistent trend.

Figure 13.FigureComparison 13. Comparison of glacial lake of glacial classification lake classification results between results SAR between images andSAR optical images images and optical acquired im- on the same day. The firstages to acquired third columns on the are same the trueday. color The first images, to th theird NDWI columns color are relief the maps,true color and theimages, classification the NDWI results derived color relief maps, and the classification results derived from NDWI maps. The fourth to sixth col- from NDWI maps. The fourth to sixth columns are the 8-bit SAR intensity maps, the ratio color relief maps, and the classified umns are the 8-bit SAR intensity maps, the ratio color relief maps, and the classified binary maps binary maps based on SAR images. The seventh column is the comparison of the glacial lake boundaries delineated by SAR based on SAR images. The seventh column is the comparison of the glacial lake boundaries delin- image and optical image. eated by SAR image and optical image. However, there were some differences between the boundaries mapped based on SAR Table 2. Comparisonimages of the and area optical of the images. XGL extracted (1) The shapefrom Sentinel-2 of glacial A/B lakes optical is slightly images inconsistent with that because of extracted from Sentinel-1the unique SAR geometric image acquired distortion on the and same projection day. distortion of the side-looking imaging SAR system. (2) The glacial lakes area varies because of the different spatial resolutions and Date Sentinel-2 A/B Sentinel-1 Difference Accuracy separate data processes of these two satellites. Table2 shows the areas and the differences 23 August 2018,of the XGL extracted64,500 from m2 the three-period62,400 m optical2 and−2100 SAR m images.2 96.74% We assume the results derived from NDWI maps to be the ground truth and calculate the relative error of area for 2 2 2 22 October 2018the outlines identified49,900 m based on the46,800 proposed m method.−3100 The m testing93.79% results show that the 11 October 2020accuracy for identifying65,900 m2 the outlines66,600 of the m glacial2 lakes700 withm2 Sentinel-1A98.94% SAR images can reach up to 96.49%. / / Average 1500 m2 96.49%

Table 2. Comparison of the area of the XGL extracted from Sentinel-2 A/B optical images with that However, extractedthere were from some Sentinel-1 differences SAR image between acquired the on theboundaries same day. mapped based on SAR images and optical images. (1) The shape of glacial lakes is slightly inconsistent because of the unique geometricDate distortion Sentinel-2 and A/B projection Sentinel-1 distortion of the Difference side-looking im- Accuracy aging SAR system.23 August(2) The 2018,glacial lakes64,500 area m 2varies because62,400 mof2 the different−2100 spatial m2 resolu-96.74% 2 2 2 tions and separate22 data October processes 2018 of 49,900these mtwo satellites.46,800 Table m 2 shows− 3100the areas m and the93.79% 11 October 2020 65,900 m2 66,600 m2 700 m2 98.94% differences of the XGL extracted from the three-period optical and SAR images. We as- / / Average 1500 m2 96.49% sume the results derived from NDWI maps to be the ground truth and calculate the relative error of area for the outlines identified based on the proposed method. The testing results show that the accuracy for identifying the outlines of the glacial lakes with Senti- nel-1A SAR images can reach up to 96.49%.

Remote Sens. 2021, 13, 1313 18 of 21

5. Discussion In this contribution, we developed and showed the use of a simple but effective data processing method for classifying glacial lakes and monitoring their dynamic evolution using time series of SAR images acquired by the Sentinel-1 A satellite for two glacial lakes at the terminus of Gongba Glacier. Instead of calculating thresholds directly using SAR intensity images, we improved the neighborhood ratio model to enhance the contrast between water bodies and their surroundings in each SAR image. Based on the proposed method, we recovered the entire process of area change of these two glacial lakes from 2014 to 2020 and analyzed their evolutionary characteristics by comparing UAV images and meteorological data. Approximately 6 years of Sentinel-1 A SAR images were used for monitoring the dynamic evolution of the glacial lakes and further addressing the spatio-temporal behavior, e.g., filling, expansion, and even outburst. A series of results showed that the proposed method has excellent persistence and adaptability in this typical application. Above all, the periodic filling and release processes can be tracked for these two lakes, as well as their expansion and the outburst. Furthermore, the proposed method can be developed into an automated processing procedure that allows the acquired data to be used in a real-time environment, which further makes timely and reliable GLOF risk evaluation under the high-frequency SAR image acquisition condition. The intensity values of homogeneous regions in SAR images could be assumed to follow an approximately log-normal distribution [51]. When the water and non-water bodies in the SAR scene are comparable, the optimal threshold can be calculated utilizing the nature of the bimodal distribution of the log-transformed intensity images. Most of the current methods aim to ensure that the number of samples involved in the optimal threshold calculation is comparable between the water body and the surroundings, as previous authors have investigated [52,53]. However, images with histograms having extremely unequal peaks or only one peak are unsuitable for this assumption, as in this study when the water of the two glacial lakes was about to release or completely released. The proposed method does not respect the distribution of image intensity values and can effectively reduce such errors. Furthermore, the design of RII in the NR model enables the involved SAR images to be under the same criteria, which ensures it is possible to classify multi-temporal SAR images using a single fixed threshold. The developed method allows the classification of glacial lakes in the area of interest, regardless of the specific gravity of the water area in the SAR scene, even for zero. Regardless of the different imaging modes used, the removal topography-induced effects should be an important factor in the automatic classification of the glacial lake. Basically, features located on the slope facing the radar present higher intensity compared to the intensity from that located in areas of shadow. These noises are often corrected by calculating the local incidence angle and the pixel area normalization factor based on the DEM, as described by O’Grady et al. [54]. Instead of direct correction of backscattered intensity, this study reduces these noises using the same imaging geometry between the generated RII and each SAR image for ratio offset. The proposed method simplifies the data processing and is efficient, avoiding the error propagation caused by the resolution and accuracy of the DEM. One of the objectives of the proposed method was to develop a generic processing chain applicable to the dynamic monitoring of various types of glacial lakes. In the current work, we studied a pair of types with periodic filling and release, both of them with distinct periods of complete release, providing a basis for the RII generation. For periodic freeze–thaw glacial lakes, the frozen glacial lake surface would become rough and present a backscattering intensity similar to that of the surroundings when snow is attached to it. The work in this paper can be directly applied to such cases since the images acquired after the freezing of the glacial lake can be assumed as zero-area. However, the proposed method may not be available for the glacial lake where liquid water exists all year round, which would present a challenge in knowing how to select the images to generate the RII. Remote Sens. 2021, 13, 1313 19 of 21

Therefore, it may be necessary to adapt the current method so that the zero-area represented by the RII is no longer an underlying condition. For example, multiple images with the smallest area of the glacial lake are visually interpreted to generate the RII, and then the optimal threshold or manual digitization is used to produce a mask representing the RII water area. The area of change is accumulated on the basis of the mask area during the classification of the glacial lake. In addition, this approach can be further extended to rapid assessment of flooded areas using the RII produced from SAR images before the rainy season. Nevertheless, given the small size of the two glacial lakes studied, we did not consider the wind-induced effect on the backscattered intensity from the water surface. Indeed, wind may influence the smoothness of the water surface, causing a change in the local incidence angle of the radar and, thus, a change in the backscattered intensity [55]. In addition, we also did not consider whether the turbidity of the glacial lake water bodies would influence the classification results for lack of ground truth data. Therefore, the robustness of the new method needs to be tested on more different types of glacial lakes. This task will be studied in future work.

6. Conclusions In this study, we proposed an improved neighborhood-based ratio method to map the boundaries of glacial lakes and detect their spatiotemporal evolution based on the time series of SAR images. We first generated the reference intensity image by averaging the SAR images that selected the water of glacial lakes when it was discharged entirely, and then calculated the ratio between the reference intensity image and the time series of SAR images after smoothing filtering image by image. Two glacial lakes at the terminus of the Gongba Glacier in the southeastern Tibetan Plateau were selected as the study site, and we achieved the mapping and dynamic monitoring of the two glacial lakes based on 144 Sentinel-1A SAR images acquired from 2014 to 2020. Through neighborhood ratio operations, the proposed method has advantages in reducing the influences of the topography-induced effects and speckle noises, as well as amplifying inter-class differences between glacial lakes and surroundings. The shared reference intensity image ensures the possibility of classifying water bodies with a single fixed threshold for time series of ratio maps, thus, ensuring the validity and accuracy of glacial lake classification regardless of the percentage of water area in the SAR scene. Furthermore, by comparing the results derived from the multispectral images acquired from Sentinel-2 A/B satellites, the accuracy for identifying the boundaries of the glacial lakes with Sentinel-1A SAR images averaged 96.49%. Our study covered the entire ascending orbit data of Sentinel-1 A satellite during the monitoring period, recovering the dynamic evolution of the glacial lakes in the study area, including one GLOF. Relevant results showed that the developed data processing chain has excellent persistence and adaptability in addressing glacial lake dynamics monitoring. Furthermore, timely and reliable GLOF risk evaluation can be conducted under the high- frequency SAR image acquisition condition.

Author Contributions: Conceptualization, B.Z., G.L., and R.Z.; methodology, B.Z.; validation, B.Z. and Y.F.; investigation, B.Z., Q.L., and J.C.; data curation, B.Z. and Y.F.; writing—original draft preparation, B.Z.; writing—review and editing, B.Z., G.L., R.Z., X.W., and Z.L.; funding acquisition, G.L., Q.L., and X.W. All authors have read and agreed to the published version of the manuscript. Funding: This research was jointly funded by the National Natural Science Foundation of China (Grant Number: 41771402, 41871069, 42071410, 41804009) and National Key R&D Program of China, (Grant Number: 2017YFB0502700). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data is contained within the article. Remote Sens. 2021, 13, 1313 20 of 21

Acknowledgments: The Sentinel-1 and Sentinel-2 A/B satellite images were provided by the Coper- nicus mission of the European Space Agency. The SRTM DEM data was downloaded from the United States Geological Survey. The temperature and precipitation data recorded by the Jiulong County Meteorological Station were provided by the China Meteorological Data Service Center (http://data.cma.cn/). The outlines of the Gongba Glacier were provided by National Cryosphere Desert Data Center. (http://www.ncdc.ac.cn). Conﬂicts of Interest: The authors declare no conﬂict of interest.

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