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The perennially ice-covered lakes of the McMurdo Dry Valleys (MDVs), Antarctica, are an important reservoir of liquid water in an arid and largely frozen environment. During the austral summer, the margins of these ice covers melt, forming a "moat" of liquid water and thin ice, allowing exchange between lake waters and the atmosphere to occur and serving as an interface between lake, soil, and stream ecosystems. The size of these moats varies from year to year. Here, we have established the first published record of moat area changes at MDVs' Lake Fryxell through time using manual traces of the moat as observed via satellite imagery. We have also tested a semi-automated approach for measuring moat area and found that it consistently underestimated the manual record, which we suspect may be due to the lower spatial resolution of images used in this versus the manual approach. Finally, we developed a predictive model based on readily available climate data, allowing moat area to be predicted beyond the limits of the satellite-based records. We found that functional moat area varies annually, potentially influencing ecosystem processes in the moats. [ABSTRACT FROM AUTHOR]

Maintaining the operability of a hydroelectric power station at a scale originally designed is being compromised by continuous reservoir sedimentation. The underlying factors include a complex mix of landscape alterations owing to natural and anthropogenic activities around dam areas, such as gully erosion, landslides, floods triggered by heavy rainfall, climate change, and construction activities. The hydropower projects in the low-to-mid mountain regions of Nepal are witnessing a combination of these phenomena, affecting their optimal performance in meeting long-term sustainable power supply targets. This paper presents a combination of geo-spatial analysis and field evaluations to identify the trends from Kulekhani-I, one of the oldest storage-type hydropower projects in Nepal, using long-term time series remote sensing satellite imagery from 1988 to 2020. Our analysis shows an expansion of the surface water content area over time, attributed mainly to high sedimentation deposition owing to multiple factors. This study has identified an urgent need for addressing the following two key contributory factors through an effective control mechanism to avoid rapid sedimentation in the reservoirs: natural—landslides and floods leading to mainly silt deposition during heavy rainfalls; and anthropogenic—road construction materials dumped directly in the reservoir. Effective implementation of a remote sensing monitoring scheme can safeguard future damages to dam environments of more recently built storage-type hydropower projects. [ABSTRACT FROM AUTHOR]

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications. [ABSTRACT FROM AUTHOR]

Iraq is considered to amongst those countries in the Middle Eastern region that are most exposed to the effects of climate change, which will have notable effects on wet areas and lakes. Natural or industrial water resources must be paid particular attention due to their importance in preserving environmental and biological systems, in addition to their economic and social importance. As a result of the effects of climatic change, water resources in Iraq have seen a multitude of changes. The aim of this study is to determine changes in the wetland area around AL-Razzaza Lake, Karbala province, Iraq, during the years 2000, 2005, 2010, 2015, and 2023. Landsat 5 satellite data from 2000, 2005, and 2010, and Landsat 8 and 9 data for 2015 and 2023, respectively, were used in this analysis, which was conducted using NDWI as a free, open-source program (ArcMap 10.8) to detect these changes; NDWI is a natural water anisotropy index used to detect the surface area of bodies of water in satellite images. The results revealed a clear decrease throughout the study period, as the wetland area of the lake in 2000 was 1189.7 km2, which represents a decrease of 34.3% compared to the total area of the lake (1810 km²); it decreased by 52.7% in 2005 (855.5 km²) and continued to decrease for 2010, 2015, and 2023, by 79.2%, 80%, and 85%, (376.5 km², 362.9 km², and 270.4 km², respectively). The wetland area of Al-Razzaza Lake decreased between 2000 and 2023 by 919.3 km², that is, an average of 40 km² per year. It was found that the lake wetland area sharply declined over the study period due to a lack of water surface resources via the Euphrates River, as well as climatic changes and poor water resource management. It is anticipated that the lake will lose more than half its current wetland area by 2040 if the current decline continues. These results are considered important in terms of preparing a strategic plan to preserve water bodies and wet areas in Iraq, including Al-Razzaza Lake. Remote sensing and GIS technologies have played a major and essential role in detecting such changes. [ABSTRACT FROM AUTHOR]

In the article, the development of the cities of the Shirvan plain, located in the central part of the Republic of Azerbaijan, and the impact of this development on the loss of biodiversity of the cities were analyzed. Currently, cities, where more than 50% of the world’s population live, are the main reasons for the change of natural landscapes. The development of cities and the intensity of urbanization accelerate these changes. Therefore, our research is dedicated to the current issue. During the research, preference was given to modern methods. Landsat 5, 8 and 9 satellite images of 6 cities located on the Shirvan plain were obtained. As a result of the processing and analysis of these satellite images, the development of the territory and population of the cities was studied, and their effects on the natural landscape, its components, vegetation and water bodies were analyzed. The boundaries of cities in 1975 and 2023 were determined and compared. As a result of the conducted studies, it was determined by the processing of the 1975 topo plans and the 2023 satellite images that the area of the cities has increased by at least 2 times. The high average annual growth of the population has led to the transformation of cities from small towns to medium and large cities in a short period of time. This causes the loss of biodiversity as soon as possible and accelerates this process. [ABSTRACT FROM AUTHOR]

Marks, S. and Potter, C., 2024. Mapping recent coastal shoreline changes in southwestern Alaska using Landsat 8 satellite imagery. Journal of Coastal Research, 40(4), 661–671. Charlotte (North Carolina), ISSN 0749-0208. Coastal communities in Alaska are frequently subjected to storm surges that erode shorelines and riverbanks, which increases flood risks around critical village infrastructure. Coastal erosion in western Alaska may be occurring currently at rates as high as 15 m per year, but the region has not been mapped in enough detail to accurately assess recent shoreline and inland riverbank erosion rates. To update the mapping of changes in all of SW Alaska's coastal margins, Landsat 8 satellite imagery was analyzed to detect trends in the normalized difference water index collected from 2013 to 2023. The normalized difference water index values range from -1 to 1, with negative values indicating majority land that includes soil and live vegetation cover and positive values indicating majority water coverage. Results showed marked coastal shoreline erosion at Chagvan Bay, Hagemeister Island, and Nanvak Bay over the past decade. Additional locations of recent bank erosion were detected on the Kuskokwim River, from its upriver mouth past the city of Bethel. Riverbank erosion was also detected in northern Nushagak Bay, and extensive surface-wetting trends around wetlands and riverbanks were detected near the village of Togiak. Moreover, many villages on the west coast of Alaska and in the Kuskokwim River Delta have recently documented climate change case studies highlighting bank erosion, indicating that a variety of related ecological disturbances are ongoing across the region. [ABSTRACT FROM AUTHOR]

The increasing significance of remote sensing and Geographic Information Systems (GIS) can be attributed to their extensive applicability across various domains, including urban planning, natural resource management, environmental monitoring, land use change assessment, disaster forecasting, and management. This paper presents a comprehensive evaluation of the evolving patterns of land use in Khordha District, Odisha, over a 30-year period from 1990 to 2020, focusing on key features such as vegetation, water resources, and built-up areas through the application of geospatial technologies. Satellite imagery from the Landsat 8 series was analyzed, employing various spectral indices calculated using ArcGIS 10.8.2 software. The indices -- specifically the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Enhanced Built-up and Bareness Index (EBBI) -- were generated from the corresponding satellite images, followed by correlation analysis to understand the relationships among different land use categories. This study serves as a critical resource for planners and decision-makers, facilitating informed and sustainable resource management practices aimed at mitigating the over-exploitation of soil, water, and other natural resources in Khordha District. The findings highlight the importance of integrating remote sensing and GIS methodologies in land use planning and environmental conservation strategies. [ABSTRACT FROM AUTHOR]

Copyright of lnternational Journal of Geography & Geography Education is the property of Marmara University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of lnternational Journal of Geography & Geography Education is the property of Marmara University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Muriganga River, also known as channel creek, underwent morphological changes often since it is an alluvial as well as a tidal river. The present study analyses the morphological changes in the Muriganga River and its islands with the help of the Remote Sensing and Geographical Information System (GIS) and digital shoreline analysis tool (DSAS 5.0). Moreover, the computation of morphological changes was also performed on two islands, i.e. Sagar and Ghoramara, which are situated just outside the river reach. Eight cloud-free satellite images of Landsat MSS (1972–1980), Landsat TM (1988–2011) and Landsat OLI (2017–2021) have been used to investigate the river shoreline shifting and island dynamics of the Muriganga River resulted from the erosion–accretion process during the last 49 years. For the short-term study, the erosion–accretion rates are derived from one Landsat image to the next, whereas for long-term analysis, the erosion–accretion rates are estimated based on the difference between 1972 as the reference image and the succeeding images. Short-term and long-term analysis shows that the average rate of erosion is more than that of accretion in Muriganga River. It is also found that the areas of Sagar, Ghoramara, Mousuni and Pushpa islands are shrinking continuously, whereas the Niogi and Basit islands are expanding enormously. These may indicate that the shoreline erosion results in widening the river and the eroded materials are accumulated in Niogi and Basit islands. The results suggest that there is an urge for a better coastal management strategy for the erosion control scheme. This study also helps in gaining knowledge of maintaining the navigability in the Muriganga River. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Non‐human primate species are threatened worldwide. Their population declines go along with the loss of ecological functions such as seed dispersal that plays a crucial role in plant regeneration. Restoring essential habitat structures could thus not only protect primates, but also facilitate forest regeneration. We used classical vegetation description on the ground and a remote sensing analysis to describe habitat use of crowned lemurs (Eulemur coronatus), a seed‐dispersing primate endemic to northern Madagascar. Our aim was to find vegetation characteristics important for lemurs that might be targeted in a restoration approach. For this, we applied both methods in differently degraded forest types. Both classical vegetation description and remote sensing analysis were able to distinguish these forest types. The habitat use of our two study groups was associated consistently with vegetation structures measured on a small scale such as tree height and density of thick trees. In contrast, vegetation productivity and water content derived from satellite imagery on a larger scale could not consistently explain habitat use of lemurs. Thus, measurements on the ground can identify suitable microhabitats that do not show on the satellite imagery scale. These microhabitats might be very important conservation elements to create buffer zones and corridors. Further, they might attract seed‐dispersing species into degraded areas targeted for forest restoration, acting as natural regeneration nuclei. The potential of these microhabitats for conservation would not be recognized when analyses were based solely on landscape analyses on large scales. [ABSTRACT FROM AUTHOR]

In recent years, land cover / use has undergone major changes, and this aspect participate along with other factors in climate change both in our country and globally. Vegetation indices based on satellite images are increasingly used to characterize the response of vegetation to climate variability and change over large spatial scales. One of the parameters estimated based on remote sensing and used in the calculation of temperature interception is the LST index. This study proposes to evaluate the trend of the spatial-temporal relationship between land surface temperature (LST) and normalized difference vegetation index (NDVI) in different ranges of LST and NDVI values for Timis County. Vegetation is very sensitive to climate changes and various anthropogenic influences, and these variations of the NDVI index can cause changes in the temperature of the land surface. In the final part, the correlation between the spectral indices of land cover (NDVI and NDWI) with the soil surface temperature (LST) was made. The remote sensing images were processed using the Geographic Information System (GIS). This study highlights the association of the LST index with land cover indices, and from the obtained data it can be observed that the correlation between NDVI and LST is one negative. [ABSTRACT FROM AUTHOR]

Copyright of Entorno Geográfico is the property of Universidad del Valle and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, the most appropriate algorithm and water index to determine the boundaries of the dam water surface using remote sensing (RS) techniques were investigated. Water surface boundaries of Demirkopru Dam were determined using Sentinel-2 L2A (MSI) and Landsat-8 (OLI) satellite images. Demirkopru Dam was chosen as the study area as it is suitable for floating photovoltaic (FPV) solar power plant installation. Normalized difference water index (NDWI) and modified NDWI indices were used to determine the water surface boundaries of the dam. Thirty-six classification results were obtained using K-means, maximum likelihood classification (MLC), and random forest (RF) algorithms. The best classification accuracies of the produced maps have been calculated as 80.3%, 73.1%, and 73.2% by RF, MLC, and K-means, respectively. In addition, the water coastlines determined by classifications were compared with the continuously operating reference station (CORS-TR) data in a local area by calculating the root-mean-square error (RMSE). Compared with the CORS-TR measurements of the dam coastline obtained from the images classified by the RF algorithm, the minimum RMSE values were calculated as 13.8 m and 10.1 m for Landsat and Sentinel images, respectively. While the minimum RMSE value for coastlines obtained with various layer stacks of Landsat images classified by the MLC algorithm is 36.7 m, it could not be calculated in Sentinel images due to poorer classification results. For the coastlines obtained from the images classified by the K-means algorithm, the minimum RMSE values were calculated as 14.5 m and 9.6 m for Landsat and Sentinel images, respectively. According to the comparisons based on classification accuracy and CORS-TR measurements, it is concluded that the RF algorithm performs better than others for the dam water surface. Moreover, it was determined that the NDWI presented better results when the water level was the lowest for Demirkopru Dam. Also, in this study, the MLC algorithm has better results in detecting water surfaces using Landsat images. It was concluded that the K-means algorithm is also very effective in water surface detection. In this study, various water extraction indices, algorithms and free Landsat and Sentinel images were used to extract the water surface in a selected reservoir for the FPV installation. This study guides a series of algorithms and indexes used to detect water surfaces. In addition, it has been shown that the use of RS techniques, which are more practical than classical approaches in determining water boundaries, will be more effective in planning and design in terms of engineers, investors and various organizations who will realize the FPV installation. [ABSTRACT FROM AUTHOR]

In this study, trend analysis of the lake surface areas was performed on the Google Earth Engine (GEE) platform in the period of 1985–2022 with Landsat 5/7/8/9 (TM) (ETM +), and (OLI) satellite images. The study analyzed 10 lakes, including Acigol, Aksehir, Beysehir, Burdur, Egirdir, Ilgin, Isikli, Karatas, Salda, and Yarisli in the Türkiye Lakes Region. In this analysis, the normalized differentiated water index was calculated for each of the 3147 satellite images, and water surfaces were extracted from other details using Otsu's threshold method. In the study's accuracy, the overall accuracy and F1-score values were calculated to be over 90% for all lakes. Moreover, the relationship between the changes in the surface areas of the lakes was evaluated using correlation analysis, with the sea surface temperature obtained from the NOAA satellite and the evaporation, temperature, and precipitation parameters obtained from the Era-5 satellite being used. In addition, the change of the area on the lake surface was analysed using Mann–Kendall (MK), Sen's slope, and sequential MK test statistics. During the 37 years between 1985 and 2022, there was no significant change in the Acigol surface area, but a slight increasing trend was observed. Decreases of 76.07, 4.68, 41.77, 5.44, 37.56, 28.97, 78.65, 7.26, and 81.02% were determined in the lakes of Aksehir, Beysehir, Burdur, Egirdir, Ilgin, Isikli, Karatas, Salda, and Yarisli, respectively. The application of this method in the lakes region and monitoring these lakes, which are of great importance for Türkiye, provide valuable information in determining the lakes' organizational strategies. [ABSTRACT FROM AUTHOR]

This short communication discusses how a specific geoarchaeological remote-sensing (RS) method, such as analyzing satellite images through NDWI (Normalized Difference Water Index), can be used to aid in searching and locating persons missing in watercourses. Thanks to its high capacity to analyze changes in the surface water area, this index can remotely detect the presence of anomalies related to disappearances in water bodies and provide valuable information that can reduce the use of human resources and help pinpoint likely areas of search. Two real-life cases of missing persons in rivers in which the NDWI index was used are presented, and the results obtained are discussed, emphasizing the importance of NDWI analysis as a complementary method to different approaches, especially non-invasive and remote-sensed ones, when positively searching for missing persons. [ABSTRACT FROM AUTHOR]

The climate changes had been recognized as one of the major factors responsible for land degradation, which has a significant impact on diverse aspects. The present study aims to estimate how the climate change can influence land degradation in the south areas of Baghdad province (Al-Rasheed, Al-Mahmudiyah, Al-Yusufiyah, AlMadaen, and Al-Latifiyah). The Satellite Landsat-8 OLI and satellite Landsat-5 TM sensor imagery were used to extent land degradation for the period (2010-2019). ArcGIS V.10.4 was applied to manage and analysis the satellite image dataset, including the use of climate factors data from the European Center for Climate Forecasts (ECMWF) by reanalyzes and extraction datasets. To achieve work objectives, many ground data were collected, including the temperature, rain precipitation, evaporation, and relative humidity from 30 meteorological monitoring stations. These data help us to utilize the interpolation methods for the extraction process of contour lines maps, to be scientific indicators of the relationship between climatic factors and satellite images classifications, involving the spectral indicators of the vegetation cover and water bodies. The results showed the agriculture degradation through the decreasing of vegetation cover rate from 56.57% in (2010) to 43.43% in )2019 (. This deterioration is thought to be related to climate changes with other factors such as water shortage that was 0.52 and 0.44, respectively, the greatest temperature reading was (24.57), the greatest precipitation was (0.21), the greatest relative humidity was (60.73), and vapor rate (-0.2) for the studied period. [ABSTRACT FROM AUTHOR]

Shoreline and coastline are two important features of the coastal landscape that reflect the natural processes of erosion as well as deposition. Hence, extracting this information has become decisive for the study and analysis of the coastal region including its management and monitoring. Several methods of choosing and extracting the coastline and shoreline were analyzed from various research papers. The manual process of extraction is time-consuming and not efficient. Since automating the extraction using satellite images is ideal but expensive, the purpose of this research is to propose an effective low-cost methodology to achieve easy, accurate, and quick extraction of the coastal features. The study area considered for this research is the 120-acre Jupiter Inlet Lighthouse Outstanding Natural Area located in Jupiter, Florida, the USA that has more than 1,000 m (3,500 ft) of dynamic shoreline and coastline individually. Over the past few decades, significant erosion has affected this area due to various natural and anthropogenic factors. In this research, the extraction process was improved by using unmanned aerial systems based multispectral images, especially through RedEdge Micasense sensor to perform Modified Normalized Difference Water Index approach. Shoreline features have been generated through segmentation; vectorization followed by extraction processes in a short span of time without much human intervention. The extracted shoreline was compared with respect to the manually digitized shoreline using transect-based analysis to measure its accuracy [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Environmental Impact Assessment (EIA) is a prominent technology to assess the environmental changes in the open cast Limestone mining region. However, traditional EIA methods and Limestone deposit identification involves time consuming, cost effective and manpower requirements. We cope-up these problem and incorporated with different satellite image processing techniques and field studies to estimate the impact on environment. Landsat imageries have been used to calculate NDVI, NDWI, LULC, LST for the buffer zone within a 10 km radius of the mines from the period between 1997–2017. The noise pollution of the mining region is identified using a random sample method. To access water quality analysis in the area, the Water Quality Index (WQI) is generated using 12 water quality parameters. EIA is analyzed using Analytical Hierarchy Process (AHP), Multi-Criteria Decision Analysis (MCDA) and future EIA is also predicted using empirical Bayesian Kriging approach. [ABSTRACT FROM AUTHOR]

Appropriate crop type mapping to monitor and control land management is very important in developing countries. It can be very useful where digital cadaster maps are not available or usage of Remote Sensing (RS) data is not utilized in the process of monitoring and inventory. The main goal of the present research is to compare and assess the importance of optical RS data in crop type classification using medium and high spatial resolution RS imagery in 2018. With this goal, Landsat 8 (L8) and Sentinel-2 (S2) data were acquired over the Tashkent Province between the crop growth period of May and October. In addition, this period is the only possible time for having cloud-free satellite images. The following four indices "Normalized Difference Vegetation Index" (NDVI), "Enhanced Vegetation Index" (EVI), and "Normalized Difference Water Index" (NDWI1 and NDWI2) were calculated using blue, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 bands. Support-Vector-Machine (SVM) and Random Forest (RF) classification methods were used to generate the main crop type maps. As a result, the Overall Accuracy (OA) of all indices was above 84% and the highest OA of 92% was achieved together with EVI-NDVI and the RF method of L8 sensor data. The highest Kappa Accuracy (KA) was found with the RF method of L8 data when EVI (KA of 88%) and EVI-NDVI (KA of 87%) indices were used. A comparison of the classified crop type area with Official State Statistics (OSS) data about sown crops area demonstrated that the smallest absolute weighted average (WA) value difference (0.2 thousand ha) was obtained using EVI-NDVI with RF method and NDVI with SVM method of L8 sensor data. For S2-sensor data, the smallest absolute value difference result (0.1 thousand ha) was obtained using EVI with RF method and 0.4 thousand ha using NDVI with SVM method. Therefore, it can be concluded that the results demonstrate new opportunities in the joint use of Landsat and Sentinel data in the future to capture high temporal resolution during the vegetation growth period for crop type mapping. We believe that the joint use of S2 and L8 data enables the separation of crop types and increases the classification accuracy. [ABSTRACT FROM AUTHOR]

Early warning systems help combat crop diseases and enable sustainable plant protection by optimizing the use of resources. The application of remote sensing to detect plant diseases like wheat stripe rust, commonly known as yellow rust, is based on the presumption that the presence of a disease has a direct link with the photosynthesis capability and physical structure of a plant at both canopy and tissue level. This causes changes to the solar radiation absorption capability and thus alters the reflectance spectrum. In comparison to existing methods and technologies, remote sensing offers access to near real-time information at both the field and the regional scale to build robust disease models. This study shows the capability of multispectral images along with weather, in situ and phenology data to detect the onset of yellow rust disease. Crop details and disease observation data from field trials across the globe spanning four years (2015–2018) are combined with weather data to model disease severity over time as a value between 0 and 1 with 0 being no disease and 1 being the highest infestation level. Various tree-based ensemble algorithms like CatBoost, Random Forest and XGBoost were experimented with. The XGBoost model performs best with a mean absolute error of 0.1568 and a root mean square error of 0.2081 between the measured disease severity and the predicted disease severity. Being a fast-spreading disease and having caused epidemics in the past, it is important to detect yellow rust disease early so farmers can be warned in advance and favorable management practices can be implemented. Vegetation indices like NDVI, NDRE and NDWI from remote-sensing images were used as auxiliary features along with disease severity predictions over time derived by combining weather, in situ and phenology data. A rule-based approach is presented that uses a combination of both model output and changes in vegetation indices to predict an early disease progression window. Analysis on test trials shows that in 80% of the cases, the predicted progression window was ahead of the first disease observation on the field, offering an opportunity to take timely action that could save yield. [ABSTRACT FROM AUTHOR]

In recent years, the accelerated development of the remote sensing domain and the improvement of the resolution and frequency of satellite images allowed the increase in the accuracy of the evaluation of morphometric characteristics and the spatiotemporal distribution of pit lakes, including the small ones. Our study quantitatively analyzes small-scale pit lakes in the piedmont and subsidence plains from contact with the Getic and Curvature Subcarpathians from Romania using the normalized difference water index (NDWI) and data series, with different resolutions, from Landsat 8, Google Earth, and Sentinel 2A. The problems encountered in extracting the contours of the gravel pit lakes were determined by the different resolution of the images, the uneven quality of the images exported from Google Earth, and an additional challenge was given by the diversity of the analyzed land surfaces, the land use, and the optical properties of the lakes. A comparison of the obtained NDWI values using data series from Sentinel 2A and Landsat 8 highlighted the importance of resolution and also showed a larger spectral difference between the identified water bodies and the surrounding land in favor of Sentinel 2A. Regarding the vegetation-derived indices, superior leaf area index (1.8–3) was recorded in low-lying plains and mixed areas (tall shrubs, wetlands, etc.) because the river banks have increased moisture that supports taller species with denser foliage and the sparsely vegetated areas are located in agricultural crops and in/near villages. Changes in vegetation richness and abundance can be spatiotemporally monitored using indices derived from the spectral bands of satellite imagery. [ABSTRACT FROM AUTHOR]

Marshlands in arid and semi-arid areas are considered constantly changing environments due to unsecured water supplies as a result of high evapotranspiration and limited and highly variable rainfall. Classification of marshlands in these regions and mapping of their land cover is not an easy task and maps need to be upgraded frequently. Satellites provide enormous amounts of information and data for the continuous monitoring of changes. The aim of this paper is to introduce an approach using multispectral satellite imagery that was adopted to classify and monitor the Al Hammar Marsh (Iraq) over several years and to suggest a relationship between the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and the Normalized Difference Water Index (NDWI), using Landsat 8 data with a resolution of 30 m × 30 m, validated with Sentinel-2 datasets at 10 m × 10 m. Six land cover classes were used: (1) open water, (2) dry area, (3) dense vegetation, (4) medium-density vegetation, (5) sparse vegetation, and (6) wet soil. Three indices, NDWI, NDMI, and NDVI, were chosen for the automatic classification of each pixel and the creation of a time series of land cover maps. The proposed method can efficiently classify and monitor marshlands and can be used to study different marshlands by adjusting the thresholds for NDVI, NDMI, and NDWI. Overall, the correlation for all classes (R) between Landsat 8 and Sentinel-2 is about 0.78. Thus, this approach will help to preserve marshes through improved water management. [ABSTRACT FROM AUTHOR]

Recently, the satellite images have been used in remote sensing allowing observations with high temporal and spatial distribution. The use of water indices has proved to be an effective methodology in the monitoring of surface water resources. However, precise or automatic methodologies using satellite imagery to determine reservoir volumes are lacking. To fulfill that gap, this methodology proposes three stages: use Google Earth Engine to select images; automatically calculate flooded surface areas applying water indices; determine the volume stored in reservoirs over those years based on the relation between the flooded area and the stored volume. The method was applied in four reservoirs and contemplate Landsat 4 and 5 ETM and Landsat 8 Operational Land Imager. For the calculation of the flooded area the Normalized Difference Water Index Indexes (Gao, 1996, https://doi.org/10.1016/s0034-4257(96)00067-3; McFeeters, 1996, https://doi.org/10.1080/01431169608948714) and the MNDWI index (Xu, 2006, https://doi.org/10.1080/01431160600589179) were applied and tested. The estimation of stored volume of water was made based on the area indices and a cross‐check between real stored volume and calculated volume was made. Finally, an analysis on the selection of the best‐fit water indices was made. The results of every case studies herein displayed showed a quantifiable proficiency and reliability for quite a varied natural conditions. As a conclusion, this methodology could be seen as a tool for water resources management in developing countries, not only, to measure automatically trends of stored volumes and its relation with the precipitation, and could eventually be extended to other types of surface water bodies, as lakes and coastal lagoons. Key Points: Automatically calculate the area of flooded water surfaces and calculate the stored volumes of water in reservoirsMethodology to complete series of monitoring data, the storage volumes of reservoirs, allowing a more precise assessment of its evolutionMapping surface water bodies using satellite images in an automatic way allowing the monitoring of water resources on a large scale [ABSTRACT FROM AUTHOR]

Monitoring and determining the amount of water in reservoirs is of great importance in terms of water planning and management. This study proposes a geographic information system (GIS)-based methodology to estimate the water volume changes in water reservoirs. Two specific methods are proposed using Australian National University's Digital Elevation Model (ANUDEM) raster surface and Triangulated Irregular Network (TIN) surface models, both utilizing normalized difference water index (NDWI) of Sentinel 2A satellite images for water-covered area and coastline and digital elevation model (DEM) for 3D modelling of the reservoir. The most crucial part of this study is the comprehensive evaluation of the model findings considering hydrological, meteorological and anthropogenic factors, simultaneously. Application of the proposed methods is provided for the analysis of the multi-temporal water volume changes of Bayramiç Dam Lake (Çanakkale, Turkey) in two hydrological periods covering the 2015–2016 and 2016–2017 water years. The results indicate that the TINS model produced water volume values much closer to the in situ Turkish General Directorate of State Hydraulic Works (DSI) values than the ANUDEM model. The performance of these methods was also assessed by the temporal dynamics of surface hydrological processes. Regarding the water storage dynamics, hydro-meteorological factors influence the water input, while anthropogenic factors strongly influence the water output. Water consumption for irrigation and electricity generation was found to be the most important water budget components of the total water consumption. [ABSTRACT FROM AUTHOR]

One of the serious environmental challenges that Iraq faces is climate changes and impacts of changing weather patterns and extreme global weather events. This paper addresses changes in the temporal and spatial characteristics of water levels of Razzaza Lake and response to climatic changes using archived series of Multispectral satellite images Landsat. TM, ETM+ and OLI images acquired on 1990, 2000 and of 2016. In order to extract, mapping the water surface area of the Razzaza Lake, Multispectral spectral band rationing the Normalized Difference Water Index (NDWI) technique was adopted, and the climatic elements data for the period (1990-2016) were analyzed which provide significant information of surface water. The results show that Razzaza Lake has a particularly sharp change rate in the water level and there are significant fluctuations on lake level and water surface area over the time. [ABSTRACT FROM AUTHOR]

The present study examines the seasonal relationship between land surface temperature (LST) and normalized difference water index (NDWI) on various land surfaces in Raipur City of India by using a series of Landsat images for four specific seasons since 1991-92. The LST is retrieved using the mono-window algorithm technique. The results show that the LST of the study area is noticeably affected by surface composition. The best correlation (correlation coefficient r = 0.42) between the LST and NDWI is achieved in the post-monsoon season, followed by the monsoon season (r = 0.33), pre-monsoon season (r = 0.25), and winter season (r = 0.04). There is a moderate negative correlation (r = -0.49, -0.33, -0.31, and -0.25 in the premonsoon, monsoon, post-monsoon, and winter season, respectively) generated between the LST and NDWI on water bodies. On green vegetation, this LST-NDWI correlation is moderate positive (r = 0.67, 0.43, 0.50, and 0.25 in the pre-monsoon, monsoon, post-monsoon, and winter season, respectively). On human settlement and barren land surface, the correlation is weak positive (r = 0.24, 0.21, 0.27, and 0.15 in the pre-monsoon, monsoon, post-monsoon, and winter season, respectively). The output of the research work can be used in the town planning section of any urban agglomeration. [ABSTRACT FROM AUTHOR]

Copyright of Al-Mustansiriyah Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Lámpsakos is the property of Fundacion Universitaria Luis Amigo, Facultad de Ingenierias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water body extraction plays a key role in water resource management and development which is one of the important issues in research topics related to remote sensing in recent decades. Pixel-based; supervised and unsupervised classification methods and water indices have been developed for extracting water body from satellite imagery while for some cases they are unable to differentiate water body from low albedo features with different spectral characteristics. The objective of this paper is to evaluate the efficiency of object-based, pixel-based and water indices for water body extraction. Landsat-8 imagery used for this study which has 30m resolution in Visible, NIR (Near-Infrared), SWIR (Shortwave Infrared) spectrum and panchromatic band with 15m resolution. The Edge-Based Segmentation (EBS) algorithm and the Support Vector Machine (SVM) classification method has been used for object-based approach, on the other hand, Maximum Likelihood and K-means methods has been selected for pixelbased classification. As a water index, Normalized Difference Water Index (NDWI) has been selected to extract water body from satellite imagery. The study area selected in both urban and mountainous regions with different characteristics both located in Turkey. Results show that object-based water body extraction is more accurate than the other methods tested. [ABSTRACT FROM AUTHOR]

This paper presents a new coastline extraction method that improves water classification accuracy by benefitting from an ever-increasing volume of repeated measurements from commercial satellite missions. The widely-used Normalized Difference Water Index (NDWI) method is tested on a sample of around 12,600 satellite images for statistical analysis. The core of the new water classification method is the use of a water probability algorithm based on the stacking of repeat measurements, which can mitigate the effects of translational offsets of images and the classification errors caused by clouds and cloud shadows. By integrating QuickBird, WorldView-2 and WorldView-3 multispectral images, the final data product provides a 2 m resolution coastline, as well as a 2 m water probability map and a repeat-count measurement map. Improvements on the existing coastline (GSHHS-the Global Self-consistent, Hierarchical, High-resolution Shoreline Database, 50 m–5000 m) in terms of resolution (2 m) is substantial, thanks to the combination of multiple data sources. • Improved water classification by the adaptive thresholding of the NDWI formula • A new coastline extraction method using water probability from repeated measurements • Substantial improvement of the spatial resolution and precision of detected coastlines [ABSTRACT FROM AUTHOR]

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rapid flood mapping is critical for timely damage assessment and post-event recovery support. Remote sensing provides spatially explicit information for the mapping process, but its real-time imagery is often not available due to bad weather conditions during the event. Using the 2015 South Carolina Flood in downtown Columbia as a case study, this article proposes a novel approach to retrieve near real-time flood probability map by integrating the post-event remote sensing data with the real-time volunteered geographic information (VGI). Relying on each VGI point, an inverse distance weighted height filter was introduced to build a probability index distribution (PID) layer from the high-resolution digital elevation model (DEM) data. For each PID layer, a Gaussian kernel was developed to extract its moisture weight from the normalized difference water index (NDWI) of an EO-1 Advanced Land Imager (ALI) image. Finally, a normalized flood probability map was produced by chaining the moisture weighted PIDs in a Python environment. Results indicate that, by adding the wetness information from post-event satellite observations, the proposed model could provide near real-time flood probability distribution with real-time social media, which is of great importance for emergency responders to quickly identify areas in need of immediate attention. [ABSTRACT FROM AUTHOR]

This research article is summarize the applications of remote sensing and GIS to study the urban floods risk in Al Mukalla. Satellite acquisition of a flood event on October 2015 in Al Mukalla (Yemen) by using flood risk mapping techniques illustrate the potential risk present in this city. Satellite images (The Landsat and DEM images data were atmospherically corrected, radiometric corrected, and geometric and topographic distortions rectified.) are used for flood risk mapping to afford a hazard (vulnerability) map. This map is provided by applying image-processing techniques and using geographic information system (GIS) environment also the application of NDVI, NDWI index, and a method to estimate the flood-hazard areas. Four factors were considered in order to estimate the spatial distribution of the hazardous areas: flow accumulation, slope, land use, geology and elevation. The multi-criteria analysis, allowing to deal with vulnerability to flooding, as well as mapping areas at the risk of flooding of the city Al Mukalla. The main object of this research is to provide a simple and rapid method to reduce and manage the risks caused by flood in Yemen by take as example the city of Al Mukalla. [ABSTRACT FROM AUTHOR]

Mapping and monitoring of wetlands as one of the world's most valuable natural resource has gained importance with the developed of the remote sensing techniques. This paper presents the capabilities of Sentinel-2 successfully launched in June 2015 for mapping and monitoring wetlands. For this purpose, three different approaches were used, pixel-based, object-based and index-based classification. Additional, for more successful extraction of wetlands, a combination of object-based and index-based method was proposed. It was proposed the use of object-based classification for extraction of the wetlands boundaries and the use of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for classifying the contents within the wetlands boundaries. As a study area in this paper Sakarbasi spring in Eskisehir, Turkey was chosen. The results showed successful mapping and monitoring of wetlands with kappa coefficient of 0.95. [ABSTRACT FROM AUTHOR]

• We improved the assessment of grassland response to drought using satellite images. • Grassland sensitivity to drought highly varies within an upland livestock farming area. • Soil properties, biodiversity, and management unequally affect sensitivity to drought. • The relative importance of response-to-drought drivers changes with drought timescales. Drought is expected to increase in frequency and severity with climate change, leading to more intense impacts on grasslands and their associated ecosystem services. Complementary to ground experiments, remote sensing technologies allow for the study of drought impacts with large spatio-temporal coverage in real-life-conditions. We aimed to quantify the variability of grassland sensitivity to drought using a long-term satellite image time series of 394 temperate permanent grassland plots to identify factors influencing these sensitivities. Accordingly, we assessed the slope of the linear relationship between satellite-based vegetation status, using the standardized anomalies of the vegetation indices (VIs), and drought severity, using a modified version of the Standardized Precipitation Evapotranspiration Index (SPEI), from 1985 to 2019. The process was repeated for 24 VIs and five SPEI timescales. We then conducted a linear model selection procedure, using the grassland sensitivity derived from the most responsive VIs (i.e., VIs for which anomalies indicated a tighter linear relationship with the modified SPEI), to identify which grassland properties influenced sensitivity to drought. A total of 29 properties, grouped into pedoclimate, agricultural management, and vegetation diversity factors, were derived from ground measurements. Overall, we demonstrated that the influence of predictors on grassland sensitivity to drought varied across the drought integration timescales. Our results highlighted the significant mitigating effect of soil water holding capacity on sensitivity to drought for short timescales of fewer than 30 days. The date of first herbage use by farmers was positively related to grassland sensitivity to drought across all timescales. We also demonstrated that higher vegetation diversity significantly reduced sensitivity to drought. However, for the long timescales of drought integration, such influence was mainly redundant with management (i.e., shared partition of variance) suggesting complex cascading effects between agricultural practices and plant community structure that still need to be addressed comprehensively in future studies. [ABSTRACT FROM AUTHOR]

Monitoring water resource changes is essential in water resources management and in flood or drought prevention. This research aims to monitor water resource changes using the multi-temporal Landsat imagery taken in December 2006 and 2014 through the following steps. First, the multiple NDWI (Normalized Difference Water Index) images are generated from the Landsat images. Next, multiple water resources such as the Nakdong River and the Dalchang reservoir are extracted from each NDWI image. Finally, the areas of the Nakdong River and the Dalchang reservoir in each NDWI image are measured to assess the water resource changes in the study area. The statistical results show that the Nakdong River had significantly increased its water storage capacity between 2006 and 2014, while the Dalchang reservoir had the constant area during the same period. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the effect of land use expansion on the natural environment of the Little Zab River basin (LZRB). The specific objectives were to: (i) prepare a land use land cover (LULC) map for the LZRB; (ii) detect changes between five dates within the time period 1976-2014 and to identify spatial and temporal changes that occurred within this time period using image indices; (iii) assess LULC classes results and compare the differences between them. Different remote sensing and GIS techniques were applied, such as digital image processing using supervised classification and image indices. Supervised classification using a maximum likelihood algorithm was only applied on the Landsat 8 OLI data (September 2014) to classify the LULC map. The classified image was modified with the help of image indices and visual interpretation to produce a more accurate map. The image obtained with supervised classification has an overall accuracy of 83.43 % and an overall Kappa coefficient of 0.811. The results were validated using 507 ground truth points distributed all over the study area. Six Landsat images from September 1976, 1984, 1990, 2000 and two Landsat 8 OLI 2014 images from April and from September were georeferenced, radiometrically and atmospherically calibrated to detect the changes in the LZRB and to identify seasonal changes. Changes have been calculated for the vegetation cover (natural vegetation vs. cropland and pasture), surface water features and urban and built-up land. Change detection of the LZRB was performed using three indices: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI) and the Normalized Difference Built-Up Index (NDBI). From the results from supervised classification six main LULC classes have been distinguished in the LZRB. They involve barren land, agricultural land, natural vegetation, urban and built-up land, burned land and water. Barren land is the main class in the basin; it is subdivided into six subclasses. Agricultural land is the second largest class; it is subdivided into three subclasses. In this study, change detection analysis over the period (1976-2014) has revealed highly dynamic interchanges between the LULC classes. The change detection results show a rapid increase in urban and built-up land. A significant increase of the population number, the migration from small villages to the main cities and economic growth has fueled the rise of the urbanization rate within the last decades. Surface water features were shown to shrink mainly due to the shrinkage of Dokan Lake under the pressure of climate change and due to the construction of dams in the upper watershed area of the LZRB. The natural vegetation displayed a wavy or irregular trend while cropland and pasture showed an increasing trend. With the NDVI two classes of natural vegetation, and one cropland and one pasture class have been identified based on reflectance values. Areas with relatively high NDVI values indicate the cropland and pasture class, while lower values represent natural vegetation. Two spectral water index methods were employed in this study, an NDWI (Ji et al. 2009), which was applied on Landsat 8 OLI and TM5 images and an NDWI (El-Asmar et al. 2013), which was only applied on Landsat MSS images. Different water indices have been applied for the MSS sensor due to the large difference in the spectral range between MSS bands and other Landsat sensor bands. Both NDWI indices were successfully applied to extract water features. Finally, the NDBI changes were calculated based on visual interpretation. [ABSTRACT FROM AUTHOR]

The aim of this paper is to remark possibilities to use WorldView-2 imagery for coastline extraction. Applications are conducted on a Phlegrean area in the Campania Region (Italy): the considered range of coastline is particularly interesting because it shows two typologies of shoreline including reefs interspersed with segments of sandy beach. Two indices are used: Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI).To enhance geometric resolution of the results pan-sharpening is applied so as to obtain maps with the same pixel dimensions of the panchromatic data. To solve the problem of thresholds determination that typically affects the classification, Maximum Likelihood method based on training sites is adopted to distinguish bare soil and sea water. Best results are given by NDWI and, comparing the resultant coastline with that obtained with visual interpretation of images, shifts of less than 1 m outcome from pan-sharpened data. [ABSTRACT FROM AUTHOR]

Deltas and lagoons, which contain many flora and fauna, have rich coastal ecological and biological environments, and are wetlands of vital importance for humans. In this study, the current problems in all coastal Ramsar sites in Turkey are summarized, and changes in water surface areas are investigated using Landsat and Sentinel 1/2 satellite images on the Google Earth Engine (GEE) cloud computing platform. Landsat TM and OLI images were used in the long-term analysis, and time series were created by taking annual and July to September averages between 1985 and 2020. In the short-term analysis, monthly averages were determined using Sentinel 2 images between 2016 and 2020. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in the months when optical data were not suitable for use in monthly analysis. The Normalized Difference Water Index (NDWI) was used to extract water surface areas from the optical images. Afterwards, a thresholding process was used for both optical and radar images to determine the changes. The changes were analyzed together with the meteorological data and the information obtained from the management plans and related studies in the literature. Changes in the water surface areas of all coastal Ramsar sites in Turkey were determined from 1985 to 2020 at different rates. There was a decreasing trend in the Goksu and Kızılırmak Deltas, which also have inland wetlands. The decreasing rates from 1985 to 2020 were −24.52% and −2.86%, for annual average water surfaces for the Goksu and Kızılırmak Deltas, respectively, and −21.64% and −6.34% for the dry season averages, respectively. However, Akyatan Lagoon, which also has inland wetlands, showed an increasing trend. Observing the annual average surface area from 1985 to 2020, an increase of 438 ha was seen, corresponding to 7.65%. Every year, there was an increasing trend in the Gediz Delta and Yumurtalık Lagoons, that do not have inland wetlands. The increasing rates from 1985 to 2020 were 46.01% and 17.31% for the annual average surface area, for the Gediz Delta and Yumurtalık Lagoons, respectively, and 38.34% and 21.04% for the dry season average, respectively. The obtained results reveal the importance of using remote sensing methods in formulating strategies for the sustainable management of wetlands. [ABSTRACT FROM AUTHOR]

Lake Salda's extreme environment is geologically similar to Jezero Crater paleolake on Mars due to the formation of stromatolites and its extremely alkaline and cold water. It is critical to accurately determine the shoreline in the littoral zone where stromatolite formation occurs, in alkaline clean lakes like Salda, which contain traces of life on Mars, and in monitoring the change that occurs with climate and anthropogenic effect. The performance of global automatic thresholding algorithms on shoreline determination from NDWI and mNDWI water indices is compared in this study using Sentinel-2 and Landsat-8 images atmospherically corrected by different algorithms. Satellite images data acquired on August 12, 2020 for Sentinel-2 and August 11, 2020 for Landsat-8 on Lake Salda were used to determining the shoreline. The shoreline data measured in situ concurrently with the Sentinel-2 satellite acquisition was used as reference data. In the accuracy analysis, ground control points created inside and outside the lake at a distance of 1 pixel and 0.5 pixel to the reference shoreline for each satellite image were used. The performance of the optimal threshold values determined by each thresholding algorithm in the water index images was assessed using Kappa coefficient, Overall Accuracy (OA), %OA of Inside (%OA inside) and %OA of Outside (%OA outside) statistics metrics. The optimal threshold values vary depending on the image and the atmospheric correction algorithm applied to the image. The NDWI index produces more accurate results in both Sentinel-2 and Landsat-8 satellite images. While atmospheric correction algorithms do not affect the results in Landsat-8 images, the Sen2Cor algorithm outperforms iCOR in Sentinel-2 images. For thresholding algorithms to be used in different water index and satellite images, Intermode, Isodata, IJ_Isodata, Minimum and Otsu algorithms in Landsat-8_LaSRC_NDWI and Landsat-8_iCOR_NDWI images, and Intermode, Minimum and Huang algorithms in Sentinel-2_Sen2Cor_NDWI images produce the best results. Because the Minimum algorithm causes significant gaps in the lake surface, the Huang and Intermode algorithms should be used for Sentinel-2_Sen2Cor_NDWI images. The 0 (zero) threshold value in the water indices images has a high accuracy only in the NDWI water indices generated from the Landsat-8 image. • Appropriate automatic thresholding algorithm for shoreline detection in extreme lakes. • NDWI index produces more accurate results in extreme alkaline Case-1 lakes like Lake Salda. • Intermode Thresholding Algorithm is successful on Landsat-8 and Sentinel-2 NDWI images. • Threshold value of 0 is reliable for only Landsat-8 NDWI. [ABSTRACT FROM AUTHOR]

Wetlands play a significant role in flood mitigation. Remote sensing technologies as an efficient and accurate approach have been widely applied to delineate wetlands. Supervised classification is conventionally applied for remote sensing technologies to improve the wetland delineation accuracy. However, performing supervised classification requires preparing the training data, which is also considered time-consuming and prone to human mistakes. This paper presents a deterministic topographic wetland index to delineate wetland inundation areas without performing supervised classification. The classic methods such as Normalized Difference Vegetation Index, Normalized Difference Water Index, and Topographic Wetness Index were chosen to compare with the proposed deterministic topographic method on wetland delineation accuracy. The ground truth sample points validated by Google satellite imageries from four different years were used for the assessment of the delineation overall accuracy. The results show that the proposed deterministic topographic wetland index has the highest overall accuracy (98.90%) and Kappa coefficient (0.641) among the selected approaches in this study. The findings of this paper will provide an alternative approach for delineating wetlands rapidly by using solely the LiDAR-derived Digital Elevation Model. [ABSTRACT FROM AUTHOR]

Ramsar Convention (RC) is the first of modern intergovernmental agreement on the conscious use and conservation of natural resources. It provides a platform for contracting parties working together to develop the best available data, advice, and policy recommendations to increase awareness of the benefits of wetlands in nature and society. Turkey became a party of the RC in 1994, and in the years 1994 to 2013, 14 wetlands that reached the Ramsar criteria were recognized as Ramsar sites (RS). With this study, all inland RS in Turkey from 1985 to 2020 were examined, and changes in the water surface areas were evaluated on the GEE cloud computing platform using Landsat satellite images and the NDWI index. The closest meteorological station data to each RS were evaluated and associated with the surface area changes. The reasons for the changes in these areas, besides the meteorological effects, have been scrutinized using management plans and publications. As a result, inland wetlands decreased at different rates from 1985 to 2020, with a total loss of 31.38% and 21,571.0 ha for the spring months. Since the designation dates of RS, the total amount of water surface area reduction was 27.35%, constituting 17,758.90 ha. [ABSTRACT FROM AUTHOR]

Over the last 50 years, countries across North Africa and the Middle East have seen a significant increase in dam construction which, notwithstanding their benefits, have endangered archaeological heritage. Archaeological surveys and salvage excavations have been carried out in threatened areas in the past, but the formation of reservoirs often resulted in the permanent loss of archaeological data. However, in 2018, a sharp fall in the water level of the Mosul Dam reservoir led to the emersion of the archaeological site of Kemune and allowed for its brief and targeted investigation. Reservoir water level change is not unique to the Mosul Dam, but it is a phenomenon affecting most of the artificial lakes of present-day Iraq. However, to know in advance which sites will be exposed due to a decrease in water level can be a challenging task, especially without any previous knowledge, field investigation, or high-resolution satellite image. Nonetheless, by using time-series medium-resolution satellite images, combined to obtain spectral indexes for different years, it is possible to monitor "patterns" of emerging archaeological sites from three major Iraqi reservoirs: Mosul, Haditha and Hamrin lake. The Normalised Difference Water Index (NDWI), generated from annual composites of Landsat and Sentinel-2 images, allow us to distinguish between water bodies and other land surfaces. When coupled with a pixel analysis of each image, the index can provide a mean for highlighting whether an archaeological site is submerged or not. Moreover, using a zonal histogram algorithm in QGIS over polygon shapefiles that represent a site surface, it is possible to assess the area of a site that has been exposed over time. The same analyses were carried out on monthly composites for the year 2018, to assess the impact of monthly variation of the water level on the archaeological sites. The results from both analyses have been visually evaluated using medium-resolution true colour images for specific years and locations and with 3 m resolution Planetscope images for 2018. Understanding emersion "patterns" of known archaeological sites provides a useful tool for targeted rescue excavation, while also expanding the knowledge of the post-flooding impact on cultural heritage in the regions under study. [ABSTRACT FROM AUTHOR]

The Aral Sea was the fourth largest lake in the world before 1960. Reduction of water resources and the growth of water intake and reservoirs in the source rivers have severely desiccated the Aral Sea. This study aimed to model the spatiotemporal and surface temperature changes of the Aral Sea in the period 1986–2017 using the Landsat multi-temporal satellite data and remote sensing techniques. Since the desiccation period was accompanied by climate change, hydrological data of source rivers, as well as climate data were collected from meteorological stations near the Aral Sea, to study the causes of changes. To achieve the research objectives, the Normalized Difference Water Index (NDWI) was selected among the other indices to extract and quantify the water area changes. Land and water surface temperature values were calculated using the thermal bands to examine the surface temperature change, which has rarely been discussed in the literature. The results revealed a significant decrease in the Aral Sea surface area (~ 78%) and a considerable increase in the land surface summertime temperature (~ 12 °C). The statistical results showed that the Aral Sea water extent decreased from 44,164 km2 in 1986 to 9772 km2 in 2017. [ABSTRACT FROM AUTHOR]