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.

Methodology

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.

Conclusions

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. 

Reference
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 

Reference
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 

Vegetation mapping using LiDAR

Introduction

LIght Detection And Ranging (LiDAR) technology is capable of making spatially located point elevation measurements to generate precise and high resolution Digital Elevation Model (DEM) of a chosen canopy or particular structure. Vegetation and trees mapping can be expertly done using LiDAR technology. LiDAR’s fast, dense, and systematic dataset permits mapping of land-use classification, vegetation canopy, ground elevations in dense vegetation covers, areas of minute textural differences, areas of minute elevation differences, and point and line features i.e. trees, water lines etc.         

Why vegetation mapping is important?

Natural resources when managed for a positive reason of saving the ecosystem in an optimistic way, can offer benefits for longer time period without getting deteriorated. Natural resources management is not comprehensive unless vegetation is properly identified with its characteristics, uses and impacts on the environment. Such vegetation mapping is sure to be efficiently done using LiDAR for determining species and groups of vegetation, measuring vegetation in three dimensions, and mapping vegetation spectrally, spatially, and temporally. If and when vegetation mapping is done in a resourceful and well-organized manner, it can solve widespread concerns during forest inventories, ecological studies, environmental modeling, hazards control, risk mapping, and wildlife safety. LiDAR data can be used to identify distinct structures in any canopy such as trees in parks, fruit orchards or forests. Distinct 3D models of trees can be created using LiDAR data. LiDAR can also acquire data on leaves’ characteristics, diversity of microhabitat, and transpiration.

Factors that effect LiDAR data

The accuracy of LiDAR data depends upon the factors like alignment of the coordinate system, quality of point data, point density, vegetation height thresholds, vegetation density in the canopy, wind that affects the leaves, leaf-on and leaf-off seasons, forest cover effects, complex vegetation cover, size of individual trees, undergrowth of herbs, shrubs’ areas, wood quality, birds in the vicinity, slope and elevation of the terrain, terrains with high and low reliefs, data recruitment times and dates, aircraft fluctuations, the distance between LiDAR sensor and trees, pulse mode, site conditions, weather, interpolation of points, stitching accuracy, and the LiDAR sensor itself. The task of creating a well-defined 3D model of tree is accomplished when the modeling of leaves, twigs, branches, tree height, tree crown, and crown diameter are incorporated into it.    

The DEM

When LiDAR data is achieved, generation of DEM can be done flawlessly after filtering of errors and outliers removal from LiDAR points, interpolating and reorganization of the points, and separation of ground points i.e. ground filtering. DEM which is representation of the landscape along with its vegetation quantitatively can be used to assess the terrain, vegetation, and trees conditions and their effects on the surroundings. DEM provides a broad vision and is used to get the spatial information about processes occurring within the forest canopy. After evaluation of DEM, the decision can be made for the management and control of vegetation areas for the betterment of neighboring flora and fauna, wildlife, water bodies, human settlement, and the vegetation itself.

Conclusion

LiDAR technology acquires accurate and workable 3D data swiftly and competently. The use of LiDAR technology in forest environments can speedily attain precise spatial data of trees and vegetation temporally. When LiDAR data is presented in form of DEM, it enables researchers to visualize the canopies’ physical, chemical, and biological scenarios through an unprecedented visualization for the quantitative analysis of the forest’s canopy.     

References

Reconstruction of individual trees based onLiDAR and in situ data 

Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues

A Vegetation Mapping Strategy for ConiferForests by Combining Airborne LiDAR Data andAerial Imagery

Analysis of the factors affecting LiDAR DTM accuracy in a steep shrub area

Written by,

Engr. Ayesha Alam Khurram

LiDAR – The Technological Glaze

LiDAR is a remote sensing technology which is used to measure elevation of objects from a specified level. This technology uses laser to detect the objects. LiDAR can be used from ground, air or space. When collecting LiDAR data from an aircraft, the system mounted in the aircraft scans the ground or any required surface. A GPS (Global Positioning System) is also integrated with LiDAR that keeps track of aircraft’s location and attaches the locational parameter with the collected data. Since the elevation and position of the aircraft changes during the flight, the system calculates precise location of aircraft and with respect to it, locates the rest of the data. This information is continuously recorded in the computer automatically.

Airborne LiDAR

What are pulse and return?

When the light is emitted towards the ground from an airborne LiDAR system, one single dispatch of light is called a pulse. When the light hits the respective object, it reflects back towards the LiDAR system, that reflected light when it reaches and detected by the system is called a return.

What is LiDAR output?

High-resolution maps are the output of LiDAR procedure. LiDAR technology provides fine-scale and spatially defined three-dimensional landscape information and map structural components like elevation, dimension and density of the structures. At the start, LiDAR data is stored as a series of points. LiDAR data can also be stored in the form of contours lines. The most fascinating form of output of LiDAR is DEM or digital elevation model. DEM is made when the scanned surface is represented with continuous elevation values in 3D format along with the values’ co-ordinates.      

DEM

How the height of the object is calculated?

When the light is emitted from the LiDAR system, the time is recorded. The time is again recorded when the light reaches back after hitting the object below. Using the speed of light and the time difference between pulse & return, the distance between the LiDAR system and the top of that object is calculated. Then the distance between the aircraft and the ground surface is determined. The ground elevation is also calculated using GPS.

Is LiDAR data accurate?

The turbulence of the aircraft during flight and its tilt are incorporated into the system so that accurate data can be recorded. The angular measurements of pulse & return are taken into account as well when calculating the elevation that gives precise elevations of the objects and ground surface.

Where are applications of LiDAR?

LiDAR is used to monitor, record, store, quantify, classify, predict, and manage data in the fields of surveying, construction site selection & evaluation, large scale land use planning, terrestrial elevation modeling, flood modeling, hydrographic models, power lines management, piping networks, forestry, archeology, agriculture, urban modeling, transport planning, environmental monitoring & modeling, climate change, atmospheric physics, geosciences, geography, geology, tectonic activity mapping, and glacial data recording etc.

What are the benefits of using LiDAR technology?

LiDAR increases humans’ capability to gather data, represent data, and to respond to that data.

Greater efficiency, faster results.

Automatic 3D modeling.  

Automatic locational tag.

Large scale topographic maps are achievable.

Making night time data collection possible.

Urban and even rural areas are quicker to survey.

Cost effective.

References:

Into the Third Dimension: Benefits of Incorporating LiDAR Data in Wildlife Habitat Models. (WWW)

LiDAR applications in surveying and engineering. (WWW)

A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. (WWW)

A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models. (WWW)

What is LIDAR? (WWW)

Lidar remote sensing of laser-induced incandescence on light absorbing particles in the atmosphere. (WWW)

Assessing the transferability of statistical predictive models for leaf area index between two airborne discrete return LiDAR sensor designs within multiple intensely managed Loblolly pine forest locations in the south-eastern USA. (WWW)

By LiDAR geek:

Ayesha Alam Khurram

Dust Transport Model

The emission of dust or mineral dust has impact on the environment, climate, health, flora & fauna, wild life, vehicle transport visibility, and ocean biodiversity. The dust dispersion and transport is determined by atmospheric conditions. The dust transport scheme includes dust uplifting into the atmosphere, dust entrainment, atmospheric advection and mixing, and gravitational sedimentation.

A model can be designed that can include the dust dispersion, the dust plume transport mechanism, the adsorption of various gaseous and chemical materials on the dust particles and the fall out of the dust particles. Dust aerosol or dust particle modeling is essential for the knowledge of nutrient transport mechanism, land-use change, and ecosystem health. An efficient model can tell the amount and distribution of these dust particles where they are finally deposited and thus their harmful effects can be predicted. There are many pre-existing related models like, Dust Entrainment and Deposition (DEAD) model, Model of Atmospheric Chemistry and Transport (MATCH) and Chemical Transport Model (CTM). These models can be further explored and combined to form a new model that should be efficient in modeling the processes of dust uplifting, transportation and deposition. Long term data collection is a must for such model.

Image

Many factors like wind friction speed, soil moisture content and vegetation cover are important for dust uplifting and dispersion model. The total vertical mass flux of dust is also required for calculating dust entrainment. The vegetation acts as a constraint and a sink to atmospheric momentum for significant dust plumes. The vegetation area index and stem/leaves index are essential to model this phenomenon. After being dispersed into the air and after their transport and adsorption processes, dust particles finally fall out due to condensation of water and other gases. These dust particles act as carriers of reactants while in the atmosphere. Dust particles mainly contain Al, Ca, Fe, K, Mg, Mn, Si, and Ti. The impacts of dust on the geochemical cycle can be found out by modeling the phenomenon of adsorption of the reactants into the dust particles. The physical-chemical properties of individual dust particles are essential for the model. The particles at last settle gravitationally at their terminal velocities. The drag coefficient and the slip correction factor are required for determination of this velocity. The effect of all these factors can be studied and included in the design of dust transport model.

When the source of dust is not properly characterized the dust transport modeling becomes tricky. The dust model can be improved when factors like land use, vegetation cover, soil composition, presence of micronutrients in mineral dust, presence of aerosols, sedimentation, and deposition (wet & dry) are properly incorporated into the model. If the data is collected fairly continuously with predetermined and close intervals, it can be comfortable extrapolated to large scale. The data should be collected from near source till to the deposition point at proper points and distances.  

Geographic information systems (GIS) and remote sensing (RS) can also be integrated into the model to further enhance the results. Using GIS and RS, the vastness of dust emission and the accurate hotspots and be identified and mapped accordingly.         

References:

1.      E. Khodabandehloo et al., “Spatiotemporal Modeling of Dust Storm Sources Emission in West Asia”, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2013) Vol. XL-1/W3: 235-239. (WWW)

2.      Prof. Robert A. Duce and Prof. Peter Liss, “Workshop on Modelling and Observing the Impacts of Dust Transport/Deposition on Marine Productivity”, Sliema, Malta, 7-9 March 2011. (WWW)

Written by,

Engr. Ayesha Alam Khurram

RECONSTRUCTION OF URBAN 3D MODELS FROM LIDAR DATA

1 Introduction

The needs for 3D city models are rapidly growing in various fields such as virtual city reality, town planning, microclimate investigation, transmitter placement in telecommunication, monitoring and protection of coastal zones, pipelines and corridor mapping and exhaust spreading in urban areas, Et cetera . There is a steady shift from 2D-GIS toward 3D-GIS now, therefore a great amount of accurate 3D city models have become essential to be produced in a short period of time and provided on the market [TAKASE et al]. Now a day, Light Detection and Ranging (LiDAR) is widely applied in urban 3D data analysis.

2 LiDAR Overview

Mounted on the aircraft, collecting the LiDAR data, is a Global Positioning System (GPS), a LiDAR sensor system and an Inertial Navigation System (INS). The GPS returns the longitude and latitude coordinates of the aircraft’s actual position. The LiDAR sensor system is a remote sensing instrument which consists of an emitter and a receiver. The INS tracks the altitude of the LiDAR sensor. The emitter sends out electromagnetic radiation (a pulse of light) into the atmosphere down to the target. The receiver (telescope) measures the intensity of the signal scattered back to the sensor. The time from sending to return of pulse is also recorded thus the distance of the sensor to the target can be calculated. In the case of a building surface, the laser will reflect off of the building’s surface and return to the sensor. Intensity and path of the electromagnetic radiation (laser beam) are distorted by the interaction between the beam and the atmosphere itself [NICHOLAS]. An example of LiDAR sensor system is shown in figure 1.

When two different heights of one point are received, this gives an indication of the presence of a penetrable object (tree). In contrast, for a non-penetrable object (building), data point has the same height for first and last returns. Figure 2 shows first and last return in case of a tree [ABDULLATIF].

2.1 Ground Surface

The point density is high around the scanner and gradually decreases with distance away from the scanner, the region of interest can be selected for example as an area 20 m x 20 m around the instrument to reduce the amount of no data areas [CHRISTOPH]. Figure 3 shows the representation of a manmade object (building) and a natural feature (tree) [VOLKER].

2.2 Tin Procedures

Triangulated Irregular Networks (TINs) are used for representing or interpolating point clouds by surfaces. TINs represents a meshing of points (xk, yk, zk) in the form of triangular surface in 3D with these points as vertices, constructed above a 2D triangulation of the surface (xk, yk) of the vertices in a base plane. Triangulations are usually constructed using the ‘Delaunay’ principle, which tells that circumcircles of the triangles do not contain the locations of vertices in their interior [CHRISTOPH]. Figure 4 shows the mesh points and TIN surface.

2.3 Greedy Insertion Triangulation

For all triangles, the distances between the triangles (planes) as shown in figure 5 and the points that they encompass (in x and y or longitude and latitude spacing) are calculated [NICHOLAS].

3 3D Reconstruction Algorithm

The following attributes should be a part of an ideal 3D reconstruction algorithm. The algorithm should be able to intelligently recognize complex (non planar) building structures besides simple ones. The algorithm should be generalized such that it has capability of analyzing raw (irregularly spaced) LiDAR data as well as where the LiDAR data is rasterized / grid-ed. The algorithm should reconstruct the buildings in the form of simple structures (roof planes, walls, etc.) as opposed to a collection of LiDAR points [NICHOLAS]. Figure 6 shows system block diagram.

3.1 Building Detection

The first step of building reconstruction algorithm is the detection of buildings. There have been many attempts to detect buildings using LiDAR data. The LiDAR points are classified according to terrain, buildings or others like vegetation. Morphological opening filters are have been used to determine a digital terrain model (DTM) which is subtracted from the digital surface model (DSM). An initial building mask is obtained by applying height thresholds to the normalized DSM [ROTTENSTEINER et al]. Figure 7 shows an example of a DSM [NORBERT et al].

3.2 Building Extraction

Figure 8 shows work flow for building extraction from LiDAR data [ROTTENSTEINER et al].

The algorithms for extraction of the building geometrical parameters will be performed after the complete edges of buildings have been detected. The LiDAR data interpretation incorporates following facts: (1) The buildings are higher than the surrounding topographic surface; (2) The laser penetrates into vegetation, thus giving echo from various heights, makes it possible to distinguish between man-made objects and vegetation [GUOQING et al].

3.3 Building Reconstruction

Object reconstruction or recognition presumes knowledge about the perceived objects by some kind of object model. Model that is used for building reconstruction should be able to describe buildings of various complexities and it should allow the representation of geometric constraints during the reconstruction. Object models can be visualized as abstractions of real world objects. The most important part in a model definition is the proper balance between the tractability and correctness, i.e. the outcome of the model must be adequate both in terms of the solution attained and the cost effectiveness [NORBERT et al]. Figure 9 shows an example of a refined reconstruction.

4 Uncertainty

Representing uncertainty can be a difficult problem. The uncertainty of all geometric entities like points, lines, planes and objects, can be represented derived by construction from those entities. But how certain is a segmentation result, for example, when multiple cues from different sources need to be combined? How that case is handled when a small (continuous) difference in geometry leads to a different roof topology (a discrete difference)? [CLAUS].

5 Conclusion

Recent development in 3D displays, computer graphics hardware and real-time texturing as well as the increasing availability of animation software tools have resulted in an increased demand for realistic three- dimensional virtual reality city models. This demand can only be satisfied by capturing urban scenes efficiently, which presumes the integrated use of various data sources [NORBERT et al]. In the near future, research will have to find a way to develop new specialized algorithms for the case, and devise methods to combine aerial and terrestrial sources efficiently [CLAUS]. Figure 10 shows an example of a virtual city model.

Written by,

Engr. Ayesha Alam Khurram