Accurate Estimation of Forest Carbon Stocks by 3-D Remote Sensing of Individual Trees

2003 ◽  
Vol 37 (6) ◽  
pp. 1198-1201 ◽  
Author(s):  
Kenji Omasa ◽  
Guo Yu Qiu ◽  
Kenichi Watanuki ◽  
Kenji Yoshimi ◽  
Yukihide Akiyama
Keyword(s):  
Remote Sensing ◽  
Carbon Stocks ◽  
Forest Carbon ◽  
Accurate Estimation ◽  
Individual Trees

scholarly journals Overview of Research Trends in Estimation of Forest Carbon Stocks Based on Remote Sensing and GIS

2011 ◽  
Vol 14 (3) ◽  
pp. 236-256 ◽  
Author(s):  
Kyoung-Min Kim ◽  
Jung-Bin Lee ◽  
Eun-Sook Kim ◽  
Hyun-Ju Park ◽  
Young-Hee Roh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis ◽  
Carbon Stocks ◽  
Research Trends ◽  
Forest Carbon

scholarly journals Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science

2019 ◽  
Vol 11 (4) ◽  
pp. 463 ◽  
Author(s):  
Céline Boisvenue ◽  
Joanne White
Keyword(s):  
Remote Sensing ◽  
Information Needs ◽  
Large Scale ◽  
Remotely Sensed ◽  
Forest Carbon ◽  
Carbon Accumulation ◽  
Accurate Estimation ◽  
Next Generation ◽  
Remotely Sensed Data ◽  
Large Area

Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key research priorities for remote sensing science. However, estimating and understanding forest carbon and its spatiotemporal variations requires diverse knowledge from multiple research domains, none of which currently offer a complete understanding of forest carbon dynamics. New large-area forest information products derived from remotely sensed data provide unprecedented spatial and temporal information about our forests, which is information that is currently underutilized in forest carbon models. Our goal in this communication is to articulate the information needs of next-generation forest carbon models in order to enable the remote sensing community to realize the best and most useful application of its science, and perhaps also inspire increased collaboration across these research fields. While remote sensing science currently provides important contributions to large-scale forest carbon models, more coordinated efforts to integrate remotely sensed data into carbon models can aid in alleviating some of the main limitations of these models; namely, low sample sizes and poor spatial representation of field data, incomplete population sampling (i.e., managed forests exclusively), and an inadequate understanding of the processes that influence forest carbon accumulation and fluxes across spatiotemporal scales. By articulating the information needs of next-generation forest carbon models, we hope to bridge the knowledge gap between remote sensing experts and forest carbon modelers, and enable advances in large-area forest carbon modeling that will ultimately improve estimates of carbon stocks and fluxes.

scholarly journals Carbon Measurement: An Overview of Forest Carbon Estimation Methods and the Role of Geographical Information System and Remote Sensing Techniques for REDD+ Implementation

2016 ◽  
Vol 13 (1) ◽  
pp. 69-86 ◽  
Author(s):  
Thakur Bhattarai ◽  
Margaret Skutsch ◽  
David Midmore ◽  
Him Lal Shrestha
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Information System ◽  
Developing Countries ◽  
Geographical Information System ◽  
Carbon Stocks ◽  
Forest Carbon ◽  
Geographical Information ◽  
Successful Implementation

Many scientists and policy makers consider payment for environmental services, particularly carbon payment for forest management, a cost-effective and practical solution to climate change and unsustainable development. In recent years an attractive policy has been discussed under the United Nation Framework Convention on Climate Change (UNFCCC): Reducing Emissions from Deforestation and Forest Degradation (REDD+), sustainable management of forest, and conservation and enhancement of carbon in developing countries. This could potentially reward forest-managing communities in developing countries. One of the challenging tasks for the successful implementation of this policy is setting up reliable baseline emissions scenarios based on the historical emissions as input for business as usual projections. Forest biomass measurements, the quantification of carbon stocks, their monitoring, and the observation of these stocks over time, are very important for the development of reference scenario and estimation of carbon stock. This paper reviews a numbers of methods available for estimating forest carbon stocks and growth rates of different forest carbon pools. It also explores the limitations and challenges of these methods for use in different geographical locations, and suggests ways of improving accuracy and precision that reduce uncertainty for the successful implementation of REDD+. Furthermore, the paper assesses the role of remote sensing (RS) and geographical information system (GIS) techniques in the establishment of a long-term carbon inventory.Journal of Forest and Livelihood 13(1) May, 2015, Page:69-86

scholarly journals A 100-year conservation experiment: Impacts on forest carbon stocks and fluxes

2013 ◽  
Vol 310 ◽  
pp. 242-255 ◽  
Author(s):  
Tara Sharma ◽  
Werner A. Kurz ◽  
Graham Stinson ◽  
Marlow G. Pellatt ◽  
Qinglin Li
Keyword(s):  
Carbon Stocks ◽  
Forest Carbon

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

<p>Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake Överuman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km<sup>2</sup> grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.</p>

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