A Semi-Automated Geological Model from Remotely Sensed Data for GIS Mapping and Analysis

2008 ◽  
Author(s):  
A. Dadon ◽  
A. Peeters ◽  
A. Karnieli ◽  
E. Ben-Dor ◽  
Klaudia Oleschko ◽  
...  
Keyword(s):  
Remotely Sensed ◽  
Gis Mapping ◽  
Geological Model ◽  
Remotely Sensed Data

A primer on mapping vegetation using remote sensing

2001 ◽  
Vol 10 (4) ◽  
pp. 277 ◽  
Author(s):  
Tom Bobbe ◽  
Henry Lachowski ◽  
Paul Maus ◽  
Jerry Greer ◽  
Chuck Dull
Keyword(s):  
Remote Sensing ◽  
Remotely Sensed ◽  
Vegetation Mapping ◽  
Project Planning ◽  
Gis Mapping ◽  
Remotely Sensed Data ◽  
Geospatial Information ◽  
Ecological Principles ◽  
Vegetation Maps ◽  
Key Steps

This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 The use of information based upon remotely sensed data is a central factor in our 21st Century society. Scientists in land management agencies especially require accurate and current geospatial information to effectively implement ecosystem management. The increasing need to collect data across diverse landscapes, scales, and ownerships has resulted in a wider application of remote sensing, Geographic Information Systems (GIS) and associated geospatial technologies for natural resource applications. This paper summarizes the use of digital remotely sensed data for vegetation mapping. Key steps in preparing vegetation maps are described. These steps include defining project requirements and classification schemes, use of reference data, classification procedures, and assessing accuracy. The role of field personnel and inventory data is described. Case studies and applications of vegetation mapping on national forest land are also included. remote sensing, GIS, mapping, geospatial, project planning.

THE ROLE OF REMOTE SENSING DATA FOR COASTAL ZONE MONITIORING AND MANAGEMENT (CASE STUDY FOR THE EAST PART OF GULF OF FINLAND)

2017 ◽  
Author(s):  
Nikifor Ostanin ◽  
Nikifor Ostanin
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Environmental Changes ◽  
Rapid Development ◽  
Water Environment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Gulf Of Finland ◽  
Coastal Protection ◽  
Remotely Sensed Data

Coastal zone of the Eastern Gulf of Finland is subjected to essential natural and anthropogenic impact. The processes of abrasion and accumulation are predominant. While some coastal protection structures are old and ruined the problem of monitoring and coastal management is actual. Remotely sensed data is important component of geospatial information for coastal environment research. Rapid development of modern satellite remote sensing techniques and data processing algorithms made this data essential for monitoring and management. Multispectral imagers of modern high resolution satellites make it possible to produce advanced image processing, such as relative water depths estimation, sea-bottom classification and detection of changes in shallow water environment. In the framework of the project of development of new coast protection plan for the Kurortny District of St.-Petersburg a series of archival and modern satellite images were collected and analyzed. As a result several schemes of underwater parts of coastal zone and schemes of relative bathymetry for the key areas were produced. The comparative analysis of multi-temporal images allow us to reveal trends of environmental changes in the study areas. This information, compared with field observations, shows that remotely sensed data is useful and efficient for geospatial planning and development of new coast protection scheme.

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

Sample size for ground and remotely sensed data

1986 ◽  
Vol 20 (1) ◽  
pp. 31-41 ◽  
Author(s):  
P.J. Curran ◽  
H.D. Williamson
Keyword(s):  
Sample Size ◽  
Remotely Sensed ◽  
Remotely Sensed Data
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