Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

/pdf/a-survey-of-image-classification-methods-and-techniques-for-5bc5ltr9dq.pdf

A survey of image classification methods and techniques for improving classification performance

https://researchweb.iiit.ac.in/~sai.deepak/lectures/14.%20Thermal%20remote%20sensing%20of%20urban%20climates%202003%20Voogt%20and%20Oke.pdf

Thermal remote sensing of urban climates

Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies

This paper presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decisionmaking, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas. We discuss the potential strengths of MAS/LUCC models and suggest that these strengths guide researchers in assessing the appropriate choice of model for their particular research question. We find that MAS/LUCC models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We discuss a range of possible roles for MAS/LUCC models, from abstract models designed to derive stylized hypotheses to empirically detailed simulation models appropriate for scenario and policy analysis. We also discuss the challenge of validation and verification for MAS/LUCC models. Finally, we outline important challenges and open research questions in this new field. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers whose goal is to create fine-scale models of LUCC phenomena that focus on human-environment interactions.

http://www.dpi.inpe.br/cursos/tutoriais/modelagem/referencias/agents_lucc_overview.pdf

Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review

Day and night airborne thermal infrared image data at 5 m spatial resolution acquired with the 15-channel (0.45 micron - 12.2 micron) Advanced Thermal and Land Applications Sensor (ATLAS) over Alabama, Huntsville on 7 September, 1994 were used to study changes in the thermal signatures of urban land cover types between day and night. Thermal channel number 13 (9.6 micron - 10.2 micron) data with the best noise-equivalent temperature change (NEAT) of 0.25 C after atmospheric corrections and temperature calibration were selected for use in this analysis. This research also examined the relation between land cover irradiance and vegetation amount, using the Normalized Difference Vegetation Index (NDVI), obtained by ratioing the difference and the sum of the red (channel number 3: 0.60-0.63 micron) and reflected infrared (channel number 6: 0.76-0.90 micron) ATLAS data. Based on the mean radiance values, standard deviations, and NDVI extracted from 351 pairs of polygons of day and night channel number 13 images for the city of Huntsville, a spatial model of warming and cooling characteristics of commercial, residential, agricultural, vegetation, and water features was developed using a GIS approach. There is a strong negative correlation between NDVI and irradiance of residential, agricultural, and vacant/transitional land cover types, indicating that the irradiance of a land cover type is greatly influenced by the amount of vegetation present. The predominance of forests, agricultural, and residential uses associated with varying degrees of tree cover showed great contrasts with commercial and services land cover types in the center of the city, and favors the development of urban heat islands. The high-resolution thermal infrared images match the complexity of the urban environment, and are capable of characterizing accurately the urban land cover types for the spatial modeling of the urban heat island effect using a GIS approach.

Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect

Scale and resolution have long been key issues in geography. The rapid development of analytical cartography, GIS, and remote sensing (the mapping sciences) in the last decade has forced the issues of scale and resolution to be treated formally and better defined. This paper addresses the problem of scale and resolution in geographical studies, with special reference to the mapping sciences. The fractal concept is introduced, and its use in identifying the scale and resolution problem is discussed. The implications of the scale and resolution problem on studies of global change and modeling are also explored.

https://www.rsgis.envs.lsu.edu/docs/Lam-Paper1992.pdf

On the Issues of Scale, Resolution, and Fractal Analysis in the Mapping Sciences*

Introduction: Scale, Multiscaling, Remote Sensing, and GIS, M.F. Goodchild and D.A. Quattrochi Multiscale Nature of Spatial Data in Scaling Up Environmental Models, L. Bian Scale Dependence of NDVI and its Relationship to Mountainous Terrain, S.J. Walsh, A. Moody, T.R. Allen, and D.G. Brown Understanding the Scale and Resolution Effects in Remote Sensing and GIS, C.-Y. Cao and N. S.-N. Lam Multiresolution Covariation among Landsat and AVHRR Vegetation Indices, L. De Cola Multiscaling Analysis in Distributed Modeling and Remote Sensing: An Application Using Soil Moisture, R. Dubayah, E.F. Wood, and D. Lavallee Examining the Effects of Sensor Resolution and Sub-Pixel Heterogeneity on Spectral Vegetation Indices: Implications for Biophysical Modeling, M.A. Friedl Multiscale Vegetation Data for the Mountains of Southern California: Spatial and Categorical Resolution, J. Franklin and C.E. Woodcock The Use of Remotely Sensed Surface Temperatures from an Aircraft-Based Thermal Infrared Multispectral Scanner (TIMS) to Estimate the Spatial and Temporal Variability of Latent Heat Fluxes and Thermal Response Numbers from a White Pine (Pinus strobus L.) Plantation, J.C. Luvall Scaling Predicted Pine Forest Hydrology and Productivity across the Southern United States, S.G. McNulty, J.M. Vose, and W.T. Swank Modeling Effects of Spatial Pattern, Drought, and Grazing on Rates of Rangeland Degradation: A Combined Markov and Cellular Automaton Approach, H. Li and J.F. Reynolds Scaling Land Cover Heterogeneity for Global Atmosphere-Biosphere Models, R.S. DeFries, J.R. Townshend, and S.O. Los Quadtrees: Hierarchical Multiresolution Data Structures for Analysis of Digital Images, F. Csillag Statistical Models for Multiple Scaled Analysis, D.E. Myers Image Characterization and Modeling System (ICAMS): A Geographic Information System for the Characterization and Modeling of Multiscale Remote Sensing Data, D.A. Quattrochi, N. S.-N. Lam, H.-L. Qiu, and W. Zhao Approaches to Scaling of Geo-Spatial Data, Z.-G. Xia and K.C. Clarke Multifractals and Remotely Sensed Data: Generalized Scale Invariance, Geographical Information Systems and Resolution Dependence, S. Pecknold, S. Lovejoy, D. Schertzer, and C. Hooge Index

Scale in Remote Sensing and GIS

http://web1.sph.emory.edu/remote-sensing/my_papers/Hu_GWR_PM25_model_southeastern_US.pdf

Estimating ground-level PM2.5 concentrations in the southeastern U.S. using geographically weighted regression

Land use and land cover maps of Atlanta Metropolitan Area in Georgia were produced from Landsat MSS and TM images for 1973,1979,1983,1987,1992, and 1997, spanning a period of 25 years. Dramatic changes in land use and land cover have occurred with loss of forest and cropland to urban use. In particular, low-density urban use, which includes largely residential use, has increased by over 119% between 1973 and 1997. These land use and land cover changes have drastically altered the land surface characteristics. An analysis of Landsat images revealed an increase in surface temperature and a decline in NDVI from 1973 to 1997. These changes have forced the development of a significant urban heat island effect and an increase in ground level ozone production to such an extent, that Atlanta has violated EPA's ozone level standard in recent years. The urban heat island initiated precipitation events that were identified between 1996 and 2000 tended to occur near high-density urban areas but outside the I-285 loop that traverses around the Central Business District, i.e. not in the inner city area, but some in close proximity to the highways. The health implications were investigated by comparing the spatial patterns of volatile organic compounds (VOC) and nitrogen oxides (NOx) emissions, the two ingredients that form ozone by reacting with sunlight, with those of rates of cardiovascular and chronic lower respiratory diseases. A clear core-periphery pattern was revealed for both VOC and NOx emissions, but the spatial pattern was more random in the cases of rates of cardiovascular and chronic lower respiratory diseases. Clearly, factors other than ozone pollution were involved in explaining the rates of these diseases. Further research is therefore needed to understand the health geography and its relationship to land use and land cover change as well as urban heat island effect. This paper illustrates the usefulness of a remote sensing approach for this purpose.

Dale A. Quattrochi

Papers

Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect

On the Issues of Scale, Resolution, and Fractal Analysis in the Mapping Sciences*

Scale in Remote Sensing and GIS

Estimating ground-level PM2.5 concentrations in the southeastern U.S. using geographically weighted regression

Land-Use and Land-Cover Change, Urban Heat Island Phenomenon, and Health Implications: A Remote Sensing Approach