The assessment of the importance of biodiversity maintenance functions is a significant content in the delineation of regional ecological protection red lines of China. However, estimation in this respect is less in previous studies involved in Chuxiong, an important ecological conservation area in Yunnan Province. Therefore, this study quantitative estimation of the functional importance of biodiversity maintenance in Chuxiong with the Net Primary Productivity model. Then, the spatial distribution pattern of the importance of biodiversity maintenance functions was analyzed after quantile classification, and the distribution characteristics of importance levels in different land use types were explored with land use classification data. The results show that: (1) The importance of biodiversity maintenance function in Chuxiong shows a decreasing trend from the north and east to the west and south. (2) Lufeng, Wuding, and Yongren have a relatively high proportion of areas with extremely important biodiversity maintenance functions, while Chuxiong City and Shuangbai are mainly important districts. In addition, Dayao and Nanhua are mainly distributed in general important areas. (3) The important and extremely important areas of biological diversity in Chuxiong are mainly distributed in grassland, forest land and other land use types. The research results can provide a scientific basis for the delimitation of Chuxiong state nature reserves and ecological corridors, and are of great significance to biodiversity conservation. © 2023 SPIE.

【Objective】In order to grasp the quantity of cultivated land in Yangzonghai basin, the temporal and spatial variation characteristics of cultivated land was analyzed, which could provide a reference basis for the sustainable utilization of cultivated land resources and land spatial planning in Yangzonghai basin. 【Method】Based on ArcGIS, Fragstats 4.2 and other software, the RS data from 2005, 2010, 2015 and 2020 were selected to analyze the dynamic variation and spatial distribution pattern of cultivated land in the study area using different methods such as dynamic variation degree, different terrain conditions, transition matrix, geo-informatic atlas, and landscape pattern index. 【Result】From 2005 to 2020, the number of cultivated lands in the study area showed a decreasing trend with a reduced area of 816.45hm2 and a dynamic variation degree of -0.66%. From the county (district) scale, Yiliang County showed a continuous decline trend in the past 15 years, while the other three counties (districts) increased and decreased in each period; The topographic conditions had a certain impact on the cultivated land spatial distribution. The cultivated land in the study area occupies a large proportion in the cloudy aspect and semi-cloudy aspect with the altitude © 2023 SPIE.

Potassium is an important nutrient element for plant growth. The traditional integer-order differential transformation methods at first order and second order tend to reduce the accuracy of the total potassium content quantitative inversion model, and there are few reports on the use of the fractional differential algorithm for the prediction of soil total potassium content. In this paper, the use of the fractional differential algorithm to predict total potassium content is introduced. Field soils collected in the Xinjiang Uygur Autonomous Region from May 9 to 23, 2017, were used as the data sources. Firstly, we calculated the correlation between spectral reflectance and total potassium content for the original spectrum (R) and the root mean square spectrum (root R) under different fractional differential orders. Secondly, bands whose maximum absolute correlation coefficient was greater than 0.5 were selected as sensitive bands. R had seven bands: 562, 596, 1177, 2155, 2156, 2364, and 239 nm. root R had six bands: 596, 1177, 2155, 2156, 2364, and 2398 nm. Finally, a multiple regression analysis method was employed to quantitatively estimate the optimal model. The ratio of performance to deviation (RPD) evaluation index of a good model should be greater than or equal to 1.4. The simulation results showed that the optimal models for R and root R were the 0.8-order differential and the 0.6-order differential, respectively. The corresponding RPD values were 1.700182 and 1.783319, respectively. We found that the prediction model of root R was more accurate.