Land Use Land Cover Changes in Detection of Water Quality: A Study Based on Remote Sensing and Multivariate Statistics

Babr

Mehran Univ. res. j. eng. technol.

2020

Under the current scenario of diminishing Indus River flows and changing the climate, the Indus Delta, the world’s 5th largest delta which has undergone rapid changes in water bodies and vegetative cover since last few decades, is under serious risk of losing its ecological functions. Assessing the temporal variation in vegetative cover and water bodies of the Indus Delta is essential for the future planning and ecosystem management in this region. The present study quantified the temporal patterns of the surface water bodies and vegetation cover, including crops, mangroves and other natural vegetation in the Indus Delta, by using field survey and remote sensing technique during the last 27 years. Results showed that the area covered by vegetation declined from 3002.35 km2 (22.98% of the entire delta) to 2817.03 km2 (21.56%) from 1990 to 2017, within which the area covered by mangrove forests declined from 1032.49 km2 (7.90%) to 812.55 km2 (6.22%). However, the area of water bodies increased from 1611.67 km2 (12.39%) to 3007.15 km2 (23.8%) in the same period. The reduction in freshwater flow to the delta, surface and subsurface seawater intrusion from the Arabian Sea and irrigation waters are the potential causes. The study would be helpful for policymakers to mitigate negative impacts and protect the ecosystem of the Indus Delta.

Section: Introductionmentioning

confidence: 99%

Temporal Dynamics of Vegetative Cover and Surface Water Bodies in the Indus Delta, Pakistan

Babr

Mehran Univ. res. j. eng. technol.

2020

“…Several scientific works have used different analysis techniques, including regression modeling, time series analysis, and geospatial modeling (e.g., using remote sensing and geographical information systems), to identify the spatio-temporal relationships between groundwater quality changes and different anthropogenic activities [19][20][21][22][23][24]. With the above background, it is of utmost importance to understand the relationship between land use changes and water quality in developing countries like India with burgeoning populations.…”

Section: Introductionmentioning

confidence: 99%

Effect of Land Use Changes on Water Quality in an Ephemeral Coastal Plain: Khambhat City, Gujarat, India

Kumar

Dasgupta

Johnson

et al. 2019

Water

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.

“…The land is one of the most important natural resources, as life and various developmental activities are based on it. Land use land cover change has been identified as one of the most important drivers of changes in ecosystems and its services [1]. These changes result from population growth, urbanisation, and migration of poor rural people to urban areas for economic opportunities.…”

Section: Introductionmentioning

confidence: 99%

Application of GIS and Remote Sensing to Analyse Land Use Land Cover Change Detection and Vegetation Dynamics Using Multi-temporal Satellite Images; The Case of Mariamdehan Kebele, Tigray Region, Northern Ethiopia

Meresa¹,

Gebrewhid²

2019

AJGR

Detecting Land use and land cover change and vegetation condition has become a central component in current strategies for managing and monitoring of environmental changes caused by anthropogenic activities. To come up with such decisions, geoinformatics technology is providing new tools to conduct vegetation and land use land cover change detection analysis for managing and wise utilisation of natural resources as well as to provide information for policymakers in a given study area. This study examines the use of geoinformatics technology to analyse land use land cover (LULC) change and vegetation dynamics using multi-temporal satellite images for the maryamdehan kebele in the years 1984, 2005 and 2015. Both primary and secondary data were used from different sources. Satellite images of the year 1984, 2005 and 2015 were downloaded from the govis.usgs.gov website and ground control points (GCP) data were collected by handheld GPS for supervised image classification in Erdas imagine and ArcGIS environment. The findings show that six main land use land cover classes were detected and vegetation values were also computed in each period. As a result, the total area of the kebele was 3646.49 hectare, from which in 1984 forest area (40.691%), grassland (26.15%) and farmland (10.81%) were dominant classes and in 2005 settlement (52.41%), forest area (25.04%) & farmland (11.71%) and in 2015, 35.14% was covered by forest land, 30.04% by Settlement, and 14.74% by farmland. Water resource decreases from 9.3% to 0.64% in 2015 and the bare land also changes from 3.18% to 0.903% because of urban expansion and agricultural activities in the kebele. In addition, the vegetation condition looks like a sinusoidal trend from the year 1984 up to 2015 because of climate change and human interventions in the kebele. To conclude that detecting LULC change and analysis of vegetation dynamics plays a great role in land use management and wise utilisation of natural resources by applying Geoinformatics tools in the kebele and it provides information for the policymakers to prepared future plan and for sustainable development.