scholarly journals Assessment of Terengganu Coastal Change

2020 ◽  
Vol 8 (5) ◽  
pp. 3333-3337
Keyword(s):  
Landsat 8 ◽  
Intensity Increase ◽  
Coastal Change ◽  
Water Index ◽  
The Past ◽  
The North ◽  
Normalized Difference Water Index ◽  
North Part ◽  
Dominant Process ◽  
Reclamation Project

Coastal erosion, accretion, and reclamation along Terengganu coastal line occur irregularly but it is noticed that their intensity increase during the past decade. The main objective of this study is to investigate the change in the coastal area for the period of 1989 to 2018. Dataset acquired from Landsat 5 TM and Landsat 8 OLI have been used in this study. Normalized Difference Water Index (NDWI) is used to differentiate land and water body. The result of this study shows that erosion is the dominant process over Tok Jembal before the reclamation work for the extension of the airport runaway. The erosion occurs from 2006 through 2014 have eroded 0.0299 km2 of land especially at the north of the runaway. However, the reclamation project has injected 0.972 km2 new land. Overall the reclamation and accretion activities have contributed 1.337 km2 of land to this area for the 1989-2018 period. Meanwhile the result Teluk Lipat shows that the worst erosion event occurred between the 2004-2008 periods. In this period 0.092 km2 of land was eroded. Meanwhile, the highest accretion event occurs between 1988-1992. During this period, 0.299 km2 of accretion take place especially in the north part of the study area.

scholarly journals AUTOMATIC PADDY RICE MAPPING INTERFACE USING ARCENGINE AND LANDSAT8 IMAGERY (CASE STUDY IN NORTH PART OF IRAN)

2014 ◽  
Vol XL-2/W3 ◽  
pp. 79-83 ◽  
Author(s):  
Sh. Bahramvash Shams
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Initial Period ◽  
Rice Paddy ◽  
Paddy Rice ◽  
Landsat 8 ◽  
Water Index ◽  
The North ◽  
North Part

Recognition of paddy rice boundaries is an essential step for many agricultural processes such as yield estimation, cadastre and water management. In this study, an automatic rice paddy mapping is proposed. The algorithm is based on two temporal images: an initial period of flooding and after harvesting. The proposed method has several steps include: finding flooded pixels and masking unwanted pixels which contain water bodies, clouds, forests, and swamps. In order to achieve final paddy map, indexes such as Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) are used. Validation is performed by rice paddy boundaries, which were drawn by an expert operator in Google maps. Due to this appraisal good agreement (close to 90%) is reached. The algorithm is applied to Gilan province located in the north part of Iran using Landsat 8 date 2013. Automatic Interface is designed based on proposed algorithm using Arc Engine and visual studio. In the Interface, inputs are Landsat bands of two time periods including: red (0.66 μm), blue (0.48 μm), NIR (0.87 μm), and SWIR (2.20 μm), which should be defined by user. The whole process will run automatically and the final result will provide paddy map of desire year.

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 AUTOMATED GIS-BASED TECHNIQUE FOR EVALUATION OF INDIRECT GROWING SEASON ESTIMATIONS

2019 ◽  
Vol XLII-4/W14 ◽  
pp. 209-211
Author(s):  
I. Rykin ◽  
E. Panidi ◽  
V. Tsepelev
Keyword(s):  
Meteorological Data ◽  
Growing Season ◽  
Google Earth ◽  
Water Quantity ◽  
Special Significance ◽  
Water Index ◽  
Atmospheric Scattering ◽  
The Past ◽  
Normalized Difference Water Index ◽  
Automated Technique

<p><strong>Abstract.</strong> This article is based on NDWI (Normalized Difference Water Index) which is automatically computed from the daily MODIS data. The main purpose of the article is to tell how the evaluation of NDWI-based growing season estimations can be automated. The NDWI is used as an indicator of liquid water quantity in vegetation, which is less sensitive to atmospheric scattering effect then the famous growing index (NDVI). The NDWI is computed using cloud-based platform (Google Earth Engine was applied) and compared with the daily meteorological data. The available meteorological data is collected for the past 130 years and NDWI data is collecting for the past 20 years. An automated technique has been probated on the example of republic of Komi, as it has a different climate forming factors. This approach can be used to evaluate growing season estimations for other territories that contain vegetation. Due to the accumulated amount of data, the study is relevant and has a special significance for areas with sparse hydrometeorological network.</p>

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.

scholarly journals The Dynamic Change of Bosten Lake Area in Response to Climate in the Past 30 Years

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 4 ◽  
Author(s):  
Xiaoai Dai ◽  
Xingping Yang ◽  
Meilian Wang ◽  
Yu Gao ◽  
Senhao Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Area ◽  
Water Body ◽  
Meteorological Factors ◽  
Lake Basin ◽  
Bosten Lake ◽  
Water Index ◽  
The Past ◽  
Normalized Difference Water Index

The widely distributed lakes, as one of the major components of the inland water system, are the primary available freshwater resources on the earth and are sensitive to accelerated climate change and extensive human activities. Lakes play an important role in the terrestrial water cycle and biogeochemical cycle and substantially influence the health of humans living in the surrounding areas. Given the importance of lakes in the ecosystem, long-term monitoring of dynamic changes has important theoretical and practical significance. Here, we extracted water body information and monitored the long-term dynamics of Bosten Lake, which is the largest inland lake in China. We quantified the meteorological factors of the study area from the observation data of meteorological stations between 1988 and 2018. The characteristics of climate change and its correlation with the change of area in the Bosten Lake Basin in the past 30 years were analyzed. The major contributions of this study are as follows: (1) The initial water body was segmented based on the water index model Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) with a pre-assigned threshold value. The results were evaluated with the area extracted through artificial visual interpretation. Then we conducted mathematical morphology operators, opening and closing operations, and median filter to eliminate noise to ensure the accuracy of water body information extraction from the Bosten Lake. A long-term water surface area database of the Bosten Lake was established from high-resolution remote sensing images during 1988–2018. (2) Due to the seasonal difference of snow, ice content, and other objects on images, the areadynamics of Bosten Lake in the recent 30 years were analyzed separately in dry season and rainy season. The water surface area of Bosten Lake showed large inter-annual variations between 1988–2018. (3) Based on the assumption that climatic change has more direct effects on lake than human activities, six meteorological factors were selected to analyze the impacts of climate change on the annual mean lake surface area. The result indicated that in the past 30 years, climate conditions in the Bosten Lake Basin fluctuated greatly. We conducted correlations analysis between the areal dynamics of the Bosten Lake and the meteorological factors. Here, the annual average evaporation had the highest correlation with the areal dynamics of Bosten Lake followed by air temperature, precipitation, sunshine hours, and relative humidity, while the annual average wind speed had the weakest correlation.

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 Sensing Images for Assessing the Minimum Ecological Flux by Automatically Extracting River Surface Width

2020 ◽  
Vol 12 (18) ◽  
pp. 2899
Author(s):  
Wenting Xu ◽  
Qian Shen ◽  
Xuelei Wang ◽  
Qian Wang ◽  
Yue Yao ◽  
...  
Keyword(s):  
Water Surface ◽  
Large Scale ◽  
North China ◽  
Dynamic Monitoring ◽  
Water Index ◽  
River Waters ◽  
The North ◽  
Analysis Technique ◽  
Normalized Difference Water Index

Global warming and economic development have intensified the evaporation and exploitation of river waters, resulting in reduced global river runoff. In minimum ecological flux management, objective determination of the minimum ecological flux and evaluation of whether a river complies with standards are urgently required. Satellite remote sensing allows for rapid, large-scale, and dynamic monitoring. Herein, the Tangmazhai cross-section of the Taizi River was analyzed using the Chinese Gaofen (GF) series satellite that comprises panchromatic multi-spectral sensors and the Sentinel-2 multi-spectral images to automatically extract the water surface width. We applied the Normalized Difference Water Index (NDWI)-Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) to 225 cloudless scenes from January 2015 to November 2019. We proposed a method to evaluate the minimum ecological flux using water surface width. The river surface width at this location increased from January 2015 to November 2019, and all widths exceeded the minimum river surface water width for the month. The degree of the minimum ecological flux guarantee was determined to be satisfactory. Because there are less clouds and rain in the North China than South China, our results may be used for evaluating the degree of minimum ecological flux guarantee of many river sections in the north China through monthly monitoring.

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