Remote Sensing Information Research Articles

Comparing Satellite, Reanalysis, Fused and Gridded (In Situ) Precipitation Products Over Türkiye

ABSTRACTPrecipitation is the fundamental source for various research areas, including hydrology, climatology, geomorphology, and ecology, serving essential roles in modelling, distribution, and process analysis. However, the accuracy and precision of spatially distributed precipitation estimates is a critical issue, particularly for daily scale and topographically complex areas. Although many datasets have been developed based on different algorithms and sources are developed for this purpose, determining which of these datasets best reflects actual conditions is quite challenging. This study, hence, aims to compare the 25 global distributed precipitation estimates (gridded, satellite, model, and fused) concerning 221 ground‐based observations based on the ranking of 18 continuous (evaluation statistics), eight categorical (precipitation indices), and two seasonality metric (high and low precipitation). Upon examining the results, gridded and model precipitation data including APHRODITE (Asian Precipitation—Highly‐Resolved Observational Data Integration Towards Evaluation), CPC (Global Unified Gauge‐Based Analysis of Daily Precipitation), ERA5‐Land (ECMWF Reanalysis 5th Generation for Lands), and CFSR (Climate Forecast System Reanalysis) occupy the top four positions in continuous metrics. In contrast, satellite data such as PERSIANN‐PDIR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), CMORPH (Climate Prediction Center morphing method), IMERG (The Integrated Multi‐Satellite Retrievals for GPM), and TRMM‐TMPA (Tropical Rainfall Measuring Mission/Multi‐satellite Precipitation Analysis) dominate in the top four positions in categorical metrics. For seasonality of high and low precipitation, fused, gridded, and reanalyses products such as CPC, MSWEP (Multi‐Source Weighted‐Ensemble Precipitation, version 2), HydroGFD (Hydrological Global Forcing Data), CFSR rank among top four. Based on the first five rankings of all metrics, fused (multiple sourced) and gridded datasets accurately reflect the actual situations compared to other precipitation products. Reanalysis (model) and satellite‐based follow this rank, respectively. The results clearly indicate that fused precipitation derived products from multiple sources offer better accuracy and precision in representing the spatial distribution of precipitation on a daily scale.

Remote Sensing Identification and Information Extraction Method of Glacial Debris Flow Based on Texture Variation Characteristics

The formation mechanism of glacial debris flows in alpine gorge mountain areas is complex, with varying characteristics across different regions. Due to the influence of mountain shadows and the accumulation and ablation of ice and snow, accurately identifying and rapidly extracting glacial debris flows using optical images remains challenging. This study utilizes the Random Forest method to develop a multi-feature spatiotemporal information extraction model based on Landsat-8 images and a glacial debris flow gully identification model. These models were applied to the Songzong–Tongmai section of the Sichuan–Tibet Highway to identify glacial debris flows. The results showed that (1) the multi-feature spatiotemporal extraction model effectively eliminated the interference of mountain shadows and ice–snow phase changes, resulting in a higher accuracy for identifying and extracting glacial debris flows in areas with significant information loss due to deep shadows. The total accuracy was 93.6%, which was 8.9% and 4.2% higher than that of the Neural Network and Support Vector Machine methods, respectively. (2) The accuracy of the glacial debris flow gully identification model achieved 92.6%. The proposed method can accurately and rapidly identify glacial debris flows in alpine gorge mountain areas, facilitating remote sensing dynamic monitoring. This approach reduces the damage caused by debris flows to both transportation and the environment, ensuring the safe passage of highways and promoting the sustainable development of the region.

Remote Sensing - Based Analysis of LST and NDVI Correlation for the Coolest and Hottest Month of 2023 in Khanaqen City

Urban land cover characteristics often lead to variations in surface temperature. Remote Sensing (RS) and Geographic Information Systems (GIS) are valuable tools for obtaining detailed information about surface indices. This study evaluates the temporal variation of Land Surface Temperature (LST) in Khanaqen city, located in South eastern Iraqi Kurdistan and Eastern Iraq. The research investigates the relationship between LST and the Normalized Difference Vegetation Index (NDVI) for the coolest and hottest month of 2023 using Landsat images from the USGS Earth Explorer. The objective is to explore how green spaces influence LST during the cool, humid winter and hot, dry summer. Two satellite images from Landsat 9 were used to retrieve LST and NDVI for the study periods. The results indicate that green spaces significantly reduce LST in July, so that the result of the correlation analysis of variables, significant inverse correlation between LST and NDVI (R = -0.53, R2 = 0.28 and P value = 0.00) highlights the effect of vegetation cooling. Conversely, January shows a modest correlation (R = -0.01, R2 = 0.00, and P value = 0.90), indicating a minimal effect of vegetation cover on LST.

Potential Sites for Rainwater Harvesting Focusing on the Sustainable Development Goals Using Remote Sensing and Geographical Information System

An innovative way to combat water scarcity brought on by population increase and climate change is rainwater harvesting (RWH), particularly in arid and semiarid areas. Currently, Pakistan is facing major water issues due to underprivileged water resource management, climate change, land use changes, and the sustainability of local water resources. This research aims to find out the suitable sites and options for RWH structures in the Quetta district of Pakistan by integrating the depression depth technique, Boolean analysis, and weighted linear combination (WLC) with hydrological modeling (HM), multicriteria analysis (MCA), a geographic information system (GIS), and remote sensing (RS). To find suitable sites for RWH, a collection of twelve (12) thematic layers were used, including the slope (SL), land use land cover (LULC), subarea (SA), runoff depth (RD), drainage density (DD), lineament density (LD), infiltration number (IFN), distance from built-up area (DB), distance from roads (DR), distance from lakes (DL), maximum flow distance (MFD), and topographic wetness index (TWI). The Boolean analysis and WLC approach were integrated in the GIS environment. The consistency ratio (CR) was calculated to make sure the assigned weights to thematic layers were consistent. Overall, results show that 6.36% (167.418 km2), 14.34% (377.284 km2), 16.36% (430.444 km2), 18.92% (497.663 km2), and 18.64% (490.224 km2) of the area are in the categories of very high, high, moderate, low, and very low suitability, respectively, for RWH. RWH potential is restricted to 25.35% (666.86 km2) of the area. This research also identifies the five (5) best locations for checking dams and the ten (10) best locations for percolation tanks on the streams. The conducted suitability analysis will assist stakeholders in selecting the optimal locations for RWH structures, facilitating the storage of water, and addressing the severe water scarcity prevalent in the area. This study proposes a novel approach to handle the problems of water shortage in conjunction with environmental and socioeconomic pressures in order to achieve the sustainable development goals (SDGs).

Role of Remote Sensing (RS) and Geographic Information Systems (GIS) in Water Resource Management in India

Role of Remote Sensing (RS) and Geographic Information Systems (GIS) in Water Resource Management in India

An approach to demarcate prospective areas for groundwater recharge a case study of Upper Thurinjalar Tamil Nadu, India

India, as the world's largest consumer of groundwater, faces an alarming water crisis, particularly in southern regions like Upper Thurinjalar, Tamil Nadu. The relentless extraction of groundwater has severely depleted reserves, making it increasingly difficult to meet the water demands of households, agriculture, and industry. This unsustainable usage endangers the region's water security, emphasizing the urgent need for sustainable management practices. Immediate action is essential to safeguard these vital resources and secure a sustainable water future for the region. The present study addresses the critical need to identify optimal groundwater recharge areas to mitigate the adverse effects of groundwater depletion. Utilizing Remote Sensing (RS) and Geographic Information Systems (GIS), this research focuses on delineating suitable recharge zones in Upper Thurinjalar, Tamil Nadu, to promote sustainable water resource management. Through geospatial analysis, thematic maps were generated, incorporating variables such as geomorphology, geology, subsurface lithology, lineaments, land use, drainage density, soil types, and slope. Utilizing satellite data within the GIS framework, groundwater recharge zones were classified into three categories: highly suitable (61.06 km2), moderately suitable (214.18 km2), and least suitable (48.07 km2). Additionally, prioritized interventions for 18 rural reservoirs within the same catchment area included sediment removal, depth enhancement, and infrastructure upgrades. The application of artificial groundwater recharge in these targeted areas is anticipated to significantly alleviate irrigation water deficits, thereby advancing sustainable development and rehabilitating degraded land. The use of a weighted overlay technique within Geographic Information Systems (GIS) has proven to be a highly effective method for optimizing water resource management, facilitating the sustainable development of groundwater resources. The results emphasize the importance of promptly undertaking focused interventions, such as prioritizing tank improvements and deploying artificial recharge procedures, in order to guarantee the long-term availability of water. Implementing these proactive measures is essential for reducing water scarcity and promoting the development of unused land in the designated recharge areas.

Integrated Morphometric and Hypsometric Analysis of Niragantipalli Micro Watershed Using Remote Sensing and Geographical Information System Techniques

Employing Remote sensing (RS) and geographic information system (GIS) for the morphometric parameters analysis are discovered to be tremendous usefulness in the prioritization of watersheds for soil, water conservation and natural resource management at micro watershed level. The analysis of morphometric parameters plays a crucial role for understanding and managing watersheds. In current study an, attempt was to determine the morphological parameters of Niragantipalli micro-watershed in Chikkaballapura District of Karnataka, India. For detail study, Google earth pro, GPS visualizer conversion toll and Arc GIS were used for preparation of DEM and delineation of the watershed boundary. GIS was used for evaluation of basic, linear, areal and relief aspects of morphometric parameters. The Niragantipalli micro-watershed occupies an area of 632 ha with third order stream (truck order) and dendritic drainage pattern. The mean bifurcation ratio of the micro-watershed is 2.5 it suggests that the drainage system formed on homogeneous rock when the influences of geologic structures on the network of streams were negligible and generate sharp peak flow. With a drainage density of 1.31 km/km2, the drainage network is extremely coarse. The watershed is elongated, as shown by the form factor of 0.27 and elongation ratio of 0.58. The results from the morphometric analysis of the watershed are very beneficial for developing and designing conservation structures of soil and watershed management measures. The hypsometric curves’ structure as well as estimated hypsometric integral results reflects the Niragantipalli Micro Watershed erosional stages. As a result, the study concludes that morphometric and hypsometric analysis findings may be useful to stakeholders participating in catchment development and management projects.

Analysis of the Morphometric Characteristics of Wadi Rajil in the Azraq Basin using Geographic Information Systems

The study aimed to Analyse the Hydrometric Characteristics of Wadi Rajil basin and assess the flood hazards in the basin using Remote Sensing and Geographic Information Systems Techniques. The study used Models of Digital Visualizations of the basin and processed them using ARC GIS 10.5 and ARC HYDRO TOOLS. The result was that the Hypsometric Coefficient reached (21.4%) of the valley area that was exposed to erosion and (78.6%) of it is still waiting for its turn to erosion, indicating that the valley has reached the stage of youth. The analysis of Hot Spots in the GIS program resulted in negative values for internal Drainage Basins greater than -0.5 extending to the northern and northwestern parts of the basin. It also showed that the flow coefficient exceeds the internal Drainage Coefficient or Leakage. As for the positive values, they are distributed in the southern and southeastern parts, where the (Hot) values prevail and where the leakage coefficient exceeds the Flow Coefficient. We find that the Hot Spots are concentrated in the southeastern parts, which are mostly extended. The Rift and thus the most suitable areas for Groundwater Charging through it.

Integrated geophysical and GIS approaches for groundwater potential assessment: a case study of Aladja, Delta State, Nigeria

ABSTRACT This study explores the groundwater potential in Aladja, Udu, Local Government Area of Delta State, Nigeria, using an integrated approach combining electrical resistivity techniques with Remote Sensing (RS) and Geographic Information System (GIS) methods. The primary aim is to delineate aquifers and comprehensively assess groundwater resources. Methods include Vertical Electrical Sounding (VES) to gather resistivity data, RS and GIS for analyzing geological and hydrological parameters such as lineament density, drainage density, slope, soil type, and land use. The resistivity values ranged from 147 to 460.50 Ωm, with aquifer thicknesses varying between 6.50 and 36.30 m. The RS and GIS analysis indicated high groundwater potential in regions characterized by significant lineament density and moderate slopes. The study highlights the complementarity of VES and RS/GIS in groundwater exploration, revealing substantial groundwater resources in the northwestern regions of the study area. This approach underscores the importance of integrating these methods for a more accurate assessment of groundwater potential. The novelty of this study lies in validating RS and GIS findings with VES data, ensuring reliable groundwater potential mapping and effective resource management. This integrated methodology offers a robust framework for sustainable groundwater resource assessment and management in similar geological settings.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

ML Approaches for the Study of Significant Heritage Contexts: An Application on Coastal Landscapes in Sardinia

Remote Sensing (RS) and Geographic Information Science (GIS) techniques are powerful tools for spatial data collection, analysis, management, and digitization within cultural heritage frameworks. Despite their capabilities, challenges remain in automating data semantic classification for conservation purposes. To address this, leveraging airborne Light Detection And Ranging (LiDAR) point clouds, complex spatial analyses, and automated data structuring is crucial for supporting heritage preservation and knowledge processes. In this context, the present contribution investigates the latest Artificial Intelligence (AI) technologies for automating existing LiDAR data structuring, focusing on the case study of Sardinia coastlines. Moreover, the study preliminary addresses automation challenges in the perspective of historical defensive landscapes mapping. Since historical defensive architectures and landscapes are characterized by several challenging complexities—including their association with dark periods in recent history and chronological stratification—their digitization and preservation are highly multidisciplinary issues. This research aims to improve data structuring automation in these large heritage contexts with a multiscale approach by applying Machine Learning (ML) techniques to low-scale 3D Airborne Laser Scanning (ALS) point clouds. The study thus develops a predictive Deep Learning Model (DLM) for the semantic segmentation of sparse point clouds (<10 pts/m2), adaptable to large landscape heritage contexts and heterogeneous data scales. Additionally, a preliminary investigation into object-detection methods has been conducted to map specific fortification artifacts efficiently.

Identification of groundwater recharge potential zone using geospatial approaches and multi criteria decision models in Udham Singh Nagar district, Uttarakhand, India

Groundwater is a valuable natural resource, which is extensively used in agricultural, industrial as well as daily activities of human beings. It is an essential source of freshwater need of half of the world’s population. In this study Remote Sensing (RS) and Geographic Information Systems (GIS), along with Multi criteria decision models were used. The Analytical Hierarchy Process (AHP) was used to determine the weights of various themes and their classes in the study to identify the groundwater recharge zones in Udham Singh Nagar district. Nine thematic maps/layers were created using GIS methods and employed for the demarcation of groundwater potential zone (GWPZ). It was concluded that around 82% of the area is very suitable (very good and good) for ground water recharge, which suggests that there is a significant amount of potential for artificial recharge systems to be implemented. This can help to augment the natural recharge process, improve water quality, and reduce the strain on existing water sources. Whereas only 18% (very poor and poor) of the area has low potential to groundwater recharge is still important to consider, as it may require different approaches or strategies to improve its suitability. Validation shows that, wells data; (n=38) have water level <5 mbgl (83%), whereas only (n=8) have water less >5 mbgl (17%). This area could be prioritized for more extensive data collection or monitoring to better understand its hydrogeological characteristics. The findings will indeed be helpful for decision-makers in selecting suitable locations for groundwater recharge structures.

GIS-based multi-criteria approach for ranking suitable areas for the implementation of PV-Powered MCDI desalination plants in the Rheris Watershed, Southeast Morocco

In Morocco's oases, which rely heavily on surface water from mountain sources, the threat of decreasing rainfall presents a significant challenge, leading to a decline in surface water availability. This study focuses on the Rheris Watershed in Southeast Morocco, where brackish water desalination emerges as a viable alternative to ensure sustainable water supply for agriculture. Prior to the deployment of a pilot plant integrating a desalination unit, selecting an optimal site for PV-powered MCDI desalination plants was critical. This selection was based on a range of criteria, including Groundwater Quantity, Global Horizontal Irradiation, Temperature, Evaporation, Water Salinity, Water Quantity, Well Density and Slope. These criteria were evaluated using a combination of the Analytical Hierarchy Process (AHP), and geospatial data obtained from Remote Sensing (RS) and Geographic Information System (GIS) techniques.This case study in the Rheris watershed identified potential sites for future Membrane Capacitive Deionization (MCDI) desalination projects, High suitability areas encompass 280.45 km² (30.91 %), located west of Tinejdad, Khettarat Moulay Hachem, north of Goulmima, and south of Tinghir. Low suitability areas cover 63.75 km² (7.02 % of the study area), including regions south of Tadighoust and Tinejdad. Moderate suitability areas span 562.86 km² (62.05 %), involving central regions of Goulmima, Tadighoust, Tinejdad, and Tinghir. Based on the results of the study, the recommendations include focusing on areas identified as highly suitable for MCDI technology for sustainable agriculture, refining selection criteria based on local data, exploring brine management strategies, and adopting integrated water management approaches to ensure sustainable resource use.

Tracking Urban Sprawl: A Systematic Review and Bibliometric Analysis of Spatio-Temporal Patterns Using Remote Sensing and GIS

The urban sprawl phenomenon refers to the expansion of urban areas driven by high population growth and migration. A spatio-temporal approach is indispensable in urban sprawl research. Monitoring and evaluating urban sprawl in a region is crucial for controlling drastic environmental changes. Integrated Remote Sensing (RS) and Geographic Information System (GIS) technologies can serve as essential tools for this purpose. The aim of this systematic literature review paper is to gather information on the latest data, methods, and findings to be considered in future urban sprawl research. The PRISMA method was employed, involving filtering from the Scopus database, resulting in 30 papers selected for an in-depth review to address the objectives of this paper. Landsat data remains the preferred choice for monitoring changes due to its extensive historical archive compared to other data sources. Landscape metrics represent a more advanced method com-pared to conventional change detection in quantifying urban sprawl. Other indices and quantifiers are also used to support the quantification of urban sprawl. Two perspectives exist in selecting the study's temporal intervals: consistent and inconsistent, which are adjusted based on the natural characteristics of "change," namely "abrupt" and "gradual." Suggestions for future research include using data with detailed spatial resolution and narrow study intervals while considering the patterns of urban sprawl formation.

Identifying hidden groundwater reserves: GIS advances and multi-criteria decision analysis for enhanced potential assessment

The aquifer system may become susceptible as a result of excessive groundwater extraction without regard for scientific principles. This study evaluates the Groundwater Potential (G.P.) using multicriteria decision analysis (MCDA) and geospatial modelling. Here in MCDA, the G.P. is measured using Remote Sensing (R.S.) and Geographic Information System (GIS), together with Analytical Hierarchical Process (AHP), Fuzzy Analytical Hierarchical Process (FAHP), and Best Worst Method (BWM). Analysis and model creation took into account 15 groundwater affecting elements that affect the G.P. Groundwater Potential Zones (GPZs) in Cachar District, India varied by the following percentages: 46.97%, 37.41%, and 15.62% for AHP; 46.13%, 40.92%, and 12.95% for FHP; and 50.65%, 36.51%, and 12.84% for BWM, respectively, for low, medium, and high zones. The FAHP and BWM models outperformed the AHP model, according to the results of the qualitative validation. The concerned authorities might identify vulnerable locations and plan sustainable groundwater management with the use of these MCDA approaches combined with GIS.

Monitoring Soybean Soil Moisture Content Based on UAV Multispectral and Thermal-Infrared Remote-Sensing Information Fusion.

By integrating the thermal characteristics from thermal-infrared remote sensing with the physiological and structural information of vegetation revealed by multispectral remote sensing, a more comprehensive assessment of the crop soil-moisture-status response can be achieved. In this study, multispectral and thermal-infrared remote-sensing data, along with soil-moisture-content (SMC) samples (0~20 cm, 20~40 cm, and 40~60 cm soil layers), were collected during the flowering stage of soybean. Data sources included vegetation indices, texture features, texture indices, and thermal-infrared vegetation indices. Spectral parameters with a significant correlation level (p < 0.01) were selected and input into the model as single- and fuse-input variables. Three machine learning methods, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Genetic Algorithm-optimized Backpropagation Neural Network (GA-BP), were utilized to construct prediction models for soybean SMC based on the fusion of UAV multispectral and thermal-infrared remote-sensing information. The results indicated that among the single-input variables, the vegetation indices (VIs) derived from multispectral sensors had the optimal accuracy for monitoring SMC in different soil layers under soybean cultivation. The prediction accuracy was the lowest when using single-texture information, while the combination of texture feature values into new texture indices significantly improved the performance of estimating SMC. The fusion of vegetation indices (VIs), texture indices (TIs), and thermal-infrared vegetation indices (TVIs) provided a better prediction of soybean SMC. The optimal prediction model for SMC in different soil layers under soybean cultivation was constructed based on the input combination of VIs + TIs + TVIs, and XGBoost was identified as the preferred method for soybean SMC monitoring and modeling, with its R2 = 0.780, RMSE = 0.437%, and MRE = 1.667% in predicting 0~20 cm SMC. In summary, the fusion of UAV multispectral and thermal-infrared remote-sensing information has good application value in predicting SMC in different soil layers under soybean cultivation. This study can provide technical support for precise management of soybean soil moisture status using the UAV platform.

Uncertainty estimation of machine learning spatial precipitation predictions from satellite data

Abstract Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to optimally provide such estimates by benchmarking six algorithms, mostly novel even for the more general task of quantifying predictive uncertainty in spatial prediction settings. On 15 years of monthly data from over the contiguous United States (CONUS), we compared quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machines (LightGBM), and quantile regression neural networks (QRNN). Their ability to issue predictive precipitation quantiles at nine quantile levels (0.025, 0.050, 0.100, 0.250, 0.500, 0.750, 0.900, 0.950, 0.975), approximating the full probability distribution, was evaluated using quantile scoring functions and the quantile scoring rule. Predictors at a site were nearby values from two satellite precipitation retrievals, namely PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals), and the site’s elevation. The dependent variable was the monthly mean gauge precipitation. With respect to QR, LightGBM showed improved performance in terms of the quantile scoring rule by 11.10%, also surpassing QRF (7.96%), GRF (7.44%), GBM (4.64%) and QRNN (1.73%). Notably, LightGBM outperformed all random forest variants, the current standard in spatial prediction with machine learning. To conclude, we propose a suite of machine learning algorithms for estimating uncertainty in spatial data prediction, supported with a formal evaluation framework based on scoring functions and scoring rules.

Spatio-temporal analysis of riverbank changes using remote sensing and geographic information system

Fluvial hazards cause severe damage to human lives and properties. The increased intrusion of anthropogenic activities into natural hydrological cycles has increased hazard severity and confounded river channel dynamics. The present article studies the epochal channel erosion, accretion, and unaltered sites along the 42 km stretch between the Polavaram project and Dowleshwaram Barrage on the Godavari River. Geographic Information System (GIS) and Remote Sensing (RS) techniques were used to identify changes in river morphology. A total of five decades of Landsat images were analyzed, classifying the land use with a supervised learning Support Vector Machine (SVM) algorithm and an ISO cluster unsupervised algorithm to delineate the rivers and study the influence of land use land cover change (LULCC) on river morphology. Accretion and erosion channel changes during the assessment period were measured at all reaches, and the possible driving mechanisms of morphological alterations were studied.The results indicate the susceptibility of human dwellings and agricultural lands to floods due to the shifting of the highly vibrant river shoreline. The study provides the most recent information on Godavari River dynamics. These aspects of the river's evolution will aid in planning and management in the lower portions, which have not been well understood. River channel alterations can be quantitatively and economically analyzed using Remote Sensing (RS) and Geographic Information System (GIS) data.

Koreksi Bias Data Hujan Satelit Persiann Sebagai Alternatif Pos Stasiun Hujan dengan Pendekatan Quantile Mapping di Sub Das Brantas Hulu

The rainfall data is usually obtained from meteorological stations, which many sill observed manually in Indonesia and can lead to an observation error. Remote sensing rainfall data observed by satellite offer interesting alternatives to overcome the limitation of existing meteorological stations in Indonesia. It offers accuracy, spatial coverage, timeliness and cost effectiveness. However it contains bias if compared to land based station. This research will evaluate rainfall data observed in PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) in Brantas Hulu Watershed. The rainfall data of 13 years from 12 stations is evaluated to obtain area averaged rainfall using Thiessen Polygon method. Three datasets from PERSIANN, PERSIANN CCS and PERSIANN CDR are compared statistically with the area averaged area for daily, monthly and yearly rainfall. Bias correction is performed using Quantile Mapping to match the cumulative distribution curve from the area averaged rainfall. Verification of bias correction is performed using 2 years data from 2019 to 2020. Finally, the extreme rainfall analysis is performed to test the corrected data for design rainfall calculations. The correlation of daily data shows poor agreement for all three datasets with correlation coefficient below 0,40. The monthly and yearly data gives better correlation with coefficient above 0,80. It shows that for time series event, the satellite data gives poor correlation with the area averaged rainfall. However, quantile mapping correction gives consistent cumulative distribution agreement for correction and model verification. The verification analysis shows an increase in coefficient correlation from 0.387 to 0.43 (daily) and 0.887 to 0.915 (monthly). In addition, there is a decrease in normalized root mean square error (NRMSE) from 31.03% to 16.91% (daily) and 46.03% to 14.03% (monthly). A decrease in normalized mean absolute error (NMAE) occurred from 16.12% to 9.74% (daily) and 31.67% to 10.07% (monthly). Beside that, the results of extreme rainfall analysis produce values that are very close to the rain station. So it is found that the PERSIANN CCS quantile mapping regression model is the best in improving data quality.

Oxytenanthera abyssinica (A. Rich.) Munro land suitability evaluation in the Kurar watershed, Abay Gorge, Upper Blue Nile River Basin, Ethiopia

O. abyssinica, known in Ethiopia as lowland bamboo, is a solid-stemmed clump-forming bamboo species widely distributed in the western dry regions of Ethiopia. The versatility of the species means that it has enormous potential for land restoration in arid and semi-arid areas, in addition to bringing socio-economic benefits. It also displays remarkable adaptability, allowing it to thrive in the challenging ecological conditions of areas such as the Abay Gorge. The Abay Gorge, situated within the Upper Blue Nile River Basin of Northwestern Ethiopia is characterized by problems associated with soil erosion and land degradation. This study, therefore, aimed to evaluate and recommend a suitable spatial analysis for the adoption and development of lowland bamboo (O. abyssinica). Fifteen primary influencing factors were chosen according to the needs of O. abyssinica, the accessibility of data, and the financial implications associated with data analysis, particularly the expenses related to soil laboratory testing. Eight composite soil samples were taken directly by dividing the watershed into two categories (upper and lower catchment areas). These composite soil samples were analyzed. Data analysis was performed via Analytical Hierarchy Process (AHP) in conjunction with Multi-Criteria Decision-Making (MCDM) analysis and the use of Remote Sensing (RS) and Geographic Information System (GIS). The spatial analysis employed in this study was a weighted sum overlay analysis, which was applied by considering the criteria weight assigned to each factor. The analysis revealed that 21.2 % (666.5 ha) of the total area was highly suitable for the growth and development of O. abyssinica. About 56 % (1753 ha) of the land was moderately and marginally suitable for the desired land utilization type. However, 23 % (723.8 ha) of the study area was unsuitable for O. abyssinica. O. abyssinica has the potential to rehabilitate large parts of challenging areas such as the Abay Gorge due to its naturally invase rhizomes and drought-tolerance. It can also be introduced into agroforestry systems and used as living fences for villages.