Spatially explicit ecosystem services valuation and change is a newly developing area of research in the field of ecology. Using the Beijing region as a study area, the authors have developed a spatially explicit ecosystem services value index and implemented this to quantify and spatially differentiate ecosystem services value at 1-km grid resolution. A gravity model was developed to trace spatial change in the total ecosystem services value of the Beijing study area from a holistic point of view. Study results show that the total value of ecosystem services for the study area decreased by 19.75% during the period 1996-2006 (3,226.2739 US$×10(6) in 1996, 2,589.0321 US$×10(6) in 2006). However, 27.63% of the total area of the Beijing study area increased in ecosystem services value. Spatial differences in ecosystem services values for both 1996 and 2006 are very clear. The center of gravity of total ecosystem services value for the study area moved 32.28 km northwestward over the 10 years due to intensive human intervention taking place in southeast Beijing. The authors suggest that policy-makers should pay greater attention to ecological protection under conditions of rapid socio-economic development and increase the area of green belt in the southeastern part of Beijing.
Background
The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development.
Methods
In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed.
Results
The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%.
Conclusion
This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.
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