scholarly journals Monitoring of Land Degradation in Alluvial Plain in Iraq by Using Geomatics Techniques

2022 ◽  
Vol 961 (1) ◽  
pp. 012023
Author(s):  
Abdulrazak T. Ziboona ◽  
Sajad Abdullah Abdul-Husseinlb ◽  
Muthanna M. Albayatic ◽  
Student Fadhaa Turkey Dakheld
Keyword(s):  
Land Degradation ◽  
Sand Dunes ◽  
Unsupervised Classification ◽  
Alluvial Plain ◽  
Dust Storms ◽  
Erdas Imagine ◽  
Arc Gis ◽  
Remote Sensing Techniques ◽  
Different Sources ◽  
Sentinel 2

Abstract Iraq faces a major environmental problem represented by severe deterioration, which threatens its food security. Many natural and human factors combine to make it, and it has dire environmental, economic, social and cultural consequences, most notably the loss of productive lands, the movement of sand dunes, severe sand and dust storms, and the resulting increase in air pollution. This study attempts to identify the development of the problem, analyze its causes and consequences, and propose a number of solutions to address it. In this article Remote Sensing techniques have been used to monitoring land degradation in ( Alluvial Plain ) of Iraq for the stage (1976 - 2021) using different sources of data such as satellite images (Landsat 1-5 MSS 1976, Landsat 5 1996 TM, Landsat8 2016 and sentinel 2 2021), also more than one software was used such as ENVI 5.3 and Erdas image 2015 to extract information from above images, Erdas imagine 2015 was use to sub set area of study, layer stack, merge resolution and classification stage, Arc GIS 10.7 use to make database and maps production), the article used supervise and unsupervised classification techniques to obtain the results, the article indicated that there is a big problem in the year 1976, this problem almost disappeared in the second station of work 1996, but it returned back after that through the results for the years 2016 and 2021. Finally, the article found a deterioration in the soil class during the stages from 2016 (988.547 Km2) to 2021(1342.398 Km2) and a decrease in the area of vegetation cover from (1931.596 Km2) in (2016) to (1632.695 Km2) in (2021).

scholarly journals Detection of Changes in Land Use/ Land Cover Using Remote Sensing and GIS Techniques in Area East of the Nile Khartoum State-Sudan

2018 ◽  
Vol 10 (2) ◽  
pp. 12
Author(s):  
Babiker, E.M.A ◽  
Ibrahim, M.M ◽  
Elhag, A.M.H ◽  
Nser, S.H ◽  
Elsheikh, M.A ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Sandy Soil ◽  
Sand Dunes ◽  
Unsupervised Classification ◽  
Bare Soil ◽  
Water Bodies ◽  
Sand Sheet ◽  
Remote Sensing Techniques

<p>The study area lies to the east of the Nile (Sharg Elneel), Khartoum State (latitudes 15<sup>o</sup> 25̎ 1̍ and 16° 19̎ 1̍ N and longitudes 33° 19̎ 8̍ and 33°02̎ 9̍ E). Using remote sensing techniques and geographic information system (GIS), the changes in land cover/land use have been estimated using two methods: supervised and unsupervised classification. the images were those of the years 1973, 2001, and 2015 MSS, ETM, ETM+, respectively(173/49 &amp; 173/48 path/ row). The study area was classified into the following nine LU/LC types: water bodies, vegetation, rocky area, sandy soil, sandy sheet, clayey soil, bare soil, sand dunes and settlement areas. The individual areas covered by each type of land use/ land cover were calculated for each image using supervised and unsupervised classification. Then the areas were compared among the different years (images). The results indicated a decrease in areas of sandy soil, water bodies, vegetation cover, sand dunes, clay soil, and bare soil for years 1973-2001 and 1973-2015.  That was associated with significant increase in settlement area, sand sheet for the same period. As for the period 2001 and 2015 was an increase in the areas of vegetation, sandy soil, dunes, clay soil, and settlement. While there was a decrease in water bodies, rocky area, sand sheet and bar soil. A striking result of his study was an increase of 50% in the settlement area for the period 1973 – 2015. This indicated that more drift of people towards the Capital took place during this period possibly due to drought and civil strife. Also people come to Khartoum to have better living conditions, education, health care and to work and may be they look at Khartoum as a spring board for going abroad. This study recommended the use of remote sensing techniques and geographic information system in the follow up of desertification and land degradation by following changes in land cover and land use. It also recommended that sand movement (sand encroachment) shall be retarded possibly through increasing vegetation cover through seed broadcasting of pasture and range plants during the rainy season and to exploit the ground water of the NSS aquifer for irrigation.</p>

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

scholarly journals A Study of Desertification Using Remote Sensing Techniques in Basra Governorate, South Iraq

2021 ◽  
pp. 912-926
Author(s):  
Fadel Abbas Zwain ◽  
Thair Thamer Al-Samarrai ◽  
Younus I. Al-Saady
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arid Zone ◽  
Sand Dunes ◽  
Risk Index ◽  
Anthropogenic Factors ◽  
Landsat Images

Iraq territory as a whole and south of Iraq in particular encountered rapid desertification and signs of severe land degradation in the last decades. Both natural and anthropogenic factors are responsible for the extent of desertification. Remote sensing data and image analysis tools were employed to identify, detect, and monitor desertification in Basra governorate. Different remote sensing indicators and image indices were applied in order to better identify the desertification development in the study area, including the Normalized difference vegetation index (NDVI), Normalized Difference Water Index (NDWI), Salinity index (SI), Top Soil Grain Size Index (GSI) , Land Surface Temperature (LST) , Land Surface Soil Moisture (LSM), and Land Degradation Risk Index (LDI) which was used for the assessment of degradation severity .Three Landsat images, acquired in 1973, 1993, and 2013, were used to evaluate the potential of using remote sensing analysis in desertification monitoring. The approach applied in this study for evaluating this phenomenon was proven to be an effective tool for the recognition of areas at risk of desertification. The results indicated that the arid zone of Basra governorate encounters substantial changes in the environment, such as decreasing surface water, degradation of agricultural lands (as palm orchards and crops), and deterioration of marshlands. Additional changes include increased salinization with the creeping of sand dunes to agricultural areas, as well as the impacts of oil fields and other facilities.

scholarly journals Sediment Transport Dynamism in the Confluence Area of Two Rivers Transporting Mainly Suspended Sediment Based on Sentinel-2 Satellite Images

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3132
Author(s):  
Ahmed Mohsen ◽  
Ferenc Kovács ◽  
Gábor Mezősi ◽  
Tímea Kiss
Keyword(s):  
Sediment Transport ◽  
Suspended Sediment ◽  
Temporal Dynamics ◽  
Pearson Correlation ◽  
Unsupervised Classification ◽  
Mixing Process ◽  
Hydrological Parameters ◽  
Flow And Sediment Transport ◽  
Spatio Temporal ◽  
Sentinel 2

Downstream of the confluence of rivers, complex hydrological and morphological processes control the flow and sediment transport. This study aimed to analyze the spatio-temporal dynamics of suspended sediment in the confluence area of the Tisza and its main tributary Maros River using Sentinel-2 images and to reveal the correlation between the hydrological parameters and the mixing process through a relatively long period (2015–2021). The surficial suspended sediment dynamism was analyzed by applying K-means unsupervised classification algorithm on 143 images. The percentages of the Tisza (TW) and Maros (MW) waters and their mixture (MIX) were calculated and compared with the hydrological parameters in both rivers. The main results revealed that the areal, lateral, and longitudinal extensions of TW and MIX have a better correlation with the hydrological parameters than the MW. The Pearson correlation matrix revealed that the discharge ratio between the rivers controls the mixing process significantly. Altogether, 11 mixing patterns were identified in the confluence area throughout the studied period. The TW usually dominates the confluence in November and January, MW in June and July, and MIX in August and September. Predictive equations for the areal distribution of the three classes were derived to support future water sampling in the confluence area.

scholarly journals Estimation of flooded area in the Bahr El-Jebel basin using remote sensing techniques

2006 ◽  
Vol 3 (4) ◽  
pp. 1851-1877 ◽  
Author(s):  
M. A. H. Shamseddin ◽  
T. Hata ◽  
A. Tada ◽  
M. A. Bashir ◽  
T. Tanakamaru
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Lake Victoria ◽  
Open Water ◽  
Unsupervised Classification ◽  
Water Balance Model ◽  
Balance Model ◽  
Water Evaporation ◽  
Flooded Area ◽  
Remote Sensing Techniques

Abstract. In spite of the importance of Sudd (swamp) area estimation for any hydrological project in the southern Sudan, yet, no abroad agreement on its size, due to the inaccessibility and civil war. In this study, remote sensing techniques are used to estimate the Bahr El-Jebel flooded area. MODIS-Terra (Moderate Resolution Imaging Spectroradiometer) level 1B satellite images are analyzed on basis of the unsupervised classification method. The annual mean of Bahr El-Jebel flooded area has been estimated at 20 400 km2, which is 96% of Sutcliffe and Park (1999) estimation on basis of water balance model prediction. And only, 53% of SEBAL (Surface Energy Balance Algorithm for Land) model estimation. The accuracy of the classification is 71%. The study also found the swelling and shrinkage pattern of Sudd area throughout the year is following the trends of Lake Victoria outflow patterns. The study has used two evaporation methods (open water evaporation and SEBAL model) to estimate the annual storage volume of Bahr El-Jebel River by using a water balance model. Also the storage changes due time is generated throughout the study years.

scholarly journals Estimation of Wheat Area using Sentinel-1 and Sentinel-2 Datasets (A Comparative Analysis)

2019 ◽  
Author(s):  
Ayesha Behzad ◽  
Muneeb Aamir ◽  
Syed Ahmed Raza ◽  
Ansab Qaiser ◽  
Syeda Yuman Fatima ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Random Forest ◽  
Water Body ◽  
Supervised Classification ◽  
Wheat Crop ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Wheat Cultivation ◽  
Erdas Imagine ◽  
Sentinel 2

Wheat is the basic staple food, largely grown, widely used and highly demanded. It is used in multiple food products which are served as fundamental constituent to human body. Various regional economies are partially or fully dependent upon wheat production. Estimation of wheat area is essential to predict its contribution in regional economy. This study presents a comparative analysis of optical and active imagery for estimation of area under wheat cultivation. Sentinel-1 data was downloaded in Ground Range Detection (GRD) format and applied the Random Forest Classification using Sentinel Application Platform (SNAP) tools. We obtained a Sentinel-2 image for the month of March and applied supervised classification in Erdas Imagine 14. The random forest classification results of Sentinel-1 show that the total area under investigation was 1089km2 which was further subdivided in three classes including wheat (551km2), built-up (450 km2) and the water body (89 km2). Supervised classification results of Sentinel-2 data show that the area under wheat crop was 510 km2, however the built-up and waterbody were 477 km2, 102 km2 respectively. The integrated map of Sentinel-1 and Sentinel-2 show that the area under wheat was 531 km2 and the other features including water body and the built-up area were 95 km2 and 463 km2 respectively. We applied a Kappa coefficient to Sentinel-2, Sentinel-1 and Integrated Maps and found an accuracy of 71%, 78% and 85% respectively. We found that remotely sensed algorithms of classifications are reliable for future predictions.

scholarly journals Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽  
2019 ◽  
Author(s):  
Juan Carlos Valdiviezo-Navarro ◽  
Adan Salazar-Garibay ◽  
Karla Juliana Rodríguez-Robayo ◽  
Lilián Juárez ◽  
María Elena Méndez-López ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Food Sovereignty ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Multispectral Images ◽  
Crop Fields ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

scholarly journals Fine dust emissions from active sands at coastal Oceano Dunes, California

2019 ◽  
Vol 19 (5) ◽  
pp. 2947-2964 ◽  
Author(s):  
Yue Huang ◽  
Jasper F. Kok ◽  
Raleigh L. Martin ◽  
Nitzan Swet ◽  
Itzhak Katra ◽  
...  
Keyword(s):  
Climate Models ◽  
Hydrological Cycle ◽  
Large Fraction ◽  
Sand Dunes ◽  
Dust Emission ◽  
Sandy Soils ◽  
Field Measurements ◽  
Dust Storms ◽  
Dust Emissions ◽  
Mineral Coatings

Abstract. Sand dunes and other active sands generally have a low content of fine grains and, therefore, are not considered to be major dust sources in current climate models. However, recent remote sensing studies have indicated that a surprisingly large fraction of dust storms are generated from regions covered by sand dunes, leading these studies to propose that sand dunes might be globally relevant sources of dust. To help understand dust emissions from sand dunes and other active sands, we present in situ field measurements of dust emission under natural saltation from a coastal sand sheet at Oceano Dunes in California. We find that saltation drives dust emissions from this setting that are on the low end of the range in emissions produced by non-sandy soils for similar wind speed. Laboratory analyses of sand samples suggest that these emissions are produced by aeolian abrasion of feldspars and removal of clay-mineral coatings on sand grain surfaces. We further find that this emitted dust is substantially finer than dust emitted from non-sandy soils, which could enhance its downwind impacts on human health, the hydrological cycle, and climate.

Assessment of Open-source Software, QGIS, To Estimate Hurricane Matthew Flood Extent in Robeson County, North Carolina, Using Unsupervised Classification

2019 ◽  
Vol 25 (4) ◽  
pp. 359-367
Author(s):  
Cortney Cameron ◽  
Chibuike Madumere
Keyword(s):  
North Carolina ◽  
Open Source ◽  
Open Source Software ◽  
Unsupervised Classification ◽  
Landsat 8 ◽  
Aid Workers ◽  
Practical Application ◽  
Robeson County ◽  
Remote Sensing Techniques ◽  
Relief Efforts

ABSTRACT The spatial extent of flooding caused by Hurricane Matthew in Robeson County, NC, in October 2016 was investigated by comparing two Landsat-8 images (one flood and one non-flood) following K-means unsupervised classification for each in both ENVI, a proprietary software, and QGIS with Orfeo Toolbox, a free and open-source software. In this study, unsupervised classification was capable of rapidly producing regional maps, but poor accuracy constrained practical application. Of particular note, the open-source setup performed on par with the proprietary option for each of the classifications. Overall, remote sensing techniques using open-source software show promise in helping aid workers to cost-effectively conduct post-event analyses and relief efforts.

scholarly journals Application of Sentinel-2 Multispectral Data for Habitat Mapping of Pacific Islands: Palau Republic (Micronesia, Pacific Ocean)

2019 ◽  
Vol 7 (9) ◽  
pp. 316 ◽  
Author(s):  
Francesco Immordino ◽  
Mattia Barsanti ◽  
Elena Candigliota ◽  
Silvia Cocito ◽  
Ivana Delbono ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Pacific Ocean ◽  
Large Scale ◽  
Pacific Islands ◽  
Satellite Image ◽  
Unsupervised Classification ◽  
Habitat Mapping ◽  
Ancillary Data ◽  
Marine Habitats ◽  
Sentinel 2

Sustainable and ecosystem-based marine spatial planning is a priority of Pacific Island countries basing their economy on marine resources. The urgency of management coral reef systems and associated coastal environments, threatened by the effects of climate change, require a detailed habitat mapping of the present status and a future monitoring of changes over time. Here, we present a remote sensing study using free available Sentinel-2 imagery for mapping at large scale the most sensible and high value habitats (corals, seagrasses, mangroves) of Palau Republic (Micronesia, Pacific Ocean), carried out without any sea truth validation. Remote sensing ‘supervised’ and ‘unsupervised’ classification methods applied to 2017 Sentinel-2 imagery with 10 m resolution together with comparisons with free ancillary data on web platform and available scientific literature were used to map mangrove, coral, and seagrass communities in the Palau Archipelago. This paper addresses the challenge of multispectral benthic mapping estimation using commercial software for preprocessing steps (ERDAS ATCOR) and for benthic classification (ENVI) on the base of satellite image analysis. The accuracy of the methods was tested comparing results with reference NOAA (National Oceanic and Atmospheric Administration, Silver Spring, MD, USA) habitat maps achieved through Ikonos and Quickbird imagery interpretation and sea-truth validations. Results showed how the proposed approach allowed an overall good classification of marine habitats, namely a good concordance of mangroves cover around Palau Archipelago with previous literature and a good identification of coastal habitats in two sites (barrier reef and coastal reef) with an accuracy of 39.8–56.8%, suitable for survey and monitoring of most sensible habitats in tropical remote islands.

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