An Introduction to MODISI and SCMOD Methods for Correction of the MODIS Snow Assessment Algorithm

Detection, monitoring and precise assessment of the snow covered regions is an important issue. Snow cover area and consequently the amount of runoff generated from snowmelt have a significant effect on water supply management. To precisely detect and monitor the snow covered area we need satellite images with suitable spatial and temporal resolutions where we usually lose one for the other. In this study, products of two sensors MODIS and ASTER both on board of TERRA platform having low and high spatial resolution respectively were used. The objective of the study was to modify the snow products of MODIS by using simultaneous images of ASTER. For this, MODIS snow index image with high temporal resolution were compared with that of ASTER, using regression and correlation analysis. To improve NDSI index two methods were developed. The first method generated from direct comparison of ASTER averaged NDSI with those of MODIS (MODISI). The second method generated by dividing MODIS NDSI index into 10 codes according to their percentage of surface cover and then compared the results with the difference between ASTER averaged and MODIS snow indices (SCMOD). Both methods were tested against some 16 MODIS pixels. It is found that the precision of the MODISI method was more than 96%. This for SCMOD was about 98%. The RMSE of both methods were as good as 0.02.     

Simulating urban growth in a megalopolitan area using a patch‐based cellular automata

Although traditional cellular automata (CA)‐based models can effectively simulate urban land‐use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell‐based simulation strategies. This research proposes a new patch‐based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a pattern‐calibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000–2006–2012. The results from this model were compared with two common models: cell‐based CA and logistic‐patch CA. The proposed model yields a degree of patch‐level agreement that is 23.4 and 7.5% higher than those of these pre‐existing models, respectively. This reveals that the patch‐based CA model simulates actual development patterns much better than the two other models.     

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and <Emphasis Type="Italic">t</Emphasis>-distributed deviations

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student’s) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.     

Evaluation of Land Suitability for Urban Land‐Use Planning: Case Study Dhaka City

The aim of this research is evaluation of land suitability for urban land‐use planning. Four factors and fourteen criteria were selected for suitability analysis and land‐use planning. Factors and criteria were defined based on literature survey, experts’ opinions, local contexts and availability of data. GIS‐based Analytical Hierarchy Process (AHP) was used as a Multi‐criteria Decision Making model. The study was conducted on a selected area of Dhaka city, which is one of the fastest growing mega‐cities of the world. The research result shows that highly suitable area (13%) should be used for urban residential zone; moderately suitable area (35%) should be designated as mixed use zone; low suitable area (42%) should be reserved for agricultural use and open spaces; and not suitable area (10%) should be protected from any types of activities except agriculture. The research approached an urban land‐use planning at a regional scale.     

An Agent‐Based Simulation of Residential Location Choice of Tenants in Tehran,Iran

Residential location choice modeling is one of the substantial components of land use and transportation models. While numerous aggregated mathematical and statistical approaches have been developed to model the residence choice behavior of households, disaggregated approaches such as the agent‐based modeling have shown interesting capabilities. In this article, a novel agent‐based approach is developed to simulate the residential location choice of tenants in Tehran, the capital of Iran. Tenants are considered as agents who select their desired residential alternatives according to their characteristics and preferences for various criteria such as the rent, accessibility to different services and facilities, environmental pollution, and distance from their workplace and former residence. The choice set of agents is limited to their desired residential alternatives by applying a constrained NSGA‐II algorithm. Then, agents compete with each other to select their final residence among their alternatives. Results of the proposed approach are validated by comparing simulated and actual residences of a sample of tenants. Results show that the proposed approach is able to accurately simulate the residence of 59.3% of tenants at the traffic analysis zone level.     

Exploring the applications of 3D proximity analysis in a 3D digital cadastre

Increasing population in urban areas and limitations of suitable lands for developing houses and urban infrastructure have led to the vertical development in cities. However, these developments are managed by a cadastral system which is mainly two-dimensional and cannot efficiently represent Rights, Restrictions, and Responsibilities (RRRs) in complex scenarios. In fact, a three-dimensional cadastre is required for efficiently registering and representing RRRs. In this paper, a 3D proximity analysis was proposed and implemented to determine RRRs and associated easement rights in non-topology-based data structures. This method can be used to investigate the surrounding spaces of a subject apartment unit or storage in a high-rise. The performance of the developed method was evaluated in a large complex high-rise in Tehran, Iran. The results confirmed that the proposed method can correctly identify the neighbor spaces in complex scenarios.     

Estimation of ship velocities from MODIS and OCM

Detection of ships and their tracks in the atmosphere from satellites was earlier demonstrated by Porch, Noone, and Kaufman, among others. In this letter, we have gone one step further to estimate the ship speed and direction by locating them and their tracks from multisatellite imagery. Exhausts from the ships create streaks of clouds in the atmosphere that help identify the same ship from two satellites. Ship velocities are estimated from displacements of ships. We have used optical sensors data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean Color Monitor (OCM) to demonstrate this technique. Estimated velocities of ships are within the expected range. Application of this approach has general interest to the navy, coast guards, shipping corporations, commercial ship owners, and fishermen. More satellite observations can be used to continuously monitor the ship velocities.     

Interannual Variation of Eddy Kinetic Energy from TOPEX Altimeter Observations

Monthly mesoscale eddy kinetic energy (EKE) per unit mass has been computed for four years, 1993-1996, from TOPEX altimeter data in the Indian Ocean. It ranges from 50 cm²/s² to 2,700 cm²/s² (about 4,000 cm²/s² near the Somali region in a few months). In the Arabian Sea and the Bay of Bengal, regions of high energies associated with various current systems under the influence of monsoonal winds have been delineated. Monthly variation of EKE near the Somali region has been studied. In this region the maximum EKE per unit mass has been observed during August every year, with variations in magnitude from year to year. The mesoscale eddy kinetic energy computed from TOPEX altimeter-derived SSH during 1993-1996 is highest near the Somali region during the SW monsoon, due to formation of mesoscale eddies and also because of upwelling. In the Bay of Bengal, high eddy kinetic energy is seen toward the western side during nonmonsoonal months due to the western boundary current. In the South Indian Ocean, it is high at a few places in some of the months. A large part of the Indian Ocean exhibits low eddy kinetic energy (less than 300 cm²/s²) year-round.     

Modelling and predicting the spatial dispersion of skin cancer considering environmental and socio-economic factors using a digital earth approach

ABSTRACT

Almost all causative factors of diseases depend on location. The Digital Earth approach is suitable for studying diseases globally. Geospatial information systems integrated with statistical models can be used to model the relationship between a disease and its causative factors. Through modelling, the most important causative factors can be extracted and the epidemiology of the disease can be observed. In this paper, skin cancer (the most common type of cancer) has been modelled based on its causative factors, including climate factors, people's occupations, nutrition habits, socio-economic factors, and usage of chemical fertiliser. To fit the model, a data framework was first designed, and then data were gathered and processed. Finally, the disease was modelled using Generalised Linear Models (GLM), a statistical model based on the location of the factors. The results of this study identify the most important causative factors together with their relative priority. Furthermore, a model was used to predict the change in skin cancer occurrences caused by a change in one of its causative factors. This work illustrates the ability of the model to predict disease occurrence. Thus, by using this Digital Earth approach, skincancer can be studied in all the key countries around the world.