Some novel approaches in laser remote sensing of natural waters based on recognition of spectral patterns with the help of artificial neural networks

2007 ◽  
Author(s):  
T. A. Dolenko ◽  
I. V. Fadeeva ◽  
S. A. Burikov ◽  
S. A. Dolenko ◽  
V. V. Fadeev
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Natural Waters ◽  
Novel Approaches ◽  
Artificial Neural ◽  
Spectral Patterns ◽  
Laser Remote Sensing

Determination of application volume for coffee plantations using artificial neural networks and remote sensing

2021 ◽  
Vol 184 ◽  
pp. 106096
Author(s):  
Mailson Freire de Oliveira ◽  
Adão Felipe dos Santos ◽  
Elizabeth Haruna Kazama ◽  
Glauco de Souza Rolim ◽  
Rouverson Pereira da Silva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coffee Plantations ◽  
Artificial Neural ◽  
Application Volume

scholarly journals Climate change or regional human impacts? remote sensing tools, artificial neural networks, and wavelet approaches aim to solve the problem

2020 ◽  
Author(s):  
Ehsan Foroumandi ◽  
Vahid Nourani ◽  
Elnaz Sharghi
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Water Level ◽  
Positive Trend ◽  
Water Level Change ◽  
Lake Urmia ◽  
Level Change ◽  
Artificial Neural

Abstract Lake Urmia, as the largest lake in Iran, has suffered from water-level decline and this problem needs to be investigated accurately. The major reason for the decline is controversial. The current paper aimed to study the hydro-environmental variables over the Lake Urmia basin using remote sensing tools, artificial neural networks, wavelet transforms, and Mann–Kendall trend tests from 1995 to 2019 in order to determine the primary reason of the decline and to find the most important hydrologic periodicities over the basin. The results indicated that for the monthly-, seasonally-, and annually-based time series, the components with 4-month and 16-month, 24- and 48-month, and 2- and 4-year, respectively, are the most dominant periodicities over the basin. The agricultural increase according to the vegetation index and evapotranspiration and their close relationship with the water-level change indicated that human land-use is the main reason for the decline. The increasing agriculture, in the situations that the precipitation has not increased, caused the inflow runoff to the lake to decline and the remaining smaller discharge is not sufficient to stabilize the water level. Temperature time series, also, has experienced a significant positive trend which intensified the water-level change.

Artificial neural networks paddy-field classifier using spatiotemporal remote sensing data

2010 ◽  
Vol 15 (2) ◽  
pp. 221-224 ◽  
Author(s):  
Takashi Yamaguchi ◽  
Kazuya Kishida ◽  
Eiji Nunohiro ◽  
Jong Geol Park ◽  
Kenneth J. Mackin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Paddy Field ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Artificial Neural
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