Monitoring of Land Use and Land Cover Changes Using Remote Sensing and Geographic Information System

Abstract

The Land use Land cover (LULC) change due to rapidly growing population is a common feature of urban area. This research aims to detect the variations in LULC from 1991-2017 of Malakand Division in the Khyber Pakhtunkhwa province of Pakistan. The study relies on secondary dataset downloaded from United State Geological Survey (1991, 2001, 2011 & 2017 imageries) and UN OCHA website. Maximum Likelihood technique under supervised image classification was adopted to analyze the LULC changes in between 1991 and 2017. Total six land use classes were generated including agriculture built-up area, vegetation cover, water bodies, snow cover and barren land. The results from 1991 to 2017 show a substantial reduction in snow cover and barren land which is consequence of climate change. Whereas the areas underbuilt-up, vegetation cover, and water bodies were increased. The vegetation cover increased from 28.89% to 44.67% while barren land decreased from 45.68% to 40.29 of the total area. Furthermore, the built-up area increased from 1.02% to 6.2%, whereas water covers increased from 0.63% (1991) up to 0.86% (2017) of the total area. The study concludes that there is immense need of planning to preserve natural habitat for sustainable development in the area.

Spatiotemporal Analysis of Urban Sprawl and Ecological Quality Study Case: Chiba Prefecture, Japan

“…Vegetation encompassed agricultural lands, grasslands, forests, and woodlands. Waterbodies represented open water, such as lakes, rivers, streams, and reservoirs [21][22][23].…”

MAFNet: A Multiangle Attention Fusion Network for Land Cover Classification

“…It doesn't analyze the association between data, where these problems have led researchers to notice the importance of data analysis in land cover tasks. In the past 10 years, many researchers combined machine learning with remote sensing images, using low-level information based on spectral features and geometric features to realize classification, such as decision tree [12], random forest [13]- [15], maximum likelihood method [16]- [18], etc. Initially, most researchers choose the maximum likelihood method to calculate the mean and variance of each category in the region of interest in the image, and classify each pixel through the classification function [17].…”

Change Detection of Vegetative Covers in Kalara Nala Watershed of Damoh District Using Remote Sensing and GIS

“…Watershed management entails the sensible use of land and water resources with the least amount of risk to the environment. Its primary objective is to conserve the soil and water in the watershed, which entails using the land properly and guarding it against all kinds of deterioration [6,7]. Additionally, it implies the preservation of soil fertility, the preservation of water for agricultural use, and an increase in resource productivity [8].…”