Sunday, 25 August 2019

Sunday, 18 August 2019

RS and GIS for mapping of coastal landforms

Paper Review Report

Background and Goal of Study
The Coast landform are undergoing various changes due to various factors like environmental change i.e. Global warning and Construction on the Coastal water. These changes need to quantified, what may be the reason, which will be helpful for the Coastal water & land Management Departments for predicting the various changes in the geography. These can only be Quantified (database creation) if some technique and software is use.
The primary aim of the present study is to map coastal landforms and assess the volumetric change of sediment load over a decade along the south-west coast of Kanyakumari using integrated remote sensing and GIS techniques. The present study therefore used different change detection techniques such as (i) topographical change analysis, (ii) cross-shore profile change analysis, (iii) DEM of Difference (DoD) algorithm based Geomorphic Change Detection (GCD) analysis for estimating the volumetric changes (land loss or land gain) along the coastal stretch using the ArcGIS platform.

Mapping of coastal landforms:
The GIS and integrated Remote Sensing has been used for creating the coastal geomorphological landforms at high resolution.  The various spatial source used as input i.e. topographical map (scale 1:25,000), Landsat ETM+ image & IKONOS multi-spectral images. Then ASTER and SRTM DEM datasets are used in Arc GIS to make Map. The Garmin ETREX 30 GPS are use for ground truth verification, pre and post field verification.

DEM of Differencing of volumetric change analysis:
The GIS-based Geomorphic Change Detection (GCD) analysis provides volumetric change of sediment load in the landforms using DEM datasets acquired over periods of interval. The GCD method use the Difference of Digital Evaluation Model (DEM) of two different time using algorithm to estimate the quantitative changes of landforms of the earth surface, in a diverse set of environments, and at a range of spatial scales and temporal frequencies. In this research Geomorphic Change Detection of coastal landforms is estimated from SRTM and ASTER DEM datasets acquired for the years 2000 and 2011 respectively using DEM of Difference (DoD) method. The DoD is a mathematical algorithm for quantifying the volumetric change of the landforms using DEM datasets acquired on two different periods. 
The DoD algorithm computes the differences by subtracting pixel values of two DEMs using the equation δE = Z2 -Z1, where δE is a output DEM showing changes in volumetric scale (m3 ); Z1 is a DEM of earlier period (i.e. SRTM DEM acquired on February 2000, and Z2 is a DEM of later period (i.e. ASTER DEM acquired on October 2011). Thus, the output DEM provides volumetric change of sediment load (δE) on various landforms due to erosion and deposition with time. In which, the negative and positive values represent the land lost (erosion) and land gain (deposition)

Finally, the output map is converted into vector layer for preparation of geo-database of landform features with attributes including name, areal extent, and volumetric change rate using ESRI-ArcGIS 10.2 software.

This research paper Demonstrated the use of GIS and integrated remote Sensing for Mapping of coastal landforms and Volumetric change analysis.
The DoD analysis of geomorphic change assessment reveals changes in morphologies due to erosion or deposition processes. The spatial variation of sediment load suggests morphologies of the landforms are closely related to the marine and terrestrial processes. 

Mapping of coastal landforms and volumetric change analysis in the south west coast of Kanyakumari, South India using remote sensing and GIS techniques. S. Kaliraj et al. / The Egyptian Journal of Remote Sensing and Space Sciences 20 (2017) 265–282.

Review by,
Kamran ullah Khan 

GIS and RS for soil loss estimation using

Research paper review

Background and Goal of paper
Thus, an attempt was made to estimate and map the spatial pattern of annual soil loss rate by water using Revised Universal Soil Loss Equation (RUSLE) simulated by GIS and Remote sensing techniques. Therefore, this research has given answers to four core research questions; how much of soil is lost per unit area of land annually in Koga watershed? How is the spatial distribution of soil loss rate in Koga watershed? Does the estimated soil loss rate exceed the tolerable limit of soil erosion set by FAO? And where are erosion hotspot areas located for conservation prioritization?
Methodology  and Results
To calculate the Soil loss in this koga watershed (KW) a Revised Universal Soil Loss Equation (RUSLE) is used , empirically expressed as
            A (metric tons ha-1 year-1) = L*S*R*K*C*P

where A is the mean annual soil loss (metric tons ha1 year1 ); R is the rain fall erosivity factor [MJ mm h-1 ha-1 year-1]; K is the soil erodibility factor [metric tons ha-1 MJ-1mm-1]; LS is the slope length–steepness factor (dimensionless); C is the cover and management factor (dimensionless, ranges from zero to one); and P is the erosion support practice or land management factor (dimensionless, and ranges from zero to one).
Raster map of each RUSLE parameters derived from different data source were produced and finally the Soil Loss Map was generated 

Soil loss estimation using GIS and Remote sensing techniques: A case of Koga watershed, Northwestern Ethiopia. H.S. Gelagay, A.S. Minale / International Soil and Water Conservation Research 4 (2016) 126–136.

Review by,
Kamran ullah Khan 

Friday, 16 August 2019

Geometrical Characterization of Landfill Site

Paper reviewk


disposal of municipal waste is always of great concern due to its environmental impacts and heath threatening effects. Due to rapid urbanization it is now a common practice that people are dumping  municipal waste into quarries or oceans with out any proper treatment . Using an old quarries into a well designed landfill is considered a good management of a solid waste. Land fill siting is major task for the planners because factor like geology , topography ,subsurface  groundwater and surface water are associated with it. There still exist a places which were used for dumping of waste In past but due to rapid urbanization and passage of time they are now  brought in to use for different purposes. Detection and characterization of buried land fill  Via different approaches is  the aim of this paper.
The research paper is  focusing  on the detection and geometrical characterization of a hidden landfill site located along the coastline of the Campi Flegrei, near Naples, Italy.                                                                                                                                 In order to examine different Topographic changes topographic historical maps at different scales from 1887 to 2004 and white aerial photographs from 1843 to 1998 are acquired from the Italian military geographic institute (IGM).
two couples of historical aerial stereo pairs acquired by the IGM, dated 1956 and 1974, along with topographic data  derived from an airborne lidar survey (ALS)
These data set was used to produce multi-temporal digital elevation models (DEMs) of the study area, with the aim of comparing the obtained DEMs by a GIS-based change detection analysis.

Comparative analyses of the topographic maps  clearly showed that in 1943 There is little dumping in the quarry it increases in 1956 and 1974 and in 1998 it is completely filled with waste and reclaimed for construction as shown in fig below, this shows the variation of urban development and anthropic activity with respect to time.

Detection and Geometrical Characterization of a Buried Landfill Site by Integrating Land Use
Historical Analysis, Digital Photogrammetry and Airborne Lidar Data. Giuseppe Esposito , Fabio Matano * and Marco Sacchi. Geosciences 2018, 8, 348.

Paper review by,
Abdullah Kakar

GIS Modeling for Groundwater Vulnerability

Paper review

As the world is developing day by day, along with it, it also gives birth to many of the environmental problems like contamination of the groundwater which is mostly because of increase in industrialization, urbanization, and the leachate from the waste. The research was undertaken to know the possibility of groundwater being contaminated in the areas of the solid waste disposal site, Njelianparamba, a municipal dumping site in Kozhikode, Kerala, India. Using ESRI GIS Software the map of the area was created showing the possibilities of the contamination. The area was divided into three classes moderate vulnerable, high vulnerable, and very high vulnerable. It was concluded that eastern and south-eastern areas to be affected more.

Njelianparamba the dumping site is located in the area of Cheruvannur Nallalam of the district Kozhikode, Kerala, India. Daily 200 tons of the waste is dumped on the site. Monsoons are responsible for 82.77% of the total rainfall in the area. Groundwater level before monsoons rains are observed 2-16 meters and after rainfall is 0.38-9 meters because of such low level of the water table the leachate gets mixed with the groundwater.

DRASTIC Model was developed considering the seven factors Depth of the water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone media, and hydraulic Conductivity of the aquifer. For the calculation of the DRASTIC Index (DI), each factor is assigned ratings and weights, rates and weights of the same factor are multiplied and then linear addition is performed. The data obtained using DRASTIC Model is combined in GIS to develop the map of the area showing possibilities to contamination of each region.

For sampling and analysis of groundwater 29 sampling sites were chosen randomly. 20 groundwater samples were taken within a buffer zone of 1 km around the dumping site, and 9 samples were taken outside the zone to check the accuracy of the map. Samples were analyzed for total dissolved solid and E-coli. For sampling and analysis of the soil 57 soil samples were taken. 49 from the buffer zone, and 8 outside the zone. Soil samples were analyzed to check soil media map.

After we had both the studies Vulnerability Map, and Sampling and Analysis reports. The both are then compared and the outcomes are: the leachate percolation is maximum at 1 km distance from the dumping site. High total dissolved solids concentrations were seen in the buffer zone, and outside of it, there were low except eastern and south-eastern sides which also are very high vulnerable in the map. The E-Coli bacteria were found to be present in most of the samples in the vicinity to the dumping site particularly within the buffer zone of 1 km. Samples outside the zone were seen to have no E-Coli except eastern and south-eastern side samples which also are very high vulnerable according to the map.

It can be concluded that the eastern and south-eastern sides of the Njelianparamba are most vulnerable to the contamination from the results of both Vulnerability map and Reports obtained from Sampling and analysis.

Application of GIS and DRASTIC Modeling for Evaluation of Groundwater Vulnerability near a Solid Waste Disposal Site. Chonattu Jaseela, Kavya Prabhakar, Puthenveedu Sadasivan Pillai HarikumarInternational Journal of Geosciences, 2016, 7, 558-571

Paper review by,
Sohail Ahmed

GIS and RS for wetland mapping

Paper review
The aim of this paper is to map vernal pools in the Northeastern United States using high-resolution LiDAR data and aerial imagery.

Background of the study
In this study the importance of wetlands has been emphasized. Wetland are the natural water resources inundated or perennial. Although a distinct definition of wetland is not available but defined by various authors have some common features such as aquatic habitats, including marshes, swamps, bogs, fens, peatlands, prairie potholes, vernal pools, and aquatic beds, among others. In general, wetlands are transitional habitats situated between wet (e.g., lakes, rivers, streams, estuaries) and dry environments. Thus, the demarcation of a wetland lies along a continuum of water gradient and is somewhat arbitrary. Some wetland definitions include open-water habitats (e.g., lakes, rivers, streams) as wetlands, while others exclude permanent deep water and focus more on shallow water habitats. Wetlands exist in numerous sorts of atmospheres, on each landmass except Antarctica. They vary in size from disconnected prairie potholes to immense salt bogs. They are found along coasts and inland. A few wetlands are forests. Others are like watery fields.

Benefits of wetlands
Wetlands provide abundant ecological and socioeconomic benefits, such as providing habitats for fish, wildlife, and plant, storing floodwater and reducing peak runoff, recharging groundwater, filtering impurities in water, acting as nutrient and sediment sinks, protecting shorelines from erosion, and providing a range of recreational opportunities (e.g., boating, fishing, hunting).

In this case study, 1-m resolution light detection and ranging (LiDAR)derived digital elevation modelling (DEM) in conjunction with LiDAR intensity imagery was used to map prairie wetlands and surface hydrologic flow pathways. The LiDAR intensity imagery was used to delineate wetland inundation areas, where as the LiDAR DEM was used to delineate wetland depressions, catchments, and surface hydrologic flow pathways.
Arc GIS is used to streamline the procedures for automated delineation of wetland catchments and flow paths, the proposed framework the toolbox consists of three tools: Wetland Depression Tool, Wetland Catchment Tool, and Flow Path Tool.
The Wetland Catchment Tool uses the digital elevation modelling (DEM) grid and the wetland polygon layers resulted from the Wetland Depression Tool as input, and exports wetland catchment layers in both vector and raster format. Various morphometric properties (e.g., width, length, area, perimeter, maximum depth, mean depth, volume, elongations, and compactness) are computed and included in the attribute table of the wetland vector layers.
The wetlands were identified for prairie pothole region (PPR) in north America
The chart given below is proposed framework for outlining wetland catchments and flow paths.

The results obtained by comparing a small portion of the prairie pothole region of Dakota to the inundation polygons derived from the 2011 LiDAR intensity data and the NWI polygons created in the early 1980s by the U.S. It was observed that the national wetlands inventory (NWI) in this region is significantly out of date. The acquired light detecting and ranging data in October 2011 relatively shows large disjointed NWI wetlands coalesced and formed even larger wetland complexes during the extremely wet period.

According to the author except north America and parts of Europe, comprehensive national-scale wetland inventories are not available foremost countries. The author argues that technologies like GIS and remote sensing has greatly improved the geo-mapping of wetlands.

GIS and remote sensing applications in Wetland mapping and monitoring. Qiusheng Wu.

Review by,

Ehsan Nazeer