[null,null,["最后更新时间 (UTC)：2025-07-25。"],[[["\u003cp\u003eThe \u003ccode\u003efeatureCollection.reduceToImage()\u003c/code\u003e method in Earth Engine converts vector data (like county boundaries) into raster images by assigning pixel values based on a specified property (e.g., land area).\u003c/p\u003e\n"],["\u003cp\u003eTo avoid errors, it's crucial to pre-filter the data to remove any features with null values for the property being used to create the image.\u003c/p\u003e\n"],["\u003cp\u003eWhen dealing with overlapping features, you need to specify a reducer (like \u003ccode\u003eee.Reducer.first()\u003c/code\u003e) to determine how their properties are aggregated within each pixel.\u003c/p\u003e\n"],["\u003cp\u003eThe output image's scale is dynamic, adjusting to the current zoom level in the Code Editor, ensuring visual clarity at different scales.\u003c/p\u003e\n"],["\u003cp\u003eThis technique allows you to visualize feature properties geographically, using a color gradient to represent variations in values, as demonstrated in the county land area example.\u003c/p\u003e\n"]]],["Vector to raster conversion is achieved using `featureCollection.reduceToImage()`. This method assigns pixel values based on specified feature properties. The example converts US county data to an image representing land area. It filters out null values, applies `ee.Reducer.first()` to handle property aggregation, and sets color gradients based on county size. `geemap` is used for Python interactive development and displaying the output image, similar to the JavaScript example. The scale is dynamically set by the zoom level.\n"],null,["# Vector to Raster Conversion\n\nVector to raster conversion in Earth Engine is handled by the\n`featureCollection.reduceToImage()` method. This method assigns pixels under\neach feature the value of the specified property. This example uses the counties data\nto create an image representing the land area of each county:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load a collection of US counties.\nvar counties = ee.FeatureCollection('TIGER/2018/Counties');\n\n// Make an image out of the land area attribute.\nvar landAreaImg = counties\n .filter(ee.Filter.notNull(['ALAND']))\n .reduceToImage({\n properties: ['ALAND'],\n reducer: ee.Reducer.first()\n});\n\n// Display the county land area image.\nMap.setCenter(-99.976, 40.38, 5);\nMap.addLayer(landAreaImg, {\n min: 3e8,\n max: 1.5e10,\n palette: ['FCFDBF', 'FDAE78', 'EE605E', 'B63679', '711F81', '2C105C']\n});\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Load a collection of US counties.\ncounties = ee.FeatureCollection('TIGER/2018/Counties')\n\n# Make an image out of the land area attribute.\nland_area_img = counties.filter(ee.Filter.notNull(['ALAND'])).reduceToImage(\n properties=['ALAND'], reducer=ee.Reducer.first()\n)\n\n# Display the county land area image.\nm = geemap.Map()\nm.set_center(-99.976, 40.38, 5)\nm.add_layer(\n land_area_img,\n {\n 'min': 3e8,\n 'max': 1.5e10,\n 'palette': ['FCFDBF', 'FDAE78', 'EE605E', 'B63679', '711F81', '2C105C'],\n },\n)\nm\n```\n\nSpecify a reducer to indicate how to aggregate properties of\noverlapping features. In the previous example, since there is no overlap, an\n`ee.Reducer.first()` is sufficient. As in [this\nexample](/earth-engine/guides/reducers_reduce_columns), pre-filter the data to eliminate nulls that can not be turned into an image.\nThe output should look something like Figure 1, which maps a color gradient to\ncounty size. Like all image-outputting reducers in Earth Engine, the\nscale is dynamically set by the output. In this case, the scale corresponds to the\nzoom level in the Code Editor.\nFigure 1. The result of `reduceToImage()` using the 'ALAND' (land area) property of the 'TIGER/2018/Counties' `FeatureCollection`."]]
