Understanding the water use characteristics of vegetation is crucial for guiding the rational allocation of water resources and the restoration of sustainable vegetation in natural oases in arid desert areas. To analyze the water use characteristics of Populus euphratica Oliv. and Tamarix chinensis Lour. at different stages of growth in the Daliyabuyi natural pristine oasis in northwestern China, the δ¹⁸O values of plant xylem of 15 sample trees with different sizes per species, potential water sources (i.e., river water, soil water, and groundwater), and the δ¹³C values of plant leaves of sample trees were measured in August 2019 and June 2020. The results show that the δ¹⁸O values of the xylem water of P. euphratica at different growth stages were similar, but water uptake proportions from deep soil water changed in P. euphratica trees at different growth stages between years with (2019) and without (2020) river water. In contrast, the main water source of young T. chinensis shrubs was deep soil water, and those of mature and overmature shrubs were deep soil water and groundwater in 2020. However, the plant leaf water use efficiency (WUE) of the P. euphratica and T. chinensis were higher without river water. Overall, the water uptake proportion from groundwater and the WUE for T. chinensis were higher than those for P. euphratica, and thus, T. chinensis was more dependent on groundwater. This means that vegetation types and growth stages are the essential factors to be considered in ecological restoration management, which can enhance the effectiveness of vegetation restoration strategies.

Understanding the effect of flooding on groundwater quality is imperative for oasis vegetation protection and local ecological environment development. We used geochemical and remote sensing inversion methods to evaluate the effects of flood recharge on the groundwater hydrochemical and geochemical processes in the Daliyaboy Oasis. Groundwater samples were collected from 30 ecological observation wells in the study area before (PRF) and after (POF) the flood. Except for small changes in HCO₃⁻ and K⁺ and a decrease in pH, ion levels were higher POF than PRF, and the water chemistry was essentially unchanged. In the POF groundwater, HCO₃⁻ was correlated with Cl⁻, Na⁺, Mg²⁺, total soluble solids (TDS), and electrical conductivity (EC), but not with SO₄²⁻, Ca²⁺, K⁺, or pH, and was positively correlated with all other variables, while the remaining variables, except for pH, were strongly positively correlated with each other. PRF water chemistry was controlled by silicate and evaporite mineral weathering and evaporation processes, resulting in high groundwater TDS, EC, and a major ion content, while POF major groundwater ions were regulated by mineral weathering and flood recharge. We demonstrated the high accuracy of remote sensing inversion, confirming this as a reliable method for evaluating groundwater chemistry. The results of the study help to reshape and predict the history of the regional hydrogeological environment and hydrogeochemical development, and provide a theoretical basis for assessing the rational use of local water resources and protecting the ecological environment.

Warming and persistent droughts may exacerbate drought stress in water-scarce areas, thereby negatively affecting tree growth. When riparian plants in arid regions experience severe drought stress, they sacrifice non-dominant branches with less competitive sap flow to improve the sap flow of dominant branches and thus ensure strong plant growth. Populus euphratica is one of the most dominant tree species in the riparian forest ecosystems of inland river basins in arid zones and is a reliable indicator of ecological change because of its diversity in growth and environment. To understand the adaptability of P. euphratica to the environment, the relationship between radial growth and meteorological factors, the growth decline and resistance of different heights and components of P. euphratica, as well as the resilience and resilience after recession were investigated. The results indicated that tree-ring width decreased gradually with increasing height and branching class of P. euphratica. Growth decreased at the bottom of the stem earlier than at the middle and top. Temperature, precipitation, and the Palmer drought index contributed to the growth at the bottom of P. euphratica, while precipitation contributed to growth at the top. The decline in the P. euphratica growth change rate was highly synchronized across heights and parts, with relatively high declines at the bottom and top. There were no significant differences in the recovery values for different heights and parts of P. euphratica, but the resistance, resilience, and relative resilience for the bottom and top were significantly lower than those for the other components, indicating vulnerability in the bottom and top of P. euphratica to drought. The relative resilience gradually decreased with the increase in branching class, and that of the secondary lateral branches at different heights was the lowest. In conclusion, the sensitivity of the top and lateral branches of P. euphratica to drought would cause the phenomenon of “breaking its arm” under drought disturbance in the future.

It is highly necessary to apply unmanned aerial vehicle (UAV) remote sensing technology to forest health assessment. To prove the feasibility of quantitative inversion of photosynthetic pigment content (PPC) in Populus euphratica Oliv. individual tree canopy (PeITC) by using multispectral UAV images, in this study, Parrot Sequoia+ multispectral UAV system was manipulated to collect the images of Populus euphratica (Populus euphratica Oliv.) sample plots in Daliyabuyi Oasis from 2019 to 2020, and the canopy PPCs of five Populus euphratica sample trees per plot were determined in six plots. The Populus euphratica crown regions were extracted by grey wolf optimizer-OTSU (GWO-OTSU) multithreshold segmentation algorithm from the normalized difference vegetation index (NDVI) images of Populus euphratica sample plots obtained after preprocessing, and the PeITCs were segmented by multiresolution segmentation algorithm. The mean values of 27 spectral indices in the PeITCs were calculated in each plot, and the optimal model was constructed for quantitative estimation of the PPCs in the PeITCs, then the inversion results were compared and verified based on GF-6 and ZY1-02D satellite imageries respectively. The results were as follows. (1) The average value of canopy chlorophyll content (Chl) was 2.007 mg/g, the mean value of canopy carotenoid content (Car) was 0.703 mg/g. The coefficient of variation (C.V) of both were basically the same and they were both of strong variability. The measured PPCs of the PeITCs in Daliyabuyi Oasis was generally low. The average contents of chlorophyll and carotenoid in PeITC in June were more than twice those in August, while the mean ratio between them was significantly lower in June than in August. The measured PPCs had no obvious spatial distribution law. However, that could prove the rationality of sample selection in this study. (2) NDVI had the best effect of highlighting vegetation among all quadrats in the study area. Based on the GWO-OTSU multithreshold segmentation method, the canopy area of Populus euphratica could be quickly and effectively extracted from the quadrat NDVI map. The best segmentation effect of PeITCs was obtained based on a multiresolution segmentation method when the segmentation scale was 120, the shape index was 0.7, and the compactness index was 0.5. Compared with manual vectorization method of visual interpretation, the root mean square error (RMSE) and Pearson correlation coefficient (R) values of the mean NDVI values in PeITCs obtained by these two methods were 0.038 and 0.951. (3) Only 12 of the 27 spectral indices were significantly correlated with Chl and Car at the significance level of 0.02. Characteristics of the calibration set and validation set were basically consistent with those of the entire set. The classification and regression tree-decision tree (CART-DT) model performed best in the estimation of the PPCs in the PeITCs, in which, when estimating the Car, the calibration coefficient of determination (R²_C) was 0.843, the calibration root mean square error (RMSE_C) was 0.084, the calibration residual prediction deviation (RPD_C) was 2.525, the validation coefficient of determination (R²_V) was 0.670, the validation root mean square error (RMSE_V) was 0.251, the validation residual prediction deviation (RPD_V) was 1.741. (4) Qualitative comparison of spectral reflectance and NDVI values between GF-6 multispectral imagery and Parrot Sequoia+ multispectral image on the 172 PeITCs can show the reliability of Parrot Sequoia+ multispectral image. The comparison results of five PeITCs relative health degree judged by field vision judgment, measured SPAD value, predicted value of Chl (Chl_pre), the red edge value calculated by ZY1-02D (ZY1-02D_{red edge}) and the Carotenoid Reflection Index 2 (CRI2) value calculated by ZY1-02D (ZY1-02D_CRI2) can further prove the scientificity of inversion results to a certain extent. These results indicate that multispectral UAV images can be applied for quantitative inversion of PPC in PeITC, which could provide an indicator for the construction of a Populus euphratica individual tree health evaluation indicator system based on UAV remote sensing technology in the next step.

Populus euphratica and Tamarix chinensis hold significant importance in wind prevention, sand fixation, and biodiversity conservation. The precise extraction of these species can offer technical assistance for vegetation studies. This paper focuses on the Populus euphratica and Tamarix chinensis located within Daliyabuyi, utilizing PointCNN as the primary research method. After decorrelating and stretching the images, deep learning techniques were applied, successfully distinguishing between various vegetation types, thereby enhancing the precision of vegetation information extraction. On the validation dataset, the PointCNN model showcased a high degree of accuracy, with the respective regular accuracy rates for Populus euphratica and Tamarix chinensis being 92.106% and 91.936%. In comparison to two-dimensional deep learning models, the classification accuracy of the PointCNN model is superior. Additionally, this study extracted individual tree information for the Populus euphratica, such as tree height, crown width, crown area, and crown volume. A comparative analysis with the validation data attested to the accuracy of the extracted results. Furthermore, this research concluded that the batch size and block size in deep learning model training could influence classification outcomes. In summary, compared to 2D deep learning models, the point cloud deep learning approach of the PointCNN model exhibits higher accuracy and reliability in classifying and extracting information for poplars and tamarisks. These research findings offer valuable references and insights for remote sensing image processing and vegetation study domains.

The phenological characteristics of plants can reflect both their responses to environmental changes as well as an ecosystem’s sensitivity to climate change. Although there have been several phenological studies of plant species worldwide, there is minimal research on the phenology of vegetation found in extremely arid environments within the context of climate change. To address this research gap, this study investigated the effects of climate–hydrological factors, including temperature, precipitation, surface temperature, and surface humidity, on the phenological characteristics (start of the growing season [SOS] and end of the growing season [EOS]) of Populus euphratica in the Tarim Desert Oasis. Using Landsat 7/8 satellite imagery and field data, we analyzed the spatial and temporal variations in the SOS and EOS from 2004 to 2023. The availability of water, particularly changes in groundwater depth and surface water, directly played a key role in shaping the spatial distribution and temporal dynamics of P. euphratica phenology. The impact of increasing temperatures on P. euphratica phenology varied under different moisture conditions: in high-moisture environments, increased temperatures promoted earlier SOS and delayed EOS, with the opposite conditions occurring in low-moisture environments. This study highlights the profound influence of moisture conditions on P. euphratica phenology in the context of climate change, especially in extreme arid regions. To accurately predict the response of P. euphratica phenology to climate change, future ecological models should incorporate hydrological factors, particularly changes in soil moisture, in cold and dry regions. These findings provide important insights for developing effective ecological protection and management strategies.