Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme

Bernardo, Nariane; Carmo, Alisson Fernando Coelho do; Park, Edward; Alcântara, Enner

doi:10.3390/rs11192283

Cited by 18 publications

(12 citation statements)

References 60 publications

(100 reference statements)

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“…For example, after detecting anomalies, decision-making systems can guide computers to give adequate and fast responses to prevent water pollution as an environmental disaster or to analyze the extent to which they occur. Furthermore, the proposed strategy broadens a list of studies, such as [55][56][57][58][59][60]101], engaged to analyze images from remote sensing to offer possible solutions to detect and monitor water pollution.…”

Section: Discussionmentioning

confidence: 99%

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

et al. 2019

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The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings. Kittler et al. in [13] proposed a taxonomy of anomalies which expanded the concept of anomaly beyond the conventional meaning of outlier. They used sensory data quality assessment [26], contextual [9] and non-contextual [27][28][29][30] classifiers [5,[15][16][17][18], and an incongruence indicator [16][17][18] to identify each type of anomaly [13]. According to this taxonomy [13] anomalies can be, for example, of the types unknown object, measurement model drift, unknown structure, unexpected structural component, component model drift, and unexpected structure and structural components. This taxonomy [13] is well-known and widely accepted by the scientific community because it has the potential to be applied for solving problems in many different research areas [13]. Therefore, studies related to the application of the taxonomy [13] on synthetic data are common, such as in [16][17][18]. However, to the knowledge of the authors, studies have not addressed the practical application of the taxonomy [13] to solve real-world problems [2,[31][32][33], because it remains a challenge for all research areas.Anomaly detection [13] and incongruence [15][16][17][18] are two powerful computational tools from pattern recognition (PR) [3,[11][12][13][14][15][16][17][18]34,35] and computer vision (CV) [3,36]. PR is a scientific area of study dedicated to analyze patterns and regularities in data [3]. PR provides powerful tools [34] for many different applications and research areas [35] such as scientific research, private and public industries, military activities, etc., [14,[21][22][23][24]32,[37][38][39][40][41][42][43][44][45][46][47][48]. For example, PR is important for geosciences as its tools are used to analyze geographical features of environments in digital images from remote sensing, i.e., scenes [11,12,[49][50][51][52][53][54]. Additionally, PR also provides powerful tools to help machine perception...

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Section: Discussionmentioning

confidence: 99%

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

et al. 2019

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“…Hal ini juga terlihat pada hasil penelitian yang menunjukkan bahwa nilai MPT yang dihasilkan dengan semianalitik memiliki korelasi yang lebih baik daripada dengan model empiris. Bernardo et al (2019) (Dolara et al, 2018). Nilai RMSE merupakan standar deviasi residual atau kesalahan prediksi.…”

Section: Hasilunclassified

Deteksi Sebaran Muatan Padatan Tersuspensi Dengan Model Empiris Dan Model Semi-Analitik Di Perairan Bekasi

Afgatiani

Hartuti

Budhiman

2020

J. Ilmu dan Teknologi Kelautan Tropis

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Salah satu parameter dalam kualitas air adalah muatan padatan tersuspensi (MPT). Muatan padatan tersuspensi terdiri dari lumpur, pasir dan jasad renik yang disebabkan pengikisan tanah yang terbawa ke badan air. Penelitian ini bertujuan untuk mendeteksi sedimen tersuspensi di perairan Bekasi. Landsat 8 digunakan untuk analisis padatan tersuspensi dengan platform Google Earth Engine dengan membandingkan antara model empiris dan semi-analitik. Alur studi ini meliputi deliniasi wilayah non air menggunakan data citra surface reflectance, analisis MPT, dan visualisasi. Selanjutnya dilakukan validasi dengan data in situ, pemilihan model dan implementasi time series. Hasil deteksi MPT tertampil dengan tampilan warna yang berbeda sesuai dengan konsentrasinya. Hasil uji validasi dengan data in situ menunjukkan nilai Normalized Mean Absolute Error (NMAE) model semi-analitik lebih mendekati syarat minimum yaitu sebesar 66,8%, berbeda jauh dengan model empiris sebesar 43768%. Nilai Root Mean Square Error (RMSE) pun terlihat bahwa model semi-analitik menghasilkan nilai yang jauh lebih kecil sebesar 51,4 dan model empiris sebesar 58577,2. Hal ini menunjukkan bahwa model semi-analitik memiliki nilai yang lebih baik dalam mendeteksi sebaran MPT. Analisis time series menunjukkan bahwa persebaran MPT tahun 2015 – 2019 di perairan pesisir memiliki sebaran MPT yang sangat tinggi, karena banyaknya tambak dan muara sungai. Oleh karena itu, model semi-analitik lebih direkomendasikan untuk mengestimasi konsentrasi MPT dibandingkan dengan model empiris.

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“…Artificial reservoirs represent a two-way issue for worldwide water demand because they can cause environmental impacts, yet are necessary for human water resource use. If not well planned and managed, they represent threats to species health and diversity, contribute to losses of fish and ecosystem services by affecting the penetrability of light within the water column and reducing the river connectivity, and may become sources of greenhouse gases [5][6][7][8][9]. They are also major sinks for sediment and nutrients in river systems along with wetlands and floodplains [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR)

et al. 2020

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Global measurements of reservoir water levels are crucial for understanding Earth’s hydrological dynamics, especially in the context of global industrialization and climate change. Although radar altimetry has been used to measure the water level of some reservoirs with high accuracy, it is not yet feasible unless the water body is sufficiently large or directly located at the satellite’s nadir. This study proposes a gauging method applicable to a wide range of reservoirs using Sentinel–1 Synthetic Aperture Radar data and a digital elevation model (DEM). The method is straightforward to implement and involves estimating the mean slope–corrected elevation of points along the reservoir shoreline. We test the model on six case studies and show that the estimated water levels are accurate to around 10% error on average of independently verified values. This study represents a substantial step toward the global gauging of lakes and reservoirs of all sizes and in any location where a DEM is available.

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Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme

Cited by 18 publications

References 60 publications

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

Deteksi Sebaran Muatan Padatan Tersuspensi Dengan Model Empiris Dan Model Semi-Analitik Di Perairan Bekasi

A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR)

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