Autonomous landing scene recognition based on transfer learning for drones

In this paper, we study autonomous landing scene recognition with knowledge transfer for drones. Considering the difficulties in aerial remote sensing, especially that some scenes are extremely similar, or the same scene has different representations in different altitudes, we employ a deep convolutional neural network(CNN) based on knowledge transfer and fine-tuning to solve the problem. Then, LandingScenes-7 dataset is established and divided into seven classes. Moreover, there is still a novelty detection problem in the classifier, and we address this by excluding other landing scenes using the approach of thresholding in the prediction stage. We employ the transfer learning method based on ResNeXt-50 backbone with the adaptive momentum(ADAM) optimization algorithm. We also compare ResNet-50 backbone and the momentum stochastic gradient descent(SGD) optimizer. Experiment results show that ResNeXt-50 based on the ADAM optimization algorithm has better performance. With a pre-trained model and fine-tuning, it can achieve 97.845 0% top-1 accuracy on the LandingScenes-7dataset, paving the way for drones to autonomously learn landing scenes.

基于Landsat影像的城市土地利用动态监测

土地是自然界不可或缺的资源,土地利用反映了人地关系。随着人口数量的持续增加和社会工业化、城市化的持续推进,如何科学地开发利用宝贵的土地资源越来越受到人们的重视。本文通过2013年和2023年的Landsat影像数据,结合遥感与GIS,对10年间合肥市土地资源进行监测与分析,结果表明,经过10年的城市发展,合肥市耕地面积减少了65707.92 hm 2,建设用地增长了39181.05 hm 2,林地减少了1728.27 hm 2,水域增加了28255.14 hm 2。

PP-GAN:Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN

The objective of style transfer is to maintain the content of an image while transferring the style of another image.However,conventional methods face challenges in preserving facial features,especially in Korean portraits where elements like the“Gat”(a traditional Korean hat)are prevalent.This paper proposes a deep learning network designed to perform style transfer that includes the“Gat”while preserving the identity of the face.Unlike traditional style transfer techniques,the proposed method aims to preserve the texture,attire,and the“Gat”in the style image by employing image sharpening and face landmark,with the GAN.The color,texture,and intensity were extracted differently based on the characteristics of each block and layer of the pre-trained VGG-16,and only the necessary elements during training were preserved using a facial landmark mask.The head area was presented using the eyebrow area to transfer the“Gat”.Furthermore,the identity of the face was retained,and style correlation was considered based on the Gram matrix.To evaluate performance,we introduced a metric using PSNR and SSIM,with an emphasis on median values through new weightings for style transfer in Korean portraits.Additionally,we have conducted a survey that evaluated the content,style,and naturalness of the transferred results,and based on the assessment,we can confidently conclude that our method to maintain the integrity of content surpasses the previous research.Our approach,enriched by landmarks preservation and diverse loss functions,including those related to“Gat”,outperformed previous researches in facial identity preservation.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Causal Analysis Between Rice Growth and Cadmium Accumulation and Transfer under Arbuscular Mycorrhizal Inoculation

Cadmium(Cd)contamination in rice has been a serious threat to human health.To investigate the effects of arbuscular mycorrhizal fungi(AMF)on the Cd translocation in rice,a controlled pot experiment was conducted.The results indicated that AMF significantly increased rice biomass,with an increase of up to 40.0%,particularly in root biomass by up to 68.4%.Notably,the number of prominent rice individuals also increased,and their plasticity was enhanced following AMF inoculation.AMF led to an increase in the net photosynthetic rate and antioxidant enzyme activity of rice.In the AMF treatment group,the Cd concentration in the rice roots was significantly higher(19.1%‒68.0%)compared with that in the control group.Conversely,the Cd concentration in the rice seeds was lower in the AMF treatment group,indicating that AMF facilitated the sequestration of Cd in rice roots and reduced Cd accumulation in the seeds.Path coefficients varied across different treatments,suggesting that AMF inoculation reduced the direct impact of soil Cd concentration on the total Cd accumulation in seeds.The translocation of Cd was consistently associated with simultaneous growth dilution and compensatory accumulation as a result of mycorrhizal effects.Our study quantitatively analyzed this process through path analysis and clarified the causal relationship between rice growth and Cd transfer under the influence of AMF.

Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

Generation of mitochondrial replacement monkeys by female pronucleus transfer

Mutations in mitochondrial DNA(mtDNA)are maternally inherited and have the potential to cause severe disorders.Mitochondrial replacement therapies,including spindle,polar body,and pronuclear transfers,are promising strategies for preventing the hereditary transmission of mtDNA diseases.While pronuclear transfer has been used to generate mitochondrial replacement mouse models and human embryos,its application in non-human primates has not been previously reported.In this study,we successfully generated four healthy cynomolgus monkeys(Macaca fascicularis)via female pronuclear transfer.These individuals all survived for more than two years and exhibited minimal mtDNA carryover(3.8%–6.7%),as well as relatively stable mtDNA heteroplasmy dynamics during development.The successful establishment of this nonhuman primate model highlights the considerable potential of pronuclear transfer in reducing the risk of inherited mtDNA diseases and provides a valuable preclinical research model for advancing mitochondrial replacement therapies in humans.

Sustainable Intensification and Large-scale Operation of Cultivated Land Use at the Farmers’ Scale:A Case Study of Shandong Province,China

Sustainable intensification of cultivated land use(SICLU) and large-scale operations(LSO) are widely acknowledged strategies for enhancing agricultural performance.However,the existing literature has faced challenges in precisely defining SICLU and constructing comprehensive indicators,which has hindered the exploration of factors influencing LSO within the SICLU framework.To address this gap,we integrated self-efficacy theory into the design of an index framework for evaluating SICLU.We subsequently employed econometric models to analyze the significant factors that impact LSO.Our findings reveal that SICLU can be divided into four key dimensions:intensive management,efficient output,resource conservation,and ecological environment optimization.Furthermore,it is crucial to incorporate belief-based cognitive factors into the index system,as farmers’ understanding of fertilizer and pesticide application significantly influences their willingness to engage in LSO.Moreover,we identify grain market turnover as the most influential factor in promoting LSO,with single-factor contribution rates reaching 70.9% for cultivated land transfer willingness and 62.5% for the total planting areas.Interestingly,unlike irrigation and agricultural machinery inputs,increased labor inputs correspond to larger planting areas for farmers.This trend may be attributed to reduced labor availability because of rural labor migration,whereas the reduction in irrigation and agricultural input is contingent on innovations in production practices and the transfer of cultivated land management rights.Importantly,SICLU dynamically influences LSO,with each index related to SICLU having an optimal range that fosters LSO.These insights offer valuable guidance for policymakers,emphasizing farmers as their central focus,with the adjustment of input and output factors as a means to achieve LSO as the ultimate goal.In conclusion,we propose research avenues for further enriching the SICLU framework to ensure that it aligns with the specific characteristics of regional agricultural development.

Effects of projectile parameters on the momentum transfer and projectile melting during hypervelocity impact

The effects of projectile/target impedance matching and projectile shape on energy,momentum transfer and projectile melting during collisions are investigated by numerical simulation.By comparing the computation results with the experimental results,the correctness of the calculation and the statistical method of momentum transfer coefficient is verified.Different shapes of aluminum,copper and heavy tungsten alloy projectiles striking aluminum,basalt,and pumice target for impacts up to 10 km/s are simulated.The influence mechanism of the shape of the projectile and projectile/target density on the momentum transfer was obtained.With an increase in projectile density and length-diameter ratio,the energy transfer time between the projectile and targets is prolonged.The projectile decelerates slowly,resulting in a larger cratering depth.The energy consumed by the projectile in the excavation stage increased,resulting in lower mass-velocity of ejecta and momentum transfer coefficient.The numerical simulation results demonstrated that for different projectile/target combinations,the higher the wave impedance of the projectile,the higher the initial phase transition velocity and the smaller the mass of phase transition.The results can provide theoretical guidance for kinetic impactor design and material selection.

Coherent optical frequency transfer via 972-km fiber link

We demonstrate coherent optical frequency dissemination over a distance of 972 km by cascading two spans where the phase noise is passively compensated for.Instead of employing a phase discriminator and a phase locking loop in the conventional active phase control scheme,the passive phase noise cancellation is realized by feeding double-trip beat-note frequency to the driver of the acoustic optical modulator at the local site.This passive scheme exhibits fine robustness and reliability,making it suitable for long-distance and noisy fiber links.An optical regeneration station is used in the link for signal amplification and cascaded transmission.The phase noise cancellation and transfer instability of the 972-km link is investigated,and transfer instability of 1.1×10^(-19)at 10^(4)s is achieved.This work provides a promising method for realizing optical frequency distribution over thousands of kilometers by using fiber links.

Understanding the Relationship Between Shrinking Cities and Land Prices:Spatial Pattern,Effectiveness,and Policy Implications

Urban shrinkage has emerged as a widespread phenomenon globally and has a significant impact on land,particularly in terms of land use and price.This study focuses on 2851 county-level cities in China in 2005–2018(excluding Hong Kong,Macao,Taiwan,and‘no data’areas in Qinhai-Tibet Plateau)as the fundamental units of analysis.By employing nighttime light(NTL)data to identify shrinking cities,the propensity score matching(PSM)model was used to quantitatively examine the impact of shrinking cities on land prices,and evaluate the magnitude of this influence.The findings demonstrate the following:1)there were 613 shrinking cities in China,with moderate shrinkage being the most prevalent and severe shrinkage being the least.2)Regional disparities are evident in the spatial distribution of shrinking cities,especially in areas with diverse terrain.3)The spatial pattern of land price exhibits a significant correlated to the economic and administrative levels.4)Shrinking cities significantly negatively impact on the overall land price(ATT=–0.1241,P<0.05).However,the extent of the effect varies significantly among different spatial regions.This study contributes novel insights into the investigation of land prices and shrinking cities,ultimately serving as a foundation for government efforts to promote the sustainable development of urban areas.

Thermo-hydro-mechanical (THM) coupled simulation of the land subsidence due to aquifer thermal energy storage (ATES) system in soft soils

Aquifer thermal energy storage(ATES)system has received attention for heating or cooling buildings.However,it is well known that land subsidence becomes a major environmental concern for ATES projects.Yet,the effect of temperature on land subsidence has received practically no attention in the past.This paper presents a thermo-hydro-mechanical(THM)coupled numerical study on an ATES system in Shanghai,China.Four water wells were installed for seasonal heating and cooling of an agriculture greenhouse.The target aquifer at a depth of 74e104.5 m consisted of alternating layers of sand and silty sand and was covered with clay.Groundwater level,temperature,and land subsidence data from 2015 to 2017 were collected using field monitoring instruments.Constrained by data,we constructed a field scale three-dimensional(3D)model using TOUGH(Transport of Unsaturated Groundwater and Heat)and FLAC3D(Fast Lagrangian Analysis of Continua)equipped with a thermo-elastoplastic constitutive model.The effectiveness of the numerical model was validated by field data.The model was used to reproduce groundwater flow,heat transfer,and mechanical responses in porous media over three years and capture the thermo-and pressure-induced land subsidence.The results show that the maximum thermoinduced land subsidence accounts for about 60%of the total subsidence.The thermo-induced subsidence is slightly greater in winter than that in summer,and more pronounced near the cold well area than the hot well area.This study provides some valuable guidelines for controlling land subsidence caused by ATES systems installed in soft soils.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

The regulation of ferrocene-based catalysts on heat transfer in highpressure combustion of ammonium perchlorate/hydroxyl-terminated polybutadiene/aluminum composite propellants

The regulation of the burning rate pressure exponent for the ammonium perchlorate/hydroxylterminated polybutadiene/aluminum(AP/HTPB/Al)composite propellants under high pressures is a crucial step for its application in high-pressure solid rocket motors.In this work,the combustion characteristics of AP/HTPB/Al composite propellants containing ferrocene-based catalysts were investigated,including the burning rate,thermal behavior,the local heat transfer,and temperature profile in the range of 7-28 MPa.The results showed that the exponent breaks were still observed in the propellants after the addition of positive catalysts(Ce-Fc-MOF),the burning rate inhibitor((Ferrocenylmethyl)trimethylammonium bromide,Fc Br)and the mixture of Fc Br/catocene(GFP).However,the characteristic pressure has increased,and the exponent decreased from 1.14 to 0.66,0.55,and 0.48 when the addition of Ce-FcMOF,Fc Br and Fc Br/GFP in the propellants.In addition,the temperature in the first decomposition stage was increased by 7.50℃ and 11.40℃ for the AP/Fc Br mixture and the AP/Fc Br/GFP mixture,respectively,compared to the pure AP.On the other hand,the temperature in the second decomposition stage decreased by 48.30℃ and 81.70℃ for AP/Fc Br and AP/Fc Br/GFP mixtures,respectively.It was also found that Fc Br might generate ammonia to cover the AP surface.In this case,a reaction between the methyl in Fc Br and perchloric acid caused more ammonia to appear at the AP surface,resulting in the suppression of ammonia desorption.In addition,the coarse AP particles on the quenched surface were of a concave shape relative to the binder matrix under low and high pressures when the catalysts were added.In the process,the decline at the AP/HTPB interface was only exhibited in the propellant with the addition of Ce-Fc-MOF.The ratio of the gas-phase temperature gradient of the propellants containing catalysts was reduced significantly below and above the characteristic pressure,rather than 3.6 times of the difference in the blank propellant.Overall,the obtained results demonstrated that the pressure exponent could be effectively regulated and controlled by adjusting the propellant local heat and mass transfer under high and low pressures.

Reliability analysis of slope stability by neural network,principal component analysis,and transfer learning techniques

The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering.Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-demanding.To assess the slope stability problems with a more desirable computational effort,many machine learning(ML)algorithms have been proposed.However,most ML-based techniques require that the training data must be in the same feature space and have the same distribution,and the model may need to be rebuilt when the spatial distribution changes.This paper presents a new ML-based algorithm,which combines the principal component analysis(PCA)-based neural network(NN)and transfer learning(TL)techniques(i.e.PCAeNNeTL)to conduct the stability analysis of slopes with different spatial distributions.The Monte Carlo coupled with finite element simulation is first conducted for data acquisition considering the spatial variability of cohesive strength or friction angle of soils from eight slopes with the same geometry.The PCA method is incorporated into the neural network algorithm(i.e.PCA-NN)to increase the computational efficiency by reducing the input variables.It is found that the PCA-NN algorithm performs well in improving the prediction of slope stability for a given slope in terms of the computational accuracy and computational effort when compared with the other two algorithms(i.e.NN and decision trees,DT).Furthermore,the PCAeNNeTL algorithm shows great potential in assessing the stability of slope even with fewer training data.

Vertical distribution characteristics of soil saturated hydraulic conductivity under different land use patterns in the Mu Us sandy land

Background,aim,and scope Soil saturated hydraulic conductivity(K_(s))is a key parameter in the hydrological cycle of soil;however,we have very limited understanding of K_(s) characteristics and the factors that inf luence this key parameter in the Mu Us sandy land(MUSL).Quantifying the impact of changes in land use in the Mu Us sandy land on K_(s) will provide a key foundation for understanding the regional water cycle,but will also provide a scientific basis for the governance of the MUSL.Materials and methods In this study,we determined K_(s) and the basic physical and chemical properties of soil(i.e.,organic matter,bulk density,and soil particle composition)within the first 100 cm layer of four different land use patterns(farmland,tree,shrub,and grassland)in the MUSL.The vertical variation of K_(s) and the factors that influence this key parameter were analyzed and a transfer function for estimating K_(s) was established based on a multiple stepwise regression model.Results The K_(s) of farmland,tree,and shrub increased gradually with soil depth while that of grassland remained unchanged.The K_(s) of the four patterns of land use were moderately variable;mean K_(s)values were ranked as follows:grassland(1.38 mm·min^(-1))<tree(1.76 mm·min^(-1))<farmland(1.82 mm·min^(-1))<shrub(3.30 mm·min^(-1)).The correlation between K_(s) and organic matter,bulk density,and soil particle composition,varied across different land use patterns.A multiple stepwise regression model showed that silt,coarse sand,bulk density,and organic matter,were key predictive factors for the K_(s) of farmland,tree,shrub,and grassland,in the MUSL.Discussion The vertical distribution trend for K_(s) in farmland is known to be predominantly influenced by cultivation,fertilization,and other factors.The general aim is to improve the water-holding capacity of shallow soil on farmland(0-30 cm in depth)to conserve water and nutrients;research has shown that the K_(s) of farmland increases with soil depth.The root growth of tree and shrub in sandy land exerts mechanical force on the soil due to biophysical processes involving rhizospheres,thus leading to a significant change in K_(s).We found that shallow high-density fine roots increased the volume of soil pores and eliminated large pores,thus resulting in a reduction in shallow K_(s).Therefore,the K_(s) of tree and shrub increased with soil depth.Analysis also showed that the K_(s) of grassland did not change significantly and exhibited the lowest mean value when compared to other land use patterns.This finding was predominantly due to the shallow root system of grasslands and because this land use pattern is not subject to human activities such as cultivation and fertilization;consequently,there was no significant change in K_(s) with depth;grassland also had the lowest mean K_(s).We also established a transfer function for K_(s) for different land use patterns in the MUSL.However,the predictive factors for K_(s) in different land use patterns are known to be affected by soil cultivation methods,vegetation restoration modes,the distribution of soil moisture,and other factors,thus resulting in key differences.Therefore,when using the transfer function to predict K_(s) in other areas,it will be necessary to perform parameter calibration and further verification.Conclusions In the MUSL,the K_(s) of farmland,tree,and shrub gradually increased with soil depth;however,the K_(s) of grassland showed no significant variation in terms of vertical distribution.The mean K_(s) values of different land use patterns were ranked as follows:shrub>farmland>tree>grassland;all land use patterns showed moderate levels of variability.The K_(s) for different land use patterns exhibited differing degrees of correlation with soil physical and chemical properties;of these,clay,silt,sand,bulk density,and organic matter,were identified as important variables for predicting K_(s) in farmland,tree,shrub,and grassland,respectively.Recommendations and perspectives In this study,we used a stepwise multiple regression model to establish a transfer function prediction model for K_(s) for different land use patterns;this model possessed high estimation accuracy.The ability to predict K_(s) in the MUSL is very important in terms of the conservation of water and nutrients.

Modelling Land Use/Land Cover Change of River Rwizi Catchment, South-Western Uganda Using GIS and Markov Chain Model

Analysis of catchment Land use/Land cover (LULC) change is a vital tool in ensuring sustainable catchment management. The study analyzed land use/land cover changes in the Rwizi catchment, south western Uganda from 1989-2019 and projected the trend by 2040. Landsat images, field observations, key informant interviews and focus group discussions were used to collect data. Changes in cropland, forestland, built up area, grazing land, wetland and open water bodies were analyzed in ArcGIS version 10.2.2 and ERDAS IMAGINE 14 software and a Markov chain model. All the LULC classes increased in area except grazing land. Forest land and builtup area between 2009-2019 increased by 370.03% and 229.53% respectively. Projections revealed an increase in forest land and builtup area by 2030 and only built up area by 2040. LULCC in the catchment results from population pressure, reduced soil fertility and high value of agricultural products.

Spatiotemporal landscape pattern changes and their effects on land surface temperature in greenbelt with semi-arid climate:A case study of the Erbil City,Iraq

Urban expansion of cities has caused changes in land use and land cover(LULC)in addition to transformations in the spatial characteristics of landscape structure.These alterations have generated heat islands and rise of land surface temperature(LST),which consequently have caused a variety of environmental issues and threated the sustainable development of urban areas.Greenbelts are employed as an urban planning containment policy to regulate urban expansion,safeguard natural open spaces,and serve adaptation and mitigation functions.And they are regarded as a powerful measure for enhancing urban environmental sustainability.Despite the fact that,the relation between landscape structure change and variation of LST has been examined thoroughly in many studies,but there is a limitation concerning this relation in semi-arid climate and in greenbelts as well,with the lacking of comprehensive research combing both aspects.Accordingly,this study investigated the spatiotemporal changes of landscape pattern of LULC and their relationship with variation of LST within an inner greenbelt in the semi-arid Erbil City of northern Iraq.The study utilized remote sensing data to retrieve LST,classified LULC,and calculated landscape metrics for analyzing spatial changes during the study period.The results indicated that both composition and configuration of LULC had an impact on the variation of LST in the study area.The Pearson's correlation showed the significant effect of Vegetation 1 type(VH),cultivated land(CU),and bare soil(BS)on LST,as increase of LST was related to the decrease of VH and the increases of CU and BS,while,neither Vegetation 2 type(VL)nor built-up(BU)had any effects.Additionally,the spatial distribution of LULC also exhibited significant effects on LST,as LST was strongly correlated with landscape indices for VH,CU,and BS.However,for BU,only aggregation index metric affected LST,while none of VL metrics had a relation.The study provides insights for landscape planners and policymakers to not only develop more green spaces in greenbelt but also optimize the spatial landscape patterns to reduce the influence of LST on the urban environment,and further promote sustainable development and enhance well-being in the cities with semi-arid climate.

Review of coastal land transformation:Factors,impacts,adaptation strategies,and future scopes

Coastal land transformation has been identified as a topic of research in many countries around the world.Several studies have been conducted to determine the causes and impacts of land transformation.However,much less is understood about coupling change detection,factors,impacts,and adaptation strategies for coastal land transformation at a global scale.This review aims to present a systematic review of global coastal land transformation and its leading research areas.From 1,741 documents of Scopus and Web of Science,60 studies have been selected using the PRISMA-2020 guideline.Results revealed that existing literature included four leading focus areas regarding coastal land transformation:change detection,driving factors,impacts,and adaptation measures.These focus areas were further analyzed,and it was found that more than 80%of studies used Landsat imagery to detect land transformation.Population growth and urbanization were among the major driving factors identified.This review further identified that about 37%of studies included impact analysis.These studies identified impacts on ecosystems,land surface temperature,migration,water quality,and occupational effects as significant impacts.However,only four studies included adaptation strategies.This review explored the scope of comprehensive research in coastal land transformation,addressing change detection,factor and impact analysis,and mitigation-adaptation strategies.The research also proposes a conceptual framework for comprehensive coastal land transformation analysis.The framework can provide potential decision-making guidance for future studies in coastal land transformation.

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.