Python遥感开发之数据趋势分析Sen+mk

Python遥感开发之数据趋势分析Sen+mk

前言：主要使用Python完成遥感时间序列数据趋势分析Sen+mk，得到slope、trend、p、s、tau、z指标。

1 方法介绍

1.1 Theil-Sen Median方法

又被称为 Sen 斜率估计，是一种稳健的非参数统计的趋势计算方法。该方法计算效率高，对于测量误差和离群数据不敏感，常被用于长时间序列数据的趋势分析中。对于后续代码计算结果中的slope.tif解读,当slope大于0表示随时间序列呈现上升趋势；slope小于0表示随时间序列呈现下降趋势。

1.2 Mann-Kendall方法

Mann-Kendall是一种非参数统计检验方法，最初由Mann在1945年提出，后由Kendall和Sneyers进一步完善，其优点是不需要测量值服从正态分布，也不要求趋势是线性的，并且不受缺失值和异常值的影响，在长时间序列数据的趋势显著检验中得到了十分广泛的应用。对于后续代码计算结果中的z.tif,当|Z|大于1.65、1.96和2.58时，表示趋势分别通过了置信度为90%、95%和99%的显著性检验。

2 Python实现Sen+mk

2.1 代码（作者实现，推荐使用）

# coding:utf-8
import numpy as np
import pymannkendall as mk
import os
from osgeo import gdal


def read_tif(filepath):
    dataset = gdal.Open(filepath)
    col = dataset.RasterXSize  # 图像长度
    row = dataset.RasterYSize  # 图像宽度
    geotrans = dataset.GetGeoTransform()  # 读取仿射变换
    proj = dataset.GetProjection()  # 读取投影
    data = dataset.ReadAsArray()  # 转为numpy格式
    data = data.astype(np.float32)
    no_data_value = data[0][0]  # 设定NoData值
    data[data == no_data_value] = np.nan  # 将NoData转换为NaN
    return col, row, geotrans, proj, data


def save_tif(data, reference_file, output_file):
    ds = gdal.Open(reference_file)
    shape = data.shape
    driver = gdal.GetDriverByName("GTiff")
    dataset = driver.Create(output_file, shape[1], shape[0], 1, gdal.GDT_Float32)  # 保存的数据类型
    dataset.SetGeoTransform(ds.GetGeoTransform())
    dataset.SetProjection(ds.GetProjection())
    dataset.GetRasterBand(1).WriteArray(data)
    dataset.FlushCache()


def get_tif_list(directory):
    """
    获取目录下所有TIF文件的完整路径
    """
    return [os.path.join(directory, f) for f in os.listdir(directory) if f.endswith('.tif')]


if __name__ == '__main__':
    # 输入文件夹路径
    input_directory = r"E:\AAWORK\学校相关\rsei\data\四季\rsei-winter"
    output_directory = r"E:\AAWORK\学校相关\rsei\data\四季\sen+mk"

    # 读取文件列表
    file_list = get_tif_list(input_directory)

    # 读取第一个文件获取基本信息（列、行、仿射变换、投影）
    col, row, geotrans, proj, _ = read_tif(file_list[0])

    # 预分配结果数组
    slope_data = np.zeros((row, col), dtype=np.float32)
    s_data = np.zeros((row, col), dtype=np.float32)
    z_data = np.zeros((row, col), dtype=np.float32)
    trend_data = np.zeros((row, col), dtype=np.float32)
    tau_data = np.zeros((row, col), dtype=np.float32)
    p_data = np.zeros((row, col), dtype=np.float32)

    # 将所有 TIFF 文件的数据堆叠为一个三维数组 (时间, 行, 列)
    tif_stack = np.array([read_tif(f)[4] for f in file_list])

    # 遍历每个像素
    for i in range(row):
        print(f"Processing row {i+1}/{row}...")
        for j in range(col):
            pixel_values = tif_stack[:, i, j]  # 取出所有时间点的该像素值
            if not np.any(np.isnan(pixel_values)):  # 过滤掉包含NaN的像素
                trend, h, p, z, Tau, s, var_s, slope, intercept = mk.original_test(pixel_values)
                trend_data[i, j] = 1 if trend == "increasing" else -1 if trend == "decreasing" else 0
                slope_data[i, j] = slope
                s_data[i, j] = s
                z_data[i, j] = z
                p_data[i, j] = p
                tau_data[i, j] = Tau

    # 输出文件路径
    save_tif(slope_data, file_list[0], os.path.join(output_directory, 'winter_slope.tif'))
    save_tif(s_data, file_list[0], os.path.join(output_directory, 'winter_s.tif'))
    save_tif(z_data, file_list[0], os.path.join(output_directory, 'winter_z.tif'))
    save_tif(trend_data, file_list[0], os.path.join(output_directory, 'winter_trend.tif'))
    save_tif(p_data, file_list[0], os.path.join(output_directory, 'winter_p.tif'))
    save_tif(tau_data, file_list[0], os.path.join(output_directory, 'winter_tau.tif'))

    print("Processing completed!")

2.2 另外版本

（参考《遥感数据趋势分析Sen+mk》）

# coding:utf-8
import numpy as np
import pymannkendall as mk
import os
import rasterio as ras

# 写影像
def writeImage(image_save_path, height1, width1, para_array, bandDes, transform1):
    with ras.open(
            image_save_path,
            'w',
            driver='GTiff',
            height=height1,
            width=width1,
            count=1,
            dtype=para_array.dtype,
            crs='+proj=latlong',
            transform=transform1,
    ) as dst:
        dst.write_band(1, para_array)
        dst.set_band_description(1, bandDes)
    del dst

def sen_mk_test(image_path, outputPath):
    # image_path:影像的存储路径
    # outputPath:结果输出路径
    global path1
    filepaths = []
    for file in os.listdir(path1):
        filepath1 = os.path.join(path1, file)
        filepaths.append(filepath1)

    # 获取影像数量
    num_images = len(filepaths)
    # 读取影像数据
    img1 = ras.open(filepaths[0])
    # 获取影像的投影，高度和宽度
    transform1 = img1.transform
    height1 = img1.height
    width1 = img1.width
    array1 = img1.read()
    img1.close()
    # 读取所有影像
    for path1 in filepaths[1:]:
        if path1[-3:] == 'tif':
            print(path1)
            img2 = ras.open(path1)
            array2 = img2.read()
            array1 = np.vstack((array1, array2))
            img2.close()
    nums, width, height = array1.shape
    # 输出矩阵，无值区用-9999填充
    slope_array = np.full([width, height], np.nan)
    z_array = np.full([width, height], np.nan)
    Trend_array = np.full([width, height], np.nan)
    Tau_array = np.full([width, height], np.nan)
    s_array = np.full([width, height], np.nan)
    p_array = np.full([width, height], np.nan)
    # 只有有值的区域才进行mk检验
    c1 = np.isnan(array1)
    sum_array1 = np.sum(c1, axis=0)
    nan_positions = np.where(sum_array1 == num_images)
    positions = np.where(sum_array1 != num_images)
    # 输出总像元数量
    print("all the pixel counts are {0}".format(len(positions[0])))
    # mk test
    for i in range(len(positions[0])):
        print(i)
        x = positions[0][i]
        y = positions[1][i]
        mk_list1 = array1[:, x, y]
        trend, h, p, z, Tau, s, var_s, slope, intercept = mk.original_test(mk_list1)
        '''        
        trend: tells the trend (increasing, decreasing or no trend)
                h: True (if trend is present) or False (if trend is absence)
                p: p-value of the significance test
                z: normalized test statistics
                Tau: Kendall Tau
                s: Mann-Kendal's score
                var_s: Variance S
                slope: Theil-Sen estimator/slope
                intercept: intercept of Kendall-Theil Robust Line
        '''
        if trend == "decreasing":
            trend_value = -1
        elif trend == "increasing":
            trend_value = 1
        else:
            trend_value = 0
        slope_array[x, y] = slope  # senslope
        s_array[x, y] = s
        z_array[x, y] = z
        Trend_array[x, y] = trend_value
        p_array[x, y] = p
        Tau_array[x, y] = Tau
    all_array = [slope_array, Trend_array, p_array, s_array, Tau_array, z_array]
    slope_save_path = os.path.join(result_path, "slope.tif")
    Trend_save_path = os.path.join(result_path, "Trend.tif")
    p_save_path = os.path.join(result_path, "p.tif")
    s_save_path = os.path.join(result_path, "s.tif")
    tau_save_path = os.path.join(result_path, "tau.tif")
    z_save_path = os.path.join(result_path, "z.tif")
    image_save_paths = [slope_save_path, Trend_save_path, p_save_path, s_save_path, tau_save_path, z_save_path]
    band_Des = ['slope', 'trend', 'p_value', 'score', 'tau', 'z_value']
    for i in range(len(all_array)):
        writeImage(image_save_paths[i], height1, width1, all_array[i], band_Des[i], transform1)

if __name__ == '__main__':
    # 调用
    path1 = r"E:\AAWORK\work\研究方向\rsei\data\年\rsei_year-25km"
    result_path = r"E:\AAWORK\work\研究方向\rsei\data\年\out"
    sen_mk_test(path1, result_path)

3 最终结果

3.1 对slope.tif以及z.tif进行重分类

3.2 对分类结果相乘

3.3 最终结果Python代码实现

import numpy as np
import rasterio as ras

def classify_rasters(slope_path, z_path, output_path):
    with ras.open(slope_path) as slope_src, ras.open(z_path) as z_src:
        slope = slope_src.read(1)
        z = z_src.read(1)
        profile = slope_src.profile

    # 重分类
    classified_slope = np.full(slope.shape, 0, dtype=np.int8)
    classified_slope[slope > 0] = 1
    classified_slope[slope < 0] = -1

    classified_z = np.full(z.shape, 1, dtype=np.int8)
    classified_z[np.abs(z) > 1.96] = 2
    classified_z[np.abs(z) <= 1.96] = 1

    # 相乘得到最终分类结果
    final_class = classified_slope * classified_z

    # 保存结果
    with ras.open(
        output_path, 'w',
        driver='GTiff',
        height=final_class.shape[0],
        width=final_class.shape[1],
        count=1,
        dtype=final_class.dtype,
        crs=profile['crs'],
        transform=profile['transform']
    ) as dst:
        dst.write(final_class, 1)

if __name__ == '__main__':
    # 调用
    slope_path = r"E:\AAWORK\work\研究方向\rsei\data\年\out\slope.tif"
    z_path = r"E:\AAWORK\work\研究方向\rsei\data\年\out\z.tif"
    output_path = r"E:\AAWORK\work\研究方向\rsei\data\年\out\final_classification.tif"
    classify_rasters(slope_path, z_path, output_path)

作者：等待着冬天的风