GEE计算遥感生态指数并运用元胞自动机预测遥感生态指数的变化实现代码

这是一个比较复杂的项目，需要用到遥感数据处理、GEE平台、元胞自动机等技术。以下是一个简单的实现代码供参考：

import ee
import numpy as np
from skimage.transform import resize
from sklearn.preprocessing import MinMaxScaler

# 初始化GEE
ee.Initialize()

# 定义区域和时间范围
region = ee.Geometry.Polygon([[xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]])
start_date = ee.Date('2015-01-01')
end_date = ee.Date('2020-12-31')

# 定义遥感数据集
dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR') \
            .filterDate(start_date, end_date) \
            .filterBounds(region) \
            .sort('CLOUD_COVER') \
            .select(['B2', 'B3', 'B4', 'B5', 'B6', 'B7'])

# 计算NDVI
def compute_ndvi(image):
    ndvi = image.normalizedDifference(['B5', 'B4'])
    return image.addBands(ndvi.rename('NDVI'))

dataset = dataset.map(compute_ndvi)

# 计算生态指数
def compute_ecology_index(image):
    # 从图像中提取NDVI和LST数据
    ndvi = image.select('NDVI')
    lst = image.select('B6')
    
    # 将数据转化为Numpy数组并进行归一化
    ndvi_np = np.array(ndvi.getInfo())
    lst_np = np.array(lst.getInfo())
    scaler = MinMaxScaler()
    ndvi_np = scaler.fit_transform(ndvi_np.reshape(-1, 1)).reshape(ndvi_np.shape)
    lst_np = scaler.fit_transform(lst_np.reshape(-1, 1)).reshape(lst_np.shape)
    
    # 计算生态指数
    ecology_index = 0.5 * ndvi_np + 0.5 * (1 - lst_np)
    
    # 将生态指数转化为图像并添加到图像集中
    return image.addBands(ee.Image(ecology_index).rename('ECOLOGY_INDEX'))

dataset = dataset.map(compute_ecology_index)

# 定义元胞自动机
class EcologyIndexCA:
    def __init__(self, ecology_index, kernel_size=3):
        self.ecology_index = ecology_index
        self.kernel_size = kernel_size
        self.kernel = np.ones((kernel_size, kernel_size))
        
    def step(self):
        # 对生态指数进行卷积
        ecology_index_np = np.array(self.ecology_index.getInfo())
        ecology_index_np = resize(ecology_index_np, (ecology_index_np.shape[1] * 2, ecology_index_np.shape[2] * 2))
        ecology_index_np = np.pad(ecology_index_np, ((1, 1), (1, 1)), mode='edge')
        ecology_index_np = np.float32(ecology_index_np)
        ecology_index_np = np.nan_to_num(ecology_index_np)
        ecology_index_np = np.convolve(ecology_index_np, self.kernel, mode='same')
        ecology_index_np = np.convolve(ecology_index_np, self.kernel.T, mode='same')
        ecology_index_np /= self.kernel_size ** 2
        
        # 将生态指数转化为图像并返回
        return ee.Image(ecology_index_np.reshape((1, ecology_index_np.shape[0], ecology_index_np.shape[1]))) \
                    .rename('ECOLOGY_INDEX')
                    
# 进行元胞自动机预测
ca = EcologyIndexCA(dataset.first().select('ECOLOGY_INDEX'))
predicted_ecology_index = []
for i in range(100):
    predicted_ecology_index.append(ca.ecology_index)
    ca.ecology_index = ca.step()

# 将结果转化为图像并显示
predicted_ecology_index = np.array(predicted_ecology_index)
predicted_ecology_index = np.float32(predicted_ecology_index)
predicted_ecology_index = predicted_ecology_index / np.max(predicted_ecology_index)
predicted_ecology_index = np.rollaxis(predicted_ecology_index, 0, 3)
predicted_ecology_index *= 255
predicted_ecology_index = np.uint8(predicted_ecology_index)
predicted_ecology_index_image = ee.Image(predicted_ecology_index).rename('PREDICTED_ECOLOGY_INDEX')

请注意，由于数据集和区域的选择会影响结果，因此需要根据实际情况进行调整。