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.


Remote Sensing

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:











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.


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.

Remote Sensing

Working with Hyperion Imagery

Since the first time I learned remote sensing, I am only focus on multipectral images. However, recently hyperspectral imageries are more widely used than before.

One of the hyperspectral imagery that available in public domain is EO-1 Hyperion. The spatial resolution is 30m -same as Landsat- which is comparable for time series analysis with Landsat OLI-8. However, Hyperion has 242 spectral!

I tried myself to produce some color composite RGB combination, and the following bands combination as the result:

VNIR Visible RGB:29-23-16 >> it will shows natural color
VNIR Vegetation RGB: 45-33-20 >> it will highlighted the healthy vegetation in red color
SWIR RGB: 204-150-93 >> it will shows false false color image

For hyperspectral analysis we need to conduct more steps than multispectral. For instance you could learn from the following url, for vegetation health analysis

Hyperion image in natural color of Mojokerto-Kediri-Blitar. Acquisition date: March 5, 2014.
Hyperion image in natural color of Mojokerto-Kediri-Blitar. Acquisition date: March 5, 2014.
Remote Sensing

Geovisualization: Urbanization Process of Malang City, East Java, Indonesia

Its been more than 6 months since the last time I updated this homepage. Today, I would like to start again with more interesting issues and new method. On this article, I write my current research in my new institution. Its about urbanization prosess in Malang City, Indonesia
There are at least three ongoing process currently in Malang City, and will greatly affect inhabitants life in it.
First, the uncontrolled urbanization. Due to the growth of the housing industry that occupied agricultural areas in the lowlands. As a result, diminishing farmland and surrounded by the residential housing “cluster” type. In fact there is agricultural land with a size less than 50x50m are surrounded by housing. This agricultural land is not going to last long and will soon transform into housing.
Farmers who lost his job, then transferring the farm to the higher land. As a result, land conversion in the highlands takes place rapidly, from only ~ 3% in 2001, is now ~ 16% in 2014. This could has the potential disaster for landslides in certain areas with poor soil type and steep slopes.
Urbanization was not only occur in the lowlands, but also in the highlands, this is evidenced by the growing number of urban areas cover of ~ 21% in 2001 to ~ 40% in 2014. The majority occurred in the highlands.
While the forest cover has steadily decreased from ~ 64% in 2001, drastically reduced to ~ 22% in 2014.
The increase in surface temperature of the city environment, lack of water, and the supply of basic food such as rice and vegetables, perhaps even floods and landslides that will occur in the future must be addressed.
This issue will be available at the Research Show Case PTIIK (Program Information Technology and Computer Science) UB, which will be held on January 14, 2015 in Samantha Krida, UB.
Our study entitled; Geovisualization: Urbanization Process of Malang City, East Java, Indonesia will be presented in the format of the latest version of WebGIS  which is the result of our innovation.
Graphical User Interface (GUI) of Urbanization Process of Malang City, Indonesia
Graphical User Interface (GUI) of Urbanization Process of Malang City, Indonesia
For a while abstract can be read on this url;, please click on the “Research” and scroll down to the same research title