Monday, October 24, 2016

Saturday, September 3, 2016

Survei dan Pemetaan Menggunakan GPS dan GIS (Ebook)

Saya baru mempublish salah satu kumpulan tutorial (lama tapi) baru tentang pengolahan gps di dalam GIS di GPlay Books, dengan harga yang (semoga) cukup murah, Hasil penjualan insya Allah sebagian besar digunakan untuk membeli kopi agar otak bisa memproduksi buku2 tutorial lain di masa mendatang, Terimakasih,

Link Buku di Google Play Books di sini

https://play.google.com/store/books/details?id=wILvDAAAQBAJ




Saturday, August 6, 2016

Open File Geodatabase in QGIS

How to open File Geodatabase in QGIS, a nice new features from QGIS team, Great!





 

Wednesday, July 20, 2016

Mengubah Satuan Grid (E menjadi BT, U menjadi LU) di ArcGIS

Bagaimana cara mengubah satuan grid/gratikul di ArcGIS dari E/S/W/N menjadi BT/BW/LU/LS



ini penting sebenarnya, tapi jarang orang yang bisa, hehehe



Wednesday, June 8, 2016

Thursday, April 7, 2016

Overpass Turbo Open Street Map

I wont give a long clues and sentences in this post, because this is just another workaround to download OSM Data.

One thing that matters just, this tool support BBOX downloading and Javascript Query to download specific layer or subtypes

Let the picture tells itself


Tuesday, April 5, 2016

Adding Online Map Services in Global Mapper

Actually, we could display online map services into global mapper and further leverage the software capabilities to deal with certain tasks.

This can be achieved using protocol similar to what we do in SASPlanet, i.e creating an xml file which contain the URL and tags, import it to global mappers's online data sources and just go mapping.

Start by copy the code below in an XML file



<source_list creator="Global Mapper - http://www.globalmapper.com" version="1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.globalmapper.com/OSL/1/0" xsi:schemalocation="http://www.globalmapper.com/OSL/1/0 http://www.globalmapper.com/OSL/1/0/gmc.xsd">

 

  <source type="GMAP"></source>

    <name>Bing Maps Hybrid</name>

    <group_list>

      <group>&lt;![CDATA[*** POPULAR SOURCES ***]]&gt;</group>

      <group>IMAGERY</group>

      <group>WORLDWIDE DATA</group>

    </group_list>

    <fileext>jpg</fileext>

    <latlonbounds>-180,-90,180,90</latlonbounds>

    <numzoomlevels>19</numzoomlevels>

    <tilesize>256</tilesize>

    <baseurl>&lt;![CDATA[http://ecn.t0.tiles.virtualearth.net/tiles/h%quad?g=604]]&gt;</baseurl>

    <nativeproj>EPSG:900913</nativeproj>

    <metadataurl>&lt;![CDATA[http://www.microsoft.com/maps/product/terms.html]]&gt;</metadataurl>

 


  <source type="GMAP"></source>

    <name>Bing Maps Imagery</name>

    <group_list>

      <group>&lt;![CDATA[*** POPULAR SOURCES ***]]&gt;</group>

      <group>IMAGERY</group>

      <group>WORLDWIDE DATA</group>

    </group_list>

    <fileext>jpg</fileext>

    <latlonbounds>-180,-90,180,90</latlonbounds>

    <numzoomlevels>19</numzoomlevels>

    <tilesize>256</tilesize>

    <baseurl>&lt;![CDATA[http://ecn.t0.tiles.virtualearth.net/tiles/a%quad?g=1239]]&gt;</baseurl>

    <nativeproj>EPSG:900913</nativeproj>

    <metadataurl>&lt;![CDATA[http://www.microsoft.com/maps/product/terms.html]]&gt;</metadataurl>

 


  <source type="GMAP"></source>

    <name>Bing Maps Street Maps</name>

    <group_list>

      <group>&lt;![CDATA[*** POPULAR SOURCES ***]]&gt;</group>

      <group>WORLDWIDE DATA</group>

    </group_list>

    <fileext>jpg</fileext>

    <latlonbounds>-180,-90,180,90</latlonbounds>

    <numzoomlevels>19</numzoomlevels>

    <tilesize>256</tilesize>

    <baseurl>&lt;![CDATA[http://ecn.t0.tiles.virtualearth.net/tiles/r%quad?g=604&amp;lbl=l1&amp;stl=h&amp;shading=hill]]&gt;</baseurl>

    <nativeproj>EPSG:900913</nativeproj>

    <metadataurl>&lt;![CDATA[http://www.microsoft.com/maps/product/terms.html]]&gt;</metadataurl>

 


<source type="GMAP"></source>

  <name>Google Maps Hybrid</name>

  <group_list>

    <group>&lt;![CDATA[*** POPULAR SOURCES ***]]&gt;</group>

    <group>IMAGERY</group>

    <group>WORLDWIDE DATA</group>

  </group_list>

  <fileext>jpg</fileext>

  <latlonbounds>-180,-90,180,90</latlonbounds>

  <numzoomlevels>20</numzoomlevels>

  <tilesize>256</tilesize>

  <baseurl>&lt;![CDATA[http://mt.google.com/vt/lyrs=y]]&gt;</baseurl>

  <nativeproj>EPSG:900913</nativeproj>

  <metadataurl>&lt;![CDATA[http://www.google.com/intl/en_us/help/terms_maps.html]]&gt;</metadataurl>


<source type="GMAP"></source>

  <name>Google Maps Imagery</name>

  <group_list>

    <group>&lt;![CDATA[*** POPULAR SOURCES ***]]&gt;</group>

    <group>IMAGERY</group>

    <group>WORLDWIDE DATA</group>

  </group_list>

  <fileext>jpg</fileext>

  <latlonbounds>-180,-90,180,90</latlonbounds>

  <numzoomlevels>19</numzoomlevels>

  <tilesize>256</tilesize>

  <baseurl>&lt;![CDATA[http://khm.google.com/kh?v=131]]&gt;</baseurl>

  <nativeproj>EPSG:900913</nativeproj>

  <metadataurl>&lt;![CDATA[http://www.google.com/intl/en_us/help/terms_maps.html]]&gt;</metadataurl>


<source type="GMAP"></source>

  <name>Google Maps Street Maps</name>

  <group_list>

    <group>&lt;![CDATA[*** POPULAR SOURCES ***]]&gt;</group>

    <group>WORLDWIDE DATA</group>

  </group_list>

  <fileext>jpg</fileext>

  <latlonbounds>-180,-90,180,90</latlonbounds>

  <numzoomlevels>20</numzoomlevels>

  <tilesize>256</tilesize>

  <baseurl>&lt;![CDATA[http://mt.google.com/vt/lyrs=m@140]]&gt;</baseurl>

  <nativeproj>EPSG:900913</nativeproj>

  <metadataurl>&lt;![CDATA[http://www.google.com/intl/en_us/help/terms_maps.html]]&gt;</metadataurl>


</source_list>


Then just copy the code into Global Mapper online data list, and then a new list called "IMAGERY" will be displayed.



Expand the group and you can start using the online data sources inside global mapper


Sunday, March 13, 2016

Another Cheap way to bring your maps to the field (Geospatial Portable Document Format)

Nowadays, bringing maps to the field is not as complex/expensive as in the past. There are numerous software and tools that could be utilized to do that. A range of IOS/Android/Windows phone gadgets can do it using various methods either online or offline then synchronize all the fieldwork to the source data back in the office.

But people usually looking for simple and cheap way no matter what is the purpose to do the fieldworks. Am I right ? so if you are one of them (like me too), I will give some insight that may  could be useful.

PDF format since few years ago has been incorporated geospatial information as part of the auxiliary information stored in the file. This cool feature enabling us to save a map in PDF format (include its geospatial coordinates) and then transfer it to our phones and brings it to the field to do field check (integrated with our phones GPS Capabilities). This method sure saving us more resources instead of field online mapping (in aspect of carrier data charge, battery power, gadget storage capacity, rendering performance etc). Furthermore, PDF is well known for great compression rate so a satellite imagery in 6GB size could be stored just for 400 MB size.

One of mobile tools that can read geospatial PDF is  Avenza PDF maps that you can download for free from Google Play Store . This tool also support import/export KML file if you want to overlaying vector maps or generate vector data (includes its attribute table could you designed directly) in the field, either using waypoint marking or line tracking (using phones built in GPS receiver). Here is below a screenshot of what I just test. Saving a my country official topographic map in geospatial PDF and bring it to the field. Using this method I could bring old maps to the field to do change detection field inspecting without needs to host it to online GIS Server.



And then, what software can be used to write Geospatial PDF from geospatial data ? well, you could use TerraGO, ArcGIS, Global Mapper to do it. And from the Open source/freeware side, QGIS could also make it with some efforts.


Suddenly I remember when  I was still in my second semester at college. Doing fieldwork check for Map Use Course, brings A1 size printed topographic map to the field, sweep away the map with the help of some friends, using stones to hold it so the wind cant slam it. Such a great and tedious times.

A lot of things has changed since then.....

Friday, March 11, 2016

Identifying Correct Datum Transformation For Old Maps Produced By U.S Army Map Services at World War II Era, Covering Nederlands East Indies Southern Zone Grid (Java and Lesser Sunda islands)

Saya akan menggunakan bahasa indonesia untuk postingan ini agar lebih mudah dipahami. 

Jadi ceritanya, saya sedang mengumpulkan peta-peta tua yang pernah merekam wilayah kepulauan nusantara/timur jauh/republik indonesia untuk mendukung rencana penelitian thesis (soon to be). Salah satu diantaranya adalah peta-peta yang pernah diproduksi oleh U.S Army Map Services (1943-1945) yang dapat diunduh di SINI, dan banyak digunakan dalam Perang Dunia ke II Teater Asia Pasifik. Peta-peta ini dikompilasi dari Peta-peta yang dibuat jawatan topografi hindia belanda (Topografische Dienst) dari tahun - tahun sebelumnya. Peta-peta ini juga selanjutnya di-update dan dikompilasi ulang oleh JANTOP TNI-AD dalam bentuk Peta Topografi dan selanjutnya BAKOSURTANAL dalam bentuk Peta Rupabumi pada masa Negara Kesatuan Republik Indonesia. 

Dikarenakan saya akan menggunakan peta ini sebagai sumber informasi, maka peta ini harus match dengan peta-peta lain (baik yang lebih lama maupun baru), misalnya dengan Sistem proyeksi geografis/UTM dengan Datum WGS84/DGN95/SRGI2013 yang digunakan di Indonesia sekarang. 

Untuk melakukan matching ini ada dua cara, 

1. Image to Image Registration (menggunakan peta raster/vektor yang lebih baru sebagai referensi)

    Cara pertama ini terkesan tidak keren, terlalu gampang, anak TK juga bisa, ga perlu kuliah geografi/geodesi yang mahal2 dan terlalu basic sekali. 


2. Georeferencing menggunakan koordinat asli pada peta, dan kemudian menyusun parameter transformasi di dalam Software. 

Saya suka cara yang kedua ini. Lebih intelek, saintifik, tidak empiris, pake mikir, harus paham ilmu dasar proyeksi peta, dan cocok lah diterapkan oleh orang-orang yang menghabiskan duit sekian puluh juta dan waktu minimal 4 tahun untuk kuliah ilmu kebumian di kampus-kampus ternama. 

Jadi kita pakai cara ke 2. 

Yang pertama kita identifikasi adalah informasi proyeksi peta yang biasanya tercantum di petanya. Misalnya seperti di bawah ini. 


Dari informasi di atas kita tahu bahwa Projection yang digunakan adalah Lambert Conical Orthomorphic, dengan spheroid Bessel 1841 (kenapa saya tahu ? karena nggak ada spheroid Bessel yang lain), origin, northing dan faktor skala. Masih ada informasi yang kurang ? ya, Datumnya belom ada. Datum ternyata disebut di sisi sebelah kanan bawah peta di bagian indeks. 



Datum nya pake Batavia dengan Central meridian sekian sekian dari Bujur 0 derajat (lihat gambar). 


Oke sekarang kita coba praktekan. 

1. Boot Up ArcMap (yang paling enak buat georeferencing 2D dibanding yang lain) 

2. Buka Data Frame Properties > Coordinate System, Bikin New Coordinate System > Projected Coordinate System

3.  Kita cari nama proyeksi dari peta yaitu Lambert Conical Orthomorphic di ArcGIS, ternyata nggak ada, coba kita cek ke Google, ternyata namanya sudah ganti jadi Lambert Comformal Conic, oke ketemu, pilih. Kemudian masukkan informasi yang kita dapat dari peta di atas ke kolom yang sesuai. misalnya False Easting, Northing, Scale Factor, Central meridian dll. Nah dari kolom parameter yang ada, ada dua parameter yang nggak ketemu nilainya di peta, yaitu Standard Parallel 1 dan Standard parallel 2. Oke pikir belakangan karena ada informasi lain yang bisa dimasukkan. Sejauh ini kita sudah memasukkan parameter linear dari proyeksi custom yang kita buat. 





4. Dari gambar diatas klik tombol Change, Sekarang kita akan masukkan parameter Angular dari proyeksi custom, yaitu spheroid dan datum, Diatas sudah disebut, pakenya datum batavia spheroid Bessel, cari deh tuh di ArcGIS, ternyata ada. Kita OK kan saja. 


5. Kita lanjutin ngurusin Standard Parallel, setelah googling ternyata standard parallel adalah nilai paralel yang kita atur dalam proyeksi lambert dimana di antara dua paralel ini tidak ada kesalahan proyeksi (jarak, sudut, luasan), atau kesalahannya sekecil mungkin. Dan karena kita tinggal di Indonesia berarti udah ketebak nilainya berapa, yaitu 6 LU untuk Standard Parallel 1 dan 11 LS untuk Standard Parallel 2. Masukin dan klik OK. Nah ArcMap Canvas sekarang sudah menggunakan Lambert Conical Orthomorphic. 

6. Lanjutkan dengan Add Peta nya dan georeferencing minimal dua koordinat sudut (kiri atas dan kanan bawah misalnya, atau amannya 4 sudut sekalian). 

7. Di bawah ini contoh hasilnya. 




8. Setelah ditampilkan dengan data OSM yang sudah direproject ke Proyeksi Geografis Datum WGS84, ternyata peta-nya masih belom pas-pas banget. Semprul!!!!

9. Kesalahannya kemungkinan besar karena Standard Parallel-nya tidak terlalu presisi, sehingga masih terpengaruh distorsi transformasi, sehingga harus di-tweak, misalnya dengan mencari batas lintang yang benar untuk Wilayah Indonesia (baru dugaan sih, nanti dicoba-coba lagi). 


Memang tampak ribet sih, tapi cara ini jelas sangat keren dan tidak ndeso kayak Image to Image itu. Selain itu, juga (relatif) bebas dari kegoblokan si pembuat peta referensi (jika pakai cara 1). Misalnya, pas lagi nggambar jalan tiba-tiba si pembuat peta bersin, dan jalannya jadi mencong tapi nggak nyadar, dan kita-kita yang memakai peta yang dia buat pas lagi sial, terus memakai jalan mencong itu sebagai referensi titik ikat/GCP, bisa dibayangkan mau pake transformasi polynomial orde 500 juga nggak bakal bagus tu RMSE-nya. 


Yaa, inti-nya sih gitu... :P


UPDATE 26 MEI 2016

Ada yang membuat dengan cara yang lebih menarik di QGIS, silahkan cek 

http://roselabs.wix.com/roselabs#!Georeferensing-Peta-Topografi-AMS-Army-Map-Service-menggunakan-QGIS/c1mbt/68A46011-45CE-4D14-99DA-48FEB362A066

Parameter transformasi proyeksi Lambert Conical Orthomorphic di QGIS/PROJ4 adalah :

+proj=lcc +lat_1=-8 +lat_2=-8 +lat_0=0 +lon_0=110 +k_0=0.9997 +x_0=550000 +y_0=4000000 +ellps=bessel +units=m +no_defs


UPDATE 26 MEI 2016 (sore hari)

Metode diatas nampaknya gagal mengidenfikasi koordinat lintang dengan benar. Pada dasarnya, permasalahan tentang Lambert Conical Orthomorphic adalah tentang standar Pararel 1 dan 2 yang belum diketahui karena Grid Nederlands East Indie Southern Zone yang digunakan di Peta AMS tidak diketahui batas-batasnya. 

Namun setelah mencari tahu di google dan ketemu paper di bawah ini (googling sendiri ya untuk link-nya),


saya mendapat informasi bahwa batas standard parallel NEISZ bisa jadi antara 5 derajat LS dan 11 derajat LS atau 7 derajat LS dan 11 derajat LS. Dan setelah mengulang membuat Custom Projection di ArcGIS, dapat diketahui bahwa nilai Standard paralel 7 LS dan 11 LS adalah yang paling mendekati. Nampak dari hasil On The Fly Reprojection overlay dengan Peta RBI (Latlong WGS84) di gambar di bawah ini. 



Its been  three Months, but finally this little shit has been resolved. Sometimes it is good to leave behind your problems and resolve it later. Because sometimes time is not allowing us to find the answer at this moment






Monday, February 29, 2016

Using Dropbox to Host your webmap

Dropbox, surprisingly could be used to host a static web page. There are other providers too like Google Drive. But its capabilities will be discontinued after August 2016. And by the this functionality, we can host a simple webmap (or little bit complex, I dont know, didnt test it yet) using this service.

The hosting procedure is quite easy,

1. Make a dropbox account and login

2. Looks for "Public" folder and make a directory there.

3. Write your own webmap, for example I copy an html file somewhere, and edit it to include my Mapbox webmap into its DIV Tag, then I just place the html to the directory created on step 2.

The code looks like this :



4. Save it to Html and place to the directory on the step 2.

5.  Then left clik on it, > Looks for "Copy Public Link"

6. Paste into browser.

7. Click this LINK to see how the code in step 3 looks like

Saturday, February 27, 2016

Surface Reflectance in ArcGIS

I just noticed that at the recent version, ArcGIS has been implemented some sort of physical liked surface reflectance generation function in its image analysis module. The function is called apparent surface reflectance. An algorithm which takes into consideration all the iradiance related information stored in the metadata (sun azimuth angle, sun elevation angle, radiance gain/bias etc) to derive surface reflectance dataset, although it is not pure physical (because it does not consider the atmospheric condition at the acquisition date). The algorithm target the illumination condition which is theoretically should be similar between dates and wavelengths (differs in reality due to atmospheric effects, sun angle/position and sun-earth distance in every season etc) so it could be usable for color balancing and mosaicking (not sure if it will add certain degrees of accuracy in image classification).

I make a guide if you want to try it by yourself


Sunday, February 14, 2016

Download Landsat 8 Data Using SnapSat.Org

A new landsat-8 data provider has been added
Using simple interface you can straightforwardly download landsat-8 data either just some bands or full dataset.
This portal utilizing Amazon S3 free access Landsat-8 repository, so it will have some data acquisition delay compared with official USGS repository (EarthExplorer, GLOVIS, REVERB).
Based on the test I conducted, I think data access thru this portal is greatly useful if you just need some quicklook or visual interpretation needs.
Oh btw, Landsat-8 data obtained from this portal is reprojected to Web Mercator Projection, so if you need the data in UTM or Geographic Projection, just reproject it using ArcGIS, QGIS or other Geospatial softwares.


Saturday, February 6, 2016

Creating Simple Web Map Using QGIS

From day to day, QGIS is getting bigger and bigger. Continuous and expanded support from open source geospatial developer has made this software go beyond its initial development. More and more tools/plugins has been created and offering more flexibility and features to the software. It is fair to said that this software is now became an ideal example of successful non commercials geospatial movement. Personally I am starting to count more on this software because it is now offering some distinct tools unavailable on commercial counterparts. The keys of QGIS for me are simplicity, straightforward how to, and free to use.

I am not kidding, below is an example how the simplicity means. A Tutorial about how Online Map could be deployed easily from QGIS. Using fast growing to use WebMap Frameworks like Openlayers or leaflet, a not experienced and programming literate user could make an online webmap with sophisticated looks just with some clicks. Check it out below.







And last but not least, innovation is not coming from the "follow the leader" attitude (IFYWIM) :P

Sunday, January 31, 2016

Trying out NNDiffuse Pan Sharpening in ENVI 5.2

Its been a while since last time I am exploring pan sharpening. Recently I am stumbled on ENVI which had  just released version 5.3 (version 5.4 will be released around summer 2016 as far I know) and it is brings some new tools in their arsenal. One of them is NNDiffuse Pan Sharpening Algorithm. ENVI has known for very long time only have 4 common image fusion/pan sharpening algorithm (PCA, Brovey, CN, and IHS/HSV) thats not quite good to maintain spectral quality of the fusion/pan sharpening result. At later version, they are adding Gram Schmidt Spectral Sharpening which is one of the most advanced pan sharpening algorithm to date and making them equal with ERDAS (Ehlers/wavelet fusion) and PCI (UNB Fusion) as the most advanced Pan Sharpening Software. Now they are adding NNDiffuse which is the latest algorithm developed by Weihua Sun et al,


NNDiffuse utilizing regression approach combined with spatial frequency extraction to generate pan sharpened dataset which not only maintain the spectral quality of multispectral bands, but also well preserve the spatial details of panchromatic band. And more of it, this algorithm also works for SWIR bands which its spectral range are beyond the spectral range of panchromatic band (similar with Gram Schmidt or other frequency based pan sharpening algorithm which also works for SWIR Bands). 


So I have been recorded my NNDiffuse testing using Landsat-8 Data into a video tutorial which you can check it out below.


Thursday, January 21, 2016

Download Landsat 8 Data Using Libra Development Seed

Another platform to download Landsat-8 Data

No login and registration needs, more intuitive than Glovis/EarthExplorer