Jumat, 05 Desember 2008

Penginderaan Jauh Vegetasi - Indeks Vegetasi

Penginderaan Jauh merupakan seni dan ilmu pengetahuan yang cukup luar biasa dan semakin berkembang pesat dengan teknologi yang ada, sehingga mampu merekam fenomena yang terjadi di bumi ini.

Obyek di bumi yang diindera oleh "penginderaan jauh" jika disederhanakan ke dalam tiga klas yakni air, tanah, dan vegetasi. Ketiga hal tersebut yang mengisi kehidupan di bumi ini...
Obyek air yang begitu luas meliputi permukaan bumi ini, dengan penginderaan jauh untuk aplikasi kelautan, maupun untuk aplikasi tubuh air di daerah daratan..menggunakan berbagai citra satelit penginderaan jauh dari level rendah, menengah hingga tingkat detil..berbagai algoritma digunakan untuk ekstraksi informasi dari tubuh air.
Obyek tanah, biasanya lahan atau lebih ke penutup lahan ataupun penggunaan lahan banyak dikaji menggunakan penginderaan jauh, baik skala global maupun tingkat perkotaan...biasanya perubahan penggunaan lahan..
Obyek vegetasi kini mendapat sorotan yang cukup mencolok.,.apalagi kondisi lingkungan yang bersedih dan menangis...

Vegetasi biasanya diamati melalui pendakatan indeks vegetasi..

Vegetation Indices are robust spectral measures of the amount of vegetation present on the ground in a particular pixel. They typically involve transformations of two or more bands designed to enhance the vegetation signal and allow for precise inter-comparisons of spatial and temporal variations in terrestrial photosynthetic activity.

Indeks vegetasi yang umum digunakan ialah NDVI atau Normalized Difference Vegetation Index.

The NDVI is calculated from these individual measurements as follows:

NDVI=(NIR-RED)/(NIR+RED)

where RED and NIR stand for the spectral reflectance measurements acquired in the red and near-infrared regions, respectively. These spectral reflectances are themselves ratios of the reflected over the incoming radiation in each spectral band individually, hence they take on values between 0.0 and 1.0. By design, the NDVI itself thus varies between -1.0 and +1.0.

Kemunculan citra satelit Aqua/Terra MODIS dengan algoritmanya untuk vegetasi yaitu Enhanced Vegetation Index (EVI) yang merupakan turunan dari NDVI.

Perbandingan NDVI dan EVI
Ndvi merupakan indeks vegetasi yang banyak dikenal dan digunakan untuk analisa vegetasi.

Normalized Difference Vegetation Index (NDVI)
NDVI = (NIR – Red) / (NIR + Red)
- Most common vegetation index
- Value is always between +1 and -1 with green vegetation closer to +1 and no vegetation close to 0
- Negative values are possible for non-vegetated features
- Somewhat sensitive to radiometric contaminants

Enhanced Vegetation Index (EVI)

- Developed to improve upon NDVI
- Output as a standard MODIS product
EVI = G*[(NIR-Red)/NIR+C1*Red-C2*Blue+L)] where G, C1, C2, and L are coefficients:
G = Gain factor (2.5 for MODIS)
L = Canopy background adjustment (1 for MODIS)
C2 = Atmospheric aerosol resistance (6 for MODIS)
C2 = Atmospheric aerosol resistance (7.5 for MODIS)
- These coefficients reduces atmospheric effects in the red band and compensates for the different ways in which near-IR and red light behaves inside and below a canopy thereby reducing the effect of soil type, soil moisture, surface litter, and snow cover.
- EVI is more sensitive to canopy structure than NDVI

Applications - Aplikasi penginderaan jauh dengan memanfaatkan Indeks Vegetasi, misalnya:

Some of the more common applications of the vegetation index include:
•Global warming/ climate
•Global biogeochemical and hydrologic modeling
•Agriculture; precision agriculture; crop stress, crop mapping
•Rangelands; water supply forecasting; grazing capacities; fuel supply
•Forestry, deforestation, and net primary production studies
•Pollution/ Health issues (rift valley fever, mosquito-producing rice fields)
•Desertification studies
•Anthropogenic change detection and landscape disturbances.


MODIS VEGETATION INDEX

Read the technical description of this product (ATBD)
atbd_mod13.pdf (Adobe Acrobat PDF Format)

best regards,
Aji Putra Perdana
GIS and Remote Sensing...Learn in everytime..

Tidak ada komentar:

Poskan Komentar