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This guide has been crafted to facilitate basic training in Geographic Information Systems (GIS), offering a swift introduction to QGIS, the foundational software upon which other applications and plugins relevant to Sustainable Land Management and Land Degradation Mapping operate, such as Trends.Earth by Conservation International and the Watershed Tool by WOCAT. The document is designed to impart fundamental knowledge of this computational tool's applications, enabling users to address georeferenced information management challenges and create maps effectively. It provides comprehensive, step-by-step explanations of the practices conducted during the training, serving as a valuable reference material. Moreover, individuals keen on self-directed learning in GIS can also utilize this manual. Further practical and theoretical guidance, including sample data and examples, can be accessed via the QGIS page.

This guide was developed for a basic training in Geographic Information Systems (GIS), to serve as a quick introduction to QGIS, which is the base software on which other applications and plugins, relevant to Sustainable Land management and Land Degradation Mapping, run, i.e: Trends.Earth (Conservation International) and Watershed Tool (WOCAT). The document aims to convey basic applications offered by this computer tool to produce practical knowledge, solve problems of georeferenced information management and creation of maps. This manual has step-by-step explanations of the practices carried out in the training to serve as a reference material. However, it can also be used by those who want to venture into GIS (self-taught). Please see the QGIS page for further practical and theoretical guidance with sample data and examples:

📢🌍📚 Registration is now open for a private 15-days of complete online training on Google Earth Engine for Advanced Remote Sensing Analysis 🌱 Are you passionate about remote sensing and GIS applications? Do you want to enhance your skills and knowledge in this cutting-edge field? We are thrilled to announce our upcoming Private Online Training Program, designed to equip you with the latest tools and techniques for analyzing and monitoring our changing environment. 🌍 Course Contents: 1) RUSLE Model - Understand erosion risk and soil loss assessment using this powerful model. 2) NDVI Change Detection - Learn how to detect changes in vegetation over time using NDVI. 3) Monthly, Annual, and Seasonal Rainfall Analysis - Analyze rainfall patterns and trends at different temporal scales. 4) Land Use and Land Cover Classification using Machine Learning - Master the art of classifying land cover using machine learning algorithms. 5) LST and NDVI Time Series Chart for Various Land Use and Land Cover Classes - Visualize and interpret the relationship between land use, temperature, and vegetation health. 6) Satellite-Based Aridity Index to Monitor Drought Conditions - Monitor and assess drought using satellite-derived data. 7) LST Monitoring using Landsat Imagery - Utilize Landsat data to monitor land surface temperature changes. 8) Linear Regression Model and Trend Analysis Time Series Chart - Employ linear regression for trend analysis over time. 9) Analyzing Long-term Trend of NDVI by Mann Kendall’s Test - Use statistical analysis to detect trends in NDVI data. 10) Monitoring VCI, TCI, VHI - Understand and analyze vegetation health, thermal condition, and overall vegetation condition indices. 11) Detecting Deforestation using Machine Learning - Learn how to identify deforestation using machine learning techniques. 12) Classification Slope in GEE and Calculated Area - Classify slope and calculate areas using Google Earth Engine. 13) Classification of NDVI and Calculated Area - Classify NDVI data and calculate corresponding areas. 14) Extraction of Raster Values from Point Samples in Excel File - Extract and analyze raster values from point samples in Excel. 15) Monitoring Bioclimatic Variables - Analyze bioclimatic variables to understand their impact on ecosystems. 16) How to Monitor Monthly Evapotranspiration - Monitor the water cycle through evapotranspiration analysis. 17) Monitoring Soil Salinity Index - Assess soil salinity using remote sensing data. 18) Landslide Susceptibility using GEE+ArcGIS - Use GEE and ArcGIS to assess landslide susceptibility. 19) Flood Mapping using Sentinel-1 SAR Data and Calculated Flooded Area - Utilize SAR data for flood mapping and area estimation. 20) Trend Analysis of NDVI - Continue your trend analysis journey, focusing on NDVI. #RemoteSensing #GIS #EarthObservation #EnvironmentalMonitoring #TrainingProgram #OnlineEducation #SatelliteImagery #DataAnalysis #SkillDevelopment

📢🌍📚 Registration is now open for a private 15-days of complete online training on Google Earth Engine for Advanced Remote Sensing Analysis 🌱 Are you passionate about remote sensing and GIS applications? Do you want to enhance your skills and knowledge in this cutting-edge field? We are thrilled to announce our upcoming Private Online Training Program, designed to equip you with the latest tools and techniques for analyzing and monitoring our changing environment. 🌍 Course Contents: 1) RUSLE Model - Understand erosion risk and soil loss assessment using this powerful model. 2) NDVI Change Detection - Learn how to detect changes in vegetation over time using NDVI. 3) Monthly, Annual, and Seasonal Rainfall Analysis - Analyze rainfall patterns and trends at different temporal scales. 4) Land Use and Land Cover Classification using Machine Learning - Master the art of classifying land cover using machine learning algorithms. 5) LST and NDVI Time Series Chart for Various Land Use and Land Cover Classes - Visualize and interpret the relationship between land use, temperature, and vegetation health. 6) Satellite-Based Aridity Index to Monitor Drought Conditions - Monitor and assess drought using satellite-derived data. 7) LST Monitoring using Landsat Imagery - Utilize Landsat data to monitor land surface temperature changes. 8) Linear Regression Model and Trend Analysis Time Series Chart - Employ linear regression for trend analysis over time. 9) Analyzing Long-term Trend of NDVI by Mann Kendall’s Test - Use statistical analysis to detect trends in NDVI data. 10) Monitoring VCI, TCI, VHI - Understand and analyze vegetation health, thermal condition, and overall vegetation condition indices. 11) Detecting Deforestation using Machine Learning - Learn how to identify deforestation using machine learning techniques. 12) Classification Slope in GEE and Calculated Area - Classify slope and calculate areas using Google Earth Engine. 13) Classification of NDVI and Calculated Area - Classify NDVI data and calculate corresponding areas. 14) Extraction of Raster Values from Point Samples in Excel File - Extract and analyze raster values from point samples in Excel. 15) Monitoring Bioclimatic Variables - Analyze bioclimatic variables to understand their impact on ecosystems. 16) How to Monitor Monthly Evapotranspiration - Monitor the water cycle through evapotranspiration analysis. 17) Monitoring Soil Salinity Index - Assess soil salinity using remote sensing data. 18) Landslide Susceptibility using GEE+ArcGIS - Use GEE and ArcGIS to assess landslide susceptibility. 19) Flood Mapping using Sentinel-1 SAR Data and Calculated Flooded Area - Utilize SAR data for flood mapping and area estimation. 20) Trend Analysis of NDVI - Continue your trend analysis journey, focusing on NDVI. #RemoteSensing #GIS #EarthObservation #EnvironmentalMonitoring #TrainingProgram #OnlineEducation #SatelliteImagery #DataAnalysis #SkillDevelopment

Before we all head off to our well-deserved vacation this summer, I wanted to share the fruits of my labour from a little side project. Promoting openness in science is one of my highest priorities, and with that comes free and easy-to-understand tutorials that allows anyone to learn the 'science'. It's 2024, let's stop gatekeeping. The first tutorial I have published using Notion will teach you how to conduct a Trace Network Analysis with open data and GIS. Right now, if you have access to ArcGIS Pro, you are able to complete the tutorial. I am also working on a QGIS version to open up the floor for free GIS users to make it even more accessible. If you find this interesting, please engage with the content however you like. If there are any GIS or remote sensing based tutorials you would like to see, comment below! 👇 https://lnkd.in/dUK5W7jB

https://lnkd.in/dWTiCcGc 16 new lessons as part of a fundamentals of GIS short course that I have created. 🌎

Such a comprehensive course! the course started from explaining the Interface of ArcGIS desktop software, Changing and Viewing of Layer properties, usage of feature classes and attribute tables, different projections used in GIS, geoprocessing specially focusing on spatial joins and relates tools and finally generating a publishable map using layout view, changing symbology, Labelling map features, Creating map books and finally focusing on Metadata creating and editing and troubleshooting problems occur in ArcGIS. The final assignment was about creating a Publishable map of wildfire impacted areas of counties in california. It used Spatial joins and updating attribute tables, Dissolve and intersect tools in its workflow. which helped to grasp handling real life data sets in ArcGIS. This course equipped me with the knowledge needed to create publishable maps handling geographical data, which will come in handy in Digital soil mapping and Environmental mapping. University of California, Davis by Coursera . . . #GIS #ArcGIS

After more than 10 years of collecting spatial data, I’m excited to finally be taking the next step to learn the art of cartography and spatial analysis. My first exposure to GIS was back in college when I conducted invasive plant field surveys for the UC Santa Cruz Reserves Invasive Plant Management Program. In this project, I drew populations on paper maps, which I later helped digitize into ArcMap. Since college, I’ve utilized maps made in GIS for numerous projects. Some examples include: 1) Navigating terrain and locating field sites using a GPS unit; 2) Mapping the distribution and location of special status and non-native plant species; and 3) Mapping the location of field plots, sample collections, and photo stations. On many of these projects, I also cleaned up and prepared field data for import into GIS, and utilized GIS spatial analyses for conducting statistical analyses and reporting. Now I’m enrolled in the UC Davis GIS Certificate program offered through Coursera. This program has provided a solid (and affordable) foundation to expanding my knowledge and skills in ArcGIS Pro. The program is self-paced, can be started at any time, and only requires that the coursework to be completed within one year to earn a certificate. I highly recommend this program to folks working full-time but looking for some structure to learn GIS and specifically ArcGIS Pro. With the integration and utility of GIS in forest management and ecological restoration being so immense, I am super excited to continue on this journey and have this new skill in my toolbox for future projects and analyses. 

Thrilled to share my colleague Dr. Mukharhee's new book: Introduction to GIS! A fresh take on introducing geospatial concepts that blends theory with hands-on applications. #GIScience #SpatialAnalysis #RemoteSensing #GeospatialEducation #QGIS Falguni Mukherjee

Congratulations on the publication of your new book, Dr Falguni Mukherjee. Cannot wait to see future grad and undergrad students of geospatial technologies benefit immensely from it as part of their coursework. Fundamental grounding in geospatial technologies is highly relevant today due to their transformative impact across various sectors. These technologies enable precise mapping, site selection, and resource management, crucial for infrastructure development and urban planning. They enhance disaster response by providing real-time data for effective emergency management. Additionally, geospatial technologies support sustainable development by optimizing resource use and monitoring environmental changes. Their integration with AI and ML further enhances data analysis capabilities, making them indispensable for informed decision-making in diverse fields.