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REMOTE SENSED DATA

L.D. COLLEGE OF

ENGINEERING

Dhaval A. Jalalpara

What is remote Sensing?

Remote sensing means collecting data from aremote location without coming in contact with theobject.

But present day remote sensing meanstechnology where images or photographs aretaken by sensors mounted on satellite transmittedto ground station , where images are interpretedfor creating maps or GIS databases for variety offield applications.

What is Remotely Sensed Data?

Remotely gathered data is available from a rangeof sources and data collection techniques and is oftenthe only type od data that is not always easily foundwithin the public domain.

This is largely due to the fact that most of this datais required by equipment that is expensive to build andmaintain.

However, there are many types of basic imagenyof high-quality that are readily available at largelysubsidized costs, particularly within the United States.

Mangrove forest distributions and dynamics

(1975–2005) of the tsunami-affected region of

Asia

AIM :-

We aimed to estimate the present extent of tsunami-

affected mangrove forests and determine the rates and causes

of deforestation from 1975 to 2005.

LOCATION :-

Our study region covers the tsunami-affected coastal

areas of Indonesia, Malaysia, Thailand, Burma (Myanmar),

Bangladesh, India and Sri Lanka in Asia.

METHOS :-

We interpreted time-series Landsat data using a hybrid supervised and

unsupervised classification approach.

Landsat data were geometrically corrected to an accuracy of plus-or-

minus half a pixel, an accuracy necessary for change analysis. Each image

was normalized for solar irradiance by converting digital number values to the

top-of-the atmosphere reflectance.

Ground truth data and existing maps and data bases were used to

select training samples and also for iterative labelling. We used a post-

classification change detection approach.

Results were validated with the help of local experts and/or high-

resolution commercial satellite data.

RESULTS :-

The region lost 12% of its mangrove forests from 1975 to 2005, to a

present extent of c. 1,670,000 ha. Rates and causes of deforestation varied

both spatially and temporally.

Annual deforestation was highest in Burma (c. 1%) and lowest in Sri

Lanka (0.1%). In contrast, mangrove forests in India and Bangladesh

remained unchanged or gained a small percentage.

Net deforestation peaked at 137,000 ha during 1990–2000, increasing

from 97,000 ha during 1975–90, and declining to 14,000 ha during 2000–05.

The major causes of deforestation were agricultural expansion (81%),

aquaculture (12%) and urban development (2%).

MAIN CONCUSION :-

We assessed and monitored mangrove forests in the tsunami-affected

region of Asia using the historical archive of Landsat data.

We also measured the rates of change and determined possible

causes.

The results of our study can be used to better understand the role of

mangrove forests in saving lives and property from natural disasters such as

the Indian Ocean tsunami, and to identify possible areas for conservation,

restoration and rehabilitation.

Remotely sensed temperature and

precipitation data improve species

distribution modelling in the tropics

AIM :-

Species distribution modelling typically relies completelyor partially on climatic variables as predictors, overlooking thefact that these are themselves predictions with associateduncertainties. This is particularly critical when such predictorsare interpolated between sparse station data, such as in thetropics.

LOCATION :-

Rain forests areas of Central Africa, the Western Ghats ofIndia and South America.

METHOD :-

We compared models calibrated on the widely used WorldClim station-

interpolated climatic data with models where either temperature or precipitation

data from WorldClim were replaced by data from CRU, MODIS, TRMM and

CHIRPS. Each predictor set was used to model 451 plant species distribution.

RESULT :-

Fewer than half of the studied rain forest species distributions matched the

climatic pattern better than did random distributions. The inclusion of MODIS

temperature and CHIRPS precipitation estimates derived from remote sensing

each allowed for a better than random fit for respectively 40% and 22% more

species than models calibrated on WorldClim. Furthermore, their inclusion was

positively related to a better transferability of models to novel regions.

REFERENCE :-

WWW.ONLINELIBRARY.WILEY.COM