Remote Sensing, Indonesia, Urban environment, Image interpretation, Education

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.

Education, Indonesia, Remote Sensing, Urban environment

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.

disaster, Indonesia, Remote Sensing

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
Indonesia, Remote Sensing

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.


Indonesia, Remote Sensing

Spatial distribution of mangrove forests of Indonesia

During the last 6 months I have been writing another scientific paper. It is about classification technique for mangrove forests assessment. And for the accuracy assessment, I am using not only data derived from field work but also data from Giri et al 2011 (Full paper available at

Spatial distribution of mangroves forest of Indonesia
Spatial distribution of mangrove forests of Indonesia. Black color represented for mangroves

For everyone who need the data for another research activity, please do not hesitate to download from the following url

Indonesia, Remote Sensing, Urban environment

Automatic cloud cover assessment (Implementation of GRASS)

On of the important thing in land use land cover mapping is cloud cover assessment. Fortunately, free open source software for imagery processing such as GRASS provides us with the Automatic Cloud Cover Assessment (ACCA) module; i.landsat.acca.

i.landsat.acca implements the ACCA Algorithm from Irish (2000) with the constant values for pass filter one from Irish et al. (2006). To do this, it needs Landsat band numbers 2, 3, 4, 5, and 6 (or band 61 for Landsat 7 ETM+) which have already been processed from DN into reflectance and band-6 temperature with i.landsat.toar.

In this case we will assess the cloud cover over Surabaya City, East Java Province, Indonesia. Data used in this cloud assessment is Landsat 7 ETM+, it was acquired on December 27, 2013. Figure 1 shows the study area with the cloud cover in the northern part, and some part with the cloud shadow and haze. We will remove this cloud cover, by employ the i.landsat.acc module in GRASS.

Natural color of Surabaya City before cloud and haze assessment
Natural color of Surabaya City before cloud and haze assessment


Note: I always working with command console (Figure 2) instead of GUI, it is more convenient, so please learn how to get used with it.

Command console in GRASS
Command console in GRASS

1. Set region

g.region rast: b1

2. Perform i.landsat.toar

i.landsat.toar input_prefix=B output_prefix=Refl metfile=C:\your metadata folder location\LE71180652013361EDC00_MTL.txt sensor=tm7 method=corrected
>> Please select the metadata file

Top-of-atmospheric correction using i.landsat.toar module
Top-of-atmospheric correction using i.landsat.toar module

In this step, I imported all of the Landsat 7 ETM+ bands with prefix “B” into GRASS environment, and converted the DNs into top-of-atmospheric reflectance, and used the prefix “Refl”.

3. Perform i.landsat.acca

i.landsat.acca -f input_prefix=Refl output=Acca

Automated Cloud-Cover Assessment (ACCA) using i.landsat.acca
Automated Cloud-Cover Assessment (ACCA) using i.landsat.acca

Please note: I used “Refl” as input prefix and “Acca” as output for the result

Cloud and haze assessment
Cloud and haze assessment

4. Using MASK

r.mapcalc “MASK = if(isnull(acca))”

Note: use command console for using MASK

5. Display RGB

d.rgb -o red=Refl3 green=Refl2 blue=Refl1

Final result after using MASK
Final result after using MASK
Indonesia, Remote Sensing

Flame on the coastline and another unique phenomenon

This image shows the “flame” on the coastline environment of Banda Aceh, Nanggroe Aceh Darussalam Province, Indonesia.

"Flame" along the coastline of Nanggroe Aceh Darussalam Province, Indonesia
“Flame” along the coastline of Nanggroe Aceh Darussalam Province, Indonesia

In fact, it should be sedimentation process captured by Landsat 8 OLI sensor on October 11, 2013. As we can see from the image, there are two main streams empties in the Strait of Bengal.

There are many variables involve to create this “flame”, such as wind speed, pressure, sediments source, and bathymetry. To produce the image above, I employed MNF transformation and used 765 MNF band output for RGB color composite.

Another unique phenomenon

There is another interesting earth surface phenomenon on the northern part of the image. Large size of wave!

Large wave. Generated by the interaction of a strong internal  solitary wave with an underwater bank
Large wave. Generated by the interaction of a strong internal
solitary wave with an underwater bank in the Dreadnought Bank.

At first I thought this was related to an earthquake events, because in the northwestern part of the image there is a plate boundary (ridge in Andaman Sea). Then I was looking at the USGS earthquake database for an earthquake event with scale ranging from Mw 1, in accordance with the date of the acquisition, however I could not find any related earthquake event.

Then I compare the image with the bathymetry and I found the answer of the large waves. For more details please read the following paper:

Keyword of this phenomenon is:
1. Sea ​​surface manifestations of internal solitary waves, and
2. Interaction of barotropic tides and bathymetry

The two images above, have informed us that rather than static our earth is completely dynamic and life.