Monitoring the eruption of Mt. Sinabung through Google Earth Engine

Hi there,

It’s been while since the last time I write an article in this page. Due to teaching and research I almost forget that I have my own page that need to be updated. Ok, let’s go for the very last thing I have learned, the cloud-computing platform of Google Earth Engine (GEE).

To learn more about the GEE platform you could go to https://earthengine.google.com/ and explore many things there. As written, the GEE “combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth’s surface.” It is very efficient and effective. No more large space of hard disk drive (HDD) needed to store hundreds of satellite imageries. No more wasting time to wait the download process of gigabytes datasets.

Lets give it a try, we will monitor the eruption of Mt. Sinabung and see the spatial distribution of emitted SO2. Please make sure you already sign-up and sign-in to GEE platform (https://code.earthengine.google.com/). To work with GEE we need to input the script into the code editor panel. There are some panel in the GEE as shown in Figure below

The GEE panels

Firstly, we need to define the date parameter before and after the eruption, use the following Javascript to do it

var sebelum = ee.ImageCollection(‘COPERNICUS/S5P/NRTI/L3_SO2’)
.select(‘SO2_column_number_density’)
.filterDate(‘2019-08-01’, ‘2019-08-31’);

var setelah = ee.ImageCollection(‘COPERNICUS/S5P/NRTI/L3_SO2’)
.select(‘SO2_column_number_density’)
.filterDate(‘2020-08-10’, ‘2020-08-14’);

 

Then we need to define the visualization parameters, used this script

var band_viz = {
min: 0.0,
max: 0.0005,
opacity:0.5,
palette: [‘white’, ‘purple’, ‘cyan’, ‘green’, ‘yellow’, ‘red’]
}; 

 

Next is to visualize the map within the main map window using the following script

Map.addLayer(setelah.mean(), band_viz, ‘Emisi SO2 Setelah’);
Map.setCenter(98.39,3.17, 7);

 

For information to readers, we need to add some title, subtitle, etc. To do it you can use my script as follow

var header = ui.Label(‘Map of SO2 Emission After M. Sinabung Eruption’, {fontSize: ’25px’, color: ‘darkSlateGrey’});
var text_1 = ui.Label(
‘Map of SO2 Emission around Mount Sinabung captured by Sentinel-5P, August 2020’,
{fontSize: ’15px’});
var text_2 = ui.Label(
‘Developed by: Dr. Fatwa Ramdani, Geoinformatics Research Group, Brawijaya University (2020)’,
{fontSize: ’11px’});

var toolPanel = ui.Panel([header, text_1, text_2], ‘flow’, {width: ‘400px’});

ui.root.widgets().add(toolPanel);

 

Finally, we need to add the legend to make it easier to understand the map.

//Membuat legenda
var legendTitle2 = ui.Label({
value: ‘SO2 Emission’,
style: {
fontWeight: ‘bold’,
fontSize: ’15px’,
margin: ’10px 0 0 0′,
padding: ‘0’
}
});

//Membuat panel aksesoris dan komponen kartografi
var legend = ui.Panel({
style: {
position: ‘bottom-right’,
padding: ‘8px 15px’,
}
});

var titleTextVis = {
‘margin’:’0px 0px 15px 0px’,
‘fontSize’: ’18px’,
‘font-weight’:”,
‘color’: ‘3333ff’
};

//Membuat judul legenda
var legendTitle = ui.Label(‘Legenda’,titleTextVis);

//Menambahkan judul legenda kedua
legend.add(legendTitle2);

//Membuat gambar legenda
var lon = ee.Image.pixelLonLat().select(‘latitude’);
var gradient = lon.multiply((band_viz.max-band_viz.min)/100.0).add(band_viz.min);
var legendImage = gradient.visualize(band_viz);

//Membuat teks di atas legenda
var panel = ui.Panel({
widgets: [
ui.Label(‘> ‘.concat(band_viz[‘max’]))
],
});

legend.add(panel);

//Menampilkan gambar legenda
var thumbnail = ui.Thumbnail({
image: legendImage,
params: {bbox:’0,0,10,100′, dimensions:’10×50′},
style: {padding: ‘1px’, position: ‘bottom-center’}
});

//Menambahkan gambar ke legenda
legend.add(thumbnail);

//Membuat teks di bawah legenda
var panel = ui.Panel({
widgets: [
ui.Label(band_viz[‘min’])
],
});

legend.add(panel);

//Menampilkan legenda di peta utama
Map.add(legend);

 

And the final result is as shown in Figure below. If you have any question just drop it to the comments below. The apps is accessible through url https://fatwaramdani.users.earthengine.app/view/eruption-of-mt-sinabung-2020

SO2 emission from the eruption of M. Sinabung, North Sumatera, Indonesia

Working with Sentinel-1 SAR data for Lombok earthquake rapid assessment

Lombok Island located in the far-eastern part of Java Island just hit by the large scale earthquake in the early August of 2018 (the second strike).

I am interest in the impact of the earthquake within the affected areas. For rapid assessment I am working with Sentinel-1 SAR datasets of Lombok Island for acquisition date 30 July 2018 and 11 August 2018.

Four main steps were developed to generate the displacement map of Lombok Island, that is:

  1. S1-TOPS Split for faster computing time
  2. InSAR processing using Graph in SNAP Software
  3. DinSAR processing using Graph in SNAP Software
  4. Displacement measurement

The final result then exported into KML format and open in Google Earth is as shown in Figure below. There is uplift of the affected area up to 29 cm in the northern part (visualized in blue colour). While land subsidence is up to 15 cm in the southern part of Lombok Island (visualized in red colour). The white colour visualized the unaffected area or there is no land displacement, especially in the center part.

Displacement map of Lombok Island generated using DinSAR interferometric algorithm. Disclaimer: This is not validated in the field

Sentinel 5P: Monitoring the atmospheric condition

The availability of cutting-edge sensor today makes the monitoring of atmospheric condition become easier than before. The Copernicus program of European now provides Sentinel 5P TROPOMI (TROPOspheric Monitoring Instrument) with so many product type and level. For detail please refer to their data products (available here https://sentinel.esa.int/web/sentinel/missions/sentinel-5/data-products).

I tried to learn and visualize the data of Sentinel 5P (L2_NO2 – Level 2 of Nitrogen Dioxide) using Panoply (available here: https://www.giss.nasa.gov/tools/panoply/) and save the image as KMZ to make it able to overlay with another dataset.

To download the data of Sentinel-5P L1B and L2 data are available for download from the following URL: https://s5phub.copernicus.eu/dhus/#/home. Please login using >> s5pguest:s5pguest.

The result is as shown in Figure below. It is shown that Jakarta produced the higher amount of NO2 compared to another region.

Tropospheric vertical column of nitrogen dioxide (NO2) at western part of Indonesia. Acquisition date: 24 July 2018.

If we zoom deeper, we will get more detail information. Where Jakarta Megapolitan dan Cilegon Industrial Area is producing a higher amount of NO2 in the western part of Java Island, Indonesia.

Nitrogen dioxide of Jakarta Megapolitan and Cilegon Industrial Area. Acquisition date: 24 July 2018.

Seeing the unseen: Forest fire through optical satellite-based imagery

The forest fire is a very common event during summer and it is very difficult to monitor since its produced huge amount of smoke.

Sentinel-2 with its SWIR band provides beneficial opportunity to monitor and map the forest fire.

The figures below shows the location of forest fire event captured in November 2017 on the west coast of United States. RGB combination of SWIR, Red, and NIR provide the clear location of fire within the image, compared to the natural color composite bands.

Natural Color. We can see nothing.
SWIR-Red-NIR composite bands. Now we can see the unseen, fire. Fire locations are shown in light orange color within the center of the image.

This information could provide the forest fireman with the full information of the fire and could handle the fire faster and saver. The information about the size, location (longitude, latitude, and altitude), accessibility, and distance will be available after classification or post-processing work.

 

New Highway at Malang and Solo as captured from the space-based sensor

Sentinel-2 is earth observation satellite own by European Space Agency (ESA) that provides a free dataset for public. This sensor is an optical-based sensor, which is captured the earth surface within the visible light spectrum.

One of the benefits of this data is sustainable monitoring of earth surface transformation could be monitored and mapped clearly since it has 10 meters spatial resolution.

Malang and Solo are two cities located on Java island, these cities have similar characteristics, which is inhabitant are mostly college or university students. They are mostly commuters or people that living in the city. These people are depending on the street infrastructures for their daily activity. Furthermore, the region around Malang and Solo needs to be supported by road infrastructures, since we all know the road will boost the regional economy.

To support the development of region around Malang and Solo, the government is now building the new highway thet connected the Solo City and Semarang City (Fig. 1)

Figure 1. New highway of Solo-Semarang as captured by Sentinel-2 of ESA. Acquisition date October 31, 2017

As shown in Figure 1, the development of new highway is still progressing, and it is planned to be fully operated by 2018.

Malang and Surabaya are already connected by highway, however, it is only to Pandaan gate. From Pandaan gate to Malang, we still need to drive through national road, which needs another one hour to reach Malang City. A new highway is undergoing to connected Pandaan directly to Sawojajar (Malang City). This new highway (Fig. 2) will cut-down the travel time to less then an hour.

Figure 2. New highway of Malang-Pandaan as captured from Sentinel-2 data. Acquisition date is September 18, 2017

These highways are expected to improve the regional economy, not only the huge private company but also the people around the highway. Can the government make it happen? Let’s wait and see.

We are living in Mega-city

This satellite photo was taken by Sentinel-2 sensor of the European Space Agency (ESA). Acquisition date was April 16, 2017.

This amazing image shows the current situation of the large scale of Indonesian urban structures, which is connected the small-medium-large cities and merged into single mega-city.

People of this mega-city is living in high mobility level. They are working in the southern part, while living in the north and vice versa.

For instance, someone is working as Government officer in Malang Regency, while she is living in Surabaya, another is working as Private Consultant in Surabaya City, and he is living in Malang City.

Bima’s Flood 2017: Rapid Assessment through Space-based data

Bima City, located in the eastern coast of the island of Sumbawa, West Nusa Tenggara (NTB), Indonesia was hit by huge flood in the late 2016 to early 2017.

From the rapid assessment of space-based dataset of Landsat OLI 8, we found that during 20 years (1997-2016) span, there are uncontrolled land use land cover transformation.

Area in the upper stream of DAS (Watershed) Sari of NTB completely transformed and lost many of its vegetation covers, while in the down stream, agricultural areas was transformed into man-made structures. Furthermore, river flood area was gone and changed into agriculture or man-made structures.

These transformations lead to huge volume of run-off when heavy rain event happened. The water will follow the physic’s rule, and flow to the lower elevation profile of land.

Rapid assessment of Bima’s Flood

The most recent land use land cover of Cimanuk Watershed

By the end of last month, flood was hit Garut City, where located in the center of Cimanuk watershed area. Many believed that flood event this time was due to mismanagement of upper stream area.

To have better understanding, we need to employ space-based image analysis to identify the most recent LULC (Land Use Land Cover) of Cimanuk Watershed.

Based-on Landsat OLI 8 acquired on August 10, 2016. The result are as shown in Figure 1, below

LULC of Garut Year 2016
LULC of Garut Year 2016

I used supervised neural netwok (NN) algorithm to extract the above figure. Based-on my evaluation, supervised NN is better than maximum likelihood or minimum distance algorithms. The class is defined in very smooth visualization.

The man-made structures (red color) are appeared in the upperstream, while agriculture (light green) and industrial forest (dark green) are dominant in this region. No doubt, when huge amount of rainfall drop, there will huge run-off be generated. This run-off will bring huge amount of materials.

Affected locations are highlighted in yellow (Sukakarya, Paminggir, Muarasanding, Pakuwon, Haurpanggung, and Sumantri). As we could see, this location is place when main streams ‘meet each other’. These streams loaded with huge amount of material, not only water but also suspended sediments (i.e., soil, mud, rock, logs, etc)

Multispectral data is very hard to be analyzed for high percentage of cloud cover. Fortunately, we have anothe space-based image of radar data of SENTINEL-1 to assess the same area. Data was acquired on October 4, 2016. The following is the result for man-made structures of upper stream of Cimanuk Watershed.

LULC of Cimanuk Watershed from Sentinel-1 data
LULC of Cimanuk Watershed from Sentinel-1 data

 

Sentinel-1 data enhanced the visualization, and we could confirmed, that there are extensive land use transformation in the upper stream.

The Sentinel-1 data has been calibrated and speckle filtering has been employed. Geometric correction also has been done based-on SRTM 3Sec data.

 

Gaza Strip captured from satellite

When the world blind but satellite “speak-up” clearly about the real situation faced by Gaza’s peoples.

This image was acquired by Landsat 8 OLI on September 12, 2015. I used 4-3-2 band combination to highlight vegetation cover and dense-urbanized area of Gaza Strip, Palestine.

Gaza Strip captured from satellite land observation
Gaza Strip captured from satellite land observation

It is clearly showed that Gaza is very dense environment with low vegetation cover. While, the ‘neighbor’, in contrast has very nice agriculture area and nice urban structures. This situation draws the real figure of socio-economic condition between two, Palestine and its neighbor.

Along the “modern apartheid wall”, there are large size of agricultural area belongs to the neighbor. This area could be considered as the “buffer zone” since there are no urbanized area along the wall. Inside the Gaza Strip, the vegetation seems not to much and not to healthy.

Vegetation really pops in red, with healthier vegetation being more vibrant. It's also easier to tell different types of vegetation.
Vegetation really pops in red, with healthier vegetation being more vibrant. It’s also easier to tell different types of vegetation.

The situation becomes worst when the winter or rainy seasons comes. Gaza Strip could transform into the biggest jail in the modern world.

What is Geoinformatics?

During my academic life, up to recently, my students sometime confuse with the following terms; Geoinformatics, Geomatics, Geoscience, GIS, etc. On this post I would like to make easier to understand the differences between them.

  1. Geoinformatics: according to Encyclopedia of Information Science and Technology, Third Edition (10 Volumes), Geoinformatics is referred to the academic discipline or career of working with geo-data for better understanding and interpretation of human interaction with the earth’s surface. Geoinformatics might be defined in a relatively broad term as a number of different technologies, approaches, processes, and methods to interpreter issue and controversy relating to the earth’s surface for collaborative decision making. Geoinformation can combine different types of dataset, from GIS, remote sensing and non-remote sensing, and socio-economic to generated results inform of maps or other forms of reports which allow better interpretation, management and decision making about human activities upon earth’s surface. Geoinformatics refers to two words; ”Geo” which is refer to “Geospatial” and “informatics” which is refer to “Information Science” multidisciplinary science (e.g., computer science, software engineering, computer vision, mobile and game technology, intelligent system, internet of things)
  2. Geomatics: according to many sources I have read, geomatics related to acquire and manage spatial data from the engineering point-of-view. It is is consist of two words; 1. “Geo” which is refer to “Geodesy” and, 2. “Matics” which refer to “Mathematics”. It is engineering discipline that mostly used and applied for land-ocean-surveying-base
  3. Geoscience: it very broad terminology, covers from geography to geological discipline. It is focused on the Earth and its systems, history, and resources. Includes the way that it interacts with the atmosphere, oceans and biosphere, making it one of the most wide-ranging of all scientific disciplines. However, geoscience now not only “on” earth issues but also “on” planetary issues also cover this terminology.
  4. GIS (Geographical Information System): it is firstly introduced by Canadian researcher, Roger Tomlinson on 1968. It is used for visualize, question, analyze, and interpret data to understand relationships, patterns, and trends. Mostly employ in the field of geography, to explore and find the answer. Recently, many discipline employ GIS to answer their research question, since GIS is provide powerful tools in spatial analysis.

Visualization for easier understanding

Two most confusing terminology are; Geomatics and Geoinformatics. If I transformed two terminologies into figures , it might be like following:

Geoinformatics
Geomatics

 

 

 

 

 

 

 

 

 

Geioinformatics research involves using modern information methods and technologies, application programs, databases, the internet and software development constitute the foundation for the deployment of Geoinformatics. In time-line, GIS is the oldest brother, following by Geomatics, and the youngest is Geoinformatics, as shown in the figure below.

Timeline
Timeline

At my research center, we focus on Geoinformatics research field. We are developing new types of geovisualization through WebGIS technology and employ cutting-edge technology in order to capture geospatial data sets. Our challenge is how to improve the visualization consisting of big-data sets to provide new insight of geo-information to solve human and earth issues.