Forest Timber Volume Research Articles

Large-Scale Wildfire Mitigation Through Deep Reinforcement Learning

Forest management can be seen as a sequential decision-making problem to determine an optimal scheduling policy, e.g., harvest, thinning, or do-nothing, that can mitigate the risks of wildfire. Markov Decision Processes (MDPs) offer an efficient mathematical framework for optimizing forest management policies. However, computing optimal MDP solutions is computationally challenging for large-scale forests due to the curse of dimensionality, as the total number of forest states grows exponentially with the numbers of stands into which it is discretized. In this work, we propose a Deep Reinforcement Learning (DRL) approach to improve forest management plans that track the forest dynamics in a large area. The approach emphasizes on prevention and mitigation of wildfire risks by determining highly efficient management policies. A large-scale forest model is designed using a spatial MDP that divides the square-matrix forest into equal stands. The model considers the probability of wildfire dependent on the forest timber volume, the flammability, and the directional distribution of the wind using data that reflects the inventory of a typical eucalypt (Eucalyptus globulus Labill) plantation in Portugal. In this spatial MDP, the agent (decision-maker) takes an action at one stand at each step. We use an off-policy actor-critic with experience replay reinforcement learning approach to approximate the MDP optimal policy. In three different case studies, the approach shows good scalability for providing large-scale forest management plans. The results of the expected return value and the computed DRL policy are found identical to the exact optimum MDP solution, when this exact solution is available, i.e., for low dimensional models. DRL is also found to outperform a genetic algorithm (GA) solutions which were used as benchmarks for large-scale model policy.

A low-cost integrated sensor for measuring tree diameter at breast height (DBH)

The measurement of tree diameter at breast height (DBH) is the basis for estimating forest timber volume, biomass, and carbon fluxes. The traditional contact methods of measuring DBH are time-consuming and labor-intensive. Thus, it is important to realize a low-cost and rapid method for measuring DBH. In this paper, a non-contact method was proposed by integrating passive (a smartphone) and active optical sensors (a laser ranger). With this device, the horizontal distance from the sensor to the tree trunk acquired by the laser ranger and the image of the target tree acquired by the smartphone were collected simultaneously. An autodetection algorithm was employed to identify the tree trunk within the image, and the diameter of the tree was then measured in combination with the horizontal distance based on the photogrammetry principle. The performance of the proposed method was validated using measuring tapes across 371 trees, the main species of which were Italian Poplar (Populus euramevicana) and Pine (Pinus tabuliformis) with diameters ranging from 6 to 51 cm. To investigate the factors that might affect the method, the results were further analyzed under four different conditions, i.e., varied illumination conditions, urban and natural forest conditions and different tree species with varied surface texture features. The results suggested that the measurements using the proposed device were in good agreement with those of the traditional contact method, with an absolute mean error (MAE) of 1.12 cm and RMSE of 1.55 cm. The attraction of the proposed method is that it is low-cost, portable, easy to use and sufficiently accurate. It is also expected that the proposed method can facilitate the measurement of DBH-related canopy structure parameters, such as tree volume, and other parameters, such as tree height, with little adaptation to the current version.

Estimation of pine forest timber volume in the European part of Russia’s Subarctic region: Evidence from the Kola Peninsula recovering from catastrophic disturbances

The study provides a comparative assessment of the volume of pine forests growing in the European part of the Subarctic region of the Russian Federation (the Kola Peninsula). This characteristic is one of the most important indicators in the forest inventory during the economic assessment of forest stands. The study considers forest communities at various stages of recovery after catastrophic disturbances with a special focus on forest cutting and forest fires. These catastrophic violations are of a different nature when exposed to elements of the forest ecosystem. Therefore, it can be assumed that different communities may experience differences in the estimated characteristics in the process of staged recovery. An alternative hypothesis of this study is the assumption that a comparative assessment of timber volume will vary between communities with different types of catastrophic disturbances (cutting and forest fire) at least in the early stages of recovery.

Effects of tree age and waterlogging duration on the form factors of eastern cottonwood (Populus deltoides Bartr. ex Marsh.)

Abstract Stem form is the short name of the tree trunk shape. Stem form determines the quantity and quality of trunk volume, and affects forest timber volume and stand merchantable volume. Based on Populus deltoides Bartr. ex Marsh. in the Dongting Lake region of Hunan Province in China, changes in the breast-height form factor (f 1.3) and experimental form factor (f a) under different waterlogging durations and stand ages were analyzed using the two-way analysis of variance (ANOVA). The results showed that the differences in waterlogging duration for f 1.3 were not significant, but were significant for f a. Tree age has significant effects on f 1.3 and f a. In addition, the f a of P. deltoides grown in stands with a waterlogging duration of 24 days and 33 days were slightly larger than the other three 50, 64, 86 days, which indicated that trees grown in these stands (24, 33 days) had larger tree volumes. In addition, the waterlogging duration of longer than 86 days was not suitable for the growth of P. deltoides.

Comparing Airborne Laser Scanning, and Image-Based Point Clouds by Semi-Global Matching and Enhanced Automatic Terrain Extraction to Estimate Forest Timber Volume

Information pertaining to forest timber volume is crucial for sustainable forest management. Remotely-sensed data have been incorporated into operational forest inventories to serve the need for ever more diverse and detailed forest statistics and to produce spatially explicit data products. In this study, data derived from airborne laser scanning and image-based point clouds were compared using three volume estimation methods to aid wall-to-wall mapping of forest timber volume. Estimates of forest height and tree density metrics derived from remotely-sensed data are used as explanatory variables, and forest timber volumes based on sample field plots are used as response variables. When compared to data derived from image-based point clouds, airborne laser scanning produced slightly more accurate estimates of timber volume, with a root mean square error (RMSE) of 26.3% using multiple linear regression. In comparison, RMSEs for volume estimates derived from image-based point clouds were 28.3% and 29.0%, respectively, using Semi-Global Matching and enhanced Automatic Terrain Extraction methods. Multiple linear regression was the best-performing parameter estimation method when compared to k-Nearest Neighbour and Support Vector Machine. In many countries, aerial imagery is acquired and updated on regular cycles of 1–5 years when compared to more costly, once-off airborne laser scanning surveys. This study demonstrates point clouds generated from such aerial imagery can be used to enhance the estimation of forest parameters at a stand and forest compartment level-scale using small area estimation methods while at the same time achieving sampling error reduction and improving accuracy at the forest enterprise-level scale.

Forest biomass and volume estimation using airborne LiDAR in a cool-temperate forest of northern Hokkaido, Japan

Trees are recognized as a carbon reservoir, and precise and convenient methods for forest biomass estimation are required for adequate carbon management. Airborne light detection and ranging (LiDAR) is considered to be one of the solutions for large-scale forest biomass evaluation. To clarify the relationship between mean canopy height determined by airborne LiDAR and forest timber volume and biomass of cool-temperate forests in northern Hokkaido, Japan, we conducted LiDAR observations covering the total area of the Teshio Experimental Forest (225km2) of Hokkaido University and compared the results with ground surveys and previous studies. Timber volume and aboveground tree carbon content of the studied forest stands ranged from 101.43 to 480.40m3ha–1 and from 30.78 to 180.54MgC ha–1, respectively. The LiDAR mean canopy height explained the variation among stands well (volume: r2=0.80, RMSE=55.04m3ha–1; aboveground tree carbon content: r2=0.78, RMSE=19.10MgC ha–1) when one simple linear regression equation was used for all types (hardwood, coniferous, and mixed) of forest stands. The determination of a regression equation for each forest type did not improve the prediction power for hardwood (volume: r2=0.84, RMSE=62.66m3ha–1; aboveground tree carbon content: r2=0.76, RMSE=27.05MgC ha–1) or coniferous forests (volume: r2=0.75, RMSE=51.07m3ha–1; aboveground tree carbon content: r2=0.58, RMSE=19.00MgC ha–1). Thus, the combined regression equation that includes three forest types appears to be adequate for practical application to large-scale forest biomass estimation.

Air quality enhancement zones in Taiwan: A carbon reduction benefit assessment

Air pollution control is an important aspect of environmental protection in Taiwan. In 1995, Taiwan's government began, via afforestation, to establish air quality enhancement zones (AQEZs) in order to promote multiple environmental benefits, including improved air quality, increased carbon sequestration, and enhanced environmental quality of life. In 2010, there were over 504 such zones. In addition to providing urban forests and green spaces, tree growth in AQEZs can sequester atmospheric carbon dioxide and, by shading and evapotranspiration, reduce energy demand and increase soil storage of organic carbon. In addition, afforestation can offset the carbon footprint of the world's population and is one of the best approaches to implementing carbon neutrality. The purpose of this study was to analyze the carbon reduction benefits of afforested AQEZs. Investigations of 48 AQEZs showed (1) a high diversity of trees planted (168 species), (2) an average density of trees of 1142/ha, (3) a forest timber volume of 23.92m3/ha, lower than in standard plantations, (4) a carbon storage capacity of 9.25tons/ha, and (5) average annual carbon sequestration of 1.34ton-C/ha/yr. Carbon sequestration by AQEZs in Taiwan, although not remarkably high, is nevertheless, along with other ecosystem services provided by AQEZs, a significant and direct benefit to the urban public. This study provides a reliable basis of measureable results with which decision makers can evaluate benefits of urban forest carbon sequestration.

A CLASS OF REGRESSION-CUM-RATIO ESTIMATORS IN TWO-PHASE SAMPLING FOR UTILIZING INFORMATION FROM HIGH RESOLUTION SATELLITE DATA

Abstract. Two-phase sampling design offers a variety of possibilities for effective use of auxiliary information such as those from high resolution remote sensing data. Continuous satellite data with large area coverage provide scope for deriving population values of the auxiliary variables, which can effectively be used for estimating the population parameters of the variable of interest. This study has been made to examine the possibilities of different forms of auxiliary information derived from remote sensing data in two-phase sampling design and suggest an appropriate estimator that will be of broad interest and applications. A new class of regression-cum-ratio estimators has been proposed for two-phase sampling using information on two auxiliary variables derived from high resolution satellite data. The bias and the mean square error (MSE) of the proposed estimators have been obtained up to first order approximation. Efficiency comparison of the proposed estimators has been made with some traditional estimators. The proposed estimator is found to be more efficient than the usual regression and ratio estimators. Numerical illustration has been carried out to examine the efficiency of the estimator in case of forest timber volume estimation utilizing tree crown diameter and tree height as auxiliary variables. It is shown that these estimators can be employed in a variety of conditions where there is strong correlation of satellite derived information with sample based ground measurements and when the cost of ground measurements is relatively high.

Capability of ETM+ in estimates standing stock in beech stands of mountain forest

Stand volume is an important criterion in forest sciences for monitoring status and function of forests, estimation of productivity, prediction and modeling of forest disturbance, economic and environmental issues, and forest planning. The aim of this research is to evaluate the ETM+ sensor of Landsat 7 satellite data ability for forest timber volume estimation. For this purpose, 40 selective sample plots with 60 × 60 m dimension were selected, and in each sample plot, standing volume was calculated. Correspondent digital data to plots were extracted from spectral and considered as independent variables. Original stand volume data, square root, and logarithm of them were considered as dependent potential for stand volume estimation. Variables using stepwise regression, the best model with coefficient of determination, and adjusted coefficient of determination with regard to determining the appropriate model were modified Log V= 07.546 + 0.85Greenness − 10.564NDWI − 28 0.063Band1. In this model, the coefficient of determination and adjusted coefficient of determination were obtained 0.778 and 0.770, respectively. Result showed that spectral data of the mentioned sensor have a moderate.

Estimating forest timber volume by means of “low-cost” LiDAR data

The main purpose of the present research is to verify whether medium resolution LiDAR data, not forest-specific, can be used to carry out statistical models for estimating timber volume. LiDAR data with different characteristics, and taken in different seasons (winter and summer) on the same forest area (Foresta di Paneveggio, Trentino, Italy), were processed, and an estimation model using winter low-resolution data was performed. Such model was then applied to a different territory, having similar forest characteristics. Moreover, LiDAR and ground data, surveyed during the management plan inventory, were integrated. The final model allows to obtain good volume estimates with fair precision if applied at forest compartments level, and can produce detailed timber volume maps, very useful when

Forest stand volume estimation using IRS_P6 (LISS_IV) data (Case study: Lirehsar, Tonekabon)

Stand volume is an important criterion in forest sciences for monitoring status and function of forests, estimation of productivity, prediction and modeling of forest disturbance, economic and environmental issues and forest planning. The aim of this research is evaluation of the LISS_IV sensor of IRS_P6 satellite data ability for forest timber volume estimation. The study area (1240 ha) is located in watershed No. 35 (Lirehsar) of Mazandaran province. Using systematic random method, 87 circular plots with 0.1 ha area were measured to study the relationship between forest stand volume and satellite data. Correspondent digital data to plots were extracted from spectral and considered as independent variables. Original stand volume data, square root and logarithm of them were considered as dependent variables. Using stepwise regression, the best model (Log V= 8.64–0.19Mb3–0.044Rb3) respect to some criteria including RMSE, bias and correlation coefficient was chosen, while the value of criteria were 32.5%, 12.6% and 0.83%, respectively. Result showed that spectral data of the mentioned sensor have a moderate potential for stand volume estimation.

Inventario forestal bietápico-bifásico combinado

This paper presents an inventory method for natural forests, using a double-sampling scheme with data from plots as well as aerial photographs. This method proved useful for assessment of native forest timber volume.