Modifications in Coverage Patterns and Land Use around the Huizache-Caimanero Lagoon System, Sinaloa, Mexico: A Multi-temporal Analysis using LANDSAT Images

1999 ◽  
Vol 49 (1) ◽  
pp. 37-44 ◽  
Author(s):  
A. Ruiz-Luna ◽  
C.A. Berlanga-Robles
Keyword(s):  
Land Use ◽  
Temporal Analysis ◽  
Landsat Images ◽  
Lagoon System ◽  
Multi Temporal

Land Planning, Agro-forest Systems, and Implications for Ecosystem Services: Insights from Northern Italy

2021 ◽  
Author(s):  
Antonio Tomao ◽  
Barbara Ermini ◽  
Marcela Prokopov ◽  
Adriano Conte
Keyword(s):  
Land Use ◽  
Environmental Changes ◽  
Land Use Changes ◽  
Value Added ◽  
Temporal Analysis ◽  
Land Use Land Cover ◽  
Development Patterns ◽  
The North ◽  
Multi Temporal ◽  
Potential Impact

Negative environmental changes generally addressed as ‘syndromes’ are evaluated in the context of Soil Degradation (SD) and interpreted by using a ‘Land-Use/Land Cover Changes’ (LULCCs) framework in order to disentangle ‘past trajectories’, ‘present patterns’, and ‘future changes’. This approach allows to discuss the potential impact on SD processes and it represents an informed basis for identifying measurable outcomes of SD. This study focuses on the case of Emilia Romagna, a region located in the North of Italy with high-value added agricultural productions. A multi-temporal analysis of land-use changes between 1954 and 2008 has been proposed, discussing the evolution of associated SD syndromes in Emilia Romagna. The contributing information have been used as a baseline for Sustainable Land Management (SLM) strategies. This framework of analysis provides useful tools to investigate and to monitor the effects of SD in the Mediterranean basin where several regions underwent common development patterns yelding global pathological symptoms of environmental degradation.

Monitoring changes in land use land cover of Yamuna riverbed in Delhi: a multi-temporal analysis

2011 ◽  
Vol 32 (24) ◽  
pp. 9547-9558 ◽  
Author(s):  
Rucha R. Joshi ◽  
Mangesh Warthe ◽  
Sharad Dwivedi ◽  
Ritesh Vijay ◽  
Tapan Chakrabarti
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Temporal Analysis ◽  
Land Use Land Cover ◽  
Multi Temporal

scholarly journals Multi-temporal analysis of urban and periurban land use changes in medieval towns of central Italy

2014 ◽  
pp. 77-92 ◽  
Author(s):  
Bruno Ronchi ◽  
◽  
Andrea Amici ◽  
Carlo Maía Rossi ◽  
Riccardo Primi ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Central Italy ◽  
Temporal Analysis ◽  
Multi Temporal

scholarly journals Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa

2015 ◽  
Vol 7 (9) ◽  
pp. 12076-12102 ◽  
Author(s):  
Benewinde Zoungrana ◽  
Christopher Conrad ◽  
Leonard Amekudzi ◽  
Michael Thiel ◽  
Evariste Da ◽  
...  
Keyword(s):  
Land Use ◽  
West Africa ◽  
Burkina Faso ◽  
Ancillary Data ◽  
Landsat Images ◽  
Multi Temporal

scholarly journals Classification of Land Use/Land Cover Changes Using GIS and Remote Sensing Technique in Lake Kenyir Basin, Terengganu, Malaysia

2018 ◽  
Vol 7 (3.14) ◽  
pp. 12 ◽  
Author(s):  
Mohd Khairul Amri Kamarudin ◽  
Kabir Abdulkadir Gidado ◽  
Mohd Ekhwan Toriman ◽  
Hafizan Juahir ◽  
Roslan Umar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Water Body ◽  
Geographical Information ◽  
Landsat Images ◽  
Gis And Remote Sensing ◽  
Remote Sensing Technique ◽  
Multi Temporal ◽  
Vegetation Water

Geographical information system (GIS) techniques and Remote Sensing (RS) data are fundamental in the study of land use (LU) and land cover (LC) changes and classification. The aim of this study is to map and classify the LU and LC change of Lake Kenyir Basin within 40 years’ period (1976 to 2016). Multi-temporal Landsat images used are MSS 1976, 1989, ETM+ 2001 and OLI 8 2016. Supervised Classification on Maximum Likelihood Algorithm method was used in ArcGIS 10.3. The result shows three classes of LU and LC via vegetation, water body and built up area. Vegetation, which is the dominant LC found to be 100%, 88.83%, 86.15%, 81.91% in 1976, 1989, 2001 and 2016 respectively. While water body accounts for 0%, 11.17%, 12.36% and 13.62% in the years 1976, 1989, 2001 and 2016 respectively and built-up area 1.49% and 4.47 in 2001 and 2016 respectively. The predominant LC changes in the study are the water body and vegetation, the earlier increasing rapidly at the expense of the later. Therefore, proper monitoring, policies that integrate conservation of the environment are strongly recommended. 

Multi-temporal analysis for land use and land cover changes in an agricultural region using open source tools

2017 ◽  
Vol 8 ◽  
pp. 278-290 ◽  
Author(s):  
Antonio J. Sanhouse-Garcia ◽  
Yaneth Bustos-Terrones ◽  
Jesús Gabriel Rangel-Peraza ◽  
Alberto Quevedo-Castro ◽  
Carlos Pacheco
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Open Source ◽  
Temporal Analysis ◽  
Land Cover Changes ◽  
Agricultural Region ◽  
Multi Temporal

scholarly journals Forest and Industrial Plantation Mapping with Multi-Temporal Dual Polarization by ALOS PALSAR Data In Riau Province

2018 ◽  
Vol 2 (1) ◽  
pp. 35
Author(s):  
Elis Molidena ◽  
Takahiro Osawa ◽  
Putu Gede Ardhana ◽  
Abd. Rahman As-syakur
Keyword(s):  
Land Use ◽  
Ground Truth ◽  
Temporal Analysis ◽  
Alos Palsar ◽  
Ground Truth Data ◽  
Multi Temporal ◽  
Industrial Plantation ◽  
Changes Detection ◽  
Accuracy Result ◽  
Color Composite

Backscattering characteristics of land use has been analyzed using ALOS PALSAR data. The purpose of this research are mapping of land use by five categories such as forest, acacia, oil palm, open area and water, and to identify the changes of environmental. Analysis Pixel-by-pixel average of ALOS PALSAR level 1.5 backscattering used from five of category land use was to estimate the spectral characteristic of each object in difference HH and HV polarization. Ground truth data was taken from 169 locations which used for classification, 119 locations and 50 locations used for validation. Two different times of ALOS PALSAR level 1.0 2009 and 2010 data, was used for changes detection by multi temporal color composite combination. The accuracy result for classification map shows 62% of ground truth database, and multi temporal analysis showed the possibility of changes.

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