Hello everyone and welcome to the last lecture of introduction to remote sensing course and
we have seen a lot of we have throughout this course we have discussed a lot of benefits
of remote sensing But as you know that not technology or tool is universal so remote
sensing Every tool is having some limitations so the remote sensing also and one by one
we will see what are the limitations and and may be today’s in present world we we might
be considering them as a limitations but may be tomorrow we have to remove from list of
limitations of remote sensing
So but whatever the present remote sensing which is available to us we will consider
this because in past remote sensing has been oversold and some people have got impression
that remote sensing can solve each and every (shal) problems which are being faced by the
human So this is not true and this are what we are going to see Generally it is thought
that for even a small area the solution is expensive because if only 1 time analysis
is required then we have to have data and that too data generally nowadays it is in
digital form
Then we have to have some expert system who will analyze the data interpret the data make
inferences of the data So initially it is thought that if for a small areas this is
not a very cost effective solution it is expensive solution which a as I have already mentioned
that it requires a specialized training for images because everyone cannot start interpreting
images everyone cannot process the remote sensing data and therefore you require a specialist
to do this thing or you require training for people those who are already working especially
for analyzing remote sensing data
Large scale engineering maps may not be prepared from satellite data This is another limitations
and that if we are looking for very high for a small area a large scale map and where some
engineering structures are going to be umm created then probably remote sensing cannot
play measure role So that is 1 sort of limitation because as you know that one of the biggest
advantages of remote sensing is synoptic view Now what is if it’s changing People are
looking for higher and higher spatial resolution data and therefore they are looking small
areas but in much more detail much more clarity they are looking and therefore up to certain
extent this may work but (af) beyond that it starts giving some problem so large scale
engineering maps may not be prepared from satellite data
If we continue on this the 4th limitation one can identify is sometimes satellite images
are costly If repetitive coverage are required to study the dynamic features but as as we
have been discussing that the many satellite images especially from Landsat series or some
other satellites if coarser resolution satellites if you talk then more or less that data is
free For example from NOAA AVHRR that data is free from Modis data is free even from
Landsat which is a moderate resolution a special resolution data that is also free
But none the less if somebody is really looking for a a complete continuous time series data
and (re) for such analysis which requires the you know to study the dynamic features
then one has to buy the data and the when you have to buy the successive satellite images
for the same area it may be expensive especially like studying agricultural related you know
outcrop outputs or crops or disease in the plant and so and so forth For that purpose
you really require a data of certain time that may not be available free of cost and
therefore you are might be having they say you have to buy and it may be expensive as
well
Now another thing is that sometimes though remote sensing completely um records things
unbiasedly but human is human role is always always there so human slagged the most appropriate
already acquired data (especia) especially finding the resolution to calibrate the sensor
determine date of data pre decide that which date data now this may give (sa) some problem
that how the this data will be processed thus human induced errors may be introduced because
if we don’t have the complete information about that particular area
And for example as in previous discussion we were suppose working on groundwater recharge
point of view if wrongly I choose a pre monsoon and post monsoon image then I am going to
make wrong interpretation though (re) remote sensing recording is completely unbiased the
but the choosing of the data of scene or date of data is human judgment and that may for
that particular year that may be not good so one has to before one decide certain things
for chain detection or such kind of a studies for some time at least some back you know
some time series data has to be studied first for that area if we start employing for that
So this is very important to choosing a data date is chosen by the human Sometimes it is
also depending because the (sa) a moderate resolution or high resolution satellites are
not orbiting everyday of the same part of the earth so whenever it is available you
have to have and that may be little different what one is looking so that may include or
induce some errors of some biased in our interpretations
If we go further on this powerful active remote sensing systems mainly I am talking about
radar systems and which emits the (ya) EMR EMR electromagnetic radiation can be intrusive
and affect the phenomena being investigated So this radiation which is being caused by
the satellite may affect a particular phenomena so in very in a sensitive area that might
be a limitation Remote sensing instruments often become uncalibrated resulting in uncalibrated
remote sensing data
Before the launching everything was in order it was all calibrated everything got tested
and further tested but after the launch you don’t have much controls left and therefore
the calibrations if (grow) goes bad then you don’t have much choice so that is another
a big problem that though on ground it was calibrated but later on if sometimes goes
wrong it cannot be calibrated because nobody can go and hold that satellite and do the
calibrations again so that is another limitations especially from those who are operating the
satellites remote sensing satellites
Another one is the distinct phenomena can be confused if they look the same by to sensor
leading to classification errors Of course (class) when we were discussing the classification
of remote sensing data (I) this this point came that even if I employ the classification
technique or same data at training sets of the same area but 2 different dates images
I am going to have different kind of errors or different kind of accuracies on my classification
data So classification of satellite images is always there is a some issues will always
be there especially about the accuracy of the data because if I can go and check everything
on the ground then you don’t really require then remote sensing
So we we always minimize or optimize the field work or ground truthing and just employing
the remote sensing so but these things are improving further because of some other techniques
which are coming based on artificial intelligence or neural network and others which are improving
our classification of satellite data and that become very reliable Phenomena which we are
not meant to measure for the application at hand can interfere with the image and must
be counted for Anything which is there may not be useful if I am looking something which
is little different than what is present and that may create some confusion
For example a land cover classification is done then atmospheric water vapor sun versus
shadow are also present in the images These are creating problems these are reducing my
or creating errors in my classification image classification but these are already inbuilt
in the image so we have to handle these things very very carefully so this is another very
big limitations of remote sensing data that whatever is present in 1 scene within 1 satellite
scene or image one has to handle Even if one is working for groundwater it has to be handled
If it is one one working for to prepare a land use land cover map that has to be handled
very carefully
Now one more is the remote sensing science has various limitations Perhaps the greatest
limitation its utility to often oversold This I have already mentioned this point that it
is oversold People have advocated that it can solve each and every problem of the world
but this is not true and that is one of the you know high expectations people are having
and it is not always remote sensing can solve but of course indirectly or directly it can
provide certain inputs towards the (natu) problems especially related with natural features
natural disasters natural phenomena and it is not panacea that that this will provide
all information needed for conducting physical biological or a science this is not a complete
solution it’s not a universal tool
Every technique is having its own limitations and unnecessarily if one apply which may not
be applicable then you may create results which um which are going to be highly unreliable
and may create some problems in future so one has to think that whether this tool that
is remote sensing technique can really be employed in a to solve certain problem or
not If it is not then one should not go but sometime may not be directly sometimes indirectly
the information which can be gathered from remote sensing can go into to solve certain
problems as one of the inputs as layer one of layers data layers or (dat) theme to solve
a problem So this this is another limitation of remote sensing It is a what basically remote
sensing providing is spatial spectral and temporal information at few radiometric resolutions
so for everything for every place every information is not available
So you sometimes you get the high spatial resolution but you don’t get the high radiometric
resolution and may be you are getting like hyperspectral data so very high spectral resolution
but you are having relatively very small strip very small coverage of that part Now that
means what Suppose there is um you know from mineral mineral resource point of view there
might be a mineralization zone but the (a) the data hyperspectral data I am having for
a small area so that kind of analysis a reliable analysis cannot be performed so all these
all all 4 types of resolutions there is 1 desires the data at those resolution may not
be available for all the area so this we can consider as one of the limitations as well
Other technical constraints are the locations accuracy which can be solved to some extent
by employing differential GPS Google Earth data and better geometric or georeferencing
but still it may suffer from some local accuracy or location problem Image quality always a
issue though we have moved much faster and higher in terms of a spatial resolution but
still image quality remain the concern and the virtual delineation will never replace
a surveyor That use whatever we employ computer artificial intelligence neural network but
the interpretation of satellite image the analysis is always a human intensive thing
also in some countries remote sensing data is being used as in legal system as as evidence
and so that where also there are some limitations of having legal value of a satellite image
but still it can serve lot of purpose there
Legal boundaries do not always fit with the visible boundaries because the legal boundaries
are on the maps not on the ground and therefore not in the satellite images so therefore there
might be some mismatches and some problems are there There are some technical constraints
local accuracy part we have already discussed image qualities is also a big issue
So it can provide though it provides very valuable help and to save and share the information
more securely and replace traditional delineation in specific situations and when the boundary
position is not accessible this is this is another advantage that remote sensing provides
the data where human can sometimes not reach very easily So inaccessible areas you can
still have the data and boundary when the boundary is curvilinear and so one still you
have the things
Now if we start looking little bit about the future of remote sensing so we have seen the
limitation there are various limitations and some of over some of the limitations we are
going to overcome but on some limitations they might remain there For example we are
moving towards higher and higher spatial resolution data and this is what where we are moving
and where we we from where we started basically we started with multi spectral data
Especially I am talking since the inception of Landsat 1 and a MSS sensor and then we
moved to the Spot and ERS radar set IRS Landsat and Envisat and all kinds of data so we are
having multispectral data we are still having panchromatic data we are also having radar
data we are also having hyperspectral so all kinds of data as we time passing this cluster
of this cluster of different satellites as you are seeing here is becoming you know really
dense compared to what it was in the 1972 So all kinds of options are available all
kinds of data is available some of them are free some in costing but all kinds of options
are available to employ such data in remote sensing
Now there is a always discussion this whether a spatial resolution is preferable or temporal
resolution As we have been discussing on this that if you go higher and higher spatial resolution
and the temporal resolution reduces because higher resolution means the narrow swath less
ground coverage by an orbit of a satellite and therefore the repetitivity becomes poor
that means the temporal resolution reduces so it’s a tradeoff between temporal resolution
versus spatial resolution
If you want higher temporal resolution may be on daily on daily basis images like NOAA
AVHRR then you have to compromise on special resolution you have to remain in say 1 kilometer
spatial resolution but if you want to (go) go for 30 centimeter resolution then you have
to be prepared that the next data you are going to have have for the same area may be
after 28 days or so on So these are the limitations that spatial versus temporal resolution and
therefore there is a tradeoff different scenes are available different types of data is available
but still the limitation continues
Also as you can see that this is spatial resolution and temporal resolution there are choices
are there lot of systems are not available and even in our image processing becomes reliable
if the bends are located on the same (EM) same parts of EM spectrum then data from 2
satellites can also be used in tandem so that that approach is also being followed for certain
type of applications especially applications related with natural disasters and others
So may be in if we get some confusion we can always say might be the (li) limitation of
remote sensing and also this spatial resolution as as we have been discussing is improving
original advantages and though it is going against the original as a original advantage
with remote sensing work it it provides the synoptic view but if you go for higher resolution
you are covering a very small area but in 2 in good details so it’s a tradeoff
Do you want a large area to cover or do you want a small area but in detail so both options
are available that’s the good part of current remote sensing and due to some limitations
of physics or electronics though we want very spectral resolution and here spatial resolution
data simultaneously but it is not possible so that kind limitations and and are also
resolving and things are improving on these fronts as well So this brings to the end of
this particular discussion about limitations of remote sensing and a little future about
GIS future about remote sensing but also it brings to the end of this very small course
on introduction to remote sensing so thank you very much all the best thank you