scholarly journals Forest cover type classification at Mt. Asama using ALOS fully Polarimetric PALSAR data, in Nagano prefecture, central Japan

2009 ◽  
Vol 48 (3) ◽  
pp. 188-194
Author(s):  
Masato KATOH ◽  
Joji IISAKA
Keyword(s):  
Forest Cover ◽  
Central Japan ◽  
Forest Cover Type ◽  
Nagano Prefecture ◽  
Cover Type ◽  
Type Classification

Stratification approach for forest cover type and landuse mapping using IRS-1A LISS-II data — A case study

1994 ◽  
Vol 22 (1) ◽  
pp. 21-29 ◽  
Author(s):  
S. Sudhakar ◽  
R. K. Das ◽  
D. Chakraborty ◽  
B. K. Bardhan Roy ◽  
A. K. Raha ◽  
...  
Keyword(s):  
Forest Cover ◽  
Forest Cover Type ◽  
Cover Type

scholarly journals ABOVE GROUND TREES BIOMASS OF LORE LINDU NATIONAL PARK-CENTRAL SULAWESI : A STUDY COMBINING FIELD MEASUREMENT AND REMOTE SENSING

Agromet ◽  
2010 ◽  
Vol 24 (1) ◽  
pp. 33
Author(s):  
Naimatu Solicha ◽  
Tania June ◽  
M. Ardiansyah ◽  
Antonius B. W.
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Field Data ◽  
Forest Cover ◽  
Satellite Image ◽  
Forest Biomass ◽  
Allometric Equation ◽  
Natural Forest ◽  
Forest Cover Type ◽  
Cover Type

Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The information on biomass is essential to assess the total and the annual capacity of forest vigor. Estimation of aboveground biomass is necessary for studying productivity, carbon cycles, nutrient allocation, and fuel accumulation in terrestrial ecosystem. The possibility that above ground forest biomass might be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The analysis showed that field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The forest cover type A and B (natural forest with the minor timber extraction) has the higher biomass than C and D (natural forest with the major timber extraction and agro forestry), it is about 607 ton/ha and 603 ton/ha. Forest cover type C is 457 ton/ha. Forest cover type D has the lowest biomass is about 203 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees heterogeneity. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass and carbon estimation for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass and for the economic carbon analysis.Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The information on biomass is essential to assess the total and the annual capacity of forest vigor. Estimation of aboveground biomass is necessary for studying productivity, carbon cycles, nutrient allocation, and fuel accumulation in terrestrial ecosystem. The possibility that above ground forest biomass might be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The analysis showed that field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The forest cover type A and B (natural forest with the minor timber extraction) has the higher biomass than C and D (natural forest with the major timber extraction and agro forestry), it is about 607 ton/ha and 603 ton/ha. Forest cover type C is 457 ton/ha. Forest cover type D has the lowest biomass is about 203 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees heterogeneity. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass and carbon estimation for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass and for the economic carbon analysis.

scholarly journals Estimates of Down Woody Materials on Fort A.P. Hill, Virginia

2008 ◽  
Vol 32 (2) ◽  
pp. 53-59 ◽  
Author(s):  
Jason R. Applegate
Keyword(s):  
Coarse Woody Debris ◽  
Forest Inventory ◽  
Forest Cover ◽  
Forest Type ◽  
Wildland Fire ◽  
Woody Debris ◽  
Forest Cover Type ◽  
Cover Type ◽  
Forest Cover Types ◽  
Structure Class

Abstract An inventory of down woody materials (DWM) was conducted on Fort A.P. Hill, Virginia, to develop a baseline of DWM abundance and distribution to assist in wildland fire management. Estimates of DWM are necessary to develop accurate assessments of wildfire hazard, model wildland fire behavior, and establish thresholds for retaining DWM, specifically CWD (coarse woody debris), as a structural component of forest ecosystems. DWM were sampled by forest type and structure class using US Forest Service, Forest Inventory and Analysis (FIA) field procedures. DWM averaged 12–16 tn/ac depending on forest cover type and structure class. Coarse woody debris (CWD) averaged 2.7–13.0 tn/ac depending on forest cover type and structure class. CWD comprised more than 70% of DWM across all forest cover types and structure classes. Fine woody debris (FWD) averaged 0.05–3.2 tn/ac depending on fuel hour class, forest cover type, and structure class. DWM was consistently higher in mature (sawtimber) forests than in young (poletimber) forests across all forest cover types, attributed to an increased CWD component of DWM. The variability associated with DWM suggests that obtaining robust estimates of CWD biomass will require a higher sampling intensity than FWD because of its nonuniform distribution in forest systems. FIA field procedures for tallying and quantifying DWM were practical, efficient, and, subsequently, included as permanent metrics in Fort A.P. Hill's Continuous Forest Inventory program.

scholarly journals Forest Cover Type Prediction using Cartographic Variables

2018 ◽  
Vol 182 (30) ◽  
pp. 14-18
Author(s):  
Tejas Anant ◽  
R. Bhargavi ◽  
Tanmay Anant ◽  
R. M.
Keyword(s):  
Forest Cover ◽  
Forest Cover Type ◽  
Cover Type

Experimental Study III

2018 ◽  
pp. 133-150
Keyword(s):  
Experimental Study ◽  
Forest Cover ◽  
Aggregate Data ◽  
Data Set ◽  
Forest Cover Type ◽  
Cover Type ◽  
Proposed Model ◽  
Type Data ◽  
Nominal Variables ◽  
Pattern Prediction

This chapter provides implementation of the proposed model on Forest Cover Type data set. The chapter includes the implementation of pattern extraction from this dataset by following a series of steps discussed in the proposed model chapter. It also includes detailed implementation of pattern prediction from Automobile dataset for prediction of numeric variables, nominal variables, and aggregate data. The implementation of pattern prediction is also a series of steps as discussed before.

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