Saturday, August 10, 2024

🎁 Advanced Remote Sensing Techniques 🌍


All course offered from Udemy : Discounted links updated on November 2024

Course 1: Landuse Landcover with Machine Learning Using ArcGIS Only

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🔹 Highlights:

  • Master the application of machine learning in ArcGIS for land use and land cover analysis.

  • Learn advanced techniques for image classification using only ArcGIS tools.

  • Gain hands-on experience with real-world data and case studies.

Course 2: Crop Yield Estimation using Remote Sensing and GIS ArcGIS
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🔹 Highlights:

  • Discover the power of remote sensing and GIS in predicting crop yields.

  • Step-by-step guidance on data collection, processing, and analysis.

  • Real-world applications and project-based learning to enhance your skills.

Course 3: SWAT CUP Calibration Validation and Write Values to ArcSWAT
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🔹 Highlights:

  • Understand the complete process of SWAT model calibration and validation.

  • Learn how to integrate SWAT CUP with ArcSWAT for enhanced hydrological modeling.

  • Practical examples to solidify your understanding and application.

Course 4: Complete Remote Sensing and GIS - ArcGIS – ERDAS
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🔹 Highlights:

  • Comprehensive coverage of GIS and remote sensing fundamentals using ArcGIS and ERDAS.

  • Learn how to perform essential GIS tasks with ease.

  • Perfect for beginners and those looking to refresh their knowledge.

Course 5: Groundwater Potential Zones GIS - Complete Project ArcGIS
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🔹 Highlights:

  • Conduct a complete groundwater potential zone assessment using GIS.

  • Learn through a hands-on project that simulates real-world scenarios.

  • Enhance your understanding of water resource management with GIS.

Course 6: Land Use Land Cover Classification GIS, ERDAS, ArcGIS, ML
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🔹 Highlights:

  • Dive deep into land use and land cover classification using multiple GIS platforms.

  • Explore machine learning techniques integrated with GIS and remote sensing.

  • Apply your skills in ERDAS, ArcGIS, and other industry-standard tools.

Course 7: Future Land Use with GIS - TerrSet - CA Markov – ArcGIS
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🔹 Highlights:

  • Predict future land use patterns using the CA-Markov model in TerrSet.

  • Integrate results with ArcGIS for comprehensive spatial analysis.

  • Practical exercises to master future land use modeling.

Course 8: ArcSWAT Model with ArcGIS - Run for Any Study Area – GIS
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🔹 Highlights:

  • Learn how to run the ArcSWAT model for any geographic area.

  • Comprehensive coverage of model setup, data preparation, and analysis.

  • Perfect for professionals working in hydrology and water resource management.




Sunday, June 9, 2024

Landuse landcover with machine learning using ArcGIS only

Creating land use maps is a complex process often plagued by errors and low accuracy rates. For instance, barren land or riverbeds may be misclassified as urban areas. To address these errors, it typically requires the use of multiple software tools, as a single software package may not be sufficient for all processes, such as post-classification pixel corrections. Often, other software is used alongside ArcGIS to correct these misclassifications because ArcGIS lacks a dedicated tool for pixel correction.

However, in this course, we have successfully managed to perform all tasks using ArcGIS alone. Land use classification is accomplished using machine learning techniques, specifically the Support Vector Machine (SVM) and Random Forest methods, through supervised training. Even post-classification pixel correction is handled within ArcGIS using specific strategies.

If you prefer to rely solely on ArcGIS without using any additional software, this course is designed for you. The first few videos are free. Here is the link.

Link to course: https://www.udemy.com/course/landuse-landcover-with-machine-learning-using-arcgis-only/?couponCode=AILANDUSE

Things that covered
  • Land use mapping
  • Land cover classification
  • ArcGIS machine learning
  • SVM land use classification
  • Random Forest land use mapping
  • Post-classification pixel correction
  • ArcGIS pixel correction
  • Supervised training ArcGIS
  • Land use errors correction
  • Urban area misclassification corrections
  • ArcGIS land use course
  • Barren land classification corrections 
  • Riverbed misclassification corrections
  • Single software land use mapping
  • ArcGIS only land use mapping
  • High accuracy land cover mapping
  • Machine learning in ArcGIS
  • Land use mapping techniques
  • Land cover classification errors corrections 
  • Saturday, May 25, 2024

    How to calculate feels like temperature

    Let’s consider a wind speed of 11km/hr., actual temperature of 30 degrees C, and humidity of 79%. Now we calculate feels like temperature as follows:


    Feels Like Temperature Calculation

    Feels Like Temperature Calculation

    To calculate the "feels like" temperature, we can use the Heat Index formula. The Heat Index (HI) is a measure of how hot it really feels when relative humidity is factored in with the actual air temperature. The formula for calculating the Heat Index (HI) is:

    HI = c1 + c2T + c3R + c4TR + c5T2 + c6R2 + c7T2R + c8TR2 + c9T2R2

    Where:

    • T is the temperature in degrees Fahrenheit.
    • R is the relative humidity as a percentage.
    • The constants are:
      • c1 = -42.379
      • c2 = 2.04901523
      • c3 = 10.14333127
      • c4 = -0.22475541
      • c5 = -6.83783 × 10-3
      • c6 = -5.481717 × 10-2
      • c7 = 1.22874 × 10-3
      • c8 = 8.5282 × 10-4
      • c9 = -1.99 × 10-6

    First, we need to convert the temperature from Celsius to Fahrenheit:

    T(°F) = T(°C) × (9/5) + 32

    T(°F) = 30 × (9/5) + 32

    T(°F) = 86

    Next, we can plug the values into the Heat Index formula:

    HI = -42.379 + 2.04901523 × 86 + 10.14333127 × 79 + (-0.22475541 × 86 × 79)
    + (-0.00683783 × 862) + (-0.05481717 × 792)
    + (0.00122874 × 862 × 79) + (0.00085282 × 86 × 792)
    + (-0.00000199 × 862 × 792)

    HI ≈ 99.3

    Converting this back to Celsius:

    HI(°C) = (99.3 - 32) × (5/9) ≈ 37.4

    So, the "feels like" temperature is about 37.4°C.

    Calculations Summary:

    1. Convert temperature from Celsius to Fahrenheit:
    2. T(°F) = 30 × (9/5) + 32 = 86

    3. Plug values into the Heat Index formula:
    4. HI = -42.379 + 2.04901523 × 86 + 10.14333127 × 79 + (-0.22475541 × 86 × 79)
      + (-0.00683783 × 862) + (-0.05481717 × 792)
      + (0.00122874 × 862 × 79) + (0.00085282 × 86 × 792)
      + (-0.00000199 × 862 × 792)

      HI ≈ 99.3

    5. Convert Heat Index back to Celsius:
    6. HI(°C) = (99.3 - 32) × (5/9) ≈ 37.4

    Therefore, the "feels like" temperature is 37.4°C.

    Tuesday, April 30, 2024

    Research level GIS Courses: Coupon Applied Udemy

    1. Crop Yield Estimation using Remote Sensing and GIS ArcGIS Enhance your proficiency in estimating crop yields using cutting-edge techniques in remote sensing and Geographic Information Systems (GIS) with a focus on ArcGIS. This course delves deep into the methodologies and tools essential for accurate crop yield estimation, equipping you with the skills needed to address contemporary agricultural challenges. By leveraging remote sensing data and GIS analysis within the ArcGIS platform, Enroll now to unlock the potential of remote sensing and GIS in revolutionizing crop yield estimation methodologies.

    Link to course : https://www.udemy.com/course/crop-yield-estimation-using-remote-sensing-and-gis-arcgis/?couponCode=CLIMATECHANGE01

    1. SWAT CUP Calibration Validation and write values to ArcSWAT Master the intricacies of SWAT (Soil and Water Assessment Tool) calibration and validation techniques through this comprehensive course. Explore how to effectively calibrate and validate SWAT models, ensuring their accuracy and reliability in simulating hydrological processes. Additionally, learn how to seamlessly integrate these calibrated values into ArcSWAT, empowering you to conduct sophisticated hydrological analyses with ease. Whether you're a seasoned GIS professional or a newcomer to hydrological modeling, this course offers invaluable insights to enhance your skills in watershed management and water resources assessment. Enroll today and elevate your proficiency in SWAT modeling and ArcSWAT integration.

    Link to course : https://www.udemy.com/course/swat-cup-calibration-validation-and-write-values-to-arcswat/?couponCode=CLIMATECGANGE1

    1. Complete Remote Sensing and GIS - ArcGIS - Erdas Embark on a journey to mastery in remote sensing and GIS with a focus on industry-leading software like ArcGIS and ERDAS Imagine. This comprehensive course covers fundamental concepts as well as advanced techniques essential for conducting diverse geospatial analyses. From image interpretation to spatial data manipulation, you'll develop a robust skill set that empowers you to tackle real-world challenges across various domains. Whether you're a student, researcher, or GIS professional, this course provides the knowledge and hands-on experience needed to excel in the field of geospatial science. Enroll now to unlock the full potential of remote sensing and GIS technologies.

    Link to course : https://www.udemy.com/course/complete-basic-gis-tasks-arcgis-erdas-remote-sensing/?couponCode=CLIMATECHANGE1

    1. Groundwater Potential Zones GIS - Complete Project ArcGIS Dive into the fascinating realm of groundwater potential mapping using Geographic Information Systems (GIS) and ArcGIS. This course offers a comprehensive guide to assessing and delineating groundwater potential zones, crucial for sustainable water resource management. Learn advanced GIS techniques for analyzing geological, hydrological, and environmental data to identify areas with high groundwater potential. With hands-on projects and practical exercises, you'll gain the skills and confidence to undertake groundwater assessments and inform decision-making processes. Whether you're a hydrogeologist, environmental scientist, or GIS specialist, this course equips you with the tools to make informed decisions regarding groundwater resource utilization. Enroll today and unlock the power of GIS in groundwater studies.

    Link to course : https://www.udemy.com/course/groundwater-potential-zones-using-gis-full-project-arcgis-tutorial/?couponCode=CLIMATECHANGE1

    1. Land use Land cover classification GIS, ERDAS, ArcGIS, ML Elevate your expertise in land use and land cover classification using a blend of GIS, ERDAS, ArcGIS, and machine learning techniques. This course provides a comprehensive overview of the methodologies and tools essential for accurate land cover mapping and classification. Explore advanced image processing techniques, including object-based classification and machine learning algorithms, to extract meaningful information from satellite imagery. Whether you're involved in environmental monitoring, urban planning, or natural resource management, this course equips you with the skills to generate detailed land use maps and analyze land cover dynamics. Enroll now to unlock the full potential of GIS and remote sensing in land cover analysis.

    Link to course: https://www.udemy.com/course/land-use-land-cover-classification-gis-erdas-arcgis-envi/?couponCode=CLIMATECHANGE1

    1. Future Land Use with GIS - TerrSet - CA Markov – ArcGIS Gain insights into future land use dynamics and scenarios using cutting-edge GIS technologies like TerrSet, CA Markov, and ArcGIS. This course explores advanced modeling techniques for projecting future land use changes and assessing their implications on the environment and society, land cover information, and spatial modeling tools to develop robust land use scenarios and forecasts. Whether you're involved in urban planning, environmental conservation, or policy development, this course offers invaluable insights into anticipating and managing future land use challenges. Enroll today to unlock the power of GIS in shaping sustainable land use policies and strategies.

    Link to course: https://www.udemy.com/course/prediction-of-future-land-use-remote-sensing-and-gis-terrset/?couponCode=CLIMATECHANGE1

    1. ArcSWAT Model with ArcGIS - Run for any Study Area - GIS Master the ArcSWAT modeling framework and unleash its potential for watershed management and hydrological modeling. This course provides a step-by-step guide to setting up and running ArcSWAT models for any study area, allowing you to simulate hydrological processes and assess water resource dynamics with precision. Explore advanced GIS techniques for model calibration, validation, and scenario analysis, enabling you to make informed decisions for sustainable water resource management. Whether you're a hydrologist, environmental engineer, or GIS professional, this course equips you with the skills to leverage ArcSWAT for tackling complex water resource challenges. Enroll now and harness the power of ArcSWAT in your hydrological modeling endeavors.

    Link to course: https://www.udemy.com/course/running-arcswat-model-with-arcgis-for-any-study-area-gis/?couponCode=CLIMATECHANGE1

    Tuesday, April 23, 2024

    ArcGIS vs ArcGIS Pro

     Overall :

    FeatureArcGISArcGIS Pro
    User InterfaceTraditional desktop application with a ribbon-style interfaceModern ribbon-style interface with contextual tabs
    3D VisualizationBasic 3D capabilities through ArcSceneEnhanced 3D visualization with integrated 3D scene views
    2D MappingComprehensive 2D mapping tools and functionalityAdvanced 2D mapping tools with improved cartography
    GeoprocessingGeoprocessing tools available through toolboxUpdated geoprocessing tools with more options and speed
    Python IntegrationPython scripting available for automation and customizationStronger Python integration with a more user-friendly environment
    Web GIS IntegrationLimited web GIS integrationSeamless integration with ArcGIS Online and Portal for ArcGIS
    Layouts and PrintingLayouts created using ArcMapImproved layout and printing capabilities with dynamic elements
    PerformanceSingle-threaded processing, can be slower for large datasetsMulti-threaded processing, optimized for better performance
    CollaborationLimited collaboration featuresEnhanced collaboration tools with project sharing
    LicensingLicense based on concurrent useLicense based on named users with subscription model


    Other features:
    Analysis ToolArcGISArcGIS Pro
    Spatial AnalysisOffers a wide range of spatial analysis tools such as buffer, overlay, and spatial statisticsExpanded spatial analysis toolbox with additional tools and improvements
    Network AnalysisBasic network analysis tools for routing, service area, and network optimizationEnhanced network analysis tools with improved performance and additional functionalities
    Geostatistical AnalysisProvides basic geostatistical tools for interpolation, kriging, and spatial modelingImproved geostatistical analysis tools with additional methods and model validation
    Image AnalysisBasic image analysis tools for remote sensing and raster data processingEnhanced image analysis capabilities with improved raster functions and deep learning tools
    Terrain AnalysisBasic tools for terrain analysis such as slope, aspect, and hillshadeAdvanced terrain analysis tools with better visualization and terrain processing capabilities
    3D AnalysisLimited 3D analysis capabilities for surface analysis and 3D visualizationExpanded 3D analysis tools for terrain modeling, viewshed analysis, and 3D feature extraction
    Time Series AnalysisBasic tools for temporal analysis and time series visualizationEnhanced time series analysis capabilities with better temporal aggregation and trend analysis

    Difference between wind speed and wind gusts in weather

     Wind speed and wind gusts are both measurements related to the movement of air, but they represent slightly different aspects of wind behavior:

    Wind Speed: Wind speed refers to the average speed of the wind over a specific period of time, usually measured over intervals such as minutes or hours. It is a continuous measurement that indicates how fast the air is moving in a particular direction at a given moment. Wind speed is typically reported in units such as miles per hour (mph), kilometers per hour (km/h), or knots (nautical miles per hour).


      Wind Gusts: Wind gusts, on the other hand, represent sudden increases in wind speed above the prevailing or average wind speed. Gusts are temporary bursts of stronger wind that occur over short periods, usually lasting only a few seconds to a minute. These sudden increases in wind speed can be caused by various factors, such as atmospheric disturbances, passing weather fronts, or local terrain features. Wind gusts are often measured alongside average wind speed and are reported as peak wind speeds reached during a particular time frame, such as within the past hour.


    In summary, while wind speed represents the average velocity of the wind over a given time period, wind gusts indicate short-lived bursts of stronger wind speed above the prevailing conditions. Both measurements are important for understanding and forecasting weather conditions, especially in terms of their impact on activities such as sailing, aviation, and outdoor events.

    Friday, January 27, 2023

    Crop yield estimation using remote sensing and GIS Tutorial

    Crop yield estimation is a crucial aspect of agricultural management and planning. Accurate and timely yield estimates can help farmers make informed decisions about planting, fertilization, irrigation, and harvest timing. Remote sensing and geographic information systems (GIS) are powerful tools that can be used to estimate crop yields with a high degree of accuracy. One of the most commonly used indices in remote sensing for crop yield estimation is the normalized difference vegetation index (NDVI).  we will explore the use of NDVI in conjunction with remote sensing and GIS to estimate crop yields.
    Remote sensing is the process of collecting and analyzing data about the earth's surface using sensors mounted on aircraft or satellites. These sensors can detect and measure various characteristics of the earth's surface, such as temperature, humidity, and vegetation cover. NDVI is a commonly used index in remote sensing that measures the amount of vegetation cover in an area. NDVI is calculated by taking the difference between the near-infrared and red bands of a multispectral image and dividing that difference by the sum of the near-infrared and red bands. NDVI values range from -1 to 1, with higher values indicating more vegetation cover.
    GIS is a collection of software and data that can be used to analyze, visualize, and manage geographic data. GIS can be used to analyze remote sensing data, such as NDVI images, to estimate crop yields.By developing a regression model on NDVI and crop yield. GIS can also be used to create maps that show crop yields across an entire region, making it easier to identify areas that may need additional resources or attention.
    ArcGIS is a popular GIS software that can be used to analyze remote sensing data and estimate crop yields. It can be used to process NDVI images and create maps that show crop yields across an entire region. The software also has a wide range of tools that can be used to analyze and manipulate spatial data, making it an ideal tool for crop yield estimation.
    Model development required a number of tools and logic to use together.  



    Tutorial Highlights :

    1. Use Machine learning method for crop classification in ArcGIS, separate crops from natural vegetation

    2. The model was developed using the minimum observed data available online

    3. Crop NDVI separation

    4. Crop Yield model development

    5. Crop production calculation from GIS model data

    6. Identify the low and high-yield zones and area calculation

    7. Calculate the total production of the region

    8. Validation of developed model on another study area

    9. Validate production and yield of other areas using a developed model of another area

    10. Convert the model to the ArcGIS toolbox




    Saturday, December 31, 2022

    GIS Udemy Tutorial Coupon codes 2023

    ArcSWAT: Covers hydrological simulations and weather data preparation using R; use it for any research area, and scripts are available for download.

    Link: https://www.udemy.com/course/running-arcswat-model-with-arcgis-for-any-study-area-gis/?couponCode=HAPPYNEWYEAR2023

    SWAT CUP: The simulated flow may not always completely match the observed flow. In this scenario, we must establish hydrological parameters. SWATCUP handles it automatically and recommends the best match settings.

    Link: https://www.udemy.com/course/swat-cup-calibration-validation-and-write-values-to-arcswat/?couponCode=HAPPYNEWYEAR2023

     

    GROUNDWATER: This course covers the use of GIS for groundwater investigation using theoretical parameters. Create a live project from scratch.

    Link: https://www.udemy.com/course/groundwater-potential-zones-using-gis-full-project-arcgis-tutorial/?couponCode=HAPPYNEWYEAR2023

     

    LANDUSE LANDCOVER: Covers landuse classification of high-resolution data. You will learn how to correct error pixels such as urban and barren land, or agriculture and natural vegetation. It covers most classification methods, such as machine learning, supervised, unsupervised, and pixel-level recoding of challenging images.

    Link: https://www.udemy.com/course/land-use-land-cover-classification-gis-erdas-arcgis-envi/?couponCode=HAPPYNEWYEAR2023

     

    FUTURE LANDUSE: Remote sensing has impressive capabilities to generate future landuse classified image of  2090, even if it is not captured in the present; see machine learning in action. No coding is required.

    Link: https://www.udemy.com/course/prediction-of-future-land-use-remote-sensing-and-gis-terrset/?couponCode=HAPPYNEWYEAR2023

     

    BASIC GIS: This course covers more than a university lab class if you are new to GIS. This course will teach you about 3D, data analysis, and satellite image processing. 11hrs of hands-on videos of practicals.

    Link: https://www.udemy.com/course/complete-basic-gis-tasks-arcgis-erdas-remote-sensing/?couponCode=HAPPYNEWYEAR2023