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.

scholarly journals Dynamics of Lower-Barun Glacier and Glacial Lake and its GLOF Susceptibility Using Geospatial Analysis and Modelling

Jalawaayu ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 57-77
Author(s):  
Nabin Gurung ◽  
Sudeep Thakuri ◽  
Raju Chauhan ◽  
Narayan Prasad Ghimire ◽  
Motilal Ghimire
Keyword(s):  
Failure Time ◽  
Peak Discharge ◽  
Mitigation Measures ◽  
Glacial Lake ◽  
Water Volume ◽  
Glacial Lakes ◽  
Water Index ◽  
Outburst Floods ◽  
Normalized Difference Water Index ◽  
Multi Temporal

Shrinkage of some of the glaciers has direct impacts on the formation and expansion of glacial lakes. Sudden glacial lake outburst floods (GLOFs) are a major threat to lives and livelihoods downstream as they can cause catastrophic damage. In this study, we present the dynamics of the Lower-Barun glacier and glacial lakes and their GLOF susceptibility. We used multi temporal Landsat and Sentinel satellite imagery and extracted the lake outlines using the Normalized Difference Water Index (NDWI) with manual post-correction while the glacier outline was digitized manually. Multi-criteria decision-based method was used to assess the GLOF susceptibility. For the estimation of peak discharge and failure time, an empirical model developed by Froelich (1995) was used. The surface area of the Lower-Barun glacial lake was increased by 86% in the last 40 yrs (from 1979 to 2018), with a mean increase of 0.0432 km2/yr. The shrinkage in the glacier area is around 0.49 km2/yr and has shrunk by 8% in the last four decades. The retreat of the Lower-Barun glacier was 0.20% per year in the last four decades. The susceptibility index was 0.94, which suggests that the lake is very highly susceptible to the GLOF. The peak discharge of 5768 m3/s is produced when the breach depth is 20 m and the entire water volume is released. Likewise, in the case of 15 m breach depth, the peak discharge of 4038 m3/s is formed. Breach depth scenario of 10 m, peak discharge of 2442 m3/s is produced and in case of breach depth of 5 m produces the peak discharge of 1034 m3/s. If GLOF occurs, it can exert disastrous impacts on the livelihood and infrastructure in the downstream. So, it is necessary to examine such lakes regularly and mitigation measures to lower the GLOF susceptibility should be emphasized.

scholarly journals Temporal monitoring of Ghamu bar glacial lakes using remote sensing and GIS

2019 ◽  
Vol 7 (1) ◽  
pp. 18
Author(s):  
Alamgeer Hussain ◽  
Dilshad Bano
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Remote Sensing And Gis ◽  
Glacial Lake ◽  
Lake Area ◽  
Glacial Lakes ◽  
Landsat Images ◽  
Water Index ◽  
Normalized Difference Water Index

The trends of glacial lakes formation and glacial lake outburst flooding events have been increased across Himalayan Karakorum Hindu Kush (HKH) ranges during last decade due to increase in global warming. This research is addressing the temporal monitoring of ghamu bar glacial lakes using remote sensing and GIS. Landsat images of 1990, 2000, 2010 and 2015 were used to map temporal glacial lakes using normalized difference water index (NDWI) index. The results of normalized difference water index were validated through modified normalized difference water index and field photographs. Temporal variability shows that, glacier lake area has been increase from 1990 to 2015. In 1990 total area of lake was 0.052 sq, which further increased 0.0423 in 1995 than it decreases to 0.314 in 2000 due to detached of debris cover moraine from glacier tongue and it reach 0.0846 sq.km in 2005. The area gradually increased up to 0.1296 sq.km in 2010 and it goes up to 0.157 sq.km 2015. The overall increase in area are expanding at an accelerated rate in past two decades, indicating that Darkut glacier is more vulnerable toward climate change through increase in size and volume ofghamu bar glacial lakes. There is need for vigilance in monitoring of ghamu bar glacial lake through high resolution remote sensing data and development of Geo-database enabling more details about past and future lakes behaviors toward climate change impacts.    

scholarly journals Geospatial Analysis and Simulation of Glacial Lake Outburst Flood Hazard in Hunza and Shyok Basins of Upper Indus Basin

2020 ◽  
Author(s):  
Syed Naseem Abbas Gilany ◽  
Javed Iqbal ◽  
Ejaz Hussain
Keyword(s):  
Flood Hazard ◽  
Indus Basin ◽  
Glacial Lake ◽  
Glacial Lakes ◽  
Digital Repository ◽  
Adaptation Measures ◽  
High Discharge ◽  
Upper Indus Basin ◽  
Study Results ◽  
The Impact

Abstract. The UIB (Upper Indus Basin) is prone to GLOFs (Glacial Lake Outburst Floods). Physical monitoring of such a large area on regular basis is a challenging task especially when the temporal and spatial extent of the hazard is highly variable. The purpose of this study was to map the potentially dangerous glacial lakes and simulate the associated hazard in the downward settlements using HEC-RAS in the GIS environment using Landsat 7 remote sensing data. The study was conducted in Hunza and Shyok sub-basins of UIB where there are several human settlements which are endangered due to the GLOF hazard. Sudden breaches in the unstable moraine dams adjoining receding glaciers may occur because of rapid and huge accumulation of turbulent water in the glacial lakes. The ASTER DEM (Digital Elevation Model) is utilized to detect flow accumulation of glacial hazard involving slope, elevation, and orientation of the mountain glaciers. The study results revealed that settlements of Hunza and Shyok basins are threatened by the GLOFs hazard. Keeping in view the seasonal growth of the potentially dangerous glacial lakes of Hunza basin, a low discharge of 3500 m3/s from potentially dangerous glacial lake can affect 40 %, whereas, a moderate discharge of 5000 m3/s can affect 60% and a high discharge of 7000 m3/s can affect 80 % of the Shimshal village habitat. In Shyok basin, a low discharge of 100 m3/s from both lakes can affect 20 %, whereas, a moderate discharge of 300 m3/s can affect 30 % and a high discharge of 500 m3/s can affect 40 % of the Barah village habitat. The results of the study can provide a platform for the establishment of an early warning and monitoring system to minimize the impact of future GLOFs. Accurate and comprehensive knowledge of potentially dangerous GLOFs is of utmost importance for risk management. A digital repository of GLOFs can enhance the ability to inform policy makers on the vulnerability, risk mitigation and action/adaptation measures.

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 GAN-GL: Generative Adversarial Networks for Glacial Lake Mapping

2021 ◽  
Vol 13 (22) ◽  
pp. 4728
Author(s):  
Hang Zhao ◽  
Meimei Zhang ◽  
Fang Chen
Keyword(s):  
Large Scale ◽  
Glacial Lake ◽  
Background Information ◽  
Generative Adversarial Networks ◽  
High Mountain ◽  
Landsat 8 ◽  
Glacial Lakes ◽  
Ancillary Data ◽  
Generative Adversarial Network ◽  
Adversarial Network

Remote sensing is a powerful tool that provides flexibility and scalability for monitoring and investigating glacial lakes in High Mountain Asia (HMA). However, existing methods for mapping glacial lakes are designed based on a combination of several spectral features and ancillary data (such as the digital elevation model, DEM) to highlight the lake extent and suppress background information. These methods, however, suffer from either the inevitable requirement of post-processing work or the high costs of additional data acquisition. Signifying a key advancement in the deep learning models, a generative adversarial network (GAN) can capture multi-level features and learn the mapping rules in source and target domains using a minimax game between a generator and discriminator. This provides a new and feasible way to conduct large-scale glacial lake mapping. In this work, a complete glacial lake dataset was first created, containing approximately 4600 patches of Landsat-8 OLI images edited in three ways—random cropping, density cropping, and uniform cropping. Then, a GAN model for glacial lake mapping (GAN-GL) was constructed. The GAN-GL consists of two parts—a generator that incorporates a water attention module and an image segmentation module to produce the glacial lake masks, and a discriminator which employs the ResNet-152 backbone to ascertain whether a given pixel belonged to a glacial lake. The model was evaluated using the created glacial lake dataset, delivering a good performance, with an F1 score of 92.17% and IoU of 86.34%. Moreover, compared to the mapping results derived from the global–local iterative segmentation algorithm and random forest for the entire Eastern Himalayas, our proposed model was superior regarding the segmentation of glacial lakes under complex and diverse environmental conditions, in terms of accuracy (precision = 93.19%) and segmentation efficiency. Our model was also very good at detecting small glacial lakes without assistance from ancillary data or human intervention.

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.

ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5091
Author(s):  
Jinxiao Wang ◽  
Fang Chen ◽  
Meimei Zhang ◽  
Bo Yu
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Glacial Lake ◽  
Landsat 8 ◽  
Glacial Lakes ◽  
Sar Images ◽  
Radar Images ◽  
Optical Images ◽  
Sar Data ◽  
Aperture Radar

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction.

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