Interpretation of remotely sensed data on black spruce and paper birch communities in Alaskan boreal forest enhanced by fire with extended SAIL model

2001 ◽  
Author(s):  
Keiji Kushida ◽  
Masami Fukuda
Keyword(s):  
Boreal Forest ◽  
Black Spruce ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Paper Birch

Carbon balance of the taiga forest within Alaska: present and future

2002 ◽  
Vol 32 (5) ◽  
pp. 757-767 ◽  
Author(s):  
John Yarie ◽  
Sharon Billings
Keyword(s):  
Boreal Forest ◽  
Black Spruce ◽  
White Spruce ◽  
Carbon Dynamics ◽  
Mean Annual Temperature ◽  
Century Model ◽  
Inventory Data ◽  
Balsam Poplar ◽  
Taiga Forest ◽  
Paper Birch

Forest biomass, rates of production, and carbon dynamics are a function of climate, plant species present, and the structure of the soil organic and mineral layers. Inventory data from the U.S. Forest Service (USFS) Inventory Analysis Unit was used to develop estimates of the land area represented by the major overstory species at various age-classes. The CENTURY model was then used to develop an estimate of carbon dynamics throughout the age sequence of forest development for the major ecosystem types. The estimated boreal forest area in Alaska, based on USFS inventory data is 17 244 098 ha. The total aboveground biomass within the Alaska boreal forest was estimated to be 815 330 000 Mg. The CENTURY model estimated maximum net ecosystem production (NEP) at 137, 88, 152, 99, and 65 g·m–2·year–1 for quaking aspen (Populus tremuloides Michx.), paper birch (Betula papyrifera Marsh.), balsam poplar (Populus balsamifera L.), white spruce (Picea glauca (Moench) Voss), and black spruce (Picea mariana (Mill.) BSP) forest stands, respectively. These values were predicted at stand ages of 80, 60, 41, 68, and 100 years, respectively. The minimum values of NEP for aspen, paper birch, balsam poplar, white spruce, and black spruce were –171, –166, –240, –300, and –61 g·m–2·year–1 at the ages of 1, 1, 1, 1, and 12, respectively. NEP became positive at the ages of 14, 19, 16, 13, and 34 for aspen, birch, balsam poplar, white spruce, and black spruce ecosystems, respectively. A 5°C increase in mean annual temperature resulted in a higher amount of predicted production and decomposition in all ecosystems, resulting in an increase of NEP. We estimate that the current vegetation absorbs approximately 9.65 Tg of carbon per year within the boreal forest of the state. If there is a 5°C increase in the mean annual temperature with no change in precipitation we estimated that NEP for the boreal forest in Alaska would increase to 16.95 Tg of carbon per year.

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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