Python实现遥感生态指数计算
最近在做一些遥感相关的图像处理项目，涉及到遥感生态指数的计算。由于项目要求Python实现，搜索互联网关于Python实现的遥感生态指数计算程序资料很少，于是就自己实现了一个并分享在这里，供需要的朋友参考。
首先需要了解遥感生态指数是什么，不是很清楚的朋友可以参考下面的几篇文章：
城市遥感生态指数的创建及其应用
区域生态环境变化的遥感评价指数
基于遥感生态指数的南京市生态变化分析
知道了遥感生态指数是什么后，程序实现其实比较简单，这里直接贴出程序。
"""
利用湿度、绿度、热度、干度计算遥感生态指数
"""
import os
import time
import numpy as np
from scipy import misc
from osgeo import gdal, gdalconst
from sklearn.decomposition import PCA
class CalIntegratedEcoIndex:
def __init__(self, img_path, res_save_dir):
self.img_width, self.img_height, self.img = self.read_img(img_path)
self.deno_bias = 0.00001 # 分母偏置，防止除0
self.res_save_dir = res_save_dir
def read_img(self, img_path):
"""读取遥感数据信息"""
dataset = gdal.Open(img_path, gdalconst.GA_ReadOnly)
img_width = dataset.RasterXSize
img_height = dataset.RasterYSize
img_data = np.array(dataset.ReadAsArray(0, 0, img_width, img_height), dtype=float) # 将数据写成数组，对应栅格矩阵
del dataset
return img_width, img_height, img_data
def get_wet_degree(self):
"""获取湿度指标"""
return 0.2626 * self.img[0] + 0.2141 * self.img[1] + 0.0926 * self.img[2] + \
0.0656 * self.img[3] - 0.7629 * self.img[4] - 0.5388 * self.img[6]
def get_green_degree(self):
"""获取绿度指标"""
return (self.img[3] - self.img[2]) / (self.img[3] + self.img[2] + self.deno_bias)
def get_temperature(self):
"""获取热度指标"""
gain = 3.20107 # landsat5 第6波段的增益值
bias = 0.25994 # 第6波段的偏置值
K1 = 606.09
K2 = 1282.71
_lambda = 11.45
_rho = 1.438e10-2
_epsilon = 0.96 # 比辐射率
DN = self.img[4] * 299/1000 + self.img[3] * 587/1000 + self.img[2] * 114/1000 # 获取象元灰度值
L6 = gain * DN + bias
T = K2 / np.log(K1 / L6 + 1)
LST = T / (1 + (_lambda * T / _rho) * np.log(_epsilon))
return LST
def get_dryness_degree(self):
"""获取干度指标"""
band5_plus_band3 = self.img[4] + self.img[2]
band4_plus_band1 = self.img[3] + self.img[0]
SI = (band5_plus_band3 - band4_plus_band1) / (band5_plus_band3 + band4_plus_band1 + self.deno_bias)
left_expr = 2 * self.img[4] / (self.img[4] + self.img[3] + self.deno_bias)
right_expr = self.img[3] / (self.img[3] + self.img[2] + self.deno_bias) + \
self.img[1] / (self.img[1] + self.img[4] + self.deno_bias)
IBI = (left_expr - right_expr) / (left_expr + right_expr + self.deno_bias)
NDBSI = (IBI + SI) / 2
return NDBSI
def arr2img(self, save_path, arr):
misc.imsave(save_path, arr)
def normlize(self, img_arr):
arr = np.array(img_arr)
return (arr - arr.min()) / (arr.max() - arr.min())
def get_rsei(self):
"""获取遥感生态指数"""
wet = self.get_wet_degree()
ndvi = self.get_green_degree()
lst = self.get_temperature()
ndbsi = self.get_dryness_degree()
data = np.array([self.normlize(wet), self.normlize(ndvi), self.normlize(lst), self.normlize(ndbsi)])
data = data.reshape(data.shape[0], -1).T
pca = PCA(n_components=1)
rsei = self.normlize(1 - np.reshape(pca.fit_transform(data), newshape=(self.img_height, self.img_width)))
return wet, ndvi, lst, ndbsi, 1 - rsei
def save_result(self, wet, ndvi, lst, ndbsi, rsei):
"""将各指数结果保存为图片"""
res_dict = {"wet": wet, "green": ndvi, "temperature": lst, "dry": ndbsi, "rsei": rsei}
for k, v in res_dict.items():
self.arr2img(os.path.join(self.res_save_dir, k + ".jpg"), v)
def get_average_index(self, wet, ndvi, lst, ndbsi, rsei):
"""获取归一化后各指标的平均值"""
return np.mean(self.normlize(wet)), np.mean(self.normlize(ndvi)), \
np.mean(self.normlize(lst)), np.mean(self.normlize(ndbsi)), np.mean(self.normlize(rsei))
程序可以按照下面的方式进行使用（注意输入图像是Landsat七波段的图像）：
if __name__ == '__main__':
start = time.time()
cal = CalIntegratedEcoIndex("/your/path/to/Landsat/image", "./result")
wet, ndvi, lst, ndbsi, rsei = cal.get_rsei()
aver_wet, aver_ndvi, aver_lst, aver_ndbsi, aver_rsei = cal.get_average_index(wet, ndvi, lst, ndbsi, rsei)
cal.save_result(wet, ndvi, lst, ndbsi, rsei)
end = time.time()
print("归一化后各指数的平均值\n湿度", aver_wet, "绿度", aver_ndvi, "热度", aver_lst, "干度", aver_ndbsi,
"遥感生态指数", aver_rsei, "Total cost time %.2f s" % (end - start))
因为对遥感领域不很了解，这也是我的初次尝试。如果程序中有什么错误及不足，欢迎读者朋友们在评论区批评指正。