scholarly journals assessment of the probable impacts of land use changes on water quality in shadeghan wetland using remotely sensed data

2020 ◽  
Vol 7 (2) ◽  
pp. 33-48
Author(s):  
behzad raygani ◽  
fargol goodarzi ◽  
ahmad talebi ◽  
mohammad talaeian araghi ◽  
hadi hashemi ◽  
...  
Author(s):  
Ali Ben Abbes ◽  
Imed Riadh Farah

Due to the growing advances in their temporal, spatial, and spectral resolutions, remotely sensed data continues to provide tools for a wide variety of environmental applications. This chapter presents the benefits and difficulties of Multi-Temporal Satellite Image (MTSI) for land use. Predicting land use changes using remote sensing is an area of interest that has been attracting increasing attention. Land use analysis from high temporal resolution remotely sensed images is important to promote better decisions for sustainable management land cover. The purpose of this book chapter is to review the background of using Hidden Markov Model (HMM) in land use change prediction, to discuss the difference on modeling using stationary as well as non-stationary data and to provide examples of both case studies (e.g. vegetation monitoring, urban growth).


2019 ◽  
pp. 1178-1197
Author(s):  
Ali Ben Abbes ◽  
Imed Riadh Farah

Due to the growing advances in their temporal, spatial, and spectral resolutions, remotely sensed data continues to provide tools for a wide variety of environmental applications. This chapter presents the benefits and difficulties of Multi-Temporal Satellite Image (MTSI) for land use. Predicting land use changes using remote sensing is an area of interest that has been attracting increasing attention. Land use analysis from high temporal resolution remotely sensed images is important to promote better decisions for sustainable management land cover. The purpose of this book chapter is to review the background of using Hidden Markov Model (HMM) in land use change prediction, to discuss the difference on modeling using stationary as well as non-stationary data and to provide examples of both case studies (e.g. vegetation monitoring, urban growth).


2021 ◽  
Vol 776 ◽  
pp. 146034
Author(s):  
Cira Buonocore ◽  
Juan Jesús Gomiz Pascual ◽  
María Luisa Pérez Cayeiro ◽  
Rafael Mañanes Salinas ◽  
Miguel Bruno Mejías

2021 ◽  
Vol 933 (1) ◽  
pp. 012010
Author(s):  
S A Nurhayati ◽  
M Marselina ◽  
A Sabar

Abstract Increasing population growth is one of the impacts of the growth of a city or district in an area. This also happened in the Cimahi watershed area. As the population grows, so does the need for land which increases the land-use change in the Cimahi watershed. Land-use changes will affect the surrounding environment and one of them is the river, especially river water quality. As a watershed area, there is one main river that is the source of life as well as the Cimahi watershed, whose main river is the Cimahi River. The purpose of this study was calculated the relationship between land-use change in the Cimahi watershed and the water quality parameters of the Cimahi River. The correlation between the two was calculated using Pearson correlation. Water quality parameters can be seen based on BOD and DO values. BOD and DO values are the opposite because good water quality has high DO values and low BOD values. The correlation between land-use change and BOD was 0.328 is in the area of settlements area. In contrast, to DO values, an increase in settlements/industrial zones will further reduce DO values so that both have a negative correlation, which is indicated by a value of -0,535. The correlation between settlements with pH and temperature values is 0.664 and 0.812. While the correlation between settlements with TSS and TDS values are 0.333 and 0.529, respectively. In this study, it can be seen that there is a relationship between the decline in water quality and changes in land use.


2014 ◽  
pp. 269-283 ◽  
Author(s):  
Mohamed S. Dafalla ◽  
Elfatih M. Abdel-Rahman ◽  
Khalid H. A. Siddig ◽  
Ibrahim S. Ibrahim ◽  
Elmar Csaplovics

Author(s):  
Ned Horning ◽  
Julie A. Robinson ◽  
Eleanor J. Sterling ◽  
Woody Turner ◽  
Sacha Spector

In terrestrial biomes, ecologists and conservation biologists commonly need to understand vegetation characteristics such as structure, primary productivity, and spatial distribution and extent. Fortunately, there are a number of airborne and satellite sensors capable of providing data from which you can derive this information. We will begin this chapter with a discussion on mapping land cover and land use. This is followed by text on monitoring changes in land cover and concludes with a section on vegetation characteristics and how we can measure these using remotely sensed data. We provide a detailed example to illustrate the process of creating a land cover map from remotely sensed data to make management decisions for a protected area. This section provides an overview of land cover classification using remotely sensed data. We will describe different options for conducting land cover classification, including types of imagery, methods and algorithms, and classification schemes. Land cover mapping is not as difficult as it may appear, but you will need to make several decisions, choices, and compromises regarding image selection and analysis methods. Although it is beyond the scope of this chapter to provide details for all situations, after reading it you will be able to better assess your own needs and requirements. You will also learn the steps to carry out a land cover classification project while gaining an appreciation for the image classification process. That said, if you lack experience with land cover mapping, it always wise to seek appropriate training and, if possible, collaborate with someone who has land cover mapping experience (Section 2.3). Although the terms “land cover” and “land use” are sometimes used interchangeably they are different in important ways. Simply put, land cover is what covers the surface of the Earth and land use describes how people use the land (or water). Examples of land cover classes are: water, snow, grassland, deciduous forest, or bare soil.


Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 724 ◽  
Author(s):  
Pankaj Kumar ◽  
Rajarshi Dasgupta ◽  
Brian Johnson ◽  
Chitresh Saraswat ◽  
Mrittika Basu ◽  
...  

Rapid changes in land use and land cover pattern have exerted an irreversible change on different natural resources, and water resources in particular, throughout the world. Khambhat City, located in the Western coastal plain of India, is witnessing a rapid expansion of human settlements, as well as agricultural and industrial activities. This development has led to a massive increase in groundwater use (the only source of potable water in the area), brought about significant changes to land management practices (e.g., increased fertilizer use), and resulted in much greater amounts of household and industrial waste. To better understand the impacts of this development on the local groundwater, this study investigated the relationship between groundwater quality change and land use change over the 2001–2011 period; a time during which rapid development occurred. Water quality measurements from 66 groundwater sampling wells were analyzed for the years 2001 and 2011, and two water quality indicators (NO3− and Cl− concentration) were mapped and correlated against the changes in land use. Our results indicated that the groundwater quality has deteriorated, with both nitrate (NO3−) and chloride (Cl−) levels being elevated significantly. Contour maps of NO3− and Cl− were compared with the land use maps for 2001 and 2011, respectively, to identify the impact of land use changes on water quality. Zonal statistics suggested that conversion from barren land to agricultural land had the most significant negative impact on water quality, demonstrating a positive correlation with accelerated levels of both NO3− and Cl−. The amount of influence of the different land use categories on NO3− increase was, in order, agriculture > bare land > lake > marshland > built-up > river. Whereas, for higher concentration of Cl− in the groundwater, the order of influence of the different land use categories was marshland > built-up > agriculture > bare land > lake > river. This study will help policy planners and decision makers to understand the trend of groundwater development and hence to take timely mitigation measures for its sustainable management.


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