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19.2 Data Collection and Analysis Techniques

9 min read•july 30, 2024

(GIS) and are powerful tools for collecting and analyzing spatial data. These technologies enable researchers to gather information about the Earth's surface, from primary sources like to secondary sources like existing maps and databases.

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Remote sensing techniques, including , , and , provide diverse data for studying landscapes. GIS tools then allow for sophisticated analysis of this data, supporting applications in environmental monitoring, urban planning, and natural resource management.

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Data Sources and Collection Methods in GIS

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Primary and Secondary Data Sources

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  • Primary data sources include direct observations, field surveys, and remote sensing imagery
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 - Direct observations (in-situ measurements, [GPS data collection](https://www.fiveableKeyTerm:gps_data_collection))
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 - Field surveys (questionnaires, interviews, sampling)
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  • Secondary data sources encompass existing maps, databases, and statistical datasets
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 - Existing maps ([topographic maps](https://www.fiveableKeyTerm:topographic_maps), [thematic maps](https://www.fiveableKeyTerm:thematic_maps))
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 - Databases ([census data](https://www.fiveableKeyTerm:census_data), [environmental monitoring data](https://www.fiveableKeyTerm:Environmental_Monitoring_Data))
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 - Statistical datasets (socioeconomic indicators, [climate data](https://www.fiveableKeyTerm:climate_data))
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Data Collection Methods in GIS

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  • Digitizing analog maps involves converting paper maps into digital format using manual or automatic digitization techniques
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  • Importing digital data from various file formats (, , ) into GIS software for analysis and visualization
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  • Collecting GPS data points, lines, and polygons using handheld GPS devices or mobile apps for accurate spatial data capture in the field
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 - GPS data points (locations of sampling sites, points of interest)
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 - GPS lines (roads, rivers, transects)
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 - GPS polygons (land parcels, study areas, vegetation patches)
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Remote Sensing Data Collection Techniques

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  • Aerial photography captures high- images of the Earth's surface using aircraft-mounted cameras
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 - [Orthophotos](https://www.fiveableKeyTerm:Orthophotos) are geometrically corrected aerial photographs that provide a uniform scale and can be used as base maps
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  • Satellite imagery is acquired by sensors aboard Earth observation satellites, covering large areas at various spatial, spectral, and temporal resolutions
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 - [Multispectral imagery](https://www.fiveableKeyTerm:multispectral_imagery) (Landsat, Sentinel-2) captures data in multiple spectral bands for land cover classification and monitoring
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 - [High-resolution imagery](https://www.fiveableKeyTerm:high-resolution_imagery) (WorldView, Pleiades) provides detailed information for urban planning, infrastructure mapping, and disaster response
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  • LiDAR (Light Detection and Ranging) uses laser pulses to measure the distance between the sensor and the Earth's surface, creating high-resolution and 3D point clouds
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 - [Airborne LiDAR](https://www.fiveableKeyTerm:airborne_lidar) systems are mounted on aircraft for large-area mapping
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 - [Terrestrial LiDAR](https://www.fiveableKeyTerm:terrestrial_lidar) systems are ground-based and used for smaller-scale, high-detail surveys
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  • Radar (Radio Detection and Ranging) sensors emit microwave energy and record the backscattered signal to create images of the Earth's surface
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [Synthetic Aperture Radar](https://www.fiveableKeyTerm:synthetic_aperture_radar) ([SAR](https://www.fiveableKeyTerm:SAR)) systems (TerraSAR-X, Sentinel-1) provide all-weather, day-and-night imaging capabilities for monitoring surface deformation, sea ice, and forest structure
Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Metadata and Data Quality Considerations

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Top images from around the web for Data Sources and Collection Methods in GIS

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  • provides essential information about spatial data, such as , , resolution, and
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Data quality describes the [overall accuracy](https://www.fiveableKeyTerm:Overall_Accuracy) and completeness of the dataset
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Accuracy refers to the closeness of measurements or estimates to the true values
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Resolution indicates the level of detail captured in the data (spatial, temporal, spectral)
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Lineage documents the data sources, processing steps, and transformations applied to the dataset
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Top images from around the web for Data Sources and Collection Methods in GIS

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  • Assessing data suitability and reliability is crucial for selecting appropriate data sources for GIS and remote sensing projects
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Top images from around the web for Data Sources and Collection Methods in GIS

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  • , , and impact the selection and use of data sources
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Spatial resolution determines the smallest detectable feature or area on the ground
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Temporal resolution refers to the frequency of data acquisition or the time interval between observations
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Thematic accuracy measures the correctness of attribute information or classification results
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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Image Processing Techniques for Remote Sensing

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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Image Enhancement Techniques

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Top images from around the web for Data Sources and Collection Methods in GIS

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  • improves the visual interpretability of remotely sensed imagery by adjusting the range of pixel values to span the full display range
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Linear contrast stretch applies a linear transformation to the pixel values
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [Histogram equalization](https://www.fiveableKeyTerm:Histogram_Equalization) redistributes pixel values to achieve a more balanced distribution across the available range
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Top images from around the web for Data Sources and Collection Methods in GIS

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  • applies mathematical operations to pixel neighborhoods to emphasize or suppress specific features or patterns in the image
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [Low-pass filters](https://www.fiveableKeyTerm:Low-pass_filters) (mean, median) smooth the image and reduce noise
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [High-pass filters](https://www.fiveableKeyTerm:high-pass_filters) (Laplacian, Sobel) enhance edges and highlight fine details
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Top images from around the web for Data Sources and Collection Methods in GIS

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  • combine three spectral bands (red, green, blue) to create a color image that highlights specific land cover types or features
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [True color composites](https://www.fiveableKeyTerm:true_color_composites) (RGB = visible bands) resemble natural colors
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [False color composites](https://www.fiveableKeyTerm:false_color_composites) (RGB = near-infrared, red, green) emphasize vegetation health and vigor
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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Image Classification Methods

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Top images from around the web for Data Sources and Collection Methods in GIS

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  • assigns pixels to predefined land cover classes based on training samples provided by the analyst
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [Maximum Likelihood Classifier](https://www.fiveableKeyTerm:maximum_likelihood_classifier) (MLC) calculates the probability of a pixel belonging to each class and assigns it to the class with the highest probability
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [Support Vector Machines](https://www.fiveableKeyTerm:support_vector_machines) (SVM) find the optimal hyperplane that separates classes in a high-dimensional feature space
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Top images from around the web for Data Sources and Collection Methods in GIS

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  • groups pixels into statistically derived clusters based on their spectral characteristics without prior knowledge of the land cover classes
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [K-means clustering](https://www.fiveableKeyTerm:k-means_clustering) iteratively assigns pixels to a specified number of clusters based on their spectral similarity
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [ISODATA](https://www.fiveableKeyTerm:Isodata) (Iterative Self-Organizing Data Analysis Technique) allows for merging and splitting of clusters based on predefined thresholds
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Top images from around the web for Data Sources and Collection Methods in GIS

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  • evaluates the performance of image classification results by comparing classified pixels with ground truth data
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Overall accuracy measures the percentage of correctly classified pixels across all classes
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [Producer's accuracy](https://www.fiveableKeyTerm:producer's_accuracy) indicates the probability of a reference pixel being correctly classified
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [User's accuracy](https://www.fiveableKeyTerm:user's_accuracy) represents the probability that a pixel classified into a given class actually belongs to that class
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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Change Detection and Object-Based Image Analysis

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Top images from around the web for Data Sources and Collection Methods in GIS

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  • identify and quantify land cover changes over time using multi-temporal remotely sensed data
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [Image differencing](https://www.fiveableKeyTerm:image_differencing) subtracts pixel values of one image from another to highlight areas of change
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [Principal component analysis](https://www.fiveableKeyTerm:Principal_Component_Analysis) (PCA) transforms multi-temporal data into uncorrelated components, with the higher-order components representing change information
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [Post-classification comparison](https://www.fiveableKeyTerm:post-classification_comparison) detects changes by comparing independently classified images from different dates
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Top images from around the web for Data Sources and Collection Methods in GIS

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  • (OBIA) segments imagery into meaningful objects based on spectral, spatial, and contextual information
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [Segmentation algorithms](https://www.fiveableKeyTerm:Segmentation_Algorithms) ([multiresolution segmentation](https://www.fiveableKeyTerm:multiresolution_segmentation), [watershed segmentation](https://www.fiveableKeyTerm:Watershed_Segmentation)) group pixels into homogeneous objects at multiple scales
Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Object-based classification assigns objects to land cover classes based on their spectral, geometric, and contextual properties
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - OBIA provides an alternative to pixel-based classification, particularly for high-resolution imagery with increased spatial heterogeneity
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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Spatial Analysis with GIS Tools

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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Vector and Raster Overlay Analysis

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Top images from around the web for Data Sources and Collection Methods in GIS

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  • combines multiple vector layers using operations like intersection, union, and erase to identify spatial relationships and create new datasets
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Intersection creates a new layer containing only the features that overlap between input layers
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Union combines all features from the input layers into a single layer, preserving the attributes from each layer
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Erase removes features from one layer that overlap with features from another layer
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Top images from around the web for Data Sources and Collection Methods in GIS

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  • performs mathematical operations, such as and , on multiple raster layers to generate suitability maps or risk assessments
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Weighted sum assigns weights to each input raster layer and calculates the sum of the weighted pixel values to create a suitability or risk index
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - Boolean operators (AND, OR, NOT) combine binary raster layers based on logical conditions to identify areas that meet specific criteria
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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Top images from around the web for Data Sources and Collection Methods in GIS

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Proximity and Terrain Analysis

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Top images from around the web for Data Sources and Collection Methods in GIS

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  • assess the spatial relationship between features based on distance or adjacency
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Top images from around the web for Data Sources and Collection Methods in GIS

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 - [Buffer](https://www.fiveableKeyTerm:Buffer) creates polygons around input features at a specified distance, useful for identifying areas within a certain proximity to a feature of interest
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 - [Thiessen polygons](https://www.fiveableKeyTerm:Thiessen_Polygons) (Voronoi diagrams) divide a plane into regions based on the nearest input point, used for defining service areas or zones of influence
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 - [Distance matrices](https://www.fiveableKeyTerm:Distance_Matrices) calculate the distances between all pairs of input features, helpful for accessibility analysis or facility location problems
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  • derives topographic attributes from digital elevation models (DEMs) to characterize land surface properties and processes
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 - [Slope](https://www.fiveableKeyTerm:slope) calculates the rate of change in elevation between neighboring pixels, expressed in degrees or percent
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 - [Aspect](https://www.fiveableKeyTerm:Aspect) determines the compass direction that a slope faces, influencing solar radiation, vegetation growth, and microclimate
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 - [Curvature](https://www.fiveableKeyTerm:Curvature) measures the rate of change in slope or aspect, with positive values indicating convex surfaces and negative values indicating concave surfaces
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  • determines areas visible from a specific location or set of locations, considering factors like terrain, observer height, and viewing distance
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 - Viewsheds are used in landscape planning, archaeological studies, and military applications to assess visibility and line-of-sight
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  • analyze surface water movement and delineate drainage networks and catchment areas
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 - [Flow direction](https://www.fiveableKeyTerm:flow_direction) calculates the direction of water flow from each pixel to its steepest downslope neighbor
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 - [Flow accumulation](https://www.fiveableKeyTerm:Flow_Accumulation) computes the number of upslope pixels that drain into each pixel, identifying areas of concentrated water flow
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 - [Watershed delineation](https://www.fiveableKeyTerm:watershed_delineation) defines the boundaries of drainage basins based on the flow direction and accumulation grids
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Interpreting Remotely Sensed Imagery

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Spectral Signatures and Vegetation Indices

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  • of land cover types, such as vegetation, water, and soil, form the basis for interpreting and classifying remotely sensed imagery
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 - Vegetation has high reflectance in the near-infrared and low reflectance in the red due to chlorophyll absorption
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 - Water has low reflectance in the near-infrared and visible spectrum due to absorption
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 - Soil reflectance varies depending on moisture content, organic matter, and mineral composition
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  • quantify vegetation health, density, and productivity using mathematical combinations of spectral bands
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 - [Normalized Difference Vegetation Index](https://www.fiveableKeyTerm:normalized_difference_vegetation_index) (NDVI) is calculated as (NIR - Red) / (NIR + Red), with higher values indicating healthier or denser vegetation
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 - [Enhanced Vegetation Index](https://www.fiveableKeyTerm:Enhanced_Vegetation_Index) (EVI) minimizes soil background effects and atmospheric influences, making it more sensitive to vegetation changes
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 - [Soil-Adjusted Vegetation Index](https://www.fiveableKeyTerm:soil-adjusted_vegetation_index) (SAVI) accounts for the influence of soil brightness in areas with sparse vegetation cover
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Multispectral, Hyperspectral, and Thermal Imagery

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  • Multispectral imagery captures data in multiple spectral bands (typically 3-10) for discriminating land cover types and detecting variations in surface properties
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 - Landsat and Sentinel-2 satellites provide multispectral imagery with spatial resolutions of 10-60 meters, suitable for regional-scale mapping and monitoring
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  • measures hundreds of narrow, contiguous spectral bands, enabling the identification of specific materials and subtle variations in surface composition
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 - [Airborne Visible/Infrared Imaging Spectrometer](https://www.fiveableKeyTerm:airborne_visible/infrared_imaging_spectrometer) (AVIRIS) and [Hyperion satellite sensor](https://www.fiveableKeyTerm:Hyperion_Satellite_Sensor) are examples of hyperspectral imaging systems
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 - Hyperspectral data is used in mineral exploration, precision agriculture, and environmental monitoring
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  • measures land surface temperature by detecting the emitted infrared radiation from the Earth's surface
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 - Landsat 8 Thermal Infrared Sensor (TIRS) provides thermal imagery at 100-meter resolution
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 - Thermal data is used to study urban heat islands, evapotranspiration, and surface energy balance
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Radar, LiDAR, and Time Series Analysis

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  • Radar and LiDAR data offer unique insights into surface roughness, elevation, and forest structure, complementing optical remote sensing techniques
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 - Synthetic Aperture Radar (SAR) captures information about surface roughness, moisture content, and structure, useful for monitoring deforestation, floods, and sea ice
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 - LiDAR point clouds provide high-resolution 3D information about terrain, buildings, and vegetation structure, supporting applications in forestry, urban planning, and coastal management
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  • Time series analysis of remotely sensed data enables the monitoring of dynamic processes over multiple time scales
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 - Vegetation phenology studies the seasonal patterns of plant growth and senescence using multi-temporal vegetation indices (NDVI, EVI)
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 - Glacier retreat and snow cover changes can be monitored using multi-temporal optical and thermal imagery
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 - Urbanization and land use/land cover changes can be assessed by analyzing time series of high-resolution satellite imagery (Landsat, Sentinel) to quantify the extent and rate of change
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