scholarly journals Application of satellite images and object-oriented processing in land use/land cover map extraction to model ecosystem services (case study: Lorestan Province)

2020 ◽  
Vol 18 (3) ◽  
pp. 49-73
Author(s):  
زهرا اسدالهی ◽  
مصطفی کشتکار ◽  
ضیاءالدین باده‌یان
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Cover ◽  
Satellite Images ◽  
Object Oriented ◽  
Land Use Land Cover ◽  
Model Ecosystem ◽  
Land Cover Map

scholarly journals Urban Growth Assessment of Chennai District by Remote Sensing and Gis Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 5119-5125
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Satellite Images ◽  
Remote Sensing And Gis ◽  
Land Use Land Cover ◽  
Gis Techniques ◽  
Land Cover Map ◽  
Landsat Satellite

Urban growth of Chennai district is exponential and heading towards extreme urbanisation. Hence this necessitates the study of urban growth in Chennai district. The recent advancement in Remote sensing and GIS has an excellent ability to derive various data from the satellite images obtained .This helps us to map, monitor and picturise various aspects of development with respect to their demands. The basic principle of remote sensing is followed as the methodology. By following the methodology correctly and by proper processing of the data acquired from the satellite images, the exact requirements of information can be obtained. The Change in the urban growth of the Chennai district for three decades from 1989 to 2019 have been found by using remote sensing and GIS techniques. The satellite images of various years are obtained from Landsat satellite from the USGS Earth Explorer .The Land use characteristics of Chennai district of each year can be obtained by preparing the land use land cover map of Chennai district by the use of landsat satellite images. The two software namely ArcGIS and ERDAS Imagine are used to create the Land use land cover map. From the Land use land cover map of Chennai district, the change detection and statistical analysis of three decades are done and these analysis clearly shows that the urban growth of Chennai district is constantly increasing and there is a huge decrease in other natural features such as vegetation, water body and barren land. By performing urban trend analysis the urban growth of Chennai district for the upcoming years are predicted to prove the urban agglomeration in Chennai district.

Land use/land cover changes and its impact on ecosystem services in ecologically fragile zone: A case study of Zhangjiakou City, Hebei Province, China

2019 ◽  
Vol 104 ◽  
pp. 604-614 ◽  
Author(s):  
An Huang ◽  
Yueqing Xu ◽  
Piling Sun ◽  
Guiyao Zhou ◽  
Chao Liu ◽  
...  
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Cover ◽  
Hebei Province ◽  
Land Cover Changes ◽  
Land Use Land Cover

scholarly journals CELLULAR AUTOMATA (CA) CONTIGUITY FILTERS IMPACTS ON CA MARKOV MODELING OF LAND USE LAND COVER CHANGE PREDICTIONS RESULTS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1585-1591
Author(s):  
M. S. Mondal ◽  
N. Sharma ◽  
M. Kappas ◽  
P. K. Garg
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Cover ◽  
Land Cover Change ◽  
Correlation Coefficient ◽  
Satellite Images ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Land Cover Map ◽  
Liss Iii

Abstract. In this study, attempts has been made to find out cellular automata (CA) contiguity filters impacts on Land use land cover change predictions results. Cellular Automata (CA) Markov chain model used to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India, using land use land cover map derived from multi-temporal satellite images. Land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. The validity of the Cellular Automata Markov process for projecting future land use and cover changes calculates using various Kappa Indices of Agreement (Kstandard) predicted (results) maps with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The validation shows Kstandard is 0.7928. 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict LULC in 2007 using 1987 and 1997 LULC maps. Regression analysis have been carried out for both predicted quantity as well as prediction location to established the cellular automata (CA) contiguity filters impacts on predictions results. Correlation established that predicted LULC of 2007 and LULC derived from LISS III Image of 2007 are strongly correlated and they are slightly different to each-other but the quantitative prediction results are same for when 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict land use land cover. When we look at the quantity of predicted land use land cover of 2007 area statistics are derived by using 3x3, 5x5 and 7x7 CA contiguity filters, the predicted area statistics are the same. Other hands, the spatial difference between predicted LULC of 2007 and LULC derived from LISS III images of 2007 is evaluated and they are found to be slightly different. Correlation coefficient (r) between predicted LULC classes and LULC derived from LISS III image of 2007 using 3x3, 5x5, 7x7 are 0.7906, 0.7929, 0.7927, respectively. Therefore, the correlation coefficient (r) for 5x5 contiguity filters is highest among 3x3, 5x5, and 7x7 filters and established/produced most geographically / spatially distributed effective results, although the differences between them are very small.

scholarly journals Assessing the effects of different land-use/land-cover input datasets on modelling and mapping terrestrial ecosystem services - Case study Terceira Island (Azores, Portugal)

One Ecosystem ◽  
2021 ◽  
Vol 6 ◽  
Author(s):  
Ina M. Sieber ◽  
Malte Hinsch ◽  
Marta Vergílio ◽  
Artur Gil ◽  
Benjamin Burkhard
Keyword(s):  
Decision Making ◽  
Land Use ◽  
Ecosystem Services ◽  
Land Cover ◽  
Input Data ◽  
Skewed Distribution ◽  
Delivery Ratio ◽  
Land Use Land Cover ◽  
Nutrient Delivery

Modelling ecosystem services (ES) has become a new standard for the quantification and assessment of various ES. Multiple ES model applications are available that spatially estimate ES supply on the basis of land-use/land-cover (LULC) input data. This paper assesses how different input LULC datasets affect the modelling and mapping of ES supply for a case study on Terceira Island, the Azores (Portugal), namely: (1) the EU-wide CORINE LULC, (2) the Azores Region official LULC map (COS.A 2018) and (3) a remote sensing-based LULC and vegetation map of Terceira Island using Sentinel-2 satellite imagery. The InVEST model suite was applied, modelling altogether six ES (Recreation/Visitation, Pollination, Carbon Storage, Nutrient Delivery Ratio, Sediment Delivery Ratio and Seasonal Water Yield). Model outcomes of the three LULC datasets were compared in terms of similarity, performance and applicability for the user. For some InVEST modules, such as Pollination and Recreation, the differences in the LULC datasets had limited influence on the model results. For InVEST modules, based on more complex calculations and processes, such as Nutrient Delivery Ratio, the output ES maps showed a skewed distribution of ES supply. Yet, model results showed significant differences for differences in all modules and all LULCs. Understanding how differences arise between the LULC input datasets and the respective effect on model results is imperative when computing model-based ES maps. The choice for selecting appropriate LULC data should depend on: 1) the research or policy/decision-making question guiding the modelling study, 2) the ecosystems to be mapped, but also on 3) the spatial resolution of the mapping and 4) data availability at the local level. Communication and transparency on model input data are needed, especially if ES maps are used for supporting land use planning and decision-making.

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