A Tutorial on Model-Assisted Estimation with Application to Forest Inventory

National forest inventories in many countries combine expensive ground plot data with remotely-sensed information to improve precision in estimators of forest parameters. A simple post-stratified estimator is often the tool of choice because it has known statistical properties, is easy to implement, and is intuitive to the many users of inventory data. Because of the increased availability of remotely-sensed data with improved spatial, temporal, and thematic resolutions, there is a need to equip the inventory community with a more diverse array of statistical estimators. Focusing on generalized regression estimators, we step the reader through seven estimators including: Horvitz Thompson, ratio, post-stratification, regression, lasso, ridge, and elastic net. Using forest inventory data from Daggett county in Utah, USA as an example, we illustrate how to construct, as well as compare the relative performance of, these estimators. Augmented by simulations, we also show how the standard variance estimator suffers from greater negative bias than the bootstrap variance estimator, especially as the size of the assisting model grows. Each estimator is made readily accessible through the new R package, mase. We conclude with guidelines in the form of a decision tree on when to use which an estimator in forest inventory applications.

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

Forests ◽

10.3390/f11080856 ◽

2020 ◽

Vol 11 (8) ◽

pp. 856 ◽

Cited By ~ 1

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.

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A questionnaire-based review of the operational use of remotely sensed data by national forest inventories

Remote Sensing of Environment ◽

10.1016/j.rse.2015.08.029 ◽

2016 ◽

Vol 174 ◽

pp. 279-289 ◽

Cited By ~ 51

Author(s):

Frank Barrett ◽

Ronald E. McRoberts ◽

Erkki Tomppo ◽

Emil Cienciala ◽

Lars T. Waser

Keyword(s):

Remotely Sensed ◽

National Forest ◽

Remotely Sensed Data ◽

Forest Inventories ◽

National Forest Inventories

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Realized and potential efficiency for post-stratified estimation in a national forest inventory

Canadian Journal of Forest Research ◽

10.1139/cjfr-2020-0379 ◽

2021 ◽

Author(s):

James A. Westfall ◽

Andrew J. Lister ◽

John W. Coulston ◽

Ronald E. McRoberts

Keyword(s):

Remotely Sensed ◽

National Forest Inventory ◽

Data Types ◽

Remotely Sensed Data ◽

Large Area ◽

Forest Inventories ◽

Temporal Misalignment ◽

Total Tree ◽

Potential Efficiency ◽

Post Stratification

Post-stratification is often used to increase the precision of estimates arising from large-area forest inventories with plots established at permanent locations. Remotely sensed data and associated spatial products are often used for developing the post-stratification, which offers a mechanism to increase precision for less cost than increasing the sample size. While important variance reductions have been shown from post-stratification, it remains unknown where observed gains lie along the continuum of possible gains. This information is needed to determine whether efforts to further improve post-stratification outcomes are warranted. In this study, two types of ‘optimal’ post-stratification were compared to typical production-based post-stratifications to estimate the magnitude of remaining gains possible. Although the ‘optimal’ post-stratifications were derived using methods inappropriate for operational usage, the results indicated that substantial further increases in precision for estimates of both forest area and total tree biomass could be obtained with better post-stratifications. The potential gains differed by the attribute being estimated, the population being studied, and the number of strata. Practitioners seeking to optimize post-stratification face challenges such as evaluation of numerous auxiliary data sources, temporal misalignment between plot observations and remotely sensed data acquisition, and spatial misalignment between plot locations and remotely sensed data due to positional errors in both data types.

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Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Forests ◽

10.3390/f10030279 ◽

2019 ◽

Vol 10 (3) ◽

pp. 279 ◽

Cited By ~ 8

Author(s):

Ernest William Mauya ◽

Joni Koskinen ◽

Katri Tegel ◽

Jarno Hämäläinen ◽

Tuomo Kauranne ◽

...

Keyword(s):

Tree Species ◽

Forest Inventory ◽

Remotely Sensed ◽

Small Scale ◽

Remotely Sensed Data ◽

Weighted Mean ◽

Alos Palsar ◽

Growing Stock ◽

Stock Volume ◽

Sentinel 2

Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

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Distribution Statistics Preserving Post-Processing Method With Plot Level Uncertainty Analysis for Remotely Sensed Data-Based Forest Inventory Predictions

Remote Sensing ◽

10.3390/rs10111677 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1677

Author(s):

Virpi Junttila ◽

Tuomo Kauranne

Keyword(s):

Forest Inventory ◽

Laser Scanning ◽

Linear Models ◽

Accuracy Assessment ◽

Remotely Sensed ◽

Processing Method ◽

Post Processing ◽

Remotely Sensed Data ◽

Training Set ◽

Study Sites

Remotely sensed data-based models used in operational forest inventory usually give precise and accurate predictions on average, but they often suffer from systematic under- or over-estimation of extreme attribute values resulting in too narrow or skewed attribute distributions. We use a post-processing method based on the statistics of a proper, representative training set to correct the predictions and their probability intervals, attaining corrected predictions that reproduce the statistics of the whole population. Performance of the method is validated with three forest attributes from seven study sites in Finland with training set sizes from 50 to over 400 field plots. The results are compared to those of the uncorrected predictions given by linear models using airborne laser scanning data. The post-processing method improves the accuracy assessment linear fit between the predictions and the reference set by 35.4–51.8% and the distribution fit by 44.5–95.0%. The prediction root mean square error declines on the average by 6.3%. The systematic under- and over-estimation are reduced consistently with all training set sizes. The level of uncertainty is maintained well as the probability intervals cover the real uncertainty while keeping the average probability interval width similar to the one in uncorrected predictions.

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Optimal resolution for linking remotely sensed and forest inventory data in Europe

Remote Sensing of Environment ◽

10.1016/j.rse.2016.05.021 ◽

2016 ◽

Vol 183 ◽

pp. 109-119 ◽

Cited By ~ 10

Author(s):

Adam Moreno ◽

Mathias Neumann ◽

Hubert Hasenauer

Keyword(s):

Forest Inventory ◽

Remotely Sensed ◽

Inventory Data ◽

Forest Inventory Data ◽

Optimal Resolution

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An Automated Approach for Extracting Forest Inventory Data from Individual Trees Using a Handheld Mobile Laser Scanner

Croatian journal of forest engineering ◽

10.5552/crojfe.2021.1096 ◽

2021 ◽

Vol 42 (3) ◽

Author(s):

Mustafa Zeybek ◽

Can Vatandaşlar

Keyword(s):

Data Processing ◽

Forest Inventory ◽

Laser Scanning ◽

Natural Forest ◽

Estimation Accuracy ◽

Future Research ◽

Inventory Data ◽

Pilot Studies ◽

Cloud Data ◽

Forest Inventories

Many dendrometric parameters have been estimated by light detection and ranging (LiDAR) technology over the last two decades. Handheld mobile laser scanning (HMLS), in particular, has come into prominence as a cost-effective data collection method for forest inventories. However, most pilot studies were performed in domesticated landscapes, where the environmental settings were far from those presented by (near)natural forest ecosystems. Besides, these studies consisted of numerous data processing steps, which were challenging when employed by manual means. Here we present an automated approach for deriving key inventory data using the HMLS method in natural forest areas. To this end, many algorithms (e.g., cylinder/circle/ellipse fitting) and machine learning models (e.g., random forest classifier) were used in the data processing stage for estimation of the tree diameter at breast height (DBH) and the number of trees. The estimates were then compared against the reference data obtained by field measurements from six forest sample plots. The results showed that correlations between the estimated and reference DBHs were very strong at the plot level (r=0.83–0.99, p<0.05). The average RMSE for tree DBHs was 1.8 cm at the forest landscape level. As for tree detection, 92.5% of 292 trunks were correctly classified on point cloud data. In general, estimation accuracy was sufficient for operational forest inventory needs. However, they could markedly decrease in »hard plots« located at rocky terrains with dense undergrowth and irregular trunks. We concluded that area-based forest inventories might hugely benefit from the HMLS method, particularly in »easy plots«. By improving the algorithmic performances, the accuracy levels can be further increased by future research.

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