Analysis Of Satellite Imagery Research Articles

Morphological Analysis of a Nearshore Nourishment along the Atlantic Coast of New Jersey, USA

Nearshore nourishment is a common coastal flood risk management technique that can be constructed beneficially by using dredged sediment from navigation channels. A nearshore nourishment project was completed during the summer of 2021 in Harvey Cedars, NJ, USA, with 67,500 m3 of dredged sediment from Barnegat Inlet placed along approximately 450 m of beach in a depth of 3–4 m. In situ instruments were installed to monitor hydrodynamic conditions before and after dredged material placement, and nine topographic and bathymetric surveys were conducted to monitor nearshore morphological response to the nourishment. Shoreline location was extracted from satellite imagery using CoastSat software to compare historical trends to the shoreline response after construction. Seven months after construction, 40% of the nearshore nourishment was transported from the initial footprint and the centroid of the nourishment migrated towards shore and alongshore (north). The sheltering capacity of the nearshore berm appears to have captured an additional 58% of the placed volume from the longshore transport system and the beach width onshore of the placement increased by 10.9 m. Measured data, satellite imagery analysis, and rapid predictions all indicate that the nearshore nourishment at Harvey Cedars had a positive impact on the adjacent beach.

Archaeological surveys of a canyon and floodplain in the Tanūf District, North‐Central Oman: Optimised methodology and applications

AbstractThe highly‐elevated and rugged topography of the Al‐Ḥajar Mountains in Northern Oman requires a topographically‐optimised survey methodology. To reconstruct the archaeological landscapes in the southern piedmont area of the Tanūf District, we applied a combined ground survey and satellite imagery analysis. In the narrow canyon, we travelled towards visible caves on the cliffs and cairns on the ridges and recorded all encountered sites on the way. In the open floodplain and surrounding hills, we visited all visible features after scanning them from higher viewpoints. We utilised satellite imagery to complement inaccessible sites. Accordingly, we identified 23 sites from the Early Bronze Age to the Early Islamic period through ground surveys, and an additional nine sites through satellite imagery analysis. In our qualitative evaluation, this combined method was effective enough to reconstruct the archaeological landscapes of the survey area.

Triggering Rainfall of Large-Scale Landslides in Taiwan: Statistical Analysis of Satellite Imagery for Early Warning Systems

Typhoon Morakot had a serious impact on Taiwan, especially the uncommon type of landslide called large-scale landslide (LSL), not many in number but serious in effect, the origin of which the study induced. To establish a specific relationship between LSL and triggering rainfall for future applications of LSL early warning predictions, relevant cases from satellite imagery, along with field investigation data, major event reports, and seismic data from 2004 to 2016, were collected. All collected cases are distributed around the mountainous area in Taiwan, and a total of 107 cases which were mainly distributed in the southern part of the mountainous area were finally selected, including 28 occurrence-time-known cases and 79 occurrence-time-unknown cases. In addition, 149 potential areas identified by the Soil and Water Conservation Bureau (SWCB) were used for improving bounding estimates. Based on the concept of safety factor, two dimensionless quantities, rainfall/landslide depth (R/D) and friction angle/slope (ϕ/θ), were analyzed by linear regression. In addition, D was assumed to be nonlinearly dependent on R, θ, and ϕ, and the parameter uncertainties were evaluated by the resampling with bootstrap method. Based on the currently obtained data, there were 8% Type-I errors in the results of the linear regression analysis, and 1% Type-II errors in the results of the nonlinear regression analysis. Through the comparison of statistical indicators, the results of nonlinear regression analysis have a better correlation trend. Based on the needs of early warning operations, more conservative indicators can reduce the risks faced by management operations. Therefore, according to the results of this study, the lower boundary values from nonlinear analysis could be used as the LSL early warning management settings. Incorporated with real-time rainfall forecasts, the variation of statistical indicators will provide the trend information dynamically, and will help to increase the response time for relevant evacuation operations, that will be welcome for the further extended applications to guide the evacuation operations of early warning systems.

Global volcano monitoring through the Normalized Hotspot Indices (NHI) system

The Normalized Hotspot Indices (NHI) is a multi-channel algorithm developed to map thermal anomalies through the Multispectral Instrument onboard the Sentinel-2 satellite and the Operational Land Imager onboard the Landsat-8 satellite. The algorithm runs operationally under the Google Earth Engine platform and allows the analysis of volcanic thermal features (e.g. lava flows/lakes) through plots of the number of hot pixels, the total shortwave infrared radiance and the area of the hotspot. We present here the automated module of this tool: the NHI system. This system provides automated notifications about volcanic thermal anomalies detected at the global scale over the previous 48 h whenever the NHI web site ( https://sites.google.com/view/nhi-tool ) is accessed. The results of the first six months of operation are assessed through the analysis of satellite imagery and comparison with well-established programmes for global volcano monitoring. The low false positive rate (around 15%, including vegetation fires and data issues) and the successful identification of small, high-temperature features show that the NHI system may successfully integrate information from high temporal/low spatial resolution satellite data, despite some limitations (e.g. temporal sampling of the combined Sentinel-2 and Landsat-8 observations; delay of data ingestion in the Google Earth Engine platform). The recent ingestion of Landsat-9 data within the system has further extended the performance of the NHI system in supporting the surveillance of active volcanoes from space.

Estimation of aboveground carbon stock using Sentinel-2A data and Random Forest algorithm in scrub forests of the Salt Range, Pakistan

Abstract Forest ecosystems play a vital role in the global carbon cycle as forests store ~283 Gt of carbon globally and hence help mitigate climate change. Carbon stock estimation is the key step for assessing the mitigation potential of a given forest. About 5–10 Gt CO2 equivalent emissions come from deforestation and forest degradation annually. Pakistan’s forest resources are currently deteriorating due to deforestation and degradation and resulting in sourcing carbon dioxide emissions. One forest type that has been examined little so far in this context is subtropical scrub forests. This research suggests a workflow to estimate the carbon stock from three carbon pools (aboveground, belowground and litter) in scrub forests of the Salt Range, Pakistan by incorporating remote sensing and geographic information system techniques. The study’s objectives include the estimation of biomass and carbon stocks by using field inventory data and allometric equations, quantifying CO2 sequestration by using the ‘IPCC 2006 Guidelines for National Greenhouse Gas Inventories’ and finally map biomass and carbon by utilizing satellite imagery and statistical analysis. For prediction and mapping of biomass and carbon, field plots data along with vegetation indices and spectral bands of the Sentinel-2A satellite imagery were fed into a Random Forest (RF) algorithm in the cloud computing Google Earth Engine platform. Our results of ground data suggest that the examined scrub forests harbour 243 917 t of biomass, 114 989 t of carbon and 422 009 t of CO2 equivalent in the three carbon pools of the study area with a mean biomass density of 12.04 t ha−1 (±5.31) and mean carbon density of 5.72 t ha−1 (±2.46). The RF model showed good performance with reasonable R2 (0.53) and root mean square error (3.64 t ha−1) values and predicted average biomass at 13.93 t ha−1 (±4.35) and mean carbon density of 6.55 t ha−1 (±2.05). The total predicted and field-measured biomass has a plausible difference in values while the mean values have a minimal difference. The red-edge region and short-wave infrared (SWIR) region of the Sentinel-2A spectrum showed a strong relationship with aboveground biomass estimates from the field. We conclude that the combination of Sentinel-2A data coupled with ground data is a cost-effective and reliable tool to estimate various carbon pools in the scrub forests at a regional scale and may contribute to formulate policies to manage forests sustainably, enhance forest cover and conserve biodiversity.

Geomorphology of the lower Mesopotamian plain at Tell Zurghul archaeological site

ABSTRACT The landscape of the Lower Mesopotamia Plain (LMP) has been moulded by water-related processes, consequently, its Holocene geomorphic evolution has been strictly connected to the fluvial process and the anthropogenic water management since 8000 BC. About 6000 years ago, during the maximum marine ingression, the modern cities of Nasiriyah and Al-Amara were close to the Persian Gulf shoreline. Successively, the Tigris and Euphrates developed two wide delta systems, that prograded south-eastward developing a complex fluvial network. Remote sensing investigations over the LMP using satellite imagery and topographic analysis revealed the surficial expression of deltaic bodies with a lobate planform and several terminal distributary channels (TDCs), classifiable as tidal-influenced river-dominated deltas. Tell Zurghul archaeological site, belonging to the ancient State of Lagash, expanded in the western part of the recognized TDC during the Mid- and Late Holocene. Indeed, the occurrence of a divergent multi-channel system supplied water for the early civilizations, which improved the water management and prevented floods through a canals network. Therefore, the multi-sensor remote sensing approach over an area of 2850 km2 allowed us to recognize several fluvial landforms, both still active and relict, attributable to the Holocene riverscape of the LMP, as well as anthropogenic features and aeolian deposits. The Main map is a geomorphological map at the scale of 1:120,000 centred on Tell Zurghul, focusing on the geometry, spatial distribution, and state of activity of erosional and constructional landforms.

Human Impact Scale on the Preservation of Archaeological Sites from Mostiştea Valley (Romania)

Through time, both natural and cultural heritage have unfortunately been under threat due to multiple environmental and human-induced factors, which are likely to trigger various hazards such as soil erosion, landslides, or land collapse. The analysis of old cartographic material, aerial imagery, and satellite imagery has been used in multiple studies to observe and understand the changes that archaeological sites have undergone over the last centuries. These efforts are intended, among other things, to raise awareness of the threats affecting cultural heritage and prevent damages and preserve tangible evidence of the distant past. In this study, historical maps and satellite imagery were analyzed to observe how the landscape in the Mostiștea Valley (Romania) has been used over the last 230 years and how the land use has affected the cultural heritage. Land cover and land use (LCLU) changes in the Mostiștea Valley have occurred due to numerous natural and anthropic forces. These changes have resulted in the damage of tangible heritage in the area with varying degrees of intensity. The results of this study allow the quantification of the magnitude of these changes and their impact on different sites in the region.

Following the herds? A new distribution of hunting kites in Southwest Asia

Remote-sensing analysis of open-source satellite imagery has identified a major, new distribution of undocumented hunting kite structures in northern Arabia. This new data has important implications on the environmental viability of hunting and on possible settlement patterns during the early and middle Holocene. Running across the eastern side of the Nafud Desert in Saudi Arabia, this research has identified star-shaped kites in a distribution that continues on to southern Iraq. From a broader perspective, this new distribution appears to represent a continuation of the well-known arc of kites recorded running principally through southern Syria and eastern Jordan. As well as representing an important archaeological identification in its own right, this new distribution also has important implications in terms of the paleoenvironment of the region, faunal dispersals and human cultural connections.

THE EFFECT OF THE DISTRIBUTION OF SEA SURFACE TEMPERATURE IN THE INDIAN OCEAN SOUTH OF JAVA ON THE CATCH OF YELLOWFIN TUNA (Thunnus albacares) USING AQUA MODIS SATELLITE IMAGERY

Tuna (Thunnus sp.) is the second largest foreign exchange earner after shrimp. One type of tuna which is an export commodity is yellowfin tuna (Thunnus albacares). Research on the effect of the distribution of sea surface temperature in the Indian Ocean south of Java on the catch of yellowfin tuna (Thunnus albacares) has been carried out at the Nizam Zachman Ocean Fishery Port (PPS) from April 8 to April 30, 2021. This study aims to determine the effect of sea surface temperature (SST) on the catch of yellowfin tuna. The research method used was the method of observation and analysis of satellite imagery. Coordinate data and the number of catches were obtained from the Ocean Fishery Port Logbook (PPS) Nizam Zachman. SPL data obtained from satellite imagery Modis Aqua Level 3. The data was obtained and then analyzed using a simple regression test and the F test results of regression analysis showed that the correlation coefficient (R) of 12.7% and the coefficient of determination (R2) of 1,6% means that 1.6% of yellowfin tuna catches are influenced by SST and 98.4% is influenced by other factors. The F test results show that the SPL significance value is 0.332. The conclusion is that changes in SST have no significant effect on the catch of yellowfin tuna

Time-series analysis of landcover dynamics and their relation with coastline migration along Kuakata coast, Bangladesh using remote sensing techniques

ABSTRACT Time-series analysis of satellite imageries is useful in studying changes in coastlines and the nature of the landcover dynamics in coastal environments. This study aimed to investigate the relationship between coastal erosion-accretion and landcover changes in Kuakata. Landsat TM and Landsat OLI/TIRS satellite imageries at a nearly 5-year interval between the years 1989 and 2020 were used to compare changes within five major landcover classes in the study area – 1) mangrove vegetation, 2) settlements, 3) agricultural land, 4) waterbody and 5) beach. Net land loss over the past 31 years was estimated to be 1.73 km2 within the 93.05 km2 study area. Linear regression rates were calculated to identify the area most prone to erosion. The average erosion rate along the coastline was estimated to be 2.09 m/year. Most erosion occurred along the western part of the coast while the highest accretion was limited to an area in the east. Study findings also suggested that changes in beach and mangrove vegetation classes have a significant spatial and statistical correlation with coastal erosion-accretion processes. These findings can help the policymakers implement coastal zonation, and preventive and rehabilitative measures to save the tourism industry and agriculture in Kuakata.

Mapping the thickness of sea ice in the Arctic as an example of using data from a ship-based television complex for operational hydrometeorological support of maritime activities

Sea ice charts produced by the ice services of the world are among the most widely used sources of information about sea ice conditions in the Arctic. The absolute majority of sea ice charts are based on visual expert analysis of satellite imagery accompanied by auxiliary data including ground information from coastal stations and ships navigating the Northern Sea Route (NSR). Ground measurements of sea ice thickness are necessary for validating the results of satellite imagery interpretation. Shipboard observations are highly valuable because, unlike coastal stations, the ships provide information about sea ice cover straight on the navigational routes, not in the coastal areas of land-fast ice, where the thickness values are not fully representative of the ice in the open sea. However, the current system of shipboard observations used by commercial fleets often does not meet the reliability requirements due to the human factor involved in the process of data collection. In the early 2000s, the Arctic and Antarctic Research Institute (AARI) suggested a new methodology for shipboard ice thickness measurement. A ship-based television complex (STC) was developed in order to exclude the human factor and standardize observations. The inaccuracy value was estimated as 3.8 % of the real thickness. By 2018, STC had been upgraded to a new ship-based television meteorological complex (STMC) allowing continuous automatic measurement of ice thickness and many other related hydrometeorological parameters during the entire voyage. The automatic and autonomous operation of the new equipment allows placing it on board the ship without the need for an ice specialist to be permanently present. It means that STMC can be used by commercial fleets, which constantly increase the number of Arc7 ice class vessels they use. For economic reasons, reinforced ice class vessels, whose number is growing, represent the only available infrastructure suitable for the deployment of distributed network providing operational hydrometeorological monitoring on the NSR. A comparison of STC data with AARI ice charts has revealed that real-time transmission of STC data from ships to the ice service office could increase the accuracy of ice charts and, as a consequence, the quality of the entire system of hydrometeorological informational support of maritime activities in the Arctic.

Land Ocean Connectivity Through Inland Water Ecosystem: A Coremap-CTI Project in The Coastal Area of Kendari Bay, Southeast Sulawesi

Coastal zone is highly valued ecosystem exposed to multiple and often cumulative natural and anthropogenic stressors such as catchment loads of nutrients and sediment, as well as climate warming. High loads of nutrients and sediment from the hinterland are dispersed into this zone leading to an increase microalgae community in which it may reduce light availability for corals and seagrasses. In this paper, we present the progress of our study in the coastal area of Kendari, Southeast Sulawesi, under the COREMAP-CTI Research Programme. Through such programme we are 1) investigating the flux of materials from the river basin to the marine environment, 2) characterising the key process that governing those fluxes, 3) developing models that capable of in predicting material fluxes under current and future environment (e.g., land-use and climate change). To do so, we integrate data and information derived from field observations and satellite imagery analysis to feed the developed rainfall-sediment-runoff and coastal ecological-hydrodynamic model. Not only focusing on the science of physical-biogeochemical processes of land-ocean interaction, the outcome of this research project is also aimed to support the development of adaptation strategy in combating climate change and anthropogenic pressure in the near future of coastal ecosystem.

Soil wind erodibility and erosion estimation using Landsat satellite imagery and multiple-criteria decision analysis in Urmia Lake Region, Iran

Assessing variations in soil wind erosion (SWE) is critical for identifying key change areas and formulating desertification control strategies. Satellite images with an expansive spatial coverage and temporal repeatability make it possible to monitor the process of soil degradation and its consequences such as SWE. This research aims to model SWE in the eastern shoreline of Urmia Lake in the 2005–2017 period through multiple-criteria decision analysis (MCDA). Soil moisture, soil erodibility (SE), soil crust index, number of snow cover days, wind field intensity, and vegetation fraction were determined as critical factors affecting SWE. The analytic hierarchy process (AHP) method was applied to determine the weight of each factor. High SE and poor vegetation were the most important factors in the developed SWE model. The SE was precisely estimated (relative percent deviation (RPD)=2.01) by the support vector regression (SVR) method using Landsat-8 image. The developed SWE estimation method had an overall accuracy of 81%. Most of the eastern shoreline of Urmia Lake Region was classified in the severe SWE class. Results showed a declining erosion intensity trend from central parts with high wind erosion (47% of the region) to northern and southern parts of the region. Increasing the distance from the lake led to an increase in SWE.

Identification of the Puting Beliung Event by Utilizing the Interpretations of Radar Products and Himawari-8 Weather Satellite (Case Study: Puting Beliung Incident, November 22, 2018 in Jakarta)

<em>Indonesia was shocked again by a Puting beliung or tornado-like incident on November 22, 2018, in the Jakarta area. This incident caused many losses. In this regard, a study was conducted to observe the reflectivity products to identifiy the hook echo or bow echo patterns, and the velocity products for mesocyclone patterns as the characteristic of the Puting Beliung used radar product. The study required the Cengkareng C-Band raw data radar that was processed to produce CMAX, VCUT, and CAPPI (V) at 0.5 km, 1.0 km, and 1.5 km elevations overlay by HWIND. The CMAX and VCUT radar products are used to identify the cloud structure that caused Puting Beliung, by observed the highest reflectivity of the Puting Beliung-producing clouds. Then the CAPPI product overlay by HWIND is used to identify the movement of the wind which is suspected to be the beginning of the formation of a Puting Beliung at that location which is characterized by the presence of a mesocyclone pattern in the form of wind components and radial velocity. It was suspected that there was a wind rotating in the Central Jakarta area which indicated a Puting Beliung in the area. The analysis of this radar interpretation was then validated using satellite imagery to detect the cumulonimbus clouds forming the Puting Beliung. From this research, it is known that the Puting Beliung occurred around 08.12 UTC. The growth of cyclone-producing clouds occurred rapidly with a reflectivity value between 35 - 45 dBZ and wind speed up to 35 knots. Analysis of satellite imagery showed a significant decrease in cloud peak temperature so that the formation of convective clouds in the form of Cumulonimbus clouds indicates the phenomenon of the Puting Beliung. However, in this case study the Puting Beliung phenomenon is indicated as seen from the mesocyclone pattern of the rotating radial velocity component, and not from the hook echo or bow echo pattern.</em>

Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery

Rapid global warming is catalyzing widespread permafrost degradation in the Arctic, leading to destructive land-surface subsidence that destabilizes and deforms the ground. Consequently, human-built infrastructure constructed upon permafrost is currently at major risk of structural failure. Risk assessment frameworks that attempt to study this issue assume that precise information on the location and extent of infrastructure is known. However, complete, high-quality, uniform geospatial datasets of built infrastructure that are readily available for such scientific studies are lacking. While imagery-enabled mapping can fill this knowledge gap, the small size of individual structures and vast geographical extent of the Arctic necessitate large volumes of very high spatial resolution remote sensing imagery. Transforming this ‘big’ imagery data into ‘science-ready’ information demands highly automated image analysis pipelines driven by advanced computer vision algorithms. Despite this, previous fine resolution studies have been limited to manual digitization of features on locally confined scales. Therefore, this exploratory study serves as the first investigation into fully automated analysis of sub-meter spatial resolution satellite imagery for automated detection of Arctic built infrastructure. We tasked the U-Net, a deep learning-based semantic segmentation model, with classifying different infrastructure types (residential, commercial, public, and industrial buildings, as well as roads) from commercial satellite imagery of Utqiagvik and Prudhoe Bay, Alaska. We also conducted a systematic experiment to understand how image augmentation can impact model performance when labeled training data is limited. When optimal augmentation methods were applied, the U-Net achieved an average F1 score of 0.83. Overall, our experimental findings show that the U-Net-based workflow is a promising method for automated Arctic built infrastructure detection that, combined with existing optimized workflows, such as MAPLE, could be expanded to map a multitude of infrastructure types spanning the pan-Arctic.

Spatio-temporal of mangrove vegetation based on the analysis of multi-temporal sentinel-2 satellite imageries at Jepara Regency, Indonesia

Mangrove areas in the northern coast of Jepara Regency have been threatened by coastal erosion and changes in land use since 1980. These areas represent coastal ecosystems with significant ecological, biological, and economic importance. The status of these ecosystems needs to be monitored; the latest condition is important to local authorities. Rate of change in mangrove conditions can be used to forecast the future condition of these coastal areas. This study aims to determine the temporal changes in the area and density of mangrove vegetation in Jepara Regency in 2017 and 2020. This study applied analysis of Sentinel 2-A multitemporal satellite imagery and classification using random forest on the Google Earth Engine platform. It was found that the mangrove area shrank from 103 ha in 2017 to 95 ha in 2020. The vegetations showed that changes in mangrove area in Jepara Regency have decreased in the rare and dense mangrove density types. The density of rare and dense mangroves experienced a decrease in area of 6 and 5 hectares, respectively. No mangroves with dense cover were found in 2020. Meanwhile, mangroves with moderate cover experienced an increase in area of 3 hectares in 2020 compared to last year 2017.

Extent, accuracy and repeatability of bare sand and vegetation cover in dunes mapped from aerial imagery is highly variable

Vegetation cover on coastal sand dunes has been increasing worldwide since at least the 1940s. Analysis of aerial and satellite imagery has been the principal source used to measure this change, however no studies have systematically evaluated the accuracy of remotely sensed estimates. Using established land cover classification methods and in-situ field measurements, we show that both the extent and accuracy of remotely sensed areas of bare sand and vegetation in dunes varies with image resolution and classification method. We found that supervised methods of classification (semi-automatic), whilst mapping a greater extent of bare sand and being more accurate than manual digitisation, had poor repeatability, exhibiting a relatively large range of bare sand and vegetation extent between classifications replicated under the same conditions. In contrast, areas of bare sand and vegetation classified by manual digitisation had high repeatability but a relatively low percentage of observed agreement with data collected in the field. For all classification methods, observed agreement with field data generally increased with image resolution. Our results demonstrate that users of land classification data in dunes should be cautious when interpreting trends of bare sand and vegetation cover due to substantial repeatability error in supervised classification methods, and relatively poor observed agreement with field data of manual classification. We recommend that analysis of bare sand and vegetation cover in dunes should be based on multiple replicates using supervised classification, employing the highest resolution imagery available and that all results presented should also include the range measured by multiple replicates.

District Cooling Network Planning. A Case Study of Tallinn

The planning procedure for district cooling as an urban system was presented and carried out using the example of the Tallinn city centre. The following steps were described in detail: cooling demand determination, cooling generation planning and cooling transition analysis. Based on the three proposed methods (average specific cooling load, satellite imagery analysis of a specific building, counting the number of fans in dry coolers and the combination method), the cooling capacity of the evaluated district was estimated at 63.2 MW. In terms of cooling generation, the analysis shows that seawater for free cooling can cover up to 55% of the annual cooling consumption. Electric chillers and absorption chillers that use surplus heat can cover the rest of the district cooling demand. The district cooling network was designed for three scenarios: with one generating unit, with two generating units and a looped network. Despite the fact that the looped network is the most expensive option, this type of solution is considered feasible as it will make it easier to connect new consumers.

Forecasting water quality using seasonal ARIMA model by integrating in-situ measurements and remote sensing techniques in Krishnagiri reservoir, India

Abstract The Krishnagiri reservoir is the main source of irrigation in Tamil Nadu, India. It has been reported to be hypereutrophic for over a decade with sediment and nutrient load sources responsible for the degradation of water quality. Remotely sensed satellite imagery analysis plays a significant role in assessing the water quality for developing a management strategy for reservoirs. The present study is an attempt to demonstrate the improvement in the chlorophyll-a (chl-a) estimation in the Krishnagiri reservoir by integrating remote sensing and in-situ measurements. Multiple regression equations were developed with the reflectance of Green, Red, NIR and SWIR1 bands of the Operational Land Imager (OLI) sensor of Landsat 8 satellite yielded the coefficient of determination for chlorophyll-a (chl-a) as 0.812, total dissolved solids (TDS) as 0.945 and electrical conductivity (EC) as 0.960 respectively. The developed regression model was further utilised to forecast chl-a and EC of the reservoir through the seasonal auto regressive integrated moving average (SARIMA) model. It is found that chl-a prediction showed that the reservoir continued to be hypereutrophic and EC significantly changed from a class C3 (high salinity) to class C4 (very high salinity). These results are alarming and an immediate reduction of the external load from the catchment through effective watershed management programs should be implemented.

Identifying Rip Channels Along RK Beach, Visakhapatnam Using Video and Satellite Imagery Analysis

Identifying Rip Channels Along RK Beach, Visakhapatnam Using Video and Satellite Imagery Analysis