【Python】遥感数据趋势分析Sen+mk

川长思鸟来 2024-04-17 06:17 61阅读 0赞

### 方法介绍 ###

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。

2.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。

### 代码介绍 ###

此代码大部分来自Github大佬分享，我修改了报错的个别代码后亲自测试pycharm下只需改路径就可以运行：

# coding:utf-8
    '''
    已全部实现
    '''
    import numpy as np
    import pymannkendall as mk
    import os
    import rasterio as ras
    
    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
    
        # 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
    
        # 输出矩阵，无值区用-9999填充
        slope_array = np.full([width, height], -9999.0000)
        z_array = np.full([width, height], -9999.0000)
        Trend_array = np.full([width, height], -9999.0000)
        Tau_array = np.full([width, height], -9999.0000)
        s_array = np.full([width, height], -9999.0000)
        p_array = np.full([width, height], -9999.0000)
        # 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)
    
    # 调用
    path1 = r"E:\Test\ACMEI\ACMEI"
    result_path = r"E:\Test\ACMEI\ACMEImk"
    sen_mk_test(path1, result_path)

### 后续处理三步走 ###

1.对于生成的tif栅格，只需要取slope.tif以及z.tif进行重分类。

slope>0赋值1表示增加 |z|>1.96赋值2表示显著

slope=0赋值0表示不变 |z|<=1.96赋值1表示不显著

slope<0赋值-1表示减少

（此处还可根据显著性阈值再细分为更多类，我仅作基本演示分为5类）

2.再相乘得到

\-2:显著减少;-1:不显著减少;0:稳定不变;1:不显著增加; 2:显著增加

3.在Arcmap出图即可！

![在这里插入图片描述][301245c4264c4b0b9a9fe61858311736.png]

[301245c4264c4b0b9a9fe61858311736.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/15/2f712a2b23be44c48711cfacee7d753d.png