收稿：2022-11-16，
纸质出版：2023-06-07
移动端阅览
海湾处于水陆交互的区域，生态系统较为脆弱，资源环境极易受损。大尺度、长时序和高精度的土地利用/覆被变化LUCC（Land Use/Cover Change）制图是海湾区域国土空间规划和环境保护的基础。现有的制图方法多是针对原始遥感影像，难以充分挖掘和联合利用特征空间和变换空间蕴含的信息潜力，导致传统方法在地表异质性较高的海湾区域应用效果较差。本文面向杭州湾区域，基于Landsat长时间序列卫星影像和谷歌地球引擎GEE（Google Earth Engine），提出了融合遥感指数和主成分分量的随机森林遥感影像分类方法，实现了1985年—2020年（5年时间间隔）的LUCC制图及时空格局分析。结果表明：（1）融合遥感指数和主成分分量的随机森林算法能够准确提取杭州湾LUCC信息，8个时相的平均总体精度OA（Overall Accuracy）和Kappa系数分别为92.83%和0.9108。（2）研究期间内，建设用地（278.26 km
2
至2984.76 km
2
，年均增加77.33 km
2
）、水体（509.32 km
2
至680.21 km
2
，年均增加4.88 km
2
）、裸地（768.99 km
2
增长至1078.13 km
2
，年均增加8.83 km
2
）的面积呈现增加趋势，而林地（2159.49 km
2
至1881.52 km
2
，年均减少7.94 km
2
）、耕地（6998.45 km
2
至4800.59 km
2
，年均减少62.80 km
2
）、滩涂（181.65 km
2
至161.50 km
2
，年均减少0.58 km
2
）的面积呈现减少趋势。（3）研究期间内，耕地是最主要的转出源，总面积占比由64.23%减少至41.43%，耕地面积转出以建设用地（2268.05 km
2
）与裸地（630.20 km
2
）为主；耕地面积转入以水体（376.22 km
2
）与林地（352.22 km
2
）为主。本研究能够为杭州湾区域土地资源的科学管理提供数据支持，所得LUCC数据集对区域可持续发展具有重要意义。
Land Use/Cover Change (LUCC) is generally defined as the use of land by humans
which is the direct result of the interaction between humans and nature
and reflects the basic process of the interaction between the Earth’s environmental system and human production systems. A bay is an area where land and water interact
with a relatively fragile ecosystem and easily damaged resources and environment. Large-scale long-time series and high-precision LUCC mapping is the basis for territorial spatial planning and environmental protection in bay regions. Random forest algorithms received considerable attention in recent years owing to their high interpretability and reliability in handling complex data. However
room for improvement exists in optimizing the performance of random forest algorithms in processing long-time series datasets. Most existing mapping methods are aimed at original remote sensing images
and fully tapping and jointly utilizing the information potential of the feature space and transformation space are difficult
resulting in the poor application effect of traditional methods in bay areas with high surface heterogeneity. The combined application of the remote sensing spectral index can effectively increase the separability of object categories in bay areas
and principal component transformation can effectively eliminate correlations between features and achieve data compression and image enhancement. Based on Landsat long-term satellite and Google Earth Engine images of the Hangzhou Bay area
this study proposes a random forest remote sensing image classification method that integrates the remote sensing spectral index and principal component transformation and analyzes LUCC mapping spatiotemporal patterns from 1985 to 2020 (at 5-year intervals). Results show that (1) the random forest algorithm
integrating the remote sensing spectral index and principal component transformation
can accurately extract Hangzhou Bay LUCC information
and the average overall accuracy and kappa coefficient of the eight time phases are 92.83% and 0.9108
respectively. (2) During the study period
the construction land area (278.26 km
2
to 2984.76 km
2
with an average annual increase of 77.33 km
2
)
water area (509.32 km
2
to 680.21 km
2
with an average annual increase of 4.88 km
2
)
and bare land area (768.99 km
2
to 1078.13 km
2
with an average annual increase of 8.83 km
2
) showed an increasing trend
whereas the wood land area (2159.49 km
2
to 1881.52 km
2
with an annual average decrease of 7.94 km
2
)
cultivated field area (6998.45 km
2
to 4800.59 km
2
with an annual average decrease of 62.80 km
2
)
and tidal-flat area (181.65 km
2
to 161.50 km
2
with an annual average decrease of 0.58 km
2
) showed a decreasing trend. (3) During the study period
the cultivated field area was the main transfer source
whose total area proportion decreased from 64.23% to 41.43%. The transfer out of the cultivated field area was mainly to construction land (2268.05 km
2
) and bare land (630.20 km
2
)
and the transfer in of the cultivated field area was mainly to water body (376.22 km
2
) and forest land (352.22 km
2
). This study provides data support for the scientific management of land resources in the Hangzhou Bay
and the obtained LUCC dataset is of considerable significance to the sustainable development of the region.
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