Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

Ota, Tetsuji; Ahmed, Oumer S.; Franklin, Steven E.; Wulder, Michael A.; Kajisa, Tsuyoshi; Mizoue, Nobuya; Yoshida, Shigejiro; Takao, Gen; Hirata, Yasumasa; Furuya, Naoyuki; Sano, Takio; Sokh, Heng; Ma, Vuthy

doi:10.3390/rs61110750

Cited by 32 publications

(25 citation statements)

References 68 publications

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“…From an overall statistical perspective, the predicted and observed volumes were equivalent, although our RF model validations showed a systematic tendency to overestimate small values and underestimate high values. The same was found in previous studies (e.g., [40,57]). According to one study [57], a possible cause might be that because the RF model estimates values by averaging the predictions of many decision trees, it might tend to underestimate when the predicted value is close to the maximum value of the training data.…”

Section: Discussionsupporting

confidence: 79%

“…The same was found in previous studies (e.g., [40,57]). According to one study [57], a possible cause might be that because the RF model estimates values by averaging the predictions of many decision trees, it might tend to underestimate when the predicted value is close to the maximum value of the training data. Similarly, when the estimated value is close to the minimum value of training data it might tend to overestimate.…”

Section: Discussionsupporting

confidence: 79%

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Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest

Silva

Klauberg

Hudak

et al. 2017

Forests

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Improvements in the management of pine plantations result in multiple industrial and environmental benefits. Remote sensing techniques can dramatically increase the efficiency of plantation management by reducing or replacing time-consuming field sampling. We tested the utility and accuracy of combining field and airborne lidar data with Random Forest, a supervised machine learning algorithm, to estimate stem total and assortment (commercial and pulpwood) volumes in an industrial Pinus taeda L. forest plantation in southern Brazil. Random Forest was populated using field and lidar-derived forest metrics from 50 sample plots with trees ranging from three to nine years old. We found that a model defined as a function of only two metrics (height of the top of the canopy and the skewness of the vertical distribution of lidar points) has a very strong and unbiased predictive power. We found that predictions of total, commercial, and pulp volume, respectively, showed an adjusted R 2 equal to 0.98, 0.98 and 0.96, with unbiased predictions of −0.17%, −0.12% and −0.23%, and Root Mean Square Error (RMSE) values of 7.83%, 7.71% and 8.63%. Our methodology makes use of commercially available airborne lidar and widely used mathematical tools to provide solutions for increasing the industry efficiency in monitoring and managing wood volume.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionsupporting

confidence: 79%

Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest

Silva

Klauberg

Hudak

et al. 2017

Forests

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“…The Random Forest (RF) technique is known to be a performant regression method that is becoming widely used by the remote sensing community for, among other, canopy height estimation (e.g., [10,37]), and biomass estimation [33,34,38]. The main advantage of Random Forest is its incorporation of continuous or qualitative predictors without making assumptions about their statistical distribution or covariance structure [36].…”

Section: Canopy Height Trend Mapping Using Random Forest Regressionsmentioning

confidence: 99%

Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana

Fayad¹,

Baghdadi²,

Bailly

et al. 2016

Remote Sensing

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LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km.

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“…rRMSE " RMSE yˆ1 00 (14) whereŷ i is the estimated basal area or canopy closure or volume, y i is the measured basal area or canopy closure or volume, n is the number of observations and y is the mean of the measured variable (basal area or canopy closure or volume). The LOOCV has the advantage of providing an unbiased estimation of the prediction error [23].…”

Section: Validationmentioning

confidence: 99%

“…Further, accurate estimates of structural variables are practical indicators for determining forest evolutionary history [8], monitoring forest sustainability [9], assessing insect infestation susceptibility [10,11], studying wildlife management and biodiversity [12], as well as forest water flux modeling [13]. Accordingly, there is an increasing need to generate accurate information regarding forest structural dynamics [14].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

et al. 2015

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Spatial information of forest structural variables is crucial for sustainable forest management planning, forest monitoring, and the assessment of forest ecosystem productivity. We investigate a complex alpine forest ecosystem located in the Swiss National Park (SNP) and apply empirical models to retrieve the structural variables canopy closure, basal area, and timber volume at plot scale. We used imaging spectrometer (IS) data from the Airborne Prism EXperiment (APEX) in combination with in-situ measurements of forest structural variables to develop empirical models. These models are based on simple and stepwise multiple regressions, while all potential two narrow-band combinations of the Simple Ratio (SR), the Normalized Difference Vegetation Index (NDVI), the perpendicular vegetation index (PVI), the second soil-adjusted vegetation index (SAVI2), and band depth indices were tested. The accuracy of the estimated structural attributes was evaluated using a leave-one-out cross-validation technique. Using stepwise multiple regression models, we obtained a moderate to good accuracy when estimating canopy closure (R 2 = 0.81, rRMSE = 10%), basal area (R 2 = 0.68, rRMSE = 20%), and timber volume (R 2 = 0.73, rRMSE = 22%). We discuss the reliability of empirical approaches for estimates of canopy structural parameters considering the causality of light interaction and surface information.

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Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

Cited by 32 publications

References 68 publications

Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest

Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest

Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

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