Estimation of Subsidence Over a Part of Kanchipuram District Using Sentinel-1 SAR Data

Journal of the Indian Society of Remote Sensing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 10, 2025

Язык: Английский

Statistical comparison of simple and machine learning based land use and land cover classification algorithms: A case study

Journal of Water Management Modeling, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

This study used three different classification models, namely Support Vector Machine (SVM), Random Forest (RFM), and Maximum Likelihood (ML) for of Landsat (7 & 8), Sentinel-2A data sets. Each case’s area interest (AOI) number training sets (within fixed AOI Chennai district boundary) were considered equal. Land use class change was observed because rapid urbanization developmental activities under urbanization, the LULC monitored using ArcGIS Pro platform 2005, 2010, 2015 2020. The overall accuracy (OA) first, second, third 89%, 88%, 82%, 80% RF, 87%, 85%, 79%, SVM. However, ML classifier provided OA as 77%, 76%, 66% 2020, respectively. Kappa coefficient (K) calculated third, 84%, 75%, 72%, 80%, 78%, 71%, 67% a K value 67%, 57% Based on quantitative assessments, RF showed good accuracy, then SVM in classifications with

Язык: Английский

Assessment of soil loss from a Trans-National Rapti River Basin using empirical and InVEST-based models

GEOMATICA, Год журнала: 2024, Номер 76(2), С. 100016 - 100016

Опубликована: Авг. 1, 2024

Presently soil erosion is a severe environmental issue. Human-induced activities and various natural processes have intensified it over time, making one of the most concerning problem worldwide. The Himalayan River Rapti, has been selected for evaluating loss along with rates using Revised Universal Soil Loss Equation (RUSLE) in Geographic Information System (GIS) InVEST model. results show that minimum maximum was estimated at 5 tons/hectare/year 1185 ton/hectare/year, respectively, north-eastern (NE) north-western (NW) sides Lesser Siwalik ranges situated upper Rapti Basin (RRB). Three susceptibility sites were identified RRB. areas around Bhutwal basin's northern part experienced significantly intensive rainfall erosivity, whereas upper-middle to lower-middle sections basin exhibit relatively moderate low intensity erosivity. large flatter alluvial plains are characterized by (0.178 ton/hectare/year) potential zones. Whereas mountainous foothills represented high sediment delivery (17,604.30 observed north north-east fringes While low-lying (0.178). steep slopes prone greater extent when contrasted will aid strategizing preventive actions against provide valuable information planners identifying heightened intensity. higher degree could be minimized introducing agricultural enhancing conservation practices implementing effective watershed management.

Язык: Английский