本文将介绍通过Python绘制地形图的方法，所需第三方Python相关模块包括 rasterio、geopandas、cartopy 等，可通过 pip 等方式安装。

1 示例代码

1.1 导入相关模块

import rasterio
import geopandas as gpd
import numpy as np
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap

1.2 设置地图字体及字号

plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams["font.size"] = 14

1.3 绘制地图

# 设置投影：墨卡托投影
# cartopy 投影说明：https://www.cnblogs.com/youxiaogang/p/14247184.html
projection = ccrs.Mercator()


# 绘制地图
fig, ax = plt.subplots(figsize=(20, 10), subplot_kw={'projection': projection})


# 设置地图范围（数值参数为对应投影下的范围坐标）
ax.set_extent([13530000, 14630000, 4960000, 5850000], crs=projection)


# 读取矢量文件
shp = gpd.read_file("Data/Jilin_Mercator.shp")
shp.plot(ax=ax, transform=projection, edgecolor="black", linewidth=1, facecolor="none")


# 创建自定义颜色映射
colors = ["#369121", "#95C769", "#FFFFBF", "#E6865A", "#D14E30", "#BA1414"]
n_bins = 100  # 定义色带的颜色数量
cmap_name = "green_brown"
cm = LinearSegmentedColormap.from_list(cmap_name, colors, N=n_bins)


# 读取地形栅格数据
dataset = rasterio.open("Data/DEM_Jilin_Mercator.tif")
data = dataset.read(1)  # 读取第一个波段的数据
nodata_value = dataset.nodata   # 获取NoData值
# 创建掩膜，去除NoData区域
data = np.ma.masked_where(data == nodata_value, data)


# 将地形栅格添加到地图中
extent = [dataset.bounds.left, dataset.bounds.right,
dataset.bounds.bottom, dataset.bounds.top]
im = ax.imshow(data, origin="upper", extent=extent,
transform=projection, cmap=cm)


# 绘制网格线并添加标签
gl = ax.gridlines(draw_labels=True, linestyle="--", color="#4F4F4F")
gl.xlocator = plt.FixedLocator(range(120, 135, 3))
gl.ylocator = plt.FixedLocator(range(40, 50, 2))


# 添加色带，设置色带的缩放比例为 0.4，主图和色带之间的间距为 0.1
cbar = plt.colorbar(im, ax=ax, orientation="horizontal", shrink=0.4, pad=0.1)
cbar.set_label("Elevation (m)", labelpad=10) # 设置色带标签与色带的距离为 10 点
cbar.ax.xaxis.set_label_position("top")  # 设置色带标签位置


plt.savefig("Pic.jpg", dpi=600)
plt.show()

2 结果图展示