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 Contributions of remote sensing in the diachronic study of the spatial and temporal evolution of the Ahmed El Hansali dam water reservoir from 2002-03 to 2018-19

2020 ◽  
Vol 183 ◽  
pp. 02004
Author(s):  
Tarik El Orfi ◽  
Mohamed El Ghachi ◽  
Sébastien Lebaut
Keyword(s):  
Remote Sensing ◽  
Negative Impact ◽  
Water Reservoir ◽  
Water Levels ◽  
Landsat 8 ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Landsat 7 ◽  
Diachronic Study ◽  
Flooded Area

The OumErRbiabasin is one of the watersheds with the largest number of hydraulic infrastructures in Morocco. These hydraulic structuressupply water for drinking, industrial and agricultural uses. The Ahmed El Hansali dam is a 740 Mm³ reservoir located near Zaouyat Cheikh andhave an active storage of473 Mm³. The succession of dry years in the OumErRbiabasin has had a negative impact on the water resource and has caused a remarkable decrease in the reservoir of the Ahmed el Hansali dam. In this paper, the MNDWI (Modified Normalized Difference Water Index) from Landsat 5-TM, Landsat 7-ETM, and Landsat 8-OLI satellite images was used to estimate the spatial and temporal fluctuations of the volumes of water stored in the reservoir between hydrological years 2002-03 and 2018-19. Results show that the volumes estimated by remote sensing reasonably match the volumes estimated by the OumErRbia Hydraulic Basin Agency (OERHBA)using recorded water levels and reservoir storage curve for years 2002-03 and 2013-14; the determination coefficient R² exceeds 0.90. The mapping of the extent of the dam’s impoundment has shown a very significant decreasein the flooded area level during dry years.

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 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 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 SUAS Based Multispectral Imagery for Monitoring Wetland Inundation and Vegetation

2019 ◽  
Author(s):  
Dustin Dehm ◽  
Richard Becker ◽  
Alexandra Godre
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Correction Method ◽  
Water Levels ◽  
Water Volume ◽  
Unmanned Aerial Systems ◽  
Short Term ◽  
Water Index ◽  
Normalized Difference Water Index

Mapping short-term wetland vegetation and water storage changes is valuable for monitoring the biogeochemical processes of wetland systems. Old Woman Creek National Estuarine Research Reserve is a dynamic freshwater estuary which experiences intermittent changes in water level over the course of a year. Small unmanned aerial systems (sUAS) are useful tools in monitoring changes as they are rapidly deployed, repeatable, and high-resolution. In this study, commercial quadcopters were paired with a red/green/near-infrared MAPIR Survey 3W camera to produce normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) maps to observe short-term changes at OWC. Orthomosaics were produced for flights on 8 days throughout 2018 and early 2019. The orthomosaics were calibrated to bottom-of-atmosphere reflectance using the Empirical Line Correction method, after which NDVI and NDWI maps were created. The NDVI maps allowed vegetation extent and density changes over time and for National Estuarine Reserve System (NERRS) Classification Codes to be applied to zones of interest. NDWI provided water extent at different water levels and when paired with LiDAR and bathymetric data yielded water volume and residence time estimates.

scholarly journals Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary

2015 ◽  
Vol 8 (3-4) ◽  
pp. 11-20 ◽  
Author(s):  
András Gulácsi ◽  
Ferenc Kovács
Keyword(s):  
Drought Index ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Short Wave ◽  
Drought Indices ◽  
High Temporal Resolution ◽  
Spectral Indices ◽  
Water Index ◽  
Water Indices

Abstract In this study a new remote sensing drought index called Difference Drought Index (DDI) was introduced. DDI was calculated from the Terra satellite’s MODIS sensor surface reflectance data using visible red, near-infrared and short-wave-infrared spectral bands. To characterize the biophysical state of vegetation, vegetation and water indices were used from which drought indices can be derived. The following spectral indices were examined: Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Difference Water Index (DWI), Normalized Difference Water Index (NDWI), Difference Drought Index (DDI) and Normalized Difference Drought Index (NDDI). Regression analysis with the Pálfai Drought Index (PaDi) and average annual yield of different crops has proven that the Difference Drought Index is applicable in quantifying drought intensity. However, after comparison with reference data NDWI performed better than the other indices examined in this study. It was also confirmed that the water indices are more sensitive to changes in drought conditions than the vegetation ones. In the future we are planning to monitor drought during growing season using high temporal resolution MODIS data products.

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.

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