找出某名珍贵药材的生长区域（ArcPy实现）

一、背景

某种珍贵药材生长于山区，通过研究了解到这种药材生长具有严格的生长条件。为了能更好地保护该药材的生长环境，现在需要使用GIS空间分析方法，将药材适合生长区域找出来，以便为该物种保护提供依据。

二、数据

(1) 山区等高线数据contour. shp;

(2)山区观测点采集的年平均温度和年总降水数据climate. txt。

三、药材生长条件

请依据以下条件，确定此区域适合种植这种药材的范围，并制作专题图，给出适宜种植的面积。

(1)这种药材一般生长在沟谷两侧较近的区域（一般不超过500m);

(2)这种药材喜阳;

(3)生长气候环境为年平均温度为10°～12°;

(4)年总降水量为550~～680mm。

四、流程

利用该山区等高线数据生成DEM数据。基于DEM进行水文分析，提取沟谷网络(汇水阈值可根据需要自行设定);基于DEM提取坡向数据，重分类划分阴阳坡。

利用观测点采集的年平均温度和年总降水数据分别进行表面内插，生成年平均温度栅格数据和年总降水栅格数据（插值方法根据需要自行选择)。提取年平均温10℃_{12℃的区域和年总降水为550}680mm的区域。

综合叠加分析满足上述4个条件的区域，得到适合该药材生长的区域，并制作专题图,计算该适合区域的面积。

流程如下图所示：

五、模型构建器

六、ArcPy实现

注：创建tin并转成栅格dem，不知道为什么老是报空间参考参数错误。一样的参数设置，在ArcMap和模型构建器又可以做得出来。

目前还未找到解决办法，知道如何解决的小伙伴，欢迎评论区留言哈~

所以，在运行下面代码时，需要先用ArcMap或模型构建器创建tin并转成栅格dem，把dem复制到工作路径。

# -*- coding: utf-8 -*-
# ---------------------------------------------------------------------------
# 13-4 找出某种珍贵药材的生成区域.py
# Created on: 2021-10-12 20:20:20.00000
#   (generated by ArcGIS/ModelBuilder)
# Description:
# ---------------------------------------------------------------------------

# Import arcpy module
import arcpy
from arcpy.sa import Raster
import os
import shutil
import time

print time.asctime()
path = raw_input("请输入数据所在文件夹的绝对路径：").decode("utf-8")
# 开始计时
time_start = time.time()
paths = path + "\\result"
if not os.path.exists(paths):
    os.mkdir(paths)
else:
    shutil.rmtree(paths)
    os.mkdir(paths)

# Local variables:
# contour = path + "\\contour"
climate_txt = path + "\\climate.txt"
tin = "tin"
dem = path + "\\dem"
constant1 = "200"
constant2 = "500"
Fill_dem = "Fill_dem"
Descent_data = "Descent_data"
Aspect_dem = "Aspect_dem"
sunny_slope = "sunny_slope"
climate_Layer = "climate_Layer"
temperature = "temperature"
proper_temp = "proper_temp"
precipitation = "precipitate"  # 格网基本名称的长度不能超过了 13。
proper_prec = "proper_prec"
FlowDir_Fill = "FlowDir_Fill"
FlowAcc_Flow = "FlowAcc_Flow"
stream = "stream"
Reclass_stre1 = "Reclass_stre1"
distance = "distance"
buffer = "buffer"
proper_area = "proper_area"
Direction_data = "Direct_data"
Inverse_data = "Inverse_data"

# Set Geoprocessing environments
print "Set Geoprocessing environments"
arcpy.env.scratchWorkspace = paths
arcpy.env.workspace = paths

# Process: 创建 TIN
# print "Process: 创建 TIN"
# arcpy.CreateTin_3d(tin, "Unknown", "{}.shp CONTOUR Soft_Line CONTOUR".format(contour), "DELAUNAY")

# Process: TIN 转栅格
# print "Process: TIN 转栅格"
# arcpy.TinRaster_3d(tin, dem, "FLOAT", "LINEAR", "CELLSIZE 100", "1")

arcpy.env.extent = dem
arcpy.env.cellSize = dem
arcpy.env.mask = dem

# Process: 填洼
print "Process: 填洼"
arcpy.gp.Fill_sa(dem, Fill_dem, "")

# Process: 流向
print "Process: 流向"
arcpy.gp.FlowDirection_sa(Fill_dem, FlowDir_Fill, "NORMAL", Descent_data, "D8")

# Process: 坡向
print "Process: 坡向"
arcpy.gp.Aspect_sa(dem, Aspect_dem, "PLANAR", "METER")

# Process: 重分类
print "Process: 重分类"
arcpy.gp.Reclassify_sa(Aspect_dem, "value", "90 270 1", sunny_slope, "NODATA")

# Process: 创建 XY 事件图层
print "Process: 创建 XY 事件图层"
arcpy.MakeXYEventLayer_management(climate_txt, "x", "y", climate_Layer, "{B286C06B-0879-11D2-AACA-00C04FA33C20};281094.800114045 3873927.75678257 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision", "")

# Process: 反距离权重法
print "Process: 反距离权重法"
arcpy.gp.Idw_sa(climate_Layer, "温度", temperature, "100", "2", "VARIABLE 12", "")

# Process: 栅格计算器
print "Process: 栅格计算器"
# arcpy.gp.RasterCalculator_sa("(\"%temperature%\" >= 10) & (\"%temperature%\" <= 12)", proper_temp)
((Raster(temperature) >= 10) & (Raster(temperature) <= 12)).save(proper_temp)

# Process: 反距离权重法 (2)
print "Process: 反距离权重法 (2)"
arcpy.gp.Idw_sa(climate_Layer, "降雨", precipitation, "100", "2", "VARIABLE 12", "")

# Process: 栅格计算器 (2)
print "Process: 栅格计算器 (2)"
# arcpy.gp.RasterCalculator_sa("(\"%precipitation%\" >= 550) & (\"%precipitation%\" <= 680)", proper_prec)
((Raster(precipitation) >= 550) & (Raster(precipitation) <= 680)).save(proper_prec)

# Process: 流量
print "Process: 流量"
arcpy.gp.FlowAccumulation_sa(FlowDir_Fill, FlowAcc_Flow, "", "FLOAT", "D8")

# Process: 大于等于
print "Process: 大于等于"
arcpy.gp.GreaterThanEqual_sa(FlowAcc_Flow, constant1, stream)

# Process: 重分类 (2)
print "Process: 重分类 (2)"
arcpy.gp.Reclassify_sa(stream, "VALUE", "0 NODATA;1 1", Reclass_stre1, "DATA")

# Process: 欧氏距离
print "Process: 欧氏距离"
arcpy.gp.EucDistance_sa(Reclass_stre1, distance, "", dem, Direction_data, "PLANAR", "", Inverse_data)

# Process: 小于等于
print "Process: 小于等于"
arcpy.gp.LessThanEqual_sa(distance, constant2, buffer)

# Process: 栅格计算器 (3)
print "Process: 栅格计算器 (3)"
# arcpy.gp.RasterCalculator_sa("\"%sunny_slope%\"  * \"%proper_temp%\" * \"%proper_prec%\"* \"%buffer%\"", proper_area)
(Raster(sunny_slope) * Raster(proper_temp) * Raster(proper_prec) * Raster(buffer)).save(proper_area)

save = ["tin", "dem", "proper_area"]
rasters = arcpy.ListRasters()
for raster in rasters:
    if raster.lower() not in save:
        print u"正在删除{}图层".format(raster)
        arcpy.Delete_management(raster)
# 结束计时
time_end = time.time()
# 计算所用时间
time_all = time_end - time_start
print time.asctime()
print "执行完毕！>>><<< 共耗时{:.0f}分{:.2f}秒".format(time_all // 60, time_all % 60)

七、结果

这节实验，太tmex o(╥﹏╥)o