MY_PORTFOLIO

NDVI (Normalized Difference Vegetation Index) is a numerical indicator used in remote sensing and geographic information systems (GIS) to assess the health and density of vegetation cover in a given area. It is calculated from the reflectance of visible and near-infrared light captured by satellite or aerial imagery. Here's a brief description of NDVI: Calculation: NDVI is calculated using the following formula:                                             where NIR represents near-infrared reflectance and Red represents red light reflectance. Values: NDVI values typically range from -1 to 1.            The interpretation of NDVI values is as follows: Values close to -1: These indicate non-vegetated surfaces, such as water bodies or barren land. Values around 0: These usually represent built-up areas or areas with no live vegetation. Values b...

Supervised image classification in Google Earth Engine is a powerful process that involves using machine learning algorithms to categorize pixels in satellite or aerial imagery into predefined land cover classes. It is a fundamental technique in remote sensing and geospatial analysis.  Here's a brief description of supervised image classification in Google Earth Engine: Data Acquisition: The process begins by selecting and acquiring the satellite or aerial imagery that covers the area of interest (AOI) for classification. Google Earth Engine provides access to an extensive collection of remote sensing data, including Landsat, Sentinel, and more. Area of Interest (AOI) Definition: The user defines the AOI, which is the geographic region where the classification will be applied. This can be done by drawing a boundary on the map or importing a shapefile. Training Data Collection: Training data is essential for supervised classification. It involves selecting sample points or poly...

Population density is a key demographic and geographical metric that measures the number of people living in a particular area in relation to the size of that area. It is typically expressed as the number of individuals per unit of land area, often square kilometers or square miles.  Here's a brief description of population density: Calculation: Population density is calculated by dividing the total population of an area by the total land area of that region. The formula for calculating population density is: Units: Population density is commonly expressed as people per square kilometer (or square mile), abbreviated as "people/km²" (or "people/mi²"). It can also be expressed in other units depending on the specific context. Significance: Population density provides insights into how concentrated or dispersed a population is within a given area. It helps in understanding the level of human settlement, urbanization, and land use in a region. High vs. Low Densi...

Terrain analysis is a geographic information system (GIS) technique that focuses on the study and interpretation of the Earth's topography or landscape features. It involves the examination of elevation, slope, aspect, and other terrain-related characteristics to gain insights into the spatial distribution of landforms and their implications for various applications.  Here's a brief description of terrain analysis: Data Acquisition: Terrain analysis begins with the collection of elevation data, often obtained through sources like digital elevation models (DEMs), LiDAR (Light Detection and Ranging), or satellite altimetry. These datasets represent the Earth's surface in a gridded format. Elevation Analysis: Elevation Models: Analyzing elevation data is a fundamental aspect of terrain analysis. DEMs provide a detailed representation of elevation across a geographic area. Slope: Slope analysis measures the steepness of the terrain at each point, often expressed in degrees o...

PAN-SHARPENING OF LANDSAT 8 Pan-sharpening is a process used to enhance the spatial resolution of multispectral satellite imagery by incorporating high-resolution panchromatic (pan) imagery into the multispectral data . In the context of Landsat 8, which captures both multispectral and panchromatic imagery, pan-sharpening is a common technique used to create a higher-resolution and more detailed composite image.  Here's a brief description of pan-sharpening in Landsat 8: Data Sources : Landsat 8 captures imagery in different spectral bands, with the panchromatic band typically having a higher spatial resolution (e.g., 15 meters) than the multispectral bands (e.g., 30 meters). Pan-sharpening combines the panchromatic and multispectral data. Enhanced Spatial Resolution: Pan-sharpening aims to create an output image with a spatial resolution similar to the panchromatic band while retaining the spectral information from the multispectral bands. This results in a composite image th...

TOPOGRAPHIC MAP A topographic map , often referred to as a "topo sheet" or "toposheet," is a detailed, two-dimensional representation of the Earth's surface that emphasizes the physical features and terrain of a specific geographic area. These maps are commonly used in cartography, geography, land surveying, and various outdoor activities.  Here's a brief description of a topo sheet map and its key characteristics: Elevation and Terrain Information: Topographic maps focus on illustrating the elevation and relief of the Earth's surface. They provide a detailed depiction of the topography, including mountains, valleys, hills, plateaus, and other landforms. Contour lines are a prominent feature, connecting points of equal elevation and helping users visualize the terrain. Scale: Topo sheets come in various scales, such as 1:24,000 or 1:50,000, which indicate the ratio between distances on the map and the actual distances on the ground. Smaller-scale maps c...

CHOROPLETH MAP A choropleth map is a thematic map that represents data for defined geographic areas or regions by shading or coloring those areas based on the value of a specific variable. Choropleth maps are a popular visualization method for displaying spatial data, as they provide a straightforward and effective way to communicate patterns, variations, and distributions across different regions. Here's a brief description of choropleth maps and their key characteristics: Variable Representation: Choropleth maps use color or shading to represent a single variable's values, often related to a specific theme or topic. This variable can be continuous (e.g., temperature, population density) or discrete (e.g., categories of land use, political parties). Geographic Areas: Choropleth maps are divided into predefined geographic areas, such as countries, states, provinces, counties, or custom regions. Each area is a polygon, and the map associates data values with these geographic ...

A False Color Composite (FCC) in the context of Landsat 8 imagery within Google Earth Engine is a composite image created by combining selected spectral bands from Landsat 8 data and assigning them to the red, green, and blue color channels of the image display. This technique is used to enhance specific features and characteristics of the Earth's surface for better visualization and analysis. Image Collection and Filtering: In Google Earth Engine, you'll typically work with an image collection, filtering it based on your area of interest and the time frame of interest (e.g., date range and cloud cover constraints). This ensures you are using the most relevant Landsat 8 images for your analysis. Creating the FCC Image: Once you have the filtered image collection, you can create the False Color Composite image by using the selected bands False Color Composites created in Google Earth Engine are valuable for various remote sensing applications, including vegetation health asses...

False Color Composite (FCC): A False Color Composite is a type of image display technique used in remote sensing and satellite imagery interpretation. It involves assigning different bands of the electromagnetic spectrum to the red, green, and blue color channels of an image to create a false-color representation. In an FCC, colors are not assigned according to their actual visible appearance but based on the specific spectral information contained in each band.  Landsat 8 is equipped with a multispectral sensor called the Operational Land Imager (OLI) , which captures data in several different spectral bands. These bands include visible, near-infrared, and shortwave infrared wavelengths . By creating a False Color Composite using Landsat 8 data, users can visualize various features and characteristics of the Earth's surface more effectively than traditional true-color images. Red Channel (R): Usually assigned to the Near-Infrared (NIR) band ( Band 5 or 6 ). Vegetation appears...