Study on method for assessment of the physical structure integrity in Chagan lake in China based on remote sensing.

This paper, taking Chagan lake as the study area, uses and improves MWR V1.0 (Rivers (Lake) Health Assessment Indicators, Standards and Methods V1.0) relating to the theory and method of physical structure integrity assessment. A 500 m × 1000 m grid on the lakeshore zone is a basic evaluation unit, and then a lakeshore physical structural integrity evaluation system using remote sensing (RS) and geographic information system (GIS) technology is established, which contains a target layer, criterion layer and indicator layer. The criterion layer consists of lakeshore condition, shoreline development rate and lake atrophy rate, and the index layer is composed of slope, vegetation cover rate and water level change rate, and another eight indicators. The results showed that for the 23 monitoring points in Chagan lake and the 15 monitoring points in Xinmiao lake, the evaluation results based on RS were 0.60-0.74 and 0.35-0.52, respectively, and the field evaluation results were 0.64-0.77 and 0.35-0.55, respectively. The evaluation results of the two methods consistently indicated that the physical structure of the lakeshore of Chagan lake was healthy and the Xinmiao's lakeshore was sub-health. On this basis, a piecewise evaluation method of physical structure integrity based on the division of the nature reserve function was proposed in this paper.

A novel hierarchical approach to insight to spectral characteristics in surface water of karst wetlands and estimate its non-optically active parameters using field hyperspectral data.

Wetlands cover only around 6 % of the Earth's land surface, and are recognized as one of the three major ecosystems, alongside forests and oceans. The ecological structure and function of karst wetlands are unique due to the influence of geologic structure. At present, the unclear spectral morphology of surface water in karst wetlands poses a significant challenge in remote sensing estimation of non-optically active water quality parameters (NAWQPs). This study proposed a novel multi-scale spectral morphology feature extraction (MSFE) method to insight to spectral characteristics in surface water of karst wetlands, and further screen the sensitive features of NAWQPs. Then we constructed three remote sensing inversion strategies for NAWQPs (TN, TP, NH3_N, DO), including direct estimation, indirect estimation, and auxiliary estimation. Finally, we constructed a novel pH-based hierarchical analysis framework (pH_HA) to thoroughly explore the influence of alkalinity-biased characteristics of karst water on the spectral domain of NAWQPs and its estimation accuracy using in-situ hyperspectral data, respectively. We found that the spectral characteristics of karst waters at the first reflectance peak (580 nm) differed significantly from other water body types. The MSFE successfully captured the sensitive spectral domains for NAWQPs, and focused on between 500 and 600 nm and 900-960 nm. The sensitive features captured by MSFE improved estimation accuracy of NAWQPs (R2 >0.9). Direct estimation presented more stable performance compared to the auxiliary estimation (average RMSE of 0.366 mg/L), and the auxiliary estimation model further improved the retrieval accuracy of TN compared to direct estimation model (R2 increasing from 0.43 to 0.56). The novel hierarchical framework clearly revealed the notable changes in the sensitive spectral domains of NAWQPs under different pH values, and enabled more precise determination of spectral subdomains of NAWQPs, and identified the optimal spectral features. The pH_HA framework effectively improved the estimation accuracy of NAWQPs (R2 increased from 0.514 to over 0.9), and the estimation accuracies (R2) of four NAWQPs were all more than 0.9 when the pH value was over 8.5. Our works provide an effective approach for monitoring water quality in karst wetlands.

Multi-sensor and multi-platform retrieval of water chlorophyll a concentration in karst wetlands using transfer learning frameworks with ASD, UAV, and Planet CubeSate reflectance data.

China has one of the widest distributions of carbonate rocks in the world. Karst wetland is a special and important ecosystem of carbonate rock regions. Chlorophyll-a (Chla) concentration is a key indicator of eutrophication, and could quantitatively evaluate water quality status of karst wetland. However, the spectral reflectance characteristics of the water bodies of karst wetland are not yet clear, resulting in remote sensing retrieval of Chla with great challenges. This study is a pioneer in utilizing field-based full-spectrum hyperspectral data to reveal the spectral response characteristics of karst wetland water body and determine the sensitive spectral bands of Chla. We further evaluated the Chla retrieval performance of multi-platform spectral data between Analytical Spectral Device (ASD), Unmanned aerial vehicle (UAV), and PlanetScope (Planet). We proposed two multi-sensor weighted integration strategies and two transfer learning frameworks for estimating water Chla from the largest karst wetland in China by combing a partial least square with adaptive ensemble algorithms. The results showed that: (1) In the range of 500-850 nm, the spectral reflectance of water bodies in the karst wetland was overall 0.001-0.105 higher than the inland water bodies, and the sensitive spectral ranges of water Chla focus on 603-778 nm; (2) UAV images outperformed ASD and Planet data, and produced the highest inversion accuracy (R2 = 0.670) for water Chla in karst wetland; (3) Multi-sensor weighted integration retrieval methods improved the Chla estimation accuracy (R2 = 0.716). Integration retrieval methods with the different weights produced the better Chla estimation accuracy than that of methods with the equal weights; (4) The transfer learning methods from ASD to UAV platform provided the better retrieval performance (the average R2 = 0.669) than that of methods from UAV to Planet platform. The transfer learning methods obtained the highest estimation accuracy of Chla (R2 = 0.814) when the ratio of the training and test data in the target domain was 7:3. The transfer learning methods produced the higher estimation accuracies with the distribution of the absolute residuals between predicted and measured values <20.957 mg/m3 compared to the multi-sensor weighted integration retrieval methods, which demonstrated that transfer learning is more suitable for estimating Chla in karst wetland water bodies using multi-platform and multi-sensor data. The results provide a scientific basis for the protection and sustainable development of karst wetlands.

Lithium-compatible and air-stable vacancy-rich Li9N2Cl3 for high-areal capacity, long-cycling all-solid-state lithium metal batteries.

Attaining substantial areal capacity (>3 mAh/cm2) and extended cycle longevity in all-solid-state lithium metal batteries necessitates the implementation of solid-state electrolytes (SSEs) capable of withstanding elevated critical current densities and capacities. In this study, we report a high-performing vacancy-rich Li9N2Cl3 SSE demonstrating excellent lithium compatibility and atmospheric stability and enabling high-areal capacity, long-lasting all-solid-state lithium metal batteries. The Li9N2Cl3 facilitates efficient lithium-ion transport due to its disordered lattice structure and presence of vacancies. Notably, it resists dendrite formation at 10 mA/cm2 and 10 mAh/cm2 due to its intrinsic lithium metal stability. Furthermore, it exhibits robust dry-air stability. Incorporating this SSE in Ni-rich LiNi0.83Co0.11Mn0.06O2 cathode-based all-solid-state batteries, we achieve substantial cycling stability (90.35% capacity retention over 1500 cycles at 0.5 C) and high areal capacity (4.8 mAh/cm2 in pouch cells). These findings pave the way for lithium metal batteries to meet electric vehicle performance demands.

Spatial-temporal changes in the degradation of marshes over the past 67 years.

Agricultural reclamation is widely regarded as a primary cause of marshes degradation. However, the process of marshes degradation on different geomorphology has rarely explored, which fail to explain the marshes degradation driven by natural restrictions in detail. The information deficiency unable propounded the targeted suggestions for the sustainable management of marshes. According to the development of China, we quantified the degradation rate of marshes on different geomorphic types from 1954 to 2020 in a typical transect in the Sanjiang Plain. The results indicated that (1) A total of 1633.92 km2 of marshes reduced from 1954 to 2020. And 97% (1582.35 km2) of marshes were converted to crop cultivation. The process of marshes degradation had obvious historical stages characteristics. The marshes degradation rate showed a trend of increasing first and then decreasing. The most serious period was 1995-2005 (6.29%) which was approximately 35 times of the period of before the reform and opening up (1954-1976) a minimal shrunk period. (2) The background of geological tectonic decided the whole trends in marshes degradation process. The degradation occurred first and worst in the meco-scale recent slow ascent region, and then extended to substantially recent slow subsidence region and the small-amplitude recent slow ascent region. (3) Significant location characteristics of marshes degradation reflected in this research. The spatial location of marshes degradation on the sub-regions sequentially consisted of alluvial plain, lower terrace, high floodplain, micro-knoll, low floodplain, and depressions. (4) Most of the existing marshes of the sub-Sanjiang Plain distribution in the national reserves. This study provides important scientific information for restoration and conservation of marshes.

Comparison of multi-class and fusion of multiple single-class SegNet model for mapping karst wetland vegetation using UAV images.

Wetland vegetation classification using deep learning algorithm and unmanned aerial vehicle (UAV) images have attracted increased attentions. However, there exist several challenges in mapping karst wetland vegetation due to its fragmentation, intersection, and high heterogeneity of vegetation patches. This study proposed a novel approach to classify karst vegetation in Huixian National Wetland Park, the largest karst wetland in China by fusing single-class SegNet classification using the maximum probability algorithm. A new optimized post-classification algorithm was developed to eliminate the stitching traces caused by SegNet model prediction. This paper evaluated the effect of multi-class and fusion of multiple single-class SegNet models with different EPOCH values on mapping karst vegetation using UAV images. Finally, this paper carried out a comparison of classification accuracies between object-based Random Forest (RF) and fusion of single-class SegNet models. The specific conclusions of this paper include the followings: (1) fusion of four single-class SegNet models produced better classification for karst wetland vegetation than multi-class SegNet model, and achieved the highest overall accuracy of 87.34%; (2) the optimized post-classification algorithm improved classification accuracy of SegNet model by eliminating splicing traces; (3) classification performance of single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy (F1-Score) ranging from 10 to 25%; (4) Fusion of single-class SegNet models and object-based RF classifier both produced good classifications for karst wetland vegetation, and achieved over 87% overall accuracy.

Spatio-temporal monitoring of marsh vegetation phenology and its response to hydro-meteorological factors using CCDC algorithm with optical and SAR images: In case of Honghe National Nature Reserve, China.

Vegetation phenology is a sensitive indicator which can comprehensively reflect the response of wetland vegetation to external environment changes. However, the time-series monitoring wetland vegetation phenological changes and clarifying its response to hydrology and meteorology still face great challenges. To fill these research gaps, this paper proposed a novel time-series approach for monitoring phenological change of marsh vegetation in Honghe National Nature Reserve (HNNR), Northeast China, using continuous change detection and classification (CCDC) algorithm and Landsat and Sentinel-1 SAR images from 1985 to 2021. We evaluated the spatio-temporal response relationship of phenological characteristics to hydro-meteorological factors by combining CCDC algorithm with partial least squares regression (PLSR). Finally, this study further explored the intra-annual loss and restoration of marsh vegetation in response to hydro-meteorological factors using the transfer entropy (TE) and CCDC-MLSR model constructed by CCDC and Multiple Linear Stepwise Regression (MLSR) algorithms. We found that the bimodal trajectory of phenology reflects two growth processes of marsh vegetation in one year, and high-frequency and high-amplitude loss occurred in shallow-water and deep-water marsh vegetation from April to October, resulting in the loss area within the year was significantly greater than the recovery area. We confirmed that the CCDC algorithm could track the evolution trajectory of time-series phenology of marsh vegetation. We further revealed that precipitation, temperature and frequency of water-level changes are the main driving factors for the spatio-temporal phenological evolution of different marsh vegetation. This study verified the effect of alternative changes of hydrology and climate on loss and recovery of marsh vegetation in each growth stage. The results of this study provide a scientific basis for wetland protection, ecological restoration, and sustainable development.

Impacts of land use change on ecosystem service value in Lijiang River Basin, China.

It is of great significance for the coordinated development of the environment and the economy to study the impact of the human driving factors of land use change (LUC) on ecosystem service value (ESV). In this study, we combined the biomass and remote sensing data of the Lijiang River Basin (LRB), which is a typical karst basin with a fragile ecological environment, to establish an ESV model to calculate the ESV. We also introduced the Lorenz curve and Gini coefficient to further analyze the impacts of the human driving factors of LUC on ESV. The results show that (1) the ESV in the LRB from 1995 to 2015 decreased from 8640.03 million yuan to 8595.38 million yuan, with a total decrease of 44.65 million yuan, indicating that the overall ESV in the region has a decreasing trend; (2) the obvious changes in land use caused a significant loss in ESV and changes in the structure of ecosystem services; and (3) the human driving factors of the total population, GDP, and urbanization rate are inversely related to the ESV in the LRB.

[Short-term effectiveness comparison of unipedicular versus bipedicular percutaneous kyphoplasty for osteoporotic vertebral compression fractures with posterior wall broken].

OBJECTIVE: To compare the short-term effectiveness and safety of unipedicular versus bipedicular percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fracture (OVCF) with posterior wall broken. METHODS: The clinical data of 68 patients with OVCF with posterior wall broken and without posterior ligament complex injury and spinal cord nerve injury between June 2013 and December 2018 were retrospectively analyzed. According to the different operative approaches, the patients were divided into two groups: group A (36 cases received PKP via bilateral pedicle puncture) and group B (32 cases received PKP via unilateral pedicle paracentesis). There was no significant difference between the two groups in gender, age, fracture vertebra distribution, time from injury to operation, preoperative pain visual analogue scale (VAS) score, Oswestry disability index (ODI), and height of injured vertebra ( P>0.05). The operation time, intraoperative fluoroscopy times, and bone cement volume were recorded and compared between the two groups. The VAS score and ODI score were used to evaluate the effectiveness before operation, at 1 day and 6 months after operation; the height of injured vertebra was measured on the lateral X-ray film, and the recovery height of injured vertebra at 1 day after operation and the loss height of injured vertebra at 6 months after operation were calculated; the intraoperative and postoperative complications of the two groups were recorded. RESULTS: The operation time, intraoperative fluoroscopy times, and bone cement volume of group B were significantly less than those of group A ( P<0.05). All patients were followed up 10-35 months, with an average of 18 months. During the operation, there were 2 cases (5.56%) of cement leakage in group A and 9 cases (28.13%) in group B, showing significant difference ( χ 2=4.808, P=0.028). There was no adverse reactions of bone cement, iatrogenic spinal cord injury, infection of puncture port, or other complications in the two groups. During the follow-up period, there were 3 cases (8.3%) of adjacent vertebral fractures in group A and 2 cases (6.3%) in group B, showing no significant difference between the two groups ( χ 2=0.027, P=0.869). The height of injured vertebra of the two groups at 1 day and 6 months after operation were significantly improved when compared with preoperative ones ( P<0.05). There was no significant difference in the height of injured vertebrae and the recovery height of injured vertebra at 1 day after operation between the two groups ( P>0.05). However, at 6 months after operation, the height of injured vertebra in group B was significantly lower than that in group A ( P<0.05), and the loss height of injured vertebra in group B was significantly higher than that in group A ( P<0.05). The VAS score and ODI score at 1 day and 6 months after operation were significantly improved when compared with preoperative ones in both groups ( P<0.05), but there was no significant difference between the two groups ( P>0.05). CONCLUSION: Both bipedicular and unipedicular PKP can obtain satisfactory effectiveness for the treatment of OVCF with posterior wall broken, but the former may have advantages of lower cement leakage rate and less height loss.

[Influence of Land Use Change on Ecosystem Service Value Based on GEE in the Beijing-Tianjin-Hebei Region from 1998 to 2018].

In sustainable development assessment of the Beijing-Tianjin-Hebei region, the ability to dynamically estimate the value of ecosystem services is of great significance. This study considers the Beijing-Tianjin-Hebei region as the research area, based on the google earth engine (GEE); the classification and decision tree (CART) classification algorithm was adopted to supervise and classify the Landsat Thematic Mapper/Operational Land Imager (TM/OLI) images in the study area in 1998, 2003, 2008, 2013, and 2018, and land use types in these five periods were obtained. Quantitative analysis of the dynamic changes of land use in the Beijing-Tianjin-Hebei region from 1998 to 2018 was carried out. Then, the ecosystem service value (ESV) equivalent estimation method was used to quantitatively estimate the ESV in the Beijing-Tianjin-Hebei region and combine it with a 15 km×15 km scale grid to detect its temporal and spatial dynamics. The main results were as follows. ① From 1998 to 2018, the area of construction land (increased by 16.67%) and grassland (reduced by 13.73%) in the six land use types in the Beijing-Tianjin-Hebei region was the largest, and the change in the proportion of water bodies (0.2%) was the smallest. ② The total value of ESV in the Beijing-Tianjin-Hebei region experienced a short-term increase from 1998 to 2003 (an increase of 91.97×108 yuan), and continued to decrease from 2003 to 2018 (a decrease of 239.07×108 yuan), mainly related to the expansion of construction land area in the other three time periods excluding 1998 and 2003. Among the six land use types, the forest provides the highest value of ecosystem services, and the construction land and unused land provide the lowest value of ecosystem services. ③ The ESV time-space analysis based on the 15 km×15 km scale grid showed that the ESV medium area in the Beijing-Tianjin-Hebei region gradually decreased from 1998 to 2018, the ESV lower area and the higher area gradually increased, and the ESV lower-area growth rate was higher than for the higher area. ④ The revised value of the Beijing-Tianjin-Hebei region (sensitivity coefficient range 0-0.83) has good significance and reliability. In future economic development, the Beijing-Tianjin-Hebei region should rationally optimize the land use pattern and strengthen the protection of forest land, grassland, water bodies and cultivated land.

Evaluation of riparian condition of Songhua River by integration of remote sensing and field measurements.

Riparian zone is crucial to the health of streams and their surrounding environment. Evaluation of riparian condition is essential to achieve and maintain good stream health, as well as to sustain ecological functions that riparian areas provide. This manuscript is aimed to evaluate riparian conditions of Songhua River, the fifth longest river in China, using physical structural integrality (PSI) values derived from remote sensing and validated by field measurements. The variation and clusters of PSI values were discriminated by the spatial statistics to quantify variation of riparian condition in each measurement section. Evaluation results derived from 13 measurement sections indicated that over 60% of the riparian zones have been disturbed by human activities. Analysis of land use patterns of riparian zone in the cold and hot spots found that land-use patterns had an important effect on riparian condition. The build-up and farmland areas had been the main human disturbances to the riparian condition, which were increased from 1976 to 2013. The low-low clusters (low PSI values with low neighbors) of PSI values can be implemented to identify the vulnerability of the riparian zone.