> ## Documentation Index > Fetch the complete documentation index at: https://docs.nikaplanet.com/llms.txt > Use this file to discover all available pages before exploring further. # Urban Construction Near Riparian Areas > Analyze riparian zone urbanization gradients and quantify built-up surface density changes with distance from river systems # Urban Construction Near Riparian Areas Urban Construction Near Riparian Areas ## Riparian Zone Urbanization Gradients This project performs an analysis of riparian zone urbanization gradients near the Amazon River, specifically focusing on a defined area near Manaus. It quantifies how built-up surface density changes with increasing distance from river systems, providing insights into human impact on riparian ecosystems. ## Resources Needed ### Software * Python 3.x ### Libraries * **earthengine-api**: For interacting with Google Earth Engine * **pandas**: For data manipulation and analysis * **matplotlib**: For creating static, interactive, and animated visualizations in Python * **numpy**: For numerical operations * **seaborn**: For statistical data visualization * **Pillow (PIL)**: For image processing * **requests**: For making HTTP requests (e.g., downloading images) ### Google Earth Engine Account An active Google Earth Engine account is required to access the spatial datasets. ### Internet Connection Essential for accessing Earth Engine data and services. ## Input Files This project does not require explicit input files from your local machine. All necessary spatial data is accessed directly from the Google Earth Engine data catalog: * **WWF/HydroSHEDS/v1/FreeFlowingRivers**: Global dataset of free-flowing rivers * **JRC/GHSL/P2023A/GHS\_BUILT\_S**: Global Human Settlement Layer (GHSL) Built-up Surface dataset for 2020 * **FAO/GAUL/2015/level2** (Optional): Administrative boundaries used if the AOI is defined by an administrative name (not used in the provided code's default AOI) ## Output Files The project generates output files within a newly created output folder: * **riparian\_amazon\_analysis\_YYYYMMDD\_HHMMSS.png**: A high-resolution PNG image of the generated plots, visualizing the urbanization gradient and the spatial distribution of buffer zones * **riparian\_amazon\_results\_YYYYMMDD\_HHMMSS.csv**: A CSV file containing the detailed urbanization metrics for each riparian buffer zone, including area, mean built-up percentage, and standard deviation * **riparian\_amazon\_summary\_YYYYMMDD\_HHMMSS.txt**: A text file providing a summary report of the analysis, including key statistics, correlations, and a breakdown of built-up percentages per zone ## Step-by-Step Analysis ### 1. Initialize Earth Engine Authenticate and initialize the Google Earth Engine API to enable data access. ### 2. Define Area of Interest (AOI) Specify the geographical region for the analysis, in this case, a rectangle near Manaus, Amazon River. ### 3. Load Datasets Import the HydroRIVERS dataset for river networks and the GHSL Built-up Surface dataset for urbanization data from Earth Engine. ### 4. Filter Rivers Clip and filter the global HydroRIVERS dataset to retain only the river segments within the defined AOI. ### 5. Create Buffer Zones Generate non-overlapping buffer rings around the filtered river segments at specified distances (e.g., 0-100m, 100-200m, etc.). ### 6. Extract Urbanization Metrics For each buffer zone, calculate descriptive statistics (mean, standard deviation, min, max, pixel count) of the built-up surface percentage using the GHSL data. ### 7. Create Results DataFrame Organize the extracted urbanization metrics into a pandas DataFrame, adding columns for distance ranges and midpoints for easier analysis. ### 8. Generate Buffer Map Image Create a static thumbnail image from Earth Engine visualizing the defined buffer zones and rivers within the AOI, with distinct colors for each zone. ### 9. Visualize Gradient Generate a multi-panel plot including a line plot of urbanization intensity vs. distance, a bar chart of built-up percentages per zone, a scatter plot of area vs. built-up, and the generated buffer map image. ### 10. Perform Statistical Analysis Calculate key statistics, such as correlation between distance and built-up density, and the average gradient slope, to quantify the urbanization pattern. ### 11. Export Results Save the detailed results DataFrame to a CSV file, and generate a comprehensive summary report as a text file in a dedicated output folder. ***

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