提取GEDI雷达数据中生物量信息的代码
时间: 2023-05-29 20:04:11 浏览: 67
由于GEDI雷达数据是高分辨率的三维点云数据,因此提取生物量信息需要进行一系列的处理和分析。以下是一个简单的Python代码,用于提取GEDI雷达数据中的生物量信息:
```python
import h5py
import numpy as np
# 打开GEDI数据文件
filename = 'GEDI01_B_2019270090744_O01822_T05368_02_003_01.h5'
f = h5py.File(filename, 'r')
# 获取数据集
beam_data = f['BEAM0000']
shot_data = beam_data['shot_data']
# 获取生物量估算因子
beta0 = beam_data.attrs['beta0']
beta1 = beam_data.attrs['beta1']
# 计算生物量
range_bias = shot_data['range_bias'][:]
shot_count = range_bias.shape[0]
range_dist = shot_data['range_dist'][:]
rh = shot_data['rh'][:]
sigma_h = shot_data['sigma_h'][:]
transmit_pulse = shot_data['tx_pulses'][:, 0]
received_pulse = shot_data['rx_pulses'][:, 0]
transmit_energy = np.sum(transmit_pulse ** 2)
received_energy = np.sum(received_pulse ** 2)
gain = beam_data.attrs['gain']
rx_sample_interval = beam_data.attrs['rx_sample_interval']
shot_area = (range_dist ** 2) * (np.pi / 4) * (sigma_h ** 2)
height = range_dist + range_bias - rh
height[height < 0] = 0
height_mask = height > 0
height_mask_count = np.sum(height_mask)
height_mask_area = shot_area[height_mask]
height_mask_energy = received_energy * height_mask_area / shot_area
height_mask_energy_sum = np.sum(height_mask_energy)
mean_energy = height_mask_energy_sum / height_mask_count
mean_power = mean_energy / rx_sample_interval
mean_power_db = 10 * np.log10(mean_power)
p0 = 10 ** (beta0 / 10)
p1 = 10 ** (beta1 / 10)
b = (p0 / p1) * mean_power_db
biomass = (10 ** (b / 10)) / gain
# 输出生物量信息
print('Biomass:', biomass)
```
该代码首先打开GEDI数据文件,获取BEAM0000数据集和shot_data子数据集。然后,从BEAM0000数据集中获取生物量估算因子,并计算每个点的生物量。最后输出生物量信息。