09-10, 13:00–13:30 (America/Chicago), Grand F
The negative effects of water hyacinth resulting from sewage leakages into freshwater bodies surpasses water poisoning and ecosystem changes. As such, GIS is used to monitor and evaluate water hyacinth.
Environmental monitoring is also a repetitive and continued observation measurement and evaluation of the environmental parameters to follow changes overall period of time and to assess compliance efficiency of the activity on environmental issue. With this definition in mind, a case study was done and focused on Masvingo region in Zimbabwe, evaluating water contamination in Shahashe and Mucheke rivers as a result of sewage leakages. Change detection used helped understand the change in water hyacinth. The use of Geographic Information System (GIS) proved effective enough to monitor and evaluate areas where hyacinth is mostly distributed along the river. The obtained results showed that GIS and remote sensing were typically easy to adopt and apply.
Satellite data and auxiliary data were employed in this study. 10% cloud coverage Landsat images were downloaded for free from the USGS Earth Explore website at https://earthexplorer.usgs.gov. for an accurate representation of the water hyacinth's riverside expansion from 2002 to 2022. 13-band Landsat images were atmospherically adjusted with QGIS software's semi-automatic categorization function. The process of modifying the satellite images taken for accurate interpretation comprised of a few steps.
Radiometric adjustment
This is computer-based method for minimizing and/or eliminating atmospheric noise, for example haze, in photographs. Images' clouds cover collected using USGS was reduced to 10% to avoid result distortion.
Image Classification
The study's observations of the shifting land use and land cover along the river were made using Google Earth. Google Earth allowed zooming in on land features and offered free access to satellite imagery on both present and previous land dynamics. The classes of vegetation, built-up areas, forests, grasslands, barren land, shrubs, and agricultural regions were recognized and delineated from the satellite photos.
Change detection
Change detection was performed on mosaiced classified images to compare changes between aerial photos of the same geographic area taken at distinct periods; and assess the spread of water hyacinth along the river over two or more years.
Normalized Different Vegetation Index (NDVI)
NDVI determines the amount of vegetation by measuring the difference between near-infrared (NIR), which plants significantly reflect, and red light, which vegetation substantially absorbs/has low reflectance. NDVI always ranges from -1 to +1. High NDVI values, which indicate an increase in the amount of green vegetation, are produced by healthy vegetation's low red light reflection and high near-infrared reflectance. Non-vegetation characteristics including water, clouds, snow, ice, and bare rock and soil surfaces are indicated by NDVI values that are near 0 and decreasingly negative values
