scholarly journals A geospatial modeling framework for assessing biofuels-related land-use and land-cover change

2012 ◽  
Vol 161 ◽  
pp. 17-26 ◽  
Author(s):  
Ruopu Li ◽  
Qingfeng Guan ◽  
James Merchant
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Modeling Framework ◽  
Geospatial Modeling

Analysis of Land Use/Land Cover Change in Yangzhou City Based on CLUE-S Model

2011 ◽  
Vol 13 (5) ◽  
pp. 695-700
Author(s):  
Zhihua TANG ◽  
Xianlong ZHU ◽  
Cheng LI
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Land Use Land Cover ◽  
S Model

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.

scholarly journals Land use land cover change in Kashmir Himalaya: Linking remote sensing with an indicator based DPSIR approach

2021 ◽  
Vol 125 ◽  
pp. 107447 ◽  
Author(s):  
Rehana Rasool ◽  
Abida Fayaz ◽  
Mifta ul Shafiq ◽  
Harmeet Singh ◽  
Pervez Ahmed
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Land Use Land Cover ◽  
Kashmir Himalaya ◽  
Dpsir Approach

Hydrological regime, water availability and land use/land cover change impact on the water balance in a large agriculture basin in the Southern Brazilian Amazon

2021 ◽  
Vol 108 ◽  
pp. 103224
Author(s):  
Tárcio Rocha Lopes ◽  
Cornélio Alberto Zolin ◽  
Rafael Mingoti ◽  
Laurimar Gonçalves Vendrusculo ◽  
Frederico Terra de Almeida ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Water Balance ◽  
Land Cover Change ◽  
Water Availability ◽  
Hydrological Regime ◽  
Brazilian Amazon ◽  
Land Use Land Cover ◽  
Change Impact
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