scholarly journals Monitoring of the 2008 Chaitén Eruption Cloud Using MODIS Data and its Impacts

10.5772/64272 ◽  
2016 ◽  
Author(s):  
Yuanzhi Zhang ◽  
Jin Yeu Tsou ◽  
Zhaojun Huang ◽  
Jinrong Hu ◽  
Wyss W.-S. Yim
Keyword(s):  
Modis Data ◽  
Eruption Cloud

Land cover classification for the monitoring of winter grain crops within the Kyiv oblast and Mykolaiv oblast (Ukraine) with the use of MODIS data

2009 ◽  
Vol 15 (5) ◽  
pp. 16-23
Author(s):  
O.I. Sakhatsky ◽  
◽  
G.M. Zholobak ◽  
A.A. Makarova ◽  
O.A. Apostolov ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Grain Crops ◽  
Modis Data

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 An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping

2021 ◽  
Vol 3 ◽  
pp. 100018 ◽  
Author(s):  
Xiao-Peng Song ◽  
Wenli Huang ◽  
Matthew C. Hansen ◽  
Peter Potapov
Keyword(s):  
Modis Data ◽  
Type Mapping ◽  
Crop Type ◽  
Sentinel 2

scholarly journals A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data

2020 ◽  
Vol 236 ◽  
pp. 111493 ◽  
Author(s):  
Joshua Lizundia-Loiola ◽  
Gonzalo Otón ◽  
Rubén Ramo ◽  
Emilio Chuvieco
Keyword(s):  
Burned Area ◽  
Modis Data ◽  
Active Fire ◽  
Burned Area Mapping ◽  
Clustering Approach ◽  
Spatio Temporal

scholarly journals Coastal zone water quality: calibration of a water-turbidity equation for MODIS data

2013 ◽  
Vol 46 (1) ◽  
pp. 333-347 ◽  
Author(s):  
Antonino Maltese ◽  
Fulvio Capodici ◽  
Giuseppe Ciraolo ◽  
Goffredo La Loggia
Keyword(s):  
Water Quality ◽  
Coastal Zone ◽  
Water Turbidity ◽  
Modis Data ◽  
Zone Water
Sign in / Sign up

Export Citation Format

Share Document