Water, Land, and Forest Susceptibility and Sustainability: Geospatial Approaches and Modeling

Front Cover
Water, Land, and Forest Susceptibility and Sustainability
Science of Sustainable Systems: Water, Land, and Forest Susceptibility and Sustainability: Geospatial Approaches and Modeling Volume I
Copyright
Contents
Contributors
Foreword
I - Introduction: Theoretical framework and
1 - Theoretical framework and approaches of susceptibility and sustainability: issues and drivers
1.1 Introduction
1.2 Global perspective of sustainability and susceptibility
1.3 Theoretical framework of sustainability and ecosystem services
1.3.1 Water, land, and forest: integral component of ecosystem
1.3.2 Sustainability and ecosystem services
1.3.3 Degradation of ecosystem services
1.4 Drivers and issues of susceptibility and sustainability of ecosystems
1.4.1 Susceptibility and sustainability of water resources
1.4.2 Susceptibility and sustainability of land resources
1.4.3 Susceptibility and sustainability of forest resources
1.5 Approaches for susceptibility/degradation assessment of ecosystems
1.6 Conclusions
References
II - Water resource susceptibility and sustainability
2 - Trap efficiency of reservoirs: concept, review, and application
2.1 Introduction
2.1.1 Reservoir sedimentation
2.1.2 Reservoir storage capacity reduction
2.1.3 Determination of quantity of sediment deposited in reservoir
2.1.3.1 Quantity of incoming sediment load
2.1.3.2 Quantity of outgoing sediment load
2.1.4 Determination of trap efficiency
2.2 Limitations of the study
2.2.1 Empirical methods
2.2.2 Artificial neural networks
2.3 Literature review
2.4 Materials and methods
2.4.1 Empirical methods
2.4.1.1 Brown's method
2.4.1.2 Capacity-inflow method (Brune method)
2.4.1.3 Gills method
2.4.1.4 Sediment index method (Churchill method)
2.4.2 Artificial neural networks
2.4.2.1 Selection of ideal structure
2.4.2.2 Selection of input variables
2.4.2.3 Selection of hidden layers
2.4.2.4 Selection of hidden neurons
2.4.2.5 Development of ANN for trap efficiency assessment
2.5 Results and discussions
2.5.1 Analysis of results for different methods
2.5.2 Discussions of the results of empirical methods
2.6 Conclusions
References
3 - GIS for Watershed Characterization and Modeling: example of the Taguenit River (Lakhssas, Morocco)∗∗Mohamed Abi ...
3.1 Introduction
3.2 Study area: geomorphology and hydro-climatology of the taguenit wadi watershed
3.3 Materials and methods
3.3.1 Flood mapping method
3.3.2 Factors use in FHI
3.3.3 Relative weight of factors
3.4 Results and discussion
3.5 Challenges and solutions
3.6 Recommendations
3.7 Conclusion
References
4 - Application of artificial intelligence to estimate dispersion coefficient and pollution in river
4.1 Introduction
4.1.1 Objectives
4.2 Methodology
4.2.1 Data collection and analysis
4.2.1.1 Data collection
4.2.1.2 Data analysis
4.2.2 ANN modeling
4.2.2.1 Multilayer Perceptrons
4.2.3 Network architecture
4.2.4 Range of various parameters in the procured data
4.2.5 Data used for training and testing of ANN
4.2.6 Development of secondary parameters
4.3 Result and discussions
4.4 Models used for comparison
4.4.1 Comparison of models
4.5 Conclusion
4.6 Appendix 1
4.7 Appendix 2
4.8 Appendix 3
4.9 Appendix 4
4.10 Appendix 5
4.11 Appendix 6
References
5 - Predicting nitrate concentration in river using advanced artificial intelligence techniques: extreme learning m ...
5.1 Introduction
5.2 Materials and methods
5.2.1 Study site
5.2.2 Performance assessment of the models
5.3 Methodology
5.3.1 Deep learning long short-term memory (LSTM)
5.3.2 Extreme learning machine (ELM)
5.3.3 Support vector regression (SVR)
5.3.4 Gaussian process regression (GPR)
5.4 Results and discussion
5.4.1 Daily time scale and scenario 01 for USGS 14211720
5.4.2 Daily time scale and scenario 02 for USGS 14211720
5.4.3 Hourly time scale and scenario 01 for USGS 14211720
5.4.4 Hourly time scale and scenario 02 for USGS 14211720
5.4.5 Discussion
5.5 Summary and conclusions
References
6 - Polluted water bodies remediation by using GIS and remote sensing approach: a deeper insight
6.1 Introduction
6.1.1 Importance of remote sensing in water quality management and monitoring
6.1.2 Importance of GIS in water quality management and monitoring
6.2 Role of these advanced techniques in monitoring water quality
6.3 Remediation of surface water—using remote sensing and GIS
6.4 Future of these advanced techniques in monitoring the quality of water
6.5 Benefits of sensing in monitoring the quality of water
6.6 Conclusions
References
7 - GIS-based spatial distribution analysis of water quality assessment using water pollution index of Yamuna river ...
7.1 Introduction
7.2 Study area
7.3 Materials and dataset
7.4 Methodology
7.4.1 Ground data
7.4.2 Remote-sensing data
7.4.2.1 Backscattering values
7.4.2.1.1 Raw data to dB conversion
7.4.2.1.1 Raw data to dB conversion
7.4.2.1.2 Backscattering values derivation
7.4.2.1.2 Backscattering values derivation
7.4.2.2 Chlorophyll Index
7.4.2.2.1 Preprocessing
7.4.2.2.1 Preprocessing
7.4.2.2.2 Chlorophyll Index calculation
7.4.2.2.2 Chlorophyll Index calculation
7.4.2.3 Normalized Coastal Aerosol Index
7.4.2.4 Derive land surface temperature
7.4.2.4.1 Top of atmospheric spectral radiance correction
7.4.2.4.1 Top of atmospheric spectral radiance correction
7.4.2.4.2 Conversion of radiance to at sensor temperature
7.4.2.4.2 Conversion of radiance to at sensor temperature
7.4.2.4.3 NDVI method for emissivity correction
7.4.2.4.3 NDVI method for emissivity correction
7.4.2.4.3.1 Land surface emissivity calculation
7.4.2.4.3.1 Land surface emissivity calculation
7.4.2.5 Water pollution index
7.5 Results
7.5.1 Backscattering SAR data values
7.5.1.1 Comparative analysis of premonsoon season in 2019 and 2020 for backscattering values
7.5.1.2 Comparative analysis of monsoon season in 2019 and 2020 for backscattering values
7.5.1.3 Comparative analysis of postmonsoon season in 2019 and 2020 for backscattering values
7.5.2 Chlorophyll Index
7.5.2.1 Comparative analysis of premonsoon season in 2019 and 2020 for Chlorophyll Index
7.5.2.2 Comparative analysis of postmonsoon season in 2019 and 2020 for Chlorophyll Index
7.5.3 Land surface temperature
7.5.3.1 Comparative analysis of premonsoon season in 2019 and 2020 for land surface temperature
7.5.3.2 Comparative analysis of postmonsoon season in 2019 and 2020 for land surface temperature
7.5.4 Normalized Coastal Aerosol Index
7.5.4.1 Comparative analysis of premonsoon season in 2019 and 2020 for NCAI
7.5.4.2 Comparative analysis of postmonsoon season in 2019 and 2020 for NCAI
7.5.5 Water pollution index
7.5.5.1 Comparative analysis of the premonsoon season in 2019 and 2020 using the WPI model
7.5.5.2 Comparative analysis of the postmonsoon season in 2019 and 2020 using the WPI model
7.6 Validation
7.7 Discussion
7.8 Conclusion
References
8 - Groundwater sustainability: role of monitoring, modeling, and management
8.1 Introduction
8.2 Limitations of study
8.3 Methodology
8.3.1 Study basin
8.3.2 Data monitoring and field campaigns
8.4 Methods
8.4.1 Groundwater flow simulation modeling
8.4.1.1 Boundary conditions
8.4.1.2 Initial condition
8.4.1.3 Calibration and validation
8.4.2 Decision support system
8.5 Results
8.5.1 Groundwater recharge
8.5.2 Pumping well boundary condition
8.5.3 River boundary conditions
8.5.4 Groundwater flow simulation
8.5.5 Decision support system
8.6 Conclusion
References
Further reading
III - Land resources susceptibility and sustainability
9 - Landslide susceptibility modeling using a generalized linear model in a tropical river basin of the Southern We ...
9.1 Introduction
9.2 Study area
9.3 Materials and methods
9.3.1 Landslide inventory and spatial database
9.3.2 Lithology
9.3.3 Soil texture
9.3.4 Land use/land cover
9.3.5 Slope angle
9.3.6 Normalized difference vegetation index
9.3.7 Slope aspect
9.3.8 Distance from road
9.3.9 Distance from Lineaments
9.3.10 Topographic wetness index
9.3.11 Terrain roughness index
9.3.12 Distance from stream
9.3.13 Stream power index
9.3.14 Profile curvature
9.3.15 Planform curvature
9.3.16 Selection of conditioning factors
9.3.17 Multicollinearity analysis
9.3.18 Generalized linear modeling
9.3.19 Model validation
9.4 Results
9.4.1 Multicollinearity and variable importance
9.4.2 Landslide susceptibility modeling
9.4.3 Model validation
9.4.4 Landslide susceptibility map (LSM)
9.5 Discussion
9.6 Summary and conclusion
References
Further reading
10 - Assessment of the land use and landcover changes using remote sensing and GIS techniques
10.1 Introduction
10.1.1 Impact of land use and landcover changes in a basin
10.1.2 Effect of land use and landcover changes on ecosystem services in a basin
10.1.3 Land use and land cover change identification using remote sensing and geographical information Systems
10.2 Literature review
10.3 Materials and methods
10.3.1 Study area
10.3.1.1 Godavari middle sub basin
10.3.1.2 Sri Ram Sagar reservoir project
10.3.2 Methodology applied
10.3.2.1 Remote sensing data applied in the study
10.3.2.2 Application of the geographical information systems
10.4 Results and discussions
10.4.1 Identification of changes in land use and land cover of the sub-basin
10.4.2 Slope maps of the river basin
10.4.3 Drainage structure of the sub-basin
10.4.4 Water spread area for Sri Ram Sagar reservoir project
10.5 Conclusions
References
11 - Mapping and monitoring land dynamics using geospatial techniques on Pathar Pratima Block, South 24 Parganas, India
11.1 Introduction
11.2 Study area
11.3 Materials and method
11.3.1 Data source
11.3.2 Image processing phase
11.3.3 LULC classification and accuracy assessment
11.3.4 Land surface temperate calculation
11.3.4.1 LST for Landsat 5 TM
11.3.4.2 LST for Landsat 8 OLI/TIRS
11.3.5 Different spectral indicator
11.3.5.1 Normalized difference vegetation index
11.3.5.2 Soil adjusted vegetation index
11.3.5.3 Normalized difference salinity index
11.4 Result and discussion
11.4.1 Land transformation
11.4.2 Variation of LST
11.4.3 Different geospatial indicators
11.4.4 Correlation analysis of this study
11.5 Challenges and solutions
11.6 Recommendations
11.7 Conclusion
References
12 - Geospatial technologies to investigate the wetland susceptibility to environmental challenges and identify sus ...
12.1 Introduction
12.2 Study area
12.3 Materials and methods
12.3.1 Materials
12.3.2 Methods
12.3.2.1 Difference in water and vegetation area of the wetland and its surroundings
12.3.2.2 Land use land cover mapping
12.3.2.3 Hydro geo morphological mapping
12.3.2.3.1 Identification of HGM units
12.3.2.3.1 Identification of HGM units
12.3.2.3.2 Evaluation of wetland health through HGM units: quantification of impact
12.3.2.3.2 Evaluation of wetland health through HGM units: quantification of impact
12.3.2.3.3 Assessment of hydrologic geomorphologic and vegetation components
12.3.2.3.3 Assessment of hydrologic geomorphologic and vegetation components
12.4 Results
12.4.1 Variation in vegetation and water area
12.4.2 Land use land cover mapping
12.4.3 HGM unit mapping
12.4.4 Hydrological assessment of the HGM units
12.4.5 Geomorphological assessment of the HGM units
12.4.6 Vegetation assessment of the HGM Units
12.4.7 Estimated wetland health score
12.5 Conclusions
References
Further reading
13 - Evaluation of spatial and temporal variability in rainfall erosivity and soil erosion risk in Maharashtra, India
13.1 Introduction
13.2 Study area
13.3 Aims and objectives
13.4 Materials and methods used
13.5 Rationale of study
13.6 Limitations
13.7 Results and discussion
13.7.1 Rainfall erosivity
13.7.2 Soil characteristics
13.7.3 Topography
13.7.4 Land use land cover
13.7.5 Soil erosion risk and rainfall erosivity
13.8 Rainfall variability
13.8.1 Yearwise rainfall variability
13.8.2 Seasonal variability in rainfall erosivity
13.8.3 Event-based rainfall variability
13.9 Recommendations
13.10 Conclusions
References
14 - Soil erosion modeling under future climate change: a case study on Marinduque Island, Philippines
14.1 Introduction
14.1.1 Soil erosion
14.1.2 Climate change
14.1.3 Soil erosion in a changing climate
14.1.4 RUSLE
14.1.4.1 Rainfall erosivity factor (R)
14.1.4.2 Soil erodibility (K)
14.1.4.3 Slope length and steepness (LS)
14.1.4.4 Vegetation cover factor (C)
14.1.4.5 Soil protection practice (P)
14.1.5 Objective of the study
14.1.6 Limitation of the study
14.2 Materials and methods
14.2.1 Study site
14.2.2 Data
14.2.3 Modeling soil erosion
14.3. Results and discussion
14.3.1 Baseline and projected increase in rainfall
14.3.2 Changes in soil erosion rates
14.4 Potential solution
14.5 Conclusion
References
IV - Forest resourcesusce ptibility and sustainability
15 - Forest fire susceptibility mapping and risk assessment using integrated AHP and DEMATEL method for Purulia Dis ...
15.1 Introduction
15.2 About study area
15.3 Materials and methods
15.3.1 Forest fire location points
15.3.2 Methodology
15.3.3 Data used
15.3.4 Forest fire influencing factors
15.3.4.1 Elevation (C1)
15.3.4.2 Slope (C2)
15.3.4.3 Aspect (C3)
15.3.4.4 Topographical wetness index (C4)
15.3.4.5 Vegetation cover (C5)
15.3.4.6 Normalized difference moisture index (C6)
15.3.4.7 River distance (C7)
15.3.4.8 Windspeed Map (C8)
15.3.4.9 Air temperature (C9)
15.3.4.10 Rainfall (C10)
15.3.4.11 Distance to settlement (C11)
15.3.4.12 Distance from tourism spots (C12)
15.3.4.13 Distance to road (C13)
15.3.4.14 Powerline (C14)
15.3.5 Integrated methods combined DEMATEL and AHP
15.3.6 The analytical hierarchy process method
15.3.6.1 Calculation procedure for AHP
15.3.7 DEMATEL method
15.3.8 Forest fire susceptibility map (FFSM)
15.3.8.1 The computation of criteria weights for the AHP method (Tables 15.5 and 15.6)
15.3.8.2 The calculation for DEMATEL method
15.4 Results and discussion
15.4.1 Results
15.4.2 Discussion
15.4.3 Validation of the forest fire risk zone
15.5 Conclusion
References
16 - Decline in vegetation cover over Kolkata city: an environmental concern from remote-sensing perspective
16.1 Introduction
16.2 Study area
16.3 Materials and methods
16.3.1 Image processing and analysis
16.3.2 Supervised classification
16.3.3 Normalized difference vegetation index estimation
16.3.4 Land surface temperature estimation
16.4 Results and discussion
16.4.1 Change in land use/land cover analysis
16.4.2 Change in normalized difference in vegetation index analysis
16.4.3 Change in land surface temperature analysis
16.4.4 NDVI and LST
16.4.5 Recommendations
16.5 Conclusion
References
17 - Understanding the forest cover dynamics and its health status using GIS-based analytical hierarchy process: a ...
17.1 Introduction
17.2 Study area
17.3 Database and methodology
17.3.1 Normalized difference vegetation index
17.3.2 Greenness index
17.3.3 Normalized difference moisture index
17.3.4 Perpendicular vegetation index
17.3.5 Transformed vegetation index
17.3.6 Soil-adjusted vegetation index
17.3.7 Normalized difference built-up index
17.3.8 Normalized difference bareness index
17.3.9 Multicriteria decision analysis
17.4 Results
17.4.1 NDVI
17.4.2 Greenness index
17.4.3 Normalized difference moisture index
17.4.4 PVI
17.4.5 Transformed vegetation index
17.4.6 Soil-adjusted vegetation index
17.4.7 Normalized difference built-up index
17.4.8 Normalized difference bareness index
17.5 Discussion
17.6 Conclusion
References
18 - Detection of forest fragmented areas of Sonitpur, Lakhimpur, and Papum Reserve Forest using the FCD model
18.1 Introduction
18.2 Limitations of the study
18.3 Materials and methods
18.3.1 Study area
18.3.2 Dataset
18.3.3 Image preprocessing
18.3.3.1 DN to radiance conversion
18.3.3.2 Radiance to TOA reflectance
18.3.3.3 Conversion of DN to radiance
18.3.4 Advanced vegetation index
18.3.5 Soil bareness index
18.3.6 Canopy shadow index
18.3.7 Vegetation density index
18.3.8 Scaled shadow index
18.3.9 Calculation of forest canopy density
18.3.10 Acquisition of change detection tool
18.4 Results and discussions
18.4.1 Assessment of advance vegetation index
18.4.2 Evaluation of bare soil index
18.4.3 Evaluation of vegetation density index
18.4.4 Evaluation of Scaled Shadow Index
18.4.5 Analysis of forest canopy density (1991–2020)
18.4.6 Examination of various changes with change detection tool
18.4.7 Comparative analysis of highly deforested area among the Papum Reserve Forest, Lakhimpur and Sonitpur
18.5 Challenges and solutions
18.6 Recommendations
18.7 Conclusions
References
Index
A
B
C
D
E
F
G
H
I
L
M
N
P
R
S
T
U
V
W
Y
Back Cover