如何统计QGIS里栅格图层的面积呢

现在有这样一个需求，就是需要统计一下加载在QGIS里的图层的面积，然后保存在csv里，第一列是图层名，第二列是面积，面积以平方千米计，直接在python控制台写python脚本比较方便。如下便是代码

# -*- coding: utf-8 -*-
from qgis.core import QgsProject
from osgeo import gdal
import numpy as np
import processing
import csv
import os

# -------------------------------
# 设置输出 CSV 路径
# -------------------------------
output_csv = r"C:\D\tmp\tmp.csv"

# 确保目录存在
output_dir = os.path.dirname(output_csv)
if not os.path.exists(output_dir):
    try:
        os.makedirs(output_dir)
        print(f"✅ 已创建目录: {output_dir}")
    except Exception as e:
        print(f"❌ 创建目录失败: {e}")
        exit()

# -------------------------------
# 收集所有栅格图层
# -------------------------------
layers = QgsProject.instance().mapLayers().values()
raster_layers = [lyr for lyr in layers if lyr.type() == lyr.RasterLayer]

if not raster_layers:
    print("❌ 错误：没有找到任何栅格图层")
else:
    print(f"✅ 找到 {len(raster_layers)} 个栅格图层，开始处理...")

# 存储结果
results = []

# -------------------------------
# 遍历每个图层进行处理
# -------------------------------
for idx, layer in enumerate(raster_layers):
    name = layer.name()
    source = layer.source()
    print(f"\n📌 正在处理 [{idx+1}/{len(raster_layers)}]: {name}")

    # --- 1. 重投影到 EPSG:3857（内存中）
    params = {
        'INPUT': source,
        'TARGET_CRS': 'EPSG:3857',
        'OUTPUT': 'TEMPORARY_OUTPUT'
    }

    try:
        result = processing.run("gdal:warpreproject", params)
        reprojected_path = result['OUTPUT']
    except Exception as e:
        print(f"   ❌ 重投影失败: {e}")
        results.append({'name': name, 'area_km2': None})
        continue

    # --- 2. 使用 GDAL 读取数据
    ds = gdal.Open(reprojected_path)
    if ds is None:
        print(f"   ❌ 无法打开重投影数据")
        results.append({'name': name, 'area_km2': None})
        continue

    try:
        band = ds.GetRasterBand(1)
        gt = ds.GetGeoTransform()
        pixel_width = abs(gt[1])
        pixel_height = abs(gt[5])
        nodata = band.GetNoDataValue()

        data = band.ReadAsArray()
        pixel_area_m2 = pixel_width * pixel_height
        total_pixels = data.size

        # 统计有效像元
        if nodata is not None:
            valid_mask = (data != nodata) & (~np.isnan(data))
            valid_count = np.sum(valid_mask)
        else:
            valid_count = total_pixels

        # 计算面积（km²）
        total_area_km2 = (valid_count * pixel_area_m2) / 1_000_000

        print(f"   ✅ 分辨率: {pixel_width:.4f}×{pixel_height:.4f} 米, 有效像元: {valid_count:,}")
        print(f"   📊 面积: {total_area_km2:.6f} 平方公里")

        results.append({'name': name, 'area_km2': round(total_area_km2, 6)})

    except Exception as e:
        print(f"   ❌ 数据处理出错: {e}")
        results.append({'name': name, 'area_km2': None})
    finally:
        ds = None  # 释放资源

# -------------------------------
# 保存结果到 CSV
# -------------------------------
try:
    with open(output_csv, 'w', encoding='utf-8', newline='') as f:
        writer = csv.writer(f)
        writer.writerow(['图层名', '面积_平方千米'])  # 表头
        for r in results:
            area_str = f"{r['area_km2']:.6f}" if r['area_km2'] is not None else ""
            writer.writerow([r['name'], area_str])
    print(f"\n🎉 成功！结果已保存至:\n   {output_csv}")
except Exception as e:
    print(f"❌ 保存 CSV 失败: {e}")