Satellite Images Research Articles

Quantifying urban air quality through multispectral satellite imagery and Google earth Engine

The escalating concerns surrounding urban air pollution's impact on both the environment and human health have prompted increased attention from researchers, policymakers, and citizens alike. As such, this study addresses growing concerns about urban air pollution's impact on the environment and human health, emphasizing the need for early, high-resolution PM2.5 pollutant measurements. Utilizing Google Earth Engine (GEE) machine learning algorithms, our study evaluates six models over four years in Tehran and Tabriz. Inputs include satellite imagery, meteorological data, and pollutant measurements from air quality stations. Four models—Histogram Gradient Boosting, Random Forest, Extreme Gradient Boosting, and Ada Boosted Decision Trees—outperform Support Vector Machine and Linear Regression. The selected model, a combination of decision tree algorithms and Ada Boost, achieves a notable correlation coefficient of 79.8% and an RMSE of 0.271 g/m3. This superior performance enables the generation of high-resolution (30-m) PM2.5 estimates for the two cities. The study's comprehensive approach, involving various data sources and advanced machine learning techniques, contributes a valuable method for accurate PM2.5 assessment. The findings hold significance for urban air quality management and provide a potential framework for generating detailed PM2.5 datasets based on Landsat images.

Analysis of Devanur and Manamedu Ophiolite Complexes in SGT, India: A detailed examination employing remote sensing techniques and Laboratory Spectral Signature investigations

This study employs advanced satellite imagery from ASTER and Sentinel-2A to conduct detailed lithological mapping of the Devanur and Manamedu ophiolite complexes in the southern Central Shear Zone (CSZ). The primary focus is on the Manamedu Ophiolite Complex (MOC) and the Devanur Ophiolitic Complex (DOC). Image enhancement techniques such as Color composites, Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) were utilized to differentiate various rock types. RGB band combinations derived from PCA and MNF outputs demonstrated effective discrimination of rock units. Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods were employed on ASTER and Sentinel-2A images, yielding classified lithologies that closely matched existing maps from the Geological Survey of India (GSI) and other studies, validating the accuracy of the findings. Additionally, Laboratory Spectral Signature Studies were conducted on 10 rock samples using an ASD FieldSpec Pro® spectroradiometer, providing reflectance spectra from 350 nm to 2500 nm. These spectra, particularly the continuum-removed reflectance, revealed diagnostic absorption features that were corroborated by geochemical analyses. A detailed analysis investigated how elemental compositions and key minerals influenced absorption bands. Major oxide geochemical compositions of DOC and MOC samples were identified using XRF methods. The aim of this research is to characterize DOC and MOC through remote sensing and spectral signature analysis. Sentinel-2A data proved more effective in lithological discrimination compared to ASTER, with spectral signatures indicating the presence of iron (Fe) and magnesium (Mg) contents. Notably, SVM classification of Sentinel-2A MNF + DEM data achieved an overall accuracy of more than 90% when compared with field investigations. This study underscores the efficacy of processing VNIR and SWIR bands from ASTER and Sentinel-2A satellite imagery alongside DEM data and ground surveys for mapping mafic-ultramafic rocks in the DOC and MOC regions of the CSZ.

ADGEO: A new shore‐based approach to improving spatial accuracy when mapping water bodies using low‐cost drones

AbstractOver the last three decades, satellite imagery has been instrumental in mapping and monitoring water quality. However, satellites often have limitations due to image availability and cloud cover. Today, the spatial resolution of satellite images does not provide finer detail measurements essential for small‐scale water pollution management. Drones offer a complimentary platform capable of operating below cloud cover and acquiring very high spatial resolution datasets in near real‐time. Studies have shown that drone mapping over water can be done via the Direct Georeferencing approach. However, this method is only suitable for high‐end drones with accurate GNSS/IMU. Importantly, this limitation is exacerbated because of the difficulty in placing targets over water, which can be used to improve the accuracy after the survey. This study explored a new method called Assisted Direct Georeferencing which combines the benefits of traditional Bundle Adjustment with Direct Georeferencing. The performance of the approach was evaluated over a variety of different scenarios, demonstrating significant improvement in the planimetric accuracy. From the results, the method reduced the error in XY of drone imagery from MAE of 18.9 to 3.4 m. The result shows the potential of low‐cost drones with Assisted Direct Georeferencing in closing the gap to high‐end drones.

Geomorphological traits of landscapes in continental rifts—From fault‐elastic rebound to sedimentary sinks

AbstractWe analyse 498 faults identified in satellite imagery and interpret the height and width of associated footwall ranges with respect to co‐seismic elastic rebound from tectonic and erosional unloading. The dynamics of footwall uplift link uplands to catchment patterns and interrelated hanging wall sedimentary fans. Height–length relations of some catchments and associated alluvial fans scale linearly whereas others, such as fault‐slope catchments and related down‐fault fans (building out from faults) show a significant scatter without an obvious trend. Perched basins abandoned in the footwalls of younger faults offer catchment‐fan height–length relations like watergap and dipslope‐related fans and, besides, hint at reduction of dip angle due to rollback of larger faults before abandonment. Analysis of the width‐to‐height ratio (W/h) of footwall ranges offer a robust linear statistical trend, h = 0.06 W and is identical between datasets. This trend is valid for both arid and tropical rifts, the latter offering smaller rebounds. Contributions of elastic rebound on fault throw in our data are simplistically considered through comparison to global trends on fault length versus throw. This allows consideration around maximum throw (Tmax) linked to the maximum height of footwall ranges (h) and to their width (W) above the reference level. Basic calculations indicate that co‐seismic rebound contributes from <1% to 17% of extensional fault throw. Width‐to‐height ratios for large faults (L > c. 50 km) show less spread than smaller faults. Such large faults expectedly dissect the brittle crust, indicating that these large faults which root in the ductile–brittle transition approach a balanced, steady‐state kinematic pattern. We speculate that significant crustal thinning associated with these large faults triggers the onset of isostatic adjustments that drive fault rotation, instigating fault abandonment and disconnected perched basins.

Mountain Peatlands and Drought: Carbon Cycling in the Pyrenees Amidst Global Climate Change

AbstractThis study provides a multi‐year (2017–2022) Net Ecosystem Carbon Balance (NECB) of a Pyrenean mountainous peatland through the integration of field data, satellite imagery, and statistical modeling. Fluvial organic carbon export was measured at 30 min frequency, while gaseous (CO2 and CH4) exchanges were measured monthly using closed chambers. These measurements were combined with Sentinel‐2 derived chlorophyll index and in situ high frequency (1 hr) measurements of key environmental variables such as air temperature, photosynthetically active radiation, and water table level, to develop hourly gaseous carbon flux models (R2 = 0.69 for GPP, R2 = 0.84 for ER, R2 = 0.59 for CH4). Over the 2017–2022 period, modeled average GPP (610 ± 39 gC.m−2.year−1) and ER (641 ± 59 gC.m−2.year−1) showed that the peatland acted as a weak source of CO2 to the atmosphere, releasing 31 ± 73 gC.m−2.year−1. Considering fluvial carbon export and CH4 exchanges, the loss of carbon from the peatland increased to 55 ± 73 gC.m−2.year−1. Dissolved organic carbon constituted 8%–106% of the NECB. The estimated long‐term organic accumulation rate indicated a steady carbon accumulation rate of 16.4 gC.m−2.year−1, contrasting with the contemporary NECB, suggesting a recent shift in ecosystem functioning from a carbon sink to a source. The study underscores the role of water availability and air temperature through a drought index (DI), in shaping the NECB. The DI correlated significantly with annual carbon gaseous fluxes, except for 2022, marked by an intense drought. During this year the peatland became a large source of carbon (189 gC.m−2.year−1) to the atmosphere.

Comparing grass biomass estimation methods for management decisions in a semi‐arid landscape

AbstractAimsEnvironmental managers require reliable and cost‐efficient monitoring methods for effective decision‐making. Understanding forage availability is important for managing wild, vertebrate herbivore populations. We developed a process for exploring the accuracy and cost efficiency of various biomass estimation techniques for a case study where semi‐arid woodland restoration is threatened by kangaroo grazing, with the aim of determining which method was most fit for purpose in a given decision context.LocationWyperfeld National Park, southeastern Australia.MethodsGrass biomass was estimated using a variety of methods, then compared to clipped biomass using linear models. Biomass estimation methods were either field‐based (i.e., rising plate meter, multispectral radiometer) or satellite‐based (i.e., Landsat satellite imagery, AussieGRASS forage production model). Sampling occurred across open and wooded semi‐arid vegetation types. We compared methods based on accuracy, the ability of each method to accurately predict a ‘forage‐switch’ threshold, cost, and the suitability for the management context.ResultsFor this case study, the multispectral radiometer was the most precise, yet most expensive, biomass estimation method over a single survey. However, satellite imagery proved to be the most cost‐efficient and fit for purpose, as it was inexpensive and most accurately estimated biomass around a forage‐switch threshold, second only to the multispectral radiometer. Accuracy of all methods was improved by including tree cover in the regression models.ConclusionsWe demonstrate a process for exploring which biomass estimation tool might be preferred for a given decision context, highlighting accuracy, consideration of tolerance to uncertainty and risk, the spatial and temporal scale of information required, and budget constraints.

Multi-scale superpixel-based nearest subspace classifier for mucilage detection from hyperspectral PRISMA data

Hyperspectral imaging has a critical role in observing the Earth's surface, providing images with rich spectral information. As a result, it has become an essential tool for monitoring and addressing environmental issues such as pollution, water balance, and climate change. Among these challenges, marine pollution poses a significant ecological threat, as evidenced by the recent mucilage incident in Turkey in April 2021. Mucilage, a dense mucus-like substance resulting from the overgrowth of marine plankton and microorganisms, detrimentally affects marine ecosystems when its levels surge. Traditional mucilage detection involves labour-intensive sampling and laboratory analysis, rendering it impractical for vast marine environments. Remote sensing techniques offer a promising alternative for automated mucilage detection from satellite imagery. In this study, we propose a multi-scale superpixel-based classification approach for mucilage detection, using the PRISMA satellite's hyperspectral mucilage datasets captured over the Sea of Marmara. Our method performs classification by analyzing the closeness between the subspace spanned by the samples in the superpixel and the subspace spanned by the within-class training samples. We formulate the solution to this approach via a Canonical Correlation Analysis framework and show that our proposed method outperforms its competitors, especially when the training data is limited. Our comprehensive experiments include assessing the transferability of classification methods on datasets collected from different locations and at different times. We compared the performance of our proposed method with state-of-the-art spectral-spatial classification methods, including deep learning-based random patches network (RPNet), structural-kernel collaborative representation (SKCR), and window based nearest subspace classifier (WBNSC). The results show that the proposed method outperforms existing techniques in most cases. The datasets and source code are publicly available on GitHub at https://github.com/kgtoker/MSBNSC, enabling more comprehensive access and collaboration in environmental monitoring efforts.

Evidence of climate change - investigating Glacial terminus and lake inventory using earth observation data for mountainous Bhutan

The mapping and monitoring of different types of Glacial lakes through the Geospatial techniques is vital to show the impact of climate changes on the Glacier and alleviate hazards that result from the bursting of Glacial Lakes and cause catastrophic consequences to human lives. The main goal of the present work was to map and analyze different types of glacial lakes in Bhutan during the years 1990, 2000, and 2017. Several sets of satellite images, Landsat-TM for 1990, Landsat ETM+ for 2000, and Landsat 8-OLI satellite image for 2017, were used to estimate the changes in the glacial lakes and the inventory study. Several glacial lakes, i.e., moraine-dammed lake, supra glacial lake, lateral moraine lake, erosional lake, medial moraine lake, and end moraine lake, were mapped within these periods. It was found that there was a rapid increase in glacial lakes from 1990 to 2017. The number of glacial lakes in 1990 was increased from 213 to 436 in 2017. It was also observed that the spatial dimensions of some of the glacial lakes increased. The study revealed five end moraine lakes, 40 lateral moraine lakes, 50 supra glacial lakes, 239 erosional lakes, and 15 other moraines dammed lakes in 2017.

Extraction of building footprint using MASK-RCNN for high resolution aerial imagery

Extracting individual buildings from satellite images is crucial for various urban applications, including population estimation, urban planning, and other related fields. However, Extracting building footprints from remote sensing data is a challenging task because of scale differences, complex structures and different types of building. Addressing these issues, an approach that can efficiently detect buildings in images by generating a segmentation mask for each instance is proposed in this paper. This approach incorporates the Regional Convolutional Neural Network (MASK-RCNN), which combines Faster R-CNN for object mask prediction and boundary box recognition and was evaluated against other models like YOLOv5, YOLOv7 and YOLOv8 in a comparative study to assess its effectiveness. The findings of this study reveals that our proposed method achieved the highest accuracy in building extraction. Furthermore, we performed experiments on well-established datasets like WHU and INRIA, and our method consistently outperformed other existing methods, producing reliable results.

Dual level attention based lightweight vision transformer for streambed land use change classification using remote sensing

Due to rapid urbanization, rising food demand, and changed precipitation patterns, the waterbodies are contracting their former beds. The continuous shrinking of waterbodies is deteriorating the vital cultural, supporting, provisioning, and regulating services. Thus, understanding and mitigating the impacts of streambed land cover change is crucial for maintaining healthy aquatic ecosystems and improving flood resilience of surrounding population. The existing works use high-resolution aerial imagery focusing on large waterbodies, while ignoring the most vulnerable floodplains of innumerous small water bodies due to high inter-class similarity. This limits the ability to perform a temporal analysis of land cover change along small water bodies. The present work aims to resolve this issue using open-source satellite imagery and taking patched samples along the boundary of small water bodies to identify long-term changes in land cover patterns. Sentinel-2 and Landsat 50 acquired satellite images were used to identify the land cover of this colonized stream bed. The data of Landsat 50 served as historical reference for identifying the changed land use. To capture spatial hierarchies in satellite images effectively, in this paper, a novel dual attention-based vision transformer has been developed for land-cover classification in four categories namely, water, built-up, siltation, and vegetation. The developed model is trained on the data collected from three potential sites in India. The experimental results are validated against seven state-of-the-art deep learning models. The results reveal that the proposed method outperformed all the considered methods by achieving accuracy and precision of 88.4% and 88.9%, respectively, while consuming the least number of parameters. The results reaffirm the concretization and erosion of nature’s flood buffers for economic advancement.

RDC-UNet++: An end-to-end network for multispectral satellite image enhancement

Multi-spectral satellite imagery is an ideal data source for comprehensive, real-time Earth observation (EO) due to its extensive coverage of Earth and regular updates. It has a wide range of applications in environment monitoring, disaster management, urban planning, weather forecasting etc. Yet, the visual aspect of these images and thus the possibility to extract useful information using image processing techniques is often degraded due to fog, rain, dust, cloud, etc. Satellite image enhancement denotes a set of techniques designed to improve the quality of a satellite image such that the result is more useful for image analysis. The image enhancement aims to improve the quality of an image such that the enhanced image is more useful for image analysis than the original image for a particular remote sensing application. This study introduces a multi-spectral satellite image enhancement architecture called Residual Dense Connection-based UNet++ (RDC-UNet++). The unique design can improve multi-spectral images by enhancing their color and texture details. Extensive experimental studies on multi-spectral image datasets containing more than 150 images prove that the proposed architecture performs better than recent state-of-the-art satellite image enhancement algorithms.

The synergistic effect of multiple design factors on building energy consumption of office blocks: A case study of Wuhan, China

Design factors such as block morphology, building form and building envelope performance are key parameters affecting building energy consumption of urban blocks. However, the synergistic effect of multi-design factors on building energy consumption has not been studied in detail. This paper aims to investigate the synergistic effect of multi-design factors on building energy consumption of office blocks in Wuhan, China. This study employs field survey, satellite map and open street map to extract the typical building forms and construct parametric models of office blocks. Then, 81 representative scenarios considering the synergy of multiple design factors in office blocks are built. Then, based on the Rhino & Grasshopper parametric platform, a building energy assessment tool that considers the synergistic effect of multiple design factors is developed to simulate and record the building energy consumption of each scenarios. Finally, the synergistic effect of multi-design parameters on building energy consumption of office blocks is quantified using correlation and multiple linear regression methods. The findings reveal that the synergistic effect of multi-design factors on building energy consumption is 18.53%. Building energy consumption for high-rise pavilion typology is saved by 9.64% than multi-storey courtyard. The key design parameter combinations affecting building energy consumption in office block are building shape factor, solar heat gain coefficient, wall K-value, building width and window-wall ratio of west and east. This study contributes to a deeper understanding of the synergistic effects of multiple design parameters on building energy consumption in office blocks and presents a scalable model that can be applied globally to promote sustainable urban development.

Floating Litter detection at the Estuary of Puducherry using Sentinel-2 data and Machine Learning model

Marine Floating Litter (MFL) comprises synthetic polymers or plastic that are purposefully tossed out or inadvertently mislaid along rivers, drains, beaches, or other coastal environments that connect the marine ecosystem. Given the adverse impact of the chemicals involved in plastic manufacturing on the marine environment, including marine biota, the need to locate, track, and remove the floating plastic has become demanding. Increasing focus has been placed on remote sensing for its multispectral imaging with high resolution and its capability to monitor large areas systematically. Compared to old-fashioned in-situ observation, satellite imaging products are pecuniary and non-chronophagous. Therefore, the current study exploited the full functionality of open-source Sentinel-2 satellite-retrieved data to detect MFL. The Union Territory of Puducherry (Southern region of India) is the study location. Field measurements were collected from the Ariyankuppam River and the Chunnambar River. A spectral signature profile was created to distinguish other floating debris from floating plastic. The Floating Debris Index (FDI) and Plastic Index (PI) were modeled to validate the performance. It has been proven that FDI performs better at detecting MFL than PI. A Random Forest machine learning model was applied to classify water, plastic debris, and slurry debris. The overall observation of the model details the distribution percentage and accuracy assessment of the proposed model. The accuracy of classifying water, slurry debris, and plastic debris are 64.91%, 61.6%, and 96.71%, respectively, indicating room for improvement in classifying water and slurry debris. The proposed model is used for the first time at the Union Territory of Puducherry and is significantly more efficient compared to in-situ observation in terms of time, cost, and workforce.

Spatiotemporal Variations of Vegetation and Its Response to Climate Change and Human Activities in Arid Areas—A Case Study of the Shule River Basin, Northwestern China

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Understanding vegetation dynamics and its response to climate change and human activities provides essential ecological and environmental resource management information. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine platform and Landsat satellite images, employing trend analysis and other methods to examine spatiotemporal changes in vegetation in the SRB. Additionally, we used partial correlation and residual analyses to explore the response of FVC to climate change and human activities. The main results were: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10−3 a–1. The area within 1 km of roads experienced a higher increase of 3 × 10−3 a–1, while the roadless areas experienced a lower increase of 1.1 × 10−3 a–1. The FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that the FVC correlates positively with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. A weak negative correlation exists between the FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) The contributions of climate change to vegetation recovery are increasing. Human activities, particularly agricultural practices, infrastructure development, and the conversion of farmland to grassland, significantly influence vegetation changes in densely populated areas. (4) The area changes of different land types are closely related to climate factors and human activities. Increased construction, agricultural activity, and converting farmland back to grassland have led to an increase in the area proportions of “impervious surfaces”, “cropland”, and “grassland”. Climate changes, such as increased rainfall, have resulted in larger areas of “wetlands” and “sparse vegetation”. These results provide valuable information for ecosystem restoration and environmental protection in the SRB.

Bacterial-fungicidal vine disease detection with proximal aerial images

Vine disease detection is considered one of the most crucial components in precision viticulture. It serves as an input for several further modules, including mapping, automatic treatment, and spraying devices. In the last few years, several approaches have been proposed for detecting vine disease based on indoor laboratory conditions or large-scale satellite images integrated with machine learning tools. However, these methods have several limitations, including laboratory-specific conditions or limited visibility into plant-related diseases. To overcome these limitations, this work proposes a low-altitude drone flight approach through which a comprehensive dataset about various vine diseases from a large-scale European dataset is generated. The dataset contains typical diseases such as downy mildew or black rot affecting the large variety of grapes including Muscat of Hamburg, Alphonse Lavallée, Grasă de Cotnari, Rkatsiteli, Napoca, Pinot blanc, Pinot gris, Chambourcin, Fetească regală, Sauvignon blanc, Muscat Ottonel, Merlot, and Seyve-Villard 18402. The dataset contains 10,000 images and more than 100,000 annotated leaves, verified by viticulture specialists. Grape bunches are also annotated for yield estimation. Further, tests were made against state-of-the-art detection methods on this dataset, focusing also on viable solutions on embedded devices, including Android-based phones or Nvidia Jetson boards with GPU. The dataset, as well as the customized embedded models, are available on the project webpage.2

Solving satellite image data downlink scheduling problem with family attribute via a bi-stage differential evolutionary algorithm

Due to the asynchronous development of observation and transition capabilities, the original image data (OID) obtained by a one-time observation cannot be completely transmitted in one transmission cycle between the EOS and GS, which is named a visible time window (VTW). First, it is needed to segment the OID into several segmented image datasets (SIDs), which are then transmitted in several VTWs. Furthermore, this can cause the satellite image data downlink scheduling problem (SIDSP). This study defines an innovative SIDSP as a satellite image data downlink scheduling problem with the family attribute (SIDSPFA), where massive OID are segmented using a fast segmentation operator, and all SIDs and other no-segmented OID are transmitted in the second step. In addition, two optimization objectives, namely the failure rate (FR) and Segmentation Time (ST) of the image data transmission, are defined to formalize the SIDSPFA as a bi-objective discrete optimization model. Further, a bi-stage differential evolutionary algorithm (DE-CD) is developed. The results of the extensive simulation tests verify the efficiency of the models, strategies, algorithms, and operators used in this study.

Unraveling the decadal–scale coastal evolution and hazards of the Salamanca Barrier (Colombia)

This study aims to characterize the primary coastal hazards operating at both medium-term and episodic scales along the Salamanca barrier in the Caribbean coast of Colombia. On a medium-term (decadal) scale, we analyzed satellite images spanning the past 40 years to characterize shoreline evolution, the associated coastal sediment budget and the responsible sediment transport pattern. At this scale, the barrier has experienced intense shoreline retreat, with erosion rates gradually decreasing over time. For the last analyzed period, 2009–2019, maximum erosion rates have exceeded −20 m/yr. This erosion is primarily driven by a net longshore sediment transport pattern directed towards the west, induced by a wave climate concentrated in a narrow direction arc from the NE. Peak transport rates, reaching an order of magnitude of 106 m3/yr, have been estimated for the westernmost (more exposed) part of the barrier. At an episodic scale, we estimated hazards by evaluating storm-induced erosion and flooding potential. The eastern zone of the barrier showed greater susceptibility to overwash, while potential erosion was only relevant in the western and central sectors. Notably, the most impactful events were associated with waves from directions of 30 and 40°. The cumulative contribution of hazards operating at both scales contribute to various challenges affecting infrastructures, ecosystems, and communities along the Salamanca barrier.

Satellite Image Classification using TensorFlow

Abstract: Satellite image classification plays a crucial role in various fields such as agriculture, urban planning, disaster management, and environmental monitoring. This paper presents a novel approach utilizing TensorFlow, a popular open-source machine learning framework, for satellite image classification. The proposed methodology leverages deep learning techniques to extract meaningful features from satellite images, enabling accurate classification into predefined categories. By harnessing the power of convolutional neural networks (CNNs) implemented in TensorFlow, this research aims to enhance the efficiency and accuracy of satellite image classification tasks. The experimental results demonstrate the effectiveness of the proposed approach in achieving high classification accuracy of about 96.5% while maintaining computational efficiency. It classifies about 10 different classes obtained from the EuroSat dataset. It is implemented in Google Collab for faster training and implementation.

Spatial analysis of electrical distribution system in obio-akpor local government area, rivers state, Nigeria

The distributions of quality electrical energy to end users and, achieving a reasonable level of customer’s satisfaction from the electricity distribution companies servicing most area are faced with divers’ spatial problems particularly with the use of dilapidated infrastructures and poor services. This study used spatial techniques to analyze the spatial characteristics of the electrical distribution system in Obio-Akpor Local Government Area, Rivers State. It also examined the ward pattern of Port Harcourt Electricity Distribution (PHED) customer’s satisfaction and also predicted the ward level of customer’s satisfaction within the study area. Primary and secondary data were used for the study. The primary data on customer’s satisfaction of PHED services was obtained from questionnaire survey. Purposive sampling method was used for this study and the population for the study is the entire customers of PHED in the study area. The secondary data consist of ward boundary map of the study area, satellite image, PHED network spatial data and attribute data of all the 3200 (three thousand two hundred) transformers (11/0.415KVA and 33/0.415KVA) within the study area as at the time of this study. Geospatial analysis such as clip analysis, buffering were carried. The data attribute were analyzed using average nearest neighbor analysis, kernel density, zonal statistics, inverse distance weighting and geographical weighted regression to obtain the desired aim of the study. The result show that, the location pattern of PHED transformers are clustered. Result further reveal that the ward pattern of customer’s satisfaction are geography dependent with either positive, negative or mixed relationship.

Estimation of Evapotranspiration in South Eastern Afghanistan Using the GCOM-C Algorithm on the Basis of Landsat Satellite Imagery

This study aims to assess the performance of the Global Change Observation Mission—Climate (GCOM-C) ETindex estimation algorithm to estimate the actual evapotranspiration (ETa) in southeastern Afghanistan. Here, the GCOM-C ETindex algorithm was adopted to estimate the monthly ETa for the period from November 2016 to October 2017 using a series of Landsat 8, Thermal Infrared Sensor (TIRS) Band 10 satellite imagery. The estimation accuracy was evaluated by comparing the results with other estimates of ETa, namely the mapping evapotranspiration with the internalized calibration (METRIC) model, the MODIS Global Evapotranspiration Project (MOD16), the surface energy balance system (SEBS) tools, and with the crop evapotranspiration under standard conditions (ETc) as estimated by the FAO-56 procedure. The evaluation was made for irrigated wheat, maize, rice, and orchards and for non-irrigated bare soil land. The comparison of ETa values showed good correlation among the GCOM-C, METRIC, and FAO-56, while the MOD16 and SEBS showed significantly lower values of ETa. The agreement with the METRIC ETa implies that the simple GCOM-C algorithm successfully estimated the ETa in the region and that the precision was similar to that of the METRIC. This study provides the first high-quality evapotranspiration data with the spatial resolution of Landsat Band 10 data for the southeastern part of Afghanistan. The estimation procedure is straightforward, and its results are anticipated to enhance the understanding of regional hydrology.