scholarly journals Carbon Stock Assessment in Pine Forest of Kedung Bulus Sub-Watershed (Gombong District) Using Remote Sensing and Forest Inventory Data

2013 ◽  
Vol 10 (1) ◽  
pp. 21-30
Author(s):  
Tyas Mutiara Basuki ◽  
Nining Wahyuningrum
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Carbon Stock ◽  
Stock Assessment ◽  
Pine Forest ◽  
Inventory Data ◽  
Forest Inventory Data

scholarly journals Carbon Stock Assessment Using Remote Sensing and Forest Inventory Data in Savannakhet, Lao PDR

2014 ◽  
Vol 6 (6) ◽  
pp. 5452-5479 ◽  
Author(s):  
Phutchard Vicharnakorn ◽  
Rajendra Shrestha ◽  
Masahiko Nagai ◽  
Abdul Salam ◽  
Somboon Kiratiprayoon
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Carbon Stock ◽  
Stock Assessment ◽  
Lao Pdr ◽  
Inventory Data ◽  
Forest Inventory Data

scholarly journals Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing

2021 ◽  
Vol 13 (8) ◽  
pp. 1592
Author(s):  
Nikolai Knapp ◽  
Andreas Huth ◽  
Rico Fischer
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Canopy Height ◽  
Biomass Estimation ◽  
Inventory Data ◽  
Border Effects ◽  
Tree Crowns ◽  
Forest Inventory Data ◽  
Estimation Uncertainty ◽  
Voxel Space

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.

scholarly journals High-resolution forest carbon flux mapping and monitoring in the Pacific Northwest with time since disturbance and disturbance legacies inferred from remote sensing and inventory data

2016 ◽  
Author(s):  
Huan Gu ◽  
Christopher A. Williams ◽  
Bardan Ghimire ◽  
Feng Zhao ◽  
Chengquan Huang
Keyword(s):  
Remote Sensing ◽  
Pacific Northwest ◽  
Forest Inventory ◽  
Stand Age ◽  
Net Ecosystem Productivity ◽  
Fine Scale ◽  
Ecosystem Productivity ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
The Pacific

Abstract. Assessment of forest carbon storage and uptake is central to understanding the role forests play in the global carbon cycle and policy-making aimed at mitigating climate change. Current U.S. carbon stocks and fluxes are monitored and reported at fine-scale regionally, or coarse-scale nationally. We proposed a new methodology of quantifying carbon uptake and release across forested landscapes in the Pacific Northwest (PNW) at a fine scale (30 m) by combining remote-sensing based disturbance year, disturbance type, and aboveground biomass with forest inventory data in a carbon modelling framework. Time since disturbance is a key intermediate determinant that aided the assessment of disturbance-driven carbon emissions and removals legacies. When a recent disturbance was detected, time since disturbance can be directly determined by remote sensing-derived disturbance products; and if not, time since last stand-clearing was inferred from remote sensing-derived 30 m biomass map and field inventory-derived species-specific biomass regrowth curves. Net ecosystem productivity (NEP) was further mapped based on carbon stock and flux trajectories that described how NEP changes with time following harvest, fire, or bark beetle disturbances of varying severity. Uncertainties from biomass map and forest inventory data were propagated by probabilistic sampling to provide a probabilistic, statistical distribution of stand age and NEP for each forest pixel. We mapped mean, standard deviation and statistical distribution of stand age and NEP at 30 m in the PNW region. Our map indicated a net ecosystem productivity of 5.2 Tg C y−1 for forestlands circa 2010 in the study area, with net uptake in relatively mature (> 24 year old) forests (13.6 Tg C y−1) overwhelming net negative NEP from tracts that have seen recent harvest (−6.4 Tg C y−1), fires (−0.5 Tg C y−1), and bark beetle outbreaks (−1.4 Tg C y−1). The approach will be applied to forestlands in other regions of the conterminous U.S. to advance a more comprehensive monitoring, mapping and reporting the carbon consequences of forest change across the U.S.

Estimation of the North–South Transect of Eastern China forest biomass using remote sensing and forest inventory data

2013 ◽  
Vol 34 (15) ◽  
pp. 5598-5610 ◽  
Author(s):  
Yanhua Gao ◽  
Xinxin Liu ◽  
Chengcheng Min ◽  
Honglin He ◽  
Guirui Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Eastern China ◽  
Forest Biomass ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
The North

scholarly journals Visual Digital Forest Model Based on a Remote Sensing Data and Forest Inventory Data

2021 ◽  
Vol 13 (20) ◽  
pp. 4092
Author(s):  
Marsel Vagizov R. ◽  
Eugenie Istomin P. ◽  
Valerie Miheev L. ◽  
Artem Potapov P. ◽  
Natalya Yagotinceva V.
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Forest Inventory ◽  
Three Dimensional ◽  
Remote Sensing Data ◽  
Digital Model ◽  
Inventory Data ◽  
Forest Model ◽  
Sensing Data ◽  
Forest Inventory Data

This article discusses the process of creating a digital forest model based on remote sensing data, three-dimensional modeling, and forest inventory data. Remote sensing data of the Earth provide a fundamental tool for integrating subsequent objects into a digital forest model, enabling the creation of an accurate digital model of a selected forest quarter by using forest inventory data in educational and experimental forestry, and providing a valuable and extensive database of forest characteristics. The formalization and compilation of technologies for connecting forest inventory databases and remote sensing data with the construction of three-dimensional tree models for a dynamic display of changes in forests provide an additional source of data for obtaining new knowledge. The quality of forest resource management can be improved by obtaining the most accurate details of the current state of forests. Using machine learning and regression analysis methods as part of a digital model, it is possible to visually assess the course of planting growth, changes in species composition, and other morphological characteristics of forests. The goal of digital, interactive forest modeling is to create virtual simulations of the future status of forests using a combination of predictive forest inventory models and machine learning technology. The research findings provide a basic idea and technique for developing local digital forest models based on remote sensing and data integration technologies.

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