scholarly journals Remote Sensing and GIS for Thematic Mapping of the Ak-Suu and Isfana River Basins in Kyrgyzstan

2021 ◽  
pp. 57-64
Author(s):  
A. Chymyrov ◽  
N. Ismailov
Keyword(s):  
Remote Sensing ◽  
River Basins ◽  
Remote Sensing And Gis ◽  
Landsat 8 ◽  
Soil Maps ◽  
Basin Boundary ◽  
Central Asian ◽  
Thematic Mapping ◽  
Glacier Mapping ◽  
Sentinel 2

Geographic information systems (GIS) play a significant role in the thematic mapping to collect, store, analyze, visualize and deliver geospatial data today. The Ak-Suu and Isfana rivers flow into the Syrdarya river, which is used for irrigation and other purposes in Uzbekistan, Tajikistan and Kazakhstan. Thematic mapping of the river basins allow efficient use of natural and water resources in the region to mitigate the existing conflicts over water use by four Central Asian neighboring countries. SRTMGL1 DEM is applied in terrain modeling and river basin boundary delineation. Multispectral Landsat 8 and Sentinel-2 images were used in land use and glacier mapping. DEM, glacier, ecosystem, emergency and soil maps are designed and updated based on the cartographic materials, remote sensing, infrastructure and statistical datasets.

scholarly journals Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

2019 ◽  
Vol 11 (15) ◽  
pp. 1744 ◽  
Author(s):  
Daniel Maciel ◽  
Evlyn Novo ◽  
Lino Sander de Carvalho ◽  
Cláudio Barbosa ◽  
Rogério Flores Júnior ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Floodplain Lakes ◽  
Empirical Modeling ◽  
Landsat 8 ◽  
Amazon Floodplain ◽  
The Impact ◽  
Sentinel 2

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

scholarly journals Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China

2020 ◽  
Vol 12 (21) ◽  
pp. 3539
Author(s):  
Haifeng Tian ◽  
Jie Pei ◽  
Jianxi Huang ◽  
Xuecao Li ◽  
Jian Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Northern China ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Winter Crops ◽  
Sentinel 2

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

scholarly journals Uma abordagem metodológica de Sensoriamento Remoto para o monitoramento da contaminação do rio Paraopeba pós-desastre de Brumadinho-MG

2021 ◽  
Vol 43 ◽  
pp. e36
Author(s):  
Neison Cabral Ferreira Freire ◽  
Admilson Da Penha Pacheco ◽  
Vinícius D'Lucas Bezerra Queiroz
Keyword(s):  
Remote Sensing ◽  
Spectral Band ◽  
Ratio Method ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Band Ratio ◽  
Hydrographic Basin ◽  
Search Area ◽  
Brazilian History ◽  
Sentinel 2

The following article aims to present and discuss the monitoring, through Remote Sensing, of the dirt displacement caused by the collapse of the Córrego do Feijão’s dam I of mining waste, which occurred on January 25, 2019, in the rural area of Brumadinho, a city located in the state of Minas Gerais, Brazil. This event is considered one of the greatest technoindustrial disasters in Brazilian history, placing in danger one of the largest hydrographic basin in Brazil: the São Francisco river basin. The search area comprises from where the sludge mud got in contact with the Paraopeba’s right bank to its mouth into the Três Marias Dam, adding up to approximately 315 km. For this monitoring the spectral band ratio method was utilized,  using images from the sensors MSI/Sentinel-2 and OLI/Landsat-8 captured at different dates, employing standardization of means and variances to harmonize the range of the surface reflectance values in each image.

scholarly journals Importance of Soil Health and It’s Estimation by Remote Sensing and GIS

2021 ◽  
Vol 9 (VII) ◽  
pp. 1262-1266
Author(s):  
Ashish Kumar Singh
Keyword(s):  
Remote Sensing ◽  
Soil Quality ◽  
Renewable Resources ◽  
Soil Health ◽  
Agricultural Practices ◽  
Uttar Pradesh ◽  
Remote Sensing And Gis ◽  
Soil Functions ◽  
Waste Land ◽  
Sentinel 2

The soil quality is defined as the measurements of an essential organic and moving composition. To conserve the environment boundary with human health as well as to maintain the soils productivity are the main objective. In order to understand, it is important to differentiate between the soil heath and soil quality. The soil functions are generally related to soil quality however the living and organic stocks are of a non renewable resources, reutilizing the nutrients is presented by the soil health. Among this the Soil Organic Carbon is most important factor. In the soil SOC plays an important role in the productivity of the soil. In this research we conducted a study on the District of Farrukhabad, Uttar Pradesh by using Remote Sensing. A Sentinel-2 data has used for the analysis of Land Use and Land Cover purposes. The result concluded the highest and minimum measurement of SOC in Agricultural and waste land respectively. The main objective of this research, to describe the quality and characteristic of the soil and consider the reasons by which the soil quality get disturb, to discover the agricultural practices which directly affects the soil quality.

scholarly journals Comparison of Different Multispectral Images to Map Hydrothermal Alteration Zones in Kokap, Kulon Progo

2021 ◽  
Vol 6 (2) ◽  
pp. 86
Author(s):  
Bayu Raharja ◽  
Agung Setianto ◽  
Anastasia Dewi Titisari
Keyword(s):  
Remote Sensing ◽  
Hydrothermal Alteration ◽  
Remote Sensing Data ◽  
Component Analysis ◽  
Landsat 8 ◽  
Accuracy Evaluation ◽  
Multispectral Images ◽  
Band Ratio ◽  
Alteration Zones ◽  
Sentinel 2

Using remote sensing data for hydrothermal alteration mapping beside saving time and reducing  cost leads to increased accuracy. In this study, the result of multispectral remote sensing tehcniques has been compare for manifesting hydrothermal alteration in Kokap, Kulon Progo. Three multispectral images, including ASTER, Landsat 8, and Sentinel-2, were compared in order to find the highest overall accuracy using principle component analysis (PCA) and directed component analysis (DPC). Several subsets band combinations were used as PCA and DPC input to targeting the key mineral of alteration. Multispectral classification with the maximum likelihood algorithm was performed to map the alteration types based on training and testing data and followed by accuracy evaluation. Two alteration zones were succeeded to be mapped: argillic zone and propylitic zone. Results of these image classification techniques were compared with known alteration zones from previous study. DPC combination of band ratio images of 5:2 and 6:7 of Landsat 8 imagery yielded a classification accuracy of 56.4%, which was 5.05% and 10.13% higher than those of the ASTER and Sentinel-2 imagery. The used of DEM together with multispectral images was increase the accuracy of hydrothermal alteration mapping in the study area.

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