Water use efficiency (WUE) links the processes of carbon and water cycling in terrestrial ecosystems and is a crucial indicator for understanding the response of vegetated ecosystems to climate change. In this study, the spatial and temporal patterns of vegetation WUE in Xinjiang of China from 1990 to 2020 were systematically analyzed based on the Carnegie-Ames-Stanford approach model inversions of net primary productivity and evapotranspiration (ET). In this method, remote sensing images and reanalysis data products from past 31 years were combined. The results revealed that vegetation WUE in Xinjiang has been decreasing for 31 years and 2003 was a pivotal year with a fluctuating downward trend before the turning point and a subsequent fluctuating upward trend. The spatial pattern of vegetation WUE in Xinjiang has not changed considerably over the past 31 years, with high values concentrated in plains, especially in the oasis and desert-oasis transition zones, and low values concentrated in mountains. The results revealed that the changes in vegetation WUE in Xinjiang can be attributed to the influence of climatic factors such as precipitation, evapotranspiration and water vapor pressure. This study can be used as a reference for screening artificial and natural vegetation structure types with reasonable structure, high water conservation and productivity, and for achieving the sustainable development of vegetation construction in arid and semiarid regions, especially for the ecosystem security and sustainable development of agriculture and animal husbandry in Xinjiang.

The accurate and dynamic monitoring of the relative content of chlorophyll in corn canopy under drought stress can improve the early warning level of corn drought and help realize precise irrigation. In this study, multispectral images captured by unmanned aerial vehicles (UAVs) were used as data sources, and various vegetation indices with clear physical significance and strong correlation with soil and plant analyzer development (SPAD) values of the chlorophyll relative content in corn canopy were selected. Multiple stepwise regression, support vector machine, the back propagation neural network (BPNN), and a remote sensing monitoring model of the corn canopy SPAD value were established and verified. The optimal estimation model was selected to extract corn canopy SPAD values under various degrees of drought stress in different growth periods. Furthermore, the changes in the corn canopy SPAD value in different growth periods were analyzed to investigate the effects of various degrees of drought stress on the corn canopy SPAD value. The results revealed that the chlorophyll-sensitive vegetation index of corn canopy was different at different growth stages. Furthermore, the inversion models with the best estimation ability varied at different growth stages. Comparing the three modeling methods revealed that the modeling results and verification results of the BPNN model were the best, which indicated that the BPNN model exhibited the best estimation ability and stability performance and can be used as the optimal method for the modeling of the SPAD value of corn canopy based on UAV multispectrum. Furthermore, drought stress can reduce the estimation accuracy of the SPAD value of corn canopy by using a remote sensing monitoring model. This reduction in accuracy affects the seedling stage. Mild drought did not significantly affect the SPAD value of corn canopy, which indicated that corn exhibited a certain adaptability and resistance to drought stress. Therefore, the BPNN model based on the vegetation index can estimate the SPAD value and can be a novel method for SPAD value monitoring based on UAV remote sensing. Furthermore, the model can be used as a reference for the nondestructive monitoring of the summer corn canopy SPAD value and precise field water management under drought stress.

This study investigated the carbon and nitrogen sequestration capacity and the recycling and influence mechanisms of the Picea crassifolia forest ecosystem in the Qilian Mountains, northwest China. The stand densities of 350 plants·hm⁻², 850 plants·hm⁻², 1000 plants·hm⁻², 1400 plants·hm⁻², 1600 plants·hm⁻², 1950 plants·hm⁻², 2100 plants·hm⁻², 2300 plants·hm⁻², and 3000 plants·hm⁻² of P. crassifolia forest were considered for field investigation, sample collection and analysis, carbon and nitrogen storage, and allocation patterns of the P. crassifolia forest ecosystem. The results revealed that: (1) The mean carbon content value of the arbor layer in the P. crassifolia forest was 497.11 g·kg⁻¹, and the nitrogen content was 4.43 g·kg⁻¹. The allocation patterns of the carbon content in each organ followed the order of stem>root>leaf>branch>bark, and the distribution pattern of nitrogen content was leaf>branch>root>bark>trunk. The carbon and nitrogen content of the understorey vegetation layer generally exhibited a sequence of shrub layer>herb layer>litter layer, and the aboveground part>underground part. The content of carbon and nitrogen in the soil layer decreased with the increase in the stand density and decreased with the increase in the soil depth. (2) The carbon storage of the P. crassifolia forest ecosystem revealed a two-peak pattern distribution with the increase in the stand density. Nitrogen storage first increased and subsequently fluctuated with the increase in the stand density. When the density was 850 plants·hm⁻², carbon and nitrogen storage were the highest (500.76 t·hm⁻² and 25.00 t·hm⁻², respectively), and when the density was 3000 plants·hm⁻², the carbon and nitrogen storage were the lowest (315.52 t·hm⁻² and 12.52 t·hm⁻², respectively). With the increase in the stand density, the proportion of vegetation carbon and nitrogen storage gradually increased, and the proportion of soil carbon and nitrogen storage gradually decreased. The allocation patterns of carbon storage followed the order of the soil layer (73.53%)>tree layer (17.03%)>understory layer (9.44%), and nitrogen storage followed the soil layer (87.63%)>understory layer (9.90%)>tree layer (2.47%). (3) The results revealed that the stand density is closely related to forest carbon and nitrogen storage and allocation patterns. Low density (850 plants·hm⁻²) can improve carbon and nitrogen sequestration capacities of vegetation and soil, which is the best retention density of middle-aged P. crassifolia forest in the Qilian Mountains. The results provide a scientific basis for explaining the influence of the stand density on the carbon and nitrogen sequestration capacity of forest ecosystems and the structural management of forests.

For efficient conservation of endangered species, it is crucial to clarify the potential distribution of the species and the gap with current nature reserves. To predict the distribution of Tetraena mongolia in the present (the 2020s) and future (2060s, 2100s), and to explore the conservation gap with natural reserves，this study estimated the potential habitat of Tetraena mongolia in the western Ordos of Inner Mongolia and Ningxia, China using the MaxEnt model and conducted a gap analysis between the potential habitat and current Tetraena mongolia nature reserves based on 23 environmental factors. The area under the receiver operator characteristic (ROC) curve of the MaxEnt model was 0.977, indicating that the prediction was accurate. The crucial environmental factors affecting the distribution of Tetraena mongolia were precipitation of the wettest month, followed by precipitation of the driest month, distance from the road, isotherm properties, mean temperature of the coldest quarter, and slope. The current suitable area of Tetraena mongolia is 4717 km²; the potential distribution area decreased from the 2020s to the 2100s and shifted to the northwest. Presently, only 14.88% of the suitable habitats of Tetraena mongolia are located in protected areas. Large areas of suitable habitats for Tetraena mongolia have not been established as nature reserves, which are mainly concentrated in Wuhai City and Hangjinqi Banner of Ordos City. This study provides scientific guidance for the conservation of Tetraena mongolia and its natural reserve development.

In order to explore the influence of microtopography on the natural regeneration of Picea crassifolia seedlings in Qilian Mountains, Picea crassifolia forest was selected as the research object, and the topographic parameters (altitude, convexity and slope) of 15 fixed plots in the watershed were divided into four different microtopographic habitats by C-mean fuzzy clustering, and the effects of microtopographic habitats on the renewal characteristics (renewal seedling density, average crown width, mean base diameter and average plant height) of the regeneration seedlings were studied. The results show that: (1) The average crown width and mean base diameter of regenerated seedlings were in the order of low-altitude convex land>high-altitude slope>low-altitude concave land>high-altitude steep slope. The average plant height on high-altitude steep slopes was considerably lower than that in the other three microtopographies. Thus, different altitudes, slopes, aspects, and slope positions considerably affected the survival rate and growth process of regeneration seedlings. (2) The renewal seedling density and average plant height under different microtopographic conditions were as follows: low-altitude concave land, low-altitude convex land, high-altitude slope, and high-altitude steep slope. (3) Most of the regenerated seedlings showed an aggregated distribution under different microtopography in the order of high-altitude steep slope>low-altitude concave>low-altitude convex>high-altitude slope. (4) The results of the correlation analysis showed that there was a significant correlation between altitude, slope, slope position, and regenerated seedlings (P<0.05). In summary, the natural regeneration of Picea crassifolia forest was significantly affected by microtopographic habitat (P<0.05), and seedlings were more suitable for settlement and growth in low-altitude convex land and low-altitude concave land.

In the context of future global warming, the vegetation in arid and semi-arid regions in China is more sensitive, and the balance between economic development and environmental protection remains challenging. Therefore, it is essential to conduct dynamic monitoring and driving factors assessment of vegetation in those areas. In this study, the spatiotemporal variation of NDVI in Otindag Sandy Land from 2000 to 2020 was first analyzed using Theil-Sen median trend analysis; next, the driving mechanism of NDVI change was analyzed using the Geodetector model. Finally, multiscenario analysis was used to reveal the future change trajectories of vegetation in the area. The results showed that the NDVI of Otindag Sandy Land exhibited upward fluctuation trends within 20 years, and grazing is the main factor affecting the changes in NDVI in Otindag Sandy Land; however, the effect of grazing to NDVI dynamics in the Otindag Sandy Land gradually weakened. In addition, the multiscenario prediction results show that, although the vegetation of Otindag Sandy Land is developing toward restoration, its fragile ecological environment and the vegetation status were still threatened by human activities and climate change. This study provides a theoretical basis for the Otindag Sandy Land ecological construction and restoration, as well as relevant countermeasures and suggestions according to the actual situation.