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Population migration brings about many problems in villages. The Ji (几)-shape bend of the Yellow River is an important ecological sensitivity study area, and some progress exists in the study of villages, still has many problems. This article addresses the inadequacy of the existing research and mainly solves the problems in three areas. It primarily used the research method of Arc GIS10.8, the big data research methods such as imbalance index, Gini coefficient, geographic concentration index, Geodetector, and other methods were used, laying the groundwork for additional analysis of various factors. Hence, the subsequent study findings were attained. 1) The distribution pattern in the village system around the Ji-shape bend of the Yellow River seems to be a positive spatial correlation, with the characteristics of random distribution, uneven spatial distribution, and low concentration, with Hohhot-Baotou serving as a center. 2) Natural factors are the most important factors in the distribution of villages. 3) Among the natural factors, elevation (0.244619), the value of slope (0.319805), and the aspect of slope (0.074089) appear to be the strongest explanatory power. Among the social factors, the rate of urbanization (0.019082), highway density (0.019082), and percentage of tertiary industry (0.018422) show the strongest explanatory power on socioeconomic factors. The natural and social characteristics of villages are important for the siting of villages. This paper put forward some suggestions for the protection and development of villages in ecologically sensitive areas.

Accurate mapping of above-ground biomass (AGB) is essential for carbon stock quantification and climate change impact assessment, particularly in mountainous areas. This study applies a random forest (RF) regression model to predict the spatial distribution of AGB in Usho (site A) and Utror (site B) forests located in the northern mountainous region of Pakistan. The predicted maps elucidate AGB variations across these sites, with non-forest areas excluded based on an normalized difference vegetation index (NDVI) threshold value of

Leaf chlorophyll content (LCC) is crucial in plant physiology and ecological research. Although several LCC products have recently been generated at a regional or global scale, understanding their accuracy is still a concern in the scientific community. We intercompared and analyzed five existing LCC products (MuSyQ LCC, MODIS LCC, MERIS LCC, GLCC, and GLOBMAP MERIS LCC) over China in terms of spatial continuity and spatiotemporal consistency over seven plant functional types. The products of 2011 and 2019 over China were used in this study. Research findings indicate (1) the 30 m-resolution MuSyQ LCC has the highest accuracy compared to field LCC of cropland and grassland types, with an RMSE of 19.5 μg/cm2, while MODIS LCC product demonstrates a more robust fit to the measured LCC, with an R2 of 0.341. (2) Interpolation of products with lower spatial resolution e.g. MODIS LCC, MERIS LCC, and GLOBMAP MERIS LCC, generally improves spatial continuity. The non-interpolated 30 m MuSyQ LCC exhibits good regional continuity due to its high spatial resolution. The lowest spatial continuity is found over shrubs for all products. (3) MODIS LCC and MuSyQ LCC of 2019 demonstrate high overall spatial consistency and exhibit the highest correlation over cropland sites. MODIS LCC and GLOBMAP MERIS LCC of 2011 demonstrate high temporal consistency over deciduous forests, evergreen forests, grasslands, and shrubs sites. The most robust overall temporal consistency is exhibited among all products in the deciduous needleleaved forest, followed by evergreen needleleaved forest and grassland. The findings of this research are essential for improving leaf chlorophyll content inversion algorithms and for understanding and better use of LCC products in land surface models.

Microgrids are the future version of advanced distribution networks due to the fast growth of renewable energy resources near consumers’ side. The microgrids are operated in on-grid mode (OGM) with the utility grid, and isolation mode (IM) without the utility grid. This dual operational mode causes protection and control challenges in the microgrids. This research paper suggests an advanced hardware-supported fault detection, phase identification & localization method for AC microgrids. The scheme deploys a Discrete Kalman Filter (DKF) for state estimation of voltage and current signals. Then, a Mathematical Morphology (MM) is engaged for generating a novel fault detection/classification index named segregated energy signature (SES) from estimated voltage and current signals. The system is considered to be faulty if the SES is higher than a predefined threshold setting, while phase identification is achieved by default because of the per-phase implementation of DKF&MM. Moreover, the directional features of the cumulative energy signature (CES) are also computed from MM-based non-fundamental current and voltage to localize the faulty section. The established scheme is tested on the CIGRE microgrid benchmark test bed on Matlab-Simulink software. In addition, the suggested method is also examined on the dSPACE MicroLab testing hardware setup in the Smart grid lab at USPCAS-E, NUST, Pakistan. The result illustrates that the proposed scheme successfully detects, classifies, and localizes the low impedance fault (LIF) as well as high impedance fault (HIF) in both operational modes and topological structures with 96.6% accuracy.

In the modern world, the growing presence of renewable energy power near the consumer end makes DC distribution systems more attractive than traditional AC ones. However, designing fault diagnosis schemes for the DC distribution system is a complicated problem due to noisy measurements and rapidly rising DC-fault currents. This article proposes an Ultra High-speed Fault Diagnosis scheme using a Discrete Median Filter and Mathematical Morphology Algorithm. In the first stage, the acquired current signal from a corresponding faulty bus is preprocessed using a Discrete Median Filter for state estimation and noise reduction. In the second stage, the proposed Scheme computes the DC Residual by deploying the Mathematical Morphology Algorithm on the DMF estimated current signal. The DC Residual represents the mathematical computed difference between the filtered output of the Mathematical Morphology Algorithm and the Discrete Median Filter-estimated current signal. Then, the proposed method cross-matches variations in the computed DC Residual with pre-defined threshold settings to detect faults successfully and promptly. In the third stage, the Mathematical Morphology Algorithm-based Energy is computed for fault classification and Section identification. The polarity of Mathematical Morphology Algorithm-based Energy is used to categorize and locate all types of DC faults in the proposed scheme. The suggested method is tested on a DC distribution test bed via MATLAB/Simulink 2022b. The results demonstrate that the suggested scheme successfully identifies all types of DC faults in less than 2.5 msec, with 99% accuracy.

Renewable energy penetrated distribution systems (REPDS) are the upcoming form of conventional distribution systems due to environmental benefits and power quality concerns. However, islanding detection is a challenging problem associated with these REPDSs. In this paper, the Particle filter (PF) is utilized to detect the islanding events in modern REPDS by utilizing a voltage signal at the point of common coupling (PCC). Initially, the PF is implemented on voltage signature at PCC for state estimation of fundamental and non-fundamental components. Then, the Particle filter estimated residuals (PFER) and non-fundamental $3^{\mathrm {rd}}$ harmonic component (NFTHC) indices are computed. The estimated PFER & NFTHC indices are matched with the pre-specified threshold setting to recognize the islanding conditions. Then, the tripping decision is taken by OR-operation of both the estimated PFER & NFTHC. The suggested method is competent in detecting islanding events in both balanced as well as unbalanced load/generation events. Moreover, the suggested method is also capable of discriminating islanding conditions from non-islanding events. Extensive simulation on MATLAB/Simulink-based IEEE 13-bus test bed and UL-1741 test bed are accomplished to authenticate the usefulness of the suggested scheme. The results illustrate that the suggested method performs well with 99% accuracy, low computational latency, and very low non-detection zone (NDZ). Furthermore, the time of action of the presented scheme is less than 2 milliseconds (m sec) deprived of any false operation.

ABSTRACTMapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security, notably from climate change and, for that purpose, remote sensing is routinely used. However, identifying specific crop types, cropland, and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures. This study applied a methodology to identify cropland and specific crop types, including tobacco, wheat, barley, and gram, as well as the following cropping patterns: wheat-tobacco, wheat-gram, wheat-barley, and wheat-maize, which are common in Gujranwala District, Pakistan, the study region. The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning (ML) methods, namely a Decision Tree Classifier (DTC) and a Random Forest (RF) algorithm. The best time-periods for differentiating cropland from other land cover types were identified, and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms. The methodology was subsequently evaluated using Landsat images, crop statistical data for 2020 and 2021, and field data on cropping patterns. The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images, together with ML techniques, for mapping not only the distribution of cropland, but also crop types and cropping patterns when validated at the county level. These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan, adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.

Abstract One of the challenging issues in the realization of medium‐voltage DC (MVDC) distribution system is high‐speed protection scheme design against quickly rising short circuit fault‐DC current. This paper proposes a high‐speed protection scheme based on the processing of energy signals through mathematical morphology (MM). The voltage and current signals measured at a distribution line section (DLS) are used to obtain the energy signal. The proposed scheme develops a modified MM‐based filter to extract the transient information in the energy signal rapidly and distinctly in the form of positive/negative polarity. The polarity detection of any disturbance in the analysed energy signal indicates the occurrence of a sudden disturbance in the system. The sudden disturbance occurrence confirmation triggers the faulty DLS identification and classification algorithms of the proposed scheme. The faulty DLS identification and classification algorithms are based on the detected polarities of the energy signal. The protection scheme requires low bandwidth communication. The proposed protection scheme is evaluated by using a ±2.5 kV radial distribution network containing 4 feeders under different fault conditions in MATLAB/SIMULINK software. Simulation results verify that the proposed scheme can isolate the faulty portion within a few milliseconds after fault inception under a variety of fault cases.

Chlorophyll is a vital indicator of vegetation growth; exploring its relationship with external influencing factors is essential for studies such as chlorophyll remote sensing retrieval and vegetation growth monitoring. However, there has been limited in-depth exploration of the spatial distribution of leaf chlorophyll content (LCC) and its influencing factors across large-scale areas with varying climates and terrains. To investigate the primary influencing factors and degrees of various environmental factors on LCC, this study employed the Geodetector Model (GDM) and the LCC satellite products in Sichuan Province in 2020 to investigate the impact of relationships between nine environmental factors (meteorology, topography, and vegetation types) and the ecosystem LCC at a regional scale. The results indicated the following: (1) Elevation (q-value = 49.31%) is the primary factor determining photosynthesis in Sichuan Province, followed by temperature (46.10%) and vegetation types (40.73%). The impact of topographical factors on LCC distribution is higher than that of meteorological factors and vegetation types in terrain with complex topography. The elevation effectively distinguishes the variations in climate factors and vegetation types. (2) Combining the influencing factors pairwise increased the combined q-values. The combination of elevation with other factors yielded the highest combined q-value. (3) The q-values for all influencing factors are higher in winter and spring and lowest in summer. Different influencing factors exhibited more substantial constraints on vegetation photosynthesis during winter and spring, significantly reducing influence during summer. (4) The different primary factors drive or constrain vegetation photosynthesis in different climate zones due to their distinct temperature and humidity characteristics. The findings of this study provide a basis for future research on vegetation change analysis and dynamic monitoring of vegetation LCC in different terrains.

Abstract One of the most challenging problems in condensed matter physics is to predict crystal structure just from the chemical formula of the material. In this work, we present a robust machine learning (ML) predictor for the crystal point group of ternary materials (A $$_l$$ l B $$_m$$ m C $$_n$$ n ) - as first step to predict the structure - with very small set of ionic and positional fundamental features. From ML perspective, the problem is strenuous due to multi-labelity, multi-class, and data imbalance. The resulted prediction is very reliable as high balanced accuracies are obtained by different ML methods. Many similarity-based approaches resulted in a balanced accuracy above 95% indicating that the physics is well captured by the reduced set of features; namely, stoichiometry, ionic radii, ionization energies, and oxidation states for each of the three elements in the ternary compound. The accuracy is not limited by the approach; but rather by the limited data points and we should expect higher accuracy prediction by having more reliable data.

Recently, the concept of microgrids has emerged in the world due to the integration of distributed energy resources (DERs) at the distribution end. The design of a reliable protection strategy is one of the top-most challenges associated with microgrids. This is because of the transition of microgrids between grid-tied and autonomous modes of operation. This paper presents a state-of-the-art microgrid protection scheme based on the Kalman filter (KF). The proposed scheme uses the one-end current signal of a distribution line for the detection and classification of faults. Firstly, the KF is applied to each phase of a three-phase current signal individually to generate residuals and total harmonic distortion (THDs). Next, the variations in the residuals and THDs of each phase are compared with pre-specified threshold values to detect the faulty events in the microgrid. As each phase is processed through KF individually, therefore, the proposed scheme is inherently phase segregated. Afterward, the KF is applied to extract the third harmonic component from the three-phase current and voltage signals. Then, the KF-based reactive power (KFBRP) is obtained from the extracted third harmonic components. Finally, the directional properties of the three-phase KFBRP are used to locate the faulty section in the microgrids. Extensive simulations in MATLAB/ Simulink software are performed for the grid-tied as well as the autonomous modes of operation under radial and meshed topologies. The results show that the proposed scheme is highly robust in all testing scenarios without any false tripping and blinding issues.

The novel infectious disease (COVID-19) took only a few weeks from its official inception in December 2019 to become a global pandemic in early 2020. Countries across the world went to lockdown, and various strict measures were implemented to reduce the further spread of the infection. Although, the strict lockdown measures were aimed at stopping the spread of COVID-19, however, Its positive implications were also observed for the environmental conditions across the global regions. The present study attempted to explore the eco-restoration of coastal marine system in response to reduced deposition of atmospheric nitrogen (NO2) emission during the substantial shift in human activities across the global metropolitan cities. Remotely data of NO2 emission were taken from Ozone Monitoring Instrument and the coastal water quality along the marine system was estimated from MODIS-Aqua Level-3 using Semi-Analytic Sediment Model (SASM). The changes in tropospheric NO2 in 2020s were also compared with the long-term average changes over the baseline period 2015 − 2019. A significant reduction in anthropogenic mobility (85 − 90%) has been observed in almost all countries over different places, especially grocery, parks, workplaces, and transit stations. A massive reduction in tropospheric NO2 was detected in Wuhan (53%), Berlin (42%), London (41%), Karachi (40%), Paris (38%), Santiago (35%), and Chennai (34%) during the strict lockdown period of the early 2020 as compared to the last five years. However, after the partial lockdown was lifted, tropospheric NO2 values bounced back and slightly increased over Karachi (6%) and Bremen (12%). For water turbidity, the rate of reduction was found to be the highest along the different coastal regions of the Mediterranean Sea and Black Sea (51%), West Atlantic Ocean (32%), East Atlantic Ocean (29%), and Indian Ocean (21%) from Apr to Jun 2020. The monthly comparison of overland-runoff in 2020 compared to 2019 across the different costal watersheds indicates that the observed decline in turbidity might have been due to the reduced deposition of atmospheric nitrogen. The findings of this study suggest that the recent decline in tropospheric NO2 and water turbidity might be associated with reduced emissions from fossil fuels and road transports followed by COVID-19 forced restrictions in the twenty-first century. The inferences made here highlight the hope of improving the global environmental quality by reducing greenhouse gas emissions using innovative periodic confinement measures on heavy transport and industries while securing public health and socioeconomics.

The novel coronavirus (COVID-19) infectious respiratory disease becomes a global pandemic in few weeks from its start in December 2019 to early 2020. Various countries across the world including China went to lockdown and several caution were implemented to reduce the further spread of this infectious disease. Wuhan (China) was the first city to impose the lockdown for controlling the impact of COVID-19. The lockdown unexpectedly gives the scientific community a chance to investigate the influence of the human activity on air pollution in real world scenario. The present study attempted to investigate the impact of lockdown during the ongoing viral disease on the changes of fine particulate matters and some unhealthy gases i.e. PM2.5, PM10, SO2, CO, O3, AQI and NO2 over Hubei province of China, by using ground station data and TROPOMI satellite data. The air pollutants were compared as, (i) pre COVID-19 period (i.e. October-December 2019), (ii) throughout the lockdown in Hubei province (i.e. January 2020-March 2020) and Post lockdown duration (i.e. April 2020-June 2020). Results clearly showed that air quality was not secured due to high emission of CO, SO2, NO2, O3, PM2.5, and PM10 on Pre COVID-19 times, but under the lockdown continuously decrease in NO2 from (54 mg/cm3 to 26 mg/cm3), SO2 (10.5 mg/cm3 to 7.77 mg/cm3) PM2.5 (49.22 mg/cm3 to 44.34 mg/cm3), PM10 concentrations (80.83 mg/cm3 to 57.04 mg/cm3) and AQI (72.95 mg/cm3 to 49.64 mg/cm3) has been observed. Because lockdown shuts all anthropogenic activities like industrial work, traffic vehicles and various socio-economic activities, which developed a healthy change on air quality. Emission of unhealthy gases and particulates were quite clear during the lockdown but again increase after finishing the lockdown period. However, we don’t support the lockdown as a measure for the betterment of air quality as this has severely posed negative impacts on the socio-economic processes and progress, but changes in human behavior of using industries and vehicles can help us to improve the air quality.

Land use and land cover transition is continue process due to various anthropogenic activities and altering the landscape pattern of the metropolitan area of Lahore over the last two decade. LULC has been emerging masses of environmental problems including land use issues for the inhabitants, city planners, and managers. This research has been focused on the LULC change from 1998 to 2018 and their impact to forecast the landscape pattern of 2023 and 2028 in metropolitan area of Lahore. Numerous satellite imageries including land sat 5 TM and landsat 8 OLI has been undergone by supervised classification for the preparation of LULC map, land change moderler MCL to calculate the transition in LULC and CA-Markov model for predicting upcoming transitions. Results have validated by transition matrix, Google Earth data and kappa statistics. Derived results has depicted the decreased in water bodies 2.70% to 0.60%, vegetation 24.90% to 22.60% and barren land 42.50% to 35.70% and increased in built up area 29.80 % to 41% between 1998 to 2018. Finding clearly represents the loss of ecological and barren landscape over the last two decade and therefore urban expansion will likely to continue the change in landscape. This study will provide a baseline reference to urban planners and policymakers to make informed decision for management of land resources, urban land planning and for maintain sustainable land development.

Global land cover dynamics alter energy, water, and greenhouse gas exchange between land and atmosphere, affecting local to global weather and climate change. Although reforestation can provide localized cooling, ongoing land use land cover (LULC) shifts are expected to exacerbate urban heat island impacts. In this study, we monitored spatiotemporal changes in green cover in response to land use transformation associated with the Khyber Pakhtunkhwa (KPK) provincial government’s Billion Tree Tsunami Project (BTTP) and the Ravi Urban Development Plan (RUDP) initiated by the provincial government of Punjab, both in Pakistan. The land change modeler (LCM) was used to assess the land cover changes and transformations between 2000 and 2020 across Punjab and KPK. Furthermore, a curve fit linear regression model (CFLRM) and sensitivity analysis were employed to analyze the impacts of land cover dynamics on land surface temperature (LST) and carbon emissions (CE). Results indicated a significant increase in green fraction of +5.35% under the BTTP, achieved by utilizing the bare land with an effective transition of 4375.87 km2. However, across the Punjab province, an alarming reduction in green fraction cover by −1.77% and increase in artificial surfaces by +1.26% was noted. A significant decrease in mean monthly LST by −4.3 °C was noted in response to the BTTP policy, while an increase of 5.3 °C was observed associated with the RUDP. A substantial increase in LST by 0.17 °C was observed associated with transformation of vegetation to artificial surfaces. An effective decrease in LST by −0.21 °C was observed over the opposite transition. Furthermore, sensitivity analysis suggested that LST fluctuations are affecting the % of CO2 emission. The current findings can assist policymakers in revisiting their policies to promote ecological conservation and sustainability in urban planning.

Spatial land surface heterogeneities are widespread at various scales and represent a great challenge to leaf area index (LAI) retrievals and product validations. In this paper, considering the mixed water and vegetation pixels prevalent at moderate and low resolutions, we propose a methodological framework for conducting global comparisons of heterogeneous land surfaces based on criterion setting and a global search of high-resolution data. We construct a global network, Heterogeneous Surface Network aiming Water and Vegetation Mixture (HESNet-WV), comprised of three vegetation types: grassland, evergreen broadleaf forests (EBFs), and evergreen needle forests (ENFs). Validation is performed using the MCD15A3H Global 500-m/4-day and GLASS 500-m/8-day LAI products. As the water area fraction (WAF), LAI values and LAI inversion errors increase in the MODIS and GLASS products, the GLASS product errors (relative LAI error (RELAI): 94.43%, bias: 0.858) are lower than the MODIS product errors (RELAI: 124.05%, bias: 1.209). The result indicates that the proposed framework can be applied to evaluate the accuracy of LAI values in mixed water-vegetation pixels in different global LAI products.

The microgrids operate in tie-up (TU) mode with the main grid normally, and operate in isolation (IN) mode without the main grid during faults. In a dynamic operational regime, protecting the microgrids is highly challenging. This article proposes a new microgrid protection scheme based on a state observer (SO) aided by a recurrent neural network (RNN). Initially, the particle filter (PF) serves as a SO to estimate the measured current/voltage signals from the corresponding bus. Then, a natural log of the difference between the estimated and measured current signal is taken to estimate the per-phase particle filter deviation (PFD). If the PFD of any single phase exceeds the preset threshold limit, the proposed scheme successfully detects and classifies the faults. Finally, the RNN is implemented on the SO-estimated voltage and current signals to retrieve the non-fundamental harmonic features, which are then utilized to compute RNN-based state observation energy (SOE). The directional attributes of the RNN-based SOE are employed for the localization of faults in a microgrid. The scheme is tested using Matlab® Simulink 2022b on an International Electrotechnical Commission (IEC) microgrid test bed. The results indicate the efficacy of the proposed method in the TU and IN operation regimes on radial, loop, and meshed networks. Furthermore, the scheme can detect both high-impedance (HI) and low-impedance (LI) faults with 99.6% of accuracy.

Due to the scarcity of studies linking the variability of rainfall and population growth in the capital cities of Northeastern Brazil (NEB), the purpose of this study is to evaluate the variability and multiscale interaction (annual and seasonal), and in addition, to detect their trends and the impact of urban growth. For this, monthly rainfall data between 1960 and 2020 were used. In addition, the detection of rainfall trends on annual and seasonal scales was performed using the Mann–Kendall (MK) test and compared with the phases of El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). The relationship between population growth data and rainfall data for different decades was established. Results indicate that the variability of multiscale urban rainfall is directly associated with the ENSO and PDO phases, followed by the performance of rain-producing meteorological systems in the NEB. In addition, the anthropic influence is shown in the relational pattern between population growth and the variability of decennial rainfall in the capitals of the NEB. However, no capital showed a significant trend of increasing annual rainfall (as in the case of Aracaju, Maceió, and Salvador). The observed population increase in the last decades in the capitals of the NEB and the notable decreasing trend of rainfall could compromise the region’s water security. Moreover, if there is no strategic planning about water bodies, these changes in the rainfall pattern could be compromising.

Increasing land surface temperature (LST) is one of the major anthropogenic issues and is significantly threatening the urban areas of the world. Therefore, this study was designed to examine the spatial variations and patterns of LST during the different seasons in relation to influencing factors in Kolkata Municipality Corporation (KMC), a city of India. The spatial distribution of LST was analyzed regarding the different surface types and used 25 influencing factors from 6 categories of variables to explain the variability of LST during the different seasons. All-subset regression and hierarchical partitioning analyses were used to estimate the explanatory potential and independent effects of influencing factors. The results show that high and low LST corresponded to the artificial lands and bodies of water for all seasons. In the individual category regression model, surface properties gave the highest explanatory rate for all seasons. The explanatory rates and the combination of influencing factors with their independent effects on the LST were changed for the different seasons. The explanatory rates of integration of all influencing factors were 89.4%, 81.4%, and 88.7% in the summer, transition, and winter season, respectively. With the decreasing of LST (summer to transition, then to winter) more influencing factors were required to explain the LST. In the integrated regression model, surface properties were the most important factor in summer and winter, and landscape configuration was the most important factor in the transition season. LST is not the result of single categories of influencing factors. Along with the effects of surface properties, socio-economic parameters, landscape compositions and configurations, topographic parameters and pollutant parameters mostly explained the variability of LST in the transition (11.22%) and summer season (15.22%), respectively. These findings can help to take management strategies to reduce urban LST based on local planning.

Anthropogenic activities and natural climate changes are the central driving forces of global ecosystems and agriculture changes. Climate changes, such as rainfall and temperature changes, have had the greatest impact on different types of plant production around the world. In the present study, we investigated the spatiotemporal variation of major crops (cotton, rice, wheat, and sugarcane) in the District Vehari, Pakistan, from 1984 to 2020 using remote sensing (RS) technology. The crop identification was pre-processed in ArcGIS software based on Landsat images. After pre-processing, supervised classification was used, which explains the maximum likelihood classification (MLC) to identify the vegetation changes. Our results showed that in the study area cultivated areas under wheat and cotton decreased by almost 5.4% and 9.1% from 1984 to 2020, respectively. Vegetated areas have maximum values of NDVI (>0.4), and built-up areas showed fewer NDVI values (0 to 0.2) in the District Vehari. During the Rabi season, the temperature was increased from 19.93 °C to 21.17 °C. The average temperature was calculated at 34.28 °C to 35.54 °C during the Kharif season in the District Vehari. Our results showed that temperature negatively affects sugarcane, rice, and cotton crops during the Rabi season, and precipitation positively affects sugarcane, rice, and cotton crops during the Kharif season in the study area. Accurate and timely assessment of crop estimation and relation to climate change can give very useful information for decision-makers, governments, and planners in formulating policies regarding crop management and improving agriculture yields.

Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes.

Climate change is likely to have serious social, economic, and environmental impacts on farmers whose subsistence depends on nature. Land Use Land Cover (LULC) changes were examined as a significant tool for assessing changes at diverse temporal and spatial scales. Normalized Difference Vegetation Index (NDVI) has the potential ability to signify the vegetation structures of various eco-regions and provide valuable information as a remote sensing tool in studying vegetation phenology cycles. In this study, we used remote sensing and Geographical Information System (GIS) techniques with Maximum Likelihood Classification (MLC) to identify the LULC changes for 40 years in the Sahiwal District. Later, we conducted 120 questionnaires administered to local farmers which were used to correlate climate changes with NDVI. The LULC maps were prepared using MLC and training sites for the years 1981, 2001, and 2021. Regression analysis (R2) was performed to identify the relationship between temperature and vegetation cover (NDVI) in the study area. Results indicate that the build-up area was increased from 7203.76 ha (2.25%) to 31,081.3 ha (9.70%), while the vegetation area decreased by 14,427.1 ha (4.5%) from 1981 to 2021 in Sahiwal District. The mean NDVI values showed that overall NDVI values decreased from 0.24 to 0.20 from 1981 to 2021. Almost 78% of farmers stated that the climate has been changing during the last few years, 72% of farmers stated that climate change had affected agriculture, and 53% of farmers thought that rainfall intensity had also decreased. The R2 tendency showed that temperature and NDVI were negatively connected to each other. This study will integrate and apply the best and most suitable methods, tools, and approaches for equitable local adaptation and governance of agricultural systems in changing climate conditions. Therefore, this research outcome will also meaningfully help policymakers and urban planners for sustainable LULC management and strategies at the local level.

Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant from cloud obstruction in optical images and the malfunction of in situ SMC-measuring instruments. With this method, data typically excluded in conventional approaches can be adequately employed. On the other hand, apart from the basic input parameters commonly discussed in previous studies, a couple of new parameters were optimized to improve the feature description. The Sentinel-1 SAR and Landsat-8 images were adopted to retrieve SMC in the study area in eastern Austria. By the SSE method, the number of available samples increased from 264 to 635 for ANN training and testing, and the retrieval accuracy could be markedly improved. Furthermore, the optimized parameters also improve the inversion effect, and the elevation was the most influential input parameter.

Due to its unique natural conditions and agricultural tradition, northeast China (NEC) has formed a distinctive open biomass burning habit with local-specific biomass burning aerosol features. In this research, with the help of a newly optimized biomass burning aerosol identification method, which combines satellite aerosol and fire observational products with the HYSPLIT model forward trajectories, a systematic and quantitative analysis of aerosol emitted from open biomass burning in the NEC region are conducted to determine in detail its local-specific features, such as influence region, aging characteristics, and seasonal variation. During the 72-h aging process after biomass burning emission, aerosol particle size growth found with the Angstrom exponent declines from 1.6 to 1.54. Additionally, the volume fraction of black carbon decreases from 4.5% to 3.1%, leading to the Single Scattering Albedo (SSA) increasing from the fresh state of 0.84 to the aged state of 0.89. The cooling effect at TOA, due to the existence of aerosol, is enhanced by more than 70%, indicating its severe and dynamic influence on climate change. The average AOD in spring is 0.63, which is higher than autumn’s value of 0.52, indicating that biomass burning is more intensive in spring. Compared to autumn, aerosols emitted from spring biomass burning in the NEC region have lower sphere fraction, smaller particle size, higher volume fraction of black carbon, higher absorbability, and weaker cooling effect at TOA, which can be partly explained by the drier ambient environment and lower water content of the burned crop straw in spring.

Land use–land cover (LULC) alteration is primarily associated with land degradation, especially in recent decades, and has resulted in various harmful changes in the landscape. The normalized difference vegetation index (NDVI) has the prospective capacity to classify the vegetative characteristics of many ecological areas and has proven itself useful as a remote sensing (RS) tool in recording vegetative phenological aspects. Likewise, the normalized difference built-up index (NDBI) is used for quoting built-up areas. The current research objectives include identification of LULC, NDVI, and NDBI changes in Jhelum District, Punjab, Pakistan, during the last 30 years (1990–2020). This study targeted five major LULC classes: water channels, built-up area, barren land, forest, and cultivated land. Satellite imagery classification tools were used to identify LULC changes in Jhelum District, northern Punjab, Pakistan. The perception data about the environmental variations as conveyed by the 500 participants (mainly farmers) were also recorded and analyzed. The results depict that the majority of farmers (54%) believe in the appearance of more drastic changes such as less rainfall, drought, and decreased water availability for irrigation during 2020 compared to 30 years prior. Overall accuracy assessment of imagery classification was 83.2% and 88.8% for 1990, 88.1% and 85.7% for 2000, 86.5% and 86.7% for 2010, and 85.6% and 87.3% for 2020. The NDVI for Jhelum District was the highest in 1990 at +0.86 and the lowest in 2020 at +0.32; similarly, NDBI values were the highest in 2020 at +0.72 and the lowest in 1990 at −0.36. LULC change showed a clear association with temperature, NDBI, and NDVI in the study area. At the same time, variations in the land area of barren soil, vegetation, and built-up from 1990 to 2020 were quite prominent, possibly resulting in temperature increases, reduction in water for irrigation, and changing rainfall patterns. Farmers were found to be quite responsive to such climatic variations, diverting to framing possible mitigation approaches, but they need government assistance. The findings of this study, especially the causes and impacts of rapid LULC variations in the study area, need immediate attention from related government departments and policy makers.

Actual evapotranspiration (ET) with high spatiotemporal resolution is very important for the research on agricultural water resource management and the water cycle processes, and it is helpful to realize precision agriculture and smart agriculture, and provides critical references for agricultural layout planning. Due to the impact of the clouds, weather environment, and the orbital period of optical satellite, there are difficulties in providing daily remote sensing data that are not contaminated by clouds for estimating daily ET with high spatial-temporal resolution. By improving the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), this manuscript proposes the method to fuse high temporal and low spatial resolution Weather Research and Forecasting (WRF) model surface skin temperature (TSK) with the low temporal and high spatial resolution remote sensing surface temperature for obtaining high spatiotemporal resolution daily surface temperature to be used in the estimation of the high spatial resolution daily ET (ET_WRFHR). The distinction of this study from the previous literatures can be summarized as the novel application of the fusion of WRF-simulated TSK and remote sensing surface temperature, giving full play to the availability of model surface skin temperature data at any time and region, making up for the shortcomings of the remote sensing data, and combining the high spatial resolution of remote sensing data to obtain ET with high spatial (Landsat-like scale) and temporal (daily) resolution. The ET_WRFHR were cross-validated and quantitatively verified with MODIS ET products (MOD16) and observations (ET_Obs) from eddy covariance system. Results showed that ET_WRFHR not only better reflects the difference and dynamic evolution process of ET for different land types but also better identifies the details of various fine geographical objects. It also represented a high correlation with the ET_Obs by the R2 amount reaching 0.9186. Besides, the RMSE and BIAS between ET_WRFHR and the ET_Obs are obtained as 0.77 mm/d and −0.08 mm/d respectively. High R2, as well as the small RMSE and BIAS amounts, indicate that ET_WRFHR has achieved a very good performance.

The authors wish to make the following corrections to this paper [...]

Land use land cover (LULC) of city regions is strongly affected by urbanization and affects the thermal environment of urban centers by influencing the surface temperature of core city areas and their surroundings. These issues are addressed in the current study, which focuses on two provincial capitals in Pakistan, i.e., Lahore and Peshawar. Using Landsat data, LULC is determined with the aim to (a) examine the spatio-temporal changes in LULC over a period of 20 years from 1998 to 2018 using a CA-Markov model, (b) predict the future scenarios of LULC changes for the years 2023 and 2028, and (c) study the evolution of different LULC categories and investigate its impacts on land surface temperature (LST). The results for Peshawar city indicate the significant expansion in vegetation and built-up area replacing barren land. The vegetation cover and urban area of Peshawar have increased by 25.6%, and 16.3% respectively. In contrast, Lahore city urban land has expanded by 11.2% while vegetation cover decreased by (22.6%). These transitions between LULC classes also affect the LST in the study areas. Transformation of vegetation cover and water surface into built-up areas or barren land results in the increase in the LST. In contrast, the transformation of urban areas and barren land into vegetation cover or water results in the decrease in LST. The different LULC evolutions in Lahore and Peshawar clearly indicate their effects on the thermal environment, with an increasing LST trend in Lahore and a decrease in Peshawar. This study provides a baseline reference to urban planners and policymakers for informed decisions.

Land degradation reflected by vegetation is a commonly used practice to monitor desertification. To retrieve important information for ecosystem management accurate assessment of desertification is necessary. The major factors that drive vegetation dynamics in arid and semi-arid regions are climate and anthropogenic activities. Progression of desertification is expected to exacerbate under future climate change scenarios, through precipitation variability, increased drought frequency and persistence of dry conditions. This study examined spatiotemporal vegetation dynamics in arid regions of Sindh, Pakistan, using annual and growing season Normalized Difference Vegetation Index (NDVI) data from 2000 to 2017, and explored the climatic and anthropogenic effects on vegetation. Results showed an overall upward trend (annual 86.71% and growing season 82.7%) and partial downward trend (annual 13.28% and growing season 17.3%) in the study area. NDVI showed the highest significant increase in cropland region during annual, whereas during growing season the highest significant increase was observed in savannas. Overall high consistency in future vegetation trends in arid regions of Sindh province is observed. Stable and steady development region (annual 48.45% and growing 42.80%) dominates the future vegetation trends. Based on the Hurst exponent and vegetation dynamics of the past, improvement in vegetation cover is predicted for a large area (annual 44.49% and growing 30.77%), and a small area is predicted to have decline in vegetation activity (annual 0.09% and growing 3.04%). Results revealed that vegetation growth in the study area is a combined result of climatic and anthropogenic factors; however, in the future multi-controls are expected to have a slightly larger impact on annual positive development than climate whereas positive development in growing season is more likely to continue in future under the control of climate variability.