波多黎各农民对 UAS 咖啡农业生态系统图像土地覆盖分类的看法

Gwendolyn Klenke, Shannon Brines, Nayethzi Hernandez, Kevin Li, Riley Glancy, Jose Cabrera, Blake H. Neal, Kevin A. Adkins, Ronny Schroeder, Ivette Perfecto
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引用次数: 0

摘要

随着人们对农场宝贵的生态系统服务的认识不断提高,高度多样化的农业生态系统日益受到关注。与此同时,无人驾驶航空系统(UAS)在遥感领域的应用也在不断增加,因为与传统卫星相比,无人机具有更精细的空间分辨率和更快的重访速度。由于无人机系统的综合效用和对农业生态系统的关注,我们有机会评估无人机系统在高度生物多样性环境中的实用性。在这项研究中,我们利用无人机系统收集了波多黎各咖啡农业生态系统的精细分辨率 10 波段多光谱图像。通过对每个农场进行基于像素的监督分类,我们绘制了土地覆被图,并对每个分类进行了准确性评估。平均总体准确率(53.9%)虽然相对较低,但对于使用精细分辨率数据的如此多样的地貌来说,这也是意料之中的。为了加深我们对分类的理解,我们采访了农民,以了解他们对如何最好地利用这些地图来支持其土地管理的想法。在与农民分享了图像和土地覆被分类后,我们发现,虽然农民通常对这些印刷品感到自豪或好奇,但他们认为将这些地图纳入农场管理是不切实际的。这些发现突出表明,虽然研究人员和政府机构可以越来越多地应用遥感技术来估算不同农业生态系统中的土地覆被等级和生态系统服务,但要使这些产品与多样化的小农相关,还需要进一步的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Farmer Perceptions of Land Cover Classification of UAS Imagery of Coffee Agroecosystems in Puerto Rico
Highly diverse agroecosystems are increasingly of interest as the realization of farms’ invaluable ecosystem services grows. Simultaneously, there has been an increased use of uncrewed aerial systems (UASs) in remote sensing, as drones offer a finer spatial resolution and faster revisit rate than traditional satellites. With the combined utility of UASs and the attention on agroecosystems, there is an opportunity to assess UAS practicality in highly biodiverse settings. In this study, we utilized UASs to collect fine-resolution 10-band multispectral imagery of coffee agroecosystems in Puerto Rico. We created land cover maps through a pixel-based supervised classification of each farm and assembled accuracy assessments for each classification. The average overall accuracy (53.9%), though relatively low, was expected for such a diverse landscape with fine-resolution data. To bolster our understanding of the classifications, we interviewed farmers to understand their thoughts on how these maps may be best used to support their land management. After sharing imagery and land cover classifications with farmers, we found that while the prints were often a point of pride or curiosity for farmers, integrating the maps into farm management was perceived as impractical. These findings highlight that while researchers and government agencies can increasingly apply remote sensing to estimate land cover classes and ecosystem services in diverse agroecosystems, further work is needed to make these products relevant to diversified smallholder farmers.
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