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.         


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


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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