In recent years, remote sensing technology has developed by leaps and bounds. Multiple remote sensing platforms such as aerospace, aviation, and near space have continued to increase. The spatial, temporal, and spectral resolution of data has continued to increase. The amount of data has soared. Remote sensing data has become more and more characteristic of big data. . The emergence of remote sensing big data provides unprecedented opportunities for related research, but how to deal with these data also poses great challenges. Traditional workstations and servers have been unable to meet the needs of large-scale, multi-scale massive remote sensing data processing.
Represented by Earth Engine (GEE), online visualization computing and analysis cloud platforms for global-scale earth science data (especially satellite remote sensing data) are becoming more and more widely used. The platform stores and synchronizes MODIS, Landsat, Sentinel and other satellite imagery, climate and weather, and geophysics data sets that are commonly used in the field of remote sensing, exceeding 60PB. The data is processed. Compared with traditional remote sensing image processing tools such as ENVI, GEE has incomparable advantages in processing massive remote sensing data. On the one hand, it provides abundant computing resources, and on the other hand, its huge cloud storage saves a large amount of data download for researchers. And the time of preprocessing, the calculation and analysis visualization of remote sensing data represent the most advanced level in this field in the world, and it is a revolution in the field of remote sensing.
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Part I: GEE Practice
Massive remote sensing data processing and GEE cloud computing technology application
1. First understanding of GEE and development platform
1. Introduction of GEE platform and typical application cases;
2. Introduction to GEE JavaScript development environment and commonly used data resources;
3.JavaScript basics, including variables, operators, arrays, judgment and loop statements, etc.;
4. Important concepts of GEE remote sensing cloud and typical data analysis process.
5. Getting to know GEE JavaScript objects and platforms
Images and Image Collections
Geometry, Features, and Feature Sets
date, character, number
arrays, lists, dictionaries
Image/image set, feature/feature set data query, spatio-temporal filtering, visualization, attribute viewing, etc.
Introduction to the most commonly used APIs for main objects
Program debugging and error reminder
2. Fundamentals of Image Big Data Processing
1.
Image mathematical operations, relational/conditional/Boolean operations, morphological filtering, texture feature extraction, etc.
Image masking, cropping and mosaicing
Loop iteration of collection objects (map/iterate)
Collection object union (Join)
More about arrays and array images
Image object-oriented analysis
2. Application of main functions
Landsat/Sentinel-2 image batch cloud removal
Landsat/Sentinel-2 sensor normalization, vegetation index calculation and Tasseled cap transformation, etc.
Smoothing and Spatial Interpolation of Time Series Optical Images
3. Data Integration Reduce
1.
Integration of images and image collections, such as annual image synthesis for specified time windows
Image area statistics and field statistics, classification post-processing
Feature Set Attribute Column Statistics
Conversion between raster and vector
Group Integration and Regional Statistics
Linear regression analysis of image sets, imagery, and feature sets
2.
Statistical analysis of the number of available Landsat images and the number of cloud-free observations in the study area
Synthesis of annual NDVI vegetation numbers in China and search for the greenest DOY time of the year Analysis of
temporal and spatial variation trends of rainfall on a national scale and a 30-year scale
4. Cloud Data Visualization
1.
Feature and feature set attribute mapping (bar charts, histograms, stacked column charts, scatter plots, etc.)
Image mapping (regional statistics, classification maps, histograms, scatter plots, line graphs, pie charts, etc.)
Image set mapping (sample point time series diagram, regional statistical time series diagram, etc.)
Array and linked list drawing (scatter plot, line transect, etc.
Graphic style and property settings
2.
Phenological Analysis and Mapping of Different Surface Vegetation Based on MODIS Time Series Images
Analysis of annual forest spatio-temporal change and thematic map drawing based on Hansen products
5. Data import and export and asset management
1.
Upload individual assets with different vector data
Image data upload personal assets, attribute settings, etc.
Batch export of images (Asset and Driver)
Vector data export (Asset and Driver)
Statistical Analysis Results Export
2.
China Flux site data upload and display, station basic weather and terrain data export
Batch export or download annual image synthesis to personal Asset or Driver platform
6. Machine Learning Algorithm
1.
Sample sampling (random sampling, stratified random sampling)
Supervised classification algorithms (random forest, CART, Bayesian, SVM, decision tree, etc.)
Unsupervised classification algorithms (wekaKMeans, wekaLVQ, etc.)
TensorFlor model
Classification Accuracy Evaluation
2.
Forest dynamic monitoring based on combined optical and radar time series images
Research on Automatic Water Extraction and Flood Monitoring
seven,
Review the main functions of GEE with a complete land use classification case. Including different ground sample preparation, multi-source remote sensing image preprocessing, algorithm development, classification post-processing, accuracy assessment, spatial statistical analysis and mapping, etc.
GEE code optimization, common errors and debugging summary
Part II: Application of GEE Remote Sensing Cloud Big Data in Forestry
1. Getting to know GEE and its development platform
Introduction to GEE platform and typical application cases;
Introduction to GEE development environment and common data resources;
Introduction to JavaScript basics;
Python-GEE environment construction;
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.
2. Basic knowledge of GEE
Basic image operations and operations: mathematical operations, relational/conditional/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.
3. Mini-cases of important knowledge points
1) Landsat, Sentinel-2 images automatically remove clouds and shadows in batches
2) Combine Landsat and Sentinel-2 to calculate vegetation indexing effect 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
7) Analysis of the trend of rainfall changes in China in the past 40 years
8) Statistical analysis of annual forest loss in a certain area (based on Hansen forest products)
Case 1: Forest identification with joint multi-source remote sensing data
Introduce in detail the complete process of combining Landsat time series optical images and PALSAR-2 radar data, as well as the decision tree algorithm to achieve remote sensing classification of typical land types such as forests. Topics involved spatiotemporal filtering of image data, batch cloud masking and vegetation index calculation for optical images; stratified random sampling and sample export, local quality control and cloud upload, random segmentation of samples, separability analysis, classification algorithm construction and application, Classification post-processing and accuracy evaluation, thematic map drawing, etc.
Case 2: Forests on a Long Time Scale
State monitoring uses long-term MODIS or Landsat image data to monitor the state of the forest for a long time and analyze the greening or browning of forest vegetation. Topics include time series image preprocessing, image set connection, image synthesis, non-parametric detection of changing trends, significance testing, quantification and grading of changing trends, spatial statistics and result visualization, and thematic map drawing, etc.
Case 3: Deforestation and Degradation Monitoring
Combining Landsat series images, spectral separation model and NDFI normalized difference score index to realize forest deforestation and degradation monitoring. Topics include image preprocessing, mixed pixel decomposition, NDFI index calculation, function encapsulation, change detection and intensity classification, result visualization, thematic map drawing, etc.
Case 4: Forest Fire Monitoring
This paper introduces in detail the use of Landsat and Sentinel-2 time series optical remote sensing images to monitor forest fire loss and achieve fire intensity classification. Topics include image filtering, Landsat and Sentinel-2 optical image cloud removal and other preprocessing, vegetation index calculation, image synthesis, fire area identification and disaster intensity classification, statistical analysis and visualization of results, etc.
Case 5: Long-term Scale Forest Disturbance Monitoring
Combining 30 years of Landsat and other optical images with the classic LandTrendr algorithm to monitor forest disturbances. Topics include preprocessing of long-term remote sensing images, batch calculation of vegetation indices, annual image synthesis, concept and usage of array images, LandTrendr algorithm principle and parameter settings, interpretation of forest disturbance results and spatial statistical analysis, visualization and thematic map drawing, etc.
Case 6: Retrieval of key physiological parameters of forests (tree height, biomass/carbon storage)
Combine GEDI lidar, Landsat/Sentinel-2 multispectral optical images, Sentinel-1/PALSAR-2 radar images, etc., and machine learning algorithms to retrieve key physical parameters of forests, such as tree height, biomass/carbon storage. Topics include introduction to GEDI lidar data, common optical and radar data processing, application of machine learning algorithms, inversion accuracy evaluation, variable importance analysis, and result visualization.
