Tuesday, 18 June 2013

Paddy Growth Changes Viewing from Satellite Images

Paddy Remote Sensing changes
Chlorophyll Changes on Satellite images


For more information education or Business queries contact me at arun.balla@gmail.com

Thursday, 13 June 2013

Tree Canopy Mapping

Tree Canopy Mapping using Remote Sensing Technique

For more information education or Business queries contact me at arun.balla@gmail.com

Landuse Landcover - Remote Sensing 1

Landuse Landcover classification using Remote Sensing Techniques

Medium Resolution satellite image Classification LU/LC



For more information education or Business queries contact me at arun.balla@gmail.com

Monday, 10 June 2013

Remote Sensing & GIS Integrated Approach For Real Time Crop Status





For more information education or Business queries contact me at arun.balla@gmail.com

Paddy - Remote Sensing

Paddy Field Gaining Biomass Viewing from Satellite images

Conversion Moisture field to Biomass field in crop season


For more information education or Business queries contact me at arun.balla@gmail.com





Tuesday, 7 May 2013

Crop Yield Estimation



Crop Yield Estimation
The sampling design generally adopted for the crop estimation surveys is one of the stratified multi stage random sampling with thesis as a strata and village within stratum as first stage unit of sampling. Field at each selected villages is sampling unit at second stage and experimental plot of a specified size and shape is the ultimate unit of the sampling.

An estimate can be made at any time. The closer to the harvest date, the more accurate the prediction. The advance estimates which are released in the month of September are related to Kharif crops, which are based on the field observations.

The second forecast, which covers both Kharif and Rabi and released in January by taking into account additional information obtained from various sources including agriculture inputs, incidence of pest and disease, various government reports, conditions of standing crops etc.

The third forecast, which is made in the month of March, the estimates of Kharif and Rabi are revised based on the information received from Market Intelligence sources, Weather Reports etc.

The final comparative report is provided based on the actual figures received from the government/authorized sources.   

Sampling Technique

In making an estimate, the objective is to obtain a 'representative' sample of the block. It is important to select samples randomly (without any human bias) throughout the block. The number of samples required will depend firstly, on the variability within the block. The greater the variability, the higher the number of samples required.
The other factor determining the number of samples is the degree of accuracy required. The more samples taken, the higher the accuracy will be of the estimate. However, there comes a point where the additional time invested will only increase accuracy marginally.

Statistical techniques to calculate the minimum number of samples needed are based on block variability, and provide an estimate within a certain level of confidence (Dunn and Martin 1998). This formula is used:
n = tx CV/ PE2
  • n is the number of samples needed
  • t is the statistical confidence level (t=1: 70% confidence; t=2: 95% confidence; t=3: 99% confidence)
  • CV is the coefficient of variation of the block (= ratio of the standard deviation to the average expressed as a percentage)
    • To calculate CV, take the standard deviation, divide it by the average, and times by 100. Excel can be used to calculate the average (AVG function) and standard deviation (STDEV function).
    • For example, where the CV is 30%, at a confidence level of 95% (t=2) to determine an average within a 10% error, you need to collect 36 samples per block.
    • An initial, random sample of 10 to 20 vines can be used to determine the variability CV. The time spent on any estimate (and thereby the cost) should always match the degree of accuracy required for the estimate.
PE is the acceptable percent of error either side of the average

GIS in Agriculture


žGeographical Information Systems (GIS)
Advantages
Storage and retrieval of data
Running any kind of query
Statistics generation at different Administrative Boundary level
Diagnostic analysis of system performance
Application Areas
Management and Planning


For more information education or Business queries contact me at arun.balla@gmail.com

Agricultural Information System - Future Requirements


žCreation of cadastral level information system
Field information
Site suitability studies
Soil, salinity, pH
Canal, distributaries network, lakes, ponds
Historical rainfall
DEM
General landuse-landcover
žSystem for on-line monitoring of crops
Satellite remote sensing
Crop acreage
Crop health
Yield & production
NDVI values
Climatic parameters
Temperature
Rainfall
Evapo-transpiration
Total precipitable water data
Agricultural practices
Additional info: fertilizers, seeds, etc


For more information education or Business queries contact me at arun.balla@gmail.com

Agriculture Information Systems


žMarket related database
Creation of field-level crop inventory
Location of different Grain Market
Market wise info on major agricultural commodities
Timely forecast of production
Demand and supply of commodities
Transportation network
Shortest route from crop field to Grain Market
Information on current price

Remote Sensing Agriculture - Complete Flow Chart

Acreage Estimation

Crop Health Monitoring


For more information education or Business queries contact me at arun.balla@gmail.com

Tuesday, 30 April 2013

Image Fusion - Satellite Images Merging




 Pan merges:
Merging of multi-sensor image data has become a widely used procedure because of the complementary nature of various data sets. High spatial resolution is necessary for an accurate description of shapes, features and structures, whereas high spectral resolution is better used for land cover classification. Hence merging of two types of data, to get multi-spectral images with high spatial resolution, is beneficial for various applications like vegetation, land-use, precision farming and urban studies. Various techniques are available for merging of multi-sensor image data. Ideally, the method used to merge data sets with high-spatial resolution and high-spectral resolution should not distort the spectral characteristics of the spectral high spectral resolution data, particularly with respect to digital classification accuracy. These merging techniques will enhance the quality as well as spatial resolution of the data. It will increase the interpretability of the images which will result in better classification.

Possible combinations:


ASTER+PAN merge
ASTER is one of the five state-of-the-art instrument sensor systems on-board Terra a satellite launched in December 1999. It was built by a consortium of Japanese government, industry, and research groups. ASTER monitors cloud cover, glaciers, land temperature, land use, natural disasters, sea ice, snow cover and vegetation patterns at a spatial resolution of 90 to 15 meters. The multispectral images obtained from this sensor have 14 different colors, which allow scientists to interpret wavelengths that cannot be seen by the human eye, such as near infrared, short wave infrared and thermal infrared.

ASTER is the high spatial resolution instrument on Terra that is important for change detection, calibration and/or validation, and land surface studies. ASTER data is expected to contribute to a wide array of global change-related application areas, including vegetation and ecosystem dynamics, hazard monitoring, geology and soils, land surface climatology, hydrology, land cover change, and the generation of digital elevation models (DEMs). Satellite Imaging Corporation (SIC) is an official distributor for ASTER Imagery through USGS.

ASTER Satellite System: Sensor Characteristics:
Launch Date
      18 December 1999 at Vandenberg Air Force Base, California, USA
Equator Crossing
     10:30 AM (north to south)
Orbit
     705 km altitude, sun synchronous
Orbit Inclination
     98.3 degrees from the equator
Orbit Period
     98.88 minutes
Grounding Track Repeat Cycle
     16 days
Resolution
      15 to 90 meters

ASTER high-resolution sensor is capable of producing stereoscopic (three-dimensional) images and detailed terrain height models. Other key features of ASTER are:
  • Multispectral thermal infrared data of high spatial resolution
  • Highest spatial resolution surface spectral reflectance, temperature, and emissivity data within the Terra instrument suite
  • Capability to schedule on-demand data acquisition requests
ASTER has 14 bands of information. For more information, please see the following table

Instrument
VNIR
SWIR
TIR
Bands
1-3
4-9
10-14
Spatial Resolution
15m
30m
90m
Swath Width
60km
60km
60km
Cross Track Pointing
± 318km (± 24 deg)
± 116km (± 8.55 deg)
± 116km (± 8.55 deg)
Quantisation (bits)
8
8
12



LISS-III+PAN
IRS LISS-III data are well suited for agricultural and forestry monitoring tasks. Because of their simultaneous acquisition with IRS PAN data and the availability of a synthetic blue band, LISS-III data are ideal for colouring IRS PAN products.

 



IRS – 1C: Sensor Characteristics:


The Indian Remote Sensing Satellite IRS-1C was successfully launched into polar orbit on December 28, 1995 by a Russian launch vehicle. Its sensors were activated in the first week of January 1996.
IRS-1C
Parameters
PAN
Bands
LISS-III
Spatial Resolution
5.8 m
Band 2 (green)
23 m
Band 3 (red)
23 m
Band 4 (NIR)
23 m
Band 5 (SWIR)
70 m
Swath-width
63 - 70 km
all Bands
127 - 141 km
Spectral Coverage
500 - 750 nm
Band 2 (green)
520-590 nm
Band 3 (red)
620-680 nm
Band 4 (NIR)
770-860 nm
Band 5 (SWIR)
1550-1700 nm

 

IRS – 1D: Sensor Characteristics

 

The Indian Remote Sensing Satellite IRS-1D was successfully launched into polar orbit on September 29, 1997 by a PSLV launch vehicle. Its sensors were activated in the middle of October 1997.

IRS-1D
Parameters
PAN
Bands
LISS-III
Spatial Resolution
5.8 m
Band 2 (green)
23 m
Band 3 (red)
23 m
Band 4 (NIR)
23 m
Band 5 (SWIR)
70 m
Swath-width
63 - 70 km
all Bands
127 - 141 km
Spectral Coverage
500 - 750 nm
Band 2 (green)
520-590 nm
Band 3 (red)
620-680 nm
Band 4 (NIR)
770-860 nm
Band 5 (SWIR)
1550-1700 nm



LISS-IV+PAN
The LISS-IV camera is a high resolution multi-spectral camera operating in three spectral bands (B2, B3, B4). LISS-IV can be operated in either of the two modes. In the multi-spectral mode (Mx),  a swath of 23 Km (selectable out of 70 Km total swath) is covered in  three bands, while in mono mode (Mono), the full swath of 70 Km can be  ... covered in any one single band, which is selectable by ground command (nominal is B3 - Red band). The LISS-IV camera can be tilted up to  +-26 degrees Celsius in the across track direction thereby providing a  revisit period of 5 days.  The Data products are categorized as Standard and have a system level   accuracy.
LISS-IV Standard Products comprise Path/Row Based products, Shift  Along Track product, Georeferenced products and Basic Stereo products. Path/Row Based products are generated based on the referencing scheme of each sensor. Shift Along Track applies to those  products covering a user's area of interest which falls in between two  successive scenes of the same path, then the data can be supplied by  sliding the scene in the along track direction. Georeferenced products are true north oriented products. These products are supplied on digital media only. Basic Stereo products comprise pairs of two images  of the same area, acquired on different dates and from different  angles. One of the parameters from which the quality of a stereo pair can be judged is the base/height (B/H) ratio. B/H ratio is the ratio of distance between two satellite passes and satellite  altitude. Stereo products are available from LISS-IV Mono mode only. The inputs required, in addition to path/row details is B/H  ratio. Two scenes selected on two different dates, satisfying the  user's B/H ratio are supplied as a stereo pair. The data is only radiometrically corrected and are supplied on digital media.



IRS-P6 Resourcesat-1
Parameters
Bands
LISS-IV
LISS-III
Mono Mode
MX Mode
Spatial Resolution
Band 2 (green)
5.8 m
5.8 m
23.5 m
Band 3 (red)
5.8 m
23.5 m
Band 4 (NIR)
5.8 m
23.5 m
Band 5 (SWIR)

23.5 m
Swath-width
all Bands
70 km
23.9 km
140 km
Spectral Coverage
Band 2 (green)
620-680 nm
520-590 nm
520-590 nm
Band 3 (red)
620-680 nm
620-680 nm
Band 4 (NIR)
770-860 nm
770-860 nm
Band 5 (SWIR)

1550-1700 nm
 

For more information education or Business queries contact me at arun.balla@gmail.com

Popular Posts