坡度坡向分析方法
坡度(slope)是地面特定区域高度变化比率的量度。坡度的表示方法有百分比法、度数法、密位法和分数法四种，其中以百分比法和度数法较为常用。本文计算的为坡度百分比数据。如当角度为45度（弧度为π/4）时，高程增量等于水平增量，高程增量百分比为100%。
坡向(aspect)是指地形坡面的朝向。坡向用于识别出从每个像元到其相邻像元方向上值的变化率最大的下坡方向。坡向可以被视为坡度方向。坡向是一个角度，将按照顺时针方向进行测量，角度范围介于 0（正东）到 360（仍是正东）之间，即完整的圆。不具有下坡方向的平坦区域将赋值为-1（arcgis处理时为-1，其他可能为0）。
坡度、坡向计算一般采用拟合曲面法。拟合曲面一般采用二次曲面，即3×3的窗口，如下图所示。每个窗口的中心为一个高程点。图中的中心点e坡度和坡向计算过程如下。
参考链接：
[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的空间坐标系一定要为投影坐标系。
ArcGIS坡度坡向分析
打开DEM数据
坡度分析
坡度结果
坡向分析
坡向结果
python-gdal坡度坡向分析
from osgeo import gdaldemfile = r"D:\微信公众号\坡度坡向\N40E117_Albers.tif"# 获取DEM信息infoDEM = gdal.Info(demfile)# 计算坡度slopfile = r"D:\微信公众号\坡度坡向\N40E117_Albers_gdal_Slope.tif"slope = gdal.DEMProcessing(slopfile, demfile, "slope", format='GTiff', slopeFormat="percent", zeroForFlat=1, computeEdges=True)# 计算坡向aspectfile = r"D:\微信公众号\坡度坡向\N40E117_Albers_gdal_Aspect.tif"b = gdal.DEMProcessing(aspectfile, demfile, "aspect", format='GTiff', trigonometric=0, zeroForFlat=1, computeEdges=True)
坡度结果
坡向结果
python坡度坡向分析
import gdalimport numpy as npfrom scipy import ndimage as ndfrom copy import deepcopydemfile = r"D:\微信公众号\坡度坡向\N40E117_Albers.tif"slopefile = r"D:\微信公众号\坡度坡向\N40E117_Albers_python_Slope.tif"#读取DEM数据ds = gdal.Open(demfile)cols = ds.RasterXSizerows = ds.RasterYSizegeo = 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[datamask = np.isnan(data)# 将无效值或背景值临近像元填充if np.sum(mask) > 0: ind = nd.distance_transform_edt(mask, return_distances=False, return_indices=True) data = data[tuple(ind)]# 计算坡度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])) * 100s_mask = s_data==-999# 边缘填充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# 写出结果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)
坡度结果
import gdal
import numpy as np
from scipy import ndimage as nd
from copy import deepcopy
demfile = r"D:\微信公众号\坡度坡向\N40E117_Albers.tif"
aspectfile = r"D:\微信公众号\坡度坡向\N40E117_Albers_python_Aspect.tif"
#读取DEM数据
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[datamask = np.isnan(data)
# 将无效值或背景值临近像元填充
if np.sum(mask) > 0:
ind = nd.distance_transform_edt(mask, return_distances=False, return_indices=True)
data = data[tuple(ind)]
# 计算坡向
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_ = 0) & (a_data_ 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.)& (ya_data[1:-1, 1:-1][(x>0.)& (y==0.)] = 90
a_data[1:-1, 1:-1][(x
# 边缘填充
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
# 写出结果
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)
坡向结果
测试数据：
链接：https://pan.baidu.com/s/1PODbTJn1JOqOA4qeaJq4Gg
提取码：l3fw
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Original: https://www.cnblogs.com/XiaoRser/p/16269417.html
Author: 小Rser
Title: 基于DEM的坡度坡向分析
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