scholarly journals Phân bố hàm lượng chất rắn lở lửng (TSS) tỉnh An Giang sử dụng ảnh viễn thám sentinel 2A

2021 ◽  
Vol 57 (1) ◽  
pp. 1-7
Author(s):  
Trần Sỹ Nam ◽  
Pham Duy Tien ◽  
Trần Bá Linh ◽  
Nguyễn Thị Hồng Điệp ◽  
Nguyen Ho ◽  
...  

Trầm tích lơ lửng (phù sa) đóng vai trò hết sức quan trọng trong việc cung cấp nguồn dinh dưỡng, có ý nghĩa rất lớn trong sản xuất nông nghiệp và cả hệ sinh thái vùng Đồng bằng sông Cửu long (ĐBSCL). Nghiên cứu đã sử dụng phương pháp hồi quy tương quan giữa giá trị chỉ số vật chất lơ lửng (Normalized Suspended Material Index) trên ảnh và lượng phù sa thực tế để thành lập bản đồ phân bố phân bố không gian hàm lượng tổng chất rắn lơ lửng trong nước mặt (phù sa). Kết quả xác định hệ số R2 trong các hàm hồi quy này đạt 0,868 cho đợt quan trắc ngày 18/10/2019. Kết quả xác định hàm lượng tổng chất rắn lơ lửng từ ảnh Sentinel 2A tỉnh An Giang có giá trị dao động trong khoảng từ 0-100mg/l. Hàm lượng tổng chất rắn lơ lửng tập trung chủ yếu trên các cánh đồng ngập nước, vùng thượng nguồn và cuối nguồn dọc theo tuyến sông Hậu thuộc tỉnh An Giang. Kết quả nghiên cứu cho thấy dữ liệu ảnh Sentinel 2 có khả năng hỗ trợ xây dựng bản đồ phân bố hàm lượng chất lơ lửng nước mặt cụ thể năm 2019 với độ tin cậy cao. Kết quả này là tiền đề cho các đề tài nghiên cứu có liên quan đến tăng giảm hàm lượng phù sa hay chất lượng phù sa vùng ĐBSCL đặc biệt là các vùng cửa sông tại Việt Nam.

2019 ◽  
Vol 11 (23) ◽  
pp. 2746 ◽  
Author(s):  
Athanasios K. Mavraeidopoulos ◽  
Emmanouil Oikonomou ◽  
Athanasios Palikaris ◽  
Serafeim Poulos

The article presents a new hybrid bio-optical transformation (HBT) method for the rapid modelling of bathymetry in coastal areas. The proposed approach exploits free-of-charge multispectral images and their processing by applying limited manpower and resources. The testbed area is a strait between two Greek Islands in the Aegean Sea with many small islets and complex seabed relief. The HBT methodology implements semi-analytical and empirical steps to model sea-water inherent optical properties (IOPs) and apparent optical properties (AOPs) observed by the Sentinel-2A multispectral satellite. The relationships of the calculated IOPs and AOPs are investigated and utilized to classify the study area into sub-regions with similar water optical characteristics, where no environmental observations have previously been collected. The bathymetry model is configured using very few field data (training depths) chosen from existing official nautical charts. The assessment of the HBT indicates the potential for obtaining satellite derived bathymetry with a satisfactory accuracy for depths down to 30 m.


2019 ◽  
Vol 11 (15) ◽  
pp. 1756 ◽  
Author(s):  
Soriano-González ◽  
Angelats ◽  
Fernández-Tejedor ◽  
Diogene ◽  
Alcaraz

Shellfish aquaculture has a major socioeconomic impact on coastal areas, thus it is necessary to develop support tools for its management. In this sense, phytoplankton monitoring is crucial, as it is the main source of food for shellfish farming. The aim of this study was to assess the applicability of Sentinel 2 multispectral imagery (MSI) to monitor the phytoplankton biomass at Ebro Delta bays and to assess its potential as a tool for shellfish management. In situ chlorophyll-a data from Ebro Delta bays (NE Spain) were coupled with several band combination and band ratio spectral indices derived from Sentinel 2A levels 1C and 2A for time-series mapping. The best results (AIC = 72.17, APD < 10%, and MAE < 0.7 mg/m3) were obtained with a simple blue-to-green ratio applied over Rayleigh corrected images. Sentinel 2–derived maps provided coverage of the farm sites at both bays allowing relating the spatiotemporal distribution of phytoplankton with the environmental forcing under different states of the bays. The applied methodology will be further improved but the results show the potential of using Sentinel 2 MSI imagery as a tool for assessing phytoplankton spatiotemporal dynamics and to encourage better future practices in the management of the aquaculture in Ebro Delta bays.


2019 ◽  
Vol 11 (10) ◽  
pp. 1151
Author(s):  
Teodor Nagy ◽  
Liss M. Andreassen ◽  
Robert A. Duller ◽  
Pablo J. Gonzalez

Satellite imagery represents a unique opportunity to quantify the spatial and temporal changes of glaciers world-wide. Glacier velocity has been measured from repeat satellite scenes for decades now, yet a range of satellite missions, feature tracking programs, and user approaches have made it a laborious task. To date, there has been no tool developed that would allow a user to obtain displacement maps of any specified glacier simply by establishing the key temporal, spatial and feature tracking parameters. This work presents the application and development of a unique, semi-automatic, open-source, flexible processing toolbox for the retrieval of displacement maps with a focus on obtaining glacier surface velocities. SenDiT combines the download, pre-processing, feature tracking, and postprocessing of the highest resolution Sentinel-2A and Sentinel-2B satellite images into a semi-automatic toolbox, leaving a user with a set of rasterized and georeferenced glacier flow magnitude and direction maps for their further analyses. The solution is freely available and is tailored so that non-glaciologists and people with limited geographic information system (GIS) knowledge can also benefit from it. The system can be used to provide both regional and global sets of ice velocities. The system was tested and applied on a range of glaciers in mainland Norway, Iceland, Greenland and New Zealand. It was also tested on areas of stable terrain in Libya and Australia, where sources of error involved in the feature tracking using Sentinel-2 imagery are thoroughly described and quantified.


Author(s):  
A. Tuzcu Kokal ◽  
A. F. Sunar ◽  
A. Dervisoglu ◽  
S. Berberoglu

Abstract. Turkey has favorable agricultural conditions (i.e. fertile soils, climate and rainfall) and can grow almost any type of crop in many regions, making it one of the leading sectors of the economy. For sustainable agriculture management, all factors affecting the agricultural products should be analyzed on a spatial-temporal basis. Therefore, nowadays space technologies such as remote sensing are important tools in providing an accurate mapping of the agricultural fields with timely monitoring and higher repetition frequency and accuracy. In this study, object based classification method was applied to 2017 Sentinel 2 Level 2A satellite image in order to map crop types in the Adana, Çukurova region in Turkey. Support Vector Machine (SVM) was used as a classifier. Texture information were incorporated to spectral wavebands of Sentinel-2 image, to increase the classification accuracy. In this context, all of the textural features of Gray-Level Co-occurrence Matrix (GLCM) were tested and Entropy, Standard deviation, and Mean textural features were found to be the most suitable among them. Multi-spectral and textural features were used as an input separately and/or in combination to evaluate the potential of texture in differentiating crop types and the accuracy of output thematic maps. As a result, with the addition of textural features, it was observed that the Overall Accuracy and Kappa coefficient increased by 7% and 8%, respectively.


Author(s):  
S. Qiu ◽  
B. He ◽  
C. Yin ◽  
Z. Liao

The Multi Spectral Instrument (MSI) onboard Sentinel-2 can record the information in Vegetation Red-Edge (VRE) spectral domains. In this study, the performance of the VRE bands on improving land cover classification was evaluated based on a Sentinel-2A MSI image in East Texas, USA. Two classification scenarios were designed by excluding and including the VRE bands. A Random Forest (RF) classifier was used to generate land cover maps and evaluate the contributions of different spectral bands. The combination of VRE bands increased the overall classification accuracy by 1.40&amp;thinsp;%, which was statistically significant. Both confusion matrices and land cover maps indicated that the most beneficial increase was from vegetation-related land cover types, especially agriculture. Comparison of the relative importance of each band showed that the most beneficial VRE bands were Band 5 and Band 6. These results demonstrated the value of VRE bands for land cover classification.


2018 ◽  
Author(s):  
Jonathan G Escobar-Flores ◽  
Carlos A Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A Marquez-Linares ◽  
Christian Wehenkel

Background. The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world's only 1-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This subspecies is distributed as a relict in the geographically isolated arid Sierra La Asamblea at elevations of between 1,010 and 1,631 m, with mean annual precipitation levels of between 184 and 288 mm. The aim of this research was i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea, Baja California (Mexico) by using Sentinel-2 images, and ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to higher resolution (x3) and increased number of bands (x2) relative to Landsat-8 , and ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine where conifers can become established and persist. Methods. An atmospherically corrected a 12-bit Sentinel-2A MSI image with ten spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index. Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest classification including cross valuation (10-fold) were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Results. We estimated, using supervised classification of Sentinel-2 satellite images, that P. monophylla covers 6,653 ± 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the most influential environmental predictor variable and indicated that the probability of P. monophylla occurrence was higher than 50% when the degree of ruggedness was greater than 17.5 m. When average temperature in the warmest month increased from 23.5 to 25.2 °C, the probability of occurrence of P. monophylla decreased. Discussion. The classification accuracy was similar to that reported in other studies using Sentinel-2A MSI images. Ruggedness is known to generate microclimates and provides shade that decreases evapotranspiration from pines in desert environments. Identification of P. monophylla in the Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.


2018 ◽  
Author(s):  
Jonathan G Escobar-Flores ◽  
Carlos A Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A Marquez-Linares ◽  
Christian Wehenkel

Background. The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world's only 1-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This subspecies is distributed as a relict in the geographically isolated arid Sierra La Asamblea at elevations of between 1,010 and 1,631 m, with mean annual precipitation levels of between 184 and 288 mm. The aim of this research was i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea, Baja California (Mexico) by using Sentinel-2 images, and ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to higher resolution (x3) and increased number of bands (x2) relative to Landsat-8 , and ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine where conifers can become established and persist. Methods. An atmospherically corrected a 12-bit Sentinel-2A MSI image with ten spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index. Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest classification including cross valuation (10-fold) were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Results. We estimated, using supervised classification of Sentinel-2 satellite images, that P. monophylla covers 6,653 ± 46 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the most influential environmental predictor variable and indicated that the probability of P. monophylla occurrence was higher than 50% when the degree of ruggedness was greater than 17.5 m. When average temperature in the warmest month increased from 23.5 to 25.2 °C, the probability of occurrence of P. monophylla decreased. Discussion. The classification accuracy was similar to that reported in other studies using Sentinel-2A MSI images. Ruggedness is known to generate microclimates and provides shade that decreases evapotranspiration from pines in desert environments. Identification of P. monophylla in the Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.


Author(s):  
Minh Hải Hoàng ◽  
Thị Thảo Kiều ◽  
Ngọc Huy Hoàng ◽  
Trọng Kha Vường
Keyword(s):  
Viet Nam ◽  

Hiện nay, trên thế giới cũng như ở Việt Nam việc sử dụng dữ liệu ảnh vệ tinh đa thời gian để tăng hiệu suất cập nhật dữ liệu đã và đang được thực hiện một cách phổ biến. Tuy nhiên, để chiết tách một cách chính xác thông tin đang nghiên cứu từ nhiều nguồn ảnh đa thời gian khác nhau thì điều kiện tiên quyết cần phải thực hiện đó là chuẩn hóa giá trị phản xạ phổ nhằm giảm thiểu các tác nhân của sự thay đổi giá trị phản xạ từ ảnh vệ tinh đa thời gian gây ảnh hưởng tới việc phát hiện biến đổi giá trị của các pixcel trên ảnh vệ tinh. Hai phương pháp chuẩnhóa giá trị phản xạ phổ là tuyệt đối và tương đối thường được áp dụng trong chuẩnhóa ảnh viễn thám để hiệu chỉnh các hình ảnh vệ tinh được chụp từ các thời điểm khác nhau và các vệ tinh khác nhau. Bài báo trình bày phương pháp phát hiện biến đổi đa biến IRMAD, một trong những phương pháp chuẩn hóa phản xạ phổ tương đối ưu việt so với vác phương pháp phát hiện biến đổi truyền thống trước đây vì nó bất biến đối với các phép biến đổi tuyến tính của cường độ ảnh gốc, không nhạy cảm với sự khác biệt. Do đó, phương pháp phát hiện biến đổi đa biến IRMAD đã được nghiên cứu rộng rãi từ các khía cạnh về lý thuyết và thực nghiệm trong những năm gần đây


Author(s):  
M. Chung ◽  
M. Jung ◽  
Y. Kim

<p><strong>Abstract.</strong> Recently, the drastic climate changes have increased the importance of wildfire monitoring and damage assessment as well as the possibility of wildfire occurrence. Estimation of wildfire damage provides the information on wildfire-induced ecological changes and supports the decision-making process for post-fire treatment activities. For accurate wildfire damage assessment, the discrimination between disaster-induced and natural changes is crucial because they usually coupled together.</p> <p>In this study, Sentinel-2 images were employed to assess the damage from a wildfire, which occurred in the coniferous forest of Gangneung, Gangwon Province, South Korea on April 2019. The images were captured from both Sentinel-2A and -2B, shortening the temporal interval of available pre- and post-fire images. Multi-temporal image analysis was performed in both object and pixel-based with two commonly used spectral indices, NDVI and NBR. Additional image pair from the same period of 2018 was used to distinguish the fire-affected regions from the naturally changed area and compared with the results from using only one pair of images from 2019. The experimental results showed that the change detection performance could be affected by the number of image pairs and spectral indices used to discriminate burned region from unburned region. Thus it verified the significance of adequately employing annual multi-pair satellite images for wildfire damage assessment.</p>


Author(s):  
Alfiatun Nur Khasanah ◽  
Dian Octaviani
Keyword(s):  

Aplikasi Penginderaan Jauh dan GIS dapat digunakan untuk mendapatkan informasi spasial produksi lahan sawah. Produktivitas sawah, yang biasanya disajikan dalam data tabular, dapat dipetakan menjadi informasi spasial dengan menggunakan respon vegetasi dari citra penginderaan jauh resolusi menengah. Tujuan dari penelitian ini adalah untuk menilai rata-rata produksi sawah di Magelang menggunakan Sentinel 2A. Citra melalui tahapan pemrosesan, yaitu koreksi atmosfer serta klasifikasi multispektral untuk mendapatkan batas sawah. Survei lapangan dan analisis regresi antara survei produktivitas aktual dan indeks vegetasi dilakukan untuk mendapatkan model terbaik. Ada 8 model indeks vegetasi yang digunakan dalam penelitian ini. Indeks Vegetasi memberikan kisaran nilai koefisien korelasi 0,62 hingga 0,74. Kisaran ini dikategorikan sebagai hubungan korelasi sedang dan kuat. Nilai koefisien korelasi tertinggi ditunjukkan oleh indeks RVI sebesar 0,74, yang berarti bahwa 74% dari model dapat mewakili sampel. Produksi beras dominan di daerah penelitian adalah di kisaran 47-52 kg / 100 m2. Nilai ini di bawah rata-rata produksi di Magelang


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