ArcGIS vs ArcGIS Pro

 Overall :

Feature	ArcGIS	ArcGIS Pro
User Interface	Traditional desktop application with a ribbon-style interface	Modern ribbon-style interface with contextual tabs
3D Visualization	Basic 3D capabilities through ArcScene	Enhanced 3D visualization with integrated 3D scene views
2D Mapping	Comprehensive 2D mapping tools and functionality	Advanced 2D mapping tools with improved cartography
Geoprocessing	Geoprocessing tools available through toolbox	Updated geoprocessing tools with more options and speed
Python Integration	Python scripting available for automation and customization	Stronger Python integration with a more user-friendly environment
Web GIS Integration	Limited web GIS integration	Seamless integration with ArcGIS Online and Portal for ArcGIS
Layouts and Printing	Layouts created using ArcMap	Improved layout and printing capabilities with dynamic elements
Performance	Single-threaded processing, can be slower for large datasets	Multi-threaded processing, optimized for better performance
Collaboration	Limited collaboration features	Enhanced collaboration tools with project sharing
Licensing	License based on concurrent use	License based on named users with subscription model

Other features:

Analysis Tool	ArcGIS	ArcGIS Pro
Spatial Analysis	Offers a wide range of spatial analysis tools such as buffer, overlay, and spatial statistics	Expanded spatial analysis toolbox with additional tools and improvements
Network Analysis	Basic network analysis tools for routing, service area, and network optimization	Enhanced network analysis tools with improved performance and additional functionalities
Geostatistical Analysis	Provides basic geostatistical tools for interpolation, kriging, and spatial modeling	Improved geostatistical analysis tools with additional methods and model validation
Image Analysis	Basic image analysis tools for remote sensing and raster data processing	Enhanced image analysis capabilities with improved raster functions and deep learning tools
Terrain Analysis	Basic tools for terrain analysis such as slope, aspect, and hillshade	Advanced terrain analysis tools with better visualization and terrain processing capabilities
3D Analysis	Limited 3D analysis capabilities for surface analysis and 3D visualization	Expanded 3D analysis tools for terrain modeling, viewshed analysis, and 3D feature extraction
Time Series Analysis	Basic tools for temporal analysis and time series visualization	Enhanced 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.

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.  

Detailed step-by-step tutorial is available on this link

Click here to access tutorial 

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