Environmental information management and analysis ecosystem to global scales
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| Sprache: | English |
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London [u.a.]
Taylor & Francis
1994
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| 245 | 1 | 0 | |a Environmental information management and analysis |b ecosystem to global scales |c ed. by William K. Michener ... |
| 264 | 1 | |a London [u.a.] |b Taylor & Francis |c 1994 | |
| 300 | |a XXVI, 555 S. |b Ill., graph. Darst., Kt. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Environmental sciences -- Information services | |
| 650 | 4 | |a Management information systems | |
| 650 | 4 | |a Geographic information systems | |
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Datensatz im Suchindex
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| adam_text | Contents
List of figures ix
List of tables xv
Preface xvii
Contributors xxi
SECTION I A RESEARCH PERSPECTIVE
1 Integration of scientific information management
and environmental research 3
Susan G. Stafford, James W. Brunt and
William K. Michener
2 Grand challenges in scaling up environmental
research 21
James H. Brown
3 Sustainable Biosphere Initiative: Data management
challenges 27
James R. Gosz
4 Multiple roles for GIS in global change research:
Towards a research agenda 41
Dennis E. Jelinski, Michael F. Goodchild and Louis
T. Steyaert
SECTION II SCIENTIFIC DATABASES AND
INFORMATION SYSTEMS
5 Scientific information systems: A conceptual
framework 59
Donald E. Strebel, Blanche W. Meeson and
Alan K. Nelson
6 Development and refinement of the Kon/a Prairie
l.TER Research Information Management
Program S7
John M. Briggs and Haiping Su
7 Forest health monitoring case study 101
vi Contents
Charles I. Liff, Kurt H. Riiters and
Karl A. Hermann
8 Bigfoot: An earth science computing environment
for the Sequoia 2000 Project 113
James Frew
9 Representing spatial change in environmental
databases 127
John L. Pfaltz and James C. French
SECTION III QUALITY ASSURANCE/QUALITY CONTROL
10 Automated smoothing techniques for visualization
and quality control of long term environmental data 141
Scott E. Chapal and Don Edwards
11 Spatial sampling to assess classification accuracy of
remotely sensed data 159
Gretchen G. Moisen, Thomas C. Edwards, Jr and
D. Richard Cutler
12 Metadata required to determine the fitness of spatial
data for use in environmental analysis 177
Nicholas R. Chrisman
SECTION IV DATA SHARING ISSUES
13 Circumventing a dilemma: Historical approaches to
data sharing in ecological research 193
John H. Porter and James T. Callahan
14 Sharing spatial environmental information across
agencies, regions and scales: Issues and solutions 203
John Evans
15 Standards for integration of multisource and
cross media environmental data 221
Rodney L. Slagle
SECTION V DATABASES FOR BROAD SCALE RESEARCH
16 Alternative approaches for mapping vegetation
quantities using ground and image data 237
Jennifer L. Dungan, David L. Peterson and
Paul J. Curran
17 Global biosphere requirements for general
circulation models 263
Bruce P. Harden
Contents vii
18 Evaluation of soil database attributes in a
terrestrial carbon cycle model: Implications for
global change research 277
Christopher S. Potter, Pamela A. Mat son and
Peter M. Vitousek
19 Designing global land cover databases to maximize
utility: The US prototype 299
Bradley C. Reed, Thomas R. Love land,
Louis T. Steyaert, Jesslyn F. Brown,
James W. Merchant and Donald O. Ohlen
20 Global environmental characterization: Lessons from
the NOAA EPA Global Ecosystems Database
Project 315
John J. Kineman and Donald L. Phillips
SECTION VI ENVIRONMENTAL MODELLING AND
GEOGRAPHIC INFORMATION SYSTEMS
21 Integrating geographic information systems and
environmental simulation models: A status review 333
Louis T. Steyaert and Michael F. Goodchild
22 Data management and simulation modelling 357
Thomas B. Kirchner
23 GIS and spatial analysis for ecological modelling 377
Richard J. Aspinall
24 Linking ecological simulation models to geographic
information systems: An automated solution 397
Martha B. Coleman, Tamara L. Bearly,
Ingrid C. Burke and William K, Lauenroth
25 Comparison of spatial analytic applications of GIS 413
David P. Lanter
SECTION VII NEW ANALYTICAL APPROACHES
26 GIS development to support regional simulation
modelling of north eastern (USA) forest ecosystems 431
Richard G. Lathrop, Jr, John D. A her,
John A. Bognar, Scott V. Ollinger,
Stephane Casset and Jennifer M. Fllis
27 Remote sensing and GIS techniques for spatial and
biophysical analyses of alpine treeline through
process and empirical models 453
viii Contents
Daniel G. Brown, David M. Cairns,
George P. Malanson, Stephen J. Walsh and
David R. Butler
28 Using a GIS to model the effects of land use on
carbon storage in the forests of the Pacific
Northwest, USA 483
Warren B. Cohen, Phillip Sollins, Peter Homann,
William K. Ferrell, Mark E. Harmon,
David O. Wallin and Maria Fiorella
29 Coupling of process based vegetation models to GIS
and knowledge based systems for analysis of vege¬
tation change 497
David Miller
30 A knowledge based approach to the management of
geographic information systems for simulation of
forested ecosystems 511
D. Scott Mackay, Vincent B. Robinson and
Lawrence E. Band
31 Detecting fine scale disturbance in forested
ecosystems as measured by large scale landscape
patterns 535
G.A. Bradshaw and Steven L. Garman
Subject index 551
List of figures
1.1 A new dynamic between science and technology has forced
evolution of the way in which environmental information is
now managed and analysed. 4
1.2 Analytical and storage capabilities accessible from the future
user s desktop. 10
5.1 The information system management council should bring
together representatives of the scientific community, funding
agency management and information system staff for frank,
open, direct and
frequent interactions. 62
5.2 The analogy between data publication and publication of
scientific research results. 63
5.3 Three personnel scenarios for handling 50 data sets, each of
which requires 26 weeks of human effort to achieve maturity. 78
5.4 Two factors that can increase the cost of handling data sets
long after they are collected or submitted. 79
5.5 Data flow diagram illustrating how data matured in the FIFE
information system. 82
6.1 Konza Prairie research experimental design. 89
6.2 Data requests to Konza Prairie LTER data manager from 1984
to 1993 from non Konza LTER investigators. 94
7.1 Location of 1992 FHM detection monitoring activities. 103
7.2 Location of 1992 FHM pilot and demonstration activities. 104
7.3 FHM information management system data flow. 106
8.1 The Sequoia 2000 layered architecture. 118
9.1 Area as a function of some variable x. 129
9.2 The derivative of the function of Figure 9.1. 130
9.3 Pixels from three images of shrub thickets. 131
9.4 Pixel differences of images shown in Figure 9.3. 131
9.5 Approximating ellipses for areas shown in Figure 9.3. 132
9.6 Vector field denoting change of a spatial boundary. 133
9.7 Observed area values and the finite difference structure. 134
10.1 Flow diagram of the components of research data management
taken from the North Inlet LTER, illustrating the integral
importance of quality assurance. 144
10.2 AIC versus the number of model parameters for Method I
(parametric) (a), Method II (seasonal semiparametric) (b), and
x List of figures
Method III (adaptive semiparametric) (c) when given a range
for their required input parameters. 146
10.3 The results of the minimum AIC fits for total nitrogen at Clam
Bank: (a) parametric method; (b) seasonal semiparametric
method; (c) adaptive semiparametric method. 148
10.4 The results of the minimum AIC fits for total phosphorous at
Clam Bank: (a) parametric method; (b) seasonal semiparametric
method; (c) adaptive semiparametric method. 149
10.5 The results of the minimum AIC fits for total macrobenthic
organisms at Bread and Butter Creek: (a) parametric method;
(b) seasonal semiparametric method; (c) adaptive semipara¬
metric method. 150
11.1 CAN 1 population. 165
11.2 SDS 2 population. 166
11.3 MTN 3 population. 167
11.4 Examples of (a) square grid, (b) offset grid and (c) linear
clusters. 168
11.5 Relative efficiency of two systematic designs applied to the (a)
CAN 1, (b) SDS 2 and (c) MTN 3 landscapes. 171
11.6 Intracluster correlation under different orientations of linear
cluster designs applied to the (a) CAN 1, (b) SDS 2 and (c)
MTN 3 landscapes. 172
11.7 For cost ratios ranging from 1:1 to 1:25 the relative efficiency
for a fixed cost of linear cluster designs applied to the (a)
CAN 1, (b) SDS 2 and (c) MTN 3 landscapes. 172
11.8 For cost ratios 1:2, 1:10 and 1:20, the relative efficiency for a
fixed cost bounded by plus or minus 20 per cent uncertainty in
intracluster correlation. Linear cluster designs were applied to
the (a) CAN 1, (b) SDS 2 and (c) MTN 3 landscapes. 173
12.1 Histosols, soils with a surface layer of accumulated organic
matter that is usually acid and wet (reproduced from Gersmehl,
1985, p. 332, Professional Geographer, with permission of the
Association of American Geographers). 178
12.2 Peatlands of the United States, from a Department of Energy
publication (reproduced from Gersmehl, 1985, p. 333, Profes¬
sional Geographer, with permission of the Association of
American
Geographers). 179
13.1 Time between termination of data collection and publication in
Ecology for 25 randomly selected articles which provided
information on dates of data collection. 201
16.1 Grey scaled images from bands of AVIRIS data used to
construct data set: (a) near infrared band, used as true map of
primary variable; (b) middle infrared band, used as map of
secondary variable. 245
List of figures xi
16.2 Scatterplot of primary and secondary data. 246
16.3 Locations of 300 sample pixels from the 90 000 pixels of the
true map. 247
16.4 Scatterplot of primary samples with collocated secondary data. 248
16.5 Map predicted with regression. 249
16.6 Plots of means of pairs at h pixels apart: (a) of primary
variable sample; (b) of secondary variable sample. 250
16.7 Semivariograms used for co kriging. 251
16.8 Map predicted with co kriging. 252
16.9 Semivariograms used for conditional simulation. 253
16.10 Four conditional simulations. 254
16.11 50 pixel x 50 pixel subsets from: (a) the true map; (b) the map
predicted with regression; (c) the map predicted with co kriging;
(d) a single simulated map. 255
18.1 CAS A Biosphere model database and submodel integration
scheme. 280
18.2 Framework for spatial modelling and evaluation of potential
aggregation errors in environmental information systems. 281
18.3 Ecosystem carbon nitrogen model. 283
18.4 Sensitivity of carbon storage in the SLOW C pool to uniform
texture settings over the global latitude range. 285
18.5 Composite distribution of SLOW C predicted by the CAS A
Biosphere model using global soil texture settings from Zobler
(1986). 286
18.6 Probability density functions for texture scaling factors at two
standard deviation (a) levels: (a) normal distributions, (b) log
normal distributions. 289
18.7 Soil inundation effects on total carbon storage. 292
19.1 Spatial heterogeneity within a BATS GCM grid cell (2° x 4°
latitude, longitude) in the central USA. 305
19.2 NDVI temporal profile for land cover characteristics database
classes 74 and 78 (big sage, rabbitbrush, wheatgrass and
fescue). 308
20.1 From data to information. 317
20.2 Characterization and modelling as complementary activities. 318
20.3 Intermediate scale of global characterization data. 321
22.1 A compartmental simulation model represents the system of
interest as a series of compartments and flows linking those
compartments. 359
22.2 Object orientated languages enable the construction of models
which can expand their structure automatically during
execution. 362
22.3 Estimating doses to residents surrounding the Nevada Test Site
required consideration of agricultural practices across the
region, radionuclide specific transport information, and
xii List of figures
location specific information about lifestyles and amounts of
fallout. 371
23.1 Model output of the climate regime of Scots pine in Scotland. 382
23.2 Graphs showing relationships between the distribution of Scots
pine and rainfall. 383
23.3 Curlew habitat maps. 388
23.4 Golden plover habitat maps. 389
24.1 Conceptual flow of spatially derived data from the GIS to the
ecological simulation model and back into the GIS. 398
24.2 EcoVision system architecture reveals a modular software
design. 401
24.3 Interface consistency is illustrated in the Eco View ecological
modelling set up menu through the use of menu titles, place¬
ment of menu elements and button functionality. 402
24.4 Eco Vision data flow begins with the selection of a region or
area of interest and GIS data layers. 406
24.5 EcoMap user interface for selection of input GIS data layers. 407
24.6 Combination of selected GIS data layers for Weld County,
Colorado form a list of codes describing each unique land area
to be modelled through Century. 408
24.7 Eco View user interface linking Century ecosystem model
(Parton et ah, 1987) submenu system EventlOO (Metherell et
al., 1993). 409
24.8 EcoMap user interface for viewing results of model simulations. 410
25.1 Model of data relationships in spatial analytic transformations. 416
25.2 Semantic parent/child links between source and derived layers. 417
25.3 Lineage knowledge representation structures lineage attributes
concerning source, derived and product layers and data
dependency relationships between them. 419
26.1 Map of north eastern US forest regional change study area. 432
26.2 PnET input and output GIS data layers. 435
26.3a Plot of PnET outputs of water drainage (mm yr ), evapotran
spiration (mm yr ) and water availability (mm) vs varying grid
cell resolution (m). 437
26.3b Plot of PnET outputs of net primary production (gC m 2.yr)
and wood production (gC m .yr) vs varying grid cell resolution
(m). 437
26.4a Plot of CONUS vs FIA total forest area (km2) by county
(R: = 0.88) with one to one line. 445
26.4b Plot of AVHRR LU/LC (predicted) vs FIA (observed) total
forest area (km2) by county (R2 = 0.98) with one to one line. 445
26.5a Plot of CONUS vs FIA coniferous forest area (km2) by county
(R = 0.76) with one to one line. 447
26.5b Plot of AVHRR LU/LC vs FIA coniferous forest area (km2)
by county (R2 = 0.94) with one to one line. 447
List of figures xiii
26.6a Plot of CONUS vs FIA deciduous forest area (km2) by county
(R = 0.63) with one to one line. 448
26.6b Plot of AVHRR LU/LC vs FIA deciduous forest area (km2) by
county (R2 = 0.44) with one to one line. 448
27.1 The forest alpine tundra ecotone, with several krummholz
islands or patches visible, eastern Glacier National Park. 455
27.2 Scale hierarchy of patterns and processes affecting treeline. 456
27.3 The basic map of study area. 461
27.4 Residual pattern for the closed canopy forest TEMTREE
model. 465
27.5 Symmetry index calculated from confusion matrix. 468
27.6 Dominance and contagion index values for land cover, eleva¬
tion, slope angle, slope aspect, surficial geology and substrate
geology by elevation zone. 468
27.7 Relationship between mean basin solar radiation and symmetry
index (R2 = 0.70). 471
27.8 Changes in net primary productivity (NPP) with decreasing
temperature predicted by FOREST BGC. 472
28.1 Study area for the Pacific Northwest region carbon project,
which includes forested portions of western Oregon and
Washington. 484
28.2 Conceptual models for (a) the creation of carbon stores maps
for five different years between 1972 and 1992, and (b) calcula¬
tion of carbon fluxes both within the forest ecosystem (on site)
and within the forest products sector (off site). 485
28.3 Schemes for detection of changes in forest age in: (a) the
forward direction, and (b) the backward direction. 489
28.4 Calculation of harvested carbon for input to the OFFSITE
module of DFC. 493
29.1 Schematic diagram of the SBS expert system shell with exam¬
ple system contents (based on Baldock et al., 1987). 500
29.2 Successional transitions between common grazing types of
semi natural vegetation with low grazing pressures (based on
Miles, 1988). 504
30.1 KBLIMS is managed by query models which integrate
structural schemes, database facts and methods, terrain analysis
tools and simulation models. 516
30.2 Topographic object and hillslope descriptions are used in creat¬
ing an object for Hillslope No. 7. 520
30.3 The terrain analysis tools transform layer based image data
into object based symbolic data for use within the knowledge
base. 522
30.4 A simulation object is the integration of a hillslope partition,
leaf area index and soil saturation capacity and hydraulic
conductivity. 525
xiv List of figures
30.5 Leaf area index (LAI) of Soup Creek, looking upstream from
the basin outlet. 527
30.6 Leaf area index is correlated with hydrologic similarity to pro¬
vide a spatial distribution within simulation objects. 528
30.7 A layer of hillslopes and streams: (a) is transformed into a
graph of topological links, (b) which are stored as binary
topological relations. 529
30.8 KBLIMS provides multiple windows for interaction with the
system. 530
30.9 KBLIMS will do recursive queries on spatial relations, and
then aggregate results of simulations for objects retrieved in
recursive search of space. 531
31.1 Examples of simulated patterning of total basal area resulting
from different combinations of bark beetle infestation and
wildfire. 543
31.2 Non spatial and spatial statistics of simulated landscapes under
varying drought conditions and number of initiations of bark
beetle and wildfire, and for different sequencing of dis¬
turbances. 545
31.3 Examples of simulated patterning of total basal area under
different sequences of a 1000 ha wildfire and bark beetle infes¬
tation under the no drought condition, showing importance of
location of disturbance initiation. 546
List of tables
1.1 Advance database management system (DBMS) strategies with
potential for scientific information management. 12
5.1 Matrix of data maturation stages and information management
services. 72
5.2 Information system personnel requirements for handling data. 74
7.1 Detection monitoring and research measurements made by
FHM in 1992. 105
8.1 Examples of earth science data sets. 115
8.2 Data types used in the Sequoia 2000 Project. 116
8.3 Bigfoot storage devices. 119
10.1 Documentation categories taken from the North Inlet LTER
long term database illustrate the detail required for continued
utility of data. 143
10.2 Results from the three methods tested, showing estimated dis¬
ruptions at intervention points tested. 155
13.1 LTER Guidelines for Site Data Management Policies issued in
1990 by the LTER ad hoc Committee on Data Access. 197
13.2 Responsibilities of the data management system and data pro¬
viders at LTER sites, as stated in site data management
policies. 198
13.3 Limitations on data access and responsibilities of data users, as
stated in LTER site data management policies. 199
13.4 NASA SCAR A Project Data Access Guidelines. 200
16.1 Set of exponential models of the form
y(h) = C(0) + C(l)Expr(h) used for indicator variograms. 254
16.2 Comparison of whole map summary statistics for the results of
the regression, co kriging and two of the many possible simula¬
tions. 255
17.1 Increased vegetation cover impacts on important climatic attri¬
butes. 264
17.2 Surface roughness used in GCMs. 266
17.3 Changes in evapotranspiration and precipitation for specified
changes in albedo. 268
17.4 Albedos for natural surfaces. 269
17.5 Data needs and data sources. 273
18.1 Texture attributes in the FAO SMW classes. 282
18.2 Soil carbon storage (SLOW pool) in biomes under the lumped
xvi List of tables
FAO SMW (Composite) texture settings and four hypothetical
probability density functions (CV = 0.2 and 0.5) for texture
scaling factors. 290
19.1 Diverse land cover data requirements of land atmosphere inter¬
action models. 300
19.2 Biome classes developed for the Simple Biosphere Model. 303
19.3 California chaparral translated into three classification schemes. 304
23.1 Specification of classes of data quality and spatial analysis
functionality required for use of GIS and spatial analysis in
ecological research and modelling. 379
26.1 PnET model input parameters and data source. 434
26.2 Land use/land cover classification categories. 435
27.1 Significant variables included in the logistic regression models
for each of the four treeline components. 464
27.2 Dominance and contagion index values for land cover, eleva¬
tion, slope angle and slope aspect, by drainage basin. 469
27.3 Significant variables for each treeline component by elevation
zone, with Kappa values. 470
30.1 Mean surface property attributes generated by the terrain
analysis system, for each hillslope object of Soup Creek water¬
shed. 526
|
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| author_GND | (DE-588)1142162737 |
| building | Verbundindex |
| bvnumber | BV009933370 |
| callnumber-first | G - Geography, Anthropology, Recreation |
| callnumber-label | GE30 |
| callnumber-raw | GE30.E57 1994 |
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| callnumber-sort | GE 230 E57 41994 |
| callnumber-subject | GE - Environmental Sciences |
| classification_rvk | AR 12350 AR 13200 QT 200 |
| classification_tum | UMW 009f |
| ctrlnum | (OCoLC)231632557 (DE-599)BVBBV009933370 |
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| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 025 - Operations of libraries and archives |
| dewey-raw | 025.06/3637 20 |
| dewey-search | 025.06/3637 20 |
| dewey-sort | 225.06 43637 220 |
| dewey-tens | 020 - Library and information sciences |
| discipline | Allgemeines Wirtschaftswissenschaften Geographie |
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| genre_facet | Aufsatzsammlung Konferenzschrift |
| id | DE-604.BV009933370 |
| illustrated | Illustrated |
| indexdate | 2024-12-23T13:45:14Z |
| institution | BVB |
| isbn | 0748401237 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006580896 |
| oclc_num | 231632557 |
| open_access_boolean | |
| owner | DE-12 DE-M49 DE-BY-TUM DE-19 DE-BY-UBM DE-706 DE-521 DE-634 DE-11 |
| owner_facet | DE-12 DE-M49 DE-BY-TUM DE-19 DE-BY-UBM DE-706 DE-521 DE-634 DE-11 |
| physical | XXVI, 555 S. Ill., graph. Darst., Kt. |
| publishDate | 1994 |
| publishDateSearch | 1994 |
| publishDateSort | 1994 |
| publisher | Taylor & Francis |
| record_format | marc |
| spellingShingle | Environmental information management and analysis ecosystem to global scales Environmental sciences -- Information services Management information systems Geographic information systems Geoinformationssystem (DE-588)4261642-6 gnd Management (DE-588)4037278-9 gnd Umweltinformationssystem (DE-588)4267581-9 gnd Umweltschutz (DE-588)4061644-7 gnd |
| subject_GND | (DE-588)4261642-6 (DE-588)4037278-9 (DE-588)4267581-9 (DE-588)4061644-7 (DE-588)4143413-4 (DE-588)1071861417 |
| title | Environmental information management and analysis ecosystem to global scales |
| title_auth | Environmental information management and analysis ecosystem to global scales |
| title_exact_search | Environmental information management and analysis ecosystem to global scales |
| title_full | Environmental information management and analysis ecosystem to global scales ed. by William K. Michener ... |
| title_fullStr | Environmental information management and analysis ecosystem to global scales ed. by William K. Michener ... |
| title_full_unstemmed | Environmental information management and analysis ecosystem to global scales ed. by William K. Michener ... |
| title_short | Environmental information management and analysis |
| title_sort | environmental information management and analysis ecosystem to global scales |
| title_sub | ecosystem to global scales |
| topic | Environmental sciences -- Information services Management information systems Geographic information systems Geoinformationssystem (DE-588)4261642-6 gnd Management (DE-588)4037278-9 gnd Umweltinformationssystem (DE-588)4267581-9 gnd Umweltschutz (DE-588)4061644-7 gnd |
| topic_facet | Environmental sciences -- Information services Management information systems Geographic information systems Geoinformationssystem Management Umweltinformationssystem Umweltschutz Aufsatzsammlung Konferenzschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006580896&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT michenerwilliamk environmentalinformationmanagementandanalysisecosystemtoglobalscales |