TM Data Research Articles

Quantitative Analysis of Land Use and Land Cover Dynamics using Geoinformatics Techniques: A Case Study on Kolkata Metropolitan Development Authority (KMDA) in West Bengal, India

One of the most valuable approaches in spatial analysis for a better understanding of the hydrological response of a region or a watershed is certainly the analysis of the well-known land use land cover (LULC) dynamicity. The present case study delves deeper into the analysis of LULC dynamicity by using digital Landsat TM and Landsat OLI data to classify the Kolkata Metropolitan Development Authority (KMDA) into seven classes with over 90% classification accuracy for decadal level assessments of 30 years (for the years 1989, 1999, 2009, and 2019). The change index, the Dematel method for analyzing the cause-effect relationship among the LULC classes, the Jaccard Similarity Index for measuring the nature of similarity among the LULC classes, and the Adherence Index for measuring the consistency of the LULC classes after the transition was used in this study to analyze the LULC transformation. In more detail, the present study considers how urban land use is altering at the expense of other land uses. Besides the shifting pattern of mean centers of the LULC classes through time, also gives a very significant insight into the LULC dynamics over 30 years of span. The current study of LULC dynamicity and transformation patterns over the 30 years of the KMDA area is expected to assist land and urban planners, engineers, and administrators in sustainable decisions and policies to ensure inclusive urbanization that accommodates population growth while minimizing the impact on potential natural resources within the whole study area.

Desertification Susceptibility Assessment Using Expert-Based Approach in Al-Khidhir District, Southern Iraq

Desertification is a serious threat to environment, agriculture, and public health all over the world. Iraq is concerned about desertification, particularly in the southern regions. As a result, this project will use geospatial methodologies to measure desertification susceptibility in Al-Khidhir, Al-Muthanna. The assessment was based on Landsat 5 TM data from 1998, Landsat 7 ETM+ data from 2008, and Landsat 8 OLI data from 2018. A Multicriteria Decision-Making (MCDM) technique based on the Analytic Hierarchy Process (AHP) was developed to assess the spatial distribution of desertification in the research area and the likelihood that it will occur soon. An examination of the geographical distribution of desertification in Al-Khidhir in 1998 indicated that desertification was most prevalent in the southern region, accounting for 25.9% of the total. Desertification spread to neighboring areas and increased (50.8%) in the research region’s north in 2008. The findings suggested that using satellite photos, such as Landsat, can be extremely effective for assessing desertification.

A Novel Global Grid Model for Atmospheric Weighted Mean Temperature in Real-Time GNSS Precipitable Water Vapor Sounding

The atmospheric weighted mean temperature (Tm) is an important parameter in calculating the precipitable water vapor from Global Navigation Satellite System (GNSS) signals. As both GNSS positioning and GNSS precipitable water vapor detection require high-precision spatial and temporal resolutions for calculating Tm, high-precision modeling of Tm has gained widespread attention in recent years. The previous models for calculating Tm have the limitation of too many model parameters or single grid data. Therefore, this study presents a global high-precision Tm model (GGTm-H model) developed from the latest MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version-2) atmospheric reanalysis data provided by United States National Aeronautics and Space Administration (NASA). The accuracy of GGTm-H model was verified by combining the MERRA-2 surface Tm data and 319 radiosonde data. The results highlighted that: (1) When the MERRA-2 Tm data was used as a reference value, the mean annual RMSE of the GGTm-H model was observed to be 2.72K. When compared with the Bevis model, GPT2w-5 and GPT2w-1 models, GGTm-H model showed an improvement of 1.5K, 0.33K, and 0.21K, respectively. (2) When the radiosonde data was used as a reference value, the mean bias and RMSE of the GGTm-H model were <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-0.41$</tex-math></inline-formula> K and 3.82K, respectively. Compared with the other models, the GGTm-H model had the lowest mean annual bias and RMSE. The developed model does not consider any meteorological parameters while calculating Tm. Therefore, it has important applications in the real-time and high-precision monitoring of precipitable water vapor from GNSS signal.

DeepTM: A deep learning algorithm for prediction of melting temperature of thermophilic proteins directly from sequences

Thermally stable proteins find extensive applications in industrial production, pharmaceutical development, and serve as a highly evolved starting point in protein engineering. The thermal stability of proteins is commonly characterized by their melting temperature (Tm). However, due to the limited availability of experimentally determined Tm data and the insufficient accuracy of existing computational methods in predicting Tm, there is an urgent need for a computational approach to accurately forecast the Tm values of thermophilic proteins. Here, we present a deep learning-based model, called DeepTM, which exclusively utilizes protein sequences as input and accurately predicts the Tm values of target thermophilic proteins on a dataset consisting of 7790 thermophilic protein entries. On a test set of 1550 samples, DeepTM demonstrates excellent performance with a coefficient of determination (R2) of 0.75, Pearson correlation coefficient (P) of 0.87, and root mean square error (RMSE) of 6.24 ℃. We further analyzed the sequence features that determine the thermal stability of thermophilic proteins and found that dipeptide frequency, optimal growth temperature (OGT) of the host organisms, and the evolutionary information of the protein significantly affect its melting temperature. We compared the performance of DeepTM with recently reported methods, ProTstab2 and DeepSTABp, in predicting the Tm values on two blind test datasets. One dataset comprised 22 PET plastic-degrading enzymes, while the other included 29 thermally stable proteins of broader classification. In the PET plastic-degrading enzyme dataset, DeepTM achieved RMSE of 8.25 ℃. Compared to ProTstab2 (20.05 ℃) and DeepSTABp (20.97 ℃), DeepTM demonstrated a reduction in RMSE of 58.85% and 60.66%, respectively. In the dataset of thermally stable proteins, DeepTM (RMSE=7.66 ℃) demonstrated a 51.73% reduction in RMSE compared to ProTstab2 (RMSE=15.87 ℃). DeepTM, with the sole requirement of protein sequence information, accurately predicts the melting temperature and achieves a fully end-to-end prediction process, thus providing enhanced convenience and expediency for further protein engineering.

Multi-scenario simulation of land use/cover change and carbon storage assessment in Hainan coastal zone from perspective of free trade port construction

Accurate prediction of future Land Use/Cover Change (LULCC) and its impact on ecosystem carbon storage is of great scientific significance for regional land use decision making and carbon management. However, there are few previous studies that combine spatial and temporal scales to explore the impact of future land use changes on ecosystem carbon storage in coastal zones at the provincial scale. Taking the coastal zone of Hainan Island as an example, Landsat TM (OLI) data from 1990, 2000, 2010 and 2020 were used as the main data sources to analyze the spatial and temporal evolution of land use from 1990 to 2020. The GeoSOS-FLUS model was used to predict land use patterns under baseline, development priority and ecological priority scenarios in 2030. Based on land use data, ArcGIS and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models were used to evaluate the carbon storage of ecosystems from 1990 to 2030, to explore the spatial and temporal evolution of LULCC and response of the LULCC to carbon storage, and to reveal the process and mechanism of the impact of the complex land use change process on the carbon sink service function of regional ecosystems in tropical and subtropical coastal zones. The results showed that: 1) From 1990 to 2020, the area of cropland, forest land, grassland and unused land decreased, while the area of water and built-up land increased. Unused land and cropland area continues to decrease, built-up land continues to increase. 2) LULCC has led to a continuous decrease in regional carbon storage over 30 years, reaching 1.5 × 106 t, with an annual change rate of 5.0 × 104 t/a. The extensive expansion of built-up land and the degradation of forest land are significant reasons for the decrease in carbon density, and “unused land→grassland” is the most obvious reason in the carbon storage increase. “Grassland→forest land (artificial)" is the most significant mapping reason in the decrease of carbon storage. The spatial and temporal characteristics of regional carbon storage showed a significant positive correlation with the region, and the wetland and mangrove areas in the southwest and northeast directions were the high-high concentration areas of carbon storage. 3) The built-up land in 2020–2030 shows different degrees of expansion in the baseline, development priority and ecological priority scenarios, with an increase of 1.94%, 2.13% and 0.70% respectively, and the development priority scenario has the largest increase. The cropland showed a shrinking trend in 3 simulated scenarios, and the area decreased by 0.87%, 0.97% and 0.87%, respectively. 4) The regional carbon storage in 2020–2030 decreases significantly under the baseline and development priority scenarios, at 1.29 × 105 t and 2.45 × 105 t respectively, while it increases significantly under the ecological priority scenario, at 1.34 × 105 t. The ecological priority scenario can effectively enhance the carbon sink capacity and control the reduction of cropland area. Optimizing the land use structure, controlling the growth rate of built-up land and increasing the area of forest land can have a positive effect on the development and use of coastal zone space and the carbon cycle.

Remote Sensing Monitoring and Evaluation of Vegetation Changes in Hulun Buir Grassland, Inner Mongolia Autonomous Region, China

Constantly increasing vegetation changes pose serious challenges to the sustainable use of global ecosystems. Thus, facing the increasingly serious climate and ecological environment problems and improving vegetation coverage is crucial to the sustainable development of the region. Along these lines, in this work, a monitoring model of vegetation cover change was proposed and developed by using Landsat TM (1989, 1999, and 2011) and Landsat OLI-TIRS (2021) data. More specifically, it was used to assess vegetation change. Based on this model, the vegetation change in the core area of Hulun Buir Grassland was systematically analyzed., From the acquired results, the existence of spatial differences in the vegetation coverage changes in the study area were demonstrated. The total area of vegetation coverage changes was 758.95 km2, and the area from low vegetation coverage to high vegetation coverage was 456.41 km2, accounting for 60.14% of the total change area. The area from high vegetation coverage to low vegetation coverage was 302.57 km2, accounting for 39.86% of the total change area, whereas the area of the area without vegetation coverage was 1963.92 km2, accounting for 72.13% of the study area, and the overall vegetation coverage is improving. Vegetation cover change monitoring models can also be used to reveal and describe large-scale vegetation landscape changes and obtain clear vegetation change results through easy-to-obtain data; our work suggests that in the process of pursuing regional economic development and accelerating urbanization, industrialization, and agricultural modernization, human beings should assume more responsibilities and pursue the sustainable development of the natural environment. The results of this work are of great importance to further study the potential driving mechanism of the vegetation coverage changes and provide theoretical guidance for relevant managers to formulate vegetation restoration measures.

Spatiotemporal Distribution Characteristics of Fire Scars Further Prove the Correlation between Permafrost Swamp Wildfires and Methane Geological Emissions

Affected by global warming, methane gas released by permafrost degradation may increase the frequency of wildfires, and there are few studies on wildfires in permafrost regions and their correlation with climate and regional methane emissions. The northwestern section of the Xiaoxing’an Mountains in China was selected as the study area, and the spatial relationship between permafrost and spring wildfires was studied based on Landsat TM and Sentinel-2 data. Combined with monitoring data of air temperature, humidity, and methane concentration, the impact of methane emissions on spring wildfires was analyzed. The study shows that the spatial distribution of fire scars in spring is highly consistent with permafrost, and the change trend of fire scars is in line with the law of permafrost degradation. Wildfires occur intensively during the snow melting period in spring, and the temporal variation pattern is basically consistent with the methane concentration. The number of fire points was positively correlated with air temperature and methane concentration in March and April, and spring wildfires in permafrost regions are the result of a combination of rising seasonal temperatures, surface snow melting, and concentrated methane emissions. Larger areas of discontinuous permafrost are more prone to recurring wildfires.

GGTm-Ts: A global grid model of weighted mean temperature (Tm) based on surface temperature (Ts) with two modes

With the development of Global Navigation Satellite System (GNSS), the detection of precipitable water vapor (PWV) using the GNSS atmospheric sounding technique becomes a research interest in GNSS meteorology. In the conversion of zenith tropospheric delay (ZTD) to PWV, the weighted mean temperature (Tm) plays a crucial role. Generally, the Tm estimated by the linear regression models based on surface temperature (Ts) cannot meet the requirement for global use, and the accuracy of Tm derived from the empirical models is limited. In this study, a new Tm model, named GGTm-Ts model, was developed using the global geodetic observing system (GGOS) atmosphere Tm data and European Centre for Medium-Range Weather Forecasts (ECMWF) data from 2011 to 2015. Resting upon a global 2.5°*2° grid of coefficients of Tm-Ts linear function, the new model can provide Tm at any site in two modes, one for the case with measured Ts provided, i.e., the accurate mode, the other for the case that Ts provided by a subroutine, i.e., the normal mode. The performance of GGTm-Ts model was assessed against the Bevis formula, GPT2w and GPT2wh model using different data sources in 2016-the GGOS atmosphere and radiosonde data. The results show that the GGTm-Ts model in accurate mode achieves best performance with an improvement of 46.9 %/15.3 %, 37.8 %/19.5 % and 34.4 %/14.2 % over other three models in the GGOS atmosphere/radiosonde comparison. For the normal mode, the GGTm-Ts model outperforms the GPT2w model and achieves equivalence results with the GPT2wh model. Moreover, the impact of Tm on GNSS-PWV was analyzed to validate the performance of the GGTm-Ts model.

Changes in River Channel Dynamics of the Satluj River, Punjab: Using a Geospatial Approach

Abstract River channel change is a dynamic process which involves erosion, accretion, lateral migration and changes in its width through the geomorphological process. Shifting of a river causes a direct impact on natural and manmade constructions which are located near the floodplain with consistent damage and devastation of the area. The objective of this study is to analyze the spatiotemporal variation along the Satluj river during the period 1990 to 2019 using Landsat TM, ETM+ and OLI satellite data. Spatiotemporal analysis of the river channel has been carried out to analyze the river channel change morphology, mid-line channel shifting and changes in surface water area of the Satluj river using twenty randomly distributed cross-sections (X1-X20). For surface water area change analysis, the satellite derived normalized difference water index (NDWI) has been used. The obtained results indicates that the river width increased by 0.033 km in overall cross-section (X1-X20) in the year 2019 as compared to the year 1990. Mid-line shifting of the river is mostly towards the left bank of the river. The maximum channel shifting of 1.364 km took place from Chak Bandala village in Jalandhar district to Madarpur village in Moga district between the years 1990 and 2019. However, the NDWI results indicate that the river surface area has been reduced by -90.33 km2 between the years 1990 and 2019. Additionally, the average shifting rate of the Satluj river during the period 1990 to 2019 is by 0.778 km.

Proximal sensor data fusion and auxiliary information for tropical soil property prediction: Soil texture

• Does combining proximal sensor always bring synergy for predicting soil texture? • Vis-NIR, NixPro TM , and pXRF data were tested for soil texture predictions. • PXRF provided key information to create accurate prediction models. • Smoothed Vis-NIR data preprocessing and dry NixPro TM condition positively influenced the results. • Soil texture and textural classes were accurately predicted by at least one approach. Soil texture is a primary variable influencing many soil chemical-physical-biological processes, providing important information for decision-making regarding sustainable soil management. The standard traditional methods for determining soil texture, however, are performed manually and are time-consuming, costly, and generate chemical wastes. As an alternative, portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared spectroscopy (Vis-NIR) have been increasingly used worldwide to predict soil attributes. Other sensors (e.g., NixPro TM color sensor) are also promising, but less evaluated to date. Thus, investigations towards proximal sensor data fusion for prediction of soil textural separates (clay, silt, and total, coarse, and fine sand contents) and soil textural classes (loam, loamy sand, etc) in tropical soils are rare. Therefore, this study aimed to evaluate proximal sensor data for predicting soil particle size fractions and soil textural classes (both Family particle size classes and USDA soil texture triangle) via random forest algorithm in tropical regions. A total of 464 soil samples were collected from A (n = 208) and B (n = 256) horizons in Brazil. Soil samples were submitted to laboratory analyses for soil texture and proximal sensor (pXRF, Vis-NIR, and NixPro TM ) scanning. Samples were randomly split into 70% for modeling and 30% for validation. The best approach varied according to the predicted attribute; however, pXRF data were key information for soil texture prediction accuracy. The best results delivered highly accurate predictions via the aforementioned proximal sensors for rapid assessment of soil texture (total sand R 2 = 0.84, RMSE = 7.60%; silt 0.83, 6.11%; clay 0.90, 5.64%; coarse sand 0.87, 6.30%; fine sand 0.82, 5.27%). Categorical prediction accuracy for soil textural classes (Family particle size classes, overall accuracy = 0.97, Kappa index = 0.95; USDA soil texture triangle, 0.83, 0.73) was enhanced when the predictions were made by soil order sub-datasets. Smoothed Vis-NIR preprocessing and dry NixPro TM color data positively influenced the results. The results reported here represent alternatives for reducing costs and time needed for evaluating soil texture, supporting agronomic and environmental strategies in Brazilian conditions. Further works should extend the results of this study to temperate regions to corroborate the conclusions presented herein regarding the fusion of these three proximal sensors.

An Improved QAA-Based Method for Monitoring Water Clarity of Honghu Lake Using Landsat TM, ETM+ and OLI Data

Secchi disk depth (ZSD) is used to quantify water clarity as an important water-quality parameter, and one of the most used mechanistic models for ZSD is the quasi-analytical algorithm (QAA), of which the latest version is QAA_v6. There are two models in QAA for clear and turbid waters (referred to as QAA_clear and QAA_turbid). QAA_v6 switches between the two models by setting a threshold value for the remote sensing reflectance (Rrs, sr−1) at the selected reference band of 656 nm. However, some researchers found that this reference band or the threshold value does not apply to many turbid inland lakes. In Honghu Lake, the Rrs (656) (Rrs at 656 nm) in the whole lake is less than 0.0015 sr−1; therefore, only QAA_turbid can be applied. Moreover, we found that QAA_clear resulted in overestimation while QAA_turbid resulted in significant underestimations. The waters of inland lakes usually continuously vary between clear and turbid water. We proposed a hypothesis that QAA_turbid and QAA_clear transition evenly, rather than being distinguished by one threshold value, and we developed a model that combined QAA_clear and QAA_turbid according to our assumption. This model simulated the process of continuous change in water clarity. The results showed that our model had a better performance with an RMSE that reduced from 0.5 to 0.28, an MAE that reduced from 0.43 to 0.21, and bias that reduced from −0.4 to −0.05 m compared with QAA_v6. We applied QAA_Honghu to Landsat TM, ETM+, and OLI data and obtained 205 ZSD maps with high spatial resolution in Honghu Lake. The results were consistent with the existing in situ measurements. From 1987–2020, the ZSD results of Honghu Lake showed an overall downward trend and a distinct seasonal pattern.

Evaluation of important phenotypic parameters of tea plantations using multi-source remote sensing data

Tea height, leaf area index, canopy water content, leaf chlorophyll, and nitrogen concentrations are important phenotypic parameters to reflect the status of tea growth and guide the management of tea plantation. UAV multi-source remote sensing is an emerging technology, which can obtain more abundant multi-source information and enhance dynamic monitoring ability of crops. To monitor the phenotypic parameters of tea canopy more efficiently, we first deploy UAVs equipped with multispectral, thermal infrared, RGB, LiDAR, and tilt photography sensors to acquire phenotypic remote sensing data of tea canopy, and then, we utilize four machine learning algorithms to model the single-source and multi-source data, respectively. The results show that, on the one hand, using multi-source data sets to evaluate H, LAI, W, and LCC can greatly improve the accuracy and robustness of the model. LiDAR + TC data sets are suggested for assessing H, and the SVM model delivers the best estimation (Rp2 = 0.82 and RMSEP = 0.078). LiDAR + TC + MS data sets are suggested for LAI assessment, and the SVM model delivers the best estimation (Rp2 = 0.90 and RMSEP = 0.40). RGB + TM data sets are recommended for evaluating W, and the SVM model delivers the best estimation (Rp2 = 0.62 and RMSEP = 1.80). The MS +RGB data set is suggested for studying LCC, and the RF model offers the best estimation (Rp2 = 0.87 and RMSEP = 1.80). On the other hand, using single-source data sets to evaluate LNC can greatly improve the accuracy and robustness of the model. MS data set is suggested for assessing LNC, and the RF model delivers the best estimation (Rp2 = 0.65 and RMSEP = 0.85). The work revealed an effective technique for obtaining high-throughput tea crown phenotypic information and the best model for the joint analysis of diverse phenotypes, and it has significant importance as a guiding principle for the future use of artificial intelligence in the management of tea plantations.

Assessment of Forest Cover Changes in Vavuniya District, Sri Lanka: Implications for the Establishment of Subnational Forest Reference Emission Level

Assessment of forest cover changes is required to establish the forest reference emission level (FREL) at any scale. Due to civil conflict, such assessments have not yet been undertaken in Sri Lanka, especially in the conflict zone. Here, we assessed the forest cover changes in Vavuniya District, Sri Lanka, from 2001 to 2020, using a combination of the Google Earth Engine (GEE) platform and the phenology-based threshold classification (PBTC) method. Landsat 5 TM data for 2001, 2006, and 2010, and Landsat 8 OLI data for 2016 and 2020 were used to classify forest cover by categories, and their related changes could be assessed by four categories, namely dry monsoon forest, open forest, other lands, and water bodies. With an overall average accuracy of 87% and an average kappa coefficient of 0.83, forest cover was estimated at 57.6% of the total land area in 2020. There was an increase of 0.46% per annum for the entire district between 2001 and 2010, but a drastic loss of 0.60% per year was observed between 2010 and 2020. Specifically, the dry monsoon forest lost 0.30%, but open forest gained 3.62% annually over the same period. Loss and gain of forest cover resulted in carbon emissions and removals of 165,306.6 MgCO2 and 24,064.5 MgCO2 annually, respectively, over the same period. Our findings could be used to set the baseline trend of deforestation, based on which, a subnational forest reference emission level can be established as an emission benchmark, against which comparisons of carbon emissions following the implementation of REDD+ activities can be made, and result-based payment can be claimed under the Paris Agreement.

Decline of suspended particulate matter concentrations in Lake Taihu from 1984 to 2020: observations from Landsat TM and OLI.

Suspended particulate matter (SPM) affects the optical properties of water, which can be used as a marker of water quality. The water quality of Lake Taihu has changed immensely since the 1980's. However, despite the link between water quality and SPM, long-term systematic studies on SPM have not been conducted in this lake. Here, we used Landsat-5 TM and Landsat-8 OLI data to model changes in the SPM concentration of Lake Taihu from 1984 to 2020. Various models were generated, calibrated, and finally validated using in situ SPM, remote sensing reflectance (Rrs) data, and synchronous satellite data. After comparing various commonly used models, it was found that the exponential model based on band combination [Rrs(red) + Rrs(NIR)/Rrs(green)] had the highest accuracy, with an average unbiased relative error greater than 35%. Subsequently, the SPM products of Lake Taihu during 1984-2020 were generated. Overall, the SPM concentration showed a downward trend over the study period, which might be primarily attributable to a decline in wind speed. These findings may assist in the conservation of Lake Taihu and its associated water resources.

Monthly, annual and decadal variability of extreme and mean air temperatures in Parnaguá – Pi, Brazil

Aiming at the delimitation of the buoyancy of the maximum temperature (TX), minimum temperature (Tn) and average temperature (Tm) of the air in the municipal area of ​​Parnaguá and the lagoon of that municipality. With the development and agricultural expansion, large areas are being deforested, not taking into account the contribution of meteorological factors among them, especially temperatures, can minimize the occurrence of damage from anomalous effects that may happen. TX, Tn and Tm data estimated by the estima software were used - T between the years 1960 - 2019, monthly, annual, maximum and minimum values ​​were obtained. Observing the variability of TX, Tn and Tm, the hottest quarter and its monthly and annual values ​​were delimited, as well as the observed absolute maximum and minimum values. The results show that in the hot period the possibilities of fires and fires increase and in green areas it is beneficial to the production of pastures and grain. Such delimitations of the hottest quarters and the information on the times of lowest maximum air temperature served as a warning to federal, state, municipal authorities and decision makers to carry out better planning. The average temperature is the representation of the variability and its oscillations of extreme temperatures, any variability in these elements the average temperatures adapt to their oscillations and result from the synoptic systems active in the rainy and dry season as well as from the impacts on the environment and its elevation in relation to sea level.

Melting point and microhardness of Cu2-II-IV-VI4 compounds

In the present study, we investigate the correlation between the melting point Tm and the microhardness H of some tetragonal and ortho-rhombic Cu2-II-IV-VI4 semiconducting compounds. After analyzing the experimental data of both Tm and H, we found that the melting point Tm of Cu2-II-IV-VI4 compounds correlates linearly with the microhardness H. With a coefficient of the correlation of around 0.98, the best fit was obtained using the linear model as follow: Tm = 175.54 H +792.27. The significance of the regression is given as the probability P of the null hypothesis (that there is no correlation) P &lt; 0.0001, while the average error on the estimation of the melting point Tm was found only at around 1.54%. Our expression related Tm and H was used to predict the microhardness H of Cu2ZnSnS4 (CZTS) material. Our value (2.68 GPa) of H of CZTS deviates from the theoretical one (2.7 GPa) by only around 0.74%.

Study on the Aridity in the North-Western (NW) Part of Bangladesh through Combined Application of Remote Sensing, GIS and Geophysical Data

In the present paper, hydro-environmental behaviour of two adjacent but characteristically different land areas of (i) semi-arid Barind Tract and (ii) Ganges flood plain situated in the north-western part of Bangladesh have been studied and possible mechanisms of variability in the two study sites have been investigated. Time series remote sensing data supplemented with climatic, geophysical and groundwater data and other ancillary information have been analysed for the purpose. Coupling of Landsat TM derived vegetation cover information with meteorological data depicts higher sensitivity of Barind Tract than the other study site towards vegetation cover changes in response to rainfall amount. Temporal sequence of albedo value as derived from Landsat TM data manifests appropriate seasonal trend that is consistent with field observations. Analysis of rainfall data over a period of about 50 years depicts substantially higher rainfall in both the study sites as compared to a typical semiarid region. However, temporal distribution of rainfall events is mostly concentrated within very short period of time in the year. Lithological information on the subsurface configuration depicts the existence of a relatively impermeable clayey layer near the top surface in these areas. Relatively higher thickness of this impermeable clayey layer over the semiarid Barind areas has been noticed that slows down the infiltration process significantly as compared to that in adjacent floodplain areas. Analysis of drainage network exhibits relatively higher drainage density in the Barind Tract area as compared to that in floodplain area. The drainage pattern shows predominantly unidirectional and distributary nature of drainage in the Barind Tract area and in general, distribution is oriented towards basin direction and most of the branches of the drainage system. Ultimately, relatively higher elevation, steeper slope result in relatively quicker surface runoff to carry out excess rain water to the nearest basins, results in decreased infiltration of rainwater. Eventually, the conversion efficiency of Barind Tract area to transform intercepted rainwater into underground reservoir water through infiltration seems to be lower as compared to that of floodplain areas.

Development and Assessment of an ALLSSA‐Based Atmospheric Weighted Mean Temperature Model With High Time Resolution for GNSS Precipitable Water Retrieval

AbstractThe atmosphere weighted mean temperature, Tm, is an essential parameter for retrieving precipitable water from the ground‐based Global Navigation Satellite Systems (GNSS). The accuracy of high temporal resolution GNSS precipitable water vapor (PWV) estimation requires wideband Tm information and its magnitude. However, existing Tm empirical models use trigonometric functions with only fixed amplitude and low‐frequency for time fitting, which limits real‐time or near real‐time PWV retrieval from GNSS observation. Thus, an improved Tm model for China, LTCm, containing more frequency information of Tm, based on the antileakage least‐squares spectrum analysis by utilizing the ERA5 pressure‐level products during the years 2015–2019, is developed. Both Tm data from ERA5 pressure‐level products and radiosonde stations distributed in China over 2020 are selected as reference values to verify the performance of the LCTm model. The results show that the LCTm model yields significant performance against other models in Tm estimation over China, especially in marine regions and high‐altitude areas. Furthermore, the LCTm model can generally achieve a mean Bias/root mean square (RMS) of −0.33 K/2.06 K in contrast to ERA5 pressure‐level products and 0.03 K/3.47 K in comparison with radiosonde, which corresponds to a 7.2%–13.8% improvement against GPT2w, GTm‐III, and Bevis. Moreover, LCTm has σ pwv and σ pwv /PWV values of 0.27 mm and 1.25% when used to retrieve GNSS‐PWV, respectively. Consequently, the LCTm model that considers high‐frequency information of Tm can obtain more reliable Tm values. Therefore, the LCTm model can be applied to real‐time or near real‐time GNSS PWV retrieval, which is of great significance for GNSS meteorological research.

Green space dynamics in response to rapid urbanization: Patterns, transformations and topographic influence in Chattogram city, Bangladesh

Green spaces have become an essential part of urban development due to the numerous ecosystem services they provide. Rapid urbanization in developing countries like Bangladesh has created enormous pressure on green spaces. The study investigated the changing pattern of green spaces resulting from unplanned urbanization in Chattogram city and how the topographic gradients of elevation and slope influence the change. Landsat TM and Landsat OLI data from 1990, 2000, 2010, and 2020 were used to produce land use/land cover maps. ASTER digital elevation model was used to derive topographic attributes of elevation and slope. Supervised image classification with maximum likelihood classification algorithm was applied for land use/land cover classification using ArcGIS 10.3. Five land use/land cover types were identified with an overall accuracy of 90% and kappa statistics of 87.3. Changes along topographic gradients were assessed in the GIS environment using overlay analysis. The results show that area coverage of green spaces decreased from 68.34% in 1990 to 36.51% in 2020. The study found a decrease in all green spaces- forest cover (9.9%), agricultural land (7.6%) and vegetation cover (14.3%) from 1990 to 2020. The decrease of green spaces was mainly due to the expansion of built-up areas, about 51.32 km2 area of green spaces were transformed into built-up areas during the period. The changes mostly occurred along low to moderate elevation (0–26 m) and gentle to moderate slope (0–15%) gradients. Green spaces decreased along all topographic gradients but the decreasing trend of forest cover was relatively low in high to very high elevation (26–88 m) and steep to very steep slope (15–66%) gradients. Such a setting can be used to expand forest cover in areas with similar topographic conditions. Built-up areas increased along all topographic gradients, increasing the risk of land degradation. The study has the potential to provide practical information to the local planners for sustainable land use management in Chattogram city.

Land-Use/Land Cover Changes Contribute to Land Surface Temperature: A Case Study of the Upper Indus Basin of Pakistan

Land-use/land cover (LULC) changes have an impact on land surface temperature (LST) at the local, regional, and global scales. To simulate the LULC and LST changes of the environmentally important area of northern Pakistan, this research focused on spatio-temporal LULC and associated LST changes since 1987 and made predictions to 2047. We classified LULC from Landsat TM and ETM data, using the maximum probability supervised categorization approach. LST was retrieved using the Radiative Transfer Equation (RTE) methodology. Furthermore, we simulated LULC using the integrated approaches of Cellular Automata (CA) and Weighted Evidence (WE) and used a regression model to predict LST. The built-up areas and vegetation have increased by 2.1% and 11% due to a decline in the barren land by −8.5% during the last 30 years. The LULC is expected to increase, particularly the built-up and vegetation classes by 2.74% and 13.66%, respectively, and the barren land would decline by −4.2% by 2047. Consequently, the higher LST classes (i.e., 27 °C to &lt;30 °C and ≥30 °C) soared up by about 25.18% and 34.26%, respectively, during the study period, which would further expand to 30.19% and 14.97% by 2047. The lower LST class (i.e., 12 °C to &lt;21 °C) indicated a downtrend of about −41.29% and would further decrease to −3.13% in the next 30 years. The study findings are useful for planning and management, especially for climatologists, land-use planners, and researchers in sustainable land use with rapid urbanization.