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.
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.
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.
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.
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.
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!
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)
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)