Water Quality Modeling for Watershed Management: Using AGNPS 98 and Satellite Remote Sensing Image Information

2001 ◽  
Author(s):  
D. Scott Adams ◽  
Kathleen M. Trauth ◽  
Janggam Adhityawarma ◽  
R. Lee Peyton ◽  
Aderbal C. Corrêa
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Watershed Management ◽  
Satellite Remote Sensing ◽  
Remote Sensing Image ◽  
Water Quality Modeling ◽  
Image Information ◽  
Quality Modeling

Water quality modeling in mangrove forest area due to anthropogenic waste as a prevention of global warming utilizing remote sensing satellite data

2021 ◽  
Author(s):  
Cokro Santoso ◽  
Kurnia Putri Adillah ◽  
Muhamad Hanif Resgi Putranto ◽  
Freija Maharani Yasminnajla ◽  
Muhammad Zetti Nugraha ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Global Warming ◽  
Satellite Data ◽  
Mangrove Forest ◽  
Water Quality Modeling ◽  
Forest Area ◽  
Quality Modeling ◽  
Remote Sensing Satellite

Monitoring land use change in the Lake Taupo catchment between 1975, 1990 and 2002 using satellite remote sensing data

2007 ◽  
pp. 17-22
Author(s):  
H. Lilienthal ◽  
A. Brauer ◽  
K. Betteridge ◽  
E. Schnug
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Native Vegetation ◽  
Lake Water Quality ◽  
Livestock Farming ◽  
Slow Decline

Conversion of native vegetation into farmed grassland in the Lake Taupo catchment commenced in the late 1950s. The lake's iconic value is being threatened by the slow decline in lake water quality that has become apparent since the 1970s. Keywords: satellite remote sensing, nitrate leaching, land use change, livestock farming, land management

Hydrodynamic and Water Quality Modeling of Lake Eğirdir

2014 ◽  
Vol 42 (11) ◽  
pp. 1573-1582 ◽  
Author(s):  
Meltem Kaçıkoç ◽  
Mehmet Beyhan
Keyword(s):  
Water Quality ◽  
Water Quality Modeling ◽  
Quality Modeling

scholarly journals Mathematical models for information classification and recognition of multi-target optical remote sensing images

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 951-960
Author(s):  
Haiqing Zhang ◽  
Jun Han
Keyword(s):  
Remote Sensing ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Specific Class ◽  
Three Dimensional Model ◽  
Image Information ◽  
Information Classification ◽  
Optical Remote Sensing Image

Abstract Traditionally, three-dimensional model is used to classify and recognize multi-target optical remote sensing image information, which can only identify a specific class of targets, and has certain limitations. A mathematical model of multi-target optical remote sensing image information classification and recognition is designed, and a local adaptive threshold segmentation algorithm is used to segment multi-target optical remote sensing image to reduce the gray level between images and improve the accuracy of feature extraction. Remote sensing image information is multi-feature, and multi-target optical remote sensing image information is identified by chaotic time series analysis method. The experimental results show that the proposed model can effectively classify and recognize multi-target optical remote sensing image information. The average recognition rate is more than 95%, the maximum robustness is 0.45, the recognition speed is 98%, and the maximum time-consuming average is only 14.30 s. It has high recognition rate, robustness, and recognition efficiency.

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