LiDAR-supported estimation of change in forest biomass with time-invariant regression models

2015 ◽  
Vol 45 (11) ◽  
pp. 1514-1523 ◽  
Author(s):  
S. Magnussen ◽  
E. Næsset ◽  
T. Gobakken
Keyword(s):  
Regression Model ◽  
A Priori ◽  
Forest Biomass ◽  
Tree Height ◽  
Canopy Height ◽  
Lidar Data ◽  
Explanatory Variables ◽  
Correlated Error ◽  
Data Thinning ◽  
Time Invariant

A single a priori chosen linear regression model with two alternative error structures is proposed for model-assisted (MA) and model-dependent (MD) estimation of state and change in aboveground tree biomass (AGB, Mg·ha−1) in three forest strata in the Våler forest in southeastern Norway. Field data of tree height and stem diameter were collected in 145 permanent 200 m2circular plots. Concurrent LiDAR data were collected for the entire forest. The regression model includes two LiDAR-based explanatory variables: the mean of canopy height raised to a power of 1.5 and the standard deviation of canopy heights. A nearest-neighbour thinning of the 2010 LiDAR data to the density of the 1999 data was implemented to counter density effects in the explanatory variables. Estimates of change based on a single regression model were more accurate than estimating change from year-specific models (and no data thinning). A canopy height dependent correlated error structure was preferred over a partitioning of the error to temporary and “permanent” plot effects. For point estimates of AGB in 1999 and 2010, MA and MD estimates of errors were numerically comparable, but MD errors of change were much smaller than corresponding MA errors.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

Estimation of biophysical characteristics for highly variable mixed-conifer stands using small-footprint lidar

2006 ◽  
Vol 36 (5) ◽  
pp. 1129-1138 ◽  
Author(s):  
Jennifer L. Rooker Jensen ◽  
Karen S Humes ◽  
Tamara Conner ◽  
Christopher J Williams ◽  
John DeGroot
Keyword(s):  
Regression Model ◽  
Large Scale ◽  
Canopy Height ◽  
Training Data ◽  
Lidar Data ◽  
Mixed Conifer ◽  
Wood Volume ◽  
Small Footprint ◽  
Mean Square Errors ◽  
Single Regression

Although lidar data are widely available from commercial contractors, operational use in North America is still limited by both cost and the uncertainty of large-scale application and associated model accuracy issues. We analyzed whether small-footprint lidar data obtained from five noncontiguous geographic areas with varying species and structural composition, silvicultural practices, and topography could be used in a single regression model to produce accurate estimates of commonly obtained forest inventory attributes on the Nez Perce Reservation in northern Idaho, USA. Lidar-derived height metrics were used as predictor variables in a best-subset multiple linear regression procedure to determine whether a suite of stand inventory variables could be accurately estimated. Empirical relationships between lidar-derived height metrics and field-measured dependent variables were developed with training data and acceptable models validated with an independent subset. Models were then fit with all data, resulting in coefficients of determination and root mean square errors (respectively) for seven biophysical characteristics, including maximum canopy height (0.91, 3.03 m), mean canopy height (0.79, 2.64 m), quadratic mean DBH (0.61, 6.31 cm), total basal area (0.91, 2.99 m2/ha), ellipsoidal crown closure (0.80, 0.08%), total wood volume (0.93, 24.65 m3/ha), and large saw-wood volume (0.75, 28.76 m3/ha). Although these regression models cannot be generalized to other sites without additional testing, the results obtained in this study suggest that for these types of mixed-conifer forests, some biophysical characteristics can be adequately estimated using a single regression model over stands with highly variable structural characteristics and topography.

scholarly journals Estimation of Tree Height and Forest Biomass Using Airborne LiDAR Data: A Case Study of Londiani Forest Block in the Mau Complex, Kenya

2017 ◽  
Vol 07 (02) ◽  
pp. 255-269 ◽  
Author(s):  
Faith Kagwiria Mutwiri ◽  
Patroba Achola Odera ◽  
Mwangi James Kinyanjui
Keyword(s):  
Forest Biomass ◽  
Tree Height ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data

scholarly journals A Validated and Accurate Method for Quantifying and Extrapolating Mangrove Above-Ground Biomass Using LiDAR Data

2021 ◽  
Vol 13 (14) ◽  
pp. 2763
Author(s):  
Rafaela B. Salum ◽  
Sharon A. Robinson ◽  
Kerrylee Rogers
Keyword(s):  
Tree Height ◽  
Canopy Height ◽  
Lidar Data ◽  
Mangrove Forests ◽  
Above Ground Biomass ◽  
Rhizophora Stylosa ◽  
Ground Biomass ◽  
Mangrove Tree ◽  
Canopy Height Model ◽  
Height Model

LiDAR data and derived canopy height models can provide useful information about mangrove tree heights that assist with quantifying mangrove above-ground biomass. This study presents a validated method for quantifying mangrove heights using LiDAR data and calibrating this against plot-based estimates of above-ground biomass. This approach was initially validated for the mangroves of Darwin Harbour, in Northern Australia, which are structurally complex and have high species diversity. Established relationships were then extrapolated to the nearby West Alligator River, which provided the opportunity to quantify biomass at a remote location where intensive fieldwork was limited. Relationships between LiDAR-derived mangrove heights and mean tree height per plot were highly robust for Ceriops tagal, Rhizophora stylosa and Sonneratia alba (r2 = 0.84–0.94, RMSE = 0.03–0.91 m; RMSE% = 0.07%–11.27%), and validated well against an independent dataset. Additionally, relationships between the derived canopy height model and field-based estimates of above-ground biomass were also robust and validated (r2 = 0.73–0.90, RMSE = 141.4 kg–1098.58 kg, RMSE% of 22.94–39.31%). Species-specific estimates of tree density per plot were applied in order to align biomass of individual trees with the resolution of the canopy height model. The total above-ground biomass at Darwin Harbour was estimated at 120 t ha−1 and comparisons with prior estimates of mangrove above-ground biomass confirmed the accuracy of this assessment. To establish whether accurate and validated relationships could be extrapolated elsewhere, the established relationships were applied to a LiDAR-derived canopy height model at nearby West Alligator River. Above-ground biomass derived from extrapolated relationships was estimated at 206 t ha−1, which compared well with prior biomass estimates, confirming that this approach can be extrapolated to remote locations, providing the mangrove forests are biogeographically similar. The validated method presented in this study can be used for reporting mangrove carbon storage under national obligations, and is useful for quantifying carbon within various markets.

scholarly journals Poster about mapping the risk of forest wind damage using airborne scanning LiDAR

2020 ◽  
Author(s):  
Ninni Saarinen ◽  
Mikko Vastaranta ◽  
Eija Honkavaara ◽  
Michael A. Wulder ◽  
Joanne C. White ◽  
...  
Keyword(s):  
Forest Management ◽  
Regression Model ◽  
Risk Model ◽  
Canopy Height ◽  
Wind Damage ◽  
Forest Damage ◽  
Multivariate Logistic Regression Model ◽  
Lidar Data ◽  
Vegetation Height ◽  
Damage Risk

Wind damage is known for causing threats to sustainable forest management and yield value in boreal forests. Information about wind damage risk can aid forest managers in understanding and possibly mitigating damage impacts especially when wind damage events have increased in recent years.The objective of this research was to better understand and quantify drivers of wind damage, and to map the probability of wind damage and to provide information that could be used to support decision making in forest management planning, as well as in other sectors (e.g. electricity companies). To accomplish this, we used open-access airborne scanning light detection and ranging (LiDAR) data. LiDAR data can provide wall-to-wall coverage and are best suited for monitoring of the dominant trees. In addition multitemporal LiDAR is highly capable of monitoring abiotic tree or stand level changes. The LiDAR data used are openly accessible for public from NLS and are mainly used for generating digital terrain model (DTM). Potential drivers associated with the probability of wind-induced forest damage were examined using a multivariate logistic regression model which was well suited to the discrete nature of the dependent variable (i.e., damage, no damage) and it has been used widely in the modelling of forest disturbances. Risk model predictors related to topography and vegetation height were extracted from the LiDAR-derived surface models such as DTM and canopy height model (CHM). The strongest predictors in the risk model were mean canopy height and mean elevation. Damaged sample grid cells covered 45,6% of the entire sample and they were mainly dominated by Norway spruce. CHM mean and maximum were higher in damaged sample cells which can be expected to correlate with the result where mean volume was also larger in damaged sample cells than in undamaged. Regression model output was a continuous probability surface whereby the probability for wind damage is interpreted as risk (e.g. areas with high probability of wind damage can be described as high risk areas). With increasing frequency of wind damage events, there is a need to identify areas of high wind damage risk. The selected predictor variables, mean elevation describing local topography and mean canopy height, can provide valuable information on the damage probability (i.e. risk) in a robust way.

scholarly journals Tree height integrated into pantropical forest biomass estimates

2012 ◽  
Vol 9 (8) ◽  
pp. 3381-3403 ◽  
Author(s):  
T. R. Feldpausch ◽  
J. Lloyd ◽  
S. L. Lewis ◽  
R. J. W. Brienen ◽  
M. Gloor ◽  
...  
Keyword(s):  
East Africa ◽  
Carbon Storage ◽  
Stand Structure ◽  
Forest Biomass ◽  
Small Diameter ◽  
Tree Height ◽  
Total Biomass ◽  
Guiana Shield ◽  
Tree Biomass ◽  
Continental Scale

Abstract. Aboveground tropical tree biomass and carbon storage estimates commonly ignore tree height (H). We estimate the effect of incorporating H on tropics-wide forest biomass estimates in 327 plots across four continents using 42 656 H and diameter measurements and harvested trees from 20 sites to answer the following questions: 1. What is the best H-model form and geographic unit to include in biomass models to minimise site-level uncertainty in estimates of destructive biomass? 2. To what extent does including H estimates derived in (1) reduce uncertainty in biomass estimates across all 327 plots? 3. What effect does accounting for H have on plot- and continental-scale forest biomass estimates? The mean relative error in biomass estimates of destructively harvested trees when including H (mean 0.06), was half that when excluding H (mean 0.13). Power- and Weibull-H models provided the greatest reduction in uncertainty, with regional Weibull-H models preferred because they reduce uncertainty in smaller-diameter classes (≤40 cm D) that store about one-third of biomass per hectare in most forests. Propagating the relationships from destructively harvested tree biomass to each of the 327 plots from across the tropics shows that including H reduces errors from 41.8 Mg ha−1 (range 6.6 to 112.4) to 8.0 Mg ha−1 (−2.5 to 23.0). For all plots, aboveground live biomass was −52.2 Mg ha−1 (−82.0 to −20.3 bootstrapped 95% CI), or 13%, lower when including H estimates, with the greatest relative reductions in estimated biomass in forests of the Brazilian Shield, east Africa, and Australia, and relatively little change in the Guiana Shield, central Africa and southeast Asia. Appreciably different stand structure was observed among regions across the tropical continents, with some storing significantly more biomass in small diameter stems, which affects selection of the best height models to reduce uncertainty and biomass reductions due to H. After accounting for variation in H, total biomass per hectare is greatest in Australia, the Guiana Shield, Asia, central and east Africa, and lowest in east-central Amazonia, W. Africa, W. Amazonia, and the Brazilian Shield (descending order). Thus, if tropical forests span 1668 million km2 and store 285 Pg C (estimate including H), then applying our regional relationships implies that carbon storage is overestimated by 35 Pg C (31–39 bootstrapped 95% CI) if H is ignored, assuming that the sampled plots are an unbiased statistical representation of all tropical forest in terms of biomass and height factors. Our results show that tree H is an important allometric factor that needs to be included in future forest biomass estimates to reduce error in estimates of tropical carbon stocks and emissions due to deforestation.

Deciphering Links Between Sexual Violence and Castes in India

2021 ◽  
pp. 2455328X2110325
Author(s):  
Yogendra Musahar
Keyword(s):  
Regression Model ◽  
Sexual Violence ◽  
Economic Status ◽  
Family Health ◽  
Uttar Pradesh ◽  
Binary Logistic Regression ◽  
Wealth Index ◽  
Bivariate Analysis ◽  
Binary Logistic Regression Model ◽  
Explanatory Variables

The recent incident, the gang rape and murder of a 19-year-old woman in Hathras, a small village in Uttar Pradesh of India, once again sparks a debate on links between sexual violence and castes in India. This article aims to examine the links between sexual violence and castes in India. This study utilizes the national representative National Family Health Survey 4 (NFHS-4, 2015–16) data. A bivariate analysis was carried out to analyse the data. A binary logistic regression model was applied to predict the effect of explanatory variables, viz. type of place of residence, years of schooling complete, economic status in terms of wealth index and finally castes on predicted variable, i.e. sexual violence. The binary regression model indicates that there were links between sexual violence and castes. For secured and dignified life of women, caste-based sexual violence must be annihilated.

scholarly journals Integrated radar and lidar analysis reveals extensive loss of remaining intact forest on Sumatra 2007–2010

2015 ◽  
Vol 12 (22) ◽  
pp. 6637-6653 ◽  
Author(s):  
M. B. Collins ◽  
E. T. A. Mitchard
Keyword(s):  
Average Rate ◽  
Forest Biomass ◽  
Forest Monitoring ◽  
Synthetic Aperture ◽  
Lidar Data ◽  
Above Ground Biomass ◽  
Swamp Forest ◽  
L Band ◽  
Field Plots ◽  
Change Map

Abstract. Forests with high above-ground biomass (AGB), including those growing on peat swamps, have historically not been thought suitable for biomass mapping and change detection using synthetic aperture radar (SAR). However, by integrating L-band (λ = 0.23 m) SAR from the ALOS and lidar from the ICESat Earth-Observing satellites with 56 field plots, we were able to create a forest biomass and change map for a 10.7 Mha section of eastern Sumatra that still contains high AGB peat swamp forest. Using a time series of SAR data we estimated changes in both forest area and AGB. We estimate that there was 274 ± 68 Tg AGB remaining in natural forest (≥ 20 m height) in the study area in 2007, with this stock reducing by approximately 11.4 % over the subsequent 3 years. A total of 137.4 kha of the study area was deforested between 2007 and 2010, an average rate of 3.8 % yr−1. The ability to attribute forest loss to different initial biomass values allows for far more effective monitoring and baseline modelling for avoided deforestation projects than traditional, optical-based remote sensing. Furthermore, given SAR's ability to penetrate the smoke and cloud which normally obscure land cover change in this region, SAR-based forest monitoring can be relied on to provide frequent imagery. This study demonstrates that, even at L-band, which typically saturates at medium biomass levels (ca. 150 Mg ha−1), in conjunction with lidar data, it is possible to make reliable estimates of not just the area but also the carbon emissions resulting from land use change.

Estimation of Peaks and Canopy Height Using LiDAR Data

2018 ◽  
Author(s):  
R Lavenya ◽  
Kinnera Shanmukha ◽  
Khokalay Vaishnavi ◽  
N G Abijith ◽  
J Aravinth
Keyword(s):  
Canopy Height ◽  
Lidar Data
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