With continuing proliferation of human influences on landscapes, there is mounting incentive to undertake quantification of relationships between spatial patterns of human populations and vegetation. In considering such quantification, it is apparent that investigations must be conducted at different scales and in a comparative manner across regions. At the broader scales it becomes necessary to utilize remote sensing of vegetation for comparative studies against map referenced census data. This paper explores such an approach for the urbanized area in the Tokyo vicinity. Vegetation is represented by the normalized difference vegetation index (NDVI) as determined from data acquired by the thematic mapper (TM) sensor of the Landsat satellite. Sparseness of vegetation is analyzed in relation to density of human residence, first by regression analysis involving stratified distance zones and then by the recent echelon approach for characterization of surfaces. Echelons reveal structural organization of surfaces in an objective and explicit manner. The virtual surface determined by census data collected on a grid is shown to have structural correspondence with the surface representing vegetation greenness as reflected in magnitude of NDVI values computed from red and infrared bands of image data.
Patil, G. P. 1998. Statistical ecology and environmental statistics for cost-effective ecological synthesis and environmental analysis. In: R. S. Ambash (ed.), Modern Trends in Ecology and Environment. Backhuys Publ., Leiden. The Netherlands, pp. 5-36.
Statistical ecology and environmental statistics for cost-effective ecological synthesis and environmental analysis., () 5-36.
Statistical ecology and environmental statistics for cost-effective ecological synthesis and environmental analysis.536)| false
Kurihara, K. 1995. An approach to statistical software for exploratory data analysis. Proceedings of International Conference on Statistical Methods and Statistical Computing for Quality and Productivity Improvement, pp. 435-441.
'An approach to statistical software for exploratory data analysis' , , .
An approach to statistical software for exploratory data analysis435441)| false
Myers, W. L. 1999. Remote sensing and quantitative geogrids in PHASES. Technical Report ER9901, Environmental Resources Research Institute, The Pennsylvania State University, University Park, PA. pp. 1-57.
Myers, W. L., G. P. Patil and K. Joly 1997. Echelon approach to areas of concern in synoptic regional monitoring. Environmental and Ecological Statistics 4: 131-152.
'Echelon approach to areas of concern in synoptic regional monitoring' () 4Environmental and Ecological Statistics: 131-152.
Echelon approach to areas of concern in synoptic regional monitoringEnvironmental and Ecological Statistics4131152)| false
Cardillo, M., D. W. Macdonald and S. P. Rushton. 1999. Predicting mammal species richness and distributions: testing the effectiveness of satellite-derived land cover data. Landscape Ecology 14: 423-435.
'Predicting mammal species richness and distributions: testing the effectiveness of satellite-derived land cover data' () 14Landscape Ecology: 423-435.
Predicting mammal species richness and distributions: testing the effectiveness of satellite-derived land cover dataLandscape Ecology14423435)| false
Johnson, G.D., W. M. Myers, G. P. Patil T. J. O'Connell and R. P. Brooks. 1999. Predictability of bird community-based ecological integrity using landscape measurements. Technical Report 99-0601, Center for Statistical Ecology and Environmental Statistics, Department of Statistics. The Pennsylvania State University, University Park, PA.
Madhur Anand, CAN (forest ecology, computational ecology, and ecological complexity)
S. Bagella, ITA (temporal dynamics, including succession, community level patterns of species richness and diversity, experimental studies of plant, animal and microbial communities, plant communities of the Mediterranean)
P. Batáry, HUN (landscape ecology, agroecology, ecosystem services)
P. A. V. Borges, PRT (community level patterns of species richness and diversity, sampling in theory and practice)
A. Davis, GER (supervised learning, multitrophic interactions, food webs, multivariate analysis, ecological statistics, experimental design, fractals, parasitoids, species diversity, community assembly, ticks, biodiversity, climate change, biological networks, cranes, olfactometry, evolution)
Z. Elek, HUN (insect ecology, invertebrate conservation, population dynamics, especially of long-term field studies, insect sampling)
T. Kalapos, HUN (community level plant ecophysiology, grassland ecology, vegetation-soil relationship)
G. M. Kovács, HUN (microbial ecology, plant-fungus interactions, mycorrhizas)
W. C. Liu,TWN (community-based ecological theory and modelling issues, temporal dynamics, including succession, trophic interactions, competition, species response to the environment)
L. Mucina, AUS (vegetation survey, syntaxonomy, evolutionary community ecology, assembly rules, global vegetation patterns, mediterranean ecology)
P. Ódor, HUN (plant communities, bryophyte ecology, numerical methods)
F. Rigal, FRA (island biogeography, macroecology, functional diversity, arthropod ecology)
D. Rocchini, ITA (biodiversity, multiple scales, spatial scales, species distribution, spatial ecology, remote sensing, ecological informatics, computational ecology)
F. Samu, HUN (landscape ecology, biological control, generalist predators, spiders, arthropods, conservation biology, sampling methods)