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| # GitHub URL: https://github.com/giswqs/qgis-earthengine-examples/tree/master/FeatureCollection/clipping.py | ||
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| import ee | ||
| from ee_plugin import Map | ||
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| roi = ee.Geometry.Polygon( | ||
| [[[-73.99891354682285, 40.74560250077625], | ||
| [-73.99891354682285, 40.74053023068626], | ||
| [-73.98749806525547, 40.74053023068626], | ||
| [-73.98749806525547, 40.74560250077625]]]) | ||
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| fc = ee.FeatureCollection('TIGER/2016/Roads').filterBounds(roi) | ||
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| clipped = fc.map(lambda f: f.intersection(roi)) | ||
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| Map.centerObject(ee.FeatureCollection(roi), 17) | ||
| Map.addLayer(ee.Image().paint(roi, 0, 2), {'palette': 'yellow'}, 'ROI') | ||
| # Map.setCenter(-73.9596, 40.7688, 12) | ||
| Map.addLayer(ee.Image().paint(clipped, 0, 3), {'palette': 'red'}, 'clipped') | ||
| Map.addLayer(fc, {}, 'Census roads', False) | ||
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| # GitHub URL: https://github.com/giswqs/qgis-earthengine-examples/tree/master/Image/band_stats.py | ||
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| import ee | ||
| from ee_plugin import Map | ||
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| # get highest value | ||
| def maxValue(img, scale=30): | ||
| max_value = img.reduceRegion(**{ | ||
| 'reducer': ee.Reducer.max(), | ||
| 'geometry': img.geometry(), | ||
| 'scale': scale, | ||
| 'maxPixels': 1e9 | ||
| }) | ||
| return max_value | ||
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| # get lowest value | ||
| def minValue(img, scale=30): | ||
| min_value = img.reduceRegion(**{ | ||
| 'reducer': ee.Reducer.min(), | ||
| 'geometry': img.geometry(), | ||
| 'scale': scale, | ||
| 'maxPixels': 1e9 | ||
| }) | ||
| return min_value | ||
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| # get mean value | ||
| def meanValue(img, scale=30): | ||
| mean_value = img.reduceRegion(**{ | ||
| 'reducer': ee.Reducer.mean(), | ||
| 'geometry': img.geometry(), | ||
| 'scale': scale, | ||
| 'maxPixels': 1e9 | ||
| }) | ||
| return mean_value | ||
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| # get standard deviation | ||
| def stdValue(img, scale=30): | ||
| std_value = img.reduceRegion(**{ | ||
| 'reducer': ee.Reducer.stdDev(), | ||
| 'geometry': img.geometry(), | ||
| 'scale': scale, | ||
| 'maxPixels': 1e9 | ||
| }) | ||
| return std_value | ||
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| dataset = ee.Image('USGS/NED') | ||
| dem = dataset.select('elevation') | ||
| # dem = ee.Image('srtm90_v4') | ||
| vis_params = {'min': 0, 'max': 3000} | ||
| Map.addLayer(dem, vis_params, 'NED', False) | ||
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| roi = ee.Geometry.Polygon( | ||
| [[[-120.18204899532924, 38.53481618819663], | ||
| [-120.18204899532924, 36.54889033300136], | ||
| [-116.75431462032924, 36.54889033300136], | ||
| [-116.75431462032924, 38.53481618819663]]]) | ||
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| image = dem.clip(roi) | ||
| Map.centerObject(image, 9) | ||
| Map.addLayer(image, vis_params, 'DEM') | ||
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| scale = image.projection().nominalScale() | ||
| print("Resolution: ", scale.getInfo()) | ||
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| scale = 30 | ||
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| print("Minimum value: ", minValue(image, scale).get('elevation').getInfo()) | ||
| print("Maximum value: ", maxValue(image, scale).get('elevation').getInfo()) | ||
| print("Average value: ", meanValue(image, scale).get('elevation').getInfo()) | ||
| print("Standard deviation: ", stdValue(image, scale).get('elevation').getInfo()) |
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| # GitHub URL: https://github.com/giswqs/qgis-earthengine-examples/tree/master/Image/image_stats_by_band.py | ||
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| import ee | ||
| from ee_plugin import Map | ||
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| image = ee.Image('USDA/NAIP/DOQQ/m_3712213_sw_10_1_20140613') | ||
| Map.setCenter(-122.466123, 37.769833, 17) | ||
| Map.addLayer(image, {'bands': ['N', 'R','G']}, 'NAIP') | ||
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| geometry = image.geometry() | ||
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| means = image.reduceRegions(geometry, ee.Reducer.mean().forEachBand(image), 10) | ||
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| print(means.getInfo()) |
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| # GitHub URL: https://github.com/giswqs/qgis-earthengine-examples/tree/master/Visualization/nwi_wetlands_symbology.py | ||
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| import ee | ||
| from ee_plugin import Map | ||
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| # NWI legend: https://www.fws.gov/wetlands/Data/Mapper-Wetlands-Legend.html | ||
| def nwi_add_color(fc): | ||
| emergent = ee.FeatureCollection( | ||
| fc.filter(ee.Filter.eq('WETLAND_TY', 'Freshwater Emergent Wetland'))) | ||
| emergent = emergent.map(lambda f: f.set( | ||
| 'R', 127).set('G', 195).set('B', 28)) | ||
| # print(emergent.first()) | ||
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| forested = fc.filter(ee.Filter.eq( | ||
| 'WETLAND_TY', 'Freshwater Forested/Shrub Wetland')) | ||
| forested = forested.map(lambda f: f.set('R', 0).set('G', 136).set('B', 55)) | ||
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| pond = fc.filter(ee.Filter.eq('WETLAND_TY', 'Freshwater Pond')) | ||
| pond = pond.map(lambda f: f.set('R', 104).set('G', 140).set('B', 192)) | ||
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| lake = fc.filter(ee.Filter.eq('WETLAND_TY', 'Lake')) | ||
| lake = lake.map(lambda f: f.set('R', 19).set('G', 0).set('B', 124)) | ||
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| riverine = fc.filter(ee.Filter.eq('WETLAND_TY', 'Riverine')) | ||
| riverine = riverine.map(lambda f: f.set( | ||
| 'R', 1).set('G', 144).set('B', 191)) | ||
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| fc = ee.FeatureCollection(emergent.merge( | ||
| forested).merge(pond).merge(lake).merge(riverine)) | ||
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| base = ee.Image(0).mask(0).toInt8() | ||
| img = base.paint(fc, 'R') \ | ||
| .addBands(base.paint(fc, 'G') | ||
| .addBands(base.paint(fc, 'B'))) | ||
| return img | ||
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| fromFT = ee.FeatureCollection("users/wqs/Pipestem/Pipestem_HUC10") | ||
| Map.addLayer(ee.Image().paint(fromFT, 0, 2), {}, 'Watershed') | ||
| huc8_id = '10160002' | ||
| nwi_asset_path = 'users/wqs/NWI-HU8/HU8_' + huc8_id + '_Wetlands' # NWI wetlands for the clicked watershed | ||
| clicked_nwi_huc = ee.FeatureCollection(nwi_asset_path) | ||
| nwi_color = nwi_add_color(clicked_nwi_huc) | ||
| Map.centerObject(clicked_nwi_huc, 10) | ||
| Map.addLayer(nwi_color, {'gamma': 0.3, 'opacity': 0.7}, 'NWI Wetlands Color') |
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| # GitHub URL: https://github.com/giswqs/qgis-earthengine-examples/tree/master/Visualization/random_color_visualizer.py | ||
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| import ee | ||
| from ee_plugin import Map | ||
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| dataset = ee.Image('USGS/NLCD/NLCD2016') | ||
| landcover = ee.Image(dataset.select('landcover')) | ||
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| Map.setCenter(-95, 38, 5) | ||
| Map.addLayer(landcover.randomVisualizer(), {}, 'Landcover') |