Land Use Land Cover Analysis for Godavari Basin in Maharashtra Using Geographical Information System and Remote Sensing

The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari River basin under study. These findings provide valuable insights for discerning land utilization across various categories, facilitating the adoption of appropriate strategies for sustainable land use in the region.

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating environments.In this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different sensors.Adversarial learning is further introduced to extract generalized sensor-invariant features.Through explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions.The experimental results suggest the proposed approach is well suited for real industrial applications.

Spatiotemporal detection of land use/land cover changes and land surface temperature using Landsat and MODIS data across the coastal Kanyakumari district, India

This study assesses the changes in land use/land cover(LULC) and land surface temperature(LST) to identify their impacts from 2000 to 2020 along the coast of Kanyakumari district, India using remote sensing techniques. Landsat images are used to estimate the LULC changes and the MODIS data for LST.The Maximum Likelihood Classification(MLC) method is used, and the LULC is classified into six categories: Agriculture Land, Barren Land, Salt Pan, Sandy Beach, Settlement, and Waterbody. Within the two decades of the present change detection study, upheave in the Settlement area of 49.89% is noticed, and the Agriculture Land is exploited by 20.09%. Salt Pan emits a high LST of 31.57°C, and the Waterbodies are noticed with a low LST of 28.9°C. However, the overall rate of LST decreased by 0.56°C during this period. This study will help policymakers make appropriate planning and management to overcome the impact of LULC and LST in the forthcoming years.

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Genetic dissection of N use efficiency using maize inbred lines and testcrosses

Although the use of heterosis in maize breeding has increased crop productivity,the genetic causes underlying heterosis for nitrogen(N) use efficiency(NUE) have been insufficiently investigated.In this study,five N-response traits and five low-N-tolerance traits were investigated using two inbred line populations(ILs) consisting of recombinant inbred lines(RIL) and advanced backcross(ABL) populations,derived from crossing Ye478 with Wu312.Both populations were crossed with P178 to construct two testcross populations.IL populations,their testcross populations,and the midparent heterosis(MPH)for NUE were investigated.Kernel weight,kernel number,and kernel number per row were sensitive to N level and ILs showed higher N response than did the testcross populations.Based on a highdensity linkage map,138 quantitative trait loci(QTL) were mapped,each explaining 5.6%–38.8% of genetic variation.There were 52,34 and 52 QTL for IL populations,MPH,and testcross populations,respectively.The finding that 7.6% of QTL were common to the ILs and their testcross populations and that 11.7% were common to the MPH and testcross population indicated that heterosis for NUE traits was regulated by non-additive and non-dominant loci.A QTL on chromosome 5 explained 27% of genetic variation in all of the traits and Gln1-3 was identified as a candidate gene for this QTL.Genome-wide prediction of NUE traits in the testcross populations showed 14%–51% accuracy.Our results may be useful for clarifying the genetic basis of heterosis for NUE traits and the candidate gene may be used for genetic improvement of maize NUE.

Impacts of land use/cover change on water balance by using the SWAT model in a typical loess hilly watershed of China

Land use/cover change(LUCC)plays a key role in altering surface hydrology and water balance,finally affect-ing the security and availability of water resources.However,mechanisms underlying LUCC determination of water-balance processes at the basin scale remain unclear.In this study,the Soil and Water Assessment Tool(SWAT)model and partial least squares regression were used to detect the effects of LUCC on hydrology and water components in the Zuli River Basin(ZRB),a typical watershed of the Yellow River Basin.In general,three recommended coefficients(R^(2)and E ns greater than 0.5,and P bias less than 20%)indicated that the output results of the SWAT model were reliable and that the model was effective for the ZRB.Then,several key findings were obtained.First,LUCC in the ZRB was characterized by a significant increase in forest(21.61%)and settlement(23.52%)and a slight reduction in cropland(-1.35%),resulting in a 4.93%increase in evapotranspiration and a clear decline in surface runoffand water yield by 15.68%and 2.95%at the whole basin scale,respectively.Second,at the sub-basin scale,surface runoffand water yield increased by 14.26%-36.15%and 5.13%-15.55%,respectively,mainly due to settlement increases.Last,partial least squares regression indicated that urbanization was the most significant contributor to runoffchange,and evapotranspiration change was mainly driven by forest expansion.These conclusions are significant for understanding the relationship between LUCC and water balance,which can provide meaningful information for managing water resources and the long-term sustainability of such watersheds.

Historical Changes and Multi-scenario Prediction of Land Use and Terrestrial Ecosystem Carbon Storage in China

Terrestrial carbon storage(CS)plays a crucial role in achieving carbon balance and mitigating global climate change.This study employs the Shared Socioeconomic Pathways and Representative Concentration Pathways(SSPs-RCPs)published by the Intergovernmental Panel on Climate Change(IPCC)and incorporates the Policy Control Scenario(PCS)regulated by China’s land management policies.The Future Land Use Simulation(FLUS)model is employed to generate a 1 km resolution land use/cover change(LUCC)dataset for China in 2030 and 2060.Based on the carbon density dataset of China’s terrestrial ecosystems,the study analyses CS changes and their relationship with land use changes spanning from 1990 to 2060.The findings indicate that the quantitative changes in land use in China from 1990 to 2020 are characterised by a reduction in the area proportion of cropland and grassland,along with an increase in the impervious surface and forest area.This changing trend is projected to continue under the PCS from 2020 to 2060.Under the SSPs-RCPs scenario,the proportion of cropland and impervious surface predominantly increases,while the proportions of forest and grassland continuously decrease.Carbon loss in China’s carbon storage from 1990 to 2020 amounted to 0.53×10^(12)kg,primarily due to the reduced area of cropland and grassland.In the SSPs-RCPs scenario,more significant carbon loss occurs,reaching a peak of8.07×10^(12)kg in the SSP4-RCP3.4 scenario.Carbon loss is mainly concentrated in the southeastern coastal area and the Beijing-TianjinHebei(BTH)region of China,with urbanisation and deforestation identified as the primary drivers.In the future,it is advisable to enhance the protection of forests and grassland while stabilising cropland areas and improving the intensity of urban land.These research findings offer valuable data support for China’s land management policy,land space optimisation,and the achievement of dual-carbon targets.

Land use and cover change and influencing factor analysis in the Shiyang River Basin,China

Land use and cover change(LUCC)is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface,with significant impacts on the environment and social economy.Rapid economic development and climate change have resulted in significant changes in land use and cover.The Shiyang River Basin,located in the eastern part of the Hexi Corridor in China,has undergone significant climate change and LUCC over the past few decades.In this study,we used the random forest classification to obtain the land use and cover datasets of the Shiyang River Basin in 1991,1995,2000,2005,2010,2015,and 2020 based on Landsat images.We validated the land use and cover data in 2015 from the random forest classification results(this study),the high-resolution dataset of annual global land cover from 2000 to 2015(AGLC-2000-2015),the global 30 m land cover classification with a fine classification system(GLC_FCS30),and the first Landsat-derived annual China Land Cover Dataset(CLCD)against ground-truth classification results to evaluate the accuracy of the classification results in this study.Furthermore,we explored and compared the spatiotemporal patterns of LUCC in the upper,middle,and lower reaches of the Shiyang River Basin over the past 30 years,and employed the random forest importance ranking method to analyze the influencing factors of LUCC based on natural(evapotranspiration,precipitation,temperature,and surface soil moisture)and anthropogenic(nighttime light,gross domestic product(GDP),and population)factors.The results indicated that the random forest classification results for land use and cover in the Shiyang River Basin in 2015 outperformed the AGLC-2000-2015,GLC_FCS30,and CLCD datasets in both overall and partial validations.Moreover,the classification results in this study exhibited a high level of agreement with the ground truth features.From 1991 to 2020,the area of bare land exhibited a decreasing trend,with changes primarily occurring in the middle and lower reaches of the basin.The area of grassland initially decreased and then increased,with changes occurring mainly in the upper and middle reaches of the basin.In contrast,the area of cropland initially increased and then decreased,with changes occurring in the middle and lower reaches.The LUCC was influenced by both natural and anthropogenic factors.Climatic factors and population contributed significantly to LUCC,and the importance values of evapotranspiration,precipitation,temperature,and population were 22.12%,32.41%,21.89%,and 19.65%,respectively.Moreover,policy interventions also played an important role.Land use and cover in the Shiyang River Basin exhibited fluctuating changes over the past 30 years,with the ecological environment improving in the last 10 years.This suggests that governance efforts in the study area have had some effects,and the government can continue to move in this direction in the future.The findings can provide crucial insights for related research and regional sustainable development in the Shiyang River Basin and other similar arid and semi-arid areas.

Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

Land use change and its driving factors in the ecological function area:A case study in the Hedong Region of the Gansu Province,China

Land use and cover change(LUCC)is important for the provision of ecosystem services.An increasing number of recent studies link LUCC processes to ecosystem services and human well-being at different scales recently.However,the dynamic of land use and its drivers receive insufficient attention within ecological function areas,particularly in quantifying the dynamic roles of climate change and human activities on land use based on a long time series.This study utilizes geospatial analysis and geographical detectors to examine the temporal dynamics of land use patterns and their underlying drivers in the Hedong Region of the Gansu Province from 1990 to 2020.Results indicated that grassland,cropland,and forestland collectively accounted for approximately 99% of the total land area.Cropland initially increased and then decreased after 2000,while grassland decreased with fluctuations.In contrast,forestland and construction land were continuously expanded,with net growth areas of 6235.2 and 455.9 km^(2),respectively.From 1990 to 2020,cropland was converted to grassland,and both of them were converted to forestland as a whole.The expansion of construction land primarily originated from cropland.From 2000 to 2005,land use experienced intensified temporal dynamics and a shift of relatively active zones from the central to the southeastern region.Grain yield,economic factors,and precipitation were the major factors accounting for most land use changes.Climatic impacts on land use changes were stronger before 1995,succeeded by the impact of animal husbandry during 1995-2000,followed by the impacts of grain production and gross domestic product(GDP)after 2000.Moreover,agricultural and pastoral activities,coupled with climate change,exhibited stronger enhancement effects after 2000 through their interaction with population and economic factors.These patterns closely correlated with ecological restoration projects in China since 1999.This study implies the importance of synergy between human activity and climate change for optimizing land use via ecological patterns in the ecological function area.

Multi-scenario Simulation of the Impact of Land Use Change on the Ecosystem Service Value in the Suzhou-Wuxi-Changzhou Metropolitan Area,China

As the most economically developed metropolitan area in China’s Yangtze River Delta,the rapid changing land use patterns of Suzhou-Wuxi-Changzhou(Su-Xi-Chang) metropolitan area have profound impacts on the ecosystem service value(ESV).Based on the patterns of land use change and the ESV change in Su-Xi-Chang metropolitan area from 2000 to 2020,we set up four scenarios:natural development scenario,urban development scenario,arable land protection scenario and ecological protection scenario,and simulated the impact of land use changes on the ESV in these scenarios.The results showed that:1) the area of built-up land in the Su-XiChang metropolitan area increased significantly from 2000 to 2020,and the area of other types of land decreased.Arable land underwent the highest transfer-out area,and was primarily converted into built-up land.The total ESV of Su-Xi-Chang metropolitan area increased initially then declined from 2000–2020,and the value of almost all individual ecosystem services decreased.2) Population density,GDP per area,night lighting intensity,and road network density can negatively impact the ESV.3) The total ESV loss under the natural development and urban development scenarios was high,and the expansion of the built-up land and the drastic shrinkage of the arable land contributed to the ESV decline under both scenarios.The total ESV under arable land protection and ecological protection scenarios increases,and therefore these scenarios are suitable for future land use optimization in Su-Xi-Chang.This study could provide a certain reference for land use planning and allocation,and offer guidance for the rational allocation of land resources.

Remaining Useful Life Prediction Method for Multi-Component System Considering Maintenance:Subsea Christmas Tree System as A Case Study

Maintenance is an important technical measure to maintain and restore the performance status of equipment and ensure the safety of the production process in industrial production,and is an indispensable part of prediction and health management.However,most of the existing remaining useful life(RUL)prediction methods assume that there is no maintenance or only perfect maintenance during the whole life cycle;thus,the predicted RUL value of the system is obviously lower than its actual operating value.The complex environment of the system further increases the difficulty of maintenance,and its maintenance nodes and maintenance degree are limited by the construction period and working conditions,which increases the difficulty of RUL prediction.An RUL prediction method for a multi-omponent system based on the Wiener process considering maintenance is proposed.The performance degradation model of components is established by a dynamic Bayesian network as the initial model,which solves the uncertainty of insufficient data problems.Based on the experience of experts,the degree of degradation is divided according to Poisson process simulation random failure,and different maintenance strategies are used to estimate a variety of condition maintenance factors.An example of a subsea tree system is given to verify the effectiveness of the proposed method.

Regulation effects of water and nitrogen on yield,water,and nitrogen use efficiency of wolfberry

Wolfberry(Lycium barbarum L.)is important for health care and ecological protection.However,it faces problems of low productivity and resource utilization during planting.Exploring reasonable models for water and nitrogen management is important for solving these problems.Based on field trials in 2021 and 2022,this study analyzed the effects of controlling soil water and nitrogen application levels on wolfberry height,stem diameter,crown width,yield,and water(WUE)and nitrogen use efficiency(NUE).The upper and lower limits of soil water were controlled by the percentage of soil water content to field water capacity(θ_(f)),and four water levels,i.e.,adequate irrigation(W0,75%-85%θ_(f)),mild water deficit(W1,65%-75%θ_(f)),moderate water deficit(W2,55%-65%θ_(f)),and severe water deficit(W3,45%-55%θ_(f))were used,and three nitrogen application levels,i.e.,no nitrogen(N0,0 kg/hm^(2)),low nitrogen(N1,150 kg/hm^(2)),medium nitrogen(N2,300 kg/hm^(2)),and high nitrogen(N3,450 kg/hm^(2))were implied.The results showed that irrigation and nitrogen application significantly affected plant height,stem diameter,and crown width of wolfberry at different growth stages(P<0.01),and their maximum values were observed in W1N2,W0N2,and W1N3 treatments.Dry weight per plant and yield of wolfberry first increased and then decreased with increasing nitrogen application under the same water treatment.Dry weight per hundred grains and dry weight percentage increased with increasing nitrogen application under W0 treatment.However,under other water treatments,the values first increased and then decreased with increasing nitrogen application.Yield and its component of wolfberry first increased and then decreased as water deficit increased under the same nitrogen treatment.Irrigation water use efficiency(IWUE,8.46 kg/(hm^(2)·mm)),WUE(6.83 kg/(hm^(2)·mm)),partial factor productivity of nitrogen(PFPN,2.56 kg/kg),and NUE(14.29 kg/kg)reached their highest values in W2N2,W1N2,W1N2,and W1N1 treatments.Results of principal component analysis(PCA)showed that yield,WUE,and NUE were better in W1N2 treatment,making it a suitable water and nitrogen management mode for the irrigation area of the Yellow River in the Gansu Province,China and similar planting areas.

The Influence of Internet Use on Women’s Depression and Its Countermeasures—Empirical Analysis Based on Data from CFPS

Based on China Family Panel Studies(CFPS)2018 data,the multiple linear regression model is used to analyze the effects of Internet use on women’s depression,and to test the robustness of the regression results.At the same time,the effects of Internet use on mental health of women with different residence,age,marital status and physical health status are analyzed.Then,we can obtain that Internet use has a significant promoting effect on women’s mental health,while the degree of Internet use has a significant inhibitory effect on women’s mental health.In addition,the study found that women’s age,education,place of residence,marital status,length of sleep,working status and physical health status are the main factors affecting the mental health of Chinese women.In the heterogeneity investigation of residence,age,marital status and physical health status,Internet use has a greater negative impact on the Center for Epidemiological Studies Depression Scale(CES-D8)scores of women in rural areas,has a significant positive impact on the mental health of middle-aged and elderly women or women with spouses,and has a positive impact on the mental health of physically unhealthy women.Therefore,in view of women’s mental health needs and the problems existing in the use of the Internet,this paper puts forward some suggestions to further improve the overall mental health level of women.

Response of ecosystem carbon storage to land use change from 1985 to 2050 in the Ningxia Section of Yellow River Basin,China

Regional sustainable development necessitates a holistic understanding of spatiotemporal variations in ecosystem carbon storage(ECS),particularly in ecologically sensitive areas with arid and semi-arid climate.In this study,we calculated the ECS in the Ningxia Section of Yellow River Basin,China from 1985 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model based on land use data.We further predicted the spatial distribution of ECS in 2050 under four land use scenarios:natural development scenario(NDS),ecological protection scenario(EPS),cultivated land protection scenario(CPS),and urban development scenario(UDS)using the patch-generating land use simulation(PLUS)model,and quantified the influences of natural and human factors on the spatial differentiation of ECS using the geographical detector(Geodetector).Results showed that the total ECS of the study area initially increased from 1985 until reaching a peak at 402.36×10^(6) t in 2010,followed by a decreasing trend to 2050.The spatial distribution of ECS was characterized by high values in the eastern and southern parts of the study area,and low values in the western and northern parts.Between 1985 and 2020,land use changes occurred mainly through the expansion of cultivated land,woodland,and construction land at the expense of unused land.The total ECS in 2050 under different land use scenarios(ranked as EPS>CPS>NDS>UDS)would be lower than that in 2020.Nighttime light was the largest contributor to the spatial differentiation of ECS,with soil type and annual mean temperature being the major natural driving factors.Findings of this study could provide guidance on the ecological construction and high-quality development in arid and semi-arid areas.

Effects of thinning and understory removal on water use efficiency of Pinus massoniana:evidence from photosynthetic capacity and stable carbon isotope analyses

Understanding the relationship between forest management and water use efficiency(WUE)is important for evaluating forest adaptability to climate change.However,the effects of thinning and understory removal on WUE and its key controlling processes are not well understood,which limits our comprehension of the physiological mechanisms of various management practices.In this study,four forest management measures(no thinning:NT;understory removal:UR;light thinning:LT;and heavy thinning:HT)were carried out in Pinus massoniana plantations in a subtropical region of China.Photosynthetic capacity and needle stable carbon isotope composition(δ^(13)C)were measured to assess instantaneous water use efficiency(WUE_(inst))and long-term water use efficiency(WUE_(i)).Multiple regression models and structural equation modelling(SEM)identified the effects of soil properties and physiological performances on WUE_(inst)and WUE_(i).The results show that WUE_(inst)values among the four treatments were insignificant.However,compared with the NT stand(35.8μmol·mol^(-1)),WUE_(i)values significantly increased to 41.7μmol·mol^(-1)in the UR,50.1μmol·mol^(-1)in the LT and 46.6μmol·mol^(-1)in HT treatments,largely explained by photosynthetic capacity and soil water content.Understory removal did not change physiological performance(needle water potential and photosynthetic capacity).Thinning increased the net photosynthetic rate(A_n)but not stomatal conductance(g_s)or predawn needle water potential(ψ_(pd)),implying that the improvement in water use efficiency for thinned stands was largely driven by radiation interception than by soil water availability.In general,thinning may be an appropriate management measure to promote P.massoniana WUE to cope with seasonal droughts under future extreme climates.

A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining enterprises.This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features.This study proposes a hybrid predictive model to assess the RUL of rolling element bearings.The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features.The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm.Subsequently,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal features.The model is trained by optimizing its parameters via the grid search approach.The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method.The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database.The comparison is constructed with other traditional models.The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

How Does Urban Shrinkage Affect Land Use Efficiency?A Case Study of Shrinking Cities in Northeast China

The effect of urban shrinkage has gradually become a new topic.Theoretically,urban shrinkage may exert great influence on land use efficiency(LUE)through various urban subsystems,but there is currently limited research examining these pathways.Using the Super-SBM-Undesirable model and the Structural Equation Model(SEM),this study calculates the LUE of shrinking cities in Northeast China and simulates the process of urban shrinkage affecting LUE.To quantify the process of urban shrinkage affecting LUE,three mediation variables,namely the economy,public services,and innovation,are used as latent variables to apply SEM.The results show that urban shrinkage will affect LUE through a direct path and indirect paths.In the direct path,urban shrinkage leads to an improvement in LUE.In the indirect paths,the economy and innovation will transmit the negative effect of urban shrinkage on LUE,while public services will reverse this effect.An important contribution of this study is that it quantifies the paths of urban shrinkage affecting LUE,thereby expanding the understanding of urban shrinkage effect and laying a foundation for the sustainable development of shrinking cities.

Uphill or downhill?Cropland use change and its drivers from the perspective of slope spectrum

The continuous decrease of low-slope cropland resources caused by construction land crowding poses huge threat to regional sustainable development and food security.Slope spectrum analysis of topographic and geomorphic features is considered as a digital terrain analysis method which reflects the macro-topographic features by using micro-topographic factors.However,pieces of studies have extended the concept of slope spectrum in the field of geoscience to construction land to explore its expansion law,while research on the slope trend of cropland from that perspective remains rare.To address the gap,in virtue of spatial analysis and geographically weighted regression(GWR)model,the cropland use change in the Yangtze River Basin(YRB)from 2000 to 2020 was analyzed and the driving factors were explored from the perspective of slope spectrum.Results showed that the slope spectrum curves of cropland area-frequency in the YRB showed a first upward then a downward trend.The change curve of the slope spectrum of cropland in each province(municipality)exhibited various distribution patterns.Quantitative analysis of morphological parameters of cropland slope spectrum revealed that the further down the YRB,the stronger the flattening characteristics,the more obvious the concentration.The province experienced the greatest downhill cropland climbing(CLC)was Shannxi,while province experienced the highest uphill CLC was Zhejiang.The most common cropland use change type in the YRB was horizontal expansion type.The factors affecting average cropland climbing index(ACCI)were quite stable in different periods,while population density(POP)changed from negative to positive during the study period.This research is of practical significance for the rational utilization of cropland at the watershed scale.

Discussion on Land Use Mode Reform in Coal Opencast Mining

The existing land supply mode of opencast mining is"land requisition first,then transfer",which lacks an effective recovery and withdrawal mechanism,and the reclaimed mining land is difficult to withdraw.According to the regular rules of coal opencast mining and the periodic characteristics of land use,this paper puts forward a new mode of temporary land use for coal opencast mining.It is conducive to im-proving the quality and scale of land use and reclamation utilization of opencast coal mining,and is of great significance for exploring and for-mulating reasonable land use policies for mineral resources development projects.