Monitoring Cyanobacteria Bloom in Dianchi Lake Based on Ground-Based Multispectral Remote-Sensing Imaging: Preliminary Results

Some lakes in China have undergone serious eutrophication, with cyanobacterial blooms occurring frequently. Dynamic monitoring of cyanobacterial blooms is important. At present, the traditional lake-survey-based cyanobacterial bloom monitoring is spatiotemporally limited and requires considerable human and material resources. Although satellite remote sensing can rapidly monitor large-scale cyanobacterial blooms, clouds and other factors often mean that effective images cannot be obtained. It is also difficult to use this method to dynamically monitor and manage aquatic environments and provide early warnings of cyanobacterial blooms in lakes and reservoirs. In contrast, ground-based remote sensing can operate under cloud cover and thus act as a new technical method to dynamically monitor cyanobacterial blooms. In this study, ground-based remote-sensing technology was applied to multitemporal, multidirectional, and multiscene monitoring of cyanobacterial blooms in Dianchi Lake via an area array multispectral camera mounted on a rotatable cloud platform at a fixed station. Results indicate that ground-based imaging remote sensing can accurately reflect the spatiotemporal distribution characteristics of cyanobacterial blooms and provide timely and accurate data for salvage treatment and early warnings. Thus, ground-based multispectral remote-sensing data can operationalize the dynamic monitoring of cyanobacterial blooms. The methods and results from this study can provide references for monitoring such blooms in other lakes.

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STUDY ON VORTEX CURRENT IN STRAIT WITH REMOTE-SENSING

Coastal Engineering Proceedings ◽

10.9753/icce.v17.157 ◽

1980 ◽

Vol 1 (17) ◽

pp. 157 ◽

Cited By ~ 1

Author(s):

Sotoaki Onishi ◽

Tsukasa Nishimura

Keyword(s):

Water Pollution ◽

Remote Sensing ◽

Boundary Conditions ◽

Mathematical Models ◽

Large Scale ◽

Sea Water ◽

Remote Sensing Data ◽

Field Measurements ◽

Sensing Technology ◽

Pollution Problem

With rapid increases of industrial activity in present time, water pollution in the coastal environment has "become an urgent problem to cope with. This problem is especially serious in enclosed bays or inland seas. Hydrodynamic character of the strait connecting the inland sea to the open ocean must be understood well in order to analyse the diffusion of pollutants in the Inland sea, because its character determine boundary conditions in the mathematical models of the water pollution problem. So far however, it is seemed that the main efforts exerted by coastal engineers have concentrated mainly on the development of mathematical models, lacking satisfactory knowledge of the boundary conditions through field measurements. One reason of this state is resulted from the fact that the relating phenomena in the field are of too large scale, in general, to perform the field measurements. Connecting with this point, the authors present in this paper, that remote-sensing technology is very useful to get information of the hydrodynaniical phenomena occurring in the water body around the strait. To show the above, the authors selected as an object of the study , Naruto Strait in the Seto Inland Sea, which is world famous for the existence of rapid tidal currents and dynamic vortices. Remote-sensing data both from the airplanes and from a space satellite Landsat are analysed with the aid of theoretical considerations and hydraulic mo-del tests to disclose the behavior of the vortices of various scales and the roles of them in the sea water mixing phenomena at the strait.

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Prediction of Forest Aboveground Biomass Using Multitemporal Multispectral Remote Sensing Data

Remote Sensing ◽

10.3390/rs13071282 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1282

Author(s):

Parth Naik ◽

Michele Dalponte ◽

Lorenzo Bruzzone

Keyword(s):

Remote Sensing ◽

Aboveground Biomass ◽

Large Scale ◽

Remote Sensing Data ◽

Temporal Information ◽

Observation Data ◽

Multispectral Data ◽

Sensing Data ◽

Multispectral Remote Sensing ◽

Forest Aboveground Biomass

Forest aboveground biomass (AGB) is a prime forest parameter that requires global level estimates to study the global carbon cycle. Light detection and ranging (LiDAR) is the state-of-the-art technology for AGB prediction but it is expensive, and its coverage is restricted to small areas. On the contrary, spaceborne Earth observation data are effective and economical information sources to estimate and monitor AGB at a large scale. In this paper, we present a study on the use of different spaceborne multispectral remote sensing data for the prediction of forest AGB. The objective is to evaluate the effects of temporal, spectral, and spatial capacities of multispectral satellite data for AGB prediction. The study was performed on multispectral data acquired by Sentinel-2, RapidEye, and Dove satellites which are characterized by different spatial resolutions, temporal availability, and number of spectral bands. A systematic process of least absolute shrinkage and selection operator (lasso) variable selection generalized linear modeling, leave-one-out cross-validation, and analysis was accomplished on each satellite dataset for AGB prediction. Results point out that the multitemporal data based AGB models were more effective in prediction than the single-time models. In addition, red-edge and short wave infrared (SWIR) channel dependent variables showed significant improvement in the modeling results and contributed to more than 50% of the selected variables. Results also suggest that high spatial resolution plays a smaller role than spectral and temporal information in the prediction of AGB. The overall analysis emphasizes a good potential of spaceborne multispectral data for developing sophisticated methods for AGB prediction especially with specific spectral channels and temporal information.

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Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite

Remote Sensing ◽

10.3390/rs11050595 ◽

2019 ◽

Vol 11 (5) ◽

pp. 595 ◽

Cited By ~ 7

Author(s):

Han Liu ◽

Randy Dahlgren ◽

Royce Larsen ◽

Scott Devine ◽

Leslie Roche ◽

...

Keyword(s):

Remote Sensing ◽

Net Primary Production ◽

Remote Sensing Data ◽

Field Measurements ◽

Rangeland Management ◽

Unmanned Aerial System ◽

Forage Production ◽

Cattle Industry ◽

Sensing Technology ◽

Multispectral Remote Sensing

Rangelands cover ~23 million hectares and support a $3.4 billion annual cattle industry in California. Large variations in forage production from year to year and across the landscape make grazing management difficult. We here developed optimized methods to map high-resolution forage production using multispectral remote sensing imagery. We conducted monthly flights using a Small Unmanned Aerial System (sUAS) in 2017 and 2018 over a 10-ha deferred grazing rangeland. Daily maps of NDVI at 30-cm resolution were first derived by fusing monthly 30-cm sUAS imagery and more frequent 3-m PlanetScope satellite observations. We estimated aboveground net primary production as a product of absorbed photosynthetically active radiation (APAR) derived from NDVI and light use efficiency (LUE), optimized as a function of topography and climate stressors. The estimated forage production agreed well with field measurements having a R2 of 0.80 and RMSE of 542 kg/ha. Cumulative NDVI and APAR were less correlated with measured biomass ( R 2 = 0.68). Daily forage production maps captured similar seasonal and spatial patterns compared to field-based biomass measurements. Our study demonstrated the utility of aerial and satellite remote sensing technology in supporting adaptive rangeland management, especially during an era of climatic extremes, by providing spatially explicit and near-real-time forage production estimates.

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Vehicle Emission Detection in Data-Driven Methods

Mathematical Problems in Engineering ◽

10.1155/2020/4875310 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Zheng He ◽

Gang Ye ◽

Hui Jiang ◽

Youming Fu

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Remote Sensing Data ◽

Vehicle Emission ◽

Fuel Type ◽

Essential Information ◽

Sensing Technology ◽

Main Component ◽

Emission Detection ◽

Remote Sensing Methods

Environmental protection is a fundamental policy in many countries, where the vehicle emission pollution turns to be outstanding as a main component of pollutions in environmental monitoring. Remote sensing technology has been widely used on vehicle emission detection recently and this is mainly due to the fast speed, reality, and large scale of the detection data retrieved from remote sensing methods. In the remote sensing process, the information about the fuel type and registration time of new cars and nonlocal registered vehicles usually cannot be accessed, leading to the failure in assessing vehicle pollution situations directly by analyzing emission pollutants. To handle this problem, this paper adopts data mining methods to analyze the remote sensing data to predict fuel type and registration time. This paper takes full use of decision tree, random forest, AdaBoost, XgBoost, and their fusion models to successfully make precise prediction for these two essential information and further employ them to an essential application: vehicle emission evaluation.

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Dynamic monitoring of drought in Fujian Province from MODIS remote sensing data

CHINESE JOURNAL OF ECO-AGRICULTURE ◽

10.3724/sp.j.1011.2008.01015 ◽

2009 ◽

Vol 16 (4) ◽

pp. 1015-1019 ◽

Cited By ~ 3

Author(s):

Wei-Hua PAN

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Dynamic Monitoring ◽

Fujian Province ◽

Sensing Data

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A Multispectral Remote Sensing Data Spectral Unmixing Algorithm Based on Variational Bayesian ICA

Journal of the Indian Society of Remote Sensing ◽

10.1007/s12524-012-0245-0 ◽

2012 ◽

Vol 41 (2) ◽

pp. 259-268 ◽

Cited By ~ 5

Author(s):

Chengfan Li ◽

Jingyuan Yin

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Spectral Unmixing ◽

Variational Bayesian ◽

Sensing Data ◽

Multispectral Remote Sensing

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Degraded land detection by soil particle composition derived from multispectral remote sensing data in the Otindag Sandy Lands of China

Geoderma ◽

10.1016/j.geoderma.2014.11.011 ◽

2015 ◽

Vol 241-242 ◽

pp. 97-106 ◽

Cited By ~ 13

Author(s):

Junjun Wu ◽

Zengyuan Li ◽

Zhihai Gao ◽

Bengyu Wang ◽

Lina Bai ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Soil Particle ◽

Degraded Land ◽

Particle Composition ◽

Sensing Data ◽

Multispectral Remote Sensing

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LAI Retrieval Based on PROSPECT-SAILH Model from Multi-Angular Data of WiDAS Imaging System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.518-523.5697 ◽

2012 ◽

Vol 518-523 ◽

pp. 5697-5703

Author(s):

Zhao Yan Liu ◽

Ling Ling Ma ◽

Ling Li Tang ◽

Yong Gang Qian

Keyword(s):

Remote Sensing ◽

Imaging System ◽

Remote Sensing Data ◽

Canopy Reflectance ◽

Area Index ◽

Sensing Data ◽

Spectral Bands ◽

Sensing Technology ◽

Leaf Optical Properties ◽

Angular Data

The aim of this study is to assess the capability of estimating Leaf Area Index (LAI) from high spatial resolution multi-angular Vis-NIR remote sensing data of WiDAS (Wide-Angle Infrared Dual-mode Line/Area Array Scanner) imaging system by inverting the coupled radiative transfer models PROSPECT-SAILH. Based on simulations from SAILH canopy reflectance model and PROSPECT leaf optical properties model, a Look-up Table (LUT) which describes the relationship between multi-angular canopy reflectance and LAI has been produced. Then the LAI can be retrieved from LUT by directly matching canopy reflectance of six view directions and four spectral bands with LAI. The inversion results are validated by field data, and by comparing the retrieval results of single-angular remote sensing data with multi-angular remote sensing data, we can found that the view angle takes the obvious impact on the LAI retrieval of single-angular data and that high accurate LAI can be obtained from the high resolution multi-angular remote sensing technology.

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