Temporal Assessment of Growing Stock, Biomass and Carbon Stock of Indian Forests

2006 ◽  
Vol 74 (1-3) ◽  
pp. 191-221 ◽  
Author(s):  
R. K. Manhas ◽  
J. D. S. Negi ◽  
Rajesh Kumar ◽  
P. S. Chauhan
Keyword(s):  
Carbon Stock ◽  
Growing Stock ◽  
Stock Biomass ◽  
Indian Forests ◽  
Temporal Assessment

scholarly journals Estimation of biomass density and carbon storage in the forests of Andhra Pradesh, India, with emphasis on their deforestation and degradation conditions

2015 ◽  
Vol 1 (1) ◽  
pp. 52-63
Author(s):  
P. Rama Chandra Prasad ◽  
P. Mamtha Lakshmi
Keyword(s):  
Carbon Storage ◽  
Andhra Pradesh ◽  
Carbon Sink ◽  
Forest Degradation ◽  
Current Status ◽  
Mean Values ◽  
Growing Stock ◽  
Stock Biomass ◽  
State Forest Department ◽  
Indian Forests

AbstractThe current study evaluates the growing stock, biomass and carbon content of Andhra Pradesh state’s forest (India) along with its current status of forest degradation and loss. For this purpose, the study used the growing stock data collected by state forest department in 2010 for the calculation of biomass and carbon storage using the standard conversion and expansion factors given by IPCC. The analysis shows low biomass and carbon values for the state’s forest in comparison to the mean values recorded in different studies made for Andhra Pradesh. It is also observed to be lower when compared with the average carbon and biomass for Indian forests. Overall, the analysis showed degradation and loss of forest in the state, coupled with reduction in biomass and carbon sink.

scholarly journals Spatio-temporal assessment of topsoil organic carbon stock change in Hungary

2019 ◽  
Vol 195 ◽  
pp. 104410 ◽  
Author(s):  
Gábor Szatmári ◽  
Béla Pirkó ◽  
Sándor Koós ◽  
Annamária Laborczi ◽  
Zsófia Bakacsi ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Carbon Stock ◽  
Carbon Stock Change ◽  
Spatio Temporal ◽  
Organic Carbon Stock ◽  
Stock Change ◽  
Temporal Assessment

scholarly journals Carbon Stock of Agroforestry Systems at Adjacent Buffer Zone of Lore Lindu National Park, Central Sulawesi

2013 ◽  
Vol 16 (2) ◽  
pp. 123-128
Author(s):  
. Wardah ◽  
Bau Toknok ◽  
. Zulkhaidah
Keyword(s):  
Climate Change ◽  
National Park ◽  
Carbon Stock ◽  
Buffer Zone ◽  
Total Carbon ◽  
Aboveground Carbon ◽  
Stock Biomass ◽  
Belowground Carbon ◽  
Central Sulawesi ◽  
Lore Lindu National Park

The potential of agrofrestry to sequestrate carbon varies depending on the natural quality of sites and management practices. Agroforestry is a climate change mitigation activities. The aim of study was to estimate the carbon stock of agroforestry system at adjacent buffer zone of Lore Lindu National Park (LLNP). Research was carried out in two types of agroforestry stands (simple and complex) adjacent LLNP buffer zone in Palolo Sub District, Sigi District, Central Sulawesi. Estimation of biomass of tree, herbs, litter, necromass and root was based on an allometric equation. The carbon storage in soil was estimated based on the carbon organic content and bulk density of soil in30 cm of depth. The results of study showed that the total carbon stored in the simple agroforestry (125.97 MgC ha-1) was significantly lower than in the complex agroforestry (209.39 MgC ha-1). In addition, the aboveground carbon stock biomass (tree, herbaceous, necromass and litter) and belowground carbon stock (root and soil organic) in a simple agroforestry were 42.42 MgC ha-1 and 83.55 MgC ha-1, respectively. Whereas, the aboveground carbon stock biomass and belowground carbon stock in the complex agroforestry were 98.46 MgC ha-1 and 110.93 MgC ha-1, respectively. Based on the carbon stock estimation in six agroforestry plots in the buffer zones of Lore Lindu National Park, the complex agroforestry was likely to be more stable and more longer in storing carbon comparedto the simple agroforestry.Keywords: Agroforestry, biomass, carbon, climate change, Lore Lindu National Park

Estimation of Growing Stock and Carbon Stock based on Components of Forest Type Map: The case of Kangwon Province

2014 ◽  
Vol 103 (3) ◽  
pp. 446-452 ◽  
Author(s):  
So Won Kim ◽  
Yeong Mo Son ◽  
Eun Sook Kim ◽  
Hyun Park
Keyword(s):  
Carbon Stock ◽  
Forest Type ◽  
Growing Stock

scholarly journals Growing stock-based assessment of the carbon stock in the Belgian forest biomass

2005 ◽  
Vol 62 (8) ◽  
pp. 853-864 ◽  
Author(s):  
Inge Vande Walle ◽  
Nancy Van Camp ◽  
Dominique Perrin ◽  
Raoul Lemeur ◽  
Kris Verheyen ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Forest Biomass ◽  
Growing Stock

scholarly journals A global forest growing stock, biomass and carbon map based on FAO statistics

Silva Fennica ◽  
2008 ◽  
Vol 42 (3) ◽  
Author(s):  
Georg Kindermann ◽  
Ian McCallum ◽  
Steffen Fritz ◽  
Michael Obersteiner
Keyword(s):  
Growing Stock ◽  
Stock Biomass

scholarly journals Biomass Carbon Content in Schima-Castanopsis Forest of Midhills of Nepal: A Case Study from Jaisikuna Community Forest, Kaski

2017 ◽  
Vol 5 (1) ◽  
pp. 1-8
Author(s):  
Sushma Tripathi ◽  
Chandra Bahadur Thapa ◽  
Amrit Sharma
Keyword(s):  
Carbon Content ◽  
Carbon Stock ◽  
Total Biomass ◽  
Biomass Carbon ◽  
Tree Biomass ◽  
Community Forests ◽  
Ground Biomass ◽  
Stock Biomass ◽  
Below Ground

Forests are considered as both the source and sinks of carbon. Different types of forests have different carbon stock. Nepal's midhills community forests have high potentiality to sequester carbon. This paper analyzes the biomass carbon stock in Schima-Castanopsis forest of Jaisikuna community forests of Kaski district, Nepal. Forest area was divided into two blocks and 18 sample plots (9 in each block) were laid randomly. Diameter at Breast Height (DBH) and height of trees (DBH≥5cm) were measured using the DBH tape and clinometer. Leaflitter, herbs, grasses and seedling were collected from 1*1m2 plot and fresh weight was taken. Biomass of tree was calculated and below ground biomass is assumed 15% of above ground tree biomass. For calculating carbon stock, biomass is multiplied by default value 0.47. The above ground tree biomass (AGTB) carbon of chilaune, katus and other species was found 19.56 t/ha, 18.66 t/ha and 3.59 t/ha respectively. The AGTB of chilaune dominated, katus dominated and whole forest was found 43.78 t/ha, 39.83 t/ha and 41.81 t/ ha respectively. LHG carbon was found 2.73 t/ha. Below ground biomass carbon at whole forest was found 6.27 t/ha respectively. Total biomass and carbon at forest was found 108.09 t/ha and 50.80 t/ ha respectively. Difference in biomass and carbon content at chilaune dominated block and katus dominated block was found insignificant. Carbon estimation at forest of different elevation, aspect and location are recommended for further research.

scholarly journals Unsupervised algorithms to detect single trees in a mixed-species and multi-layered Mediterranean forest using LiDAR data

2021 ◽  
Author(s):  
Cesar Alvites ◽  
Giovanni Santopuoli ◽  
Mauro Maesano ◽  
Gherardo Chirici ◽  
Federico Valerio Moresi ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Point Cloud ◽  
Laser Scanning ◽  
Sustainable Forest Management ◽  
Tree Height ◽  
Tree Level ◽  
Forest Stands ◽  
Growing Stock ◽  
Tree Detection ◽  
Large Trees

Accurate measurement of forest growing stock is a prerequisite for implementing Climate-Smart Forestry strategies. This study deals with the use of Airborne Laser Scanning data to assess carbon stock at the tree level. It aims to demonstrate that the combined use of two unsupervised techniques will improve the accuracy of estimation supporting sustainable forest management. Based on the heterogeneity of tree height and point cloud density, we classified 31 forest stands into four complexity categories. The point cloud of each stand was further splitted in three horizontal layers improving the accuracy of tree detection at tree level for which we calculated volume and carbon stock. The average accuracy of tree detection was 0.48. The accuracy was higher for forest stands with lower tree density and higher frequency of large trees, as well as dense point cloud (0.65). The prediction of carbon stock was higher with a bias ranging from -0.3 % to 1.5 % and the RMSE ranging from 0.14 % to 1.48 %.

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