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.

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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.

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

Algorithm:

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
Urban environment

Learning from river-side management in Japan: Rapid assessment using satellite images

As the previous article in this webpage, the reservation of precious waterside areas along river-side in Tokyo are under good condition (light green color along river side). The improvement and development of minor rivers to protect Tokyo from suffered serious damage from floods due to typhoon strikes are also well prepared and maintained in Japan.

The buffer zone along main river are vary, between 100 to 200 m (see Figure 1). These buffer zones are utilized as open green space for urban inhabitants in dry season. Baseball field, jogging tracks, and small parks are some of the urban green space available here. While in the rainy season or when the runoff relatively higher due to typhoon strikes, the buffer zone along river-side will work to protect the inhabitant from being submerged. River diversion in smaller channel is typical common management in Japan`s urbanized area.

Buffer zones along river-side in Tokyo.  Image acquisition of Landsat 8 OLI was May 2013
Figure 1. Buffer zones along river-side in Tokyo.
Image acquisition of Landsat 7 ETM+ was 4 May 2013. Color composite of bands 754 with image Gram-Schmidt enhancement technique to acquired 15 m resolution.

In contrary, in Great Megapolitan Jakarta, river-side environments are in bad condition. From the rapid assessment  using remotely-sensed image we can see that there is no buffer zone available (see Figure 2). Uncontrolled development can be seen from the density of man-made objects along river-side.

There is no buffer zone
Figure 2. Satellite image of Jakarta.
Image acquisition of Landsat 8 OLI was 25 August 2013. Color composite of bands 754 with image Gram-Schmidt enhancement technique to acquired 15 m resolution.

Furthermore, if we compare the topography condition between two great megapolitan area, we could understand that there is quite similar characteristics between Jakarta and Tokyo as shown in the following figures.

Comparing topography
Comparing topography of Jakarta and Tokyo. We draw line across Jakarta and Tokyo to show the cross-section of topography condition form north to southern part of the cities.
Source: Google Earth, elevation profile fuction

Tokyo`s elevation profile along 64.5 km is lower than Jakarta`s elevation profile, but there was no huge-wide floods event during the last two decades in Tokyo Megapolitan Area since the better management in buffer zone always done regularly.

Water will flow from the higher elevation to lower elevation, we need to manage many variables (including; land use land cover in the upper streams and down streams, physical condition, and  socio-environment condition) in order to overcome the flood. One of the solution of floods in Great Megapolitan Jakarta is management along river-side. It will not be achieved in short-term project, however it could begin now!

Indonesia, Remote Sensing, Urban environment

Land surface temperature of Great Megapolitan Jakarta and northern part of West Java, Indonesia

This map 100% generated using open source remote sensing and gis software, GRASS for imagery processing and Quantum GIS for cartography design.

The highest temperature recorded on Great Megapolitan Jakarta on 12 October 2013 is ~37.7 degree Celsius, while in the northern part of West Java Province is ~33.5 degree Celsius. In the urban environment, the building density play a main role regarding the surface temperature (read F. Ramdani & P. Setiani, 2013)

LST Jakarta
Land surface temperature of Great Megapolitan Jakarta and northern part of West Java, Indonesia

The land surface temperature (LST) in this map is not air temperature but land cover temperature. This is the effective at-satellite temperature of the viewed Earth-atmosphere system under an assummption of unity emissivity and using pre-launch calibration constant (available in metadata MTL.txt file)

Every object on the earth surface emitted thermal energy rather than reflected, the OLI 8 sensor allows to acquire, display, and interpret the thermal properties of the Earth`s surface.

The map above was produced from band 10 and band 11 of sensor OLI 8, we used average value of LST from the both bands. The two bands have 100 m spatial resolution.

Sensor OLI 8 save the image in the digital numbers (DNs), the DNs values of each bands were scaled from the absolute radiance measure to byte values prior to media output using the gain and bias (offset) values given for each band. The DNs values then can be converted back to the radiance units using the radiance scaling factor (see url https://landsat.usgs.gov/Landsat8_Using_Product.php)

Once the DNs for the thermal bands have been converted to radiance values, it is simply a matter of applying the inverse of the Planck function to derive temperature values. (also see url https://landsat.usgs.gov/Landsat8_Using_Product.php)

Note:

1.For anyone interested using GRASS to convert DN into Radiance or Reflectance can see the following url http://grass.osgeo.org/grass64/manuals/i.landsat.toar.html

2. To use QuantumGIS for cartography design, please visit this url http://docs.qgis.org/1.8/en/docs/user_manual/print_composer/print_composer.html