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自 1965 年以来,基于卫星数据的三江平原大规模湿地损失重建:过程、格局和驱动力。

Large-Scale Marsh Loss Reconstructed from Satellite Data in the Small Sanjiang Plain since 1965: Process, Pattern and Driving Force.

机构信息

State Key Laboratory of Resources and Environmental Information System, Institute of Geography and natural resources, Chinese Academy of Sciences, Beijing 100101, China.

出版信息

Sensors (Basel). 2020 Feb 14;20(4):1036. doi: 10.3390/s20041036.

Abstract

Monitoring wetland dynamics and related land-use changes over long-time periods is essential to understanding wetland evolution and supporting knowledge-based conservation policies. Combining multi-source remote sensing images, this study identifies the dynamics of marshes, a core part of wetlands, in the Small Sanjiang Plain (SSP), from 1965 to 2015. The influence of human activities on marsh patterns is estimated quantitatively by the trajectory analysis method. The results indicate that the marsh area decreased drastically by 53.17% of the total SSP area during the study period, which covered the last five decades. The marsh mostly transformed to paddy field and dry farmland in the SSP from 1965 to 2015, indicating that agricultural encroachment was the dominant contributor to marsh degradation in the area. Analysis of the landscape indexes indicates that marsh fragmentation was aggravated during the past five decades in the SSP. Trajectory analysis also indicated that human activities have acted as the primary driving force of marsh changes in the SSP since 1965. This study provides scientific information to better understand the evolution of the wetland and to implement ecological conservation and sustainable management of the wetlands in the future.

摘要

监测湿地动态及其相关土地利用变化的长时间变化对于了解湿地演变和支持基于知识的保护政策至关重要。本研究结合多源遥感图像,从 1965 年到 2015 年,确定了三江平原(SSP)核心湿地沼泽的动态。通过轨迹分析方法定量估计了人类活动对沼泽格局的影响。结果表明,在研究期间,沼泽面积减少了 53.17%,占 SSP 总面积的 53.17%,涵盖了过去五十年。1965 年至 2015 年,SSP 的大部分沼泽转变为稻田和旱地,表明农业侵占是该地区沼泽退化的主要原因。景观指数分析表明,在过去的五十年中,SSP 的沼泽破碎化程度加剧。轨迹分析还表明,自 1965 年以来,人类活动一直是 SSP 沼泽变化的主要驱动力。本研究为更好地了解湿地的演变以及未来实施湿地生态保护和可持续管理提供了科学信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d984/7070650/2b52254512b9/sensors-20-01036-g001.jpg

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