first part
basic practice
one
Platform and basic development platform
· Introduction of GEE platform and typical application cases;
· Introduction of GEE development environment and commonly used data resources;
Introduction to GPT models such as ChatGPT and Wenxinyiyan
· Introduction to JavaScript basics;
· GEE remote sensing cloud important concepts and typical data analysis process;
GEE basic object introduction, vector and raster object visualization, attribute viewing, API query, basic debugging and other platforms to get started.
two
GEE basic knowledge interacts with AI models such as ChatGPT
· Basic image operations and operations: mathematical operations, relations/conditions/Boolean operations, morphological filtering, texture feature extraction; image masking, cropping and mosaic, etc.;
· Element basic operations and operations: geometric buffer, intersection, union, difference operations, etc.;
Collection object operations: loop iteration (map/iterate), merge Merge, union (Join);
· Data integration Reduce: including image and image set integration, image synthesis, image area statistics and domain statistics, group integration and area neighborhood statistics, image set linear regression analysis, etc.;
· Machine learning algorithms: including supervised (random forest, CART, SVM, decision tree, etc.) and unsupervised (wekaKMeans, wekaLVQ, etc.) classification algorithms, classification accuracy evaluation, etc.;
Data asset management: including local vector and raster data upload, cloud vector and raster data download, statistical result data export, etc.;
· Drawing visualization: including bar charts, histograms, scatter plots, time series and other graphic drawing.
GPT model interaction: Combine the above basic knowledge points with AI tools such as ChatGPT for interactive demonstrations, including skills such as auxiliary question answering, code generation and correction.
the second part
advanced quiz
Mini-case lectures on important knowledge points and GPT model interactive demonstration
1) Landsat, Sentinel-2 images automatically remove clouds and shadows in batches
2) Combine Landsat and Sentinel-2 to calculate vegetation index and annual synthesis in batches
3) Statistical analysis of the number of available images and the number of cloud-free observations in the study area
4) Synthesis of annual NDVI vegetation numbers in China and search for the greenest DOY time of the year
5) Moving window smoothing of time series optical image data
6) Stratified random sampling and sample export, sample local evaluation and data upload to the cloud
Analysis on the Variation Trend of Rainfall in China in the Past 40 Years
the third part
Typical case comprehensive drill
Case 1: Flood Disaster Monitoring
Based on images such as Sentinel-1 radar, a typical flood disaster is taken as an example to monitor the affected area. The case content includes multi-source image data processing and the construction of different water body recognition algorithms, such as OSTU global automatic segmentation and local adaptive threshold method, and the use of different methods to determine the disaster area, disaster area statistics and visual output, etc.
Case 2: Flood Sensitivity and Risk Modeling
Combining spatial data sets such as ESA10m resolution land cover products, terrain (elevation and slope), MERIT global hydrological data, JRC surface water data products, etc., with the help of cloud platform to calculate the distance between different land types and open waters, the height above the nearest drainage system ( HAND) and rainfall frequency (a representative of rainfall intensity and duration) are used as input parameters for simulating flood sensitivity, and then the weighted linear combination WLC method is used to draw a flood sensitivity distribution map. The content involves reclassification and grading of different data products, Euclidean distance calculation, image set map cycle and analysis modeling, etc.
Case 3: Water Quality Monitoring
Combined with Landsat 8/9 and JRC surface aquatic products in the past ten years, use such as NDSSI normalized differential suspended sediment index, NDTI normalized differential turbidity index, etc. to monitor water quality changes in water catchment areas, and count water catchment areas Monthly water quality changes. The content involves time series image preprocessing, vegetation index calculation, monthly and yearly image synthesis, image set Reducer operation, null value filtering and mapping, etc.
Case 4: River profile monitoring
Demonstrate the application of Earth Engine to river hydrology and geomorphology. Specifically demonstrate how to use the cloud platform to distinguish rivers from other water bodies, perform basic shape analysis, extract the centerline and width of rivers, and detect changes in river shape over time. The content involves open source package calling, RivWidthCloud key code interpretation, time series image processing, water body remote sensing recognition and data export.
Case 5: Groundwater change monitoring
The use of GRACE satellite observations to assess groundwater storage changes in large river basins is described in detail, including the application of remote sensing estimated total storage anomalies, land surface model output GLDAS, and in situ observations to address groundwater storage invariance. The content involves using GRACE to draw changes in total water storage, water storage trends, and solving changes in groundwater storage in river basins. The practice knowledge points include image set filtering, set Join, map loop, trend analysis, visualization, etc.
Case 6: Remote sensing mapping of mangroves
Combining Sentinel-1/2 multi-source remote sensing images and machine learning algorithms to draw mangrove distribution maps. Topics include optical and radar data processing, machine learning algorithm application, inversion accuracy evaluation, variable importance analysis, result visualization, raster and vector conversion, etc., and will demonstrate how to use mangrove habitat feature information (such as terrain, and sea Connecting, etc.) to fine-tune the classification results and realize the drawing of high-precision distribution maps.
Link to the original text: Typical case practice and GPT model application of remote sensing cloud big data in the fields of disasters, water bodies and wetlands