Videos

• Remote Sensing Definition and Applications:

  • Remote sensing involves acquiring information about Earth's surface without direct contact.

  • Applications include land-use planning, change detection, precision agriculture, weather monitoring, climate study, and disaster management.

• Active and Passive Sensors:

  • Active sensors (e.g., LiDAR, SAR) emit energy and measure its reflection.

  • Passive sensors (e.g., MODIS, Landsat) rely on solar energy and measure reflected or emitted radiation.

• Electromagnetic Spectrum:

  • Remote sensing utilizes a small portion of the electromagnetic spectrum (400-700 nanometers).

  • It extends from radio waves to gamma radiation.

• Satellites:

  • Sensors on satellites collect data across different spectral regions, including those beyond human vision.

  • Data collection involves transmission, scattering, absorption, and re-emission of solar energy.

• Data Processing and Band Channels:

  • Satellite data is transmitted to ground stations, processed, and prepared for display.

  • Each sensor collects data in specific wavelength regions known as bands or channels.

• Base Map and Raw Data:

  • Remote sensing data replaces traditional base maps, providing raster layers representing different spectral bands.

  • Raw data appears as grayscale images with digital number values indicating brightness.

• Composite Images:

  • Composite images combine three bands to create visual representations using RGB or false color composites.

  • Different composites reveal unique information about land cover and features.

• Resolution and Sensitivity:

  • Remote sensing data varies in spatial, temporal, radiometric, and spectral resolution.

  • Higher resolution and sensitivity enable detailed analysis and detection of subtle changes.

• Landsat Missions:

  • Landsat provides free, downloadable data spanning from 1972 to present.

  • It offers moderate spatial resolution and radiometric sensitivity.

• Image Classification:

  • Image classification categorizes pixels into classes (e.g., urban, vegetation) using supervised or unsupervised methods.

  • Classification aids in land cover analysis, change detection, and monitoring.

• Spectral Signature and Band Algebra:

  • Spectral signature graphs depict reflectance values across different wavelengths, aiding feature identification.

  • Band algebra involves mathematical operations on bands to enhance specific features or patterns.

• ArcGIS Pro and Remote Sensing Exercise:

  • ArcGIS Pro facilitates automation and integration of remote sensing tasks.

  • The video mentions an upcoming exercise on San Fernando Valley, covering composite creation, NDVI calculation, signature graph generation, and potentially, classification.

• GIS (Geographic Information Systems):

  • Refers to the software, standards, systems, and tools used to manage, analyze, and visualize spatial data.

• Coordinate Systems:

  • Systems used to georeference features on Earth's surface, enabling data integration and overlay for visualization and analysis.

• Layers:

  • Components of GIS representing different aspects of spatial data, such as streets, cities, elevation, etc., which can be combined and manipulated for analysis and visualization.

• Vector Data:

  • Represents locations of features on the ground using points, lines, and polygons, suitable for discrete features and boundaries.

• Raster Data:

  • Represents data as a grid of cells, useful for continuous phenomena like elevation, land cover, and imagery.

• Shapefiles and Geodatabases:

  • File formats for storing spatial data, with shapefiles being older and capable of storing only vector data, while geodatabases can store both raster and vector data.

• Projections:

  • Mathematical transformations to represent the Earth's curved surface on a flat map, crucial for accurate spatial analysis and visualization.

• Topology:

  • Concerns the spatial relationships between features, ensuring data integrity and accuracy in GIS analysis.

• Geoprocessing:

  • Operations that modify or create spatial data, including buffering, intersections, and data export.

• Maps and Layouts:

  • Maps are collections of layers with symbology, while layouts include additional elements like legends and scale bars for printing or display.

• Base Maps:

  • Background images used in GIS maps, often provided as tile services by GIS companies.

• Query:

  • Selecting features based on attribute values or spatial relationships for analysis or visualization.

• Join:

  • Combining tables based on common attributes or spatial relationships to integrate additional data into GIS analysis.

GIS and Remote Sensing Summaries:

• Spatial Analysis:

  • Process of examining the locations, attributes, and relationships of features in spatial data through overlay and other analytical techniques.

  • Core process supported by GIS software, unique from other analytical tools.

  • Involves extracting or creating new information from spatial data.

• Vector Geoprocessing Operations:

  • Operations specific to vector-based data models (points, lines, polygons).

  • Includes operations like Buffer, Clip, Dissolve, Union, Identity, and Intersect.

• Raster Spatial Analysis:

  • Involves working with raster grid cell values.

  • Map Algebra is a common method for raster-based spatial analysis.

• Map Algebra:

  • Mathematical operations on raster cells to create new output rasters.

  • Examples include addition and multiplication of raster cell values.

• Common Raster Analysis Methods:

  • Slope: Calculates the maximum change rate between a cell and its neighbors.

  • Hillshade: Accounts for the sun's relative position to create a greyscale representation of the surface.

  • Euclidean Distance: Describes each raster cell's relationship to a source based on straight-line distance.

• Hands-On Demonstration:

  • Conducting a spatial analysis model for archaeological sensitivity using vector-based geoprocessing tools.

  • Creating buffers, multi-ring buffers, and performing operations like Union and Dissolve.

  • Generating raster effects such as hillshade and overlaying raster and vector data for visualization.

Terms and Concepts:

• Buffer:

  • Operation to create new output features at a certain distance around input features.

• Clip:

  • Extracts an area from an input feature using a clip feature, similar to making cookies with a cookie cutter.

• Dissolve:

  • Aggregates features based on a common attribute.

• Union:

  • Computes the geometric union of polygon features.

• Identity:

  • Computes the geometric intersection of input features and identity features.

• Intersect:

  • Computes the geometric intersection of input and intersect features.

• Map Algebra:

  • Performing mathematical operations on raster cells.

• Slope:

  • Calculating the maximum change rate between a cell and its neighbors in a digital elevation model.

• Hillshade:

  • Creating a greyscale representation of a surface based on sun's relative position.

• Euclidean Distance:

  • Describing raster cells' relationship to a source based on straight-line distance.

• Hands-On Demonstration:

  • Practical application of GIS techniques to conduct spatial analysis.

  • Creating, manipulating, and visualizing spatial data using geoprocessing tools.

• Spatial Analysis: It involves going beyond mapping to add value by transforming, manipulating, and applying analytical methods to spatial data. It's part of geospatial knowledge discovery, focusing on finding patterns, clusters, disparities, and causal mechanisms.

• Components of Spatial Analysis:

  • Where do things happen?

  • Why do they happen where they do?

  • How does what happens in one location affect other locations?

  • Optimization: Determining optimal locations for various entities like medical clinics or fire stations.

• Special Characteristics of Spatial Analysis:

  • Combines geographic location with attribute information.

  • Location matters, unlike in non-spatial analysis where location is invariant.

• Spatial Autocorrelation: Detecting spatial patterns in data where neighboring locations exhibit similar values.

• Spatial Analysis Techniques:

  • Box plots and box maps for visualizing distributions spatially.

  • Identifying outliers and spatial outliers.

  • Exploratory spatial data analysis to discover patterns.

• Spatial Data Science: A subset of data science focusing on spatial characteristics, treating location, distance, and spatial interaction as core aspects.

• Data Science Process: Involves data manipulation, exploration, visualization, modeling, and communication of insights.

• Software Tools: Use of various tools like Geoda, R, and Python for different tasks in spatial data science.

• Example Project: A case study on analyzing Twitter and Foursquare data to identify hot spots and spatial outliers in New York City, illustrating the spatial data science process.

• Viewsheds: Provides a detailed view from a specific point, useful for assessing visual impact in urban planning and landscape architecture.

• Zonal Statistics: Summarizes geographic regions by extracting statistical data based on defined boundaries.

• Least Cost Path: Identifies the optimal path between two points considering terrain and obstacles, applicable in various fields like pipeline routing.

• Append: Merges multiple datasets into one, commonly used for combining tables or shapefiles.

• Line of Sight: Determines visibility between two points, important in urban planning, military operations, etc.

• Mosaic: Merges raster datasets into a seamless image, useful for combining data from different locations.

• Union: Combines input data layers into a composite layer while preserving boundaries and attributes.

• Resample: Changes the spatial resolution of a raster dataset, often used for data preparation.

• Raster Clip: Extracts a portion of a raster dataset based on a specified extent, helpful for focusing on specific areas.

• Erase: Removes features within a specified polygon extent, useful in refining input data.

• Raster Calculator: Creates new raster layers by combining existing ones based on user-defined equations.

• Interpolation: Estimates values of unknown points within known data points using various techniques.

• Spatial Join: Combines spatial data from different layers based on geographic relationships.

• Dissolve: Fuses adjacent polygons based on similar attribute values, simplifying data representation.

• Table Joins: Combines data from multiple tables based on a common attribute.

• Create Contours: Represents land surface elevation using lines of equal elevation.

• Intersect: Combines spatial data layers to create a new layer with overlapping areas.

• Merge: Combines two or more data layers into a single layer, useful for managing large datasets.

• Buffer: Creates a boundary around features expanded by a specified distance, useful for assessing potential impacts.

• Clip: Extracts a subset of a data layer based on the extent of another layer, essential for creating focused datasets.