scholarly journals Effect of tide level on submerged mangrove recognition index using multi-temporal remotely-sensed data

2021 ◽  
Vol 131 ◽  
pp. 108169
Author(s):  
Qing Xia ◽  
Mingming Jia ◽  
Tingting He ◽  
Xuemin Xing ◽  
Lingjie Zhu
Keyword(s):  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Tide Level ◽  
Multi Temporal

scholarly journals Brief communication "Determination of urban growth in catchment areas in Cyprus using multi-temporal remotely sensed data: risk assessment study"

2010 ◽  
Vol 10 (11) ◽  
pp. 2235-2240 ◽  
Author(s):  
D. G. Hadjimitsis
Keyword(s):  
Risk Assessment ◽  
Catchment Area ◽  
Image Data ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Aerial Photos ◽  
Area Of Interest ◽  
Catchment Areas ◽  
Multi Temporal

Abstract. The aim of this study is to quantify the actual urbanization activity near the catchment area in the urban area of interest located in the vicinity of the Agriokalamin River area of Kissonerga Village in Paphos District. Remotely sensed data such as aerial photos, Landsat-5/7 TM/ETM+ and Quickbird image data have been used to track the urbanization activity from 1963 to 2008. In-situ GPS measurements have been used to locate in-situ the boundaries of the catchment area. The results clearly illustrate that tremendous urban development has taken place ranging from 0.9 to 33% from 1963 to 2008, respectively. A flood risk assessment and hydraulic analysis were also performed.

Wetland cover change detection using multi-temporal remotely sensed data

2017 ◽  
Vol 10 (21) ◽  
Author(s):  
Saeed Ojaghi ◽  
Farshid Farnood Ahmadi ◽  
Hamid Ebadi ◽  
Raechel Bianchetti
Keyword(s):  
Change Detection ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Multi Temporal ◽  
Wetland Cover

Prediction Changes for Nonstationary Multi-Temporal Satellite Images using HMM

2017 ◽  
pp. 387-406
Author(s):  
Ali Ben Abbes ◽  
Imed Riadh Farah
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Satellite Image ◽  
Remotely Sensed ◽  
High Temporal Resolution ◽  
Remotely Sensed Data ◽  
Vegetation Monitoring ◽  
Area Of Interest ◽  
Multi Temporal ◽  
The Difference

Due to the growing advances in their temporal, spatial, and spectral resolutions, remotely sensed data continues to provide tools for a wide variety of environmental applications. This chapter presents the benefits and difficulties of Multi-Temporal Satellite Image (MTSI) for land use. Predicting land use changes using remote sensing is an area of interest that has been attracting increasing attention. Land use analysis from high temporal resolution remotely sensed images is important to promote better decisions for sustainable management land cover. The purpose of this book chapter is to review the background of using Hidden Markov Model (HMM) in land use change prediction, to discuss the difference on modeling using stationary as well as non-stationary data and to provide examples of both case studies (e.g. vegetation monitoring, urban growth).

Detecting areas affected by flood using multi-temporal ALOS PALSAR remotely sensed data in Karawang, West Java, Indonesia

2015 ◽  
Vol 77 (2) ◽  
pp. 959-985 ◽  
Author(s):  
Fajar Yulianto ◽  
Parwati Sofan ◽  
Any Zubaidah ◽  
Kusumaning Ayu Dyah Sukowati ◽  
Junita Monika Pasaribu ◽  
...  
Keyword(s):  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Alos Palsar ◽  
West Java ◽  
Multi Temporal

Progress In Automatic Analysis Of Multi-temporal Remotely-sensed Data

2005 ◽  
Author(s):  
D.G. Corr ◽  
A.M. Tailor ◽  
A. Cross ◽  
D.C. Mason ◽  
M. Petrou ◽  
...  
Keyword(s):  
Remotely Sensed ◽  
Automatic Analysis ◽  
Remotely Sensed Data ◽  
Multi Temporal

Prediction Changes for Nonstationary Multi-Temporal Satellite Images Using HMM

2019 ◽  
pp. 1178-1197
Author(s):  
Ali Ben Abbes ◽  
Imed Riadh Farah
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Satellite Image ◽  
Remotely Sensed ◽  
High Temporal Resolution ◽  
Remotely Sensed Data ◽  
Vegetation Monitoring ◽  
Area Of Interest ◽  
Multi Temporal ◽  
The Difference

Due to the growing advances in their temporal, spatial, and spectral resolutions, remotely sensed data continues to provide tools for a wide variety of environmental applications. This chapter presents the benefits and difficulties of Multi-Temporal Satellite Image (MTSI) for land use. Predicting land use changes using remote sensing is an area of interest that has been attracting increasing attention. Land use analysis from high temporal resolution remotely sensed images is important to promote better decisions for sustainable management land cover. The purpose of this book chapter is to review the background of using Hidden Markov Model (HMM) in land use change prediction, to discuss the difference on modeling using stationary as well as non-stationary data and to provide examples of both case studies (e.g. vegetation monitoring, urban growth).

Diagnosing Cotton Farmland Quality Using Multi-Temporal Remotely Sensed Data

2012 ◽  
Vol 10 (1) ◽  
pp. 475-483 ◽  
Author(s):  
Junhua Bai ◽  
Jing Li ◽  
Qinhuo Liu ◽  
Xu Wang ◽  
Shaokun Li
Keyword(s):  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Multi Temporal

A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data

2010 ◽  
Vol 25 (5) ◽  
pp. 671-682 ◽  
Author(s):  
Xiaoping Liu ◽  
Xia Li ◽  
Yimin Chen ◽  
Zhangzhi Tan ◽  
Shaoying Li ◽  
...  
Keyword(s):  
Urban Expansion ◽  
Remotely Sensed ◽  
Landscape Index ◽  
Remotely Sensed Data ◽  
Multi Temporal
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