Inverse Distance Weighting Interpolation Research Articles

Prediction Method for Dynamic Subsidence Basin in Mining Area Based on SBAS-InSAR and Time Function

Dynamic predictions of surface subsidence are crucial for assessing ground damage and protecting surface buildings. Based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology, a method for making dynamic predictions of large-scale surface subsidence in mining areas can be established; however, the problem of phase coherence loss in InSAR data makes it impossible to predict the complete dynamic subsidence basin. In this study, a method combining the WeiBull time function and the improved probabilistic integral method (IPIM) model was established based on the PIM model, and a method for predicting the dynamic subsidence basin in the mining area was proposed by integrating the IPIM and the combined WeiBull time function. Time-series subsidence data, obtained using SBAS-InSAR, were used as fitting data, and the parameters of the combined WeiBull function were inverted, pixel by pixel, to predict the dynamic subsidence of the working face in the study area. Based on the predicted surface subsidence results of a certain moment in the working face, the parameters of the IPIM model were inverted to predict the subsidence value in the incoherent region. The subsidence predictions of the combined WeiBull time function and the IPIM model were fused using inverse distance weighting (IDW) interpolation to restore the complete subsidence basin in the mining area. This method was tested at the Wannian Mine in Hebei, and the obtained complete subsidence basin was compared with the measured data, with an absolute error range of 0 to 10 mm. The results show that the dynamic subsidence basin prediction method for the SBAS-InSAR mining area, involving the combination of the IPIM model and the combined WeiBull model, can not only accurately fit the time series of surface observation points affected by mining but also accurately restore the subsidence data in the incoherent region to obtain complete subsidence basin information in the mining area.

Integrated approach to soil salinity assessment using SEM in Sirdarya province, Uzbekistan

The majority of irrigated land in the Sirdarya area in Uzbekistan is susceptible to salinization. Yet, due to the benefits of its geographical and temporal data, satellite imaging has the ability to efficiently monitor territorial resources. The objectives of this paper are (1) to find how strong surface water mineralization used for irrigation correlated with soil salinity, and (2) to introduce a simplified approach for the soil salinity assessment using SEM based on satellite imagery analysis. The main focus of this research is to explore the potential of using SEM methodology for soil salinity assessment in the study area by streamlining spatial image analysis processes. Throughout data collection of 261 surface water samples and 206 soil samples in Sirdaria irrigation district, while satellite images and other official datasets were our secondary material. We used the Inverse Distance Weighting interpolation method to map soil salinity in ArcGIS, achieving an interpolation accuracy of 72% and a Kappa value of 81%. Statistical packages in R software served as analyst tools to validate our findings. Our findings imply that while MODIS satellite imagery has a lesser connection with EC values, however, the EC values in terms of soil salinization in Sirdarya Province can be predicted using several Landsat bands without using remote sensing indices with correlation coefficients of R ≥ 0.8. We proved that the wavelength ranges of 0.64–0.67 μm and 2.11–2.29 μm are acceptable for mapping soil salinity. Nevertheless, continuous research is needed to understand the mechanisms behind this relationship and the potential applications of these findings in other drylands, land management, and environmental monitoring.

Analysing Spatial and Temporal Rainfall Variability of Southern Rajasthan using GIS Approach

This study focuses on analysing the behavioral patterns of rainfall in southern Rajasthan, particularly in reference to its amount and variation over time. The study made use of standard meteorological weeks (SMW) and historic weekly rainfall records. IDW (Inverse Distance Weighting) interpolation technique within the ArcGIS platform was used for spatial analysis, creating spatial variation maps. It was found that significant rainfall occurred from Standard Meteorological Week (SMW)-22 to SMW-42. The weekly mean rainfall across the study area during the monsoon period ranged from 2.1 to 68.4 mm, with corresponding standard deviations ranging from 9.3 to 79.7 mm. Notably, the standard deviation was often higher than the mean, indicating substantial variability in weekly rainfall. Trend analysis using the Mann-Kendall test revealed that most stations in the study area exhibited no significant trend in annual rainfall data. Similarly, weekly rainfall data showed a trend in SMW-30 for 44 stations, while all other weekly data series did not exhibit significant trends, except for SMW-30. Stationarity was generally accepted for most weekly data series, except for SMW-30 and SMW-32, which showed non-stationary behavior. However, homogeneity tests indicated homogeneity in weekly rainfall data series for almost all selected stations. Overall, the study highlighted the significant variability in rainfall patterns in southern Rajasthan, with no significant trend in annual rainfall, except during the peak monsoon period (SMW-30). These findings contribute to a better understanding of regional rainfall behavior, which is crucial for water resource management and agricultural planning in the region.

A Two-Step Regional Ionospheric Modeling Approach for PPP-RTK.

In the precise point positioning/real-time kinematic (PPP-RTK) technique, high-precision ionospheric delay correction information is an important prerequisite for rapid PPP convergence. The commonly used ionospheric modeling approaches in the PPP-RTKs only take the trend term of the ionospheric total electron content (TEC) variations into account. As a result, the residual ionospheric delay still affects the positioning solutions. In this study, we propose a two-step regional ionospheric modeling approach that involves combining a polynomial fitting model (PFM) and a Kriging interpolation (KI) model. In the first step, a polynomial fitting method is used to model the trend term of the ionospheric TEC variations. In the second step, a KI method is used to compensate for the residual term of the ionospheric TEC variations. Datasets collected from continuously operating reference stations (CORSs) in Hunan Province, China, are used to validate the PFM/KI method by comparing with a single PFM method and a combined PFM and inverse distance weighting interpolation (IDWI) method. The experimental results show that the two-step PFM/KI modeled ionospheric delay achieves an average root mean square (RMS) error of 1.8 cm, which is improved by about 48% and 23% when compared with the PFM and PFM/IDWI methods, respectively. Regarding the positioning performance, the PPP-RTK with the PFM/KI method takes an average of 1.8 min or 4.0 min to converge to a positioning accuracy of 1.3 cm or 2.5 cm in the horizontal and vertical directions, respectively. The convergence times are decreased by about 18% and 14% in the horizontal direction and 9% and 5% in the vertical direction over the PFM and the PFM/IDWI methods, respectively.

Reconstruction of γ Dose Rate Field and Algorithm Validation Based on Inverse Distance Weight Interpolation

The inverse distance weighting (IDW) interpolation algorithm is used to reconstruct the γ radiation field. The traditional IDW interpolation algorithm is improved. The power exponent of distance P in the IDW for each interpolation point is not fixed and varies from one point to the other point. A fitting expression of P is obtained, which is a function of the coordinates of each point and can minimize the interpolation error when the number of sampling points is specified. Afterward, the improved algorithm is used to reconstruct a γ radiation field of a single source, and the theoretic results are compared with the results from Geant4, yielding an average relative error of 7.50%. The interpolated results from the experimental measurements align well with the actual data, with an average relative error of only 0.12%. The P derived from the interpolated experimental measurement data shows an error of 2.0% compared to the power exponent obtained from the Geant4 data interpolation. Then we set up a double-source γ radiation scene experiment and measured the count rate data at the grid points. At the same time, the experiment scene was simulated by Geant4. The improved IDW algorithm could not reconstruct the double-source γ radiation field well, thus further improvement is needed.

Methodology of Applying Inverse Distance Weighting Interpolation Method in Determining Normal Heights

This article explores the application of the Inverse Distance Weighting (IDW) interpolation method in determining normal heights, utilizing a network of points with measured ellipsoidal and normal heights by the use of GNSS/levelling method. The IDW method, which assumes that points closer to each other are more alike, is employed to locate points within the correct quadrant and calculate their normal height based on the determined average height anomaly across the entire area. The accuracy of this method is validated using check points. The article further discusses the methodology of normal height determination at a country level by using generated coefficients, representing the average difference between various height-systems, which could be used as an integral to the interpolation methods used for generating a refined Digital Elevation Model (DEM) across the country. This study contributes to the ongoing discourse on the effective use of interpolation methods in geospatial analysis and DEM generation. It provides valuable insights into the practical application of the IDW method and its potential for enhancing the accuracy of normal height determination and DEM refinement. Importantly, such methods hold significant value for countries lacking gravimetric data, as they provide a viable means of determining normal heights in the absence of a local quasi geoid model.

GIS-based spatial analysis for lightning scenario in Bangladesh

Bangladesh has witnessed alarmingly rising lightning frequency, particularly during pre-monsoon and monsoon seasons. This has resulted in significant annual death tolls from lightning strikes over the past decade. Recognizing this crisis, the country officially declared lightning casualties a natural disaster in 2016. This study delves deeper into the landscape of lightning fatalities and causalities in Bangladesh. Utilizing secondary data sources, this research introduces a unique approach by integrating Bangladesh Meteorological Department (BMD) data and NASA's Lightning Imaging Sensor (LIS) data from the International Space Station's (ISS) Near-real Time (NRT) mission. This combined dataset allows for a more comprehensive analysis. Furthermore, Geographic Information Systems (GIS) was employed to analyze spatial distributions and generate maps. The Inverse Distance Weighted (IDW) interpolation tool was used to create detailed spatial distribution maps of lightning fatalities, thunderstorm days (TSDs), and lightning flash frequency (LFF) across Bangladesh. The analysis revealed that farmers and fishermen were the most vulnerable populations, with the northeastern regions experiencing the highest impact. Sylhet division emerged as the area with the most fatalities, highlighting the northeastern zone's susceptibility. The study also identified monsoons as the period with the highest occurrences of lightning deaths and injuries. By combining innovative data integration and spatial analysis, this study offers valuable insights into the alarming trend of lightning fatalities in Bangladesh. These findings can inform targeted prevention strategies and interventions to safeguard vulnerable populations and communities.

Liquefaction potential analysis based on standard penetration test in coastal area (Case study: Loh Buaya, Rinca Island, Indonesia)

An area has liquefaction potential when it has a shallow groundwater level, loose sandy soil, and is prone to earthquakes. There are several areas with such criteria that have not been analysed for liquefaction potential. This study aims to analyse and plot the liquefaction potential in the coastal area of Loh Buaya, Rinca Island, East Nusa Tenggara Province. Soil investigation data, such as SPT, sieve analysis, and groundwater level, as well as earthquake history that occurred from 1922-2022, served as the main data for liquefaction potential analyses. The methods used were Ground Motion Equation Prediction (GMPE) to calculate Peak Ground Acceleration (PGA), Simplified Procedure, and Liquefaction Severity Index (LSI) to make a liquefaction hazard assessment. LSI scores were used to provide micro-zonation of liquefaction potential with Inverse Distance Weighted (IDW) interpolation in QGIS. The result obtained is very dense gravel has no liquefaction potential whereas loose sandy soil has very high in LSI classification because loose sandy soil has liquefaction potential up to 20 meters of depth. The applying of micro-zonation LSI by IDW interpolation method can estimate the potential level of liquefaction hazard on Loh Buaya, Rinca Island with limited soil investigation data.

Long-term ozone exposure and mortality in patients with chronic kidney disease: a large cohort study

BackgroundEpidemiologic studies on the effects of long-term exposure to ozone (O3) have shown inconclusive results. It is unclear whether to O3 has an effect on chronic kidney disease (CKD). We investigated the effects of O3 on mortality and renal outcome in CKD.MethodsWe included 61,073 participants and applied Cox proportional hazards models to examine the effects of ozone on the risk of end-stage renal disease (ESRD) and mortality in a two-pollutants model adjusted for socioeconomic status. We calculated the concentration of ozone exposure one year before enrollment and used inverse distance weighting (IDW) for interpolation, where the exposure was evenly distributed.ResultsIn the single pollutant model, O3 was significantly associated with an increased risk of ESRD and all-cause mortality. Based on the O3 concentration from IDW interpolation, this moving O3 average was significantly associated with an increased risk of ESRD and all-cause mortality. In a two-pollutants model, even after we adjusted for other measured pollutants, nitrogen dioxide did not attenuate the result for O3. The hazard ratio (HR) value for the district-level assessment is 1.025 with a 95% confidence interval (CI) of 1.014–1.035, while for the point-level assessment, the HR value is 1.04 with a 95% CI of 1.035–1.045. The impact of ozone on ESRD, hazard ratio (HR) values are, 1.049(95%CI: 1.044–1.054) at the district unit and 1.04 (95%CI: 1.031–1.05) at the individual address of the exposure assessment. The ozone hazard ratio for all-cause mortality was 1.012 (95% confidence interval: 1.008–1.017) for administrative districts and 1.04 (95% confidence interval: 1.031–1.05) for individual addresses.ConclusionsThis study suggests that long-term ambient O3 increases the risk of ESRD and mortality in CKD. The strategy to decrease O3 emissions will substantially benefit health and the environment.

Study on the Relationship between Hotspots and Air Pollution Standard Index Based on Carbon Monoxide Concentration in West Kalimantan

Hotspots, indicating active fires and potential sources of air pollution, are a major concern due to their impact on air quality in West Kalimantan. In this study, an analysis was carried out regarding the relationship of hotspots to the Air Pollution Standard Index (ISPU) based on the carbon monoxide (CO) parameter. This study aimed to determine the relationship between forest fires and air pollution levels. The data used in this study is in the form of monthly average CO concentration data and coordinates of hotspots in 2017-2021. To determine the effect of CO concentration, CO concentration was converted into ISPU and then mapped using the Inverse Distance Weighting (IDW) interpolation method. In addition, the overlay technique is also applied to the map and the correlation coefficient calculation between ISPU data and hotspot data. The study results show that the annual average number of hotspots ranges from 769 to 3,612, while the average ISPU ranges from 22.21 to 59.03. The highest average number of hotspots occurred in 2019, with 3,612 hotspots and the highest average ISPU value of 59.03, categorized as moderate. However, when examined monthly, the highest average number of hotspots is observed in August and September, with 8,505 and 8,321 hotspots, respectively, and average ISPU values of 55.36 and 88.32, categorized as moderate. Furthermore, the correlation coefficient between the average number of hotspots and the average ISPU per month is 0.91, and per year is 0.98, indicating a very high relationship. Spatially, clustered hotspot locations correspond to higher ISPU values. This implies that as the number of hotspots increases, the ISPU value based on CO concentration increases.

Spatial Variability of Selected Soil Chemical Parameters in Low-yielding Paddy Production Block in Mahaweli System H, Sri Lanka

Although water availability for paddy production in Mahaweli System H is abundant, paddy yield is lower in Nochchiyagama (278 km2), chosen for soil assessment. Paddy yield data were used to identify the low-yielding division using the area-weighted average. Twenty-five random locations were generated, and soil samples were collected for pH, soil conductivity (EC), salinity, and total dissolved solids (TDS) analysis. ArcGIS software was used to create spatial distribution maps and related geostatistical analyses for each parameter. Vector files were created with their associated properties, and thematic maps were generated using spatial interpolation techniques, such as universal kriging (UK), ordinary kriging (OK), and inverse distance weighted (IDW) methods of interpolation techniques to identify the best interpolation method for soil chemical parameters mapping. The entire Nochchiyagama land was observed to have a slightly acidic pH (5.6-5.9) range that may have affected rice crop growth due to nutrient mobility and uptake issues. The spatial interpolation evaluation suggests that at least two-thirds of the area observed for lower TDS levels (591-654 mg/L) is potentially unsafe paddy production. Elevated levels of EC (3.1-7.24 dS/m) along with TDS may lead to physiological drought due to interferences in ion uptake. In overall, spatial interpolation evaluation indicators suggest that the UK method was observed with a lower mean relative error (MRE) than the other two interpolations. However, EC distribution showed low MRE in both IDW and OK interpolation techniques. The IDW method was observed to have a lower RMSE (Root mean square error). The UK spatial interpolation performed better for TDS and salinity predictions than other methods. This study found consistent regional differences in low paddy yields in Mahaweli system H using the UK method for analyzing soil chemical parameters. 
 Keywords: Electrical conductivity, Interpolation errors, Salinity, Spatial interpolation

Prioritization of Milli Watershed of Kodar River Catchment Based on Integrated Approach of Morphometric and Land Use Land Cover Analysis

Watershed prioritisation is now important for effective natural resource planning and management in sustainable development, however, their implementation necessitates a careful examination of the morphometric and hydrological characteristics existing in the watershed. In the present study an attempts has been made to prioritize the Kantori nala milli watersheds of the Kodar river catchment using integrated approach of morphometric features and land use/landcover. Study area watershed was automatically delineated and divided into eleven micro watersheds Micro Watershed-1 to Micro Watershed-11 (MWS-1 to MWS-11) on the basis of topography from the Depression less DEM) prepared by Inverse Distance Weighted (IDW) interpolation technique. Morphometric analysis was done using DEM and each parameter has been assigned their ranks according to their value. Thereafter, an average value of the rank score for each of the micro watershed is calculated. The micro watershed with the lowest compound factor (Cp) was given the highest prioritized rank out of the group of micro watersheds, and vice versa. Land Use Land Cover (LULC) analysis was also done using sentinel 2 satellite data. The result from the priority ranking of morphometric analysis shows that MWS 7 is having high priority ranking while MWS 8 is having low priority ranking, while the results of the LULC analysis-based prioritizing showed that the micro watersheds MWS 10 and MWS 5 micro watersheds are of are of highest and lowest priority respectively. It is observed that upon integration of morphometric and land use/ land cover compound factor values information, MWS 3 and MWS 10 found to receive common priority falls under the highest priority, though MWS 6 and MWS 8 falls under the lowest priority ranking. The results of the study can be employed for identifying the sub-watersheds that require immediate restoration and ultimately help in managing watershed resources for sustainable development.

Development of a hybrid geoid model using a global gravity field model over Sri Lanka

Abstract Sri Lanka is still in need of a well-defined local geoid model. This geoidal void has made present-day Global Navigation Satellite System (GNSS) surveys heavily dependent on Global Geopotential Models (GGMs) for height determination. Further, in many instances, the accuracy of GGMs have shown drawbacks in elevation determination over Sri Lanka. Therefore, the study focused on developing a hybrid geoid model (HGM) for Sri Lanka by integrating the available GGMs. Five high-resolution (2190°) GGMs; EGM2008, EIGEN-6C4, GECO, XGM2019e-2159, and SGG-UGM2 were employed to extract GGM-derived geoid undulation for 21 Fundamental Benchmarks (FBMs). The residuals (geoid height deviation) were calculated relative to the observed geoid undulation using GNSS/leveling on the FBMs. The data set was clustered based on topography, and residuals were adjusted using weighted least squares adjustment (LSA). The uneven distribution of the FBMs promotes topography-based clustering. EIGEN-6C4 is found to be the robust GGM for Sri Lanka to develop a hybrid approach, with a 0.001 m RMS value of estimated residuals in LSA. The resulting HGM was interpolated at 1 arc-second grid resolution (30 m × 30 m) using the Inverse Distance Weighted Interpolation. Regression lines were generated for the interpolated HGM with respect to the interpolated observed geoid undulation for 9 transects along the parallel passing through Mount Pedro and for the 16 transects along the meridian. The coefficient of determination on both lines is 0.999. HGM generated by EIGEN-6C4 has shown reliable RMS gradient and intercept values of 8.860078 × 10−9 and 0.0039239, respectively, in first-order polynomial fitting.

Geoestadística para integrar mediciones de campo con estimaciones satelitales adecuados para escala local

In countries such as Mexico, there is a lack of rain measurement stations. Additionally, in the Bajo Grijalva Basin, data of only three or fewer stations are integrated into satellite products of missions such as Tropical Rainfall Monitoring Mission (TRMM) and Global Precipitation Mission (GPM). Although Satellite missions enable obtaining rainfall at constant spacing (e.g., 11 km for GPM), this resolution is not suitable for local management. Integrating a larger quantity of gauge data with downscaled satellite values allows for obtaining local-scale precipitation data. In this work, Ordinary kriging (OK) was applied to downscale yearly aggregated precipitation satellite data (GPM-IMERG and TRMM: TMPA/3B43) and regression kriging (RK) to integrate them with the gauge measurements available in the basin of study. The resulting data were compared with the interpolation results of gauge measurements using OK and universal kriging (UK). Leave-one-out cross-validation (Lou-CV), principal components analysis, a correlation matrix, and a heat map with cluster analysis helped to evaluate the performance and to define similarity. An Inverse Distance Weighting (IDW) interpolation was included as a low-performance criterion in the comparison. OK performed well to downscale GPM satellite estimates. The RK integration of gauge data with downscaled GPM data got the best validation values compared to the interpolation of gauge measurements. Geostatistical methods are promising for downscaling satellite estimates and integrating them with all the available gauge data. The results indicate that the evaluation using performance metrics should be complemented with methods to define similarity among the values of the obtained spatial layers. This approach allows obtaining precipitation data useful for modeling and water management at the local level.

Determination and assessment of the groundwater table and mineralization in irrigated areas (Xojeli district, Republic of Karakalpakstan)

Irrigated areas in the Aral Sea Basin are becoming highly saline due to the ascendant groundwater table and groundwater mineralization. This, in turn, leads to a decrease in soil fertility in agriculture and negatively affects the food security of basin countries. In the research work, seasonal changes in the groundwater table and mineralization during the periods of 2015 and 2023 were evaluated on the irrigated lands of the Xojeli district in the Republic of Karakalpakstan (Uzbekistan). Field monitoring was carried out on 142 observation wells using traditional methods of measuring the groundwater table and mineralization. Then, the obtained data was statistically analyzed, and maps based on the geoinformation system (GIS) were created by the inverse distance weighting (IDW) interpolation method for the study period. Furthermore, a correlation coefficient analysis was applied to find a relationship between climatic patterns, and groundwater table, and mineralization. Results based on statistical analysis present steady fluctuations in the groundwater table and wild fluctuations in mineralization over a period of nine years. The average water table below surface and mineralization in April was determined by 1.55 m and 2.94 g/l, in July by 1.78 m and 2.96 g/l, and in October by 2.18 m and 2.56 g/l. The visual maps created in accordance with the GIS-based method show that average values for the groundwater table in the irrigated areas were 1.5–2 m and 2–3 m (57.9% and 27.8%), and mineralization was 1-3 g/l and 3-5 g/l (62.4% and 36.5%). A correlation coefficient (r) shows a low relationship between the chosen variables.

The Impact of Groundwater Burial Depth on the Vegetation of the Dariyabui Oasis in the Central Desert

Vegetation and groundwater are important components of the ecological environment of oases in desert hinterlands and their relationship is crucial to ecosystem stability. In this study, Sentinel-2 data for 2016–2022 and measured groundwater burial depths were analysed for the Dariyabui Oasis in the hinterland of the Taklamakan Desert. The spatial and temporal changes in vegetation and groundwater burial depth from 2019 to 2022 were analysed based on the image–element dichotomous model of the normalised difference vegetation index, utilising the inverse distance weight interpolation method, cubic curve regression, image–element difference, slope trend analysis, and the Markov transfer matrix for determining the temporal and spatial response law between the two. Finally, the threshold value of groundwater burial depth for different vegetation cover types was clarified. The fractional vegetation cover of the Dariyabui Oasis showed a slight increase from 2016 to 2022. Vegetation in the northwest and southeast of the oasis increased, whereas vegetation decreased in the mid-north and northeast regions; 5.14% of the total area experienced increased coverage, whereas 3.35% experienced decreased coverage. The depth of groundwater in the oasis showed a pattern of gradual increase from the entrance to the end of the oasis, that is, south to north. The depth of groundwater in the oasis from 2019 to 2022 was stable, with a 4-year average depth of 4.1069 m and a maximum fluctuation of 0.4560 m. The interannual changes in the groundwater level showed an increasing trend in January–April, while groundwater levels showed a decreasing trend in May–July and August–October and remained constant in June–July and October–December. Oasis vegetation cover showed a negative correlation with groundwater depth, with a depth interval for the highest low-cover vegetation distribution of 3–6 m, and an ultimate depth threshold of 7 m. The depth interval with the highest medium-cover vegetation distribution was 3–4 m, that with the highest high-cover distribution was 2–4 m, and the ultimate depth threshold was 6 m. The depth of the oasis ranged from 3 to 6 m and the ultimate depth threshold was 7 m.

WATER QUALITY ANALYSIS USING PHYSIOCHEMICAL PARAMETERS AND GEOSPATIAL DISTRIBUTION FOR FIVE SELECTED LAKES OF BHANDARA DISTRICT IN

In order to understand the water quality of five selected fresh water lakes of Bhandara district, Maharashtra (India), physicochemical characteristics viz. water temperature, pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), dissolved oxygen (DO),chemical oxygen demand (COD) were investigated. Samples were collected from fifteen different points of five lakes (three samples from each lake) during the pre-monsoon and post-monsoon season. To understand the spatial distribution of physiochemical parameters with the help of Geospatial Technology, we used the Inverse Distance Weighted (IDW) interpolation method. The consequences of study were validated by comparing with World Health Organization (WHO) and Bureau of Indian Standard (BIS) criteria for drinking water. All physicochemical parameters were recorded more or less similar ecological status under permissible limit during the study period. This study also reveals that, water quality parameters were found apparently lower during post-monsoon season, which could have been due to dilution of water in post-monsoon season.

Potential of Geospatial technologies in Mechanized Timber harvesting planning

Mechanized timber harvesting involves various activities including; road planning and selection of harvesting systems and machineries. The emergence of geospatial technology (GSPT) i.e., geographical information system (GIS) and remote sensing in the recent decades, has been considered as the best tools to facilitate timber harvesting planning in plantation forests. GSPT provide accurate stand information enabling better decision-making and optimizing forest operations. This study was conducted at Sao hill Forest Plantation (SHFP) in Tanzania, with the objective of determining relative efficiency (RE) between geospatial approach (GSPA) and conventional approach (CA) on planning mechanized timber harvesting. 120 grapple skidder (GS) time study observations in 30 sample plots covering different elevation terrain ranges were studied in both approaches. Productivity and costs under the two approaches were estimated and modelled using generalized linear model (GLM) approach. To obtain large scale estimates of productivity and costs, Inverse Distance Weighted (IDW) interpolation approach was used. The results showed that, GSPA demonstrated higher productivity and lower unit skidding costs (i.e., 71.1m3/hr and 2.121USD/m3) compared to CA (i.e., 67.5m3/hr and 2.914USD/m3) respectively. Skidding distance and slope (p-value < 0.05) were significant predictors of the GS performance in both approaches. The pseudo R2 ranging from 58.1% to 64.3% under CA, and from 62.9% to 60.8% under GSPA. Likewise, relative root mean square error (RMSEr) for the models under CA ranged from 49.3% to 50.4% and 33.4% to 35.2% under GSPA. Generally, the results showed that, models under GSPA have better fits and accuracy, compared to CA. Furthermore, the GSPA provided a raster representation of productivity and costs over the entire study area. Moreover, computed RE values (i.e., 1.18 and 6.17) indicated that parameter estimates for the GS productivity and costs were more precise in geospatial models (GSPM) compared to conventional models (CM). These findings highlight the potential of GSPT for an efficient large scale timber harvesting planning, by considering terrain constraints.

Application of Multivariable Statistical and Geo-Spatial Techniques for Evaluation of Water Quality of Rudrasagar Wetland, the Ramsar Site of India

The water quality of Rudrasagar Lake, the second-largest natural reservoir of Tripura is of great ecological and economic importance as it serves a diverse range of purposes, including fishing, irrigation, aquaculture, domestic use, and recreation activities. This study investigates the water quality of the study area, an esteemed Ramsar site in North Eastern India, using a combined application of multivariable statistical and geospatial techniques. In this study, 24 water samples were designed based on their use and collected along the periphery and the inner areas of the lake employing the Latin Square Matrix. This research also examines the spatial variations of water quality involving quartile-based water quality categorization of parameters, with Pearson’s Correlation analysis, Principal Component Analysis (PCA), and Hierarchy Cluster Analysis (HCA) applied for dimension reduction. The analysis involved quartile-based water quality categorization of parameters, with PCA and HCA applied for dimension reduction. Meanwhile, the Inverse distance weighted (IDW) approach was used to interpolate the spatial distribution of the quartile score using the ArcGIS platform. The Bureau of Indian Standards (BIS) was followed for water quality assessment. The results revealed significant spatial variation, providing valuable insights for future water management strategies. PCA indicates 57.26% of the variance in the dataset, whereas samples were classified into three subgroups and two groups in a dendrogram representing the result of the HCA. This study demonstrates the utility of PCA, HCA, and IDW interpolation in water quality assessment, highlighting the effect of human-induced activities in the lake’s vicinity.

Distribution of Heavy Metals in Surface Sediments of a Tropical Mangrove Wetlands in Hainan, China, and Their Biological Effectiveness

The distribution and ecological risk of heavy metals in sediments were studied through the systematic collection and analysis of mangrove wetland sediments in Dongzhai Harbor, Hainan. The main insights obtained were as follows: (1) The distribution characteristics and influencing factors of heavy metals in wetland sediments were analyzed by using the inverse-distance weight interpolation method. In terms of spatial distribution, the contents of heavy metals As, Ba, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Sr, Ti, and Zn in the western part of the wetland were significantly higher than in the eastern part. The contents of heavy metals Cd, Co, Cr, Cu, Hg, Mn, Ni, Zn, and Ti near the anthropogenic area were significantly higher than at other points. (2) The pollution sources and ecological risks of heavy metals in wetland sediments were explored by using correlation analysis, cluster analysis, and potential ecological risk index analysis. The results showed that As, Ba, Pb, and Sr mainly originated from natural processes; Co, Cr, Cu, Mn, Ni, Ti, and Zn mainly originated from industry; and agricultural heavy metals mainly originated from Cd and Hg. The ecological risk analysis showed that there were obvious ecological risks of heavy metals in the western and southeastern corners of the wetland, which were both located in the vicinity of land far away from the coastline and near the human activities, and featured mangrove forests with dense vegetation characteristics.