scholarly journals Objective stratification and sampling-effort allocation of ground-truthing in benthic-mapping surveys

2009 ◽  
Vol 67 (4) ◽  
pp. 628-637 ◽  
Author(s):  
Annika J. Clements ◽  
James A. Strong ◽  
Clare Flanagan ◽  
Matthew Service
Keyword(s):  
Resource Constraints ◽  
Slope Angle ◽  
Calculated Data ◽  
Remotely Sensed ◽  
Irish Sea ◽  
Sampling Effort ◽  
Remotely Sensed Data ◽  
Sediment Grain Size ◽  
Effort Allocation ◽  
Ground Truthing

Abstract Clements, A. J., Strong, J. A., Flanagan, C., and Service, M. 2010. Objective stratification and sampling-effort allocation of ground-truthing in benthic-mapping surveys. – ICES Journal of Marine Science, 67: 628–637. The application of statistical procedures for objective stratification of sampling effort during map ground-truthing is presented. Marine benthic mapping is usually undertaken in two stages: a remotely sensed acoustic survey followed by ground-truthing to confirm ground-type and habitat classification. The objective of this study was to assess the application of optimum allocation analysis (OAA) through the use of remotely sensed data to direct expensive ground-truthing sampling effort. At an offshore site in the Irish Sea, classification of remotely sensed data, namely bathymetry and slope angle, generated six predicted ground-types. Calculated data variances within each ground-type were assumed to be a predictor of substratum heterogeneity, and these were used in an OAA to apportion ground-truthing effort objectively within each ground-type in order to achieve a set level of sampling precision. The sampling effort recommended by the OAA was realistic and practical with regard to video footage, but the collection of grabs was limited by resource constraints. The coefficient of variation (CV) of the video ground-truthing data matched that estimated by OAA, but the inability to collect all the recommended grabs resulted in CVs greater than expected for sediment grain-size parameters. The efficient identification of substratum classes using OAA represents a first stage whereby this method could direct ground-truthing that could ultimately be used for habitat mapping.

scholarly journals Tropical montane forests are a larger than expected global carbon store

2014 ◽  
Vol 11 (10) ◽  
pp. 2741-2754 ◽  
Author(s):  
D. V. Spracklen ◽  
R. Righelato
Keyword(s):  
Land Surface ◽  
Forest Cover ◽  
Slope Angle ◽  
Remotely Sensed ◽  
Data Sets ◽  
Remotely Sensed Data ◽  
Montane Forests ◽  
Tropical Montane Forests ◽  
Global Land ◽  
Steep Slopes

Abstract. Tropical montane forests (TMFs) are recognized for the provision of hydrological services and the protection of biodiversity, but their role in carbon storage is not well understood. We synthesized published observations (n = 94) of above-ground biomass (AGB) from forest inventory plots in TMFs (defined here as forests between 23.5° N and 23.5° S with elevations ≥ 1000 m a.s.l.). We found that mean (median) AGB in TMFs is 271 (254) t per hectare of land surface. We demonstrate that AGB declines moderately with both elevation and slope angle but that TMFs store substantial amounts of biomass, both at high elevations (up to 3500 m) and on steep slopes (slope angles of up to 40°). We combined remotely sensed data sets of forest cover with high resolution data of elevation to show that 75% of the global planimetric (horizontal) area of TMF are on steep slopes (slope angles greater than 27°). We used our remote sensed data sets to demonstrate that this prevalence of steep slopes results in the global land surface area of TMF (1.22 million km2) being 40% greater than the planimetric area that is the usual basis for reporting global land surface areas and remotely sensed data. Our study suggests that TMFs are likely to be a greater store of carbon than previously thought, highlighting the need for conservation of the remaining montane forests.

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.

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