在注释之后，这是适用于垂直线段的版本。请谨慎使用，因为我尚未对其进行彻底的测试！
这种方法比@whuber的答案笨拙得多-部分是因为我不是一个很好的程序员，部分是因为矢量处理有点麻烦。如果您需要垂直线段，我希望它至少可以帮助您入门。
您需要拥有Shapely，Fiona和Numpy Python软件包（及其依赖项）才能运行此软件包。
#-------------------------------------------------------------------------------
# Name: perp_lines.py
# Purpose: Generates multiple profile lines perpendicular to an input line
#
# Author: JamesS
#
# Created: 13/02/2013
#-------------------------------------------------------------------------------
""" Takes a shapefile containing a single line as input. Generates lines
perpendicular to the original with the specified length and spacing and
writes them to a new shapefile.
The data should be in a projected co-ordinate system.
"""
import numpy as np
from fiona import collection
from shapely.geometry import LineString, MultiLineString
# ##############################################################################
# User input
# Input shapefile. Must be a single, simple line, in projected co-ordinates
in_shp = r'D:\Perp_Lines\Centre_Line.shp'
# The shapefile to which the perpendicular lines will be written
out_shp = r'D:\Perp_Lines\Output.shp'
# Profile spacing. The distance at which to space the perpendicular profiles
# In the same units as the original shapefile (e.g. metres)
spc = 100
# Length of cross-sections to calculate either side of central line
# i.e. the total length will be twice the value entered here.
# In the same co-ordinates as the original shapefile
sect_len = 1000
# ##############################################################################
# Open the shapefile and get the data
source = collection(in_shp, "r")
data = source.next()['geometry']
line = LineString(data['coordinates'])
# Define a schema for the output features. Add a new field called 'Dist'
# to uniquely identify each profile
schema = source.schema.copy()
schema['properties']['Dist'] = 'float'
# Open a new sink for the output features, using the same format driver
# and coordinate reference system as the source.
sink = collection(out_shp, "w", driver=source.driver, schema=schema,
crs=source.crs)
# Calculate the number of profiles to generate
n_prof = int(line.length/spc)
# Start iterating along the line
for prof in range(1, n_prof+1):
# Get the start, mid and end points for this segment
seg_st = line.interpolate((prof-1)*spc)
seg_mid = line.interpolate((prof-0.5)*spc)
seg_end = line.interpolate(prof*spc)
# Get a displacement vector for this segment
vec = np.array([[seg_end.x - seg_st.x,], [seg_end.y - seg_st.y,]])
# Rotate the vector 90 deg clockwise and 90 deg counter clockwise
rot_anti = np.array([[0, -1], [1, 0]])
rot_clock = np.array([[0, 1], [-1, 0]])
vec_anti = np.dot(rot_anti, vec)
vec_clock = np.dot(rot_clock, vec)
# Normalise the perpendicular vectors
len_anti = ((vec_anti**2).sum())**0.5
vec_anti = vec_anti/len_anti
len_clock = ((vec_clock**2).sum())**0.5
vec_clock = vec_clock/len_clock
# Scale them up to the profile length
vec_anti = vec_anti*sect_len
vec_clock = vec_clock*sect_len
# Calculate displacements from midpoint
prof_st = (seg_mid.x + float(vec_anti[0]), seg_mid.y + float(vec_anti[1]))
prof_end = (seg_mid.x + float(vec_clock[0]), seg_mid.y + float(vec_clock[1]))
# Write to output
rec = {'geometry':{'type':'LineString', 'coordinates':(prof_st, prof_end)},
'properties':{'Id':0, 'Dist':(prof-0.5)*spc}}
sink.write(rec)
# Tidy up
source.close()
sink.close()
下图显示了脚本输出的示例。您输入一个代表中心线的shapefile，并指定垂直线的长度及其间距。输出是一个新的shapefile，该文件在此图像中包含红线，每条红线都有一个关联的属性，用于指定距轮廓起点的距离。
正如@whuber在评论中所说，一旦您进入了这个阶段，其余的工作就很容易了。下图显示了将输出添加到ArcMap的另一个示例。
使用“ 要素转栅格”工具将垂直线转换为分类栅格。将栅格设置为输出shapefile中VALUE的Dist字段。还记得设置工具Environments，这样Extent，Cell size和Snap raster是相同的底层DEM。您应该以行的栅格表示形式结束，如下所示：
最后，将此栅格转换为整数网格（使用Int工具或栅格计算器），并将其用作“ 区域统计表作为表格”工具的输入区域。您应该最终得到如下输出表：
的 VALUE表中字段给出了距原始轮廓线起点的距离。其他列为每个横断面的值提供了各种统计信息（最大值，平均值等）。您可以使用此表来绘制您的摘要配置文件。
注意：此方法的一个明显问题是，如果您的原始线非常摆动，则某些样条线可能会重叠。ArcGIS中的区域统计工具无法处理重叠的区域，因此，发生这种情况时，一条样条线将优先于另一条样条线。这可能对您正在做的事情没有问题。
祝好运！
