Integration of remote sensing, county-level census, and machine learning for century-long regional cropland distribution data reconstruction
Integration of remote sensing, county-level census, and machine learning for century-long regional cropland distribution data reconstruction
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DOI：
10.1016/j.jag.2020.102151
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发表时间：
2020-09
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影响因子：
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通讯作者：
Jia Yang;B. Tao;Hao Shi;Ouyang Ying;S. Pan;W. Ren;Chaoqun Lu
中科院分区：
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作者：
Jia Yang;B. Tao;Hao Shi;Ouyang Ying;S. Pan;W. Ren;Chaoqun Lu
密西西比河下游冲积谷(LMAV)是美国南部约1000万公顷滩地阔叶林的家园。在过去的几个世纪里，这里经历了超过80%的滩地阔叶林面积的丧失，近几十年来经历了大规模的植树造林。由于缺乏高分辨率的农田数据集，土地利用变化(LUC)对LMAV生态系统服务的影响还没有得到充分的了解。在这项研究中，我们开发了一个新的框架，通过集成机器学习算法、县级农业普查和基于卫星的农田产品来重建1850-2018年间30米分辨率的LMAV农田分布。结果表明，耕地面积从1850年的0.78 × 104km2增加到1980年的6.64 × 104km2,2018年减少到6.16 × 104km2。20世纪60年代耕地扩展速度最大(749 km2yr−1)，但此后迅速下降，而近几十年来耕地弃置率大幅增加，2010年代最大速度为514 km2 yr−1。我们的数据集有三个显著的特点：(1)描述精细的空间细节，(2)整合县级人口普查，(3)包括由基于卫星的土地覆盖产品训练的机器学习算法。最重要的是，我们的数据集很好地捕捉到了1930-1960年间耕地面积的持续增长趋势，这一趋势被从州级普查中重建的其他农田数据集所歪曲。我们的数据集将对准确评估历史上的森林砍伐和最近的植树造林对区域生态系统服务的影响，将观测到的水文变化归因于人为和自然驱动因素，以及研究社会经济因素如何控制区域LUC格局具有重要意义。我们的框架和数据集对于制定保护自然资源和加强LMAV生态系统服务的管理和政策战略至关重要。
The Lower Mississippi Alluvial Valley (LMAV) was home to about ten million hectare bottomland hardwood (BLH) forests in the Southern U.S. It experienced over 80 % area loss of the BLH forests in the past centuries and large-scale afforestation in recent decades. Due to the lack of a high-resolution cropland dataset, impacts of land use change (LUC) on the LMAV ecosystem services have not been fully understood. In this study, we developed a novel framework by integrating the machine learning algorithm, county-level agricultural census, and satellite-based cropland products to reconstruct the LMAV cropland distribution during 1850–2018 at a 30-m resolution. Results showed that the LMAV cropland area increased from 0.78 × 104km2in 1850 to 6.64 × 104km2in 1980 and then decreased to 6.16 × 104km2in 2018. Cropland expansion rate was the largest in the 1960s (749 km2yr−1) but decreased rapidly thereafter, whereas cropland abandonment rate increased substantially in recent decades with the largest rate of 514 km2yr−1in the 2010s. Our dataset has three notable features: (1) the depiction of fine spatial details, (2) the integration of the county-level census, and (3) the inclusion of a machine-learning algorithm trained by satellite-based land cover product. Most importantly, our dataset well captured the continuous increasing trend in cropland area from 1930–1960, which was misrepresented by other cropland datasets reconstructed from the state-level census. Our dataset would be important to accurately evaluate the impacts of historical deforestation and recent afforestation efforts on regional ecosystem services, attribute the observed hydrological changes to anthropogenic and natural driving factors, and investigate how the socioeconomic factors control regional LUC pattern. Our framework and dataset are crucial to developing managerial and policy strategies for conserving natural resources and enhancing ecosystem services in the LMAV.