GEG 410 GIS II Lecture 4: Raster Analysis Fall 2024 PDF

GEG 410 GIS II Lecture 4: Raster Analysis Fall 2024 Instructor: Dr. Guimin Zhu Email: [email protected] Contents Raster patterns Calculate Density tool Point density Line density Preprocessing Zonal analysis Map algebra Raster...

GEG 410 GIS II Lecture 4: Raster Analysis Fall 2024 Instructor: Dr. Guimin Zhu Email: [email protected] Contents Raster patterns Calculate Density tool Point density Line density Preprocessing Zonal analysis Map algebra Raster & machine learning Calculate Density Creates a density surface map from point or line features by spreading known quantities of some phenomenon (represented as attributes of the points or lines) across the map Density surface: Show where point or line features are concentrated Example: you might have a point value for each town representing the total number of people in the town, but you want to learn more about the spread of population over the region  Human trafficking hotspots https://gargoyle.flagler.edu/2015/12/florida-sun-fun-and-human-trafficking/ GIF: heatmap of reported crimes in Chicago by month 2001-2017 https://www.reddit.com/r/dataisbeautiful/comments/71e3pp/heatma p_gif_of_reported_crimes_in_chicago_by/  A raster cell center Point Density The number of points that fall within the neighborhood is totaled and divided by the area of the neighborhood A raster example Point Density Weighted Each point can be weighted using its attributes Some points may be weighted more heavily than others, depending on their meaning, or to allow one point to represent several observations, e.g. A condominium with six individual units Some crimes with higher crime levels ArcGIS Pro: Point Density tool https://pro.arcgis.com/en/pro-app/latest/tool- reference/spatial-analyst/point-density.htm Line Density Density is calculated in units of length per unit of area ArcGIS Pro: Line Density tool https://pro.arcgis.com/en/pro-app/latest/tool- reference/spatial-analyst/line-density.htm Contents Raster patterns: Calculate Density Preprocessing Georeferencing, Clipping, Resizing, Enhancing, Mosaicking Snap Raster Zonal analysis Map algebra Raster & machine learning Raster Preprocessing Simple raster editing that prepares images for viewing and analysis, including Georeferencing Clipping Resizing Enhancing Mosaicking Preprocessing: Georeferencing Raster data is commonly obtained by scanning maps or collecting aerial photographs and satellite images You may need to align or georeference them to a map coordinate system Aligning the raster with control points Transforming the raster The yellow cross is the control point on the vector data, while After you have created enough pairs of control points and the the green cross is the corresponding point on the raster. corresponding points on the raster, you can transform the raster to permanently match the map coordinates if the target data. Preprocessing: Clipping Cuts out a portion of a raster ArcGIS Pro: Clip Raster tool https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/clip.htm Preprocessing: Resizing Changes the spatial resolution of a raster dataset and sets rules for aggregating or interpolating values across the new pixel sizes The cell size can be changed The extent of the raster dataset will remain the same Resampling technique: Nearest, majority, linear, or user-defined ArcGIS Pro: Resample tool https://pro.arcgis.com/en/pro-app/latest/tool- reference/data-management/resample.htm Changed the spatial resolution: in this example, pixel values are assigned based on a 3*3 rectangular window using “maximum” statistics. Preprocessing: Enhancing To see features in some images or raster datasets, or to distinguish them more easily from the surroundings Alter the stretch applied to the histogram Preprocessing: Mosaicking Mosaic: two or more adjacent raster datasets need to be merged into one entity ArcGIS Pro: Mosaic to New Raster tool https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/mosaic-to-new- raster.htm 1 arc-second DEM data from SRTM (Shuttle Radar Topography Mission) by NASA. Each image is about 25MB (so a relatively small area). Snap Raster Adjust the extent so that the cells in the output raster align with the cells in the snap raster The lower left corner of the given extent is moved to the nearest snap raster cell corner The upper right corner of the given extent is moved to the nearest output raster cell corner Contents Raster patterns: Calculate Density Preprocessing Zonal analysis Zonal statistics Zonal geometry Map algebra Raster & machine learning Zonal Tools Perform analysis when the output is a result of computations performed on all cells that belong to each input zone Zones Contiguous or noncontiguous Raster or feature datasets Work on zone attributes – Zonal Statistics Work on zone shapes – Zonal Geometry Zonal Statistics Summarizes the values of a raster within the zones of another dataset (and reports the results as a table) ArcGIS Pro: Zonal Statistics tool ArcGIS Pro: Zonal Statistics as Table tool https://pro.arcgis.com/en/pro-app/latest/tool- https://pro.arcgis.com/en/pro-app/latest/tool- reference/spatial-analyst/zonal-statistics.htm reference/spatial-analyst/zonal-statistics-as- table.htm Zonal Geometry Similar to Zonal Statistics, Zonal Geometry returns the geometry information of each zone in a raster The Zonal Geometry tool takes in only 1 raster dataset Geometry information Area Perimeter Thickness Centroid ArcGIS Pro: Zonal Geometry tool https://pro.arcgis.com/en/pro-app/latest/tool- reference/spatial-analyst/zonal-geometry.htm ArcGIS Pro: Zonal Geometry as Table tool https://pro.arcgis.com/en/pro-app/latest/tool- reference/spatial-analyst/zonal-geometry-as- table.htm Contents Raster patterns: Calculate Density Preprocessing Zonal analysis Map algebra Local, focal, zonal, and global Raster functions & Raster Calculator Example: NDVI Raster & machine learning Map Algebra Map algebra refers to an informal language with syntax similar to algebra, which can be used to facilitate the manipulation and analysis of raster data Input A single raster or multiple raster datasets Expression GIS tools, mathematical operators, and constants For example, Select(Slope([emidalat], degree), 'value < 20') is a map algebra expression: It executes the Slope operation, Slope([emidalat], degree) And then selecting areas with slopes less than 20 degrees. Map Algebra Map algebra operations and functions are broken down into four groups: Local Focal Zonal Global Map Algebra: Local Local operations and functions are applied to each individual cell and only involve those cells sharing the same location One or more rasters can be involved output = (2 * raster + 1) output = (R1 + R2) Map Algebra: Local Local operations also include reclassification of values A range of values from the input raster is assigned a new value ArcGIS Pro: Reclassify tool https://pro.arcgis.com/en/pro- app/latest/tool-reference/spatial- analyst/reclassify.htm Map Algebra: Focal Also called neighborhood operations Focal operations assign to the output cells some summary value (such as the mean) of the neighboring cells from the input raster Smoothing function: The average of all 9 neighboring input cells (including the center cell) Cells surrounded by non-existent cells are assigned an NA in this example Some GIS software will ignore the missing surrounding values and just compute the average anyway Take the 3*3 neighbor Calculate the average: 1.56=(1+2+2+1+2+2+1+2+1)/9 Four common neighborhood types: Rectangle Circle Annulus Wedge The cell marked with an x is the focal cell  Neighborhood statistics include: Summary statistics: maximum, minimum, range, sum, mean, median, and standard deviation Tabulation of measures: majority, minority, and variety Map Algebra: Zonal A zonal operation computes a new summary value (such as the mean) from cells aggregated for some zonal unit Similar to Zonal Statistics This technique is often used with rasters derived from remotely sensed imagery (e.g. NDVI) where areal units (such as counties or states) are used to compute the average cell values from the raster Map Algebra: Global Global operations and functions may make use of some or all input cells when computing an output cell value Example: Euclidean Distance Each pixel is assigned its closest distance to one of the two source locations (defined as value 1 in the Input raster) Map Algebra: Operators Mathematical and logical operators Map Algebra: Operators Boolean operators AND True False True True False False False False OR True False True True True False True False Example: ((R1 > 0) & (R1 < 4)) & (R2 > 0) How is the result calculated? Raster Calculator ArcGIS Pro: Raster Calculator tool Build and run a single map algebra expression using Python syntax https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/raster- calculator.htm Rasters box will show all the available raster layers in your current TOC Tools box also has other math functions, e.g., Int(), math.Abs() Expression box Raster Functions Raster functions are operations that apply processing directly to the pixels of imagery and raster datasets, as opposed to geoprocessing tools, which write out a new raster to disk Calculations are applied to the pixels of the original data as the raster is displayed, so only pixels that are visible on your screen are processed. As you zoom and pan around, the calculations are performed on the fly Since no intermediate datasets are created, processes can be applied quickly, as opposed to the time it would take to create a processed file on disk  No new raster is physically created so the process is fast The new layer in the TOC is temporary The process is “on the fly” Example: NDVI Normalized Difference Vegetation Index Widely used metric for quantifying the health and density of vegetation using remote sensing imagery data NDVI in June (top) and October (bottom) over the British Isles (Source: NOAA) Example: NDVI Rationale The pigment in plant leaves, chlorophyll, strongly absorbs visible light (from 400 to 700 nm) for use in photosynthesis The cell structure of the leaves, on the other hand, strongly reflects near-infrared light (from 700 to 1100 nm) Calculating NDVI 𝑁𝑁𝑁𝑁𝑁𝑁 − 𝑅𝑅𝑅𝑅𝑅𝑅 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 = 𝑁𝑁𝑁𝑁𝑁𝑁 + 𝑅𝑅𝑅𝑅𝑅𝑅 Example: NDVI NDVI value ranges from +1.0 to -1.0 Negative NDVI values: correspond to water -0.1 to 0.1: areas of barren rock, sand, or snow usually show very low NDVI values (for example,) 0.2 to 0.5: sparse vegetation such as shrubs and grasslands or senescing crops may result in moderate NDVI values 0.6 to 0.9: high NDVI values correspond to dense vegetation such as that found in temperate and tropical forests or crops at their peak growth stage Contents Raster patterns: Calculate Density Preprocessing Zonal analysis Map algebra Raster & machine learning Land cover classification Building Footprints Land Use Land Cover (LULC) Land cover According to the Food and Agricultural Organization, land cover refers to the observed physical and biological cover of the earth’s land. Lan is covered by various types of vegetation, grassland, forest, water bodies, barren land, etc. Land use Land use is commonly defined as a series of operations on land, carried out by humans, with the intention to obtain products and/or benefits through using land resources it includes – wildlife management areas, agricultural land, urban, recreation areas, etc. LULC study is simply the temporal analysis of the changes of the study area’s land use and land cover Planning, management, monitoring, policies, sustainable urban development, etc. Examples National Land Cover Database USGS (US Geological Survey) 30-meters, 2001/2004/2006/2008/2011/2013/2016/2019/2021 https://www.usgs.gov/centers/eros/science/national-land- cover-database#overview Sentinel-2 Land Cover Explorer ESRI (Environmental Systems Research Institute) – ArcGIS Living Atlas of the World 10-meters, 2017-2023 https://livingatlas.arcgis.com/landcoverexplorer/#mapCent er=35.13695%2C11.13721%2C11&mode=step&timeExtent =2017%2C2023&year=2023 Methodology Data collection Satellite images: Landsat, Sentinel, etc. Topographic maps Preprocessing Radiometric correction (calibration) and geometric correction Land use classification Supervised & unsupervised Analysis Map presentation Change detection (temporal changes) Classification Image classification Assigning pixels in the image to categories or classes of interest Types of classification Supervised, unsupervised, & object-based Classification USGS LULC classification major classes: (mid 1970’s) Urban or built-up Agricultural land Rangeland Forest land Water Wetland Barren land Tundra Perennial snow or ice Analysis Map Temporal change detection LULC changes in northern Atlanta, GA in 1985-2020 LULC changes in Miami-Dade County, Florida in 2001-2021 Microsoft Building Footprints 125 million building footprints deep learning generated by Microsoft for the USA https://hub.arcgis.com/datasets/esri::microsoft-building-footprints- features/about And you can try extracting building footprints using Esri’s deep learning model yourself: https://mediaspace.esri.com/media/t/1_m5yviqru