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Terrain Analysis (Slope, Aspect, and Hillshade) with SRTM DEM Data

Terrain analysis is a fundamental aspect of geospatial studies, providing crucial insights into the physical landscape of the Earth. Among the various tools available for terrain analysis, the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data stands out for its global coverage and accessibility. When combined with the computational power of Google Earth Engine (GEE), SRTM DEM becomes an even more powerful resource for understanding terrain features like slope, aspect, and hillshade. In this blog, we explore how terrain analysis is performed in GEE using SRTM DEM data and its significance in various applications. Understanding SRTM DEM SRTM was a joint mission by NASA and the National Geospatial-Intelligence Agency (NGA), which provided the first near-global elevation data of Earth. SRTM DEM data offers a resolution of 30 meters for most parts of the world, making it suitable for a wide range of terrain analyses. Terrain Analysis in Google Earth Engine GEE provi...

Random Forest Machine Learning Classification in GEE with Sentinel-2

In satellite imagery analysis, the integration of machine learning techniques with powerful platforms like Google Earth Engine (GEE) has opened new avenues for understanding our planet. One such technique is the Random Forest (RF) classification, a popular machine learning method known for its accuracy and efficiency. When applied to Sentinel-2 imagery within GEE, it provides a potent tool for various geospatial analyses. This blog explores the importance and uses of Random Forest machine learning classification in GEE, specifically using Sentinel-2 data. Understanding Random Forest Classification Random Forest is an ensemble learning method, essentially a collection of decision trees, each contributing to the final decision. This method is not only robust against overfitting but also capable of handling large datasets with numerous input variables, making it ideal for satellite imagery classification. Why Use Random Forest with Sentinel-2 in GEE? GEE code : https://code.earthengi...

Optimizing Agriculture Monitoring : A Guide to Satellite Imagery Selection for Crop Management

When monitoring crops through satellite images based on field size, the choice of satellite imagery depends on the specific requirements and objectives of your monitoring project. Here are some commonly used satellite images and their suitability for different field sizes: Source : researchgate Sentinel-2: Spatial Resolution: 10 meters (visible, near-infrared) to 20 meters (shortwave infrared) Coverage Frequency: Every 5 days (global coverage) Suitable for: Medium to large-sized fields Why: Sentinel-2 provides high-resolution multispectral imagery, making it suitable for monitoring medium to large-sized fields. The frequent revisit time allows for regular monitoring of crop conditions and vegetation dynamics. Landsat: Spatial Resolution: 30 meters (visible, near-infrared) to 60 meters (thermal infrared) Coverage Frequency: Every 16 days (global coverage) Suitable for: Medium to large-sized fields Why: Landsat imagery offers a longer historical archive and is suitable for monitoring med...

From Pixels to Plants : Time Series NDVI Analysis for Precision Agriculture

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Google Earth Engine: Supercharging Your Remote Sensing Workflow

For those in the realm of GIS and remote sensing, the name "Google Earth Engine" (GEE) conjures up images of immense power and possibility. But what exactly is it, and why should it be essential in your geospatial toolbox? Buckle up, as we delve into the world of GEE and explore how it's revolutionizing the landscape of remote sensing. What is Google Earth Engine? Imagine a playground where massive satellite imagery datasets and powerful cloud computing capabilities coexist seamlessly. That's GEE in a nutshell. It's a cloud-based platform offering: A Petabyte Catalog: Millions of free, globally-consistent datasets including Landsat, Sentinel, MODIS, and more. Planetary-Scale Analysis: Leverage Google's cloud infrastructure to analyze massive datasets with ease. Intuitive APIs: Code in JavaScript or Python to manipulate and analyze imagery through a well-documented API. Interactive Platform: Explore data, visualize results, and s...

FIRST ORGANIC STATE - SIKKIM

WHAT’S A ORGANIC STATE? A state in which no artificial fertilizers or chemicals used for practicing agriculture. All the manures and nutrients required for plants are obtained naturally. ECOLOGICAL BENEFITS OF ORGANIC STATE · Land quality improvement · Reducing pollution . · Sustains ecological environment ECONOMIC BENEFITS OF ORGANIC STATE · Health savings . · Economic demands for organic food · Increasing income of farmers . SIKKIM In January 2016 PM Narendra Modi declared Sikkim India’s first fully organic state. The efforts of north eastern states towards achieving sustainable goals have been rewared by United Nations. The state have been awarded with “...

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Piyush manush an environment activist plus founder of salem citizen’s forum decided to grow . So bought 1.5 acre of land in dharmapuri,with the help from his friends and some volunteers he now manages 250 acres of land. Initially a drought prone area now shines with greenery everywhere because of his cooperative forest concept. OBJECTIVE: Afforestation for sustainable development in addition a good income generator. COOPERATIVE FOREST: It’s a 250 acre green patches with number of rainwater harvesting structures which was earlier a barren and undulated drought prone area in Dharmapuri district Tamil nadu. First and foremost he did was constructing trenches ,farmponds checkdams to harvest rainwater. This project was started in...

A Comprehensive Exploration of Vector Data Formats Supported by QGIS

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