Multidimensional Assessment of Food Provisioning Ecosystem Services Using Remote Sensing and Agricultural Statistics

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).