Table of contents
Chapter 2, Development Environment Construction
Chapter Four, Typical Case Operation Practice
Chapter 5, Input and Output and Efficient Management of Data Assets
Chapter 6, Publication-level Visualization of Cloud Data Papers
Integrating the most advanced AI natural language models such as ChatGPT and Wenxin Yiyan to assist teaching, assist students to answer doubts, provide targeted suggestions and guidance, and provide an efficient and personalized learning experience for future self-learning. At present, Earth Engine has been valued by more and more scientific and technological workers at home and abroad for its powerful functions, and its application is becoming more and more common.
To master the practical application ability of Earth Engine, based on the Python programming language, explanations and advanced training will be given in terms of platform construction, image data analysis, local and cloud data management, and cloud data paper publication-level visualization based on cases. In addition, batch processing and machine learning will be emphasized, which is suitable for students who have mastered some basics of Earth Engine and Python, or have a strong interest in programming.
Chapter 1, Theoretical Basis
1.Earth Engine platform and application, introduction of main data resources
2.Earth Engine remote sensing cloud important concepts, data types and objects, etc.
3.JavaScript and Python remote sensing cloud programming comparison and selection
4.Python basics (grammar, data type and program control structure , functions, classes and objects, etc.)
5. Commonly used Python software packages ((pandas, numpy, os, etc.) introduction and basic function demonstration (Excel/csv data file reading and data processing, directory operations, etc.) 6. JavaScript and Python remote
sensing Cloud API differences, learning methods and resource recommendations
7. Introduction to AI natural language models such as ChatGPT and Wenxinyiyan and their applications in the field of remote sensing
Chapter 2, Development Environment Construction
1. Introduction to local and cloud Python remote sensing cloud development environment
2. Local development environment construction
1) Anaconda installation, pip/conda software package installation method and virtual environment creation, etc.
2) Earthengine-api, geemap and other necessary software package installation
3 ) Remote sensing cloud local authorization management
4) Jupyter Notebook/Visual Studio Code installation and running debugging.
3. Cloud Colab development environment construction
4. Geemap introduction and common function demonstration
5. ChatGPT, Wenxin Yiyan account application and main function demonstration, such as remote sensing knowledge answering, data analysis and processing code generation, solution framework consultation, etc.
Chapter 3, the basis of remote sensing big data processing and the interaction with AI models such as ChatGPT
1. Introduction to the image data analysis and processing process of the remote sensing cloud platform: introduce the basic framework of the image data analysis and processing process of the remote sensing cloud platform, including data acquisition, data preprocessing, algorithm development, visualization, etc.
2. Object display and attribute field exploration of elements and images: Introduce how to display and explore attribute fields of elements and images on the remote sensing cloud platform, including how to select elements and image objects, view attribute information, filter data, etc.
3. Time, space and attribute filtering method of image/feature set: Introduce how to filter image/feature set in time, space and attribute, including how to select time period, geographical area and attribute conditions to achieve more accurate data analysis
4. Band operations, conditional operations, vegetation index calculations, clipping and mosaic, etc.: Introduce how to perform band operations, conditional operations, vegetation index calculations, clipping and mosaic operations on the remote sensing cloud platform to achieve more in-depth data analysis
5. Declouding of commonly used optical images such as Landsat/Sentinel-2: Introduce how to use different methods to remove clouds in commonly used optical images such as Landsat/Sentinel-2 on the remote sensing cloud platform to improve the quality of image data
6. Iterative cycle of images and feature sets: Introduce how to use the iterative cycle function of the remote sensing cloud platform to batch process images and feature sets to improve data analysis efficiency
7. Image data integration (Reducer): Introduce how to use the Reducer function of the remote sensing cloud platform to integrate multiple image data into one data set to facilitate subsequent data analysis
8. Neighborhood analysis and spatial statistics: Introduce how to perform neighborhood analysis and spatial statistics on the remote sensing cloud platform to obtain more in-depth spatial information
9. Common errors and code optimization: Introduce common errors in the data analysis process of the remote sensing cloud platform and how to optimize the code to improve the efficiency and accuracy of data analysis
10. Python remote sensing cloud data analysis exclusive package construction: Introduce how to use Python to build a data analysis exclusive package on the remote sensing cloud platform to facilitate multiple use and share analysis codes
Chapter Four, Typical Case Operation Practice
11. Machine learning classification algorithm case: This case combines Landsat and other long-term image series and machine learning algorithms to demonstrate the basic remote sensing classification process at the national scale. The specific content includes image statistics in the study area, spatial stratified random sampling, random sample segmentation, time series image preprocessing and synthesis, machine learning algorithm application, classification postprocessing and accuracy evaluation, etc.
12. Decision tree forest classification algorithm case: In this case, combined with L-band radar and Landsat optical time-series images, the decision tree classification algorithm is used to extract the forest distribution map of the designated area from 2007 to 2020, and the spatial comparison is made with JAXA annual forest products. The case involves joint use of multi-source data, construction of decision tree classification algorithm, dynamic optimization of threshold, spatial analysis of classification results, etc.
13. Flood disaster monitoring case: This case is based on Sentinel-1 C-band radar and other images to monitor provincial-level torrential rain disasters. The case content includes Sentinel-1 C image processing, construction of various water body recognition algorithms, image difference analysis, and result visualization.
14. Drought remote sensing monitoring case: This case uses 40-year-old satellite remote sensing rainfall data products such as CHIRPS to monitor the extreme drought at the provincial scale. The case content includes basic processing of meteorological data, integration of annual and monthly scale data, calculation of long-term average LPA/deviation, and visualization of data results, etc.
15. Case analysis of phenological characteristics: This case is based on time series images such as Landsat and MODIS, and analyzes the phenological differences of typical surface vegetation over the years (sampling point scale) and large-scale (such as China) phenological spatial variation characteristics through changes in vegetation indices. The case content includes time series image synthesis, image smoothing (Smoothing) and gap filling (Gap-filling), result visualization, etc.
16. A case of forest vegetation health monitoring: This case uses the 20-year MODIS vegetation index to conduct long-term monitoring of forests in selected areas and analyze the afforestation or browning of forest vegetation. Methods involving image concatenation and composition, trend analysis, spatial statistics, and visualization
17. Case of dynamic monitoring of ecological environment quality: This case uses RSEI remote sensing ecological index and Landsat series images to quickly monitor the ecological status of selected cities. The main technologies involved include calculation of vegetation index, extraction of surface temperature, normalization of data, PCA analysis of principal components, construction of RSEI ecological index and visualization of results, etc.
Chapter 5, Input and Output and Efficient Management of Data Assets
1. Interaction between local data and cloud: Introduce how to convert local csv, kml, vector and raster data with cloud data, and explain the method of data export
2. Batch download of server-side data: including direct local download, batch download of image sets, and how to quickly download large-scale and long-term data products, such as global forest products and 20-year MODIS data products, etc.
3. Local data upload and attribute setting: including the use of earthengine commands, how to upload a small amount of local vector and raster data and set attributes (small files), and how to upload data in batches and automatically set attributes, and also introduce how to use the fast Upload skills Upload very large image files, such as domestic high-score images
4. Personal data asset management: Introduce how to use Python and earthengine command line to manage personal data assets, including creating, deleting, moving, renaming and other operations, and also explain how to cancel upload/download tasks in batches
Chapter 6, Publication-level Visualization of Cloud Data Papers
1. Introduction to Python visualization and main software packages: introduce matplotlib and seaborn visualization packages, explain basic graphics concepts, graphics composition, and quick drawing of commonly used graphics, etc.
2. Drawing of topography and sample plot distribution map of the research area: Combine local or cloud vector files, cloud topographic data, etc. to draw a schematic diagram of the research area. It involves drawing process, Chinese display, color matching and beautification, etc. It will also introduce cpt-city exquisite palette palette online download and local application, etc.
3. Research area image coverage statistics and drawing: make statistics on the coverage quantity of Landsat and Sentinel and other series of images in the designated area, and the coverage of cloud-free images, and draw regional image statistical maps or pixel-level cloud-free image coverage thematic maps
4. Analysis and drawing of sample spectral characteristics and phenological characteristics: quickly draw spectral and phenological characteristics of different types of plots, dynamically download and integrate thumbnails of sampling points in the past 30 years and vegetation index time series, etc.
5. Thematic map drawing of classification results and Timelapse production of space-time dynamic time-lapse photography: single or multiple classification thematic map drawing and color matching beautification, making Timelapse with clear land use changes, and introducing animation text addition, etc.
6. Classification result area statistics and drawing: based on cloud classification results and vector boundary files, count the area of different land types in different regions, extract statistical results, and display the statistical area in different graphics; make land use change statistical drawings, etc.
How Xiaobai learns GEE, GEE-Python, and GEE in forestry application_Xiaoyan refueling blog-CSDN blog GEE has been valued and applied by more and more foreign scientific and technological workers for its powerful functions. https://blog.csdn.net/weixin_46747075/article/details/128216382?spm=1001.2014.3001.5502 Typical case practice of GEE remote sensing cloud big data forestry application and GPT model application_WangYan2022's blog-CSDN blog combines the most advanced ChatGPT, Wenxinyiyan and other AI natural language models assist teaching, assist students to answer doubts, provide targeted suggestions and guidance, not only allow students to grasp the learning content more deeply, but also provide efficient and personalized learning experience for future independent learning.
https://blog.csdn.net/WangYan2022/article/details/130722655?spm=1001.2014.3001.5502