DASCAN算法python代码

DASCAN（Discrete All Scale Canopy Analysis）算法是一种用于分析森林结构的方法，它可以计算各种尺度的森林结构指标，如树冠覆盖度、高度分布、树冠形态等。以下是一个简单的Python实现：

import numpy as np
import matplotlib.pyplot as plt

def dascan(heights, resolution):
    """DASCAN algorithm implementation."""
    heights = np.array(heights)
    res = resolution
    scales = np.arange(res, heights.max(), res)
    canopy_cover = []
    height_std = []
    height_cv = []
    
    for scale in scales:
        # Calculate the number of cells occupied by the tree canopy
        occupancy = np.sum(heights >= scale)
        canopy_cover.append(occupancy / heights.size)
        
        # Calculate the standard deviation of the heights within the canopy
        heights_within_canopy = heights[heights >= scale]
        height_std.append(np.std(heights_within_canopy))
        
        # Calculate the coefficient of variation of the heights within the canopy
        height_cv.append(np.std(heights_within_canopy) / np.mean(heights_within_canopy))
    
    return scales, canopy_cover, height_std, height_cv

# Example usage
heights = [10, 8, 6, 4, 2, 1, 3, 5, 7, 9, 11, 12, 13, 14, 15]
res = 1
scales, canopy_cover, height_std, height_cv = dascan(heights, res)

# Plot the results
fig, ax1 = plt.subplots()
ax1.plot(scales, canopy_cover, 'b-', label='Canopy cover')
ax1.set_xlabel('Scale (m)')
ax1.set_ylabel('Canopy cover')
ax2 = ax1.twinx()
ax2.plot(scales, height_std, 'r-', label='Canopy height std')
ax2.plot(scales, height_cv, 'r--', label='Canopy height cv')
ax2.set_ylabel('Canopy height')
fig.legend(loc='upper right')
plt.show()

在这个例子中，我们使用高度列表`heights`和分辨率`res`作为输入，然后计算树冠覆盖度、高度标准差和高度变异系数等指标，并用Matplotlib绘制了结果。