Dear All,
Best *Zia Ahmed, PhD* Research Associate Professor (Data & Visualization) RENEW (Research and Education in eNergy, Environment and Water) Institute <http://www.buffalo.edu/renew.html> University at Buffalo <http://www.buffalo.edu/> Tutorial consist following topics: *1. Spatial Data Processing <https://zia207.github.io/geospatial-r-github.io/about.html>* - Reading and Writing Spatial Data <https://zia207.github.io/geospatial-r-github.io/read-write-spatial-data.html> - Vector data - Raster data - Map Projection and Coordinate Reference Systems <https://zia207.github.io/geospatial-r-github.io/map-projection-coordinate-reference-systems.html> - Geographic coordinate system (GCS) - Projected coordinate system - Coordinate Reference System in R - Geoprocessing of Vector data <https://zia207.github.io/geospatial-r-github.io/geoprocessing-vector-data.html> - Clipping - Union - Dissolve - Intersect - Erase - Convex Hull - Buffer - Working with Spatial Point Data <https://zia207.github.io/geospatial-r-github.io/working-with-spatial-point-data.html> - Create a Spatial Point Data Frame - Extract Environmental Covariates to SPDF - Create a Prediction Grid - Exploratory Data Analysis - Plot Data on Web Map - Working with Spatial Polygon Data <https://zia207.github.io/geospatial-r-github.io/working-with-spatial-polygon.html> - Data Processing - Visualization - Animation of Time Series Data - Working with Raster Data <https://zia207.github.io/geospatial-r-github.io/working-with-raster-data.html> - Basic Raster Operation - Clipping - Reclassification - Focal Statistics - Raster Algebra - Aggregation - Resample - Mosaic - Convert Raster to Point Data - Convert Point Data to Raster - Raster Stack and Raster Brick - Digital Terrain Modeling - Slope - Aspect - Hillshade - Terrain Ruggedness Index - Topographic Position Index - Roughness - Curvature - Flow Direction - netCDF Data Processing <https://zia207.github.io/geospatial-r-github.io/netCDF-data-processing.html> *2. Spatial Statistics <https://zia207.github.io/geospatial-r-github.io/spatial-statistics.html>* - Spatial Autocorrelation <https://zia207.github.io/geospatial-r-github.io/spatial-autocorrelation.html> - Moran’s I - Geary’s C - Getis’s Gi - Point Pattern Analysis <https://zia207.github.io/geospatial-r-github.io/point-pattern-analysis.html> - Geographically Weighted Mmodels <https://zia207.github.io/geospatial-r-github.io/geographically-weighted-models.html> - Geographically Weighted Summary Statistics <https://zia207.github.io/geospatial-r-github.io/geographically-weighted-summary-statistics.html> - Geographically Weighted Principal Components Analysis <https://zia207.github.io/geospatial-r-github.io/geographically-weighted-principal-components-analysis.html> - Geographically Weighted Regression <https://zia207.github.io/geospatial-r-github.io/geographically-weighted-regression.html> - Geographically Weighted OLS Regression <https://zia207.github.io/geospatial-r-github.io/geographically-weighted-ols-regression.html> - Geographically Weighted Poisson Regression <https://zia207.github.io/geospatial-r-github.io/geographically-weighted-poisson-regression.html> - Global and local (Geographically Weighted) Random Forest <https://zia207.github.io/geospatial-r-github.io/geographically-wighted-random-forest.html> *3. Spatial Interpolation <https://zia207.github.io/geospatial-r-github.io/spatial-interpolation.html>* · Spatial Interpolation <https://zia207.github.io/geospatial-r-github.io/spatial-interpolation.html> o Deterministic Methods for Spatial Interpolation <https://zia207.github.io/geospatial-r-github.io/deterministic-methods-for-spatial-interpolation.html> § Polynomial Trend Surface § Proximity Analysis-Thiessen Polygons § Nearest Neighbor Interpolation § Inverse Distance Weighted § Thin Plate Spline o Geostatistical Methods for Spatial Interpolation <https://zia207.github.io/geospatial-r-github.io/geostatistical-methods-for-spatial-interpolation.html> § Semivariogram Modeling <https://zia207.github.io/geospatial-r-github.io/semivariogram-modeling.html> § Kriging <https://zia207.github.io/geospatial-r-github.io/kriging.html> § Ordinary Kriging <https://zia207.github.io/geospatial-r-github.io/ordinary-kriging.html> § Universal Kriging <https://zia207.github.io/geospatial-r-github.io/universal-kriging.html> § Co-Kriging <https://zia207.github.io/geospatial-r-github.io/cokriging.html> § Regression kriging <https://zia207.github.io/geospatial-r-github.io/regression-kriging.html> § Generalized Linear Model § Random Forest § Meta Ensemble Machine Learning § Indicator kriging <https://zia207.github.io/geospatial-r-github.io/indicator-kriging.html> · Assessing the Quality of Spatial Predictions <https://zia207.github.io/geospatial-r-github.io/assessing-quality-spatial-predictions.html> o Cross-validation <https://zia207.github.io/geospatial-r-github.io/cross-validation.html> o Validation with an Independent Dataset <https://zia207.github.io/geospatial-r-github.io/validation-independent-dataset.html> o Conditional Simulation for Spatial Uncertainty <https://zia207.github.io/geospatial-r-github.io/conditional-simulation-spatial-uncertainty.html> *4. Remote Sensing Data Processing and Analysis <https://zia207.github.io/geospatial-r-github.io/about-c.html>* · Remote Sensing Basic <https://zia207.github.io/geospatial-r-github.io/reomte-sensing-basic.html> · Landsat 8 Image Processing & Visualization <https://zia207.github.io/geospatial-r-github.io/landsat-8-image-processing.html> o RGB image comparison o Pan Sharpening or Image Fusion o Radiometric Calibration and Atmospheric Correction · Spectral Indices <https://zia207.github.io/geospatial-r-github.io/spectral-indices.html> o Normalized Difference Vegetation Index o Soil Adjusted Vegetation Index (SAVI) o Modified soil Adjusted Vegetation Index (MSAVI) o Enhanced Vegetation Index (EVI) o Two-bands Enhanced Vegetation (EVI2) o Normalized Difference Water Index (NDWI) · Green Ground Cover from UAV Images <https://zia207.github.io/geospatial-r-github.io/uav-ground-cover.html> · Texture Analysis <https://zia207.github.io/geospatial-r-github.io/texture-analysis.html> · Image Classification <https://zia207.github.io/geospatial-r-github.io/image-classification.html> o Ground Truth Data Processing <https://zia207.github.io/geospatial-r-github.io/ground-truth-data-processing.html> o Unsupervised Classification <https://zia207.github.io/geospatial-r-github.io/unsupervised-classification.html> § Supervised Classification <https://zia207.github.io/geospatial-r-github.io/supervised-classification.html> · Random Forest <https://zia207.github.io/geospatial-r-github.io/random-forest.html> · Support Vector Machine <https://zia207.github.io/geospatial-r-github.io/support-vector-machine.html> · Naïve Bayes <https://zia207.github.io/geospatial-r-github.io/naive-bayes.html> · eXBoost <https://zia207.github.io/geospatial-r-github.io/exboost.html> · Deep Learning-H2O <https://zia207.github.io/geospatial-r-github.io/deep-learning-h2o.html> · Stack-Ensemble-H20 <https://zia207.github.io/geospatial-r-github.io/stack-ensemble-h2o.html> · Deep Learning Keras-TensorFlow <https://zia207.github.io/geospatial-r-github.io/deep-learning-keras-tensorflow.html> [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo