River classification and change detection from landsat images by using a river classification toolbox

Abstract

<span>Water bodies especially rivers are vital to existence of all lifeforms on Earth. Therefore, monitoring river areas and water bodies is essential. In the past, the monitoring relied essentially on manpower in surveying individual areas. However, there are limitations associated wih such surveys, e.g., tremendous amount of time and labour involved in expeditions. Presently, there have been accelerated development in remote sensing (RS) and artificial intelligence (AI) technology, particularly for change monitoring and detection in different areas globally. This research presents technical development of a toolbox for rivers classification and their change detection from Landsat images, by using water index analysis and four machine learning algorithms, which are K-Means, ISODATA, maximum likelihood classification (MLC), and support vector machine (SVM). Experimental findings indicated that all presented techniques were effective in detecting hydrological changes. The most accurate algorithm, nevertheless, for river classification was the SVM, with accuracy of 96.89%, precision of 98.61%, recall of 96.59%, and F-measure of 97.59%. Herein, it was demonstrated, in addition, that the developed toolbox was versatile and could be applied in rapid river change detection in other areas.</span>

Mapping and Assessing Riparian Vegetation Response to Drought along the Buffalo River Catchment in the Eastern Cape Province, South Africa

“…The geospatial technique adopted a hybrid methodological approach using ArcGIS v.10.8 and R statistical package (RStudio v.4.1 software) to compute the inter-annual variability of vegetation indices and changes in climatic patterns in response to drought. The change detection allows the identification of changes in the state of drought incidence quantified over a repeated time interval [47]. In this study, the widely adopted change detection method was employed in mapping and assessing riparian vegetation response in a time series interval of five years [48,49].…”

“…An unsupervised classification was performed on the vegetation index imageries to create user-defined classes and harmonise them into four classes [47]. The quantified classes include dense riparian vegetation, sparse, non-vegetated areas, and water bodies.…”

Change detection of pulmonary embolism using isomeric cluster and computer vision

“…In a similar vein, segmentation inclusion is one of the other exciting hybrids that the researchers have revealed. Supattra Puttinaovarat et al [26] presents technical development of a toolbox for rivers classification and their change detection from Landsat images, by using water index analysis and four machine learning algorithms, which are KMeans, ISODATA, maximum likelihood classification (MLC), and support vector machine (SVM).…”

The relationship between the land and buildings Tax Act, B.E. 2562 (A.D. 2019), and the land use and land cover (LULC) of Bangkok, Thailand

“…Through these, the trends were assessed across the different periods and LULC classes. Once these preliminary quantitative methods were done, the spatial techniques were performed by use of the categorical compute change function of ArcGIS (Puttinaovarat et al, 2021). Every pair of chronological time skips between images from 2018 to 2021 were run through the tool.…”

“…So, this part of the literature review shall focus on identifying these changes via remote sensing and GIS. It is then revealed through the inquiry of numerous studies that first, comparing the overall area and percent coverages of LULC classes is standard for studies of this nature(Bagwan & Gavali, 2021;Butt et al, 2015;Chugtai et al, 2021;Das & Angadi, 2021;Kafi et al, 2014;Nurda et al, 2020;Puttinaovarat et al, 2021;Saini et al, 2019;Shah & Kiran, 2021;Tian et al, 2014;Vivekananda et al, 2020). In order to further deepen the insight on the quantitatively determined land transformations, some studies used cross-tabulation to know the from-to LULC conversions(Bagwan & Gavali, 2021;Butt et al, 2015;Chugtai et al, 2021;Das & Angadi, 2021;Kafi et al, 2014;Tian et al, 2014).…”