A New Time Series Change Detection Method for Landsat Land use and Land Cover Change

2019 ◽  
Author(s):  
Yukun Lin ◽  
Lifu Zhang ◽  
Nan Wang
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Detection Method ◽  
New Time

scholarly journals Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia

2018 ◽  
Vol 11 ◽  
pp. 117862211775160 ◽  
Author(s):  
Gebiaw T Ayele ◽  
Aschalew K Tebeje ◽  
Solomon S Demissie ◽  
Mulugeta A Belete ◽  
Mengistu A Jemberrie ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Population Growth ◽  
Change Detection ◽  
Land Cover Change ◽  
Training Data ◽  
Steady Increase ◽  
Northern Ethiopia ◽  
Detection Techniques

Land use planners require up-to-date and spatially accurate time series land resources information and changing pattern for future management. As a result, assessing the status of land cover change due to population growth and arable expansion, land degradation and poor resource management, partial implementation of policy strategies, and poorly planned infrastructural development is essential. Thus, the objective of the study was to quantify the spatiotemporal dynamics of land use land cover change between 1995 and 2014 using 5 multi-temporal cloud-free Landsat Thematic Mapper images. The maximum likelihood (ML)-supervised classification technique was applied to create signature classes for significant land cover categories using means and variances of the training data to estimate the probability that a pixel is a member of a class. The final Bayesian ML classification resulted in 12 major land cover units, and the spatiotemporal change was quantified using post-classification and statistical change detection techniques. For a period of 20 years, there was a continuously increasing demand for arable areas, which can be represented by an exponential growth model. Excepting the year 2009, the built-up area has shown a steady increase due to population growth and its need for infrastructure development. There was nearly a constant trend for water bodies with a change in slope significantly less than +0.01%. The 2014 land cover change statistics revealed that the area was mainly covered by cultivated, wood, bush, shrub, grass, and forest land mapping units accounting nearly 63%, 12%, 8%, 6%, 4%, and 2% of the total, respectively. Land cover change with agro-climatic zones, soil types, and slope classes was common in most part of the area and the conversion of grazing land into plantation trees and closure area development were major changes in the past 20 years.

scholarly journals An Automatic and Operational Method for Land Cover Change Detection Using Spatiotemporal Analysis of MODIS Data: A Northern Ontario (Canada) Case Study

2021 ◽  
Vol 10 (5) ◽  
pp. 325
Author(s):  
Ima Ituen ◽  
Baoxin Hu
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Basic Unit ◽  
Operational Method ◽  
Northern Ontario ◽  
Modis Data ◽  
Forest Fire Management ◽  
Land Cover Change Detection

Mapping and understanding the differences in land cover and land use over time is an essential component of decision-making in sectors such as resource management, urban planning, and forest fire management, as well as in tracking of the impacts of climate change. Existing methods sometimes pose a barrier to the effective monitoring of changes in land cover and land use, since a threshold parameter is often needed and determined based on trial and error. This study aimed to develop an automatic and operational method for change detection on a large scale from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Super pixels were the basic unit of analysis instead of traditional individual pixels. T2 tests based on the feature vectors of temporal Normalized Difference Vegetation Index (NDVI) and land surface temperature were used for change detection. The developed method was applied to data over a predominantly vegetated area in northern Ontario, Canada spanning 120,000 sq. km from 2001–2016. The accuracies ranged between 78% and 88% for the NDVI-based test, from 74% to 86% for the LST-based test, and from 70% to 86% for the joint method compared with manual interpretation. Our proposed method for detecting land cover change provides a functional and viable alternative to existing methods of land cover change detection as it is reliable, repeatable, and free from uncertainty in establishing a threshold for change.

A review on change detection method and accuracy assessment for land use land cover

2021 ◽  
Vol 22 ◽  
pp. 100482
Author(s):  
Ali Hassan Chughtai ◽  
Habibullah Abbasi ◽  
Ismail Rakip Karas
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Detection Method ◽  
Accuracy Assessment ◽  
Land Use Land Cover

scholarly journals Remote Sensing-Based Urban Land Use/Land Cover Change Detection and Monitoring

2017 ◽  
Vol 06 (02) ◽  
Author(s):  
Remote sensing ◽  
Land cover change ◽  
Urban areas ◽  
Change detection
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Land Cover ◽  
Land Cover Change Detection
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