Know Advance Technique of Remote Sensing and GIS in Hindi and English. We will update you about new satellite technology and data analysis methods. Like in ArcGIS and Erdas and How to use Landsat data including methods. More we will also provide Natural Universal Science secrets.Free GIS Data Download भारत की सर्वप्रथम तकनीक की जानकारी हिंदी में भी उपलब्ध
Friday, 6 August 2021
Thursday, 15 July 2021
Udemy GIS course coupons
How to learn GIS at home, then these courses are for you.
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Course Name: ArcSWAT Model with ArcGIS - Run for any Study Area - GIS
Duration: 4hr 26min
Highlights: Watershed simulation, amount of sediment reach at the outlet, ET, PET, Water Yield, Groundwater Recharge estimates, Streamflow in m3/sec, Hending weather data, Cover zero to simulation graphs. Applicable for ArcGIS 10.1 to ArcGIS 10.7
Course Name: SWAT CUP Calibration Validation and write values to ArcSWAT
Duration: 2hr 37min
Highlights: Multiple concepts of calibration and validation, Write calibrated parameter values to ArcSWAT.

Course Name: Land use Land cover classification GIS, ERDAS, ArcGIS, ENVI + Machine Learning in GIS
Duration: 6hr 22min
Highlights: A number of classification methods are covered including Machine learning. From data downloading to final results. Also covers pixel editing to improve LULC accuracy, Covers Accuracy assessment, Post modification and a number of basic and advanced tasks. Applicable for Erdas 14 to Erdas 18. All versions of ArcGIS.

Course Name: Future Land Use with GIS - TerrSet - CA Markov - ArcGIS
Duration: 4hr 4min
Highlights: How an urban are expand in future at which extended till the year 2030, 2060 or 2090. Based on past classification and factors affecting landuse incorporated in the software. Convert the theoretical concept of urban expension into relaity. Generate Video simulation of Urban growth. Also, generate a map of the future landuse with the machine learning approach. No coding is required. TerrSet and ArcGIS software required.

Course Name: Complete Basic GIS Tasks - ArcGIS - Erdas - Remote Sensing
Duration: 11hr 7min
Highlights: If you are new to GIS then this 11 hrs course cover everything in GIS from data management to publication-ready maps. Rea;l life tasks are covered. Covers GIS, GPS, Excel, Weather data handling, Advance scripts, Image mosaicking in the best way, image resolution increase, Processing of high-resolution free images of 10m, 30m resolution. 3D and much more. Applies to ArcGIS 10.1 to 10.8, Erdas 2014, 2015 and 2018.

Friday, 8 May 2020
Online GIS course 28 hrs
Tuesday, 14 April 2020
GIS course udemy Coupon - Complete Basic GIS Tasks - ArcGIS - Erdas - Remote Sensing
Complete GIS Course from scratch to Advance
Saturday, 11 April 2020
GIS Basics Video Tutorial
Sunday, 9 December 2018
Estimation of Groundwater Potential Zones using GIS and Remote Sensing
- Successfully
identify Groundwater Potential Zones
- Also
Drought prone Area analysis
- Flood
Risk map
- Working
with Rainfall Data
- Generate
Soil Map and identify Soil Properties
- Delineate
Watershed
- Drainage
Density and Drainage Maps
- Mosaic
of Digital Elevation Model
- Area
Calculation from Pixels
- Data
Interpolation
- Adding
Data from Excel to GIS Environment
- Perform
GIS data calculations
- Tool
used for Site suitability analysis
- Working
with CDF file and data Extraction, Map Generation, Calculation,
- Data
Reclassification
- Generate
Fluctuation maps to identify Good Aquifer.
- Data
Reproject
- Calculation
with Raster Data
- Data
management in GIS
- Basic
GIS
- Must
have Landuse image
- You
must have ArcGIS any Version
- GIS
Student
- GIS
Professionals
- Professors
- Scientists
- Master
Student of Remote Sensing and GIS
- PhD
Research Scholars
- Groundwater
Exploration Engineers
- Irrigation
Engineers
- Water
Resource Engineers
- Students
of WRDM
- Govt.
Policy Makers
- Ministry
of Earth Science
Groundwater Potential Zones using GIS |
Wednesday, 5 September 2018
CA Markov Method of Urban Growth using GIS Tutorial
- New Urban area wll grow at boundary of older
- Some factor depends like distance between settelments
- Fistance from road
- Trend of change from Agriculture and forest each location
- Rate of Growth,
- Power of factor of change
Tuesday, 17 April 2018
ArcSWAT Tutorial
Landuse Tutorial
ArcSWAT Tutorial
Link to Tutorials
Monday, 16 April 2018
ArcSWAT and Landuse Best Tutorial Ever
Sunday, 8 April 2018
Tuesday, 9 January 2018
landuse landcover classification step by step in GIS
Problem faced During classification:
Some of us faced problem during classification as:
- Urban area and barren land has same signature
- Dry river reflect same signature as urban area and barren land
- if you try to correct urban and get error in barren
- In Hilly area you cannot classify forest which is in the hill shade area.
This will cost you only 1280 Rs/-
Sunday, 17 December 2017
Saturday, 25 March 2017
Download High Resolution Satellite Images Free (10 meter)
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Wednesday, 15 March 2017
Tuesday, 10 January 2017
PC Configuration for Remote Sensing GIS - सुदूर संवेदन के लिए कंप्यूटर की कॉन्फ़िगरेशन
GIS का काम करने के लिए हमारे अनुसार ये कॉन्फ़िगरेशन होनी चाहिए
- RAM 32 GB अथवा अधिक (DDR4 OR DDR5)
- इंटेल का xeon processor 4Ghz
- SSD (Non Rotating Hard disk) Solid State Drive
Saturday, 7 January 2017
लैंडसैट 8 के डिजिटल नंबर को सतह के तापमान में बदलना
लैंडसैट 8 का प्रयोग करना
Conversion to TOA Radiance
OLI and TIRS band data can be converted to TOA spectral radiance using the radiance rescaling factors provided in the metadata file:
where:Lλ = TOA spectral radiance (Watts/( m2 * srad * μm))
ML = Band-specific multiplicative rescaling factor from the metadata (RADIANCE_MULT_BAND_x, where x is the band number)
AL = Band-specific additive rescaling factor from the metadata (RADIANCE_ADD_BAND_x, where x is the band number)
Qcal = Quantized and calibrated standard product pixel values (DN)
Conversion to TOA Reflectance
OLI band data can also be converted to TOA planetary reflectance using reflectance rescaling coefficients provided in the product metadata file (MTL file). The following equation is used to convert DN values to TOA reflectance for OLI data as follows:
where:ρλ' = TOA planetary reflectance, without correction for solar angle. Note that ρλ' does not contain a correction for the sun angle.
Mρ = Band-specific multiplicative rescaling factor from the metadata (REFLECTANCE_MULT_BAND_x, where x is the band number)
Aρ = Band-specific additive rescaling factor from the metadata (REFLECTANCE_ADD_BAND_x, where x is the band number)
Qcal = Quantized and calibrated standard product pixel values (DN)
TOA reflectance with a correction for the sun angle is then:
ρλ = | ρλ' | = | ρλ' |
cos(θSZ) | sin(θSE) |
For more accurate reflectance calculations, per pixel solar angles could be used instead of the scene center solar angle, but per pixel solar zenith angles are not currently provided with the Landsat 8 products.where:ρλ = TOA planetary reflectance
θSE = Local sun elevation angle. The scene center sun elevation angle in degrees is provided in the metadata (SUN_ELEVATION).
θSZ = Local solar zenith angle; θSZ = 90° - θSE
Conversion to At-Satellite Brightness Temperature
TIRS band data can be converted from spectral radiance to brightness temperature using the thermal constants provided in the metadata file:
T = | K2 | ||
ln( | K1 | +1) | |
Lλ |
where:T = At-satellite brightness temperature (K)
Lλ = TOA spectral radiance (Watts/( m2 * srad * μm))
K1 = Band-specific thermal conversion constant from the metadata (K1_CONSTANT_BAND_x, where x is the thermal band number)
K2 = Band-specific thermal conversion constant from the metadata (K2_CONSTANT_BAND_x, where x is the thermal band number)
Friday, 6 January 2017
Download India Shapefile with Kashmir and Process
Click Here to Download.
- ये डाटा डेसीमल डिग्री में है
- सर्वप्रथम ऐड करें
- फिर जियोग्राफिक प्रोजेक्शन पैर सेट करें
- इसको डाटा फ्रेम के साथ एक्सपोर्ट के के फिर से ऐड करें
- अब इसको अपने क्षेत्र की UTM Zone पे के साथ एक्सपोर्ट करें
- अब भारत का का डाटा तयार है