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.

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 http://www.exelisvis.com/docs/HyperionVegetationAnalysisTutorial.html, 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

Bismillah,
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; http://ptiik.ub.ac.id/info/staff/0763758, please click on the “Research” and scroll down to the same research title
Image interpretation, Remote Sensing

Land use land cover classification using QuantumGIS; Study case mangrove forest in Segara Anakan, Indonesia

This time I would like to present you how to deal with QuantumGIS, especially how to produce land use land cover (LULC) map extracted from Landsat 8 OLI

In this case, I would like to measure the present condition of mangrove forest only based on the reflectance characteristic, study case mangrove forest in Segara Anakan, Indonesia.

Study area: Segara Anakan, Indonesia
Study area: Segara Anakan, Indonesia

The algorithm of the image interpretation will be as follow;

1. DN conversion into TOA (Top of Atmospheric Correction)

2. Preparing band input for used as dataset

3. Create training area, to “guide” the software

4. Perform LULC classification

5. Calculate the mangrove forest area

 

Before we begin, its better for you to check your computer specification. If your computer is Windows 64bit, you need to download and install first the WinPython. Then you need to extract the installer with 7-zip (see Figure 1 to teach you how to extract the installer). Inside $_OUTDIR/python-2.7.6.amd64/Lib/site-packages you can find the scipy , the matplotlib, and the numpy directories that you can copy to the QGIS installation directory (see Figure 2); then install the Semi-Automatic Classification Plugin for QGIS.

Figure 1. How to extract the installer of WinPython
Figure 1. Right click, and the installer of WinPython into the folder

 

Figure 2. Directory to save the folders (scipy , matplotlib, and numpy)

To install the Semi-Automatic Classification Plugin, run your QGIS and click Plugins – Manage and Install Plugins.. – type “semi” in the Plugins window as shown in Figure 3. Then click “Install plugin”

Figure 3. How to install the Semi-Automatic Classification Plugin in QGIS
Figure 3. How to install the Semi-Automatic Classification Plugin in QGIS

1. DN conversion into TOA (Top of Atmospheric Correction)

In the satellite imagery interpretation, the conversion of DN into TOA is crucial since we need the real physical reflectance values of the specific land use land cover. In this study we employ DOS (Dark Object Subtraction). If you are interested more in DOS method, please read it from the following paper.

Run your QGIS and start the Semi-Automatic Classification Plugin; in the main interface select the tab Pre processing > Landsat > select the directory which is consist of your Landsat 8 OLI dataset with the MTL.txt file. Also select the “Output directory” of the converted bands.

Check (x) the option of “Apply DOS1 atmospheric correction” and click “Perform conversion”

Figure 4. Activating the plugin and converting the DN into TOA
Figure 4. Activating the plugin and converting the DN into TOA

After the conversion process finished, you could check the result using the “identify feature” modul. Move the mouse to the particular area and just give single click, then the new window will be appear and shows the reflectance value (see Figure 5)

Figure 5. The value of DN and reflectance
Figure 5. The value of DN and reflectance

2. Preparing band input for used as dataset

We need to produce the color composite of the study area, so it will make us easier to select the training data. We will produce the composite RGB of 543 (RGB = 432 for Landsat 7) is useful for the interpretation of mangrove forests in the image, because of healthy vegetation reflects a large part of the incident light in the near-infrared wavelength.

Go to the main toolbar of QGIS, and select Raster – Miscellaneous – Build Virtual Raster (catalog); click the button “Select” in the input file section… and select the bands 5, 4, and 3 (do not forget to hold +Ctrl); click the button “Select” in the output file section, and give the output name (for example RGB.vrt). Check “Separate” and click “OK”.

Until this step, you have been successfully produced the RGB image of the study area.

Figure 6. Producing dataset for multispectral data
Figure 6. Producing dataset for multispectral data

In the window of  Semi-Automatic Classification Plugin, click the button “Band set” then the tab Band set will open; click the button “Refresh list”, then Add rasters to set (order the band names in ascending order, from top to bottom using ↑ and ↓ arrows. Select only the dataset with RT_ in the file name. You can also check the center wavelength of the data as shown in Figure 7.

Figure 7. Producing dataset and check the center wavelength
Figure 7. Producing dataset and check the center wavelength

3. Create training area, to “guide” the software

To create the training area, in the vertical-right side of QGIS window, under the “Training shapefile” click the button “New shp”, and select where to save the shapefile, and give the name of your ROI-Region of Interest (for example ROI.shp).

In order to “guide” the software, we have to to create some ROI, considering the reflectance variability of LULC classes. It is important that ROIs represent homogeneous areas of the image, therefore we are going to draw the ROIs using a region process (i.e. a segmentation of the image, grouping similar pixels).

The following are the land cover classes that we are going to identify in the image:
1. Water (e.g. sea water, river, and lake)
2. Agriculture (e.g. paddy field areas)
3. Another crops
4. Mangrove forests
5. Forest
6. Fish pond (e.g. aquacultures)
7. Cloud cover

When you are selecting the training area, please consider to select more than 3 of the training data of each LULC. Based on my experience, if we only select a few of them, it will lead to some error classification.

Figure 8 shows how to create training data. Click the (+) symbol to add the “box” training data and click the “Create ROI polygon” to produce free shapefile. Give it the name and ID value to each training data. After finished it, do not forget to click “Save ROI” and “Add to signature”.

Figure 8. Creating shapefiles as training areas
Figure 8. Creating shapefiles as training areas

If you would like to produce some spectral signature plot or scatter plot, just select all the training data and click the two symbol next to “Add to signature” as shown in the Figure 9 below. You can also produce particular training data, for instance only “mangrove”. Then just select the “mangrove”, and click the spectral signature symbol.

Figure 9. Producing scatter plot and signature plot of the training data
Figure 9. Producing scatter plot and signature plot of the training data

4. Perform LULC classification

The remotely-sensed image classification can be performed through several algorithms; in this study we will use the Maximum Likelihood classification.

Before the software produce the final result of the classification, it is possible to preview the classification. In the “Classification algorithm”, under “Classification preview” type the “Set” Size = 500 then click the button (+) and then click on the image; instantly, the classification preview image will be appeared.

If you already satisfied with the result, then you could click “Perform classification” and give the output name. However, if you would like to modify the training data, you could go back to the stage 3.

Its done! Congratulation, you successfully produce the LULC of Segara Anakan, Indonesia.

Figure 10. Performing classification of remotely-sensed data of the study area
Figure 10. Performing classification of remotely-sensed data of the study area. Mangrove forests represented in light-green color.

 

5. Calculate the mangrove forest area

To calculate the LULC, especially the mangrove forest, go back to the Semi-Automatic Classification Plugin main window and select “Post processing – Classification report”.

In the drop down menu select the “Class_Result”, this is the result of your classification. Then click “Calculate classification report”.

 

Figure 11. Calculating the LULC
Figure 11. Calculating the LULC

This study successfully documented the present (May 1st, 2014) area of mangrove forests in Segara Anakan Indonesia. There are about 33.13 square kilometers of mangrove forests in the study area. However, this is not reflected the real number since I did not conduct any field work or accuracy assessment. This is only to show you how to work in QuantumGIS environment to produce image classification.

If you would like to do some accuracy assessment measurement, you could use the “Accuracy” modul in this software. Just input your reference data from the field work or from the official database and then calculate the accuracy of your imagery classification.