Estimation of Changes of Forest Structural Attributes at Three Different Spatial Aggregation Levels in Northern California using Multitemporal LiDAR

Mauro, Francisco; Ritchie, Martin W.; Wing, Brian M.; Frank, Bryce; Monleón, Vicente J.; Temesgen, Hailemariam; Hudak, Andrew T.

doi:10.3390/rs11080923

Cited by 16 publications

(20 citation statements)

References 30 publications

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“…A large number of studies have demonstrated the value of multi-temporal ALS data for estimating changes in forest attributes [12,16,73]. Change maps are important for understanding the state and dynamics of forests, and they can be used as an initial information source for reporting carbon emissions.…”

Section: Population Changementioning

confidence: 99%

“…Zhao et al [14] estimated ∆AGB in a forested landscape in Scotland with the indirect approaches showing slightly more precise performances. In addition, the indirect modeling has been claimed to be less sensitive to extrapolating and it has been found to be a better alternative to estimate changes more accurately at stand-level [73]. In light of the diversity of results, the most accurate and/or precise approach may have to be determined by the data available for each study.…”

Section: Population Changementioning

confidence: 99%

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Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data

et al. 2019

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Despite the popularity of random forests (RF) as a prediction algorithm, methods for constructing confidence intervals for population means using this technique are still only sparsely reported. For two regional study areas (Spain and Norway) RF was used to predict forest volume or aboveground biomass using remotely sensed auxiliary data obtained from multiple sensors. Additionally, the changes per unit area of these forest attributes were estimated using indirect and direct methods. Multiple inferential frameworks have attracted increased recent attention for estimating the variances required for confidence intervals. For this study, three different statistical frameworks, design-based expansion, model-assisted and model-based estimators, were used for estimating population parameters and their variances. Pairs and wild bootstrapping approaches at different levels were compared for estimating the variances of the model-based estimates of the population means, as well as for mapping the uncertainty of the change predictions. The RF models accurately represented the relationship between the response and remotely sensed predictor variables, resulting in increased precision for estimates of the population means relative to design-based expansion estimates. Standard errors based on pairs bootstrapping within or internal to RF were considerably larger than standard errors based on both pairs and wild external bootstrapping of the entire RF algorithm. Pairs and wild external bootstrapping produced similar standard errors, but wild bootstrapping better mimicked the original structure of the sample data and better preserved the ranges of the predictor variables.

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Section: Population Changementioning

confidence: 99%

Section: Population Changementioning

confidence: 99%

Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data

et al. 2019

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“…Models that predict change directly tend to be more accurate than those that predict change indirectly [18,53,54], but the flexibility of applying a single model to multiple image dates with the indirect method is often thought to outweigh the problems (e.g., Powell et al [55]). In many cases, ground measurements of change are not available, and indirect estimation of change is the only option (e.g., Andersen et al [56]).…”

Section: Modeling C Stocks and Stock Change With Only Spatial Predictorsmentioning

confidence: 99%

Integrating TimeSync Disturbance Detection and Repeat Forest Inventory to Predict Carbon Flux

Gray

Cohen

Yang

et al. 2019

Forests

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Understanding change in forest carbon (C) is important for devising strategies to reduce emissions of greenhouse gases. National forest inventories (NFIs) are important to meet international accounting goals, but data are often incomplete going back in time, and the amount of time between remeasurements can make attribution of C flux to specific events difficult. The long time series of Landsat imagery provides spatially comprehensive, consistent information that can be used to fill the gaps in ground measurements with predictive models. To evaluate such models, we relate Landsat spectral changes and disturbance interpretations directly to C flux measured on NFI plots and compare the performance of models with and without ground-measured predictor variables. The study was conducted in the forests of southwest Oregon State, USA, a region of diverse forest types, disturbances, and landowner management objectives. Plot data consisted of 676 NFI plots with remeasured individual tree data over a mean interval (time 1 to time 2) of 10.0 years. We calculated change in live aboveground woody carbon (AWC), including separate components of growth, mortality, and harvest. We interpreted radiometrically corrected annual Landsat images with the TimeSync (TS) tool for a 90 m × 90 m area over each plot. Spectral time series were divided into segments of similar trajectories and classified as disturbance, recovery, or stability segments, with type of disturbance identified. We calculated a variety of values and segment changes from tasseled cap angle and distance (TCA and TCD) as potential predictor variables of C flux. Multiple linear regression was used to model AWC and net change in AWC from the TS change metrics. The TS attribution of disturbance matched the plot measurements 89% of the time regarding whether fire or harvest had occurred or not. The primary disagreement was due to plots that had been partially cut, mostly in vigorous stands where the net change in AWC over the measurement was positive in spite of cutting. The plot-measured AWC at time 2 was 86.0 ± 78.7 Mg C ha−1 (mean and standard deviation), and the change in AWC across all plots was 3.5 ± 33 Mg C ha−1 year−1. The best model for AWC based solely on TS and other mapped variables had an R2 = 0.52 (RMSE = 54.6 Mg C ha−1); applying this model at two time periods to estimate net change in AWC resulted in an R2 = 0.25 (RMSE = 28.3 Mg ha−1) and a mean error of −5.4 Mg ha−1. The best model for AWC at time 2 using plot measurements at time 1 and TS variables had an R2 = 0.95 (RSME = 17.0 Mg ha−1). The model for net change in AWC using the same data was identical except that, because the variable being estimated was smaller in magnitude, the R2 = 0.73. All models performed better at estimating net change in AWC on TS-disturbed plots than on TS-undisturbed plots. The TS discrimination of disturbance between fire and harvest was an important variable in the models because the magnitude of spectral change from fire was greater for a given change in AWC. Regional models without plot-level predictors produced erroneous predictions of net change in AWC for some of the forest types. Our study suggests that, in spite of the simplicity of applying a single carbon model to multiple image dates, the approach can produce inaccurate estimates of C flux. Although models built with plot-level predictors are necessarily constrained to making predictions at plot locations, they show promise for providing accurate updates or back-calculations of C flux assessments.

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“…Regardless of the source of RS data and the type of extracted features from this data, an area-based approach (ABA) is the most appropriate method for AGB estimation over large areas [32,33]. This is because ABA makes it possible to obtain model-unbiased estimates of AGB [34], and the requirements for RS data (e.g., point cloud density) or hardware are relatively low [32]. On the other hand, up-to-date ground data are still needed for preparing a model, and when tree-level information, such as stem number or species, is desired, ABA is less suitable [35].…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have used different RS data and modelling methods to predict AGB in forest, shrub, or grassland ecosystems (e.g., [10,[32][33][34][35][38][39][40][41][42]). However, as far as we know, relatively few studies (e.g., [22,23,26]) have dealt with the spatial identification of AAL and prediction of AGB on AAL using ALS data.…”

Section: Introductionmentioning

confidence: 99%

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

2020

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Mapping aboveground woody biomass (AGB) on abandoned agricultural land (AAL) is required by relevant stakeholders to monitor the spatial dynamics of farmland afforestation, to assess the carbon sequestration, and to set the appropriate management of natural resources. The objective of this study was, therefore, to present and assess a workflow consisting of (1) the spatial identification of AAL based on a combination of airborne laser scanning (ALS) data, cadastral data, and Land Parcel Identification System data, and (2) the prediction of AGB on AAL using an area-based approach and a nonparametric random forest (RF) model based on a combination of field and ALS data. Part of the second objective was also to evaluate the applicability of (1) the author-developed algorithm for the calculation of ALS metrics and (2) a single comprehensive RF model for the whole area of interest. The study was conducted in the forest management unit Vígľaš (Slovakia, Central Europe) covering a total area of 12,472 ha. Specifically, five reference areas consisting of 11,194 reference points were used to assess the accuracy of the spatial identification of AAL, and seventy-five ground reference plots were used for the development of the ALS-based AGB model and for assessing the accuracy of the AGB map. The overall accuracy of the spatial identification of AAL was found to be 93.00% (Cohen’s kappa = 0.82). The difference between ALS-predicted and ground-observed AGB reached a relative root mean square error (RMSE) at 26.1%, 33.1%, and 21.3% for the whole sample size, plots dominated by shrub species, and plots dominated by tree species, respectively.

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Estimation of Changes of Forest Structural Attributes at Three Different Spatial Aggregation Levels in Northern California using Multitemporal LiDAR

Cited by 16 publications

References 30 publications

Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data

Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data

Integrating TimeSync Disturbance Detection and Repeat Forest Inventory to Predict Carbon Flux

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

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