PhD defence presentation, 12 July 2016 @ FU-Berlin

transcript

Trends in surface temperature from new long-termhomogenised thermal data by applying remote

sensing techniques and its validation using in-situdata of five southern European lakes

Sajid Pareeth

Department of Biology, Chemistry and Pharmacy, Freie Universität BerlinCentre for Research and Innovation, Fondazione Edmund Mach

Supervisors: Dr. Markus Neteler, Dr. Nico Salmaso,Prof. Dr. Rita Adrian, Prof. Dr. Klement Tockner

Disputatio – 12 July 2016

Presentation outline

Rationale & Research questionsObjectives & BackgroundStudy area & DataMilestones

M1: Resolve the geometrical issues of earlier satellite dataM2: New Climate Data Record (CDR) – thirty years of daily lakesurface water temperature (1986 – 2015)M3: Results – validation of new LSWT and trend analysis

Discussion & Future workThesis outcome – PublicationsAcknowledgments

Introduction Outline 2 / 44

Rationale: Climate change perspective

Lake Surface Water Temperature (LSWT) - is a good indicator inunderstanding the changes in lake characteristicsLong-term changes in LSWT could give us a cue towardschanging climate and lake’s biological propertiesGlobally the summer mean LSWT are reported to be significantlyincreasingLakes’ response to long-term warming varies regionally

O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophysical Research Letters42, 10,773–10,781 (2015)Adrian, R. et al. Lakes as sentinels of climate change. Limnology and oceanography 54, 2283–2297 (2009)

Introduction Motivation 3 / 44

Rationale: Data perspective

Need high spatio-temporal resolution data to understand thelong-term dynamics of thermal variations over lake’s surfaceThe in-situ data from the study lakes are spatially and temporallycoarseSatellite data acquired at Thermal Infra-red Region(TIR) areconsidered as a good alternativeAvailability of daily satellite observations since 1980’s at 1 kmspatial resolutionSurface temperature is one of the accurate measurements usingremote sensing

Introduction Motivation 4 / 44

Research questions

How can we develop a reliable time series of daily LSWT from thehistorical discrete satellite data?What level of accuracy can be achieved with satellite derivedsurface temperature over large lakes in Northern Italy?Are the large lakes in the north of Italy warming due to climatechange and at what rates?

Introduction Research Questions 5 / 44

Objectives

To reconstruct thirty years (1986 – 2015) of homogenised timeseries of daily LSWT for five large Italian lakes by combiningthermal data from multiple satellitesTo assess the quality of the satellite derived LSWT usinglong-term in-situ dataTo report the summer and annual trends in LSWT using statisticaltests for last thirty years (1986 – 2015)

Introduction Objectives 6 / 44

Background (1/3)

Satellite sensors record the reflected energy in the optical range,while they record the emitted energy in the thermal regionAll objects with a temperature above absolute zero(0 K or -273.15 ◦C) emit electromagnetic radiation

Introduction Background 7 / 44

Background (1/3)

Satellite sensors record the reflected energy in the optical range,while they record the emitted energy in the thermal regionAll objects with a temperature above absolute zero(0 K or -273.15 ◦C) emit electromagnetic radiation

Background (2/3)

Background (3/3)

At-sensor radiance to Brightness Temperature(BT) using theinverse Planck’s equation.BT to surface temperature is calculated using Split-Window (SW)equation.

LSWT = T i + c1(T i − T j) + c2(T i − T j)2 + c0 (1)

c0 - c2: Satellite specific split-window coefficientsTi: Thermal data (BT) acquired from satellites at 10.5 - 11.5 µmTj: Thermal data (BT) acquired from satellites at 11.5 - 12.5 µm

Jimenez-Munoz, J.-C. & Sobrino, J. Split-Window Coefficients for Land Surface Temperature Retrieval From Low Resolution

Thermal Infrared Sensors. IEEE GREL 5, 806–809 (2008).

Study lakes

Red rectangle: bounding box of processed satellite data

Introduction Study area 10 / 44

Thirty years of satellite data

NOAA11

NOAA12

NOAA14

NOAA16

NOAA17

Envisat

NOAA18

NOAA19

1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

Sensor

A(A)TSR

Introduction Data 11 / 44

Thermal data from 13 satellites (five sensors)Daily dual thermal channels

Ti [10.5 - 11.5 µm]Tj [11.5 - 12.5 µm]

Long-term in-situ data from study lakes obtained from thecollaborators

Lake Garda – Fondazione Edmund MachLake Iseo – Uni-Milano Bicocca (Dr. Barbara Leoni)Lake Como – ARPA (Dr. Fabio Buzzi)Lake Maggiore – ISE-CNR (Dr. Giuseppe Morabito)Lake Trasimeno – Uni-Perugia (Dr. Alessandro Ludovisi)

Daily acquisition time of satellite data

1986 1993 1997 2000 2003 2005 2006 2008 2010 2012 2015

Satellites

NOAA11

NOAA12

NOAA14

NOAA16

NOAA17

NOAA18

NOAA19

Envisat

Major milestones

M1: Resolve the geometrical issues of earlier AVHRR dataM2: New homogenisation method to develop daily LSWT timeseries for thirty years (1986 - 2015)M3: Validation of new LSWT and trend analysis

Miestone 1 Resolving AVHRR issues 14 / 44

Major milestones

Geometrical issues with AVHRR sensor data

Navigational discrepancies due to on board clock errors andsatellite orbital angular errorsOrbital drifts of the NOAA satellites

Geometrical issues with AVHRR sensor data

Navigational discrepancies due to on board clock errors andsatellite orbital angular errorsOrbital drifts of the NOAA satellites

Automated workflow for accurate AVHRR dataprocessing

New python readers: read raw data and calibrate, contributed tothe public repositoryFeature matching technique deployed to extract homologouspoints with respect to a reference imageGeo-rectification using the automatically extracted matching points

Feature matching based geometric correction

Input – Date of acquisition - 09 Aug 1997Red and blue points: homologous point pairs

Zoomed into study lakes

Output – Date of acquisition - 09 Aug 1997Corrected image with aligned boundaries

Zoomed into study lakes

Validation: Geometric correction of AVHRRsensor data

Using 12,000 random pixels from 2000 imagesSub-pixel accuracy with an overall RMSE of 755.63 m (Nominalpixel size is 1 km)

Major milestones

Milestone 2 Homogenisation of LSWT 23 / 44

Work flow to homogenise LSWT time series

Daily acquisition time of satellite data

1986 1993 1997 2000 2003 2005 2006 2008 2010 2012 2015

Satellites

NOAA11

NOAA12

NOAA14

NOAA16

NOAA17

NOAA18

NOAA19

Envisat

Diurnal Temperature Cycle Model (DTC)

Homogenisation using the monthly diurnal cycles derived fromLSWT using DTC modelCorrection factor (cf) is estimated for each LSWT observationusing the respective monthly diurnal cycle

cf = abs(T s(t)− T s(12)) (2)

Original LSWT observation is then adjusted using the correctionfactor to standardise it to 12:00 UTCTime correction of an image taken in June at 14:00 UTC:

Gap filling using harmonic analysis

Harmonic ANalysis of Time Series (HANTS) is applied to filteroutliers and fill the gaps due to cloud coverHANTS output for the year 2003 as an example for Lake Garda

Seasonal and annual climatologies

Summer mean LSWT (June/July/August) for the year 2003Seasonal and annual mean LSWT were developed for all lakes

Summer climatology of Lake Garda

Major milestones

M1: Resolve the geometrical issues of earlier AVHRR dataM2: New homogenisation method to develop daily LSWT timeseries for thirty years (1986 - 2015)M3: Validation of new LSWT time series and trend analysis

Milestone 3 Validation and trends 30 / 44

Validation of new LSWT time series

Cross-platform LSWT’s between same day observations from asatellite pair reported an average RMSE of 0.88 ◦CFinal homogenised and gap-filled LSWT against in-situ datareported an average RMSE of 1.2 ◦CLake wise results using respective in-situ data are given below:

Name RMSE (◦C) MAE (◦C) R2 N SamplingLake Garda 1.06 0.83 0.98 217 MonthlyLake Iseo 1.08 0.95 0.97 129 MonthlyLake Como 1.14 0.96 0.96 83 MonthlyLake Maggiore 1.13 0.97 0.97 207 MonthlyLake Trasimeno 1.38 1.13 0.98 4392 Daily

Summer mean LSWT variation: 1986 – 2016

Summer and annual trends from 1986 to 2016

Trends estimated using Sen-slope and Mann Kendal test forsignificanceSignificant trends obtained for annual and summer meansAverage summer and annual trends at the rate of 0.032 ◦C yr-1

and 0.017 ◦C yr-1 respectively

Lake Summer Annual

Lake Garda 0.036*** 0.02*Lake Iseo 0.017 0.019*Lake Como 0.032* 0.012Lake Maggiore 0.033* 0.017Lake Trasimeno 0.044*** 0.017

***(P < 0.001), **(P < 0.01), *(P < 0.05)

Temporal coherence between summer meanLSWT

Lake Garda

21.0 22.0 23.0

0.76*** 0.83***21.5 22.5 23.5 24.5

0.76***

0.84***21

Lake Iseo 0.87*** 0.73*** 0.64***

Lake Como 0.86***

0.62***

Lake Maggiore 0.64***

22.5 23.5 24.5 20 21 22 23 24.5 25.5 26.5

Lake Trasimeno

Scatter plot matrix showing temporal coherence between summermean LSWT of all the lakes

Applications of new LSWT time series

Inter-lake comparisons using data from same sourceHigh temporal frequency of new data helps to detect localminima/maxima of LSWT during a season/month.To study the ecological consequences due to warmingIt could help in understanding the variation in timing of thermalstratificationTo understand the disappearance of large lakes and its impact tosurrounding landscapesClimate Data Record (CDR) to various modelling frameworks

Discussion Applications 35 / 44

Discussion

The method is extensible and reproducible to other geographicallocationsThe new LSWT time series provides an opportunity to fill the gapdue to lack of high frequent in-situ dataGood alternative for lakes with difficult accessibilityThe satellites measure temperature over skinlayer (thin microlayer between lake surface and atmosphere), while in-situ datarepresents epilimnion layerThe skin layer explains the higher RMSE obtained in this study

Discussion Applications 36 / 44

Future work

To study the influence of larger climatic indices like North AtlanticOscillation (NAO) and Eastern Atlantic (EA) oscillations on thederived seasonal means from new LSWT seriesTo understand the ecological impacts of the reported warming onthe study lakes.

Discussion Future work 37 / 44

Conclusion

A total of 62,799 images (6 TB) were processed in highperformance computer using GRASS GIS and PythonNew method for developing homogenised time series of LSWTfrom multiple satellites is developedThe study lakes are reported to be warming at an average rate of0.032 ◦C yr-1 during summer and 0.017 ◦C yr-1 annuallyHigh coherence was reported between summer mean LSWT ofstudy lakesHighest coherence was reported between Lake Trasimeno andLake Garda during summer, depicting the higher influence ofMediterranean climate over Lake Garda.

Discussion Conclusion 38 / 44

Thesis manuscripts

M1: Pareeth, S., Delucchi, L., Metz, M., Rocchini, D., Devasthale, A., Raspaud, M.,Adrian, R., Salmaso, N., Neteler, M., 2016. New Automated method to developgeometrically corrected time series of brightness temperatures from historical AVHRR LACdata. Remote Sensing 8, 169.

M2: Pareeth, S., Salmaso, N., Adrian, R., Neteler, M. [In review]. Homogenized daily lakesurface water temperature data generated from multiple satellite sensors: A long-termcase study of a large sub-Alpine lake. Nature Scientific Reports.

M3: Pareeth, S., Bresciani, M., Buzzi, F., Leoni, B., Lepori, F., Ludovisi, A., Morabito, G.,Adrian, R., Neteler M., Salmaso N. [In review], Warming trends of perialpine lakes fromhomogenised time series of historical satellite and in-situ data, Science of the TotalEnvironment.

Discussion Publications 39 / 44

Conference contributionsPareeth, S., Salmaso, N., Adrian, R., Neteler, M. New homogenized daily lake surfacewater temperature data of three decades from multiple sensors confirm warming of largesub-alpine lake Garda (Poster). In: EGU 2016, Vienna, Austria, 17-22 April 2016Pareeth, S., Delucchi, L., Metz, M., Salmaso, N., Neteler, M. An open source frameworkfor processing daily satellite images (AVHRR) over last28 years. In: FOSS4G-Europe,Como, Milan, Italy, 14-17 July 2015Pareeth, S., Delucchi, L., Metz, M., Buzzi, Fabio., Leoni, B., Ludovisi, A., Morabito, G.,Salmaso, N., Neteler, M.. Inter-sensor comparison of lake surface temperatures derivedfrom MODIS, AVHRR and AATSR thermal bands. In: 35th EARSeL symposium 2015,Stockholm, Sweden, 15-19 June 2015Pareeth, S., Metz, M., Rocchini,D., Salmaso,N., Adrian,R., Neteler, M. (2014). Lakesurface temperature as a proxy to climate change: Satellite observations versus multiprobe data. Poster at Climate Symposium, Darmstadt, Germany, 13-17 October, 2014Pareeth, S., Metz, M., Neteler, M., Bresciani, M., Buzzi, F., Leoni, B., Ludovisi, A.,Morabito, G., Salmaso, N. (2014). Monitoring and retrieving historical daily surfacetemperature of sub-alpine Lakes from space. In: 15th World Lake Conference (WLC15),ISBN: 978-88-96504-05-5.Pareeth, S., Metz,M., Rocchini,D., Salmaso,N., Neteler,M. (2013). Warm Lakes: retrievalof lake surface water temperature (LSWT) for large sub-alpine lakes from multiple sensorsatellite imageries. In: XXI Congresso dell’AIOL, Lignano Sabbiadoro (Ud), 23-26September 2013: 9.Pareeth, S., Metz,M., Rocchini,D., Salmaso,N., Neteler,M. (2013). Lake surface watertemperature (LSWT) for large sub alpine lakes from satellite sensor derived surfacetemperature. In: EULAKES, Brescia, Italy, 30 May 2013

Discussion Publications 40 / 44

Acknowledgements

Discussion Acknowledgements 41 / 44

Acknowledgements

GIS and RS group members at FEMHydrobiology group members at FEMCollaborators - Mariano Bresciani (IREA-CNR); Barbara Leoni(Uni Milano Bicocca); Alessandro Ludovisi (Uni-Perugia); FabioLenti (SUPSI); Fabio Buzzi (ARPA); Giuseppe Morabito(ISE-CNR)Flavia Zanon, Elisabetta Perini, Alessandro GretterCRI PhD students and colleaguesData providers - NASA, ESA, NOAAGrant providers - FIRST-FEM, IRSAE, European Commission,AIOLOpen source projects - GRASS GIS, Pytroll and their wonderfulcommunities

THANK YOU !!!

References

Adrian, R. et al. Lakes as sentinels of climate change. Limnology and oceanography 54, 2283–2297 (2009).

O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophysical ResearchLetters 42, 10,773–10,781 (2015).

Jimenez-Munoz, J.-C. & Sobrino, J. Split-Window Coefficients for Land Surface Temperature Retrieval From Low-Resolution Thermal Infrared Sensors. IEEE Geoscience and Remote Sensing Letters 5, 806–809 (2008).

Riffler, M., Lieberherr, G. & Wunderle, S. Lake surface water temperatures of European Alpine lakes (1989–2013) basedon the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set. Earth System Science Data 7, 1–17 (2015)

Dokulil, M. T. et al. Twenty years of spatially coherent deepwater warming in lakes across Europe related to the NorthAtlantic Oscillation. Limnology and Oceanography 51, 2787–2793 (2006).

Pareeth, S. et al. New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperaturesfrom Historical AVHRR LAC Data. Remote Sensing 8, 169 (2016).

Jin, M. & Treadon, R. E. Correcting the orbit drift effect on AVHRR land surface skin temperature measurements.International Journal of Remote Sensing 24, 4543–4558 (2003

PhD defence presentation, 12 July 2016 @ FU-Berlin

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