scholarly journals GIS-Based Modeling for Selection of Dam Sites in the Kurdistan Region, Iraq

2020 ◽  
Vol 9 (4) ◽  
pp. 244
Author(s):  
Arsalan Ahmed Othman ◽  
Ahmed F. Al-Maamar ◽  
Diary Ali Mohammed Amin Al-Manmi ◽  
Veraldo Liesenberg ◽  
Syed E. Hasan ◽  
...  
Keyword(s):  
Active Faults ◽  
Curve Number ◽  
Landsat 8 ◽  
Severe Drought ◽  
Analytic Hierarchy ◽  
Discharge Data ◽  
Weighted Sum Method ◽  
Optical Images ◽  
Stream Width ◽  
Input Dataset

Iraq, a country in the Middle East, has suffered severe drought events in the past two decades due to a significant decrease in annual precipitation. Water storage by building dams can mitigate drought impacts and assure water supply. This study was designed to identify suitable sites to build new dams within the Al-Khabur River Basin (KhRB). Both the fuzzy analytic hierarchy process (AHP) and the weighted sum method (WSM) were used and compared to select suitable dam sites. A total of 14 layers were used as input dataset (i.e., lithology, tectonic zones, distance to active faults, distance to lineaments, soil type, land cover, hypsometry, slope gradient, average precipitation, stream width, Curve Number Grid, distance to major roads, distance to towns and cities, and distance to villages). Landsat-8/Operational Land Imager (OLI) and QuickBird optical images were used in the study. Three types of accuracies were tested: overall, suitable pixels by number, and suitable pixels by weight. Based on these criteria, we determined that 11 sites are suitable for locating dams for runoff harvesting. Results were compared to the location of 21 preselected dams proposed by the Ministry of Agricultural and Water Resources (MAWR). Three of these dam sites coincide with those proposed by the MAWR. The overall accuracies of the 11 dams ranged between 76.2% and 91.8%. The two most suitable dam sites are located in the center of the study area, with favorable geology, adequate storage capacity, and in close proximity to the population centers. Of the two selection methods, the AHP method performed better as its overall accuracy is greater than that of the WSM. We argue that when stream discharge data are not available, use of high spatial resolution QuickBird imageries to determine stream width for discharge estimation is acceptable and can be used for preliminary dam site selection. The study offers a valuable and relatively inexpensive tool to decision-makers for eliminating sites having severe limitations (less suitable sites) and focusing on those with the least restriction (more suitable sites) for dam construction.

scholarly journals Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1593
Author(s):  
Luca Cenci ◽  
Valerio Pampanoni ◽  
Giovanni Laneve ◽  
Carla Santella ◽  
Valentina Boccia
Keyword(s):  
Landsat 8 ◽  
Image Sharpness ◽  
Optical Images ◽  
Data Quality Assessment ◽  
Relevant Role ◽  
Edge Method ◽  
Selection Of ◽  
Statistical Criteria

Developing reliable methodologies of data quality assessment is of paramount importance for maximizing the exploitation of Earth observation (EO) products. Among the different factors influencing EO optical image quality, sharpness has a relevant role. When implementing on-orbit approaches of sharpness assessment, such as the edge method, a crucial step that strongly affects the final results is the selection of suitable edges to use for the analysis. Within this context, this paper aims at proposing a semi-automatic, statistically-based edge method (SaSbEM) that exploits edges extracted from natural targets easily and largely available on Earth: agricultural fields. For each image that is analyzed, SaSbEM detects numerous suitable edges (e.g., dozens-hundreds) characterized by specific geometrical and statistical criteria. This guarantees the repeatability and reliability of the analysis. Then, it implements a standard edge method to assess the sharpness level of each edge. Finally, it performs a statistical analysis of the results to have a robust characterization of the image sharpness level and its uncertainty. The method was validated by using Landsat 8 L1T products. Results proved that: SaSbEM is capable of performing a reliable and repeatable sharpness assessment; Landsat 8 L1T data are characterized by very good sharpness performance.

scholarly journals Multicriteria optimization in a fuzzy environment: The fuzzy analytic hierarchy process

2010 ◽  
Vol 20 (1) ◽  
pp. 71-85 ◽  
Author(s):  
Milanka Gardasevic-Filipovic ◽  
Dragan Saletic
Keyword(s):  
Analytic Hierarchy Process ◽  
Optimization Problems ◽  
Fuzzy Ahp ◽  
User Preferences ◽  
Analytic Hierarchy ◽  
Weighted Sum Method ◽  
Fuzzy Environment ◽  
Previously Treated ◽  
Judgment Matrix ◽  
Hierarchy Process

In the paper the fuzzy extension of the Analytic Hierarchy Process (AHP) based on fuzzy numbers, and its application in solving a practical problem, are considered. The paper advocates the use of contradictory test to check the fuzzy user preferences during fuzzy AHP decision-making process. We also propose consistency check and deriving priorities from inconsistent fuzzy judgment matrices to be included in the process, in order to check if the fuzzy approach can be applied in the AHP for the problem considered. An aggregation of local priorities obtained at different levels into composite global priorities for the alternatives based on weighted-sum method is also discussed. The contradictory fuzzy judgment matrix is analyzed. Our theoretical consideration has been verified by an application of commercially available Super Decisions program (developed for solving multi-criteria optimization problems using AHP approach) on the problem previously treated in the literature. The obtained results are compared with those from the literature. The conclusions are given and the possibilities for further work in the field are pointed out.

scholarly journals In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series

2019 ◽  
Vol 11 (2) ◽  
pp. 118 ◽  
Author(s):  
Valérie Demarez ◽  
Florian Helen ◽  
Claire Marais-Sicre ◽  
Frédéric Baup
Keyword(s):  
Time Series ◽  
Shuttle Radar Topography Mission ◽  
Irrigation Scheduling ◽  
Landsat 8 ◽  
Radar Images ◽  
Optical Images ◽  
Multi Temporal ◽  
Irrigated Crops ◽  
Elevation Model ◽  
The Impact

Numerous studies have reported the use of multi-spectral and multi-temporal remote sensing images to map irrigated crops. Such maps are useful for water management. The recent availability of optical and radar image time series such as the Sentinel data offers new opportunities to map land cover with high spatial and temporal resolutions. Early identification of irrigated crops is of major importance for irrigation scheduling, but the cloud coverage might significantly reduce the number of available optical images, making crop identification difficult. SAR image time series such as those provided by Sentinel-1 offer the possibility of improving early crop mapping. This paper studies the impact of the Sentinel-1 images when used jointly with optical imagery (Landsat8) and a digital elevation model of the Shuttle Radar Topography Mission (SRTM). The study site is located in a temperate zone (southwest France) with irrigated maize crops. The classifier used is the Random Forest. The combined use of the different data (radar, optical, and SRTM) improves the early classifications of the irrigated crops (k = 0.89) compared to classifications obtained using each type of data separately (k = 0.84). The use of the DEM is significant for the early stages but becomes useless once crops have reached their full development. In conclusion, compared to a “full optical” approach, the “combined” method is more robust over time as radar images permit cloudy conditions to be overcome.

scholarly journals ON THE ROLE OF MESOSCALE VORTICES IN THE OIL SPILL TRANSPORT IN THE SOUTH-EASTERN BALTIC SEA (SATELLITE INFORMATION)

2019 ◽  
Vol 47 (3) ◽  
pp. 8-19 ◽  
Author(s):  
A. I. Ginzburg ◽  
E. V. Krek ◽  
A. G. Kostianoy ◽  
D. M. Soloviev
Keyword(s):  
Vortex Dynamics ◽  
Vortex Motion ◽  
Landsat 8 ◽  
Radar Images ◽  
Optical Images ◽  
Total Transport ◽  
First Time ◽  
Vortex Movement ◽  
Eastern Baltic

In this paper, on the basis of an analysis of the successive satellite optical images (MODISAqua, TIRS Landsat-8, AVHRR NOAA-18) and radar images (SAR-C Sentinel-1A, SAR-C Radarsat-2) on June 8–11, 2015, the effect of the mesoscale vortex movement (anticyclone with diameter of 35 km and associated cyclone) on the transport of oil spots in the northern part of the Gdansk Bay was demonstrated for the first time. The velocities of this transport are estimated; the observed picture of the movement of the spots is compared with their transfer according to the Seatrack Web model. The largest (about 20 cm/s) drift velocity corresponded to the spot that appeared near the periphery of the anticyclonic vortex (the region of maximum velocities), the smallest one was at the spot near the center of the vortex. At a wind speed of not more than 5 m/s on June 10 and an assumed orbital velocity of the anticyclone of the order of 20 cm/s, the contribution of the vortex motion to the total transport of the spots under the influence of wind and vortex should be decisive. The observed drift of the spots did not correspond to the forecast of their movement by the Seatrack Web numerical model, which did not take into account the vortex dynamics of the waters.

scholarly journals An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018

2020 ◽  
Vol 12 (1) ◽  
pp. 345-356 ◽  
Author(s):  
Sher Muhammad ◽  
Amrit Thapa
Keyword(s):  
Water Resources ◽  
Snow Cover ◽  
Cloud Cover ◽  
High Mountain ◽  
Landsat 8 ◽  
Spatial Filters ◽  
Glacier Inventory ◽  
Input Dataset ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Improved Accuracy

Abstract. Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).

scholarly journals An Integrated Shadow-Adjusted Snow-Aging Index for Alpine Regions

2020 ◽  
Vol 12 (8) ◽  
pp. 1249
Author(s):  
Haixing Li ◽  
Jinrong Liu ◽  
Xiangxu Bu ◽  
Xuezhi Feng ◽  
Pengfeng Xiao
Keyword(s):  
Snow Cover ◽  
Aging Process ◽  
Field Measurements ◽  
Northwest China ◽  
Analytic Hierarchy ◽  
Alpine Regions ◽  
Aging Index ◽  
Optical Images ◽  
Searching Method ◽  
Steep Slopes

Detecting the variations in snow cover aging over undulating alpine regions is challenging owing to the complex snow-aging process and shadow effect from steep slopes. This study proposes a novel snow-cover status index, namely shadow-adjusted snow-aging index (SASAI), portraying the integrated aging process within the Manas River Basin in northwest China. The Environment Satellites HJ-1A/B optical images and in-field measurements were used during the snow ablation and accumulation periods. The in-field measurements provide a reference for building a candidate library of snow-aging indicators. The representative aging samples for training and validation were obtained using the proposed time-gap searching method combined with the target zones established based on the altitude of snowline. An analytic hierarchy process was used to determine the snow-aging index (SAI) using multiple optimal snow-aging indicators. After correction by the extreme value optimization algorithm, the SASAI was finally corrected for the effects of shading and assessed. This study provides both a flexible algorithm that indicates the characteristics of snow aging and speculation on the causes of the aging process. The separability of the SAI/SASAI and adaptability of this algorithm on multiperiod remote sensing images further demonstrates the applicability of the SASAI to all the alpine regions.

Data Construction and Spatiotemporal Trend Attribution of Runoff over the African Continent (1981–2016)

2021 ◽  
Author(s):  
Fidele Karamage ◽  
Yongwei Liu ◽  
Yuanbo Liu
Keyword(s):  
Climate Change ◽  
Statistical Methods ◽  
Human Activities ◽  
Water Resource Management ◽  
Agricultural Land ◽  
Curve Number ◽  
Discharge Data ◽  
Resource Managers ◽  
Runoff Change ◽  
Natural Resource Managers

AbstractThe availability of streamflow records in Africa has been declining since the 1980s due to malfunctioning gauging stations and data collection failures. Africa also has insufficient hydrological information owing to the allocation of few resources to research efforts. Unreliable runoff datasets and large uncertainties in runoff trends due to climate change patterns and human activities are major challenges to water resource management in Africa. Therefore, this study aimed to improve runoff estimates and to assess runoff trend responses to climate change and human activities in Africa during 1981–2016. Using statistical methods, monthly gridded runoff datasets were generated for the period of 1981–2016 from a modified runoff curve number method calibrated with river discharge data from 535 gauging stations. According to the cross-validation results, the constructed runoff datasets comprised the Nash and Sutcliffe coefficients ranging from 0.5 to 1, coefficients of determination ranging from 0.5 to 1 and percent biases between ±25% for a large number of stations up to 73%, 80% and 91% of the 535 gauged catchments used as references. Analysis of runoff trend responses to climate change and human activities revealed that land cover change contributed more (72%) to the observed net runoff change (0.30%•a−1) than continental climate changes (28%). These contributions were results of cropland expansion rate of 0.46%•a−1 and a precipitation increase of 0.07%•a−1. The performance and simplicity of the statistical methods used in this study could be useful for improving runoff estimations in other regions with limited streamflow data data. The results of the current study could be important to natural resource managers and decision makers in terms of raising awareness of climate change adaptation strategies and agricultural land-use policies in Africa.

ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5091
Author(s):  
Jinxiao Wang ◽  
Fang Chen ◽  
Meimei Zhang ◽  
Bo Yu
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Glacial Lake ◽  
Landsat 8 ◽  
Glacial Lakes ◽  
Sar Images ◽  
Radar Images ◽  
Optical Images ◽  
Sar Data ◽  
Aperture Radar

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction.

scholarly journals Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

Agronomy ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 327 ◽  
Author(s):  
Remy Fieuzal ◽  
Vincent Bustillo ◽  
David Collado ◽  
Gerard Dedieu
Keyword(s):  
Relative Error ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Wheat Yield ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Optical Images ◽  
Multi Temporal ◽  
Input Variables ◽  
Sentinel 2

The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat, over a study site located in southwestern France. The methodology is based on the Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the Normalized Difference Vegetation Index (NDVI) is combined with the yield maps collected during the crop rotation, the agricultural season 2014 showing the lower level of performances with a coefficient of determination (R2) of 0.44 and a root mean square error (RMSE) of 8.13 quintals by hectare (q.h−1) (corresponding to a relative error of 12.9%), the three other years being associated with values of R2 close or upper to 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%). Moreover, the proposed approach allows estimating the crop yield throughout the agricultural season, by using the successive images acquired from the sowing to the harvest. In such cases, early and accurate yield estimates are obtained three months before the end of the crop cycle. At this phenological stage, only a slight decrease in performance is observed compared to the statistic obtained just before the harvest.

scholarly journals APLIKASI METODE CURVE NUMBER UNTUK MEMPRESENTASIKAN HUBUNGAN CURAH HUJAN DAN ALIRAN PERMUKAAN DI DAS CILIWUNG HULU – JAWA BARAT

2016 ◽  
Vol 13 (1) ◽  
pp. 25
Author(s):  
Sunu Tikno ◽  
Teguh Hariyanto ◽  
Nadjadji Anwar ◽  
Asep Karsidi ◽  
Edvin Aldrian
Keyword(s):  
Land Surface ◽  
Prediction Models ◽  
Prediction Method ◽  
Curve Number ◽  
Surface Flow ◽  
Discharge Data ◽  
Run Off ◽  
Hydrological Variable ◽  
Land Cover Maps ◽  
Model Curve

Aliran permukaan/limpasan (run off) merupakan salah satu variabel hidrologi yang sangat penting di dalam menunjang kegiatan pengembangan sumber daya air. Metode prediksi yang handal untuk menghitung jumlah dan laju limpasan yang berasal dari permukaan tanah dan bergerak menuju sungai di suatu DAS yang tidak dilengkapi alat ukur (ungaged watershed) adalah suatu pekerjaan yang sangat sulit dan memerlukanwaktu yang banyak. Penelitian ini dilakukan di DAS Ciliwung Hulu, yang merupakan daerah penting dalam kotribusi banjir di Jakarta. Untuk mengetahaui run off  yang terjadi, digunakan data curah hujan dan debit Tahun 2007-2009. Sebagai model, untuk mengetahui run off menggunakan peta penggunaan lahan, peta jenis tanah, dan topografi. Peta-peta tersebut diolah dengan menggunakan Arcview, sehingga didapatkannilai CN. Berdasarkan analisis perhitungan, besarnya debit mendekati 50% dari tebal hujan. Kondisi ini mengindikasikan bahwa kondisi DAS Ciliwung Hulu sudah tidak mampu lagi menyerap curah hujan dengan baik. Korelasi antara hasil prediksi run off model yang menggunakan CN dengan perhitungan run off observasi cukup baik. Hal ini menunjukkan bahwa metode Curve Number cukup dapat mepresentaskan hubungancurah hujan dengan aliran permukaan (run off). kata kunci : Run off observasi, run off model, curve number AbstractRun off (surface flow) is one of the most important hydrological variable in supporting the activities of water resources development. A reliable prediction method to calculate the amount and rate of runoff from the land surface caused by the rain that falls in a watershed that is not equipped with measuring devices (un gauge watershed) is a verydifficult job and requires a lot of time. The research was conducted in the watershed Ciliwung Hulu, which is an important area in relation to the incidence of flooding in Jakarta. Curve Number (CN) method can be used to predict the amount of runoff from a watershed. This model required input of rainfall; land cover maps; soil type maps,and topography. The maps are processed using Arc View software, so we get the value of CN. In this study, we used of rainfall and discharge data 2007-2009. Based on the analysis of calculation, known that amount of surface flow approaching 50% of rainfall depth. This condition indicates that the Ciliwung Hulu watershed conditions were not ableand proper to absorb of rainfall. The correlation between the results of run-off prediction models using CN with run-off observation was quite good. This indicated that the Curve Number method could be able to represent the relationship of rainfall with surface flow (run off) and also to predict runoff key words: Run off observation, run-off model, curve number

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