scholarly journals WATER CLARITY MAPPING IN KERINCI AND TONDANO LAKE WATERS USING LANDSAT 8 DATA

2017 ◽  
Vol 12 (2) ◽  
pp. 117 ◽  
Author(s):  
Bambang Trisakti ◽  
Nana Suwargana ◽  
I Made Parsa
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Atmospheric Correction ◽  
Total Suspended Solid ◽  
Water Clarity ◽  
Suspended Solid ◽  
Lake Ecosystem ◽  
Landsat 8 ◽  
Wide Range ◽  
Lake Waters

Land conversion occurred in the lake catchment area caused the decreasing of water quality in many lakes of Indonesia. According to Lake Ecosystem Management Guidelines from Ministry of Environment, tropic state of lake water is one of parameters for assessing the lake ecosystem status. Tropic state can be indicated by the quantity of nitrogen, phosphorus, chlorophyll, and water clarity. The objective of this research is to develop the water quality algorithm and map the water clarity of lake water using Landsat 8 data. The data were standardized for sun geometry correction and atmospheric correction using Dark Object Subtraction method. In the first step, Total Suspended Solid (TSS) distributions in the lake were calculated using a semi empirical algorithm (Doxaran et al., 2002), which can be applied to a wide range of TSS values. Secchi Disk Transparency (SDT) distributions were calculated using our water clarity algorithm that was obtained from the relationship between TSS and SDT measured directly in the lake waters. The result shows that the water clarity algorithm developed in this research has the determination coefficient that reaches to 0,834. Implementation of the algorithm for Landsat 8 data in 2013 and 2014 showed that the water clarity in Kerinci Lake waters was around 2 m or less, but the water clarity in Tondano Lake waters was around 2 – 3 m. It means that Kerinci Lake waters had lower water clarity than Tondano Lake waters which is consistent with the field measurement results.

scholarly journals MONITORING OF LAKE ECOSYSTEM PARAMETER USING LANDSAT DATA (A CASE STUDY: LAKE RAWA PENING)

2017 ◽  
Vol 12 (1) ◽  
pp. 71 ◽  
Author(s):  
Bambang Trisakti ◽  
Nana Suwargana ◽  
Joko Santo Cahyono
Keyword(s):  
Surface Area ◽  
Lake Water ◽  
Water Hyacinth ◽  
Water Surface ◽  
Atmospheric Correction ◽  
Water Clarity ◽  
Likelihood Method ◽  
Lake Ecosystem ◽  
Water Surface Area ◽  
Water Classification

Most lakes in Indonesia have suffered (decrease in quality) caused by land conversion in the catchment area, soil erosion, and water pollution from agriculture and households. This study utilizes remote sensing data to monitor several parameters used as ecosystem status assessors in accordance with the guidelines of Lake Ecosystem Management provided by the Ministry of Environment. The monitoring was done at Lake Rawa Pening using Landsat TM/ETM+ satellite data over the period of 2000-2013. The data standardization was done for sun angle correction and also atmospheric correction by removing dark pixels using histogram adjustment method. RGB color composites (R: NIR + SWIR, G: NIR, B: NIR-RED) were used for water hyacinth identification; thus, the lake water surface area can be delineated. Further samples were collected for water hyacinth and water classification with Maximum Likelihood method. Total Suspended Matter (TSM) by Doxaran model and the water clarity from field measurement was correlated to build water clarity algorithm. The results show that Lake Rawa Pening was deterioting in term of quality during the period of 2000-2013; it can be seen from the dynamic rate of the shrinkage and the expansion of the lake water surface area, the uncontrolled distribution of water hyacinth which it covered 45% of the lake water surface area in 2013, the increased of TSM concentration, and the decreased of water clarity. Most parts of Rawa Pening’s water have clarity less than 2.5 m which indicated that the thropic status is hypertrophic class.

scholarly journals TIME SERIES ANALYSIS OF TOTAL SUSPENDED SOLID (TSS) USING LANDSAT DATA IN BERAU COASTAL AREA, INDONESIA

2017 ◽  
Vol 14 (1) ◽  
pp. 61 ◽  
Author(s):  
Ety Parwati ◽  
Anang Dwi Purwanto
Keyword(s):  
Water Quality ◽  
Coastal Area ◽  
Image Data ◽  
Total Suspended Solid ◽  
Suspended Solid ◽  
Landsat 8 ◽  
Landsat Data ◽  
Observation Area ◽  
Extraction Algorithm ◽  
Landsat 7

Water quality information is usually used for the first examination of the pollution.  One of the parameters of water quality is Total Suspended Solid (TSS), which describes the amount of matter of particles suspended in the water. TSS information is also used as initial information about waters condition of a region. TSS could be derive from Landsat data with several combinations of spectral channels to evaluate the condition of the observation area for both the waters and the surrounding land. The study aimed to evaluate Berau waters condition in Kalimantan, Indonesia, by utilizing TSS dynamics extracted from Landsat data. Validated TSS extraction algorithm was obtained by choosing the best correlation between  field data and image data. Sixty pairs of points had been used to build validated TSS algorithms for the Berau Coastal area. The algorithm was TSS = 3.3238 * exp (34 099 * Red Band Reflectance). The data used for this study were Landsat 5 TM, Landsat 7 ETM and Landsat 8 data acquisition in 1994, 1996, 1998, 2002, 2004, 2006, 2008 and 2013. For detailed evaluation, 20 regions were created along the watershed up to the coast. The results showed the fluctuation of TSS values in each selected region. TSS value increased if there was a change of any kind of land cover/land used into bareland, ponds, settlements or shrubs. Conversely, TSS value decreased if there was a wide increase of mangrove area or its position was very closed to the ocean.

Unified, high resolution water quality retrievals from Earth Observation satellites

2021 ◽  
Author(s):  
James Harding
Keyword(s):  
Water Quality ◽  
Water Resource Management ◽  
State Of The Art ◽  
Earth Observation ◽  
Water Quality Parameter ◽  
Landsat 8 ◽  
Optical Parameters ◽  
Atmospheric Conditions ◽  
Mixture Density ◽  
Wide Range

<p>Earth Observation (EO) satellites are drawing considerable attention in areas of water resource management, given their potential to provide unprecedented information on the condition of aquatic ecosystems. Despite ocean colours long history; water quality parameter retrievals from shallow and inland waters remains a complex undertaking. Consistent, cross-mission retrievals of the primary optical parameters using state-of-the-art algorithms are limited by the added optical complexity of these waters. Less work has acknowledged their non- or weakly optical parameter counterparts. These can be more informative than their vivid counterparts, their potential covariance would be regionally specific. Here, we introduce a multi-input, multi-output Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in shallow and inland water bodies. The model is trained and validated using a sizeable historical database in excess of 1,000,000 samples across 38 optical and non-optical parameters, spanning 20 years across 500 surface waters in Scotland. The single network learns to predict concurrently Chlorophyll-a, Colour, Turbidity, pH, Calcium, Total Phosphorous, Total Organic Carbon, Temperature, Dissolved Oxygen and Suspended Solids from real Landsat 7, Landsat 8, and Sentinel 2 spectra. The MDN is found to fully preserve the covariances of the optical and non-optical parameters, while known one-to-many mappings within the non-optical parameters are retained. Initial performance evaluations suggest significant improvements in Chl-a retrievals from existing state-of-the-art algorithms. MDNs characteristically provide a means of quantifying the noise variance around a prediction for a given input, now pertaining to real data under a wide range of atmospheric conditions. We find this to be informative for example in detecting outlier pixels such as clouds, and may similarly be used to guide or inform future work in academic or industrial contexts. </p>

scholarly journals Phytoplankton community structure and it’s relationships with water quality in Bangka Island, Indonesia

2021 ◽  
Vol 26 (1) ◽  
pp. 37-44
Author(s):  
Muh Yusuf ◽  
Robin Robin ◽  
Wahyu Adi ◽  
Mu’alimah Hudatwi ◽  
Widianingsih Widianingsih ◽  
...  
Keyword(s):  
Water Quality ◽  
Phytoplankton Community ◽  
Environmental Changes ◽  
Total Suspended Solid ◽  
Mining Area ◽  
Suspended Solid ◽  
Test Analysis ◽  
Mining Site ◽  
Average Value ◽  
Primary Producer

Phytoplankton plays an important role in primary productivity in marine environment. Various environmental changes in coastal area will impact the water quality and their phytoplankton compositions. The purpose of this study is to examine the abundance of phytoplankton from two different sites, i.e Tanah Merah (close to mining site) and Semujur Island (away from mining site) in Bangka Island. Phytoplankton and water sample were collected on June- August 2018. Water quality was measured using water quality checker, whereas the phytoplankton was identified under the microscope with a magnification of 100x. Non-parametric Kruskal test and T-test analysis was performed to determine the abundance, diversity, uniform, and dominance of phytoplankton between Sites, respectively. Statistical analyses showed the abundance of phytoplankton at Semujur Island was significantly higher than that at Tanah Merah (p = 0.003). In additions the diversity, uniform, and dominance were also significantly different between sites (all p <0.05). In Semujur Island, Diatoms (Thalassiothrix, Chaetoceros and Thalassionema) were more dominants than the Dinophyceae group. However, in Tanah Merah, the genera Ceratium belong to class Dinophyceae was more dominant than the class Bacillariophyceae. These results performed that the phytoplankton in Tanah Merah and Semujur Island was affected by environment, in this case the mining area. The water quality in Semujur Island (non-mining Area) might have good quality than in Tanah Merah (mining area). The average value of turbidity and Total Suspended Solid in Tanah Merah Waters causes low abundance of phytoplankton. It can be concluded that tin mining can disrupt the abundance and composition of phytoplankton as a primary producer of waters.

scholarly journals EFFECTS OF ANTHROPOGENIC ACTIVITY ON STREAM WATER QUALITY IN LANGSA, ACEH

2018 ◽  
Vol 18 (2) ◽  
Author(s):  
Helmy Akbar ◽  
Iwan Suyatna ◽  
Jailani Jailani ◽  
Singgih Afifa Putra ◽  
Fauziah Azmi
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Surface Mining ◽  
Total Suspended Solid ◽  
Suspended Solid ◽  
Low Ph ◽  
Anthropogenic Activity ◽  
Stream Water Quality ◽  
Type C

Increased human activity towards the water bodies will change the condition of water quality. Case study in Langsa, Aceh, It was found that an increase in Some physical parameter (TSS) that exceeds the value determined in PP 82 of 2001 (Indonesian government standard). The high value of TSS in Station 2 and Station 3 indicates that the sediment loading to the water body is high, especially in Station 3, where the TSS concentrations far exceed the standard. Activity of type C surface mining materials tends to affect the brightness, turbidity, depth and TSS. Water conditions with low pH were also found in this study. In location studied no EPT larvae were found Keyword: Langsa, Water Quality, Stream, Total Suspended Solid, Anthropogenic Activity

scholarly journals An Overview of River Side Pollutants and their Effects on Physico-Chemical Changes in Water Quality of River Halda, Bangladesh

2020 ◽  
Vol 32 (4) ◽  
pp. 827-834
Author(s):  
Muhammad Towhid Moula ◽  
Ranjit K. Nath ◽  
Mh. Mosfeka Chowdhury ◽  
Md. Abu Bakar Siddique
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
Anthropogenic Activities ◽  
Oxygen Demand ◽  
Aquatic Organisms ◽  
Total Suspended Solid ◽  
Total Solid ◽  
Suspended Solid ◽  
Total Alkalinity ◽  
Physico Chemical

Halda is an important river of Bangladesh, is now polluted in different ways through industrial, agricultural, domestic and sewage disposal. Increased anthropogenic activities have increased the potential pollution of the river and excessive pollutants may be toxic to humans and aquatic fauna. Presence of heavy metals in the river water causes perilous impact on the aquatic organisms. Hence, regular monitoring of pollution levels in the river is indispensable. In this study, we discuss about physico-chemical assessments of water quality parameters viz. pH, dissolve oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total solid (TS), total suspended solid (TSS), total dissolved substance (TDS), total alkalinity, turbidity, salinity, electrical conductivity (EC), hardness, chloride and heavy metals in the water of Halda river during rainy and winter seasons, at different points; sources of pollutants in water and their effects given starting from the early research until the current research.

scholarly journals Colour Classification of 1486 Lakes across a Wide Range of Optical Water Types

2018 ◽  
Vol 10 (8) ◽  
pp. 1273 ◽  
Author(s):  
Moritz Lehmann ◽  
Uyen Nguyen ◽  
Mathew Allan ◽  
Hendrik van der Woerd
Keyword(s):  
Water Quality ◽  
Environmental Control ◽  
Anthropogenic Factors ◽  
Landsat 8 ◽  
Diverse Range ◽  
Water Colour ◽  
Water Types ◽  
Wide Range ◽  
Yellow Orange ◽  
Spatio Temporal

Remote sensing by satellite-borne sensors presents a significant opportunity to enhance the spatio-temporal coverage of environmental monitoring programmes for lakes, but the estimation of classic water quality attributes from inland water bodies has not reached operational status due to the difficulty of discerning the spectral signatures of optically active water constituents. Determination of water colour, as perceived by the human eye, does not require knowledge of inherent optical properties and therefore represents a generally applicable remotely-sensed water quality attribute. In this paper, we implemented a recent algorithm for the retrieval of colour parameters (hue angle, dominant wavelength) and derived a new correction for colour purity to account for the spectral bandpass of the Landsat 8 Operational Land Imager (OLI). We used this algorithm to calculate water colour on almost 45,000 observations over four years from 1486 lakes from a diverse range of optical water types in New Zealand. We show that the most prevalent lake colours are yellow-orange and blue, respectively, while green observations are comparatively rare. About 40% of the study lakes show transitions between colours at a range of time scales, including seasonal. A preliminary exploratory analysis suggests that both geo-physical and anthropogenic factors, such as catchment land use, provide environmental control of lake colour and are promising avenues for future analysis.

scholarly journals Assessing the potential of integrating Landsat sensors for estimating chlorophyll-a concentration in a reservoir

2017 ◽  
Vol 49 (5) ◽  
pp. 1608-1617 ◽  
Author(s):  
Matias Bonansea ◽  
Claudia Rodriguez ◽  
Lucio Pinotti
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Landsat Imagery ◽  
Quality Parameters ◽  
Landsat 8 ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Sensing Applications ◽  
Wide Range ◽  
Quality Assessments

Abstract Landsat satellites, 5 and 7, have significant potential for estimating several water quality parameters, but to our knowledge, there are few investigations which integrate these earlier sensors with the newest and improved mission of Landsat 8 satellite. Thus, the comparability of water quality assessing across different Landsat sensors needs to be evaluated. The main objective of this study was to assess the feasibility of integrating Landsat sensors to estimate chlorophyll-a concentration (Chl-a) in Río Tercero reservoir (Argentina). A general model to retrieve Chl-a was developed (R2 = 0.88). Using observed versus predicted Chl-a values the model was validated (R2 = 0.89) and applied to Landsat imagery obtaining spatial representations of Chl-a in the reservoir. Results showed that Landsat 8 can be combined with Landsat 5 and 7 to construct an empirical model to estimate water quality characteristics, such as Chl-a in a reservoir. As the number of available and upcoming sensors with open access will increase with time, we expect that this trend will certainly further promote remote sensing applications and serve as a valuable basis for a wide range of water quality assessments.

Monitoring and modelling mining impacts on water quality in Chindwin River Basin, Myanmar

2020 ◽  
Author(s):  
Thanapon Piman ◽  
Chayanis Krittasudthacheew ◽  
Shakthi K. Gunawardanaa ◽  
Sangam Shresthaa
Keyword(s):  
Water Quality ◽  
Heavy Metal ◽  
Drinking Water ◽  
Water Treatment ◽  
River Basin ◽  
Total Suspended Solid ◽  
Water Quality Monitoring ◽  
Suspended Solid ◽  
Quality Monitoring ◽  
Mining Activities

&lt;p&gt;The Chindwin River, a major tributary of the Ayeyarwady River in Myanmar, is approximately 850 km long with a watershed area of 115,300 km&lt;sup&gt;2&lt;/sup&gt;. The Chindwin River is essential for local livelihoods, drinking water, ecosystems, navigation, agriculture, and industries such as logging and mining. Over the past two decades, Myanmar&amp;#8217;s rapid economic development has resulted in drastic changes to socio-economic and ecological conditions in the basin. Water users in the basin reported that there is a rapid extension of gold and jade mining and they observed a noticeable decline in water quality along with increased sedimentation and turbidity. So far, however, Myanmar has not undertaken a comprehensive scientific study in the Chindwin River Basin to assess water quality and sources of water pollution and to effectively address issues of river basin degradation and concerns for public health and safety. This study aims to assess the status of water quality in the Chindwin River and the potential impact of mining activities on the water quality and loading through monitoring program and modeling approach. 17 locations in the upper, middle and lower parts of the Chindwin River Basin were selected for water quality monitoring. These sites are located near Homalin, Kalewa, Kani and Monywa townships where human activities and interventions could affect water quality. Water quality sampling and testing in the Chindwin River was conducted two times per year: in the dry season (May-June) and in the wet season (September-October) during 2015-2017. We monitored 21 parameters including heavy metals such as Lead (Pb), Mercury (Hg), Copper (Cu) and Iron (Fe). The observed values of Mercury in Uru River in the upper Chindwin River Basin which located nearby gold mining sites shown higher than the WHO drinking standard. This area also has high values of turbidity and Total Suspended Solid. The SHETRAN hydrological model, PHREEQC geochemical model and LOADEST model were used to quantify the heavy metal loads in the Uru River. Results from scenario analysis indicate an increase in Arsenic and Mercury load under increment of concentration due to expansions in mining areas. In both baseline and future climate conditions, the Uru downstream area shows the highest load effluent in both Arsenic and Mercury. These heavy metal loads will intensify the declining water quality condition in Chindwin River and can impact negatively on human health who use water for drinking. Therefore, we recommend that water quality monitoring should continue to provide scientific-evidence for decision-makers to manage water quality and mining activities properly.&amp;#160; Water treatment systems for drinking water are required to remove turbidity, Total Suspended Solid, and Mercury from raw water sources. Raising awareness of relevant stakeholders (local people, farmers, private sectors, etc.) is necessary as many people living in the Chindwin River Basin are using water directly from the river and other waterways without proper water treatment.&lt;/p&gt;

scholarly journals KUALITAS AIR BAGI KEHIDUPAN ORGANISME BAGIAN TENGAH DAN HILIR SUNGAI MUSI BERDASARKAN PADA SUMBER POLUTAN

2017 ◽  
Vol 14 (3) ◽  
pp. 253
Author(s):  
Siswanta Kaban ◽  
Husnah Husnah ◽  
Siti Nurul Aida
Keyword(s):  
Water Quality ◽  
Oxygen Demand ◽  
Total Suspended Solid ◽  
High Water ◽  
Suspended Solid ◽  
Pollution Source ◽  
Source Distribution ◽  
Musi River

Penelitian ini dilakukan untuk mengetahui kualitas air Sungai Musi tahun 2007 sampai dengan 2008 di bagian tengah dan hilir berdasarkan pada sumber polutan. Empat belas stasiun pengambilan contoh ditetapkan sebagai sumber polutan seperti industri maupun pemukiman penduduk, dan referensi yang jauh dari industri maupun pemukiman yang digunakan sebagai pembanding. Pada setiap stasiun, pengambilan contoh dilakukan 3 kali waktu pengambilan, yaitu bulan April, Juni, dan Januari yang dapat mewakili 3 musim yang berbeda pada tahun tersebut. Beberapa parameter diukur in situ sementara beberapa lain dianalisis di laboratorium dengan standar methods (AWWAWEF, 2005). Dari hasil penelitian didapatkan bahwa industri yang bergerak di bidang pengolahan kelapa sawit dan karet cenderung menurunkan kualitas perairan di Sungai Musi. Kandungan logam berat dalam sedimen di Sungai Musi relatif rendah dengan kandungan Cr+6 dan Pb yang tertinggi masing-masing 13,481 dan 1,747 μg per g. Curah hujan cenderung menurunkan beberapa parameter fisika dan kimia kualitas perairan. Potensi pencemaran cenderung ditemukan di bagian hilir Sungai Musi, karena sebaran industri dan intensitas pemanfaatan perairan cukup tinggi di bagian sungai tersebut. Study in order to know distribution of pollution source and its effect on water quality of the middle and down stream of Musi River was conducted in April and June 2007 and January 2008. Fourteen sampling sites were selected based on the pollution source and the minimal degradation site (reference sites). Parameters observed were pollution source distribution and water and sediment parameters such as physical and chemical parameters. Water sample was collected at 0.5 m from water surface by using Kemmerer water sampler while sediment samples were taken by using Ekman grab. Some of the parameters were analyzed in situ while the rest were analyzed in laboratory. Results indicated that oil palm and rubber industries were mostly the pollution source in Musi River. Potential pollution source was mostly found in the middle and down stream of Musi River since most of pollution source and high water utilization found in this area. Water quality parameters except total suspended solid and biochemical oxygen demand, were still in the range that can be tolerated by the aquatic organisms. Rain fall tends to decrease water quality of the river. Concentration of heavy metal such as Chrom (Cr+6) and plumbum in the sediment were in still in low concentration with the highest concentration reaching 13.481 and 1.747 μg per g respectively.

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