Showing posts with label Aqua/Terra MODIS. Show all posts
Showing posts with label Aqua/Terra MODIS. Show all posts

Thursday, December 9, 2010

Bakar Hutan versus Hijaunya Vegetasi

Bakar Hutan versus Hijaunya Vegetasi

Tulisan kali ini mencoba menatap secara global aktivitas pembakaran karena di permukaan bumi ini "something is always burning".

Peta api di bawah ini menunjukkan lokasi kebakaran aktif yang terbakar di seluruh dunia setiap bulannya, berdasarkan pengamatan dari citra Moderate Resolution Imaging Spectroradiometer (MODIS) yang terpasang pada satelit Terra milik NASA.

Warna didasarkan pada penghitungan jumlah (bukan ukuran) dari kebakaran yang diamati dalam wilayah 1.000 kilometer persegi. Piksel Putih menunjukkan nilai tertinggi dari-hitungan sebanyak 100 kebakaran di area 1.000 kilometer persegi per hari.
Piksel Kuning menunjukkan sebanyak 10 kebakaran, oranye menunjukkan sebanyak 5 kebakaran, dan daerah merah sesedikitnya 1 api per hari.

Beberapa pola global yang muncul dalam peta api dari waktu ke waktu adalah hasil dari siklus alami dari curah hujan, kekeringan, dan petir.

Misalnya, kebakaran yang terjadi secara alami yang umum di hutan utara Kanada pada musim panas. Di bagian lain dunia, pola-pola merupakan hasil dari aktivitas manusia. Misalnya, pembakaran intens di jantung Amerika Selatan dari Agustus-Oktober adalah hasil dari kebakaran manusia-dipicu, baik disengaja dan tidak disengaja, di Amazon Rainforest dan Cerrado (sebuah padang rumput / ekosistem savana) ke selatan. Di seluruh Afrika, sebuah pembakaran pertanian yang meluas menyapu utara ke selatan lebih dari benua sebagai musim kemarau berlangsung setiap tahun. Pembakaran dari aktivitas pertanian terjadi pada akhir musim dingin dan awal musim semi setiap tahun di seluruh Asia Tenggara.


(a)

(b)

Gambar 1. Video (sumber: GlobalMap: Fire NASA) Maret 2000 hingga Oktober 2010 (a) dan Gambar (hasil printscreen) Kebakaran di Permukaan bumi di Bulan Oktober 2010 (b)


Lalu, apa sich yang terbakar ? Apakah benar vegetasi yang terbakar dan bagaiman kehijaun vegetasi dari pantauan MODIS Terra juga ?

Mari kita bandingkan video dan gambar kebakaran api (memerahnya bumi) dengan kehijauan vegetasi (menghijaunya bumi).

Pada peta di bawah ini, vegetasi ini digambarkan dalam skala, atau indeks, dari kehijauan (menggunakan NDVI). Kehijauan didasarkan pada beberapa faktor: jumlah dan jenis tanaman, bagaimana berdaunnya mereka, dan bagaimana sehatnya mereka. Di tempat-tempat di mana dedaunan padat dan tanaman yang tumbuh cepat, indeks tinggi, direpresentasikan dalam warna hijau gelap. Daerah di mana beberapa tanaman tumbuh memiliki indeks vegetasi rendah, ditampilkan dalam cokelat. Area dimana citra satelit tidak mengumpulkan data berwarna abu-abu.

(a)
Gambar 1. Video (sumber: GlobalMap: Vegetation) Maret 2000 hingga Oktober 2010 (a) dan Gambar (hasil printscreen) Kehijauan Vegetasi di Permukaan bumi di Bulan Oktober 2010 (b)


Pilih Bumi berwarna merah atau hijau, dan langit berwarna biru atau kelabu ???
Adakah hubungannya dengan 'perubahan alam' ataukah ada turut andil 'perubahan manusia' juga di dalam merubah warna-warni dunia ???

sumber info+video : GlobalMap: Fire and Vegetation dari earthobservatory NASA

salam 'mejikuhibiniu',

Aji PP

Tuesday, November 30, 2010

Panasnya permukaan bumi ?

Panasnya permukaan bumi ?

Fulan tiba-tiba mengeluarkan sebuah hipotesa bahwa adanya kaitan antara situasi dunia yang 'memanas' dengan kondisi permukaan bumi yang memang kian memanas.


98.4251968503937%
February 2000September 2010

Land Surface Temperature
















Skip to beginning
Step back one
Play
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Download a Quicktime animation of this dataset (4 MB)

View, download, or analyze more of these data from NASA Earth Observations (NEO):
Land Surface Temperature

Land surface temperature is how hot the “surface” of the Earth would feel to the touch in a particular location. From a satellite’s point of view, the “surface” is whatever it sees when it looks through the atmosphere to the ground. It could be snow and ice, the grass on a lawn, the roof of a building, or the leaves in the canopy of a forest. Thus, land surface temperature is not the same as the air temperature that is included in the daily weather report.

Untuk melihat lebih detil simulasi terkait Land Surface Temperature yang terekam oleh citra satelit MODIS silahkan akses Earth Observatory milik NASA .

Kemudian kita fokuskan pada beberapa Negara yang sedang bergejolak baik alam maupun manusianya. Lakukanlah analisa mendalam pada tiap bulannya dan komparasi dengan pemberitaan yang ada di dunia maya maupun dunia pertelevisian ataukah media cetak dan bukan media hiburan semata.

Lihatlah bahwa pemanasan global terjadi terhadap permukaan bumi dan juga permukaan tubuh manusia :) ... hipotesa yang belum bisa dipertanggungjawabkan secara ilmiah.

Terimakasih atas kesediaanya membaca...
Bagi para blog walker, follower, reader, etc...mari berjalan-jalan bersama-sama...for better Indonesia... MERDEKA !!!


Salam,
^app^

-end-


"Don’t be silent, do something and smile for Planet of Earth”
by Aji Putra Perdana
"The Transformer of GIS and Remote Sensing“
http://ajiputrap.blogspot.com/

Dancing in A Globalized, Dancing with Love and Peace for Our Planet of Earth”
by My Little Sister


Salam Hangat,

Mencoba berpikir sederhana untuk memecahkan kerumitan dari sebuah problematika.

Aji Putra Perdana
"....................."
http://ajiputrap.blogspot.com/
http://geospatialvision.blogspot.com/
http://bumiwisata.blogspot.com/
http://gisresetutor.blogspot.com/
diganti dengan http://geospatialinfo.blogspot.com/
http://ajiputrap.wordpress.com/

Wednesday, November 17, 2010

Informasi Geospasial untuk Bencana Gunung Merapi, sebuah trend atau kebutuhan ?

Informasi Geospasial untuk Bencana Gunung Merapi, sebuah trend atau kebutuhan ?

Berawal dari ketidaksengajaan dan rasa penasaran akan fenomena yang sedang terjadi di Bangsa Indonesia ini terkait Bencana Gunung Merapi dan Informasi Geospasial. Disadari ataupun tidak penyajian informasi berbasis keruangan atau yang kini semakin dikenal dengan informasi geospasial, tidaklah luput dari peran serta para pegiat dunia Geospasial baik dari kalangan akademisi maupun praktisi. Fenomena yang disoroti dalam postingan kali ini ialah semakin membahananya orang berbicara mengenai lokasi berkoordinat, PETA, CITRA, GoogleMaps, Google Earth, hingga tampilan 3 Dimensional yang secara visual menarik orang untuk melihat lebih jelas seakan seperti aslinya di dunia nyata.

Fenomena ini tentusaja sangatlah berkaitan erat dengan perkembangan informasi dan teknologi geospasial yang begitu cepat dan tanggap terhadap bencana. Teknologi geospasial yakni teknologi penginderaan jauh dengan berbagai satelit-satelit berkemampuan resolusi tinggi (GeoEye-1, Ikonos, WorldView-2 DigitalGlobe) * disediakan oleh www.crisp.nus.edu.sg, hingga citra satelit resolusi rendah (citra satelit Terra/Aqua MODIS oleh NASA http://earthobservatory.nasa.gov/NaturalHazards/view.php?id=46837 atau disajikan juga di dalam http://cybergisforum.blogspot.com/2010/11/citra-merapi-terbaru.html). Mereka telah merekam baik dengan disengaja ataupun memang berada di atas Indonesia (baca: Gunung Merapi) pada jam lintasnya menghasilkan potret (baca: gambaran) dari atas langit ala citra satelit. Yang sangat menarik ialah bahwa informasi geospasial yang tersaji baik berupa PETA ataupun CITRA telah membuka mata dan membuka hati (baca: semoga) untuk lebih peduli dengan lingkungan dan bencana yang terjadi.

Pertanyaan yang muncul kemudian di benak penulis ialah :

1. Apakah ini sebuah trend ?

*teringat beberapa waktu lalu kala bencana tsunami melanda Aceh, tampaklah beragam internasional NGO berdatangan dan di sanalah berkembang pemanfaatan Informasi dan Teknologi Geospasial. Bahkan pembelajaran hingga mengenai warga lokal, salah satu teman penulis adalah warga Aceh seorang PNS yang belajar SIG (Sistem Informasi Geografis) berkat kejadian di Aceh. Di lain sisi, gempa bumi yang terjadi di Jogja 27 Mei 2006 melahirkan salah satu tulisan berjudul Geoinformation-Based Response to the 27 May Indonesia Earthquake – an Initial Assessment dan berbagai tulisan-tulisan lainnya. Dimana di dalam tulisan tersebut mengemukakan informasi berbasis keruangan atau informasi geospasial sebagai respon terhadap kejadian bencana gempa bumi sangatlah penting. Trend berikutnya ialah akan muncul berbagai tulisan terkait Merapi dengan informasi geospasialnya. Semoga bukan sekedar trend dalam penulisan atau kegiatan proyek semata ?

* Tapi trend yang dimaksud di sini ialah, Apakah informasi geospasial 'muncul' dan menjadi 'perlombaan' sesaat, saat, setelah bencana terjadi ? *habis itu ya sudah dilupakan...

2. Ataukah informasi geospasial itu sebuah Kebutuhan ?

* Jika berbicara kebutuhan, ada papan sandang pangan sebagai kebutuhan pokok atau kebutuhan primer. Lalu masuk kategori yang manakah informasi geospasial ini ? Kemudian pada level apa sajakah informasi geospasial dibutuhkan dan seberapa detil yang perlu disajikan ? Sebenarnya siapa saja yang butuh akan informasi geospasial ? Bukankah dalam sebuah perencanaan pembangunan dibutuhkan informasi geospasial ? Konon kabarnya dalam rencana tata ruang hingga tingkat detil pun memerlukan apa yang disebut sebagai PETA ?
* Begitu banyak tanda tanya yang mungkin bisa dijawab oleh para ahli dan pegiat GIS di Indonesia. Yang acapkali menjadi permasalahan ialah bagaimana kita mengkomunikasikan 'Kebutuhan' serta keberadaan informasi geospasial ini kepada yang meng-ACC kebutuhan tersebut.

Informasi Geospasial untuk Bencana Gunung Merapi, sebuah trend atau kebutuhan ?
Ini hanyalah tulisan selingan semata tatkala menatap betapa indahnya dunia Geospasial dengan warna-warni PETAnya, ramai koneksi informasinya ala-webbasedmapping, hingga citra satelit yang tak kenal lelah menatap abu vulkanik Merapi. Tapi sangatlah disayangkan bahwa informasi geospasial tersebut begitu banyak terserak-serak di berbagai sudut dunia maya seakan berdiri sendiri-sendiri di setiap laci tanpa adanya komunikasi yang saling melengkapi. "Perlu sebuah WADAH, bukan sekedar menyimpannya ke dalam Project (MXD atau APR) *baca: proyek", celetuk fulan.

Solusinya : Apapun itu baik trend atau memang menjadi sebuah Kebutuhan, maka inilah saatnya Wujudkan Informasi Geospasial bagian dari Hidup Kita untuk siapapun,kapanpun, dimanapun, tidak terpatok pada koneksi internet, peta cetak pun jadi, apapun PETA/CITRAnya minumnya ambil di tempat masing-masing :)

*mengkritik itu mudah dan sederhana, sehingga kemudahan dan kesederhanaan itu tersaji di dalam postingan kali ini...hehe :)*

--- yang jelas, kalau bisa sich semua dikomunikasikan dan bukannya menjadi ajang perlombaan di tengah bencana Merapi ---

--- janganlah money-oriented tetapi kalau memang berbicara relawan ya...harus rela dunk :). It's better to be humanitarian-oriented... ----

--- kalau partisipatif, ya semoga bener-bener melibatkan masyarakat atau stakeholder2x maupun yang hendak dilibatkan secara betul-bener... ---

--- dibutuhkan peta-peta yang terupdate dan tentusaja dibutuhkan dukungan data-data yang valid hari demi hari dan semoga tidak terjadi overlapping antar pihak pengumpul data ---


*Maaf tulisan kali ini tidak memuat unsur yang menarik dibaca terkait pembelajaran geospasial, kebencanaan, dan lain sebagainya (*sok sedikit menyerempet) tidak ada maksud dan tujuan apapun, namun yang pasti tulisan kali ini ialah suatu upaya untuk membangkitkan gairah si penulis sendiri untuk bisa menulis ilmiah nantinya. Sekedar iseng semata setelah melihat kejadian nyata...upss :). piss..piss...

Terimakasih atas kesediaanya membaca...
Bagi para blog walker, follower, reader, etc...mari berjalan-jalan bersama-sama...for better Indonesia... MERDEKA !!!

Salam,
^app^

-end-


"Don’t be silent, do something and smile for Planet of Earth”
by Aji Putra Perdana
"The Transformer of GIS and Remote Sensing“
http://ajiputrap.blogspot.com/

Dancing in A Globalized, Dancing with Love and Peace for Our Planet of Earth”
by My Little Sister


Salam Hangat,

Mencoba berpikir sederhana untuk memecahkan kerumitan dari sebuah problematika.

Aji Putra Perdana
"The Transformer of GIS and Remote Sensing"
http://ajiputrap.blogspot.com/
http://geospatialvision.blogspot.com/
http://bumiwisata.blogspot.com/
http://gisresetutor.blogspot.com/ diganti dengan http://geospatialinfo.blogspot.com/
http://ajiputrap.wordpress.com/

Tuesday, May 5, 2009

Ocean Color Web Feature - Orinoco Flow

Orinoco Flow

Ocean Color Web Feature - Orinoco Flow

The Orinoco is one of the longest rivers in South America at 2,140 km, (1,330 miles). Its drainage basin, sometimes called the Orinoquia (especially in Colombia) covers 880,000 km², 76.3% in Venezuela with the rest in Colombia. The Orinoco and its tributaries are the major transportation system for eastern and interior Venezuela and the llanos of Colombia. However, since river navigation is declining in every country, many of the old waterways along the Orinoco watershed are now an obstacle to land communications rather than a useful commercial route.

Map of Lower Orinoco 1897

Orinoco Flow

The Orinoco River regularly sends a plume of water into the Caribbean in the fall. This has already been documented in the early days of satellite ocean color measurement using CZCS data. Researchers in the area have remarked that this year's plume is unusually large and occurs at an unusual time prompting some to suggest that the Amazon River may instead be the source of this plume.

You can see the green water extending northward past Puerto Rico and the Virgin Islands in the above image. Move your pointer over the image to see computed chlorophyll concentrations in the plume. Larger chlorophyll and true color images are also available.

source information : http://oceancolor.gsfc.nasa.gov/

---------------------------------------------------------------------------
Salam Penginderaan Jauh untuk Kelautan bagi Indonesia tercinta...


Aji Putra Perdana
- sok ocean, sok iso, sok tenan, sok atuh -
-------------------------------------------------------------------------------------
Perdana, Aji Putra, (2006). Kajian Suhu Permukaan Laut Berdasarkan Analisis Data Penginderaan Jauh dan Data Argo Float di Selatan Pulau Jawa, Pulau Bali, dan Kepulauan Nusa Tenggara. Skripsi. Yogyakarta: Fakultas Geografi, Universitas Gadjah Mada. ( download )
-------------------------------------------------------------------------------------
Perdana, Aji Putra, (2006). Study Of Sea Surface Temperature Based On Analysis Of Remotely Sensed Data And Argo Float Data In The South Of Java Island, Bali Island And Nusa Tenggara Archipelago. Skripsi. Yogyakarta: Faculty of Geography, Gadjah Mada University. ( download )

Tuesday, March 31, 2009

SeaDAS for Windows XP and Vista

SeaDAS Virtual Appliance
released for Windows!

SeaDAS VA 5.3b (beta) allows SeaDAS to be run on Microsoft Windows XP and Vista systems within a virtual Linux machine.
This is a fully functional version of SeaDAS and processing benchmarks show very impressive performance.

SeaDAS VA is simple to install and requires the free VMware Player.

SeaDAS VA Screen Shot

Best Regards,

Aji Putra Perdana
http://ajiputrap.blogspot.com/
http://bumiwisata.blogspot.com/

SeaDAS - Level-2 processing MODIS

What is SeaDAS


The SeaWiFS Data Analysis System (SeaDAS) is a comprehensive image analysis package for the processing, display, analysis, and quality control of ocean color data.

SeaDAS Screen Shot

Supported satellite sensors are
MODIS, SeaWiFS, OCTS, and CZCS.

SeaDAS Primary Functionalities

Processing programs:

SeaWiFS:
  • L1A, L1B, L2, L3, and SMI (Standard Mapped Image) processing
  • Map projection of L1, L2, and L3 files
  • L1 and L2 Browse product generation
  • L1A to L0 program for renavigation
  • Interactive L1 coastline registration and L2 QC
  • L1 subscene extraction
  • MODIS:
  • L0 to L1A Direct Broadcast processing
  • L1A Geolocation processing
  • L1A subscene extraction
  • L1A to L1B processing
  • L1B to L2 processing
  • L2 and L3 binning
  • SMI processing
  • Map projection of L2 and L3 files
  • CZCS:
  • L1A to L1B processing
  • L1 to L2 processing
  • L2 and L3 binning
  • SMI processing
  • OCTS:
  • L1A to L1B processing
  • L1B to L2 processing
  • L2 and L3 binning
  • SMI processing


  • SeaDAS Tutorials


    SeaWiFS Data Product Generation Programs:
    l1agen_seawifs Generate L1A file from L0 (HRPT) file
    l1bgen Generate L1B file from L0 (HRPT) file
    l2gen,0 Generate L2 file from L1A (GAC, LAC, HRPT) file
    l2bin/l3bin Generate L3 space- or time-binned files from L2 files
    smigen Generate L3 SMI standard mapped product from L3 binned data
    browse Generate L1A browse (Band 865nm) file or L2 browse (chlor_a) file
    bl1map Generate projected L1A HDF file
    bl2map Generate projected L2 HDF file
    bl3map Generate projected L3 HDF file
    l0regen_seawifs Generate L0 file from L1A (HRPT) file

    SeaWiFS Data Extraction and Quality Control Programs:
    l1aextract_seawifs L1A and L2 file extraction
    register L1A coastline registration and warping


    MODIS Data Product Generation Programs:
    l1agen_modis Generate L1A file from L0 (PDS) file
    l1bgen_modis Generate L1B file from an L1A and GEO file
    l2gen,4 Generate L2 file from L1B file
    l2bin/l3bin Generate L3 space- or time-binned files from L2 files
    smigen Generate L3 SMI standard mapped product from L3 binned data

    l2gen

    Description: This program performs Level-2 processing on MODIS, SeaWiFS, OCTS, or CZCS data and generates Level-2 geophysical products by applying atmospheric corrections and bio-optical algorithms to the sensor data. The input data levels required for l2gen processing are as follows:

    • MODIS: input must be an Aqua or Terra L1B file
    • SeaWiFS: input may be either an L1A or L1B file
    • OCTS: input must be either a NASDA-format L1B file or SIMBIOS-format L1B file
    • CZCS: input must be a CZCS L1B file

    For a thorough description of the full capabilities of l2gen and details on the UNIX command-line user interface, please refer to the L2GEN User's Guide.

    Please read more about l2gen..

    Level-2 Products Selection Window:


    Description: This window allows the user to select Level-2 output products for the output file specified in the l2gen main window.

    **************************************** download SeaDAS****************************

    SeaDAS 5.3 released

    MODISL1DB 1.5 released


    Wednesday, March 25, 2009

    SeaWIFS/MODIS data download

    SeaWIFS/MODIS data download

    SeaWIFS Background

    The purpose of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project is to provide quantitative data on global ocean bio-optical properties to the Earth science community. Subtle changes in ocean color signify various types and quantities of marine phytoplankton (microscopic marine plants), the knowledge of which has both scientific and practical applications. The SeaWiFS Project will develop and operate a research data system that will process, calibrate, validate, archive and distribute data received from an Earth-orbiting ocean color sensor.
    (http://oceancolor.gsfc.nasa.gov/SeaWiFS/BACKGROUND/SEAWIFS_BACKGROUND.html)
    -------------------------------------------------------------------------------------------------

    Topic Announcements / Ocean Color Announcements / SeaWiFS Data Available for Download from (http://oceancolor.gsfc.nasa.gov/forum/oceancolor/topic_show.pl?tid=85)

    By @gene Date 2004-05-04 20:20

    1) seawifs data now available from the oceancolor web

    although it has taken a little longer than we had initially thought, we are
    happy to announce that it is now possible to download and/or order seawifs data
    (up to data collected 14 days ago, the required embargo period) directly from
    the oceancolor web browser along with the modis/aqua data that is currently
    being provided. this service is provided in addition to the continuing access
    and user services for seawifs data that are provided by the goddard daac.

    2) you still need to be an authorized seawifs user

    as with data access from the daac, you must be an authorized seawifs data
    user and have a password. for those of you who already are authorized users,
    just request a password directly from a link in the lower right portion
    on the oceancolor web page ( http://oceancolor.gsfc.nasa.gov ) to use this
    option. if you are not currently an authorized researcher, there is also a
    link on that web page that you can use to request to become one.

    upon authorization, new users will be provided with a password. if you
    are not sure if you are on the authorized list or if you want to verify the
    e-mail address at which you are listed at (information that we require),
    you can check at:

    http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/OCDST/SeaWiFS_auth.html

    3) improved seawifs 1 kilometer data product available

    we are currently producing an improved high resolution seawifs data product
    (MLAC = Merged Local Area Coverage) derived from the 1 kilometer seawifs
    hrpt and recorded lac data that reduces geographic overlap from multiple
    receiving stations and maximizes data quality. both level-1 and level-2 data
    are available for this new data set along with the same products derived
    from the gac (4 kilometer) data. we are currently processing the entire
    MLAC archive to create a complete set of level-1 and level-2 products and
    the entire mission should be available by early next week. currently,
    1997 is online with 1998 and the rest of the mission following close behind.

    4) geographic subsetting for seawifs data

    in addition to being able to order the full level-1 and level-2 files,
    you can also now request a geographic extract from the full sized file to
    only get the data for your specific region of interest. this option only
    works through the order and ftp option (rather than the direct download from
    the web browser which provides you with the entire file). once you've used
    the browser to find all the potential files for the time frame and general
    region of interest and hit the button, you'll be given the option
    of extracting the data products into a smaller geographic box. it will be
    these extracts (all fully supported in seadas) that will be staged for you
    to download.

    5) updated seawifs calibration model

    the seawifs data that you can get from the oceancolor web is slightly
    different from the data archived at the goddard daac in that we have
    switched to a revised calibration table which better handles the temporal
    degradation of the instrument in the very early and later parts of the mission.
    a comprehensive description of this change in the level-1 calibration can be
    found under the "validation" section of the oceancolor web. our analyses
    show that the differences are relatively minor which is why we did not
    believe it necessary to replace all the data that are currently archived
    at the daac based on reprocessing #4 at this time. as was discussed at the
    recent science team meeting, we anticipate an official reprocessing of the
    entire seawifs mission either later this year or early next which will most
    likely include a number of additional parameters based on the recommendations
    of the ocean color community.

    6) help available

    for those of you who are not familiar with all the functions of the oceancolor
    web browse and order tool, there is an extensive help function available.
    we will be keeping our eyes on the oceancolor forum for feedback and questions
    regarding this new feature.

    -----------------------------------end--------------------------------------------

    To download SeaWIFS/MODIS Data from OceanColor, you may click this link/url :
    http://oceancolor.gsfc.nasa.gov/cgi/browse.pl?sen=am

    best regards,
    Aji Putra Perdana
    http://ajiputrap.blogspot.com/
    http://gisresetutor.blogspot.com/

    Wednesday, March 4, 2009

    Masking Citra Aqua/Terra MODIS

    Masking Citra Aqua/Terra MODIS

    Masking dilakukan dengan tujuan untuk membedakan daerah daratan, lautan dan daerah yang berawan.

    The purpose of masking is to distinguish the land, sea and cloudy areas.


    Proses masking daratan dan lautan dilakukan pada file Land/Sea Mask, menggunakan fasilitas Masking pada software ENVI 4.x.

    This masking process using Land/Sea Mask file from MOD03....(Terra MODIS) atau MYD03....(Aqua MODIS) and processed using ENVI 4.x


    Proses masking awan darat dan awan laut dilakukan pada saluran 3, dimana :
    Untuk tutupan awan :
    - Nilai batas awan laut adalah 0.174
    - Nilai batas awan darat adalah 0.2

    To do masking process land cloud and sea cloud use band 3 MODIS, where:
    Threshold Value for sea cloud is 0.174
    Threshold Value for land cloud is 0.2



    Langkah-langkah pemrosesan masking citra MODIS menggunakan software ENVI 4.x

    Below are the steps in masking process for Land/Sea Mask MODIS data with ENVI 4.x :

    1. Open MOD03....(Terra MODIS) atau MYD03....(Aqua MODIS) from mainmenu ENVI:
    File -> Open External File -> Generic Format -> HDF
    2. Choose your file and choose Land/Sea Mask
    3. Choose Map -> Georefence MODIS (to georeferencing land/sea mask data)
    4. Follow the instruction..choose latitude and longitude...Choose Geographic Lat/Lon, Perform Bow-Tie Correction = YES, and then click OK
    5. Choose your output file name and location..wait the process...(Building MODIS Image Geometry...Georeferencing MODIS with Bow-Tie Correction), and then you will have your land/sea mask image georeferenced in Available Bands List
    6. Open the Image, and move your mouse, click on Land and see the value...
    7. to build Mask..use tool from Main Menu choose Basic Tools -> Masking -> Build Mask
    8. choose Display#1 (display that showing the Georeferenced Land/Sea Mask Data), click OK
    9. Mask Definition, choose Option - Import Data Range (Choose Georeferenced land/sea mask filename)
    make two mask definition : first, min = 0 and max = 0
    then, min = 3 and max = 7
    10. After that, click Aplly and u will get the Mask.....

    best regards
    geografi..putra bumi..
    http://ajiputrap.blogspot.com/

    Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images

    Mapping paddy rice agriculture in South and Southeast Asia using
    multi-temporal MODIS images

    Xiangming Xiao a,*, Stephen Boles a, Steve Frolking a, Changsheng Li a, Jagadeesh Y. Babu a,b,
    William Salas c, Berrien Moore III a
    a Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH 03824, USA
    b Central Rice Research Institute, Cuttack, 753006, Orissa, India
    c Applied Geosolutions, LLC, Durham, NH 03824, USA
    Received 27 April 2005; received in revised form 12 August 2005; accepted 1 October 2005

    Abstract

    In this paper, we developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia. These countries have ¨30% of the world population and ¨2/3 of the total rice land area in the world. We used 8-day composite images (500-m spatial resolution) in 2002 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period a mixture of surface water and rice seedlings exists. We applied a paddy rice mapping algorithm that uses a time series of MODIS-derived vegetation indices to identify the initial period of flooding and transplanting in paddy rice fields, based on the increased surface moisture. The resultant MODIS-derived paddy rice map was compared to national agricultural statistical data at national and subnational levels. Area estimates of paddy rice were highly correlated at the national level and positively correlated at the subnational levels, although the agreement at the national level was much stronger. Discrepancies in rice area between the MODIS-derived and statistical datasets in some countries can be largely attributed to: (1) the statistical dataset is a sown area estimate (includes multiple cropping practices); (2) failure of the 500-m resolution MODIS-based algorithm in identifying small patches of paddy rice fields, primarily in areas where topography restricts field sizes; and (3) contamination by cloud. While further testing is needed, these results demonstrate the potential of the MODIS-based algorithm to generate updated datasets of paddy rice agriculture on a timely basis. The resultant geospatial database on the area and spatial distribution of paddy rice is useful for irrigation, food security, and trace gas emission estimates in those countries.

    ~ 2005 Elsevier Inc. All rights reserved.

    Keywords: Enhanced vegetation index; Land surface water index

    District-level spatial distribution of paddy rice sown area derived from national agricultural statistical data (described in Section 3.4). Rice area is displayed as the percent of the district land area dedicated to paddy rice in Southeast Asia.

    Thursday, January 22, 2009

    MODIS (Aqua/Terra)-Image Processing (2)

    MODIS (Aqua/Terra)-Image Processing (2)

    continues from previous entry...http://ajiputrap.blogspot.com/modis-image-processing.html

    " temperature brightness " or " temperature brightness value "

    Aqua MODIS image processing for sea surface temperature using two bands : band 31 and band 32 (that have processed for corrected radiance value).
    We need to convert radiance value both bands (31 and 32) to Brightness Value using “Planck”:

    Tb = c2/(Vi * ln (c1/(Vi5 * radiance) + 1))
    where Tb = Brightness Value (K),
    c1 = Constanta radiance (1.1910659x108 [W m -2sr-1 (µm-1 )-4 ])
    c2 = Constanta radiance (1.438833 x 104 [K µm])
    Vi = central wavelength

    below are table for central wavelength - Aqua/Terra MODIS Bands..

    For AQUA MODIS =
    Band 20 : 3.7803
    Band 22 : 3.9720
    Band 23 : 4.0617
    Band 31 : 11.0263
    Band 32 : 12.0424

    For TERRA MODIS =
    Band 22 : 3.9719
    Band 23 : 4.0567
    Band 31 : 11.0073
    Band 32 : 12.0020

    -----------------------------------------------------------------------------------

    nice to share

    Aji PP

    Wednesday, January 21, 2009

    MODIS (Aqua/Terra)-Image Processing (1)

    MODIS (Aqua/Terra)-Image Processing (1)

    this is just a little bit sharing... :)

    you may or have been read :
    http://ajiputrap.blogspot.com/search/label/Aqua
    http://ajiputrap.blogspot.com/search/label/Aqua%2FTerra%20MODIS
    http://ajiputrap.blogspot.com/search/label/MODIS

    After we download MODIS (Aqua/Terra) Level 1B Radiance Calibrated (1KM, 500m and 250m, opr you just download 1KM only (its already consist of 250m and 500m that aggregated to 1km) and Level 1A Geolocation (for extracting LAND SEA MASK).

    The next step is to open HDF MODIS data in Image Processing Software, such as ENVI...
    in this post i would like to share about open HDF MODIS DATA, View HDF DATASETS and other steps in image processing using software ENVI 4.x.

    1. In ENVI 4.x u may open HDF MODIS DATA through two ways:
    a. Open HDF MODIS data from:
    File -> Open External Files -> General Format -> HDF

    U will see HDF Dataset Selection, and then choose Datasets (that to be opened)
    Click OK
    Choose BSQ and click OK (repeated until 4 times, depend on number of dataset opened)

    b. Or u may open directly from
    File -> Open Image File

    Will open all modis data from MYD021KM: Reflectance and Radiance Bands (1- 19 and 26) also Emissive Bands (20-25 and 27-36)

    Your MODIS DATA will opened in ENVI 4.x

    2. View HDF Datasets of MODIS
    from ENVI 4.x main menu..choose Basic Tools->Preprocessing->Data Specific Utilities -> View HDF Datasets Attributes
    and then choose your MODIS DATA...
    u can read my previous posting :
    http://ajiputrap.blogspot.com/2008/12/modis-hdf-hierarchical-data-format.html

    3. Radiometric Correction
    Radiometric Correction in ENVI use Band Math (ENVI main menu, choose Basic Tool -> Band Math)

    Radiance bands calculated using this formula :
    Rb = R_scaleb ( SIb – R_offsetsb)
    where :
    Rb = Radiance of band-b
    R_Scaleb = Radiance scale of band-b
    SIb = Sign Integer of band-b
    R_offset¬sb = Radiance offsets of band-b

    Radiance value of sensor zenith calculated using this formula :
    Rz = R_scalez * iz * pi/180
    dimana :
    Rz = Radiance value of sensor zenith
    R_scalez = Radiance scale of sensor zenith
    iz = Sensor zenith


    Reflectance Bands calculated for visible and thermal infrared (1 – 19 and 26 bands) with this formula:

    Refb = Ref_Scaleb * (Bb - Ref_offsetsb)

    where: Refb = Reflectance of band- b
    Ref_Scaleb = Reflectance scale
    Bb = Band -b
    Ref_offsetsb = Reflectance offset band- b

    4. applied formula or algorithm (for Sea Surface Temperature or Chlorophyll-a)

    MODIS can help us to detecting Chlorophyll-a (Chlor-a) with formula or algorithm from Carder et al. :

    Chlor-a=(10^(0.2818-(2.783*alog10(B10/B12))+(1.863*((alog10(B10/B12))^2))-
    (2.387*((alog10(B10/B12))^3))))

    where : B10 = Reflecance of Band 10
    B12 = Reflecance of Band 12
    - above is the formula of Chlor-a in Band Math of ENVI.

    Aqua MODIS image processing for sea surface temperature using two bands : band 31 and band 32 (that have processed for corrected radiance value).
    We need to convert radiance value both bands (31 and 32) to Brightness Value using “Planck”:

    Tb = c2/(Vi * ln (c1/(Vi5 * radiance) + 1))
    where Tb = Brightness Value (K),
    c1 = Constanta radiance (1.1910659x108 [W m -2sr-1 (µm-1 )-4 ])
    c2 = Constanta radiance (1.438833 x 104 [K µm])
    Vi = central wavelength

    You can use band math ENVI 4.x and put this formula:
    (1.438833*10000)/(11.0263*(alog((1.1910659*100000000)/(11.0263^5*b31))/1))
    where :
    b31 = band 31
    to determine or get information of Sea Surface Temperature you may use this algorithm from Brown and Minnet :
    SPL =
    1.152+0.96*(B1-273)+0.151*(B1-B2)*(B3-273)+2.021*(B1-B2)*(1/COS(B4)-1)

    where
    B1 = Brightness Value of band 31
    B2 = Brightness Value of band 32
    B3 = Brightness Value of band 20
    B4 = Radiance Value of sensor zenith



    =========
    Regards,

    Aji Putra Perdana

    Thursday, January 15, 2009

    MODIS phytoplankton-ENVI

    MODIS phytoplankton-ENVI

    I would like to discuss about "MODIS phytoplankton-ENVI".
    title of this my blog post today is about Aqua/Terra MODIS image processing for detecting phytoplankton using software ENVI.

    Let's start this discussion from the last word :"ENVI"

    What is ENVI??

    1. ENVI
    We know that ENVI is great image processing and analyzing geospatial imagery.
    ENVI is the premier software solution for processing and analyzing geospatial imagery used by GIS professionals, scientists, researchers, and image analysts around the world. ENVI software combines the latest spectral image processing and image analysis technology with an intuitive, user-friendly interface to help you get meaningful information from imagery.

    ENVI provides us MODIS Toolkit and Ocean Color Plug-ins (previous posting : http://ajiputrap.blogspot.com/2008/12/modis-toolkit-and-ocean-color-plug-ins.html)
    ENVI help us in MODIS image processing with geometric correction (bow-tie correction).

    - I have uploaded in my esnips simple tutorial Aqua/Terra MODIS image processing using ENVI 4.x

    http://ajiputrap.blogspot.com/2009/01/pengolahan-citra-aquaterra-modis-dengan.html

    download this tutorial:
    pengolahan citra modis dengan envi.pdf
    -- step by step from open MODIS HDF (Level 1B), MODIS Geometric Correction (include Bow-Tie Correction), Radiometric Correction for Reflectance and Radiance Bands (use Band Math).
    Another tutorial files can be downloaded in http://www.esnips.com/web/perdana09-article :
    - SeaDAS_4_AquaMODIS.pdf
    Tutorial Aqua/Terra MODIS image processing using SeaDAS software
    - Langkah Order Citra MODIS.pdf
    How to order MODIS (level 1B at Ladsweb)

    2. phytoplankton
    phytoplankton ??
    Phytoplankton are the autotrophic component of the plankton community. The name comes from the Greek words phyton, or "plant", and πλαγκτος ("planktos"), meaning "wanderer" or "drifter".[1] Most phytoplankton are too small to be individually seen with the unaided eye. However, when present in high enough numbers, they may appear as a green discoloration of the water due to the presence of chlorophyll within their cells (although the actual color may vary with the species of phytoplankton present due to varying levels of chlorophyll or the presence of accessory pigments such as phycobiliproteins, xanthophylls, etc.).

    Phytoplankton obtain energy through a process called photosynthesis and must therefore live in the well-lit surface layer (termed the euphotic zone) of an ocean, sea, lake, or other body of water.

    Phytoplankton are a key food item in both aquaculture and mariculture. Both utilize phytoplankton for the feeding of the animals being farmed. In mariculture, the phytoplankton is naturally occurring and is introduced into enclosures with the normal circulation of seawater. In aquaculture, phytoplankton must be obtained and introduced directly.

    References

    1. ^ Thurman, H. V. (1997). Introductory Oceanography. New Jersey, USA: Prentice Hall College. ISBN 0132620723.
    2. ^ "Satellite Sees Ocean Plants Increase, Coasts Greening". NASA (2 March 2005). Retrieved on 12 January 2009.
    3. ^ Richtel, M. (May 1, 2007), "Recruiting Plankton to Fight Global Warming", New York Times, http://www.nytimes.com/2007/05/01/business/01plankton.html?ref=science
    4. ^ Hallegraeff, G.M. (2003). Harmful algal blooms: a global overview. in Hallegraeff, G.M., Andewrson, D.M. and Cembella, A.D. (eds) 2003. Manual on Harmful Marine Microalgae. UNESCO, Paris
    5. ^ G.E. Hutchinson (1961). "The paradox of the plankton". Am. Nat. 95: 137–145. doi:10.1086/282171.
    6. ^ a b c d McVey, James P., Nai-Hsien Chao, and Cheng-Sheng Lee. CRC Handbook of Mariculture Vol. 1 : Crustacean Aquaculture. New York: C R C P LLC, 1993.

    == source information : http://en.wikipedia.org/wiki/Phytoplankton ==

    interesting question about phyto...

    Where Are Phytoplankton?

    The distribution of phytoplankton in the ocean have been measured by special instruments in space since 1979. The instruments, called ocean-color scanners, measure the color of the ocean. Color is proportional to the amount of chlorophyll pigments close to the surface, except in sediment-rich water very close to coasts. And the amount of chlorophyll is proportional to the amount of phytoplankton in the water. Water with great numbers of phytoplankton are green. Pure ocean water is deep navy blue.

    The first ocean-color scanner, the Coastal Zone Color Scanner, was launched on the Nimbus-7 satellite in 1978. It was followed many years later by SeaWiFS (Sea-viewing Wide Field-of-view Sensor) on the Seastar satellite launched in 1997. The most recent color scanner is MODIS (Moderate Resolution Imaging Spectrometer) on the Terra spacecraft launched in 1999 and the Aqua satellite launched in 2002.

    more information about distribution of phyto...read this article : Distribution of Plankton

    summary from the article
    :
    Phytoplankton are abundant in regions where:
    1. Winds are able to mix nutrients up into near surface waters from deeper in the ocean, or
    2. Where Ekman transports driven by the winds pulls water up from deeper in the ocean,
    a. When winds blow toward the equator along west coasts of continents, and
    b. When the average winds blowing at different speeds and directions cause divergence of the Ekman transports,provided a small amount iron needed by the protists is in the water.

    --- MODIS (Moderate Resolution Imaging Spectrometer) on the Terra spacecraft and the Aqua satellite help us daily in monitoring or detecting phytoplankton. ---

    3. MODIS (Moderate Resolution Imaging Spectrometer)
    please read previously article about MODIS (http://ajiputrap.blogspot.com/search/label/MODIS)
    or directly from http://modis.gsfc.nasa.gov

    ===================================================================================
    Step by step Aqua/Terra MODIS (Level 1B) image processing using ENVI 4.2 software:
    1. Download MODIS data level 1B 1km resolution from ladsweb...
    2. Open HDF MODIS in ENVI 4.x
    File -> Open External File -> Generic Format -> HDF (http://ajiputrap.blogspot.com/2008/12/modis-hdf-hierarchical-data-format.html or http://ajiputrap.blogspot.com/search/label/HDF)
    3. Geometric Correction, include Bow-Tie Correction*
    Bowtie Correction aims to improve image at overlapped data. Overlap occurs because there is increasing instantaneous field of view (IFOV) of 1x1 km in the lowest point (nadir). Being close to almost 2 x 5 km at the maximum scan angle is 55 degree.
    After MODIS DATA geometrically corrected, then go to the next step
    4. Radiometric Correction
    Radiometric Correction in ENVI use Band Math (ENVI main menu, choose Basic Tool -> Band Math)

    Reflectance Bands calculated for visible and thermal infrared (1 – 19 and 26 bands) with this formula:
    Refb = Ref_Scaleb * (Bb - Ref_offsetsb)

    where: Refb = Reflectance of band- b
    Ref_Scaleb = Reflectance scale
    Bb = Band -b
    Ref_offsetsb = Reflectance offset band- b

    -- information about reflectance scale and offset we may get it from HDF Dataset (http://ajiputrap.blogspot.com/2008/12/modis-hdf-hierarchical-data-format.html or http://ajiputrap.blogspot.com/search/label/HDF)

    5. Chlorophyll-a algorithm with Band Math
    After we get geometric and radiometrically corrected MODIS image, we continue to the main step : detecting Chlorophyll-a

    Fitoplankton that is on a layer of light contains Chlorophyll-a useful for photosynthesis. Chlorophyll-a able to absorb a light blue and green, so it can detect the existence fitoplankton based on the ability of these Chlorophyll-a.

    MODIS can help us to detecting Chlorophyll-a (Chlor-a) with formula or algorithm from Carder et al. :

    Chlor-a=(10^(0.2818-(2.783*alog10(B10/B12))+(1.863*((alog10(B10/B12))^2))-
    (2.387*((alog10(B10/B12))^3))))

    where : B10 = Reflecance of Band 10
    B12 = Reflecance of Band 12
    - above is the formula of Chlor-a in Band Math of ENVI.

    In addition Chlorophyll-a , we may use sea surface temperature from MODIS to get information distribution of phytoplankton in the ocean. if we combined both we may get great information...

    ==================================================================================
    nice to share :)


    Best Regards,

    Aji Putra Perdana