GEE高阶案例——ee.Image和ee.ImageCollection的影像列表的可视化
GEE高阶案例——ee.Image和ee.ImageCollection的影像列表的可视化
ee.Image和ee.ImageCollection的利用列表进行可视化操作
这里我们主要的操作是利用简单的列表进行波段选择。可以利用[波段名称]，[波段数字]，[slice1:4]等等来实现我们遥感影像的选取。
代码：
安装python包
!pip install eemont
!pip install geemap导入安装包和验证
import ee, eemont, geemap
import geemap.colormaps as cm
Map = geemap.Map()定义研究区
poi = ee.Geometry.PointFromQuery("Oporto, Portugal",user_agent = "eemont-tutorial-024")影像处理分析
S2 = (ee.ImageCollection("COPERNICUS/S2_SR")
.filterBounds(poi)
.filterDate("2020-01-01","2020-07-01")
.preprocess()
.spectralIndices())容器仿真方法
ee.ImageCollection 如果想知道图像集合中有多少幅图像，可以使用 len() 方法：
len(S2)73
如果要从集合中选择特定频段，可以使用 collection[band] 或 collection[[band1,band2,...,bandn]]：
RGB = S2[["B2","B3","B4"]]您还可以使用波段指数，或者数字或者slice等来实现这一功能：
RGB = S2["B[2-4]"]
RGB = S2[[1,2,3]]
RGB = S2[1:4]使用容器模拟方法创建合成物！
Map = geemap.Map()
Map.addLayer(S2[[3,2,1]].median(),{"min":0,"max":0.3},"RGB")
Map.centerObject(poi)
Map如果要从集合中选择图像，可将集合转换为列表，然后使用容器模拟方法！
我们将从集合中选择第一、第三和第五幅图像。
首先，我们将集合转换为列表：
S2list = S2.toList(S2.size())
S2list.getInfo()'VEGETATION_PERCENTAGE': 6.85553, 'SOLAR_IRRADIANCE_B12': 85.25, 'SOLAR_IRRADIANCE_B10': 367.15, 'SENSOR_QUALITY': 'PASSED', 'SOLAR_IRRADIANCE_B11': 245.59, 'GENERATION_TIME': 1593324824000, 'SOLAR_IRRADIANCE_B8A': 955.32, 'FORMAT_CORRECTNESS': 'PASSED', 'CLOUD_COVERAGE_ASSESSMENT': 83.468688, 'THIN_CIRRUS_PERCENTAGE': 25.003883, 'system:time_end': 1593313613223, 'WATER_VAPOUR_RETRIEVAL_ACCURACY': 0, 'system:time_start': 1593313613223, 'DATASTRIP_ID': 'S2A_OPER_MSI_L2A_DS_EPAE_20200628T061344_S20200628T030457_N02.14', 'PROCESSING_BASELINE': '02.14', 'SENSING_ORBIT_NUMBER': 32, 'NODATA_PIXEL_PERCENTAGE': 38.487333, 'SENSING_ORBIT_DIRECTION': 'DESCENDING', 'GENERAL_QUALITY': 'PASSED', 'GRANULE_ID': 'L2A_T50TMK_A026198_20200628T030457', 'REFLECTANCE_CONVERSION_CORRECTION': 0.967843675317843, 'MEDIUM_PROBA_CLOUDS_PERCENTAGE': 47.631767, 'MEAN_INCIDENCE_AZIMUTH_ANGLE_B8': 104.057248542272, 'DATATAKE_TYPE': 'INS-NOBS', 'MEAN_INCIDENCE_AZIMUTH_ANGLE_B9': 105.861973902451, 'MEAN_INCIDENCE_AZIMUTH_ANGLE_B6': 104.999263430107, 'MEAN_INCIDENCE_AZIMUTH_ANGLE_B7': 105.19973338743, 'MEAN_INCIDENCE_AZIMUTH_ANGLE_B4': 104.599846509095, 'MEAN_INCIDENCE_ZENITH_ANGLE_B1': 9.26483116852418, 'NOT_VEGETATED_PERCENTAGE': 3.850959, 'MEAN_INCIDENCE_AZIMUTH_ANGLE_B5': 104.806888871887, 'RADIOMETRIC_QUALITY': 'PASSED', 'MEAN_INCIDENCE_AZIMUTH_ANGLE_B2': 103.845170510872, 'MEAN_INCIDENCE_AZIMUTH_ANGLE_B3': 104.253550988713, 'MEAN_INCIDENCE_ZENITH_ANGLE_B5': 9.16945293581117, 'MEAN_INCIDENCE_AZIMUTH_ANGLE_B1': 105.623602057599, 'MEAN_INCIDENCE_ZENITH_ANGLE_B4': 9.1511983118597, 'MEAN_INCIDENCE_ZENITH_ANGLE_B3': 9.12658446045674, 'MEAN_INCIDENCE_ZENITH_ANGLE_B2': 9.10159606126085, 'MEAN_INCIDENCE_ZENITH_ANGLE_B9': 9.29833105492727, 'MEAN_INCIDENCE_ZENITH_ANGLE_B8': 9.11119453954433, 'MEAN_INCIDENCE_ZENITH_ANGLE_B7': 9.21252733317911, 'DARK_FEATURES_PERCENTAGE': 0.355675, 'HIGH_PROBA_CLOUDS_PERCENTAGE': 10.833038, 'MEAN_INCIDENCE_ZENITH_ANGLE_B6': 9.18986663198901, 'UNCLASSIFIED_PERCENTAGE': 4.527044, 'MEAN_SOLAR_ZENITH_ANGLE': 22.5118973098276, 'MEAN_INCIDENCE_ZENITH_ANGLE_B8A': 9.2372672183666, 'RADIATIVE_TRANSFER_ACCURACY': 0, 'MGRS_TILE': '50TMK', 'CLOUDY_PIXEL_PERCENTAGE': 83.468688, 'PRODUCT_ID': 'S2A_MSIL2A_20200628T025551_N0214_R032_T50TMK_20200628T061344', 'MEAN_INCIDENCE_ZENITH_ANGLE_B10': 9.13676392418118, 'SOLAR_IRRADIANCE_B9': 812.92, 'SNOW_ICE_PERCENTAGE': 0, 'DEGRADED_MSI_DATA_PERCENTAGE': 0, 'MEAN_INCIDENCE_ZENITH_ANGLE_B11': 9.18102900320746, 'MEAN_INCIDENCE_ZENITH_ANGLE_B12': 9.23541231406127, 'SOLAR_IRRADIANCE_B6': 1287.61, 'MEAN_INCIDENCE_AZIMUTH_ANGLE_B10': 104.554556943066, 'SOLAR_IRRADIANCE_B5': 1424.64, 'MEAN_INCIDENCE_AZIMUTH_ANGLE_B11': 105.017646027147, 'SOLAR_IRRADIANCE_B8': 1041.63, 'MEAN_INCIDENCE_AZIMUTH_ANGLE_B12': 105.532199227014, 'SOLAR_IRRADIANCE_B7': 1162.08, 'SOLAR_IRRADIANCE_B2': 1959.66, 'SOLAR_IRRADIANCE_B1': 1884.69, 'SOLAR_IRRADIANCE_B4': 1512.06, 'GEOMETRIC_QUALITY': 'PASSED', 'SOLAR_IRRADIANCE_B3': 1823.24, 'system:asset_size': 930285505, 'WATER_PERCENTAGE': 0.939834, 'system:index': '20200628T025551_20200628T030457_T50TMK', 'DATATAKE_IDENTIFIER': 'GS2A_20200628T025551_026198_N02.14', 'AOT_RETRIEVAL_ACCURACY': 0, 'SPACECRAFT_NAME': 'Sentinel-2A', 'cloud_mask': {'type': 'Image', 'bands': [{'id': 'probability', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 255}, 'dimensions': [10980, 10980], 'crs': 'EPSG:32650', 'crs_transform': [10, 0, 399960, 0, -10, 4500000]}], 'version': 1593657504842083, 'id': 'COPERNICUS/S2_CLOUD_PROBABILITY/20200628T025551_20200628T030457_T50TMK', 'properties': {'system:time_start': 1593312951000, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[115.83391273005041, 39.65570880191806], [115.83392735250536, 39.65570819516246], [117.11372157909372, 39.66150634210722], [117.11377544025689, 39.661542869738895], [117.1138348670573, 39.66157390309806], [117.11383854756858, 39.66158869507626], [117.11550249100466, 40.650753672685155], [117.11545428566758, 40.65079522150662], [117.11541344128565, 40.65084094968039], [117.11539396165452, 40.650843793685524], [115.81688569582906, 40.6448403917732], [115.81683171569112, 40.644803233824405], [115.81677204866992, 40.64477149287509], [115.81676861849282, 40.64475673572606], [115.82539420248055, 40.150295002117005], [115.83380892864993, 39.65579703594504], [115.83385712860958, 39.655756044964406], [115.83389828811886, 39.655710764692735], [115.83391273005041, 39.65570880191806]]}, 'system:time_end': 1593399351000, 'system:asset_size': 28499638, 'system:index': '20200628T025551_20200628T030457_T50TMK'}}, 'SATURATED_DEFECTIVE_PIXEL_PERCENTAGE': 0}}]
然后，我们就可以选择图像了！
S2selected = S2list[[0,2,4]]现在，我们在 S2selected 列表中有三幅图像：
len(S2selected)这里是3景影像的长度
现在，让我们选择从第 21 张到最后一张的所有图片。
嘘！我们可以使用切片！
S2selected = S2list[20:]看看我们有多少张影像！
len(S2selected)52景影像
如果我们不想选择最后一幅图像，可以使用负指数！下面是一个例子：
S2selected = S2list[20:-5]len(S2selected)47 但它们是在 ee.List 对象中的 ee.Image 对象。我们可以保持这种状态，或者将它们转换成一个 ee.ImageCollection 对象！
S2selected = ee.ImageCollection(S2selected)ee.Image 我们还可以为 ee.Image 对象选择波段！
S2img = S2.first()让我们选择 NDVI：
NDVI = S2img['NDVI']
RGBimg = S2img[1:4]可视化
Map = geemap.Map()
Map.addLayer(S2img["NDVI"],{"min":0,"max":1,"palette":cm.palettes.ndvi},"NDVI")
Map.centerObject(poi)
Map