Proportion Of Area Research Articles

Influence of urban functional zone change on land surface temperature using multi-source geospatial data: A case study in Nanjing City, China

The urban heat island effect is a prevalent urban concern, and studies on its influencing factors are essential to mitigate its negative effects and promote sustainable urban development. Although many studies have revealed the impact of land cover change on land surface temperature (LST) during urbanization, most have focused only on built-up areas without considering the relationship between urban internal functional structure and LST. To clarify the influence of changes in urban functional zones (UFZs) on LST, focusing on the downtown area of Nanjing City, China, this study proposed an effective framework using multi-source geospatial data for qualitative and quantitative analysis. Specifically, very high spatial resolution images and point of interest data were jointly used to generate UFZ maps using an advanced deep learning approach, and LST distribution maps were retrieved from Landsat 8 images using the radiative transfer equation. The UFZ and LST maps were then overlaid and analyzed to reveal the impact of UFZ change on LST. The results showed that in 2013, 2018, and 2022, the residential region constituted the largest proportion of area, comprising 23.52%, 24.52%, and 27.28%, respectively. The areas of unused region and transport region experienced the most rapid decrease and increase of 6.08% and 4.05% from 2013 to 2022, respectively. Furthermore, the area of sub-high temperature zone decreased the most, by 3.47%, whereas that of the high-temperature zone increased the most, by 6.05%. In the study area, water and green space maintained relatively low LSTs, whereas residential region and unused region exhibited higher LSTs. From 2013 to 2022, UFZ changes contributed to a 0.71°C increase in LST. Transfers from green space, water, commercial region, transport region, and industrial region mostly resulted in a higher LST, which was associated with a decrease in fractional vegetation cover (FVC). Transfers from unused region, residential region, and public service region mostly contributed to reducing LST, which was caused by the increase in FVC. The findings and the proposed framework can provide reliable references for urban managers to optimize urban spatial layout and promote sustainable urban development.

Contrasting foliar nitrogen nutrition of coexisting temperate and boreal trees across a modest temperature cline

The temperate-boreal forest ecotone represents a transition zone from temperate to boreal forest where nitrogen (N) is frequently limiting tree growth, which is highly sensitive to climate change. However, the spatial patterns and potential drivers of plant N nutrition and soil N availability remain poorly understood. To address this, we conducted a field investigation along a temperate-boreal forest ecotone in northeastern China, characterized by a modest mean annual temperature gradient (∼1°C) within the range of current climate warming. Our goal was to evaluate the spatial variation in foliar N nutrition and soil N availability, and the potential driving factors for Mongolian oak (Quercus mongolica) and Dahurian larch (Larix gmelinii), the dominant trees of the local temperate and adjacent boreal forests, respectively. Our results revealed no significant spatial trend in topsoil N availability across the sampling transect. Overall, foliar N concentration was significantly higher, but foliar δ15N was lower, for Mongolian oak than Dahurian larch. More important, foliar N concentration for Mongolian oak increased significantly toward the boreal forest, driven by lower mean annual temperatures and mean annual precipitation, with no significant trend observed for Dahurian larch. Additionally, foliar Δδ15N (foliar δ15N−soil δ15N) decreased significantly for Mongolian oak as it approached the boreal forest, while it increased significantly for Dahurian larch toward the temperate forest. Furthermore, the foliar N concentration, δ15N, and Δδ15N for Dahurian larch were all higher with an increasing basal area proportion of Mongolian oak. Our findings reveal contrasting patterns of foliar N nutrition between co-occurring temperate and boreal trees across a temperate-boreal forest ecotone which experiences a modest climatic gradient. These results underscore the importance of incorporating interspecific interactions to enhance our understanding of future N cycling in southern boreal forests in the context of climate warming.

An Indoor Air Quality Analysis in Newly Constructed Apartments

This study analyzes the indoor air quality inspections of newly constructed apartments by the Busan Institute of Health and Environment for the last three years (2021-2023). Of the 41 inspections, 33 exceeded the recommended standard, and the average excess rate was 80.5%. When classified by measuring households, 148 out of 332 exceeded the standards, whereas, the average excess rate by household was 44.6%. Benzene was exceeded in two out of the 332 households; therefore, the excess rate was only 0.6%. However, the relative standard deviation was the largest, showing as the highest item in the scattergram. The average concentration of toluene was 1,006.0 µg/m3, which was 100.6% of the recommended standard and was exceeded by 138 households; hence, the excess rate was 41.6%. All six items(Formaldehyde, Benzene, Toluene, Ethylbenzene, Xylene, Styrene) reached the highest concentration in spring or summer when the indoor temperature increased. The average concentration of toluene in July was 1,922 µg/m3, close to 1.9 times the recommended standard; toluene is considered an item of particular concern in summer. In the correlation analysis with indoor temperature, the formaldehyde coefficient of determination was 0.4855, regarded as somewhat significant; it was difficult to confirm the correlation with temperature for the other five items, including benzene. In correlation with relative humidity, formaldehyde showed the same tendency as indoor temperature, with a coefficient of determination of 0.4404, which is believed to be due to its small molecular weight and high solubility. Due to the chimney effect, high concentrations of volatile organic compounds could be confirmed at low atmospheric pressure in the upper floors, and this trend was most evident for formaldehyde. For most items(Formaldehyde, Benzene, Toluene, Ethylbenzene, Styrene), a negative correlation was confirmed in which the emission amount decreased as the number of households increased. For large apartment houses, it is believed that it is due to sufficient bake-out and flush-out according to the adequate construction period and the use of eco-friendly and high-end building materials. Small apartments are classified together with many small houses. The emission concentration of small houses is relatively high because the proportion of the surface area where pollutants are released is high.

Alkali Metal Cations Induce Structural Evolution on Au(111) During Cathodic Polarization.

The activity of the electrocatalytic CO2 reduction reaction (CO2RR) is substantially affected by alkali metal cations (AM+) in electrolytes, yet the underlying mechanism is still controversial. Here, we employed electrochemical scanning tunneling microscopy and in situ observed Au(111) surface roughening in AM+ electrolytes during cathodic polarization. The roughened surface is highly active for catalyzing the CO2RR due to the formation of surface low-coordinated Au atoms. The critical potential for surface roughening follows the order Cs+ > Rb+ > K+ > Na+ > Li+, and the surface proportion of roughened area decreases in the order of Cs+ > Rb+ > K+ > Na+ > Li+. Electrochemical CO2RR measurements demonstrate that the catalytic activity strongly correlates with the surface roughness. Furthermore, we found that AM+ is critical for surface roughening to occur. The results unveil the unrecognized effect of AM+ on the surface structural evolution and elucidate that the AM+-induced formation of surface high-activity sites contributes to the enhanced CO2RR in large AM+ electrolytes.

A Probabilistic Statistical Risk Assessment Method for Soil Erosion Using Remote Sensing Data: A Case Study of the Dali River Basin

Soil erosion risk assessment enables the identification of areas requiring priority treatment and avoids wasting human and material resources. The factor scoring method used in existing studies has high subjectivity, and the method of expressing erosion risk according to the soil erosion intensity ignores the random nature of the occurrence of erosion; therefore, neither method accurately reflects the risk of soil erosion. In order to address this issue, this study proposes a soil erosion risk assessment method that integrates the outcome and the probability of occurrence of soil erosion by means of a probabilistic statistical model. Subsequently, experimental research is conducted in the Dali River Basin. On the basis of long time-series data, using mathematical statistics as a tool and drawing on the empirical frequency formula, the probabilistic statistical risk assessment model is combined with the Modified Universal Soil Loss Equation (RUSLE) model to account for the probability of regional soil erosion at different intensity levels in the long time-series, which is combined with the intensity of erosion to carry out soil erosion risk assessment. The results of our study show the following: (1) The central and southwestern regions of the Dali River Basin (DRB) present medium and high levels of soil erosion risk, with the proportion of low-risk areas increasing annually, accounting for 78.97% of the DRB in 2020, while extremely high-risk areas account for only 0.40% of the DRB. (2) The major components impacting soil erosion risk in the DRB, as revealed by the geodetector, are the normalized difference vegetation index (NDVI) and slope, where the interaction between the two dominated the spatial variation in soil erosion risk. (3) Comparing the soil erosion risk and its status in the coming years, the proposed assessment method based on the occurrence probability can reveal the future soil erosion risk better than the traditional assessment method.

Ultrasound Multimodality Examination Improves the Diagnostic Efficiency of Non-Mass-Like Breast Lesions

This study is focused on ultrasound multimodality examination, which refers to the combined use of three ultrasound examination modalities, ultrasound (US), acoustic radiation force impulse (ARFI) imaging, and contrast-enhanced ultrasound (CEUS). The purpose of this study is to analyze the value of applying ultrasound multimodality examination in the differential diagnosis of benign and malignant breast non-mass-like lesions (NMLs). Cases of breast NMLs were analyzed retrospectively, and the nature of all the lesions was verified by pathological examination. Based on the gray-scale ultrasound image characteristics, the cases were classified into types Ⅰ to Ⅴ, and type Ⅰ and type Ⅱ were further classified into 4 subtypes, Ⅰa, Ⅰb, Ⅱa, and Ⅱb, according to whether there was also calcification, and the proportion of malignant cases in each subtype was statistically analyzed. Logistic regression models of US, US+ARFI, US+CEUS, and US+ARFI+CEUS for the diagnosis of malignant cases were established, ROC curves were drawn, the area under the curve (AUC) was calculated, and comparisons were made accordingly. The detection rate of malignant NMLs without calcification (atypical malignant NMLs) by the combination examination of US, ARFI, and CEUS was analyzed. A total of 407 cases were included in the study. All subjects were female, aged 22 to 81 years, with the average age being (47.0±11.0) years. There were 220 benign cases and 187 malignant cases. Ranked from the highest to the lowest, the malignancy proportion of the different types was Ⅰb>Ⅱb>Ⅲ>Ⅴ>Ⅰa>Ⅱa>Ⅳ. The malignant proportion of the low echo area with calcification was significantly higher than that of the lesions without calcification. The AUC (95% confidence interval [CI]) for diagnosing malignant cases with the logistic regression models of US, US+ARFI, US+CEUS, and US+ARFI+CEUS were 0.895 (0.862-0.927), 0.908 (0.878-0.937), 0.921 (0.893-0.948), and 0.927 (0.902-0.952), respectively. Comparison of the AUC of the 4 regression models showed significant differences (P<0.001). The detection rate of US for NMLs without calcification was 80.7%. When US was used in combination with ARFI and CEUS, 86.4% of the malignant NMLs lesions without calcification could be detected if the lesion CEUS score was 4 or 5 points or if shear-wave velocity (SWV)≥4.28 m/s. Breast NMLs with calcification show high risks of malignancy, and a pathological examination is always recommended for a conclusive diagnosis. Ultrasound multimodality examination can improve the diagnostic accuracy of breast NML without calcification.

Human-derived food shrinks home ranges and alters resource selection of mammals at the urban-wild interface

Protected lands are an important source of food, shelter, and reproductive opportunities for wildlife, especially in urban landscapes. When urban development abuts the edges of undisturbed ecosystems, synanthropic species can alter their foraging behaviors and movement to utilize human-supplemented resources throughout the urban-wild interface. Therefore, urban development on the edges of protected lands can have pronounced effects on animal movement and ecosystem functions. Iconic urban adaptive mesopredators such as northern raccoons (Procyon lotor) and Virginia opossums (Didelphis virginiana) often benefit from human-supplemented food sources such as unsecured garbage, pet food, and fresh water when available. To investigate how urban edges affect the movements of urban-adapted omnivores within conservation lands, we estimated home ranges and third-order resource selection of 27 raccoons and 12 opossums with GPS collars throughout the protected areas of northern Key Largo, FL, USA between April 2022–October 2023. The proportion of urban development in an individual's home range was the most influential factor associated with home range size, followed by species and sex. Individuals with greater proportions of residential neighborhoods and commercial areas in their home ranges exhibited smaller home ranges. Third-order resource selection functions identified both mesopredator species using residential and commercial land use areas more than they were available on the landscape. These results indicate that urban areas attract urban-adapted mesopredators from protected areas and result in smaller home ranges in the face of abundant human-derived food. Reduced home ranges on edges can support higher densities of animals, which may increase rates of disease transmission, especially when the urban borders support populations of feral domestic species. Shifting foraging behaviors from the protected areas to urban edges could have cascading downward effects if seed-dispersing roles are diluted. As urbanization increases and the distance between wild lands and human disturbance decreases, it is increasingly important to study the mechanisms of how urban development on the edges of protected areas affect the movement of wildlife.

Early spectral dynamics are indicative of distinct growth patterns in post‐wildfire forests

AbstractWestern North America has seen a recent dramatic increase in large and often high‐severity wildfires. After forest fire, understanding patterns of structural recovery is important, as recovery patterns impact critical ecosystem services. Continuous forest monitoring provided by satellite observations is particularly beneficial to capture the pivotal post‐fire period when forest recovery begins. However, it is challenging to optimize optical satellite imagery to both interpolate current and extrapolate future forest structure and composition. We identified a need to understand how early spectral dynamics (5 years post‐fire) inform patterns of structural recovery after fire disturbance. To create these structural patterns, we collected metrics of forest structure using high‐density Remotely Piloted Aircraft (RPAS) lidar (light detection and ranging). We employed a space‐for‐time substitution in the highly fire‐disturbed forests of interior British Columbia. In this region, we collected RPAS lidar and corresponding field plot data 5‐, 8‐, 11‐,12‐, and 16‐years postfire to predict structural attributes relevant to management, including the percent bare ground, the proportion of coniferous trees, stem density, and basal area. We compared forest structural attributes with unique early spectral responses, or trajectories, derived from Landsat time series data 5 years after fire. A total of eight unique spectral recovery trajectories were identified from spectral responses of seven vegetation indices (NBR, NDMI, NDVI, TCA, TCB, TCG, and TCW) that described five distinct patterns of structural recovery captured with RPAS lidar. Two structural patterns covered more than 80% of the study area. Both patterns had strong coniferous regrowth, but one had a higher basal area with more bare ground and the other pattern had a high stem density, but a low basal area and a higher deciduous proportion. Our approach highlights the ability to use early spectral responses to capture unique spectral trajectories and their associated distinct structural recovery patterns.

Exploring Ecological Quality and Its Driving Factors in Diqing Prefecture, China, Based on Annual Remote Sensing Ecological Index and Multi-Source Data

The interaction between the natural environmental and socioeconomic factors is crucial for assessing the dynamics of plateau ecosystems. Therefore, the remote sensing ecological index (RSEI) and CatBoost-SHAP model were employed to investigate changes in the ecological quality and their driving factors in the Diqing Tibetan Autonomous Prefecture, China, from 2001 to 2021. The results showed an increase from 0.44 in 2001 to 0.71 in 2021 in the average RSEI for the Diqing Prefecture, indicating an overall upward trend in the ecological quality. Spatial analysis shows the percentage of the area covered by different levels of RSEI and their temporal changes. The results revealed that “good” ecological quality accounted for the largest proportion of the study area, at 42.77%, followed by “moderate” at 21.93%, and “excellent” at 16.62%. “Fair” quality areas accounted for 16.11% and “poor” quality areas only 2.57%. The study of ecological and socioeconomic drivers based on the CatBoost-SHAP framework also indicated that natural climate factors have a greater impact on ecological quality than socioeconomic factors; however, this effect differed significantly with altitude. The findings suggest that, in addition to strengthening climate monitoring, further advancements in ecological engineering are required to ensure the sustainable development of the ecosystem and the continuous improvement of the environmental quality in the Diqing Prefecture.

Integrating calibrated PTFs and modified OpenKarHydro framework to map the responses of ecohydrological processes to climate change across the Loess Plateau

Climate change is exacerbating the risk of soil water stress, soil erosion and ecological degradation in the Loess Plateau (CLP). However, the spatiotemporal dynamics of ecohydrological processes, including soil water balance (SWB), soil erosion (SE) and vegetation net primary productivity (NPP), in response to climate change remain isolated and ambiguous. Additionally, existing studies often rely on limited measured data from specific watersheds with particular land use/cover types to establish the pedotransfer functions (PTFs) for single soil hydraulic parameter (SHP). There is a notable lack of comprehensive studies that explore PTFs encompassing all SHPs applicable to the entire CLP, predict the spatiotemporal response of ecohydrological processes to climate change, and identify future risk periods and regions for SWB, SE, and NPP. To address these gaps, we first screened the PTFs applicable for the whole CLP, and then projected the spatiotemporal dynamics of ecohydrological processes from 2020 to 2030 under CMIP6 scenarios by integrating the PTFs with the OpenKarHydro, RUSLE, and CASA model, and finally classified the soil water content, SE and NPP to identify their risk periods and regions. The results showed that the PTFs achieved satisfactory accuracy, with RMSEs for Ks, θs, θr, α, n, θfc and θw being 2.682, 0.109, 0.016, 0.111, 0.897, 0.060 and 0.058, BIASs of 1.365, 0.182, 0.012, 0.031, 0.214, 0.057 and 0.048, R2s of 0.445, 0.500, 0.430, 0.400, 0.694, 0.453 and 0.453, and NSEs of 0.645, 0.737, 0.874, 0.349, 0.567, 0.756 and 0.458, respectively. SE decreased significantly from 2020 to 2030, with an average annual rate of −6.18 %. Soil water storage decreased significantly between 2020 and 2030, and declining from southeast to northwest. The proportion of area in the wet zone decreased by 4 %, while the proportion of area in the dry zone increased by 11.9 % from 2020 to 2030. The average NPP in 2020–2030 is 320.07 gC·m−2·a−1, with the largest in summer and smallest in winter, and decreasing from southeast to northwest. The NPP increased significantly in 2020–2030, with average annual value and rate of 19.61 gC·m−2·a−1 and 70.84 %, respectively. The NPP increased significantly in spring, summer and autumn, but remained stable in winter. This investigation bridges the gap between existing soil properties, missing SHPs, the strong regional applicability of PTFs, and isolated ecohydrological processes. It is promises to provide valuable insights into the response to climate change in the CLP and another water-limited regions.

Projection of the Potential Global Geographic Distribution of the Solanum Fruit Fly Bactrocera latifrons (Hendel, 1912) (Diptera: Tephritidae) Based on CLIMEX Models

The solanum fruit fly Bactrocera latifrons (Diptera: Tephritidae) is an invasive alien insect that causes huge economic losses to pepper and other solanaceous plant industries. It is mainly distributed in South and Southeast Asia, SW Europe, Western USA, and in some African countries. However, the potential global geographical distribution of B. latifrons is unknown. Therefore, in this study, based on the current (1981–2010) and future (2040–2059) climatic scenarios determined using the CNRM-CM5, Access1.0, GFDL-ESM-2M, and NorESM1-M models, we used a species distribution model (CLIMEX 4.0) to project the potential global geographic distribution of B. latifrons to prevent further invasion and harm. In the current climate scenario, South America (1286.06 × 104 km2), Africa (1435.47 × 104 km2), and Oceania (410.66 × 104 km2) have the largest proportions of suitable land areas for B. latifrons colonization. Under all four future climate models, the global potential suitable area for Bactrocera latifrons is projected to decrease and shift towards higher latitudes. This study provides an important baseline upon which researchers, quarantine personnel, and governments can develop the appropriate control strategies against B. latifrons.

Spatio-temporal interplay between ecosystem services and urbanization in the Yangtze River Economic Belt: A new perspective for considering the scarcity effect

The expeditious trajectory of urbanization has profound ramifications on the configuration and functioning of the natural milieu. Given the constrained opulence of natural capital and its indispensable bestowal of ecosystem services in different regions, meticulous contemplation of the relationship between urbanization and the ecosystem service scarcity value (ESSV) while formulating decisions is imperative. This study focuses on the Yangtze River Economic Belt (YREB), where ESSV is quantified by combining the dynamic changes in market supply and demand factors. The influence of supply and demand changes on the ESSV is discussed and analyzed in four scenarios. Subsequently, a multidimensional urbanization comprehensive evaluation index system was established to explore the spatiotemporal interactive response mode between ESSV and urbanization. Our findings manifest a notably augmented ESSV in the YREB from 1990 to 2020, particularly prominent between 2000 and 2010. Moreover, the growth in public goods services outpaced that in private goods services within the same period. Demand-driven scarcity has emerged as the foremost determinant of ESSV. Urbanization levels gradually increased from 1990 to 2020, with a uniform allocation of heightened ESSV and urbanization throughout the region. The urbanization level of YREB, as a whole, exhibited conspicuous spatial heterogeneity, with the downstream region significantly surpassing the middle and upper reaches, and the downstream Yangtze River Delta city cluster and the middle reaches of the Chengdu-Chongqing city cluster notably outpacing that in the surrounding region. The coupling coordination degree (CCD) between the ESSV and urbanization in the YREB develops in a harmonious direction during the study period, accompanied by palpable spatial disparities. The area of unbalanced development gradually decreased, and the area proportion of the coordinated development type of ESSV and urbanization increased continuously. Overall, this study elucidates regional disparities in the reciprocal relationship between the ESSV and urbanization in the YREB, thereby unveiling the extent of incongruity between regional urbanization and the ecosystem. It serves to identify the juncture at which ecological sustainability and urbanization converge harmoniously, thus offering novel insights in achieving comprehensive, high-caliber, and synchronized development in the YREB.

Study on freezing separation process through observing microstructure of NaCl solution ice

Freezing desalination is a method to purify saline water. Researchers believe that the salt inclusions distributed inside ice affect the sea ice salinity, but there is not sufficient visualized proof that has been obtained via direct and non-destructive experiments to support this view. Even less study has been carried out on the salt inclusion distribution inside ice below the eutectic temperature. In this paper, a NaCl solution ice sample was prepared with the original mass concentration of 3 wt% using unidirectional heat transfer method. The freezing temperature was −24 ℃, which is lower than −21.2 ℃, the eutectic temperature of NaCl solution. Micro-CT device was used to scan the upper, middle and lower parts of a 3 wt% NaCl solution ice sample and the 5-layer U-Net architecture in DL (deep learning) module of the Dragonfly program was used to segment images to extract the information of interest. The image characteristics of salt inclusions and air bubbles inside the ice were obtained. Very high salt inclusion area proportion was found in the surface layer images of the sample with the maximum area proportion of about 43%. Experiments were also carried out to measure the salinity distribution inside the same solution ice samples. It was found that in the surface and bottom ice layers, the salinity is higher than that of the original solution. In the experiments, for the layers cut from the top and bottom of the 3 wt% NaCl solution ice sample, each of them possesses 10% of the solution ice sample mass, their salinities were 4.28 wt% and 5.41 wt%, respectively. Comparing the measurement results with the micro-CT image processing results, it was found that the variation tendency of the area proportion of salt inclusions is consistent with that of the ice layer salinity. The combination of micro-CT technology with DL image processing method has been proved an effective way to visualize and analyze quantitatively the internal structure of salt solution ice. Then, three NaCl solution samples that have the original mass concentrations of 3 wt%, 6 wt% and 9 wt%, and a pure water sample were frozen under the same conditions. The middle parts of the four ice samples were scanned and the obtained micro-CT images were processed. It can be found that in the middle parts of the three NaCl solution ice samples, the area proportions of salt inclusions increase from top to bottom and are positively correlated with the original salinities of the solution ice. The factors affecting air bubble distribution are more complex than that affecting salt inclusion distribution.

Spatiotemporal Variations in MODIS EVI and MODIS LAI and the Responses to Meteorological Drought across Different Slope Conditions in Karst Mountain Regions

Based on monthly MODIS EVI and LAI data from 2001 to 2020, combined with the Standardized Precipitation Evapotranspiration Index (SPEI), this study employs Theil–Sen trend analysis, Mann–Kendall (MK) test, Hurst index analysis, and correlation analysis to comparatively analyze the overall vegetation trends, spatial distribution characteristics, and future trends of different vegetation types in Guizhou Province under varying slope conditions. The study also explores the response of vegetation to SPEI at different time scales across different slopes. The results indicate the following: (1) From 2001 to 2020, the average values of EVI (0.34%/a) and LAI (1.4%/a) during the growing season exhibited an increasing trend, with the improved vegetation areas primarily concentrated in the western region of Guizhou, while the degradation areas were mainly located in the central and eastern regions. (2) Under different slope conditions, EVI generally showed slight improvement, while LAI exhibited significant improvement, with dry-lands experiencing the largest changes. Future trends indicate continuous improvement, but the proportion of vegetation improvement area decreases with increasing slope. When the slope is less than 5°, the proportion of vegetation improvement area is the highest. (3) The positive correlation between EVI, LAI, and SPEI at different time scales is stronger than the negative correlation, with the strongest correlations observed when the slope is less than 5°. When the slope exceeds 35°, the relationship between vegetation and drought response is almost unaffected by the slope. These findings provide a scientific basis for vegetation growth monitoring and the study of climate change and vegetation interactions in Guizhou Province.

Classification of tomato seedling chilling injury based on chlorophyll fluorescence imaging and DBO-BiLSTM

IntroductionTomatoes are sensitive to low temperatures during their growth process, and low temperatures are one of the main environmental limitations affecting plant growth and development in Northeast China. Chlorophyll fluorescence imaging technology is a powerful tool for evaluating the efficiency of plant photosynthesis, which can detect and reflect the effects that plants are subjected to during the low temperature stress stage, including early chilling injury.MethodsThis article primarily utilizes the chlorophyll fluorescence image set of tomato seedlings, applying the dung beetle optimization (DBO) algorithm to enhance the deep learning bidirectional long short term memory (BiLSTM) model, thereby improving the accuracy of classification prediction for chilling injury in tomatoes. Firstly, the proportion of tomato chilling injury areas in chlorophyll fluorescence images was calculated using a threshold segmentation algorithm to classify tomato cold damage into four categories. Then, the features of each type of cold damage image were filtered using SRCC to extract the data with the highest correlation with cold damage. These data served as the training and testing sample set for the BiLSTM model. Finally, DBO algorithm was applied to enhance the deep learning BiLSTM model, and the DBO-BiLSTM model was proposed to improve the prediction performance of tomato seedling category labels.ResultsThe results showed that the DBO-BiLSTM model optimized by DBO achieved an accuracy, precision, recall, and F1 score with an average of over 95%.DiscussionCompared to the original BiLSTM model, these evaluation parameters improved by 9.09%, 7.02%, 9.16%, and 8.68%, respectively. When compared to the commonly used SVM classification model, the evaluation parameters showed an increase of 6.35%, 7.33%, 6.33%, and 6.5%, respectively. This study was expected to detect early chilling injury through chlorophyll fluorescence imaging, achieve automatic classification and labeling of cold damage data, and lay a research foundation for in-depth research on the cold damage resistance of plants themselves and exploring the application of deep learning classification methods in precision agriculture.

Spatial–Temporal Variations and Driving Factors of the Albedo of the Qilian Mountains from 2001 to 2022

Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI.

Mechanically and accurately calculate river width in vegetation areas by coupling Sentinel-1 and -2 imageries within land-water-mixed pixels

Accurately measuring river width has been one of greatest challenges due to the presence of mixed land–water pixels intersecting river boundaries. Therefore, this study proposed a novel mechanical method (RW-vebasud), instead of traditionally empirical models, to estimate river width within a pixel in vegetation areas based on time series analysis of Sentinel-1 and Sentinel-2 spaceborne multispectral images. We initially explored the mechanism of variation in backscatter intensity (σ) with enhanced vegetation index (EVI) whereby we successfully removed noises in σ–EVI relationship resulted from vegetation growth. Then, for the first time a smooth functional relationship between water area proportion and backscatter intensity within a ROI (or region of interest) was constructed. Consequently, subpixel water–land separation based on the mechanism process was realized. The novel method could not only work at large-scaled rivers (satellite-visible) but perform well at small-scaled rivers within a water-land mixed pixel (satellite-invisible). A total of 197 measurements for river widths in China during 2016 ∼ 2021 were used for model verification, demonstrating a positive correlation between EVI and σ, with R2 ranging from 0.16 to 0.69 (P<0.05). The RW-vebasud exhibited superior accuracy in calculating river width compared to the widely used MNDWI (modified normalized difference water index). The Root Mean Square Error (RMSE) decreased by 4.32 ∼ 6.65 m when the river width was less than 90 m and by 66.12 % when it exceeded 90 m, compared to MNDWI. Remarkably, RW-vebasud maintains satisfactorily high accuracy (the Nash-Sutcliffe efficiency coefficient: NSE=0.70 and RMSE=3.19) even at the spatial scale less than 3 times the image resolution, breaking the internationally accepted limit that river width extraction can only be accurate when the river width is greater than 3 times the satellite resolution. Moreover, the accuracy of this method is better than that with the currently well-known global river width datasets GRWL and MERIT Hydro. For the RW-Vebasud/GRWL/MERIT Hydro datasets, the NSE=0.99 /0.93/0.87, the RMSE=5.99/42.33/54.27, and the R2 = 0.99/0.91/0.74, respectively. The application of RW-vebasud in China shows that river widths in wet and dry seasons exhibited an increasing trend over the previous six years (2016–2021), as global warming accelerated glacier melting and increased rainfall quantity, with an average growth rate of 2.26 m/year (wet, P<0.05) and 2.17 m/year (dry, P<0.05), respectively. In response to the summer/winter Asian monsoons, most rivers widen in summer. The largest river width occurs in the Yellow River Basin (YLRB, 155.28 m on average), while the smallest occurs in the Hai River Basin (HARB, 22.99 m on average). The method proposed in this study can provide efficient techniques for surface river-width reconstruction which can greatly facilitate global resource and environmental modelling.

Ecological impacts and supply demand evolution of the Yangtze to Huaihe water transfer project in Anhui section

Human activities have profound impacts on land use and the supply–demand balance of ecosystem services (ESs). Various activities, such as urban construction, urban and rural planning, and inter-basin water transfer projects, continuously reshape land use patterns. This is a case study of the Anhui section of the Yangtze-Huaihe Water Diversion Project. Data from 2000, 2010, and 2020 is analyzed. Additionally, the patch-generating land use simulation (PLUS) model is utilized to quantify the specific impacts of the water diversion project construction on the supply and demand of ESs. The results indicate that the comprehensive dynamic attitude of land use during the project construction period significantly increased, rising from 0.16 to 13.79%, and mainly affected forest, water areas, construction land, and unused land. Specifically, the construction of the project led to significant changes in water purification, biodiversity, and, especially, hydrological regulation services. Additionally, the migration of residents significantly impacted the demand for ESs. The study also found a significant correlation between land use changes and the balance of ES supply and demand: the proportion of cultivated land and construction land is positively correlated with the balance, while the proportion of forest, grassland, and water areas is negatively correlated. This study provides empirical data for understanding the environmental and socio-economic impacts of large-scale water diversion projects and offers a scientific basis for local mitigation and control of adverse impacts. Through quantitative analysis and model prediction, this research effectively bridges the gap between theory and practice, providing important references for sustainable regional development.

Habitat fragmentation differentially affects invasive and native plant diversity in a human-dominated wetland island system

Managing invasive species requires identifying the factors that determine alien species invasion success. This study investigates how anthropogenic and biogeographical factors influence alien plant invasion in the Sanyang Wetlands, a human-dominated island system in Wenzhou City, China. Specifically, we analyzed whether human activities (e.g., habitat heterogeneity, proportion of road area, and cultivation) and island characteristics (e.g., island area, isolation) affect the diversity of native and invasive plant species similarly. We also assessed the applicability of the equilibrium theory of island biogeography to invasive plant species diversity and examined how these factors affect invasive plant species with different dispersal syndromes (anemochore, zoochore, and autochore). We found that both invasive and native species richness positively correlate with island area, habitat heterogeneity, and proportion of road area. However, although native species richness was negatively correlated with isolation, invasive species richness was not. The diversity and composition of invasive species with different dispersal syndromes were determined by different variables; for example, the composition and diversity of zoochores was increased by habitat heterogeneity, while anemochore species richness was increased by the proportion of road area, whereas anemochore species composition was influenced by distance to the nearest island. We conclude that habitat fragmentation differentially affects invasive and native plant diversity, aligning with the predictions of the equilibrium theory of island biogeography only for native species but not for invasive species. Our findings indicate that tailoring habitat attributes and regulating human activities could be effective strategies for mitigating the spread of invasive species in fragmented landscapes.

A study on landscape feature and emotional perception evaluation of waterfront greenway

Waterfront greenway is important spatial carriers for urban residents to engage in walking tours and fitness activities. Improving the quality of its walking spaces can have a positive impact on the mental well-being of pedestrians. This enhancement is a crucial aspect of building healthy cities. This study focused on urban waterfront greenway landscapes. Field surveys combined with questionnaire scales were used to assess pedestrians’ emotional perceptions, while concurrently collecting image data of waterfront greenway walking spaces. Semantic segmentation was employed to identify elements, and landscape metrics were calculated using relevant formulas. Correlation analysis was used to investigate the relationship between landscape metric features and emotional perceptions. And established a multiple regression model of pedestrian spatial perception on waterfront greenways to investigate the influence mechanisms of emotional perception and landscape metrics. The study indicates that greenness, proportion of water area, and vegetation diversity positively impact emotional perception of waterfront leisure greenway walking spaces, whereas path width negatively affects emotional perception. Specifically, the magnitude of influence is ranked as follows: greenness (0.491), proportion of water area (0.356), proportion of trails (−0.272), and vegetation diversity (0.130). The design guidelines for urban waterfront greenway spaces were proposed, guided by quantitative studies on landscape elements of waterfront greenway spaces and based on the measurement features of these elements. Moreover, the environmental experience of the waterfront green walking spaces was further enhanced, satisfying the public’s visual perception and psychological needs. The contribution of this study is to lay the foundation for the development of spatial control items for urban waterfront greenway.