The evaluation of ecological environment quality and the analysis of the causes of ecological change are important aspects of regional ecological management. In this study, based on the factors of greenness (NDVI), humidity (WET), heat (LST), and dryness (NDBSI), the salinity index (SI) was introduced to build an improved ecological remote sensing index (MRSEI). The spatial and temporal distribution pattern and driving mechanism of eco-environmental quality in the Beibu Gulf port area from 2000 to 2024 were analyzed. The results showed as follows: ① From 2000 to 2024, the overall ecological environment quality in the study area showed a slow improvement trend, and the MRSEI grade was mainly in the middle level, with the average annual value ranging from 0.25 to 0.68, showing a spatial distribution pattern of high in the west and low in the east. ② There was a strong spatial autocorrelation of ecological environment quality in the study area. The spatial aggregation patterns were mainly H-H and L-L. The H-H gathering area was mainly forest land and mountain, and the L-L gathering area was mainly agricultural land and construction land. ③ In 2000-2024, the area of ecological environment quality improvement was significantly larger than the area of degradation, and the area of no significant improvement and significant degradation was the most extensive. The future change trend is mainly future degradation. ④ The ecological environment quality in the study area was influenced by both natural and human factors. Among them, the average annual temperature had the strongest explanatory power, followed by evapotranspiration, slope, distance to artificial surface, and NPP. The interaction of all factors increased to a certain extent, and the interaction effect of average annual temperature and evapotranspiration was the strongest.

The carbon (C), nitrogen (N), and phosphorus (P) concentrations, along with the C:N:P stoichiometry in plants, play a vital role in regulating nutrient use efficiency in terrestrial ecosystems. However, fast and accurate estimation of the leaf C: N:P stoichiometry remains challenging, especially in complex karst areas. This study addresses this challenge by combining ground-based hyperspectral remote sensing technology with advanced neural network methods to estimate leaf C:N:P stoichiometry, based on 301 samples collected from nine typical karst areas in southern China. Our results showed that the PLSR (partial least squares regression) + BPNN (back propagation neural network), PLSR + GRNN (generalized regression neural network), and S-Transformer (simplified transformer) models achieve high performance in estimating leaf C:N:P stoichiometry in karst areas in southern China (R-2 was 0.71 0.88). Notably, we introduce fractional differentiation as a novel preprocessing step, which significantly improves model accuracy. Particularly, the predictive performances were effectively enhanced and stable when the fractional order was larger than 1.6. Furthermore, we identify distinct spectral sensitivity ranges for C and P are more sensitive to reflectance in the 400 800 nm wavelength ranges, while N exhibits sensitivity in both the 400 800 nm and 1500 2500 nm ranges after fractional differentiation. Our results also reveal that N plays an essential role in coordinating the C and P cycles in plants, and increasing leaf N concentrations could alleviate P stress on plant growth in karst areas. Compared to leaves C and N, P is more susceptible to environmental factors. Our results indicate a significant advancement in the application of groundbased hyperspectral remote sensing and neural network methods for ecological stoichiometry estimation in karst ecosystems. By successfully applying PLSR + BPNN, PLSR + GRNN, and S-Transformer models, we provide a robust framework for rapid and accurate hyperspectral inversion of leaf C:N:P stoichiometry. Our findings not only contribute to the understanding of nutrient dynamics in karst areas, but also provide practical tools for precision agriculture and ecosystem monitoring in similar environments.

Sustainable Development Goal 15 (SDG 15) specifically targets the protection, restoration, and sustainable use of terrestrial ecosystems, including forests, wetlands, mountains, and drylands, along with their biodiversity. This study localizes the SDG 15 indicator system and integrates geospatial and statistical data to construct an enhanced evaluation framework for assessing the sustainable development of terrestrial ecosystems at the county level. The proposed system encompasses key indicators such as forest coverage rate, terrestrial biodiversity, sustainable forest management, land degradation neutrality, mountain biodiversity, and mountain green cover index. Using Guilin City as a study area, the ecological status of each county was assessed over the period 2010 to 2020, providing valuable insights to guide ecological conservation and sustainable development efforts. The main results are as follows: (1) Spatial heterogeneity is evident in the distribution of key biodiversity areas, which are concentrated in the northern and southeastern mountainous regions of Guilin. (2) Land degradation during the assessment period is notably smaller than during the baseline period, though a significant gap remains toward achieving land degradation neutrality. (3) Sustainable development scores for terrestrial ecosystems show an overall upward trend across counties, but the poor performance in sustainable forest management affects the comprehensive sustainable development of terrestrial ecosystems in Guilin. The localized SDG 15 indicator system proposed in this paper can effectively quantify changes in terrestrial ecosystems and visualize their spatial distribution, and can effectively serve as a model for other sustainable development areas.

Global warming has exacerbated the impact of regional drought on vegetation ecosystems, especially in typical karst areas with fragile ecosystems that are more severely affected by drought. However, the response mechanisms of vegetation ecosystems in karst areas to drought stress are still uncertain. With drought stress in the summer of 2022, we examined the spatiotemporal patterns of drought in a World Heritage karst site, Guilin, China, and revealed the exacerbated drought impacts on vegetation ecosystems in karst areas with various vegetation indices. Firstly, we analyzed the spatiotemporal characteristics of drought from 2000 to 2022, utilizing the temperature vegetation dryness index (TVDI), highlighting the intra-annual variability of drought in 2022. Additionally, we compared the responses of different vegetation types to drought stress in karst and non-karst areas and explored the exacerbated impacts of drought stress on vegetation ecosystems within the same year with three vegetation indices, namely, the Normalized Difference Vegetation Index (NDVI), Leaf Area index (LAI), and Gross Primary Production (GPP) in karst areas. The results showed that drought started in July and persisted from August to November at moderate to severe levels (with severe drought in September), eventually easing in December. Karst areas exhibited severe drought (TVDI = 0.76), which more significantly impacted regional vegetation ecosystems than those in non-karst areas. Different vegetation types also experienced greater drought stress in karst areas compared to non-karst areas. The vegetation indices increased at the early- to mid-stages of drought (July to September) compared to those in the baseline year (2020–2021), mainly due to the increase in non-karst areas. However, vegetation indices decreased at the late drought stage (October to November), primarily due to the decrease in karst areas, indicating that the karst topography exacerbated the impact of drought on regional vegetation ecosystems. Since LAI and GPP exhibited similar changing patterns to TVDI, with GPP showing particularly strong alignment, they can be used to reveal the response mechanisms of ecosystems to drought stress in karst areas. We emphasize the importance of monitoring the responses of vegetation ecosystems to climate-induced droughts stress and enhancing their resilience to future climatic challenges, particularly in karst areas.

Potassium is a critical macronutrient for plant growth, yet accurately and rapidly estimating its content in karst regions remains challenging due to complex terrestrial conditions. To address this, we collected leaf potassium content and reflectance data from 301 plant samples across nine karst regions in Guangxi Province. Our results showed that hybrid models combining Partial Least Squares Regression (PLSR) with three machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP)—namely PLSR-RF, PLSR-XGBoost, and PLSR-MLP, demonstrated exceptional accuracy in estimating leaf potassium content. Validation coefficient of determination (R²) values reached 0.89, 0.94, and 0.96, respectively—representing improvements of 206%, 147%, and 108% over standalone algorithms. This performance gain was attributed to rigorous overfitting control: PLSR’s dimensionality reduction synergized with ensemble machine learning (RF, XGBoost, MLP) to eliminate redundant spectral features while retaining predictive signals. Furthermore, fractional differentiation preprocessing significantly improved the correlation between spectral reflectance and potassium content, enhancing model robustness. Two spectral regions (700–1100 nm, 1400–1800 nm) were identified as key predictors, aligning with known potassium-related biochemical absorption features. Collectively, the integration of these strategies offers a robust framework for nutrient monitoring in ecologically fragile karst ecosystems.

Accurately quantifying individual tree parameters is a critical step for assessing carbon sequestration in forest ecosystems. However, it is challenging to gather comprehensive tree point cloud data when using either unmanned aerial vehicle light detection and ranging (UAV-LiDAR) or terrestrial laser scanning (TLS) alone. Moreover, there is still limited research on the effect of point cloud filtering algorithms on the extraction of individual tree parameters from multiplatform LiDAR data. Here, we employed a multifiltering algorithm to increase the accuracy of individual tree parameter (tree height and diameter at breast height (DBH)) extraction with the fusion of TLS and UAV-LiDAR (TLS-UAV-LiDAR) data. The results showed that compared to a single filtering algorithm (improved progressive triangulated irregular network densification, IPTD, or a cloth simulation filter, CSF), the multifiltering algorithm (IPTD + CSF) improves the accuracy of tree height extraction with TLS, UAV-LiDAR, and TLS-UAV-LiDAR data (with R2 improvements from 1% to 7%). IPTD + CSF also enhances the accuracy of DBH extraction with TLS and TLS-UAV-LiDAR. In comparison to single-platform LiDAR (TLS or UAV-LiDAR), TLS-UAV-LiDAR can compensate for the missing crown and stem information, enabling a more detailed depiction of the tree structure. The highest accuracy of individual tree parameter extraction was achieved using the multifiltering algorithm combined with TLS-UAV-LiDAR data. The multifiltering algorithm can facilitate the application of multiplatform LiDAR data and offers an accurate way to quantify individual tree parameters.

Vulnerable ecological regions in China represent vast territories with significant potential for augmenting carbon sinks. The widespread recognition of net primary productivity (NPP) as a valuable tool for simulating regional shifts in vegetation carbon sequestration dynamics is evident. In this study, we explore the constraints and prospects for carbon sequestration in these sensitive ecological areas by leveraging NPP analysis. A model to evaluate the relative contributions of climate change (CLC) and land use change (LUC) to carbon sequestration variability was developed, providing quantitative insight into their respective impacts within these zones. Our research revealed a significant upwards trend in NPP across the study area, with an annual increase of 3.73 gCm(-2) year(-1). Notably, the Southwestern Karst Area and the Loess Plateau Area experienced the greatest increases, at rates of 9.68 gCm(-2) year(-1) and 6.95 gCm(-2) year(-1), respectively. This growth exhibited a geographical gradient that was more pronounced in the southeast and diminished toward the northwest, although the Northwest Arid Area was an exception, where the spatial trend of NPP primarily showed a downwards trajectory. This suggests resilience and the potential for enhanced carbon capture in these ecologically sensitive areas. This study identified CLC and LUC as key drivers of NPP increases, with their multiyear average impacts measured at 0.69 gCm(-2) year(-1) and 1.03 gCm(-2) year(-1), respectively, generally yielding positive effects. The impact varied across regions and land use scenarios. In areas experiencing land use changes, the positive and negative impacts of LUC on NPP were 32.47% and 22.68%, respectively, outpacing the effects of CLC. In contrast, in stable land use areas, the positive impact of CLC on NPP (26.65%) exceeded its negative effect (21.74%). Our work seeks to offer comprehensive insights into carbon sequestration capacities, elucidate multifactorial contributions, and develop targeted strategies to increase carbon sequestration in fragile ecosystems.

The construction of ecological safety network system plays a critical role in ecological protection and restoration for people’s welfare and national security. In order to construct a better ecological security pattern, we took the Lijiang River basin with typical karst landforms as a research example, to extract ecological source sites with landscape ecological risk and connectivity indices, to identify and grade the potential ecological corridors and nodes with the minimum cumulative resistance and gravity models, and finally to construct the regional ecological security pattern in a coordinated way the conservation of Mountains-RiversForests-Farmlands-Lakes-Grasslands-Deserts ecosystem. The results showed that:（1） The ecological risk of the Lijiang River basin was characterized as high in the center and south, and low in the east and north. The high and higher risk areas accounted 43.34% of the total watershed area.（2） There were five ecological source areas, mainly located in forests and nature reserves, accounting for 28.99%（1689.05km2） of the total watershed area.（3） Six potential ecological corridors and 38 potential ecological nodes were identified, which were concentrated in the vicinity of Lingtian town.（4） The Lijiang River basin was constructed as a protective pattern of ecological conservation, restoration, control, and corridor construction. In addition, more ecological compensation funds and techniques should be supported to Lingchuan and Xingan counties in order to ensure the successful construction of the corridor.We hope this study could provide scientific knowledge for improving the ecosystem function of the Lijiang River basin and the successful implementation of national land use plan.

Ecosystem water-use efficiency (WUE) has been central in revealing the variability in terrestrial carbon and water cycles. Short-rotation plantations such as Eucalyptus plantations can simultaneously impact net primary production (NPP) and actual evapotranspiration (ETa), components of WUE, resulting in changes in terrestrial carbon and water cycles. However, there are few detailed studies on the changes in the WUE of Eucalyptus plantations at the catchment scale with high spatial remote sensing imagery. Here, we present the changes in the WUE of Eucalyptus plantations and its driving factors (i.e., NPP and ETa) using satellite-based models combined with 5-m spatial resolution RapidEye imagery in a small county in South China. The increases in ETa of Eucalyptus plantations are primarily the result of climate warming and result in low WUE of Eucalyptus plantations. The management practice used (short rotation in this study) can enhance the effect of climate warming on WUE by varying the NPP of Eucalyptus plantations. A high value of NPP leads to a high WUE of Eucalyptus plantations at the end of a short rotation, while a low value of NPP results in a low WUE at the beginning of another short rotation. Changes in the WUE of Eucalyptus plantations indicated large spatial and temporal variability, associated with climate warming and short-rotation practices.

对近30年国内外桉树人工林水分利用效率研究领域的相关文献进行分析,探讨其研究进展及发展趋势.检索Web of Science(WoS)和中国知网(CNKI)数据库中1990-2023年桉树人工林水分利用效率研究的相关文献,利用文献计量可视化分析软件VOSviewer对年度发文量及其刊物、主要国家、研究机构与学者、关键词聚类和高被引论文等进行可视化处理.结果表明:共检索获取WoS和CNKI数据库中文献分别为216篇和56篇,发文量呈波动增长趋势;WoS数据库中影响因子最高的期刊为TREE PHYSIOLOGY,CNKI数据库中《桉树科技》载文数量最多;机构分布主要以圣保罗大学和华南农业大学为主;我国与国外学者间就桉树人工林水分利用效率方面的合作关系较少,国内不同作者群间也缺乏密切的合作关系:关键词表明国外研究侧重于基于碳同位素的桉树人工林与其它树种混合栽培下的水分利用效率差异,国内研究侧重于干旱条件下桉树人工林的耗水特性和水分利用效率差异.鉴于此,国内外应加强彼此间合作,探明不同尺度桉树人工林水分利用效率的区别与联系,并运用先进技术系统构建从叶片到区域乃至全球尺度间的耦合模型,模拟与预测桉树人工林水分利用效率对全球变化的反馈和适应机制,为区域桉树人工林可持续经营提供科学依据.

Given the high degree of fragmentation and poor resistance to disturbance in karst landscapes, it is important to clarify the spatial and temporal dynamics of landscape patterns in karst areas when designing karst ecological protection strategies. Using the Li River Basin as the study area, the spatial distribution and dynamic evolution of landscape patterns in the basin were analyzed at the levels of landscape utilization, landscape type dynamics and landscape pattern indices based on the Landsat series images for 2000 to 2020 obtained from the GEE platform as the data source. The results show three important aspects of this typical karst watershed. (1) There are large differences in landscape structure and landscape type trends between the karst and non-karst areas in the Li River Basin. (2) The comprehensive landscape type dynamic attitude of the Li River Basin is 0.22%, and the composite index of landscape type use varies from 239.49 to 244.88. The degree of landscape use is higher in karst areas than in non-karst areas, and the rate of landscape change in karst areas is more intense. The integrated index of landscape use in karst areas ranges from 262.32 to 270.50, and in non-karst areas it spans 225.28 to 227.01. The integrated landscape type motility in the karst areas is 0.31%, which is about twice as high as that in non-karst areas. (3) The overall landscape evolution of the Li River Basin shows trends of increasing fragmentation, decreasing connectivity, decreasing dominance and increasing heterogeneity, and these trends are particularly prominent in the karst areas. The results of this study can provide a scientific basis for realizing the construction goals of the National Sustainable Development Innovation Demonstration Zone in Guilin, and a technical reference for the ecological environmental management of the karst watershed.

为了探讨适合于喀斯特植物叶片叶绿素含量估算的光谱指数,在总结以往基于光谱指数的植物生化参数估算研究基础上发现,常用光谱指数通常采用差值、比值、归一化以及倒数差值方式来构建.因此,我们通过上述4种光谱指数构建方式对所采集的4种典型喀斯特植物——黄荆(Vitex negundo)、盐麸木(Rhus chinensis)、朴树(Celtis sinensis)和红背山麻杆(Alchornea trewioides)叶片原始光谱反射率及其一阶导数值与同步测定的叶片叶绿素含量进行遍历分析,以期获得最优光谱指数并将其应用于喀斯特植物叶片叶绿素含量定量估算研究.结果表明:(1)常用光谱指数中,改良红边归一化指数(modified red-edge normalized difference vegetation index,mND705)对喀斯特植物叶片叶绿素含量估算效果较好(决定系数为0.45,均方根误差为0.26 mg?g-1).(2)虽然荧光比值(fluorescence ratio index,FRI1)和叶绿素吸收面积光谱指数(chlorophyll absorption area index,CAAI)在估算喀斯特与非喀斯特植物叶片叶绿素含量能力相当,但是其估算精度相对较低(决定系数小于0.45).(3)通过差值、比值、归一化以及倒数差值方式构建的光谱指数无论是基于植物叶片原始光谱反射率,还是其一阶导数值,相比常用光谱指数都能更好地估算喀斯特植物叶片叶绿素含量(决定系数大于0.60).其中,基于植物叶片原始光谱反射率一阶导数值的差值光谱指数[dD(760,769)]对喀斯特植物叶片叶绿素含量的估算精度最好,其决定系数为0.71,均方根误差为0.19 mg?g-1.综上可知,结合高光谱遥感技术的光谱指数模型可快速定量估算喀斯特植物叶片叶绿素含量,为典型喀斯特地区植物生长诊断及其对环境胁迫适应性评价提供重要科学依据和技术支持.

Combining deep learning and UAV images to map wetland vegetation distribution has received increasing attention from researchers. However, it is difficult for one multi-classification convolutional neural network (CNN) model to meet the accuracy requirements for the overall classification of multi-object types. To resolve these issues, this paper combined three decision fusion methods (Majority Voting Fusion, Average Probability Fusion, and Optimal Selection Fusion) with four CNNs, including SegNet, PSPNet, DeepLabV3+, and RAUNet, to construct different fusion classification models (FCMs) for mapping wetland vegetations in Huixian Karst National Wetland Park, Guilin, south China. We further evaluated the effect of one-class and multi-class FCMs on wetland vegetation classification using ultra-high-resolution UAV images and compared the performance of one-class classification (OCC) and multi-class classification (MCC) models for karst wetland vegetation. The results highlight that (1) the use of additional multi-dimensional UAV datasets achieved better classification performance for karst wetland vegetation using CNN models. The OCC models produced better classification results than MCC models, and the accuracy (average of IoU) difference between the two model types was 3.24–10.97%. (2) The integration of DSM and texture features improved the performance of FCMs with an increase in accuracy (MIoU) from 0.67% to 8.23% when compared to RGB-based karst wetland vegetation classifications. (3) The PSPNet algorithm achieved the optimal pixel-based classification in the CNN-based FCMs, while the DeepLabV3+ algorithm produced the best attribute-based classification performance. (4) Three decision fusions all improved the identification ability for karst wetland vegetation compared to single CNN models, which achieved the highest IoUs of 81.93% and 98.42% for Eichhornia crassipes and Nelumbo nucifera, respectively. (5) One-class FCMs achieved higher classification accuracy for karst wetland vegetation than multi-class FCMs, and the highest improvement in the IoU for karst herbaceous plants reached 22.09%.

Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37~95.25%.

喀斯特地区植被相比非喀斯特地区具有更高的时空差异性,在维持脆弱生态系统稳定与可持续发展中具有极其重要的作用.西南喀斯特地区作为全球生物多样性热点地区,植被类型多样且存在显著的同物异谱/同谱异物现象.为更精准、高效地进行喀斯特地区植被定量遥感研究,本文从个体尺度到生态系统尺度,从遥感数据源选择和方法应用上回顾了西南喀斯特地区植被定量遥感的研究进展,并探讨下一步需要重点关注的研究方向.西南喀斯特地区植被定量遥感研究主要集中在群落和生态系统尺度的植被覆盖度、植被分类、生态系统服务功能与价值研究;遥感影像数据应用相对单一,主要为被动成像中低分辨率的光学影像(如Landsat和MODIS).在个体和种群尺度上,虽采用了地物高光谱遥感技术和无人机遥感技术,但该技术主要应用于小尺度近地面植物个体和种群研究,难以扩展到区域范围.亟待开展融合多源影像,尤其是激光雷达影像(Light Detection and Ranging,LiDAR)与非影像数据的应用及其先进分析方法研究,以及个体和种群尺度的喀斯特地区植被生化参数定量估算与自然植被物种精准识别,群落和生态系统尺度的生物多样性与碳循环定量遥感研究工作,以期为喀斯特地区植被格局、过程及其生态系统服务功能定量研究、脆弱生态系统植被恢复和石漠化治理决策的制定提供参考.

Due to the simultaneous impacts of economic development and climate change, the Lijiang River Basin in China-the largest karst tourist attraction in the world-has experienced dramatic water shortages during the dry season. As actual evapotranspiration (ETa) plays a critical role in the water cycle, accurate estimation of ETa and water stress are important for sustainable water resources management. In this paper, we mapped the distribution of daily ETa using a modified Operational Simplified Surface Energy Balance (SSEBop) model in combination with Landsat 8 images and assessed water stress using the Crop Water Stress Index (CWSI) during the dry season in the Lijiang River Basin. In general, the daily ETa simulated by the SSEBop model with aerodynamic resistance value of 110 s m(-1) was higher than that of satellite-based actual evapotranspiration products (i.e., MOD16A2 and Penman-Monteith-Leuning (PML)_V2 actual evapotranspiration products in this study). Aerodynamic resistance plays a critical role in the estimation of energy fluxes in the SSEBop model and should be readjusted and calibrated with available datasets to improve the model's performance in estimating actual evapotranspiration for particular regions. Readjusted values between 20 and 35 s m(-1) of aerodynamic resistance produced reasonable agreement with satellite-based actual evapotranspiration products in the Lijiang River Basin. In addition, insufficient ground-level measurements of actual evapotranspiration might have increased the uncertainty of the SSEBop model's performance. The achievement of higher accuracy in the estimation of actual evapotranspiration and water availability will require establishing local flux towers, particularly in forested areas, to collect evapotranspiration, temperature and other in situ data. For different land-cover classes, forest areas exhibited the highest actual evapotranspiration, whereas farmland and built-up areas had the lowest actual evapotranspiration values compared to the other land-cover classes. All land-cover classes, especially farmland areas, experienced severe water stress. Inadequate precipitation as a result of climate change, combined with high actual evapotranspiration will result in less water being available for the Lijiang River Basin. Additional water is required to compensate for evapotranspiration and support plant growth in the Lijiang River Basin during the growing season.

The ratio between nitrogen and phosphorus (N/P) in plant leaves has been widely used to assess the availability of nutrients. However, it is challenging to rapidly and accurately estimate the leaf N/P ratio, especially for mixed forest. In this study, we collected 301 samples from nine typical karst areas in Guangxi Province during the growing season of 2018 to 2020. We then utilized five models (partial least squares regression (PLSR), backpropagation neural network (BPNN), general regression neural network (GRNN), PLSR+BPNN, and PLSR+GRNN) to estimate the leaf N/P ratio of plants based on these samples. We also applied the fractional differentiation to extract additional information from the original spectra of each sample. The results showed that the average leaf N/P ratio of plants was 17.97. Plant growth was primarily limited by phosphorus in these karst areas. The sensitive spectra to estimate leaf N/P ratio had wavelengths ranging from 400–730 nm. The prediction capabilities of these five models can be ranked in descending order as PLSR+GRNN, PLSR+BPNN, PLSR, GRNN, and BPNN when considering both accuracy and robustness. The PLSR+GRNN model yielded high R2 and performance to deviation (RPD), and low root mean squared error (RMSE) with values of 0.91, 3.15, and 1.98, respectively, for the training test and 0.81, 2.25, and 2.46, respectively, for validation test. Compared with the PLSR model, both PLSR+BPNN and PLSR+GRNN models had higher accuracy and were more stable. Moreover, both PLSR+BPNN and PLSR+GRNN models overcame the issue of overfitting, which occurs when a single model is used to predict leaf N/P ratio. Therefore, both PLSR+BPNN and PLSR+GRNN models can be used to predict the leaf N/P ratio of plants in karst areas. Fractional differentiation is a promising spectral preprocessing technique that can improve the accuracy of models. We conclude that the leaf N/P ratio of mixed forest can be effectively estimated using combined models based on field spectroradiometer data in karst areas.

[目的]森林冠层导致的穿透雨和树干茎流中离子通量的季节变化,能够影响森林生态系统生物地球化学循环,对物候变化明显的温带落叶林的影响更为突出.探究不同物候期(展叶期、盛叶期和落叶期)森林水化学过程,深入了解森林生态系统养分元素循环过程,为温带落叶林生物地球化学循环提供基础数据.[方法]以东北林业大学城市林业示范基地内落叶松人工林为研究对象,在观测样地的中心位置十字交叉布设13个直径20 cm自制雨量筒,并选择5株落叶松安装树干茎流收集器,同时在林外布置1台翻斗式雨量计和3个自制雨量筒.在前期观察期(2015年5月1日-10月31日)每次降雨事件后,对林外降雨、穿透雨和树干茎流进行观测、取样,水样过滤酸化处理后用火焰原子吸收分光光度计测定Na+和K+质量浓度,探索冠层的物候变化对降雨分配过程中Na+和K+的质量浓度和净输入量的影响.[结果]整个观测期间,林外降雨中Na+、K+的质量浓度分别为0.45和1.89 mg/L,穿透雨中分别为0.44和2.48 mg/L,树干茎流中分别为1.98和18.63 mg/L;大气降雨中Na+质量浓度在落叶期最高,盛叶期最低,K+质量浓度则在落叶期最高,展叶期最低;各时期穿透雨中Na+和K+质量浓度大小均为落叶期＞盛叶期＞展叶期;树干茎流中Na+和K+质量浓度大小均为展叶期＞盛叶期＞落叶期;生长季内林冠对降雨中Na+的截留量为0.252 kg/hm2,其中展叶期和落叶期的截留量分别为0.143和0.193 kg/hm2,截留率分别为30.63％和48.22％,盛叶期则表现为淋溶,淋溶量为0.083 kg/hm2;生长季内降雨对林冠中K+的淋溶量为0.903 kg/hm2,其中展叶期和盛叶期的淋溶量分别为0.999和0.157 kg/hm2,落叶期则为截留,截留量为0.254 kg/hm2,截留率为20.25％.[结论]大气降雨经过森林冠层后离子质量浓度发生明显改变,且不同物候期、不同离子的变化强度不同.生长季内,兴安落叶松林对Na+总体表现为截留作用,对K+总体表现为淋溶作用.即落叶松叶片的物候变化能够影响大气降雨中Na+和K+的迁移.研究结果可为进一步探明我国温带森林生态系统伴随水文过程的养分循环过程及促进可持续经营管理提供借鉴.

Water resources play a critical role in the sustainable development of river-based tourism. Reduced stream flow on the Lijiang River, south China, may negatively impact the development of cruise tourism. We explored the effects of stream flow changes on cruise tourism by determining (1) cruise tourism development indicators, (2) stream flow regime characteristics and their impacts on cruise tourism development indicators, and (3) climate variability and socio-economic factors effecting stream flow. Cruise tourism on the river has experienced rapid growth in recent decades. Stream flow regimes displayed no significant changes between 1960 and 2016, although dry season stream flow was significantly lower than in other seasons. We found that stream flow changes did not have a significant impact on the development of cruise tourism. As precipitation has not changed significantly, policies, including regulated stream flow from hydroelectric reservoirs, are assumed to mitigate reduced stream flow. However, increased irrigation and economic development, combined with future climate change, may increase challenges to cruise tourism. Future reservoir operations should prepare for climate change-related increases in temperature and insignificant changes in precipitation, and adopt adaptive measures, such as rationing water use in various sectors, to mitigate water shortages for supporting sustainable tourism development.

改革开放以来,漓江作为国家重点保护的13条河流之一,其流域内土地利用变化的强弱、方向、稳定性状况及其驱动力组成关系着漓江流域生态系统的稳定和桂林市社会经济的发展.基于此,采用1977、1987、2000和2015年4期TM/ETM影像,通过信息熵、空间集聚性及驱动力等从多角度对漓江流域1977-2015年的土地利用/覆被变化及其驱动力进行了研究.结果 表明:①1977-2015年,漓江流域土地利用/覆被变化呈现出剧烈—平稳—再次剧烈的变化特点;②流域土地利用综合指数由1977年的228.602增加到2015年的230.878,且越接近市辖区中心土地利用程度越高,流域北部土地利用结构信息熵较小,市辖区最大,极差达到0.86 Nat,不同县域土地利用系统稳定性有所差异;③自然因素中以地形曲率、地貌类型及年均气温对土地利用的影响较大,社会经济因素及半自然半社会经济因素中以旅游景点密度、单位面积粮食产量和单位面积肉类产量这几个因素影响最为显著.