scholarly journals Spatial Distribution of Aboveground Carbon Stock of the Arboreal Vegetation in Brazilian Biomes of Savanna, Atlantic Forest and Semi-Arid Woodland

PLoS ONE ◽  
2015 ◽  
Vol 10 (6) ◽  
pp. e0128781 ◽  
Author(s):  
Henrique Ferraco Scolforo ◽  
Jose Roberto Soares Scolforo ◽  
Carlos Rogerio Mello ◽  
Jose Marcio Mello ◽  
Antonio Carlos Ferraz Filho
2022 ◽  
Vol 503 ◽  
pp. 119789
Author(s):  
Alex Josélio Pires Coelho ◽  
Pedro Manuel Villa ◽  
Fabio Antônio Ribeiro Matos ◽  
Gustavo Heringer ◽  
Marcelo Leandro Bueno ◽  
...  

2011 ◽  
Vol 26 (4) ◽  
pp. 500-512 ◽  
Author(s):  
Hossein Tabari ◽  
Ali Aeini ◽  
P. Hosseinzadeh Talaee ◽  
B. Shifteh Some'e

2019 ◽  
Author(s):  
Marion Réveillet ◽  
Shelley MacDonell ◽  
Simon Gascoin ◽  
Christophe Kinnard ◽  
Stef Lhermitte ◽  
...  

Author(s):  
Bayu Elwanto Bagus Dewanto ◽  
Retnadi Heru Jatmiko

Estimation of aboveground carbon stock on stands vegetation, especially in green open space, has become an urgent issue in the effort to calculate, monitor, manage, and evaluate carbon stocks, especially in a massive urban area such as Samarinda City, Kalimantan Timur Province, Indonesia. The use of Sentinel-1 imagery was maximised to accommodate the weaknesses in its optical imagery, and combined with its ability to produce cloud-free imagery and minimal atmospheric influence. The study aims to test the accuracy of the estimated model of above-ground carbon stocks, to ascertain the total carbon stock, and to map the spatial distribution of carbon stocks on stands vegetation in Samarinda City. The methods used included empirical modelling of carbon stocks and statistical analysis comparing backscatter values and actual carbon stocks in the field using VV and VH polarisation. Model accuracy tests were performed using the standard error of estimate in independent accuracy test samples. The results show that Samarinda Utara subdistrict had the highest carbon stock of 3,765,255.9 tons in the VH exponential model. Total carbon stocks in the exponential VH models were 6,489,478.1 tons, with the highest maximum accuracy of 87.6 %, and an estimated error of 0.57 tons/pixel.


Author(s):  
P. Wicaksono ◽  
P. Danoedoro ◽  
U. Nehren ◽  
A. Maishella ◽  
M. Hafizt ◽  
...  

Abstract. Remote sensing can make seagrass aboveground carbon stock (AGCseagrass) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGCseagrass in the field to train and assess its remote sensing-based mapping. The aim of this research is to (1) analyze the Percent Cover (PCv)-AGCseagrass relationship in seagrass at the species and community levels to estimate AGCseagrass from PCv and (2) perform AGCseagrass mapping at both levels using WorldView-2 image and assess the accuracy of the resulting map. This research was conducted in Karimunjawa and Kemujan Islands, Indonesia. Support Vector Machine (SVM) classification was used to map seagrass species composition, and stepwise regression was used to model AGCseagrass using deglint, water column corrected, and principle component bands. The results were a rapid AGCseagrass estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGCseagrass map had 58.79% accuracy (SEE = 5.41 g C m−2), whereas at the species level, the accuracy increased for the class Ea (64.73%, SEE = 6.86 g C m−2) and EaThCr (70.02%, SEE = 4.32 g C m−2) but decreased for ThCr (55.08%, SEE = 2.55 g C m−2). The results indicate that WorldView-2 image reflectance can accurately map AGCseagrass in the study area in the range of 15–20 g C m−2 for Ea, 10–15 g C m−2 for EaThCr, and 4–8 g C m−2 for ThCr. Based on our model, the AGCseagrass in the study area was estimated at 13.39 t C.


Sign in / Sign up

Export Citation Format

Share Document