基於DEM的坡度坡向分析

坡度坡向分析方法

坡度(slope)是地面特定區域高度變化比率的量度。坡度的表示方法有百分比法、度數法、密位法和分數法四種，其中以百分比法和度數法較為常用。本文計算的為坡度百分比資料。如當角度為45度（弧度為π/4）時，高程增量等於水平增量，高程增量百分比為100%。

 

坡向(aspect)是指地形坡面的朝向。坡向用於識別出從每個像元到其相鄰像元方向上值的變化率最大的下坡方向。坡向可以被視為坡度方向。坡向是一個角度，將按照順時針方向進行測量，角度範圍介於 0（正東）到 360（仍是正東）之間，即完整的圓。不具有下坡方向的平坦區域將賦值為-1（arcgis處理時為-1，其他可能為0）。

坡度、坡向計算一般採用擬合曲面法。擬合曲面一般採用二次曲面，即3×3的視窗，如下圖所示。每個視窗的中心為一個高程點。圖中的中心點e坡度和坡向計算過程如下。

參考連結：

[1]https://blog.csdn.net/zhouxuguang236/article/details/40017219

[2]https://blog.csdn.net/weixin_45561357/article/details/106677574

[3]https://www.cnblogs.com/gispathfinder/p/5790469.html

注意：DEM的空間座標系一定要為投影座標系。

ArcGIS坡度坡向分析

開啟DEM資料

坡度分析

 

坡度結果

坡向分析

 

坡向結果

python-gdal坡度坡向分析

from osgeo import gdal

demfile = r"D:\微信公眾號\坡度坡向\N40E117_Albers.tif"

# 獲取DEM資訊
infoDEM = gdal.Info(demfile)

# 計算坡度
slopfile = r"D:\微信公眾號\坡度坡向\N40E117_Albers_gdal_Slope.tif"
slope = gdal.DEMProcessing(slopfile, demfile, "slope", format='GTiff', slopeFormat="percent", zeroForFlat=1, computeEdges=True)

# 計算坡向
aspectfile = r"D:\微信公眾號\坡度坡向\N40E117_Albers_gdal_Aspect.tif"
b = gdal.DEMProcessing(aspectfile, demfile, "aspect", format='GTiff', trigonometric=0, zeroForFlat=1, computeEdges=True)

 

坡度結果

 

坡向結果

python坡度坡向分析

import gdal
import numpy as np
from scipy import ndimage as nd
from copy import deepcopy

demfile = r"D:\微信公眾號\坡度坡向\N40E117_Albers.tif"
slopefile = r"D:\微信公眾號\坡度坡向\N40E117_Albers_python_Slope.tif"

#讀取DEM資料
ds = gdal.Open(demfile)
cols = ds.RasterXSize
rows = ds.RasterYSize
geo = ds.GetGeoTransform()
proj = ds.GetProjection()
dem_data = ds.ReadAsArray()
data = deepcopy(dem_data).astype(np.float32)
band = ds.GetRasterBand(1)
nodata = band.GetNoDataValue()
data[data == nodata] = np.nan
# data[data<-999]=np.nan
mask = np.isnan(data)
# 將無效值或背景值臨近像元填充
if np.sum(mask) > 0:
    ind = nd.distance_transform_edt(mask, return_distances=False, return_indices=True)
    data = data[tuple(ind)]

# 計算坡度
xsize = np.abs(geo[1])
ysize = np.abs(geo[5])
x = ((data[:-2, 2:] - data[:-2, :-2]) + 2 * (data[1:-1, 2:] - data[1:-1, :-2]) + (data[2:, 2:] - data[2:, :-2])) / (8 * xsize)
y = ((data[2:, :-2] - data[:-2, :-2]) + 2 * (data[2:, 1:-1] - data[:-2, 1:-1]) + (data[2:, 2:] - data[:-2, 2:])) / (8 * ysize)
s_data = np.full((rows, cols), -999, dtype=np.float32)
s_data[1:-1, 1:-1] = (np.arctan(np.sqrt((np.power(x, 2) + np.power(y, 2)))))
s_data[1:-1, 1:-1] = np.abs(np.tan(s_data[1:-1, 1:-1])) * 100
s_mask = s_data==-999
# 邊緣填充
if np.sum(s_mask) > 0:
    ind = nd.distance_transform_edt(s_mask, return_distances=False, return_indices=True)
    s_data = s_data[tuple(ind)]
# 掩膜
s_data[dem_data==nodata] = -999
# 寫出結果
driver = gdal.GetDriverByName("gtiff")
outds = driver.Create(slopefile, cols, rows, 1, gdal.GDT_Float32)
outds.SetGeoTransform(geo)
outds.SetProjection(proj)
outband = outds.GetRasterBand(1)
outband.WriteArray(s_data)
outband.SetNoDataValue(-999)

 

坡度結果

import gdal
import numpy as np
from scipy import ndimage as nd
from copy import deepcopy

demfile = r"D:\微信公眾號\坡度坡向\N40E117_Albers.tif"
aspectfile = r"D:\微信公眾號\坡度坡向\N40E117_Albers_python_Aspect.tif"

#讀取DEM資料
ds = gdal.Open(demfile)
cols = ds.RasterXSize
rows = ds.RasterYSize
geo = ds.GetGeoTransform()
proj = ds.GetProjection()
dem_data = ds.ReadAsArray()
data = deepcopy(dem_data).astype(np.float32)
band = ds.GetRasterBand(1)
nodata = band.GetNoDataValue()
data[data == nodata] = np.nan
# data[data<-999]=np.nan
mask = np.isnan(data)
# 將無效值或背景值臨近像元填充
if np.sum(mask) > 0:
    ind = nd.distance_transform_edt(mask, return_distances=False, return_indices=True)
    data = data[tuple(ind)]

# 計算坡向
xsize = np.abs(geo[1])
ysize = np.abs(geo[5])
x = ((data[:-2, 2:] - data[:-2, :-2]) + 2 * (data[1:-1, 2:] - data[1:-1, :-2]) + (data[2:, 2:] - data[2:, :-2])) / (8 * xsize)
y = ((data[2:, :-2] - data[:-2, :-2]) + 2 * (data[2:, 1:-1] - data[:-2, 1:-1]) + (data[2:, 2:] - data[:-2, 2:])) / (8 * ysize)
a_data = np.full((rows, cols), -999, dtype=np.float32)
a_data[1:-1, 1:-1] = np.arctan2(y, -1 * x) * 57.29578
a_data_ = deepcopy(a_data[1:-1, 1:-1])
a_data[1:-1, 1:-1][a_data_ < 0] = 90 - a_data[1:-1, 1:-1][a_data_ < 0]
a_data[1:-1, 1:-1][a_data_ >90] = 450 - a_data[1:-1, 1:-1][a_data_ > 90]
a_data[1:-1, 1:-1][(a_data_ >= 0) & (a_data_ <= 90)] = 90 - a_data[1:-1, 1:-1][(a_data_ >= 0) & (a_data_ <= 90)]
a_data[1:-1, 1:-1][(x==0.)& (y==0.)] = -1
a_data[1:-1, 1:-1][(x==0.)& (y>0.)] = 0
a_data[1:-1, 1:-1][(x==0.)& (y<0.)] = 180
a_data[1:-1, 1:-1][(x>0.)& (y==0.)] = 90
a_data[1:-1, 1:-1][(x<0.)& (y==0.)] = 270.

# 邊緣填充
a_mask = a_data==-999
if np.sum(a_mask) > 0:
    ind = nd.distance_transform_edt(a_mask, return_distances=False, return_indices=True)
    a_data = a_data[tuple(ind)]

# 掩膜
a_data[dem_data==nodata] = -999
# 寫出結果
driver = gdal.GetDriverByName("gtiff")
outds = driver.Create(aspectfile, cols, rows, 1, gdal.GDT_Float32)
outds.SetGeoTransform(geo)
outds.SetProjection(proj)
outband = outds.GetRasterBand(1)
outband.WriteArray(a_data)
outband.SetNoDataValue(-999)

 

坡向結果

測試資料：

連結：https://pan.baidu.com/s/1PODbTJn1JOqOA4qeaJq4Gg 

提取碼：l3fw 

​

歡迎關注個人wx_gzh: 小Rser