scholarly journals Remote Sensing Retrieval of Total Phosphorus in the Pearl River Channels Based on the GF-1 Remote Sensing Data

2020 ◽  
Vol 12 (9) ◽  
pp. 1420
Author(s):  
Shijun Lu ◽  
Ruru Deng ◽  
Yeheng Liang ◽  
Longhai Xiong ◽  
Xianjun Ai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Total Phosphorus ◽  
Remote Sensing Data ◽  
Optically Active ◽  
Pearl River ◽  
Percentage Error ◽  
River Channels ◽  
Active Components ◽  
Sensing Data ◽  
The Pearl River

Total phosphorus (TP) concentration is one of the indicators for surface water quality evaluation. In this study, an indirect algorithm was proposed to retrieve TP concentration. This algorithm retrieves the TP concentration in urban waters based on Gaofen-1 (GF-1) remote sensing data. The algorithm uses the correlation between remote-sensing reflectance, optically significant constituents of water (chlorophyll, suspended sediment, and organic matter (excluding algae)), and TP to establish a retrieval model. First, the concentrations of optically active components are retrieved using a semi-analytical model. Second, the correlation between TP and optically active components is used to retrieve the TP concentration in waters. The GF-1 remote sensing data for 7 August 2015 were used to perform remote sensing retrieval of TP concentration in the Pearl River channels in Guangzhou, China. The results show that the TP concentration in most areas of the Front Channel, Western Channel, Guangzhou Channel, and the western part of the Back Channel was higher than 0.2 mg/L, while the TP concentration in the middle and eastern parts of the Back Channel was generally lower than 0.2 mg/L. The mean absolute percentage error of the retrieval is 24.18%. The experimental results show that the model is suitable for remote sensing retrieval of TP in urban waters in Guangzhou.

Estimating the soil total phosphorus content based on hyper-spectral remote sensing data in the Min River estuarine wetland

2015 ◽  
Vol 35 (24) ◽  
Author(s):  
章文龙 ZHANG Wenlong ◽  
曾从盛 ZENG Congsheng ◽  
高灯州 GAO Dengzhou ◽  
陈晓艳 CHEN Xiaoyan ◽  
林伟 LIN Wei
Keyword(s):  
Remote Sensing ◽  
Total Phosphorus ◽  
Phosphorus Content ◽  
Remote Sensing Data ◽  
Total Phosphorus Content ◽  
Sensing Data ◽  
Estuarine Wetland ◽  
Min River ◽  
Hyper Spectral ◽  
River Estuarine

Retrieval of Water Quality Parameters in Lake Ontario Based on Hyperspectral Remote Sensing Data and Intelligent Algorithms

2020 ◽  
Author(s):  
Yu Li ◽  
Youyue Sun ◽  
Jinhui Jeanne Huang ◽  
Edward McBean
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Optically Active ◽  
Water Quality Parameters ◽  
Support Vector ◽  
Sensing Data

<p>With the increasingly prominent ecological and environmental problems in lakes, the monitoring water quality in lakes by satellite remote sensing is becoming more and more high demanding. Traditional water quality sampling is normally conducted manually and are time-consuming and labor-costly. It could not provide a full picture of the waterbodies over time due to limited sampling points and low sampling frequency. A novel attempt is proposed to use hyperspectral remote sensing in conjunction with machine learning technologies to retrieve water quality parameters and provide mapping for these parameters in a lake. The retrieval of both optically active parameters: Chlorophyll-a (CHLA) and dissolved oxygen concentration (DO), as well as non-optically active parameters: total phosphorous (TP), total nitrogen (TN), turbidity (TB), pH were studied in this research. A comparison of three machine learning algorithms including Random Forests (RF), Support Vector Regression (SVR) and Artificial Neural Networks were conducted. These water parameters collected by the Environment and Climate Change Canada agency for 20 years were used as the ground truth for model training and validation. Two set of remote sensing data from MODIS and Sentinel-2 were utilized and evaluated. This research proposed a new approach to retrieve both optically active parameters and non-optically active parameters for water body and provide new strategy for water quality monitoring.</p>

scholarly journals ASSESSMENT OF NATURAL CONDITIONS FOR THE CONSTRUCTION OF LANDFALL OF TRANSITION OF THE BOVANENKOVO-UKHTA GAS PIPELINE THROUGH THE BAIDARATSKAYA GUBA (KARA SEA) ACCORDING TO REMOTE SENSING DATA

2021 ◽  
Vol 2 (6) ◽  
pp. 59-65
Author(s):  
S.G. Mironyuk ◽  
◽  
, O.M. Kasimova ◽  
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Kara Sea ◽  
Gas Pipeline ◽  
Aerial Photographs ◽  
River Channels ◽  
Sensing Data ◽  
Landsat Satellite ◽  
Sketch Map

The results of multi-temporal aerospace imagery of the coast of the Baydaratskaya Bay (Kara Sea) interpretation are presented. In order to draw up a sketch map for the estimated zoning of the territory according to the construction conditions of the onshore sections of the Bovanenkovo-Ukhta gas pipeline, studies were carried out. The methods, consisting of 3 stages, was used: 1) sequential of aerial photographs and satellite images interpretation of different scales; 2) spatial comparison of the obtained interpretation results with the available cartographic materials of geotechnical surveys; 3) drawing up maps and sketch maps of various contents, including the final sketch map of the estimated zoning according to the degree of favorableness of the territory for the construction of gas pipelines. Landsat satellite images (1999–2000) with a resolution of 30 m, as well as aerial photographs of 1949–1950, scale 1:60 000, covering the southern part of the coast of the Baydaratskaya Bay were used for interpretation. The retreat of the gulf coasts, changes in the configuration of the coastline, river channels in their deltaic parts, changes in the size of thermokarst lakes have been discovered over the past 50 years in the course of analysis and interpretation of Earth remote sensing data. The amount of coastal retreat varies in different parts of the coast of the Baydaratskaya Bay from 30 to 90 m, i.e. the rate of retreat of the coastline is between 0.6 and 1.8 m / year has been established. The places of development of thermokarst and polygonal wedge ice, as well as active river erosion, have been identified by deciphering signs. At the third, final stage of the studies performed, based on the analysis of natural factors that determine the patterns of distribution of the studied hazardous processes and phenomena, a schematic assessment zoning of the studied territory was carried out according to the degree of its favorableness for the construction of onshore sections of the gas pipeline. Three types of areas: unfavorable, relatively unfavorable and favorable are noted on the sketch map

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market
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