Quantifying Plant Species α-Diversity Using Normalized Difference Vegetation Index and Climate Data in Alpine Grasslands

Abstract

Quantitative plant species α-diversity of grasslands at multiple spatial and temporal scales is important for investigating the responses of biodiversity to global change and protecting biodiversity under global change. Potential plant species α-diversity (i.e., SRp, Shannonp, Simpsonp and Pieloup: potential species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by climate data (i.e., annual temperature, precipitation and radiation) and actual plant species α-diversity (i.e., SRa, Shannona, Simpsona and Pieloua: actual species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by normalized difference vegetation index and climate data. Six methods (i.e., random forest, generalized boosted regression, artificial neural network, multiple linear regression, support vector machine and recursive regression trees) were used in this study. Overall, the constructed random forest models performed the best among the six algorithms. The simulated plant species α-diversity based on the constructed random forest models can explain no less than 96% variation of the observed plant species α-diversity. The RMSE and relative biases between simulated α-diversity based on the constructed random forest models and observed α-diversity were ≤1.58 and within ±4.49%, respectively. Accordingly, plant species α-diversity can be quantified from the normalized difference vegetation index and climate data using random forest models. The random forest models of plant α-diversity build by this study had enough predicting accuracies, at least for alpine grassland ecosystems, Tibet. The proposed random forest models of plant α-diversity by this current study can help researchers to save time by abandoning plant community field surveys, and facilitate researchers to conduct studies on plant α-diversity over a long-term temporal scale and larger spatial scale under global change.

Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands

“…Documented results suggest that SM in non-grazing areas is not affected by anthropogenic activities (Han et al, 2022b;Tian and Fu, 2022). Refer to some previous studies (Han et al, 2022b;Tian and Fu, 2022), SM under non-grazing conditions were referred to SM p . Based on the monthly meteorological data (temperature, precipitation and radiation) from 145 meteorological stations, we obtained the raster meteorological data in 2011, 2013-2018 and 2020 (spatial resolution of 1 km × 1 km, temporal resolution of 1 month) through spatial interpolation using the Anusplin software Tian and Fu, 2022).…”

“…Refer to some previous studies (Han et al, 2022b;Tian and Fu, 2022), SM under non-grazing conditions were referred to SM p . Based on the monthly meteorological data (temperature, precipitation and radiation) from 145 meteorological stations, we obtained the raster meteorological data in 2011, 2013-2018 and 2020 (spatial resolution of 1 km × 1 km, temporal resolution of 1 month) through spatial interpolation using the Anusplin software Tian and Fu, 2022). Temperature, precipitation and radiation data were obtained from spatial interpolated meteorological data, which often have higher accuracy Tian and Fu, 2022).…”

“…Based on the monthly meteorological data (temperature, precipitation and radiation) from 145 meteorological stations, we obtained the raster meteorological data in 2011, 2013-2018 and 2020 (spatial resolution of 1 km × 1 km, temporal resolution of 1 month) through spatial interpolation using the Anusplin software Tian and Fu, 2022). Temperature, precipitation and radiation data were obtained from spatial interpolated meteorological data, which often have higher accuracy Tian and Fu, 2022). Temperature, precipitation and radiation have been validated as important variables affecting SM on Tibetan Plateau (Cui et al, 2016;Li et al, 2022;Liu and Yang, 2022).…”

“…Some studies have tried to quantify SM using various machine learning tools (e.g., random forest, extreme gradient boosting) (Cui et al, 2016;Yang et al, 2017;Cui et al, 2019;Tong et al, 2021;Guo et al, 2022;Jarray et al, 2022;Lei et al, 2022;Uthayakumar et al, 2022;Veloso et al, 2022;Wei et al, 2022). Although several previous studies confirmed that different machine learning tools can have varying performances in quantifying surface variables (Han et al, 2022b;He et al, 2022;Tian and Fu, 2022), it is still unclear which has the best performance in estimating SM (Tong et al, 2021;Zhang et al, 2022a;Nguyen et al, 2022). Earlier models for estimating SM generally have many model parameters (Wang et al, 2022a;Zhang et al, 2022a;Kisekka et al, 2022;Tramblay and Segui, 2022).…”

Different responses of soil bacterial species diversity and phylogenetic diversity to short-term nitrogen input in an alpine steppe at the source of Brahmaputra

“…In September 2021, we recorded the number of plant species, height, and coverage of each plant species within a 0.5 m × 0.5 m quadrat in the middle of each plot (Zhang and Fu, 2021;Tian and Fu, 2022). In each plot, five 3.8 cm diameter soil cores (0-10 cm and 10-20 cm) were collected from the four corners and the middle of each quadrat in cross diagonals after plant community investigation.…”