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Multidimensional Assessment of Food Provisioning Ecosystem Services Using Remote Sensing and Agricultural Statistics
Remote Sensing ( IF 5 ) Pub Date : 2020-12-03 , DOI: 10.3390/rs12233955 Donghui Shi , Yishao Shi , Qiusheng Wu , Ruibo Fang
Remote Sensing ( IF 5 ) Pub Date : 2020-12-03 , DOI: 10.3390/rs12233955 Donghui Shi , Yishao Shi , Qiusheng Wu , Ruibo Fang
With the increasing global population, human demands for natural resources continue to grow. There is a critical need for the sustainable use and development of natural resources. In this context, ecosystem services have attracted more and more attention, and ecosystem services assessment has proven to be useful for guiding research, policy formulation, and management implementation. In this paper, we attempted to assess ecosystem services more comprehensively from various perspectives. We used food provisioning ecosystem services in Minnesota as a case study and proposed two new concepts for assessing ecosystem services: efficiency and trend. We designed a multidimensional assessment framework, analyzed the total output, efficiency, and trend temporally based on both area and space with Exploratory Spatial Data Analysis (ESDA). We also identified major influencing factors based on remote sensing images in Google Earth Engine and explored the quantitative influence on each assessment dimension. We found that: (1) Food provisioning ecosystem service in Minnesota has generally been improving from 1998 to 2018. (2) We identified food provisioning ecosystem services in Minnesota as superior zones, mixed zones, and inferior zones with a ‘sandwich geo-configuration’. (3) The total output tends to be stable while the efficiency is disturbed by some natural disasters. Simultaneously, the trend index has been improving with slight fluctuations. (4) Agricultural disaster financial support has a stronger impact on stabilizing the total output of food provisioning than the other two dimensions. (5) Soil moisture, diurnal temperature difference, and crop growth are the three main influencing aspects of food provisioning ecosystem services, and the order of the influential density is: the Perpendicular Drought Index (PDI), Normalized Difference Vegetation Index (NDVI), Rainfall (RF), Daytime Temperature (DT), and Diurnal Temperature Difference (DIF).
中文翻译:
利用遥感和农业统计数据对粮食供应生态系统服务进行多维评估
随着全球人口的增加,人类对自然资源的需求持续增长。迫切需要自然资源的可持续利用和开发。在这种情况下,生态系统服务已引起越来越多的关注,并且生态系统服务评估已证明对指导研究,政策制定和管理实施很有用。在本文中,我们尝试从各种角度更全面地评估生态系统服务。我们以明尼苏达州的粮食供应生态系统服务为案例研究,并提出了两个新的概念来评估生态系统服务:效率和趋势。我们设计了一个多维评估框架,使用探索性空间数据分析(ESDA)来基于面积和空间在时间上分析总产出,效率和趋势。我们还基于Google Earth Engine中的遥感图像确定了主要的影响因素,并探讨了对每个评估维度的定量影响。我们发现:(1)从1998年到2018年,明尼苏达州的食物供应生态系统服务总体上得到了改善。(2)我们将明尼苏达州的食物供应生态系统服务确定为具有“三明治”地理构造的优势区域,混合区域和劣等区域'。(3)总产量趋于稳定,而效率却受到一些自然灾害的干扰。同时,趋势指数一直在改善,并略有波动。(4)与其他两个方面相比,农业灾害财政支持对稳定粮食供应总产出的影响更大。(5)土壤水分,日温差,
更新日期:2020-12-03
中文翻译:
利用遥感和农业统计数据对粮食供应生态系统服务进行多维评估
随着全球人口的增加,人类对自然资源的需求持续增长。迫切需要自然资源的可持续利用和开发。在这种情况下,生态系统服务已引起越来越多的关注,并且生态系统服务评估已证明对指导研究,政策制定和管理实施很有用。在本文中,我们尝试从各种角度更全面地评估生态系统服务。我们以明尼苏达州的粮食供应生态系统服务为案例研究,并提出了两个新的概念来评估生态系统服务:效率和趋势。我们设计了一个多维评估框架,使用探索性空间数据分析(ESDA)来基于面积和空间在时间上分析总产出,效率和趋势。我们还基于Google Earth Engine中的遥感图像确定了主要的影响因素,并探讨了对每个评估维度的定量影响。我们发现:(1)从1998年到2018年,明尼苏达州的食物供应生态系统服务总体上得到了改善。(2)我们将明尼苏达州的食物供应生态系统服务确定为具有“三明治”地理构造的优势区域,混合区域和劣等区域'。(3)总产量趋于稳定,而效率却受到一些自然灾害的干扰。同时,趋势指数一直在改善,并略有波动。(4)与其他两个方面相比,农业灾害财政支持对稳定粮食供应总产出的影响更大。(5)土壤水分,日温差,