Moving Window Method Research Articles

Probabilistic method for wind speed prediction and statistics distribution inference based on SHM data-driven

For wind-sensitive structures, such as long-span bridges, high-rise buildings, transmission towers, etc., the prediction of wind speed and its statistical distribution are vital steps in the design and operation stages. Specifically, wind speed prediction is directly related to the value of wind load in the next occurrence; the statistical distribution of wind speed has regular characteristics, which can represent the random characteristics of wind field. In this paper, a probabilistic prediction model of wind speed based on Bayes’ theorem is proposed and verified based on structural health monitoring (SHM) data. Firstly, the Gaussian process is derived and used as an a priori function in Bayes’ theorem. In addition, the influence of six covariance functions on the prediction performance are discussed, that is, squared exponential (SE), Matern-3/2 (MA-3/2), Matern-5/2 (MA-5/2), automatic relevance determination SE (ARDSE), ARDMA-3/2, and ARDMA-5/2. Secondly, the correlation between the next wind speed and the previous wind speed is discussed by using the moving window method. Finally, the parameters in the three wind speed probability distribution functions (PDF), that is, Gumbel distribution, Weibull distribution, Rayleigh distribution, are updated in real time by increasing the SHM data based on Bayes’ theorem.

Dynamic Changes in Agroecosystem Landscape Patterns and Their Driving Mechanisms in Karst Mountainous Areas of Southwest China: The Case of Central Guizhou

Puding County in central Guizhou is a typical karst ecologically vulnerable area integrating rural, mountainous, and ecological migration areas. It is essential to study the dynamic trajectory and direction of its agroecosystems (AESs) transformation to promote sustainable land use development in karst mountain areas. Based on high-resolution remote sensing images from 2004 to 2020, this study analyzes the transformation characteristics, typical landscape patterns, and their driving forces of AESs using the landscape pattern index, moving window method, and Geodetector model. The results show that: (i) The overall landscape pattern of AESs in the study area developed towards complexity and diversification from 2004 to 2020. The overall trend of woodland and grassland (WGL) is increasing, the slope cultivated land (SCL) is shrinking, the construction land (CL) is spreading and expanding along the vicinity of the town, and the economic and fruit forests (EFF) are increasing significantly. (ii) Three typical patterns are concluded according to the main transformation directions of AESs: WGL restoration type, CL growth type, and EFF growth type; middle and high mountains are dominated by the WGL restoration type with increased ecological functions, and the concentration of the new WGL increases with the increase in elevation; low mountain areas are dominated by the CL growth type with enhanced living functions, and the concentration of the new CL decreases with the increase in slope and elevation; valleys and hills are dominated by the EFF growth type with enhanced ecological and economic functions, and the concentration of the new EFF increases with the decrease in slope and elevation. (iii) Socio-economic factors are the dominant drivers of AES transformation. The WGL restoration type is dominated by slope and elevation, the CL growth type is mainly influenced by slope and urbanization rate, and the EFF growth type is primarily influenced by soil organic matter, slope, urbanization rate, and resident population. The study results have implications for rural land use, rural development, and ecological restoration of rocky desertification in karst mountain areas and other similar mountainous areas.

Transformation of Paddy Field Use in Intermountain-Type Basins Using Evidence from the Structure and Function Perspective of Karst Mountain Areas in Southwest China

China’s southwestern karst area exhibits many mountains and little flat lands, and intermountain basins (IBs) (locally named “bazi”) are one of their typical landform types. Comparative studies on land use in the core of typical landforms in karst mountain areas are relatively lacking. Studying the evolution and transformation patterns of intermountain basin paddy fields use (IBPFU) to optimize land resources in mountainous areas is important. Based on structural and functional perspectives, this study established a research framework on the evolution and transformation of IBPFU in karst mountainous areas, and analyzed the evolution and transformation patterns of IBPFU in Guizhou Province from 1966 to 2020 by measuring land use change and applying the moving window method and morphological spatial pattern analysis (MSPA) model. The study showed that the evolution of IBPFU is characterized by reversibility and irreversibility, diversity, and stages. The transition of IBPFU requires a trade-off among regional socioeconomic development, ecological protection, and food security. The commonality of land use transitions in IB and sloped land (SL) is reflected in the diversity of their land use functions. This study provides a reference for a comprehensive understanding of land use transitions and sustainable development in the mountainous karst regions of southwest China by providing in-depth examinations of the land use transition of IB, which is different from SL, via the long time series evolution of core land use types. The research framework and research method constructed here also apply to other regions.

Small Dataset-Based Closed-Loop State of Charge Estimation for Pure Electric Construction Machinery With Large Sensor Error: A Case Study of 5-ton Loader

Pure electric construction machinery (PECM) is the future development trend for construction machinery. Compared with other electric drive systems, the PECM systems are often accompanied by complex working conditions, frequent high current pulse signals, limited memory sources, and heavier sensor noise. To improve the robustness and accuracy of state of charge (SOC) estimation for the PECM using small dataset with large sensor error, a hybrid approach combined machine learning method with physical method is proposed in this paper. Firstly, the Gaussian process regression (GPR) is employed to train an open-loop SOC estimation model with a simple moving window method. Secondly, a framework of hybrid SOC estimation is proposed based on an improved version of the Kalman filter to improve the robustness of the GPR model with large sensor error. Finally, a series of experiments are carried out to evaluate the proposed approach considering temperature variation, measurement drift noise, and users’ habits. It is worth noting that the sampling frequency of training data is only 0.1 Hz. The proposed approach is compared with the most widely used machine learning methods, which verifies the high accuracy and strong robustness of the proposed approach.

Detecting Spatiotemporal Dynamics and Driving Patterns in Forest Fragmentation with a Forest Fragmentation Comprehensive Index (FFCI): Taking an Area with Active Forest Cover Change as a Case Study

Forests play an irreplaceable role in preserving soil and water, as well as realizing carbon neutrality. However, logging and urban expansion have caused widespread forest fragmentation globally, resulting in biodiversity loss and carbon emissions. Therefore, it is a prerequisite to develop a comprehensive index for evaluating the degree of forest fragmentation to propose effective policies for forest protection and restoration. In this study, a forest fragmentation comprehensive index (FFCI) was constructed through principal component analysis (PCA) based on land-use data from 2000 to 2020 in Fujian Province, composed of five commonly used landscape metrics: patch density (PD), largest patch index (LPI), mean patch area (MPA), aggregation index (AI), and division. Then, the semivariogram function and moving windows method were employed to explore the scale effect and spatiotemporal variations of FFCI. The spatial autocorrelation analysis was used to distinguish the spatial relationship of forest fragmentation, while the driving mechanisms were explored using the geographic detector (GD). The results show that the optimal scale to reflect forest fragmentation based on the semivariogram and moving window method was 3500 m. The proposed FFCI could explain more than 85% of the information for all landscape metrics, and the effectivity of FFCI was validated by urban–rural gradient and transect analysis. We also found that, despite having the highest forest coverage in China, Fujian Province has experienced severe forest fragmentation. High and medium fragmentation accounted for over 50% of all types of fragmentation, with decreasing trends in low and very low fragmentation and increasing trends in high fragmentation over time, indicating that the degree of forest fragmentation in the study area was aggravated over time. Moreover, the spatial distribution pattern of FFCI was mainly high–high clusters and low–low clusters, showing a decreasing trend year by year. The areas with high fragmentation were mainly distributed in the urban center of coastal cities, while the internal cities in western and central regions had a relatively low degree of fragmentation. Additionally, the spatial differentiation in the variation in FFCI was mainly influenced by elevation, slope, and nighttime light intensity. The superimposed impact of two factors on the variation in FFCI was greater than the impact of individual factors. These results provide an effective approach for assessing the degree of forest fragmentation and offer scientific support for mitigating forest fragmentation.

Geomorphic influences on land use/cover diversity and pattern

Landforms directly or indirectly affect land use/cover diversity and its spatial distribution. However, the interactions between complex topography and land use/cover patterns remain unclear. To explore this concern, a study was performed in the Altay region, Xinjiang, which is a typical region with multiple geomorphic and land use/cover types. Topographic variables, including elevation, slope, aspect, relief amplitude, topographic wetness index (TWI), surface solar radiation value (SRV) and land use/cover diversity indexes (LDIs) were extracted and calculated. Firstly, the dominant distribution area of each land use/cover type was calculated by a topographic distribution index, and the results showed that forest and steppe land covers were mainly distributed on mountains with slopes > 5°, small relief platforms and hills and large relief mountains; agricultural, urban, water and Gobi desert land uses/covers were concentrated on plains, gently rolling hills and low mountains with slopes < 5° with elevation of approximately 800 m. Further research indicated that the moving-window method providing the characteristic scale of land use/cover diversity was 210 m, and the spatial distributions of the LDIs were obtained. Combined with landscape structure information entropy (LSIE), the results showed that the LDIs and LSIE were highest on plains, hills and low-mountains. Regularity occurred between landform and land use/cover: a higher elevation, greater relief amplitude, higher TWI, and gentle slope resulted in a simpler land use/cover type and smaller LDIs. The interrelationships between LDIs and topographic variables were evaluated via redundancy analysis (RDA) and Spearman correlation coefficient analysis, and results indicated that elevation, slope and TWI, strongly influenced the land use/cover diversity and pattern. Fully considering the relationship between geomorphic conditions and land use/cover is essential for rationally developing land resources and sustainable development.

Is your neighbor your friend? Scan methods for spatial social network hotspot detection

AbstractGIS analyses use moving window methods and hotspot detection to identify point patterns within a given area. Such methods can detect clusters of point events such as crime or disease incidences. Yet, these methods do not account for connections between entities, and thus, areas with relatively sparse event concentrations but high network connectivity may go undetected. We develop two scan methods (i.e., moving window or focal processes), EdgeScan and NDScan, for detecting local spatial‐social connections. These methods capture edges and network density, respectively, for each node in a given focal area. We apply methods to a social network of Mafia members in New York City in the 1960s and to a 2019 spatial network of home‐to‐restaurant visits in Atlanta, Georgia. These methods successfully capture focal areas where Mafia members are highly connected and where restaurant visitors are highly local; these results differ from those derived using traditional spatial hotspot analysis using the Getis–Ord Gi* statistic. Finally, we describe how these methods can be adapted to weighted, directed, and bipartite networks and suggest future improvements.

Deep learning-based multi-parameter early warning model under true triaxial conditions

There are still many challenges to achieve accurate early warning of rock instability in underground excavation projects. The cusp mutation model combined with energy time series shows great potential in early warning, but this approach has two drawbacks: On the one hand, energy definition method violates the basic thermodynamic law. On the other hand, there is still a large subjectivity in the selection of the energy mutation time series. This results in a large error in the defined warning interval. In this paper, we use machine deep learning method instead of manual single selection of feature values, which greatly avoids the subjective influence of sudden change time series. Based on dynamic moving window method with improved convolutional neural network (CNN) method, a multi-parameter time series prediction of acoustic emission was established. The input features of the three convolutional layers verify and learn each other to strengthen the predictive properties of time series; The dynamic moving window method shortened the time step, deepened the visualization of mutation in the time series, finally the model realized the integration of prediction and warning. The training results show that the multi-parameter warning model has great accuracy, and the selection of warning points is highly consistent with the positioning information; The warning duration stabilizes within a reasonable range as the number of training sessions rises, and the brittleness degree of rock mass has a controllable influence on the duration. This study successfully constructed an integrated prediction and warning model for rock mass instability, which passed the test and performed well on the whole. The model is proposed to provide a more cutting-edge and improved idea for early warning of subsurface rock instability damage.

Risk Ahead: Actigraphy-Based Early-Warning Signals of Increases in Depressive Symptoms During Antidepressant Discontinuation

Antidepressant discontinuation increases the risk of experiencing depressive symptoms. In a repeated single-subject design, we tested whether transitions in depression were preceded by increases in actigraphy-based critical-slowing-down-based early-warning signals (EWSs; variance, kurtosis, autocorrelation), circadian-rhythm-based indicators, and decreases in mean activity levels. Four months of data from 16 individuals with a transition in depression and nine without a transition in depression were analyzed using a moving-window method. As expected, more participants with a transition showed at least one EWS (50% true positives; 22.2% false positives). Increases in circadian rhythm variables (25.0% true positives vs. 44.4% false positives) and decreases in activity levels (37.5% true positives vs. 44.4% false positives) were more common in participants without a transition. None of the tested risk indicators could confidently predict upcoming transitions in depression, but some evidence was found that critical-slowing-down-based EWSs were more common in participants with a transition.

Cavitation state recognition for control valve using AlexNet-type neural networks with three-channel images transformed by time series

The control valve is a crucial component with high switching frequency in coal liquefaction systems. Its performance exerts a great influence on the long-term operations in the coal chemical technology. Cavitation is a key factor affecting the control accuracy and surface morphology of the control valve; hence, it will further shorten the service life of the control valve. A cavitation state recognition model based on vibration acceleration time series is proposed in this paper, and the cavitation stage in the valve is identified accurately. Three vibration acceleration sensors are attached to the valve seat to measure cavitation-induced vibration data. The collected time series is divided into fragments based on the non-overlap moving window method, and these fragments are converted into feature maps using the Gramian angular field transform. These feature maps are then input into AlexNet to train the cavitation state recognition model. The experimental results show that recognition accuracy could be improved effectively upon the increase in the length of the time series fragment, and the proposed model has a similar prediction accuracy on unfamiliar datasets. Compared with no noise, the recognition accuracy could reach to 95% when the moving window length is 150 and the signal-to-noise ratio is equal to 5 dB. Furthermore, the proposed model could still achieve good recognition results under mixed open conditions.

Investigating shock wave propagation, evolution, and anisotropy using a moving window concurrent atomistic–continuum framework

Despite their success in microscale modeling of materials, atomistic methods are still limited by short time scales, small domain sizes, and high strain rates. Multiscale formulations can capture the continuum-level response of solids over longer runtimes, but using such schemes to model highly dynamic, nonlinear phenomena is very challenging and an active area of research. In this work, we develop novel techniques within the concurrent atomistic–continuum (CAC) multiscale framework to simulate shock wave propagation through a two-dimensional, single-crystal lattice. The technique is described in detail, and two moving window methods are incorporated to track the shock front through the domain and thus prevent spurious wave reflections at the atomistic–continuum interfaces. We compare our simulation results to analytical models as well as previous atomistic and CAC data and discuss the apparent effects of lattice orientation on the shock response of two materials. We then use the moving window techniques to perform parametric studies which analyze the shock front’s structure. Finally, we compare the efficiency of our model to molecular dynamics simulations. This work showcases the framework’s capability for simulating dynamic shock evolution over long runtimes and opens the door to more complex studies involving shock propagation through composites and alloys.

A WiFi Indoor Location Tracking Algorithm Based on Improved Weighted K Nearest Neighbors and Kalman Filter

The Weighted K Nearest Neighbor (WKNN) algorithm is a widely adopted lightweight methodology for indoor WiFi positioning based on location fingerprinting. Nonetheless, it suffers from the disadvantage of a fixed K value and susceptibility to incorrect reference point matching. To address this issue, we present a novel algorithm in this paper, referred to as Static Continuous Statistical Characteristics-Soft Range Limited-Self-Adaptive WKNN (SCSC-SRL-SAWKNN). Our algorithm not only takes into account location tracking in the motion state but also exploits the continuous statistical features of extended periods of inactivity to enhance localization. In the motion state, we initially employ the adaptive WKNN (SAWKNN) algorithm to determine the optimal K value, followed by the employment of the Soft Range Limited KNN (SR-KNN) algorithm to identify the correct reference point and ultimately estimate the position. When a prolonged stationary state is detected, we first utilize the moving window method to obtain a more stable position fingerprint (SCSC), and then proceed with the positioning process in the same motion state. Ultimately, we use Kalman filter to generate the location trajectory. Our experimental findings demonstrate that the proposed SCSC-SRL-SAWKNN algorithm outperforms traditional WKNN, SAWKNN, and SRL-KNN techniques in terms of localization accuracy and location trajectory. Specifically, the localization accuracy of our algorithm is 56.7% and 36.6% higher than that of traditional WKNN in the static state and overall situation, respectively.

Random field modeling of local strength of a randomly arranged unidirectional fiber-reinforced composite material under transverse tensile loading

This study examines the random field modeling of local strength, which is the apparent strength of each window extracted from an SEM image of a cross-section of a randomly arranged unidirectional fiber-reinforced composite material specimen. In particular, computation of the spatially varying random apparent fracture strength based on the microstructure simulation is performed. To analyze the apparent fracture strength, single-scale analyses assuming plane-stress and plane-strain conditions, as well as multiscale stress analysis based on the homogenization theory, are attempted. In addition, the moving window method is employed to compute the respective random fields and their autocorrelation functions. First, the influence of the analysis method on the identified random field is investigated. Furthermore, the influence of the window and image size is discussed. The numerical results revealed that a smaller window leads to a more accurate evaluation of the apparent strength, and thus, is preferable for the identification of the respective random field.

Spatial Effects of Landscape Patterns of Urban Patches with Different Vegetation Fractions on Urban Thermal Environment

The landscape patterns of urban green spaces have been proven to be important factors that affect urban thermal environments. However, the spatial effect of the landscape patterns of urban patches with different vegetation densities on land surface temperature (LST) has not been investigated in detail. In this study, the built-up area of Xuzhou City was taken as the study region, and the four phases of Landsat 8 images and their corresponding ground observations from 2014 to 2020 were selected as the basic data. Normalized spectral mixture analysis and an improved mono-window algorithm were used to invert the vegetation component fraction (VF) and LST maps of the study area, respectively, and the surface patches were classified into five levels according to the VF values, from low to high. Four landscape-level and five class-level metrics were then selected to represent the landscape characteristics of each VF-level patch. The tested values of 60 and 780 m were regarded as the best grain size and spatial extent, respectively, in the calculation of all landscape metrics of ALL VF-level patches (VFLM) using the moving-window method. The results of bivariate Moran’s I for VFLM and LST showed the following: (1) for landscape-level metrics, only the Shannon diversity index and patch diversity have substantial negative spatial correlations with LST (with average |Moran’s I| &lt; 0.2), indicating that the types of VF levels and the number of patches exert weak negative effects on the thermal environment for a certain area; (2) for class-level metrics such as percentage of landscape, patch cohesion index, largest patch index, landscape shape index, and aggregation index, only the class-level metrics of sub-high VF (LV4) and extreme-high (LV5) VF levels patches have significant negative spatial correlations with LST (with high Moran’s I value, and high–high and low–high distributions in local indications of spatial association cluster maps), indicating that only the patches of high VF levels can effectively alleviate LST and that patch proportion, natural connectivity degree, predominance degree, shape complexity, and aggregation degree are important landscape factors for regulating the thermal environment. Principal component analysis and multiple linear regression were applied to determine the impact weights of the class-level VFLMs of LV4 and LV5 patches on LST, which revealed the contributions of these landscape metrics to mitigating the urban heat island effect (UHI). These results signify the importance of and differences in the spatial patterns of various VF-level patches for UHI regulation; these patterns can provide new perspectives and references for urban green space planning and climate management.

Development of an Online Adaptive Parameter Tuning vSLAM Algorithm for UAVs in GPS-Denied Environments.

In recent years, unmanned aerial vehicles (UAVs) have been applied in many fields owing to their mature flight control technology and easy-to-operate characteristics. No doubt, these UAV-related applications rely heavily on location information provided by the positioning system. Most UAVs nowadays use a global navigation satellite system (GNSS) to obtain location information. However, this outside-in 3rd party positioning system is particularly susceptible to environmental interference and cannot be used in indoor environments, which limits the application diversity of UAVs. To deal with this problem, in this paper, a stereo-based visual simultaneous localization and mapping technology (vSLAM) is applied. The presented vSLAM algorithm fuses onboard inertial measurement unit (IMU) information to further solve the navigation problem in an unknown environment without the use of a GNSS signal and provides reliable localization information. The overall visual positioning system is based on the stereo parallel tracking and mapping architecture (S-PTAM). However, experiments found that the feature-matching threshold has a significant impact on positioning accuracy. Selection of the threshold is based on the Hamming distance without any physical meaning, which makes the threshold quite difficult to set manually. Therefore, this work develops an online adaptive matching threshold according to the keyframe poses. Experiments show that the developed adaptive matching threshold improves positioning accuracy. Since the attitude calculation of the IMU is carried out based on the Mahony complementary filter, the difference between the measured acceleration and the gravity is used as the metric to online tune the gain value dynamically, which can improve the accuracy of attitude estimation under aggressive motions. Moreover, a static state detection algorithm based on the moving window method and measured acceleration is proposed as well to accurately calculate the conversion mechanism between the vSLAM system and the IMU information; this initialization mechanism can help IMU provide a better initial guess for the bundle adjustment algorithm (BA) in the tracking thread. Finally, a performance evaluation of the proposed algorithm is conducted by the popular EuRoC dataset. All the experimental results show that the developed online adaptive parameter tuning algorithm can effectively improve the vSLAM accuracy and robustness.

Evaluating the influence of constant source profile presumption on PMF analysis of PM2.5 by comparing long- and short-term hourly observation-based modeling

Hourly PM2.5 speciation data have been widely used as an input of positive matrix factorization (PMF) model to apportion PM2.5 components to specific source-related factors. However, the influence of constant source profile presumption during the observation period is less investigated. In the current work, hourly concentrations of PM2.5 water-soluble inorganic ions, bulk organic and elemental carbon, and elements were obtained at an urban site in Nanjing, China from 2017 to 2020. PMF analysis based on observation data during specific pollution (firework combustion, sandstorm, and winter haze) and emission-reduction (COVID-19 pandemic) periods was compared with that using the whole 4-year data set (PMFwhole). Due to the lack of data variability, event-based PMF solutions did not separate secondary sulfate and nitrate. But they showed better performance in simulating average concentrations and temporal variations of input species, particularly for primary source markers, than the PMFwhole solution. After removing event data, PMF modeling was conducted for individual months (PMFmonth) and the 4-year period (PMF4-year), respectively. PMFmonth solutions reflected varied source profiles and contributions and reproduced monthly variations of input species better than the PMF4-year solution, but failed to capture seasonal patterns of secondary salts. Additionally, four winter pollution days were selected for hour-by-hour PMF simulations, and three sample sizes (500, 1000, and 2000) were tested using a moving window method. The results showed that using short-term observation data performed better in reflecting immediate changes in primary sources, which will benefit future air quality control when primary PM emissions begin to increase.

Effects of Farmland Landscape Fragmentation on Agricultural Irrigation in Hotan Oasis

Farmland landscape fragmentation is an important problem affecting the agricultural modernization process in China. However, farmland landscape fragmentation leads to land being wasted and increases management costs, particularly in the dryland’s oasis regions. Therefore, investigating the impact of farmland landscape fragmentation on agricultural irrigation is of great significance in developing oasis agriculture. This paper used the landscape quantitative index (DIVISION), the moving window method, and gradient analysis methods to study the temporal and spatial pattern changes in farmland fragmentation in the Hotan Oasis. Additionally, the impact of fragmentation on irrigation in the oasis was elaborated upon by exploring the relationship between evapotranspiration and its components in farmland fragmentation. The results showed that the farmland area of the Hotan Oasis increased from 1546.19 km2 in 2000 to 2068.23 km2 in 2020, and farmland landscape fragmentation increased with the expansion of the Hotan Oasis. In addition, a significant relationship between farmland fragmentation and evapotranspiration and its components was evident. A lower DIVISION value corresponded to a higher ET value, a lower ETs/ETc ratio, and a higher water use efficiency. When the total farmland area is assumed to remain unchanged, the irrigation water consumption is reduced by 4.82 × 108 m3 according to the size and proportion of arable land with the lowest degree of fragmentation (L1, division value of 0.46). In addition, with the increase in the proportion of farmland, the scale of oasis decreases by 2431.56 km2 for the reduction in field roads, shelterbelt, and bare land. These findings suggest that solving the problem of farmland fragmentation can effectively reduce irrigation water consumption, realize the internal expansion of the oasis through intensive land use, and relieve the pressure of the external expansion of the oasis.

Heterogeneity of wetland landscapes and their relationships with anthropogenic disturbances and precipitation in a semiarid region of China.

Wetlands in semiarid regions have important ecological functions and provide services that are partly dependent on the landscape pattern and structure; therefore, it is important to assess the heterogeneity of wetland landscape conditions for wetland conservation and management. Widespread wetland degradation resulting from main factors in semiarid regions highlights the need for exploring the heterogeneity of wetland landscapes and their relationships with anthropogenic disturbances and precipitation. Wetlands in our study referred to natural wetlands, including marshes, rivers, and lakes. We used overall landscape metrics to assess wetland landscape conditions and used grid landscape metrics at a scale of 30km using a moving window method to explore the heterogeneity of wetland landscapes during the period (1986-2019) for western Jilin Province. We performed Spearman correlation analyses of the landscape metrics with the Hemeroby index (HI) and mean annual precipitation (MAP). We suggested that wetland landscape composition metrics (e.g., NP and PD) and configuration metrics (e.g., ED and AI) had positive correlations with HI by 2019 (p < 0.01); in addition, wetland landscape composition metrics (e.g., NP and PD) and configuration metrics (e.g., ED) were negatively correlated with MAP during 1986-2019. In conclusion, wetland landscapes have become increasingly fragmented over the past few decades (1986-2019) in western Jilin Province and the reasons for this result mainly include the household contract responsibility system, along with rural productivity, severe overgrazing and reclamation, and climate change. Wetland landscape composition was positively correlated with human interference and negatively correlated with mean annual precipitation.

Relationships between Land Surface Temperatures and Neighboring Environment in Highly Urbanized Areas: Seasonal and Scale Effects Analyses of Beijing, China

Urban environments have a strong influence on the land surface temperature (LST) in urban areas. Understanding the relationship between LST and urban environmental factors can help develop effective strategies to reduce high LSTs in urban areas, which is critical for mitigating the urban heat island effect. Previous studies have focused on the correlation between LST and the environmental factors that drive its formation, without considering the influences of the neighboring environment and the vertical expansion of highly urbanized areas. Notably, the correlation between LST and its neighboring environment in different seasons remains unclear. In this study, we selected central Beijing in China as our study area and employed the moving window method to characterize the environmental factors of the neighboring environment of the central LST cell. We explored eight environmental factors from three layers: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), modified normalized difference water index (MNDWI), building density (BD), building height (BH), building volume (BV), sky view factor (SVF), and road density (RD). The Pearson correlation and extreme gradient boosting (XGB) regression methods were applied to measure the correlation between LST and the different factors in moving windows of different sizes. The results indicated that the correlation between NDVI, MNDWI, and LST was considerably different in the winter and other seasons. However, NDBI was positively correlated with LST in all seasons, although the correlation was strongest/weakest in summer/winter. Among building-related factors, BD and BH were more strongly correlated with LST, and the positive/negative correlation between BD/BH and LST was stronger in summer/winter. The correlation between LST and its neighboring environment varied with increasing window size, and this variation differs significantly between winter and other seasons. In spring, summer, and autumn, the strength of the correlation between LST and its neighboring environment showed an “inverted V” pattern with increasing window size. The optimal spatial scales to explore the influence of neighboring environments on the LST of 30-m cells were 210 m and 270 m. This study revealed the seasonal correlation between LST and its neighboring environment while explaining the variation at a spatial scale. Notably, this study can provide a new perspective for understanding the driving mechanism of the urban thermal environment, while contributing to its scientific optimization and management.

Research on temporal and spatial evolution of land use and landscape pattern in Anshan City based on GEE

Frequent mining activities can bring about problems such as soil erosion and environmental pollution, which are detrimental to the efficient use of land and the sustainable development of cities. Existing studies have paid little attention to mining areas and lack comparative analysis of landscape changes in multiple mining pits. In this paper, the main urban area of Anshan City, where the mining areas are concentrated, was used as the research area, and the Landsat TM/OLI surface reflectance (SR) data of the Google Earth Engine (GEE) platform and the random forest algorithm were used to map the land use in 2008, 2014, and 2020. On this basis, land use dynamics and landscape pattern indices were used to analyze the changes in land use and landscape patterns in the Anshan City area. In addition, a moving window method was combined to further analyze and compare the landscape changes between different pits. The results show that:1. From 2008 to 2020, the construction land in Anshan urban area continued to decline, the forest land continued to expand, and the construction land was shifted to the forest land and cultivated land. Mining land increased before 2014 and remained almost unchanged after 2014, which is in line with the actual situation. 2. During the study period, the landscape fragmentation degree and landscape heterogeneity in the urban area of Anshan kept increasing. The high value areas of landscape fragmentation were the urban-rural combination areas and the mining areas. Among them, the reclamation of Dagushan and Donganshan is better, while the reclamation of Anqian, Yanqianshan and Xiaolingzi mines needs to be strengthened. 3. The random forest algorithm based on GEE shows a high degree of accuracy for land use classification. The overall classification accuracy in 3 years exceeds 90% and the kappa coefficient exceeds 0.85. The study results can be used as an essential reference for optimizing the urban ecological environment and provide technical backing for the urbanization construction and rational use of land in Anshan City.