Monitoring and evaluation

July 10 2020

Monitoring mountain glacier extents

eduardo@movimentar.eu GIS, Monitoring and evaluation

Introduction

The retreat of sea ice and glaciers, sea-level rise, and extreme climate events are predicted to cause immense economic and social chaos. Around 10% of the world’s population lives in coastal areas that are less than 10 meters above sea level. Furthermore, farming requires the availability of sufficient fresh water, the largest source of which are rivers, which are largely fed by rain and mountain glaciers in the melt season. Therefore, monitoring mountain glacier extents is inevitable in the era of global warming.

A Geographic Information System (GIS) is a computer-based tool for storing, managing, analyzing, and visualizing many different types of data. GIS technology allows for deeper insights into data and supports decision-making in various fields. Geo-information is largely based on raster and numerical data collected from satellite sensors and plays a critical role in environmental data science. Satellite image processing is an important tool for any environmental scientist to research the climate and to support their research based on in-situ data. GIS also plays a key role in the evaluation of public policies, programmes, and projects related to natural resource management and other fields whenever datasets are georeferenced (include at least latitudes and longitudes of each observation).

In this study, we have analysed satellite images of the Bolivian mountain range of Cordillera Quimsa Cruz (Figure 1) to determine the glaciers’ extent of retreat. Such retreats can be measured through temporal analysis of multispectral satellite images. The spectrum-based supervised classification allowed us to distinguish snow-covered surfaces from other surface types and helped us to create a temporal glacier inventory. This post shows the step-by-step procedures to reproduce and monitor glacier changes anywhere. This aims at helping anyone interested in contributing to the evaluation of the impacts of global warming and hopefully motivate actions in terms of national and international policies, programmes, and projects.

According to Oxfam, Bolivia is particularly vulnerable to the impacts of climate change for six main reasons:

It is one of the poorest countries in Latin America and suffers from one of the worst patterns of inequality. Low-income groups in developing countries are the most exposed to climate change impacts.
It is the country in South America with the highest percentage of indigenous people and hence a concentration of poverty and inequality.
It is one of the most bio-diverse countries in the world, with a wide variety of ecosystems that are vulnerable to different impacts from climate change.
More than half of the country is Amazonian, with high levels of deforestation, which adds to the vulnerability to flooding.
Located in a climatically volatile region, it is one of the countries in the world most affected by ‘natural’ disasters in recent years.
It is home to about twenty per cent of the world’s tropical glaciers, which are retreating more quickly than predicted by many experts.

The map below shows Bolivia and the area which has been analysed here. We analysed glacier retreat in a time span of 30 years from 1985 to 2015 for the month of August with 10-year intervals. We observed one individual glacier, which showed a reduction of ice extent by 40.47% from 1985 to 2015. This indicates that global-warming consequences in Bolivia will affect the ecosystems and lives of its people, particularly those ill-equipped to adapt to the present and future impacts. Cities such as La Paz and El Alto are vulnerable to the accelerated retreat of glaciers, which are the sources of a significant amount of their drinking water (Oxfam 2009). In addition, thousands of poor Andean farmers are dependent on glacial melt for parts of their water supply to irrigate their crops. Glacier retreat can also impact in terms of unpredictability of the rainfall, more extreme weather events, and higher temperatures, with negative impacts for livelihoods and the ecosystems.

Figure 1: Mountain range of Cordillera Quimsa Cruz (in red square) located in Bolivia.

Preparative steps (data retrieval): Exploring satellite images

For creating this glacier inventory, that is, attribute tables, we used multispectral satellite images and a Digital Elevation Model (DEM). The attribute tables included latitude, longitude, area, and elevation for each polygon representing one specific glacier. In this study, images are used from satellites Landsat 05 and Landsat 08. The temporal interval is 10 years in the time span of 1985 to 2015. In case of the glacier retreat study, the end of the ablation period is important (in this study: August).

Images had to be selected that showed no visible cloud cover over the region of interest (ROI), with shadows as small as possible. The satellite images (Level 1 – Geo TIFF) and the DEM (ASTER GLOBAL DEM tiles) can be downloaded from the website of the U.S. Geological Survey.

In this study, images are retrieved from Landsat 05 (Thematic Mapper) for the years 1985, 1995, and 2005; from Landsat 08, we retrieved the Operational Land Image (OLI) and Thermal Infrared Sensor (TIRS) combined for the year 2015. The images from Landsat 07 (Enhanced TM) could not be selected due to scan-line failure.

Creating a false-colour RGB image

We created a project file in QGIS, loaded the desired bands (SWIR, NIR, and Red) of one of the downloaded satellite images taken on 5 August 1985, and created a false-colour image using the Build virtual raster tool.

Figure 2: This false-colour RGB image was created by using spectral bands of SWIR, NIR, and Red of the Landsat 05 (TM) satellite image (footprint 170 km x 180 km), taken on 5 August 1985 with a resolution of 30m.

Since our ROI is the mountain range, we cropped the remaining parts of the region using the Clip raster by extent tool. The extent of the first ROI image layer was later used to define the ROI extents of the rest of the satellite images layers in order to keep all layers of the same extent. We loaded the images of the bands SWIR, NIR, Red and clipped them using the extent of ROI. Similarly, the DEM was also clipped by using the extent of ROI.

Figure 3: Our ROI is the mountain range of Cordillera Quimsa Cruz and needs to be clipped out of the downloaded satellite images for better visualization.

Figure 4: The images of ROI of the bands SWIR, NIR (top left, top right), Red, and DEM (bottom left, bottom right) after clipping.

Creation of ratio image

A sharp reflectance of Red colour by a snow-covered surface as contrary to SWIR can be used to classify snow surfaces from other surface types. A band ratio image is one of the methods, such as Red by SWIR, NIR by SWIR, and Normalized Difference Snow Index (NDSI). In this study, a simple band ratio Red by SWIR is used. The band ratio image can be created using the Raster Calculator tool.

Figure 5: The ratio image (Red / SWIR).

Finding a suitable Threshold Value (THV) for supervised classification of glaciers

The THV can be determined by using the Identify Features tool located at the Attributes toolbar, zooming in somewhere to the edge of the glacier extent and clicking on the least visible pixel that can be identified as snow. A simultaneous look over the ratio image and false-colour image together with a google base map as shown in the figure below helped to identify the least possible THV.

Figure 6: Zoomed section of the ratio image (3 by 5) focusing on an edge of the glacier with a simultaneous view of the false-colour image.

The lowest THV was selected from the ratio image (i.e., 2.2), allowing us to assume that all pixels >2.2 were classified as snow.

Creating a reclassified image based on THV

We created a constant layer of a pixel size of 30m (same as ratio-image resolution) of the same extent as of the ratio image and saved it as a tif-file. Each pixel value of the constant layer should be 1. We now used a Raster Calculator to create a reclassified image of expression (band-ratio-image > THV) * constant layer and saved it as tif-file. The created layer was a reclassified raster image for the snow surface where pixel value 1 (white) represented snow surface and pixel value 0 (black) represented non-snow surface.

Figure 7: Reclassified image of snow cover.

Converting to vector layer and separating the catchment areas

We converted the reclassified image (Figure 7) to a vector image using the Polygonize tool and, afterwards, Digitizing tools to clean the small polygons far from mountain ridges representing leftover snow in catchment areas. We were required to fill the gap areas among glaciers (that were not classified as snow due to shadow cover) and merge those small polygons that were aligned with relatively bigger polygons.

Figure 8: Vector layer of reclassified image of snow cover.

We needed to fix the polygons’ geometry using the Fix geometries tool before we divided glaciers (polygons) based on their location relative to mountain ridges. We could then draw boundary lines (a new vector layer) over mountain ridges with the help of the DEM (using Aspect) in order to divide the glacier regions.

Figure 9: Aspect (top-right) created from DEM (top-left). Boundary lines drawn at mountain edges with the help of Aspect (bottom-right) can be used to split glaciers (bottom-left).

Creating attribute table

The attribute table contains important information such as latitude, longitude, elevation, and area size. We extracted elevation information of polygons from DEM using the Zonal statistics tool. We calculated the area of the polygons using the Field calculator tool and fixed the geometry using the Fixed geometry tool. For information on the coordinates, we created a centroids layer using the Centroids tool to get centralized coordinate values of the glacier polygons by using the Field calculator tool, and joined the attribute table of Centroids layer to the vector layer by using the Joins feature in vector layer properties.

Figure 10: Attribute table snapshot.

Figure 11: End-result glacier extent (1985) after image processing.

Repeating the procedure with satellite images of 1995, 2005 and 2015

The above procedure was repeated with other temporal images, using the same layer extent of ROI and boundary lines layer of 1985 throughout the process. The layout shows the retreating glaciers’ extent. The anomaly for a sub-divided glacier was calculated by using the attribute table below.

Figure 12 : Selected glacier to measure retreat extent 1985 (left) and 2015 (right).

Year	Latitude	Longitude	Area(sqkm)	Elev_mean	Elev_min	Elev_max
1985	-1874765	673362	2.52	5329	4945	5711
1995	-1874638	673415	2.15	5349	4995	5711
2005	-1874663	673364	1.83	5322	4951	5704
2015	-1874520	673402	1.50	5339	5034	5639

Table 1: Attribute table of the selected glacier in Figure 12.

The retreat of selected glacier extent from 1985 to 2015 equals 40.47%.

The consolidated map

The consolidated map was created using all the end-result glacier extent layers of the years 1985, 1995, 2005, and 2015 using the New Print Layout tool located at the Project toolbar.

Final remarks

Bolivia is particularly vulnerable to the impacts of climate change and is home to about twenty percent of the world’s tropical glaciers, which are retreating more quickly than predicted by many experts. This post shows the step-by-step procedures to reproduce and monitor glacier changes anywhere. This aims at helping anyone interested in contributing to the evaluation of the impacts of global warming and hopefully motivate actions in terms of national and international policies, programs and projects.

Using QGIS (version 3.10), we analyzed glacier retreat in a time span of 30 years from 1985 to 2015 for the month of August with 10-year intervals. The consolidated map shows a clear detectable temporal retreat of the extent of glaciers throughout the mountain range. The retreat is more prominent at eastern glaciers of the mountain range. We observed that one individual glacier, the glacier in Figure 12, has reduced its ice extent by 40.47% from 1985 to 2015. We can also see in the consolidated map that the overall glaciers’ extents are undergoing temporal retreat even though a lower THV is used for later years. The THV used were 2.2, 2.0, 2.0, and 1.4 for the years 1985, 1995, 2005, and 2015, respectively. This indicates that global warming consequences in Bolivia will affect the ecosystems and lives of its people, particularly those ill equipped to adapt to the present and future impacts. Cities such as La Paz and El Alto are vulnerable to the accelerated retreat of glaciers, which are the sources of a significant amount of their drinking water. In addition, thousands of poor Andean farmers are dependent on glacial melt for part of their water supply to irrigate their crops. Glacier retreat can also impact in terms of unpredictability of the rainfall, more extreme weather events and higher temperatures, with negative impacts for livelihoods and the ecosystems.

In the future, a similar study could be applied to Himalayan glaciers (up to 2000m above sea level) in southeast Asia, which provide water to five river basins of the Indus, Ganges, Yellow, Brahmaputra, and Yangtze rivers for 1.4 billion people (over 20% of the global population).

Written by Usman Ahmed (MSc) and Eduardo W. Ferreira (PhD)

Acknowledgement

We would like to express our gratitude to Annalena Oeffner Ferreira and Valerie Kateb in reviewing and providing suggestions for this post. Many thanks to Dr. Marco Möller (University of Bremen) for supervising the project which made this post possible.

Useful Links

Data and images
https://movimentar.co/glacier_data

Used GIS application: QGIS version 3.10
https://download.qgis.org

Satellite images:
https://earthexplorer.usgs.gov/
https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products

February 24 2020

Digitalisation and big data – A cost-effective and reliable way for decision-making in the international development context

eduardo@movimentar.eu Big data, Digitalisation, Machine learning, Monitoring and evaluation

The Past Has No Power over the Present Moment (1)

movimentar GmbH is a social enterprise providing advisory, training, and software development services for organisations and individuals working in sustainable development and humanitarian actions. Its founder Eduardo works as a project-management and data-science consultant, trainer, and facilitator in designing, managing, and evaluating projects and programmes across the globe. In the following interview, he outlines his work, his motivation, and why he thinks that using technology and data science in international development projects can speed up humanity’s efforts towards sustainable progress.

Eduardo, why do you do the job you do today?

Digitalisation and big data are gigantic challenges for agencies working in the international development and humanitarian sector including governments, international organisations, civil society, and the private sector. Our job is to help these actors, using technology and data science (statistical computing), to get the most of their data in a cost-effective and reliable way in time for decision-making. Science and technology can speed up humanity’s efforts towards sustainable progress by helping actions to become more evidence-based and results-driven.

Why are you working in this industry?

I do not believe that there are single industries anymore. Everything is connected, and data science can help us to see that better with increased precision and quality. My goal is to bring science and technology closer to project and programme management in different thematic areas. Independently from the topic, professionally designed digital data-collection processes together with big-data techniques (e.g., text mining) and artificial-intelligence algorithms such as machine learning methods can work just like glasses. They help one see better. They are not there to replace one’s eyes (and brains), but they allow for a more realistic picture of the world with reduced error and bias. Hans Rosling is a great inspiration to me, and I try to contribute to his legacy of promoting more factfulness in public policies and international development actions.

What do you enjoy most about your job?

Discovering new ways to contribute to sustainable development for thousands of people. Every assignment is full of discoveries. It always amazes me to see new and counter-intuitive relationships between variables and problems. As anything else, they are also continuously changing in space (geographic area) and time. Analysing the interrelations between variables and problems is the first step towards addressing the world’s problems.

What was the best decision in your career and why?

Start learning computer languages, especially R and Python. They are always evolving and one is always learning something new. I have always worked with international cooperation for development and humanitarian aid. This is a sector that has seen many impressive achievements such as the reduction of extreme poverty by half in the last 20 years. The more progress humanity achieves, the harder it gets to make further improvements. R and Python are the key languages when it comes to data science and artificial intelligence. They are free software and therefore extremely powerful through their worldwide community. Everyday one can see further developments by thousands of developers around the world. Data-science skills have increased my potential contribution to more evidence-driven decisions and transparency in the international development and humanitarian sectors.

What makes you excited about Mondays?

The possibility of taking part in the improvement of the lives of more people around the world.

What has been your greatest career disappointment? What did you learn from it?

Once I worked on a large action with environmental groups working with development education. I designed a system to provide them with real-time information on the satisfaction of participants about their activities with digital data collection. I was invited to present the system at a large initial partner conference, and I did. I had so far only exchanged ideas with the leading organisation and did not know the others. During my presentation, some of the older and more influential members were determined in not letting me even present the tools. We had to interrupt the presentation and exclude the data-collection system from our contract. My lesson from that: To never underestimate the fear of technology as well as the change-management efforts particularly among older and less technology-savvy decision-makers.

Describe the environments in which your leadership style is most effective. Where have you been frustrated and less successful?

Charles Darwin once said: “In the long history of humankind (and animal kind, too) those who learned to collaborate and improvise most effectively have prevailed.” Participation and collaboration are of crucial importance. Multiple minds can produce better than one. Technology is helping people to collaborate more effectively. Every now and then we come across cases where people still prefer a more top-down approach. Depending on the organisational culture of the client, some staff are not willing to make direct contributions to the text of a proposal, for example, and prefer to make general or subjective comments without taking ownership for the product. I understand that this is also a way to reduce risk exposure, but it can be inefficient and slows down processes. Luckily, these cases are becoming increasingly less frequent.

What does success look like for you?

Increased capacity of our clients to deliver humanitarian and development actions faster, at scale and with real-time information on their impact and beneficiary satisfaction.