python - 使用 matplotlib basemap 绘制 GDAL 栅格

Question

我想使用 matplotlib basemap 绘制光栅 tiff ( download -723Kb)。我的光栅投影坐标以米为单位:

In  [2]:
path = r'albers_5km.tif'
raster = gdal.Open(path, gdal.GA_ReadOnly)
array = raster.GetRasterBand(20).ReadAsArray()

print ('Raster Projection:\n', raster.GetProjection())
print ('Raster GeoTransform:\n', raster.GetGeoTransform())

Out [2]:
Raster Projection:
 PROJCS["unnamed",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]],PROJECTION["Albers_Conic_Equal_Area"],PARAMETER["standard_parallel_1",15],PARAMETER["standard_parallel_2",65],PARAMETER["latitude_of_center",30],PARAMETER["longitude_of_center",95],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]]]
Raster GeoTransform:
 (190425.8243, 5000.0, 0.0, 1500257.0112, 0.0, -5000.0)

如果我尝试使用 Robin 投影绘制此图，使用 contourf 和 latlon=False，则 x 和 y 被假定为 map 投影坐标(请参阅 docs，我想这就是我所拥有的)。

但是如果我看一下情节，我会注意到它位于左下角非常小:

使用这段代码:

In  [3]:
xy = raster.GetGeoTransform() 
x = raster.RasterXSize 
y = raster.RasterYSize    
lon_start = xy[0] 
lon_stop = x*xy[1]+xy[0] 
lon_step = xy[1]    
lat_start = xy[3] 
lat_stop = y*xy[5]+xy[3] 
lat_step = xy[5]

fig = plt.figure(figsize=(16,10)) 
map = Basemap(projection='robin',resolution='c',lat_0=0,lon_0=0)

lons = np.arange(lon_start, lon_stop, lon_step) 
lats = np.arange(lat_start, lat_stop, lat_step)    
xx, yy = np.meshgrid(lons,lats)

levels = [array.min(),-0.128305,array.max()] 
map.contourf(xx, yy,array, levels, cmap=cm.RdBu_r, latlon=False)

map.colorbar(cntr,location='right',pad='10%')    
map.drawcoastlines(linewidth=.5) 
map.drawcountries(color='red')

终究要的不是世界观而是细节观。但这给了我一个缩放级别，其中绘制了海岸线和国家/地区，但数据再次放置在左下角，但没有以前那么小:

使用以下代码:

In  [4]:
extent = [ xy[0],xy[0]+x*xy[1], xy[3],xy[3]+y*xy[5]]
width_x = (extent[1]-extent[0])*10
height_y = (extent[2]-extent[3])*10

fig = plt.figure(figsize=(16,10))
map = Basemap(projection='stere', resolution='c', width = width_x , height = height_y, lat_0=40.2,lon_0=99.6,)

xx, yy = np.meshgrid(lons,lats)
levels = [array.min(),-0.128305,array.max()]
map.contourf(xx, yy, array, levels, cmap=cm.RdBu_r, latlon=False)

map.drawcoastlines(linewidth=.5)
map.drawcountries(color='red')

Answer 1

您可以使用以下代码转换坐标，它会自动将栅格投影作为源坐标系，将 basemap 对象的投影作为目标坐标系。

导入

from mpl_toolkits.basemap import Basemap
import osr, gdal
import matplotlib.pyplot as plt
import numpy as np

坐标转换

def convertXY(xy_source, inproj, outproj):
    # function to convert coordinates
    
    shape = xy_source[0,:,:].shape
    size = xy_source[0,:,:].size

    # the ct object takes and returns pairs of x,y, not 2d grids
    # so the the grid needs to be reshaped (flattened) and back.
    ct = osr.CoordinateTransformation(inproj, outproj)
    xy_target = np.array(ct.TransformPoints(xy_source.reshape(2, size).T))

    xx = xy_target[:,0].reshape(shape)
    yy = xy_target[:,1].reshape(shape)
    
    return xx, yy

读取和处理数据

# Read the data and metadata
ds = gdal.Open(r'albers_5km.tif')

data = ds.ReadAsArray()
gt = ds.GetGeoTransform()
proj = ds.GetProjection()

xres = gt[1]
yres = gt[5]

# get the edge coordinates and add half the resolution 
# to go to center coordinates
xmin = gt[0] + xres * 0.5
xmax = gt[0] + (xres * ds.RasterXSize) - xres * 0.5
ymin = gt[3] + (yres * ds.RasterYSize) + yres * 0.5
ymax = gt[3] - yres * 0.5

ds = None

# create a grid of xy coordinates in the original projection
xy_source = np.mgrid[ymax+yres:ymin:yres, xmin:xmax+xres:xres]

绘图

# Create the figure and basemap object
fig = plt.figure(figsize=(12, 6))
m = Basemap(projection='robin', lon_0=0, resolution='c')

# Create the projection objects for the convertion
# original (Albers)
inproj = osr.SpatialReference()
inproj.ImportFromWkt(proj)

# Get the target projection from the basemap object
outproj = osr.SpatialReference()
outproj.ImportFromProj4(m.proj4string)

# Convert from source projection to basemap projection
xx, yy = convertXY(xy_source, inproj, outproj)

# plot the data (first layer)
im1 = m.pcolormesh(xx, yy, data[0,:,:], cmap=plt.cm.jet, shading='auto')

# annotate
m.drawcountries()
m.drawcoastlines(linewidth=.5)

plt.savefig('world.png',dpi=75)

如果您需要 100% 正确的像素位置，您可能需要自己非常仔细检查坐标数组的创建(因为我根本没有)。希望这个例子能让您走上正轨。