Relationship between extreme climate indices and spatiotemporal changes of vegetation on Yunnan Plateau from 1982 to 2019

Assessing habitat selection parameters of Arabica coffee using BWM and BCM methods based on GIS

Arabica coffee, as one of the world's three native coffee species, requires rational planning for its growing areas to ensure ecological and sustainable agricultural development. This study aims to establish a decision-making framework using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM), with a focus on assessing the habitat suitability of Arabica coffee in Yunnan Province, China. The impacts of climate, topography, soil, and socio-economic factors were considered by selecting 13 criteria through correlation analysis. Indicator weights were determined using the Best-Worst Method (BWM), while weighted processing was conducted using the Base-Criterion Method (BCM). Sensitivity analysis was performed to verify the accuracy and stability of the model. Additionally, several decision models were evaluated to investigate regionalizing Arabica coffee habitats in Yunnan. The results highlighted that minimum temperature during the coldest month is crucial for evaluation purposes. The BWM-GIS model identified suitable areas comprising 13.55% of the total area as most suitable, 27.46% as suitable, and 59.00% as unsuitable, whereas corresponding values for the BCM-GIS model were 9.97%, 30.43%, and 59.59%. Despite employing different decision-making methods, both models yielded similar and consistent results. The suitable areas mainly encompass Dehong, Pu'er, Lincang, Xishuangbanna, Baoshan, southern Chuxiong, eastern Honghe, southern Yuxi, and parts of Wenshan. BWM-GIS achieved an area under curve (AUC) value of 0.891, while BCM-GIS obtained an AUC value of 0.890, indicating the stability and reliability of the models. Among them, the evaluation process of BCM-GIS was simpler and more realistic. Therefore, it has high feasibility and practical value in practical application. The findings from this study provide a significant scientific foundation for optimizing Yunnan Province.

Investigating the long-term response of plateau vegetation productivity to extreme climate: insights from a case study in Qinghai Province, China

Over the past three decades, there has been a significant global climate change characterized by an increase in the intensity and frequency of extreme climate events. The vegetation status in Qinghai Province has undergone substantial changes, which are more pronounced than other regions in the Qinghai-Tibet Plateau. However, a clear understanding of the response characteristics of plateau vegetation to extreme climate events is currently lacking. In this study, we investigated the response of net primary productivity (NPP) to different forms of extreme climate events across regions characterized by varying levels of aridity and elevation gradients. Specifically, we observed a significant increase in NPP in relatively arid regions. Our findings indicate that, in relatively arid regions, single episodes of high-intensity precipitation have a pronounced positive effect (higher correlation) on NPP. Furthermore, in high-elevation regions (4000-6000 m), both the intensity and frequency of precipitation events are crucial factors for the increase in regional NPP. However, continuous precipitation can have significant negative impacts on certain areas within relatively wet regions. Regarding temperature, a reduction in the number of frost days within a year has been shown to lead to a significant increase in NPP in arid regions. This reduction allows vegetation growth rate to increase in regions where it was limited by low temperatures. Vegetation conditions in drought-poor regions are expected to continue to improve as extreme precipitation intensifies and extreme low-temperature events decrease.

Spatiotemporal variability characteristics of extreme climate events in Xinjiang during 1960-2019

Under the global warming, it is particularly important to explore the response of extreme climate to global climate change over the arid regions. Based on daily temperature (maximum, minimum, and average) and precipitation data from meteorological stations in Xinjiang, China, we analyzed the spatiotemporal characteristics of extreme temperature and extreme precipitation events via combining thin plate smoothing spline function interpolation, Sen's slope, and Mann-Kendall test. Our results showed that during 1960-2019, the extreme low temperature index of frost days (FD), icing days (ID), cold days (TX10p), cold nights (TN10p), and cold speel duration index (CSDI) all showed the downward trend to varying degrees, and the extreme high temperature index of summer days (SD25), warm days (TX90p), warm night (TN90p), and warm speel duration index (WSDI) all showed an upward trend to varying degrees, and the extreme low temperature index of high altitude mountains decreases more than that of the basin and plains. In addition, all the extreme temperature indices are closely related to the annual average temperature in Xinjiang (R > 0.6). Among the extreme precipitation indices, except for the consecutive dry days (CDD), the other extreme precipitation indices showed increasing trends to different degrees, but the changes in extreme precipitation in Xinjiang were mainly manifested by the increase of heavy precipitation in a short period (the increase of heavy precipitation and extreme heavy precipitation was the largest, 44.8 mm/10a and 17.6 mm/10a, respectively) and spatially concentrated in the Ili River and Altai Mountains in northern Xinjiang. Meanwhile, annual precipitation was positively correlated with the extreme precipitation index (R > 0.4), except for the CDD. This study provides theoretical support for the prevention and control of natural disasters in the dry zone.

Machine learning-based grassland aboveground biomass estimation and its response to climate variation in Southwest China

The demand for accurate estimation of aboveground biomass (AGB) at high spatial resolution is increasing in grassland-related research and management, especially for those regions with complex topography and fragmented landscapes, where grass and shrub are interspersed. In this study, based on 519 field AGB observations, integrating Synthetic Aperture Radar (SAR; Sentinel-1) and high-resolution (Sentinel-2) remote sensing images, environmental and topographical data, we estimated the AGB of mountain grassland in Southwest China (Yunnan Province and Guizhou Province) by using remote sensing algorithms ranging from traditional regression to cutting edge machine learning (ML) and deep learning (DL) models. Four models (i.e., multiple stepwise regression (MSR), random forest (RF), support vector machine (SVM) and convolutional neural network (CNN)) were developed and compared for AGB simulation purposes. The results indicated that the RF model performed the best among the four models (testing dataset: decision co-efficient (R2) was 0.80 for shrubland and 0.75 for grassland, respectively). Among all input variables in the RF model, the vegetation indices played the most important role in grassland AGB estimation, with 6 vegetation indices (EVI, EVI2, NDVI, NIRv, MSR and DVI) in the top 10 of input variables. For shrubland, however, topographical factors (elevation, 12.7% IncMSE (increase in mean squared error)) and SAR data (VH band, 11.3% IncMSE) were the variables which contributed the most in the AGB estimation model. By comparing the input variables to the RF model, we found that integrating SAR data has the potential to improve grassland AGB estimation, especially for shrubland (26.7% improvement in the estimation of shrubland AGB). Regional grassland AGB estimation showed a lower mean AGB in Yunnan Province (443.6 g/m2) than that in Guizhou Province (687.6 g/m2) in 2021. Moreover, the correlation between five consecutive years (2018–2022) of AGB data and climatic factors calculated by partial correlation analysis showed that regional AGB was positively related with mean annual precipitation in more than 70% of the grassland and 60% of the shrubland area, respectively. Also, we found a positive relationship with mean annual temperature in 62.8% of the grassland and 55.6% of the shrubland area, respectively. This study demonstrated that integrating SAR into grassland AGB estimation led to a remote sensing estimation model that greatly improved the accuracy of modeled mountain grassland AGB in southwest China, where the grassland consists of a complex mix of grass and shrubs.

Study on the Structural Properties of an Ecospatial Network in Inner Mongolia and Its Relationship with NPP

In the context of strengthening the construction of ecological civilization and accelerating the “carbon peak” in China, the regional ecological pattern and its connection with carbon sink capacity have become an urgent topic. Given that Inner Mongolia is a large carbon emission province and the conflict between economic development and ecological protection is particularly prominent, we took Inner Mongolia as an example to extract its ecospatial network, then calculated the integrity index, topological indices, and recovery robustness of the network and evaluated integrity and other properties of the ecospatial network structure by combining them with the ecological background. In addition, we analyzed the relationship between the topological indices and net primary productivity (NPP). The results showed that the network was scale-free and heterogeneous, with low integrity, connectivity and stability, which were the focus of future optimization. The nodes with important functions were mainly distributed in the farm-forest ecotone, grasslands, and the agro-pastoral ecotone; under the simulation attack, the node recovery robustness was stronger than the corridor recovery robustness, and NPP was negatively and significantly correlated with the woodland nodes and grassland nodes. In terms of ecological restoration, the unused land in the west is a key area, and it is necessary to add new ecological nodes and corridors. In terms of enhancing carbon sequestration capacity, under the premise of ensuring network connectivity, the appropriate and rational merging of ecological nodes and corridors within woodlands and grasslands is a particularly effective means. This study provides a reference for evaluating and optimizing the ecological pattern of areas with prominent ecological problems and improving the carbon sink of ecosystems in terms of their ecospatial network structure.