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EM: Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh (EM-979)
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Expand AllEM Identity and Description
EM Identification
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EM ID
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EM-979 |
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EM Short Name
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Predicting ecosystem service values, Bangladesh |
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EM Full Name
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Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh |
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EM Source or Collection
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None |
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EM Source Document ID
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457 |
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Document Author
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Morshed, S. R., Fattah, M. A., Haque, M. N., & Morshed, S. Y. |
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Document Year
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2022 |
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Document Title
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Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh |
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Document Status
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Peer reviewed and published |
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Comments on Status
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Published journal manuscript |
Software and Access
| Not applicable | |
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Contact Name
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Syed Riad Morshed |
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Contact Address
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Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh |
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Contact Email
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riad.kuet.urp16@gmail.com |
EM Description
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Summary Description
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Land Use/Land Cover (LULC) provides provisional, supporting, cultural, and regulating ecosystem services that contribute to ecological environments, enhance human health and living, have economic advantages for sustaining living organisms. LULC transformation due to enormous urban expansion diminishing Ecosystem Services Values (ESVs) and discouraging sustainability. Though unplanned LULC transformation practice became more prevalent in developing countries, comprehensive assessment of LULC changes and their influences in ESVs are rarely attempted. This study aimed to illustrate and forecast the LULC changes and their influences on ESVs change in Jashore using remote sensing technologies. ESVs estimation and change analysis were conducted by utilizing -derived LULC data of the year 2000, 2010, and 2020 with the corresponding global value coefficients of each LULC type which are previously published. For simulating future LULC and ESVs, Land Change Modeler of TerrSet Geospatial Monitoring and Modeling Software was used in Multi-Layer Perceptron-Markov Chain and Artificial Neural Network method. The decline of agricultural land by 13.13% and waterbody by 5.79% has resulted in the reduction of total ESVs US$0.23 million (24.47%) during 2000–2020. The forecasted result shows that the built-up area will be dominant LULC in the future, and ESVs of provisioning and cultural services will be diminished by $0.107 million, $63400.3 by 2050 with the declination of agricultural, waterbody, vegetation, and vacant land covers. The study signifies the importance of a strategic rational land-use plan to strictly monitor and control the encroachment of built-up areas into vegetation, waterbodies, and agricultural land in addition to scientific mitigative policies for ensuring ecological sustainability. |
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Specific Policy or Decision Context Cited
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N/A |
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Biophysical Context
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Jashore city, Bangladesh |
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EM Scenario Drivers
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No scenarios presented |
EM Relationship to Other EMs or Applications
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Method Only, Application of Method or Model Run
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Method + Application |
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New or Pre-existing EM?
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New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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Document ID for related EM
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None |
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EM ID for related EM
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None |
EM Modeling Approach
EM Relationship to Time
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EM Temporal Extent
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2000-2050 |
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EM Time Dependence
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time-dependent |
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EM Time Reference (Future/Past)
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both |
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EM Time Continuity
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discrete |
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EM Temporal Grain Size Value
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10 |
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EM Temporal Grain Size Unit
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Year |
EM Spatial Extent
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Bounding Type
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Geopolitical |
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Spatial Extent Name
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Jashore city, Bangladesh |
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Spatial Extent Area (Magnitude)
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1000-10,000 km^2. |
Spatial Distribution of Computations
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EM Spatial Distribution
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spatially distributed (in at least some cases) |
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Spatial Grain Type
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map scale, for cartographic feature |
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Spatial Grain Size
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30m |
EM Structure and Computation Approach
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EM Computational Approach
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Analytic |
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EM Determinism
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deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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Model Calibration Reported?
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Yes |
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Model Goodness of Fit Reported?
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Yes |
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Goodness of Fit (metric| value | unit)
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Model Operational Validation Reported?
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Yes |
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Model Uncertainty Analysis Reported?
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Unclear |
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Model Sensitivity Analysis Reported?
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Unclear |
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Model Sensitivity Analysis Include Interactions?
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Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| None |
Centroid Lat/Long (Decimal Degree)
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Centroid Latitude
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23.95 |
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Centroid Longitude
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89.12 |
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Centroid Datum
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other |
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Centroid Coordinates Status
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Provided |
Environments and Scales Modeled
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Urban city |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM Organismal Scale
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Not applicable |
Taxonomic level and name of organisms or groups identified
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| None Available |
EnviroAtlas URL
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| GAP Ecological Systems |
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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EM Variable Names (and Units)
Predictor
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Intermediate
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Intermediate (Computed) Variables (and Units)
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None |
Response
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Observed Response Variables (and Units)
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Computed Response Variables (and Units)
view details (4 variables)
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