scholarly journals Improving flood monitoring in rural areas using remote sensing

2018 ◽  
Vol 14 (1) ◽  
pp. 160-171
Author(s):  
Zahra Ghofrani ◽  
Victor Sposito ◽  
Robert Faggian
Keyword(s):  
Rural Areas ◽  
Flood Control ◽  
Control Measures ◽  
Landsat 8 ◽  
Protective Measures ◽  
Rainfall Events ◽  
Water Index ◽  
Flood Monitoring ◽  
Normalized Difference Water Index ◽  
Heavy Rainfall Events

Abstract Precise information on the extent of inundated land is required for flood monitoring, relief, and protective measures. In this paper, two spectral indices, Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), were used to identify inundated areas during heavy rainfall events in the Tarwin catchment, Victoria, Australia, using Landsat-8 OLI imagery. By integrating the assessed condition of levees, this research also explains the inefficiency of the flood control measures of this region of Australia. NDWI and MNDWI indices performed well, but water features were enhanced better in the NDWI-derived image, with an accuracy of 96.04% and Kappa coefficient of 0.83.

scholarly journals Analysis of riverbank changes in Ho Chi Minh city in the period 1989 – 2015

2019 ◽  
Vol 2 (2) ◽  
pp. 80-88
Author(s):  
Thu Trang Hoang ◽  
Khoi Nguyen Dao ◽  
Loi Thi Pham ◽  
Hong Van Nguyen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Ho Chi Minh City ◽  
Landsat 8 ◽  
River Bank ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Multi Temporal ◽  
Erosion And Accretion ◽  
Landsat Satellite

The objective of this study was to analyze the changes of riverbanks in Ho Chi Minh City for the period 1989-2015 using remote sensing and GIS. Combination of Modified Normalized Difference Water Index (MNDWI) and thresholding method was used to extract the river bank based on the multi-temporal Landsat satellite images, including 12 Landsat 4-5 (TM) images and 2 Landsat 8 images in the period 1989-2015. Then, DSAS tool was used to calculate the change rates of river bank. The results showed that, the processes of erosion and accretion intertwined but most of the main riverbanks had erosion trend in the period 1989-2015. Specifically, the Long Tau River, Sai Gon River, Soai Rap River had erosion trends with a rate of about 10.44 m/year. The accretion process mainly occurred in Can Gio area, such as Dong Tranh river and Soai Rap river with a rate of 8.34 m/year. Evaluating the riverbank changes using multi-temporal remote sensing data may contribute an important reference to managing and protecting the riverbanks.

scholarly journals Analisis Model Estimasi Tinggi Muka Air Tanah Menggunakan Indek Kekeringan

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Nur Febrianti ◽  
Kukuh Murtilaksono ◽  
Baba Barus
Keyword(s):  
Water Level ◽  
Drought Index ◽  
Greenhouse Gas Emission ◽  
Information Criteria ◽  
Water Levels ◽  
Drought Indices ◽  
Landsat 8 ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Shortwave Infrared

The Ground Water Level plays an important role in determining the greenhouse gas emission and, in turn, in regulating global climate system. Information on existing water levels is still using field measurements. The purpose of this study was to evaluate the best approximation model for estimating water level using drought index. This study utilizes Landsat 8 data to calculate Normalized Difference Water Index and Visible and Shortwave infrared Drought Index for 3 months (March, April and June 2016). The best estimation model is selected by the Akaike Information Criteria correction method and validated using K-Fold cross-validation. The results of this study indicate that the estimation of water level is affected by both drought indices with the TMA (mm) equation= -439,47 – 1639,7 * NDWI_Maret – 640,23 * NDWI_April + 477 * VSDI_Maret. Estimated water level began to detect hotspots ranging from 64,35 ± 36,9 6 cm (27 - 101 cm). The critical point for KHG Sei Jangkang - Sei Liong is 27 cm, thus the water level depth should be maintained less than that to avoid fire in peatlands.ABSTRAKTinggi muka air tanah lahan gambut atau secara teknis dikenal dengan kedalaman muka air tanah memegang peran penting dalam menentukan emisi gas rumah kaca dan mengatur sistem iklim global. Informasi tentang tinggi muka air yang ada saat ini masih menggunakan hasil pengukuran lapangan. Tujuan penelitian ini adalah mengevaluasi model aproksimasi terbaik untuk estimasi tinggi muka air dengan menggunakan indeks kekeringan. Penelitian ini memanfaatkan data Landsat 8 untuk menghitung Normalized Difference Water Index dan Visible and Shortwave infrared Drought Index selama 3 bulan (Maret, April dan Juni 2016). Model estimasi terbaik dipilih dengan metode koreksi Kriteria Informasi Akaike dan divalidasi menggunakan validasi silang K-Fold. Hasil penelitian ini menunjukkan bahwa estimasi tinggi muka air dipengaruhi oleh kedua indeks kekeringan tersebut dengan persamaan TMA (mm) = - 439,47 – 1639,7 * NDWI_Maret – 640,23 * NDWI_April + 477 * VSDI_Maret. Estimasi tinggi muka air mulai terdeteksi adanya hotspot berkisar antara 64,35±36,9 6 cm (27 – 101 cm). Titik kritis untuk KHG Sei Jangkang – Sei Liong adalah 27 cm, dengan demikian kedalaman tinggi muka air harus dipertahankan kurang dari itu untuk menghindari terjadinya kebakaran di lahan gambut.

scholarly journals ANALISIS PERUBAHAN GARIS PANTAI UJUNG PANGKAH DENGAN MENGGUNAKAN METODE EDGE DETECTION DAN NORMALIZED DIFFERENCE WATER INDEX (UJUNG PANGKAH SHORELINE CHANGE ANALYSIS USING EDGE DETECTION METHOD AND NORMALIZED DIFFERENCE WATER INDEX)

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Nanin Anggraini ◽  
Sartono Marpaung ◽  
Maryani Hartuti
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Shoreline Change ◽  
Landsat 8 ◽  
Landsat Data ◽  
Change Analysis ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Landsat 7 ◽  
Edge Detection Method

Besides to the effects from tidal, coastline position changed due to abrasion and accretion. Therefore, it is necessary to detect the position of coastline, one of them by utilizing Landsat data by using edge detection and NDWI filter. Edge detection is a mathematical method that aims to identify a point on a digital image based on the brightness level. Edge detection is used because it is very good to present the appearance of a very varied object on the image so it can be distinguished easily. NDWI is able to separate land and water clearly, making it easier for coastline analysis. This study aimed to detect coastline changes in Ujung Pangkah of Gresik Regency caused by accretion and abrasion using edge detection and NDWI filters on temporal Landsat data (2000 and 2015). The data used in this research was Landsat 7 in 2000 and Landsat 8 in 2015. The results showed that the coastline of Ujung Pangkah Gresik underwent many changes due to accretion and abrasion. The accretion area reached 11,35 km2 and abrasion 5,19 km2 within 15 year period. Abstrak Selain akibat adanya pasang surut, posisi garis pantai berubah akibat adanya abrasi dan akresi. Oleh karena itu diperlukan adanya deteksi posisi garis pantai, salah satunya dengan memanfaatkan data Landsat dengan menggunakan filter edge detection dan NDWI. Edge detection adalah suatu metode matematika yang bertujuan untuk mengidentifikasi suatu titik pada gambar digital berdasarkan tingkat kecerahan. Filter edge detection digunakan karena sangat baik untuk menyajikan penampakan obyek yang sangat bervariasi pada citra sehingga dapat dibedakan dengan mudah. NDWI mampu memisahkan antara daratan dan perairan dengan jelas sehingga memudahkan untuk analisis garis pantai. Penelitian ini bertujuan untuk deteksi perubahan garis pantai di Ujung Pangkah Kabupaten Gresik yang disebabkan oleh adanya akresi dan abrasi dengan menggunakan filter edge detection dan NDWI pada data Landsat temporal (tahun 2000 dan 2015). Data yang digunakan pada penelitian ini adalah citra Landsat 7 tahun 2000 dan Landsat 8 tahun 2015. Hasil penelitian menunjukkan bahwa garis pantai di Ujung Pangkah Gresik banyak mengalami perubahan akibat adanya akresi dan abrasi. Luas akresi mencapai 11,35 km2 dan abrasi 5,19 km2 dalam periode waktu 15 tahun.

PEMETAAN DAN ANALISIS PERUBAHAN GARIS PANTAI DI SEBAGIAN PESISIR BARAT LOMBOK BARAT MENGGUNAKAN NORMALIZED DIFFERENCE WATER INDEX PADA CITRA LANDSAT

2019 ◽  
Vol 3 ◽  
pp. 911
Author(s):  
Karunia Pasya Kusumawardani ◽  
Zulfian Isnaini Cahya ◽  
Wahyu Hendardi Giri Ananto ◽  
Galuh Hayun Mustika Asri
Keyword(s):  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Landsat 7

Pesisir Kabupaten Kabupaten Lombok Barat dan Kota Mataram merupakan wilayah rawan bencana dan perubahan garis pantai. Dalam 10 tahun terakhir telah terjadi abrasi sehingga pada tahun 2007 dibangun tanggul pemecah gelombang di sebagian pesisir Ampenan. Abrasi semakin parah terjadi pada dua tahun terkahir yaitu tahun 2017 dan 2018. Abrasi pantai terjadi di sepanjang Pantai Ampenan seperti di Kelurahan Bintaro sampai Mapak Indah (Radar Lombok, 2017). Penelitian bertujuan untuk memetakan garis pantai dan menganalisis perubahan garis pantai di sebagian pesisir Kabupaten Lombok Barat dan Kota Mataram. Data yang digunakan adalah data citra multitemporal yaitu citra Landsat 7 ETM+ tahun 2003 dan citra Landsat 8 OLI tahun 2018. Metode yang digunakan untuk memetakan garis pantai adalah transformasi indeks yaitu Normalized Difference Water Index (NDWI) dan filter highpass. Algoritma NDWI dapat digunakan untuk mengidentifikasi tubuh air. Transformasi NDWI pada penelitian digunakan untuk membedakan wilayah daratan dan perairan. Algoritma NDWI melibatkan band hijau dan band inframerah dekat yaitu dengan rumus NDWI = Green-NIR/Green+NIR. Pengujian model dilakukan dengan citra resolusi tinggi yaitu citra Planet dengan resolusi 3 meter. Output terdiri atas peta garis pantai tahun 2003 dan 2018 dengan skala 1: 125.000. Hasil pengujian peta garis pantai dengan citra resolusi tinggi menghasilkan nilai mean sebesar 14.972 m dengan standar deviasi sebesar 5.106 m. Perubahan garis pantai di sebagian pesisir Lombok Barat disebabkan karena adanya abrasi oleh kecepatan arus yang tinggi dan durasinya yang lama serta akresi yang disebabkan sedimentasi material dari 7 sungai di wilayah Ampenan Tengah, Ampenan Selatan, Loang Baloq, Labu Api, dan Gerung.

scholarly journals Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4333 ◽  
Author(s):  
Poliyapram Vinayaraj ◽  
Nevrez Imamoglu ◽  
Ryosuke Nakamura ◽  
Atsushi Oda
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Landsat 8 ◽  
Multispectral Images ◽  
Water Index ◽  
Tuning Parameters ◽  
Water Classification ◽  
Water Region ◽  
Normalized Difference Water Index ◽  
Perceptron Learning

Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images. Water/non-water region estimates derived from PDWF were compared with three different approaches—Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), and Deep Convolutional Neural Network—using various case studies. Our proposed method outperforms all three approaches, showing a significant improvement in water/non-water region estimation. PDWF performance is consistently better even in cases of challenging conditions such as low reflectance due to hill shadows, building-shadows, and dark soils. Moreover, our study implemented a sunglint correction to adapt water/non-water region estimation over sunglint-affected pixels.

scholarly journals Survey of efficiency of erosion and flood control measures at the Němčický stream

2008 ◽  
Vol 2 (No. 3) ◽  
pp. 85-95 ◽  
Author(s):  
J. Uhlířová
Keyword(s):  
Flood Control ◽  
Water Retention ◽  
Arable Land ◽  
Erosion Control ◽  
Control Measures ◽  
Protective Measures ◽  
Water Quality Indicators ◽  
Long Term Observation ◽  
Experimental Basin

The article presents the initial part of the research of the efficiency of erosion and flood control measures designed in the experimental basin of the Němčický stream. A long term observation of discharges, rainfalls, and some water quality indicators was introduced at 2 experimental profiles. We have elaborated a study of the erosion threat for discovered areas, where the realisation of protective measures is necessary to reduce soil loss. Besides the erosion control, the sheet grassing contributes to a better water retention by the agricultural countryside. The efficiency of the designed measures ascertained by model evaluation proved that grassing of 49 ha of arable land (from total 183 ha) and the exclusion of erosive dangerous crops growing (on 21 ha) should decrease the centenary discharge by 18% and the amount of the transported suspended matter by 29%. The observation will continue after realisation of the erosion control measures and of a polder, which was designed for sufficiently effective flood protection, and the measurements will be compared with the preliminary and model values.

scholarly journals COVERAGE CHANGES DETECTION AT CIÉNAGA GRANDE, SANTA MARTA – COLOMBIA USING AUTOMATIC CLASSIFICATION

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 195-200
Author(s):  
J. S. Vinasco ◽  
D. A. Rodríguez ◽  
S. Velásquez ◽  
D. F. Quintero ◽  
L. R. Livni ◽  
...  
Keyword(s):  
Random Forest ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Confusion Matrix ◽  
Automatic Classification ◽  
Detection Methods ◽  
Landsat 8 ◽  
High Resolution Data ◽  
Water Index ◽  
Normalized Difference Water Index

Abstract. The Ciénaga Grande, Santa Marta is the largest and most diverse ecosystem of its kind in Colombia. Its primary function is acting as a filter for the organic carbon cycle. Recently, this place has been suffering disruptions due to the anthropic activities taking place in its surroundings. The present study, the changes in the surface of Ciénaga Grande, Santa Marta, Magdalena, Colombia between 2013 and 2018 were determined using semiautomatic detection methods with high resolution data from remote sensors (Landsat 8). The zone of studies was classified in six kinds of surfaces: 1) artificial territories, 2) agricultural territories, 3) forests and semi-natural areas, 4) wet areas, 5) deep water surfaces & 6) wich is related to clouds as a masking method. Random Forest classifiers were utilized and the Feed For Ward multilayer perceptron neuronal network (ANN) was simultaneously assessed. The training stage for both methods was performed with 300 samples, distributed in equal quantities, over each coverage class. The semi-automatic classification was carried out with an annual frequency, but the monitoring was carried out throughout the analysis period through the performance of three indicators Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI). It was found from the confusion matrix that the Random Forest method more accurately classified four classes while Neural Networks Analysis (NNA) just three. Finally, taking the Random Forest results into account, it was found that the agricultural expansion increased from 7% to 9% and the urban zone increased from 20% to 30% of the total area. As well as a decrease of damp areas from 27% to 12% and forests from 4% to 3% of the total area of study.

scholarly journals Extraction of Water-body Area from High-resolution Landsat Imagery

2018 ◽  
Vol 8 (6) ◽  
pp. 4111
Author(s):  
B. Chandrababu Naik ◽  
B. Anuradha
Keyword(s):  
Surface Water ◽  
Satellite Data ◽  
Water Body ◽  
Water Area ◽  
Water Bodies ◽  
Landsat 8 ◽  
Indexing Methods ◽  
Water Index ◽  
Surface Water Body ◽  
Normalized Difference Water Index

Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously.

scholarly journals Procjena opsega poplave daljinskim istraživanjima

2021 ◽  
Vol 24 (1) ◽  
pp. 119-132
Author(s):  
Tea Butković ◽  
Andrea Maretić ◽  
Bojana Horvat ◽  
Nino Krvavica
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Water Index ◽  
Normalized Difference Water Index

U radu su, na primjeru poplave koja je u svibnju 2014. godine zadesila istočnu Hrvatsku, uspoređene tri metode kartiranja i procjene opsega poplavljenog područja: metoda analize refleksije s površine u blisko infracrvenom (IC) dijelu spektra (jednokanalna metoda) te metode vegetacijskog indeksa NDVI (Normalized Difference Vegetation Index) i vodenog indeksa NDWI (Normalized Difference Water Index). Metode kao ulazne podatke koriste snimke snimljene pasivnim senzorom ugrađenim na satelitsku platformu Landsat 8. Analizirane su četiri snimke; snimljene su prije (jedna snimka), tijekom (jedna snimka) i nakon poplave (dvije snimke). Procjena temeljena na jednokanalnoj metodi rezultirala je površinom manjom od površina procijenjenih primjenom višekanalnim metodama. Rezultati se mogu objasniti kompleksnošću spektralnog potpisa plitkih poplavnih voda s visokim udjelom suspendiranog nanosa koji će utjecati na refleksiju takvih površina u blisko IC dijelu spektra i klasificirati ih kao nevodene površine. S druge strane, kombiniranjem različitih spektralnih kanala u višekanalnim metodama kompenzira se utjecaj suspendiranog nanosa na refleksiju takvih voda te je klasifikacija na vodene i nevodene površine preciznija.

scholarly journals Glacial Lake Inventory Derived from Landsat 8 OLI in 2016–2018 in China–Pakistan Economic Corridor

2020 ◽  
Vol 9 (5) ◽  
pp. 294
Author(s):  
Da Li ◽  
Donghui Shangguan ◽  
Muhammad Naveed Anjum
Keyword(s):  
Indus Basin ◽  
Glacial Lake ◽  
Formation Mechanisms ◽  
Landsat 8 ◽  
Lateral Moraine ◽  
Glacial Lakes ◽  
Threshold Method ◽  
Water Index ◽  
Supraglacial Lakes ◽  
Normalized Difference Water Index

The China–Pakistan Economic Corridor (CPEC), a key hub for trade, is susceptible to glacial lake outburst floods. The distributions and types of glacial lakes in the CPEC are not well documented. In this study, cloud-free imagery acquired using the Landsat 8 Operational Land Imager during 2016–2018 was used to delineate the extent of glacial lakes in the mountainous terrain of the CPEC. In the study domain, 1341 glacial lakes (size ≥ 0.01 km2) with a total area of 109.76 ± 9.82 km2 were delineated through the normalized difference water index threshold method, slope analysis, and a manual rectification process. On the basis of the formation mechanisms and characteristics of glacial lakes, four major classes and eight subclasses of lakes were identified. In all, 492 blocked lakes (162 end moraine-dammed lakes, 17 lateral moraine-dammed lakes, 312 other moraine-dammed lakes, and 1 ice-blocked lake), 723 erosion lakes (123 cirque lakes and 600 other erosion lakes), 86 supraglacial lakes, and 40 other glacial lakes were identified. All lakes were distributed between 2220 and 5119 m a.s.l. At higher latitudes, the predominate lake type changed from moraine-related to erosion. From among the Gez, Taxkorgan, Hunza, Gilgit, and Indus basins, most glacial lakes were located in the Indus Basin. The number and area of glacial lakes were larger on the southern slopes of the Karakoram range.

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