scholarly journals Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico

2013 ◽  
Vol 6 (1) ◽  
pp. 80-96 ◽  
Author(s):  
Carlos A. Aguirre-Salado ◽  
Eduardo J. Treviño-Garza ◽  
Oscar A. Aguirre-Calderón ◽  
Javier Jiménez-Pérez ◽  
Marco A. González-Tagle ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Forest Inventory ◽  
Nearest Neighbor ◽  
Central Mexico ◽  
Inventory Data ◽  
K Nearest Neighbor ◽  
North Central ◽  
Forest Inventory Data

scholarly journals Landscape-scale parameterization of a tree-level forest growth model: a k-nearest neighbor imputation approach incorporating LiDAR data

2010 ◽  
Vol 40 (2) ◽  
pp. 184-199 ◽  
Author(s):  
Michael J. Falkowski ◽  
Andrew T. Hudak ◽  
Nicholas L. Crookston ◽  
Paul E. Gessler ◽  
Edward H. Uebler ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Nearest Neighbor ◽  
Forest Growth ◽  
Tree Level ◽  
Lidar Data ◽  
Inventory Data ◽  
K Nearest Neighbor ◽  
Forest Inventory Data ◽  
Forest Growth Model ◽  
Nearest Neighbor Imputation

Sustainable forest management requires timely, detailed forest inventory data across large areas, which is difficult to obtain via traditional forest inventory techniques. This study evaluated k-nearest neighbor imputation models incorporating LiDAR data to predict tree-level inventory data (individual tree height, diameter at breast height, and species) across a 12 100 ha study area in northeastern Oregon, USA. The primary objective was to provide spatially explicit data to parameterize the Forest Vegetation Simulator, a tree-level forest growth model. The final imputation model utilized LiDAR-derived height measurements and topographic variables to spatially predict tree-level forest inventory data. When compared with an independent data set, the accuracy of forest inventory metrics was high; the root mean square difference of imputed basal area and stem volume estimates were 5 m2·ha–1 and 16 m3·ha–1, respectively. However, the error of imputed forest inventory metrics incorporating small trees (e.g., quadratic mean diameter, tree density) was considerably higher. Forest Vegetation Simulator growth projections based upon imputed forest inventory data follow trends similar to growth projections based upon independent inventory data. This study represents a significant improvement in our capabilities to predict detailed, tree-level forest inventory data across large areas, which could ultimately lead to more informed forest management practices and policies.

Economic losses in carbon forestry due to errors in inventory data

2020 ◽  
Author(s):  
Roope Ruotsalainen ◽  
Timo Pukkala ◽  
Annika Kangas ◽  
Mari Myllymäki ◽  
Petteri Packalen
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Net Present Value ◽  
Nearest Neighbor ◽  
Carbon Price ◽  
Wood Products ◽  
Total Carbon ◽  
Economic Losses ◽  
Inventory Data ◽  
Forest Inventory Data

Forestry can help to mitigate climate change by storing carbon in trees, forest soils and wood products. Forest owners can be subsidized if forestry removes carbon from the atmosphere and taxed if forestry produces emissions. Errors in forest inventory data can lead to losses in net present value (NPV) if management prescriptions are selected based on erroneous data but not on correct data. This study assesses the effect of inventory errors on economic losses in forest management when the objective is to maximize the total NPV of timber production and carbon payments. Errors similar as in airborne laser scanning based forest inventory were simulated in stand attributes with a vine copula approach and nearest neighbor method. Carbon payments were based on the total carbon balance of forestry (incl. trees, soil and wood-based products) and calculations were carried out for 30 years using carbon prices of € 0, 50, 75, 100, 125 and 150 t-1. The results revealed that increasing the carbon price and decreasing the level of errors led to decreased losses in NPV. The inclusion of carbon payments for the maximization of the NPV decreased the effect of errors on the losses, which suggests that the value of collecting more accurate forest inventory data may decrease when the carbon price increases.

scholarly journals Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data?

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 856 ◽  
Author(s):  
Gretchen G. Moisen ◽  
Kelly S. McConville ◽  
Todd A. Schroeder ◽  
Sean P. Healey ◽  
Mark V. Finco ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Forest Inventory ◽  
Remotely Sensed ◽  
Data Sets ◽  
Inventory Data ◽  
Remotely Sensed Data ◽  
North Central ◽  
Forest Inventory Data ◽  
Long Time

Throughout the last three decades, north central Georgia has experienced significant loss in forest land and tree cover. This study revealed the temporal patterns and thematic transitions associated with this loss by augmenting traditional forest inventory data with remotely sensed observations. In the US, there is a network of field plots measured consistently through time from the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program, serial photo-based observations collected through image-based change estimation (ICE) methodology, and historical Landsat-based observations collected through TimeSync. The objective here was to evaluate how these three data sources could be used to best estimate land use and land cover (LULC) change. Using data collected in north central Georgia, we compared agreement between the three data sets, assessed the ability of each to yield adequately precise and temporally coherent estimates of land class status as well as detect net and transitional change, and we evaluated the effectiveness of using remotely sensed data in an auxiliary capacity to improve detection of statistically significant changes. With the exception of land cover from FIA plots, agreement between paired data sets for land use and cover was nearly 85%, and estimates of land class proportion were not significantly different for overlapping time intervals. Only the long time series of TimeSync data revealed significant change when conducting analyses over five-year intervals and aggregated land categories. Using ICE and TimeSync data through a two-phase estimator improved precision in estimates but did not achieve temporal coherence. We also show analytically that using auxiliary remotely sensed data for post-stratification for binary responses must be based on maps that are extremely accurate in order to see gains in precision. We conclude that, in order to report LULC trends in north central Georgia with adequate precision and temporal coherence, we need data collected on all the FIA plots each year over a long time series and broadly collapsed LULC classes.

scholarly journals Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data

2016 ◽  
Vol 8 (7) ◽  
pp. 565 ◽  
Author(s):  
Tianyu Hu ◽  
Yanjun Su ◽  
Baolin Xue ◽  
Jin Liu ◽  
Xiaoqian Zhao ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Forest Inventory ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
Optical Imagery ◽  
Forest Aboveground Biomass

scholarly journals Synthesising Existing Forest Inventory Datasets for Estimating Historical Aboveground Biomass Stocks, Growth and Mortality in Logged-over Tropical Dipterocarp Forests of Kalimantan, Indonesia

2020 ◽  
Author(s):  
Solichin Manuri ◽  
Cris Brack ◽  
Nurul Silva ◽  
Fatmi Noor'an
Keyword(s):  
Land Cover ◽  
Aboveground Biomass ◽  
Forest Inventory ◽  
Emission Factors ◽  
Forest Monitoring ◽  
Inventory Data ◽  
Permanent Sample Plots ◽  
Forest Inventory Data ◽  
Dipterocarp Forests ◽  
The Mean

Abstract Background: Extensive forest inventory data is available from commercial timber companies. For this study, over 20,000 plots were compiled for North, East and West Kalimantan provinces, with more than 17,000 of these exceeding our quality assurance tests. This study aimed to: (1) explore the potential use of existing permanent sample plots and forest inventory data established and measured by timber concessions; (2) assess uncertainties of aboveground biomass (AGB) estimates using various allometric models; (3) analyse the dynamics of AGB in logged-over dipterocarp forests; (4) analyse AGB stocks and emission factors in tropical dipterocarp ecosystems. Methods: Two types of forest monitoring datasets measured by timber companies in Indonesia were compiled and assessed in this study: permanent sample plots (PSPs) (24 1-ha plots), and the overall periodic timber inventory (OPTI) (17,301 plots). We compared various allometric equations for estimating AGB of the plots and developed a simple AGB equation using basal area (BA) as predictor. We further evaluated the AGB growth and mortality using the PSP plots. Results: We found that the model using only tree diameter (D) as a predictor variable tended to be unbiased when aggregating the estimates at larger plots. We also found that BA per hectare could explain the variation of AGB at plot level (adjusted r2 = 0.911; root mean square error [RMSE]: 27.8). We overlaid the OPTI plot with the land cover map and estimated the mean AGB of the associated land cover classes. The mean AGB of primary dryland forest, secondary dryland forest and bush classes were 281.1 + 4.0 Mg/ha, 231.5 + 1.7 Mg/ha and 179.0 + 5.0 Mg/ha, respectively. Nine years after logging, the mean AGB is still lower than the mean AGB two years after logging. The growth rate (2.5%) was still lower than the mortality rate (3.1%), and recruitment (0.2%) did not occur until seven years after logging. Conclusions: The results of this study suggest that the existing forest monitoring data should be incorporated into the carbon accounting system at district, province and national level to improve the estimation of forest biomass and emission factors related to forest degradation and deforestation. However, there is a need for data quality assessment prior the analysis and a standardised platform for nation-wide forest inventory database is therefore required.

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