通过卫星图像检测茶河SCI生态走廊的土地利用变化的策略
Mario García-Ontiyuelo1, Carolina Acuña-Alonso2, Christos Vasilakos3
1Universidade de Vigo, Hydro-Forestry Geomodeling Research Group, School of Forestry Engineering, 36005 Pontevedra, Spain.
The Science of the total environment
|November 17, 2024
概括
本研究使用卫星数据和先进方法量化西北西班牙的土地利用和土地覆盖面变化 (LULCC). 基于对象的图像分析与行星实验室的数据实现了最高的准确性,有助于可持续的土地管理.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
背景情况:
- 土地覆盖面的变化对生态系统,息地和气候产生重大影响.
- 量化土地利用和土地覆盖变化 (LULCC) 对环境管理至关重要.
- 西北西班牙加利西亚的茶河SCI面临着由于土地覆盖率分散而带来的挑战.
研究的目的:
- 在2015-2023年期间,量化茶河SCI中的LULCC.
- 评估不同卫星数据的准确性和用于LULCC检测的分类方法.
- 模拟2031年未来的土地覆盖场景.
主要方法:
- 基于对象的图像分析 (OBIA) 和人工神经网络 (ANN) 的分类.
- 利用了来自Sentinel-2和行星实验室 (RapidEye,PlanetScope) 的多光谱卫星图像.
- 在未来的土地覆盖模拟中采用后分类技术.
主要成果:
- 使用行星实验室的数据与OBIA实现了最高的准确性 (80%).
- 使用OBIA的Sentinel-2数据产生了大约70%的准确性.
- 星球实验室与ANN的数据结果约为55%的准确性,突出数据和方法的影响.
结论:
- 地理空间数据和所选择的方法极大地影响了LULCC分类的准确性.
- 这些发现支持该地区的领土规划和可持续森林管理.
- 有效的LULCC检测有助于明智的决策和区域治理.
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