Friday 29 July 2016
Saturday 23 July 2016
Monday 18 July 2016
Saturday 16 July 2016
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.
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)
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Saturday 9 July 2016
GIS Based Spatial Modeling of Biochar Application to the Soil for GHG Mitigation
The
phenomenon of global warming has gained immense interest among academic and
industrial researchers. Many mitigation techniques are being analyzed to
address this issue. Among these, biochar application to soil is considered as
promising technique for greenhouse gases (GHG) mitigation [1]. Biochar or
charcoal is a biomass-derived black carbon (C). It is a stable residue that is
produced after charring the biomass. Biochar when applied to soil can enhance
moisture and nutrients retention while improving fertility and microbiological
properties of soils. The average carbon content of the biochar is about 50%,
and when it is applied to the land, some research results show that even after
100 years of passage of time, biochar derived from crop residues loses less
than 10 percent of its carbon content [1]. A comparison of the process of
degradation of the C-content for biomass and biochar over a period of 100 years
is shown in Fig-1.
Biochar
shows very good stability due to the presence of charcoal which is inert and
resistant to biochemical breakdown. This property of biochar to retain
nutrients especially C-content for a long period of time originates the concept
of C sequestration potential of biochar in the context of the global C cycle.
The concept of biochar-C sequestration involves breaking the carbon cycle by
converting it into a stable form (biochar), thus effectively removing a fraction
of carbon from the cycle and limiting its release to the atmosphere. In this
sense biochar can act as a long term sink for atmospheric carbon dioxide in
land environment. The use of biochar as a C is a promising way to reduce
atmospheric concentration of carbon dioxide and thus mitigate climate change.
The effectiveness of this solution will depend on the maximization of the range
of economic and environmental benefits of this practice [3]. Production to
application costs of biochar can be fully recovered from the crop production
and savings in fertilizer cost. There are new economic opportunities for
sectors like forestry and agricultural industry, if biochar is used efficiently
and cost effectively. One of the main benefits of using biochar as a fertilizer
is that, it filters the pollutants that lead to soil remediation [4]. Hence,
biochar can be utilized in different ways that brings down the average economic
cost of implementation.
All the
steps from biochar production to its application for GHG mitigation should be
studied and analyzed for better results. These steps if and when modeled
spatially and integrated into geographic information system (GIS) layers for
clarity of observation, then the data can be managed and implemented efficiently.
The biochar can be applied into terrestrial ecosystems and its outcome as a
measure of the GHG mitigation can be studied. Utilizing GIS modeling
techniques, data layers for biochar production can integrate the resources of
biomass such as crop residues, livestock manure, pulp mill waste etc. Other
layers of analysis can also include transport analysis, land management
practices, and biochar application techniques. In addition, soil data can also
be modeled into layers including soil response to field application of biochar.
To solve
challenging problems of the project a modeling framework can be developed in
the ArcGIS-Desktop environment. ArcGIS-Desktop is used for spatial analysis and
modeling of all sorts of data as well as data management and mapping. This
software can be easily used on Windows operating system. It is a platform for
creating, editing, and analyzing geographic knowledge. The decision making can
be improved as it allows seeing data on map for the clarity of patterns and
trends in a given data. The data can be presented using separate layers and
also as integration of all or a set of layers.
The
emissions of methane and nitrous oxide from agricultural sector contribute
mainly to greenhouse gases. The application of sufficient quantity of biochar
could reduce these emissions from soils as well as development of biochar
system can provide opportunities of carbon sequestration and storage into soils
[3]. Biochar can be produced from feedstock with high lignin content like in
forest residues, crop residues and organic wastes that can result in high
biochar yields and thus waste management fee of these residues would add up to
economic benefits in terms of beneficial biochar production which would play a
role in the structure of overall economy [2].
The
regional case study of any affected locality can be modeled that would counter
most of the soils, land-uses, and environmental issues throughout the country.
Dairy lands can be chosen for such study as they generate high nitrous oxide
and nitrate leachate such as from urine patches and require foremost attention
[3]. Algorithms can be proposed for the assessment of competent biomass resource
that would give higher biochar yield and to estimate the harvesting cost of
biomass and mobility costs of biomass and biochar. The algorithms can also
include the evaluation of application rates of biochar at particular site to
determine the biochar production requirement. This would lead to propose the
optimal size of the biomass processing plant for biochar production for a
particular site. Many soil profiles are shallow and the volume of biochar
application for efficient crop productivity is questionable. For shallow soils,
even small volume of biochar if added to top few centimeters of soil might
result in high yield as well as being effective for GHG mitigation. The soil
types and conditions would govern the efficiency of biochar application [3].
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Friday 1 July 2016
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.
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