Forest Biomass Estimation Research Articles

Stand biomass model for Pinus koraiensis plantation based on different additive methods in Heilongjiang Province, China

Large-scale estimation of forest biomass has received much attention. Constructing a stand-level biomass model is a method for estimating tree layer biomass. In this study, we constructed stand biomass models of Korean pine plantations based on aggregation method 1, aggregation method 2, adjustment method, and disaggregation method. The prediction precision of four additive methods was compared and analyzed to provide theoretical basis for biomass prediction of Korean pine plantations in Heilongjiang Province. Weighted functions were used to eliminate the heteroscedasticity of each model, with the leave-one-out cross validation (LOOCV) as the validation method. The results showed that the overall prediction ability of the adjustment method was slightly better than other methods. The specific prediction precision was ranked as adjustment method > aggregation method 1 > aggregation method 2 > disaggregation method. The prediction precision of four additive methods was not consistent when considering their prediction ability of different stand basal areas. When the stand basal area of Korean pine plantations was distributed in the interval of 0-10 or 50-60 m2·hm-2, the parameter estimation values of disaggregation method performed better. When the stand basal area was distributed in other intervals, the parameter estimation values of adjustment method was better.

Forest structure dependency analysis of L-band SAR backscatter.

Forest structure plays an important role in forest biomass inversion using synthetic aperture radar (SAR) backscatter. Synthetic aperture radar (SAR) sensors with long-wavelength have the potentiality to provide reliable and timely forest biomass inversion for their ability of deep penetration into the forest. L-band SAR backscatter shows useful for forest above-ground biomass (AGB) estimation. However, the way that forest structure mediating the biomass-backscatter affects the improvement of the related biomass estimation accuracy. In this paper, we have investigated L-band SAR backscatter sensitivity to forests with different mean canopy density, mean tree height and mean DBH (diameter at breast height) at the sub-compartment level. The forest species effects on their relationship were also considered in this study. The linear correlation coefficient R, non-linear correlation parameter, Maximal Information Coefficient (MIC), and the determination coefficient R2 from linear function, Logarithmic function and Quadratic function were used in this study to analyze forest structural properties effects on L-band SAR backscatter. The HV channel, which is more sensitive than HH to forest structure parameters, was chosen as the representative of SAR backscatter. 6037 sub-compartment were involved in the analysis. Canopy density showed a great influence on L-band backscatter than mean forest height and DBH. All of the R between canopy density and L-band backscatter were greater than 0.7 during the forest growth cycle. The sensitivity of L-band backscatter to mean forest height depends on forest canopy density. When canopy density was lower than 0.4, R values between mean forest height are smaller than 0.5. In contrast, the values of R were greater than 0.8 if canopy density was higher than 0.4. The sensitivity SAR backscatter to DBH fluctuated with canopy density, but it only showed obvious sensitivity when canopy density equals to 0.6, where both the linear and non-liner correlation values are higher than others. However, their effects on L-bang HV backscatter are affected by forest species, the effects on three forest structural parameters depend on tree species.

Generalized additive models reveal among-stand variation in live tree biomass equations

Accurate estimation of forest biomass is important for scientists and policymakers interested in carbon accounting, nutrient cycling, and forest resilience. Estimates often rely on the allometry of trees; however, limited datasets, uncertainty in model form, and unaccounted-for sources of variation warrant a reexamination of allometric relationships using modern statistical techniques. We asked the following questions: (i) Is there among-stand variation in allometric relationships? (ii) Is there nonlinearity in allometric relationships? (iii) Can among-stand variation or nonlinearities in allometric equations be attributed to differences in stand age? (iv) What are the implications for biomass estimation? To answer these questions, we synthesized a dataset of small trees from six different studies in the White Mountains of New Hampshire. We compared the performance of generalized additive models (GAMs) and linear models and found that GAMs consistently outperform linear models. The best-fitting model indicates that allometries vary among both stands and species and contain subtle nonlinearities that are themselves variable by species. Using a planned contrasts analysis, we were able to attribute some of the observed among-stand heterogeneity to differences in stand age. However, variability in these results points to additional sources of stand-level heterogeneity, which if identified could improve the accuracy of live tree biomass estimation.

Model-Based Estimation of Forest Canopy Height and Biomass in the Canadian Boreal Forest Using Radar, LiDAR, and Optical Remote Sensing

One of the fundamental technical challenges of any new spaceborne vegetation remote sensing mission is the determination of what sensor(s) to place onboard and what, if any, overlapping modes of operation they will employ as each onboard sensor adds significant cost to the overall mission. In this article, the remote sensing of forest parameters using multimodal remote sensing is presented. In particular, polarimetric radar, Light Detection And Ranging (LiDAR), and near-IR passive optical sensing platforms are employed in conjunction with physics-based models. These models are used to accurately estimate forest aboveground biomass as well as canopy height in homogeneous areas. It is shown that this proposed method is capable of achieving high accuracy estimates while using minimal ancillary data in the estimation process. We present a method to combine measured data sets with our geometric and electromagnetic sensor models to develop a forest parameter estimation algorithm that fuses multimodal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height with rms errors of 1.6 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 1.68 m respectively.

An accuracy analysis of mangrove tree height mensuration using forestry techniques, hypsometers and UAVs

Tree height is a fundamental measurement in forest inventory studies and a critical variable for the assessment of tree biomass, carbon stock and site productivity. However, measuring tree height is often a challenging task and may generate errors. This study provides an accuracy analysis of tree heights measured through different methods ranging from traditional techniques (thumb rule and stick method) to trigonometric equipment (clinometer, laser rangefinder, altimeter), and advanced technologies (Unmanned Aerial Vehicle or UAV and distometer). Along with scientific insights for the in situ application of these methods, the factors generating errors in tree height mensuration and its impact on forest biomass estimation were determined. Our results showed that the amount of error varies from one method to another. The amount of error was highest with the thumb rule and stick method (15%) while the range of error was similar for the clinometer (7.7%), laser rangefinder (7.1%) and altimeter (7.5%). Parameters such as tree form, the status of tree (i.e., if a tree was either isolated or within a canopy) and height of tree were found to significantly affect error generation during tree height measurements. Information on the extent to which biomass estimation is influenced by tree height errors associated with the use of different methods and instruments and the direction of error impact (overestimation or underestimation of biomass) are discussed. Finally, we recommend that the choice of method for tree heights in field inventory depends on certain factors such as cost, available time and manpower, required skills, site of observation and amount of error generated by each of the methods.

Allometric equations for estimating biomass of community forest tree species in Madiun, Indonesia

Abstract. Wirabuana PYAP, Setiahadi R, Sadono R, Lukito M, Martono DS, Matatula J. 2020. Allometric equations for estimating biomass of community forest tree species in Madiun, Indonesia. Biodiversitas 21: 4291-4300. The capability of community forests for offsetting carbon emissions highly depends on their biomass production. Unfortunately, the measurement of tree biomass in community forests using a destructive method is expensive and time-consuming. It is also almost impossible to conduct this method for all trees in the observation area. Therefore, the development of allometric equations is essential to support tree biomass estimation in community forests. This study was designed to construct specific models for predicting individual tree biomass in community forests, located in Madiun, Indonesia. We destructively sampled approximately 120 trees from four different species (30 trees for each species), i.e., Falcataria moluccana, Melia azedarach, Swietenia macrophylla, and Tectona grandis. For every tree sample, the measurement of biomass was conducted in each tree’s component, namely roots, stem, branches, and leaves. The allometric equations were developed with regression analysis using predictor variables, like diameter at breast height (D), squared diameter at breast height combined with tree height (D2H), as well as D and H separately. Results found that for four species, the mean biomass in the stem was 50.3%, followed by branches 25.4%, roots 15.9%, and leaves 8.3%. The best equation for estimating biomass in every component and total of four species was different. However, our study showed that the equation lnŶ = -3.037 + 1.430 lnD + 1.684 was reliable to estimate total individual tree biomass of four species in the surveyed area since this model had accuracy of 90.8%. Referring to these findings, we recommended the utilization of an allometric equation as an alternative method for facilitating more efficient biomass measurement in the community forests.

Estimation of Future Changes in Aboveground Forest Carbon Stock in Romania. A Prediction Based on Forest-Cover Pattern Scenario

The aboveground forest biomass plays a key role in the global carbon cycle and is considered a large and constant carbon reservoir. Hence, exploring the future potential changes in forest-cover pattern can help to estimate the trend of forest biomass and therefore, carbon stock in a certain area. As a result, the present paper attempts to model the potential changes in aboveground forest carbon stock based on the forest-cover pattern scenario simulated for 2050. Specifically, the resulting aboveground forest biomass, estimated for 2015 using the allometric equation based on diameter at breast height and the estimated forest density, was used as baseline data in the present approach. These spatial data were integrated into the forest-cover pattern scenario, predicted by using a spatially explicit model, i.e., the Conversion of Land Use and its Effects at Small regional extent (CLUE-S), in order to estimate the potential variation of aboveground forest carbon stock. Our results suggest an overall increase by approximately 4% in the aboveground forest carbon stock until 2050 in Romania. However, important differences in the forest-cover pattern change were predicted on the regional scale, thus highlighting that the rates of carbon accumulation will change significantly in large areas. This study may increase the knowledge of aboveground forest biomass and the future trend of carbon stock in the European countries. Furthermore, due to their predictive character, the results may provide a background for further studies, in order to investigate the potential ecological, socio-economic and forest management responses to the changes in the aboveground forest carbon stock. However, in view of the uncertainties associated with the data accuracy and methodology used, it is presumed that the results include several spatial errors related to the estimation of aboveground forest biomass and simulation of future forest-cover pattern change and therefore, represent an uncertainty for the practical management of applications and decisions.

Assessment of forest carbon stocks for REDD+ implementation in the muyong forest system of Ifugao, Philippines.

Forests hold significant potential for carbon sequestration and climate change mitigation. Forest biomass estimation is vital for sustainable forest management, providing critical input data for implementing the United Nations Reducing Emissions from Deforestation and forest Degradation-plus (REDD+) mechanism. This study investigates the total carbon pools-aboveground biomass (AGB), belowground biomass (BGB), forest floor biomass, and soil carbon-using field-based information in the muyong forest management system, which is native to Ifugao in the Philippines. This study reveals that a difference may be observed between the total carbon stock of the private woodlots (muyong) and that of the communal forest (bilid). The results indicate that the bilid forest has trees with a small diameter at breast height (DBH) and high tree density in contrast to the muyong, which has trees with high DBH and low tree density. The average carbon stock per unit area is higher in muyong (150.8tC/ha) than in bilid (126.1tC/ha). These findings are valuable in determining whether Ifugao's muyong forest system should be included under the REDD+ framework. Human mediation and management helps forests to sequester a greater amount of carbon than they would without human intervention. Implementation of REDD+ should promote Ifugao's ecosystem and biodiversity conservation and agroforestry practices in addition to protecting traditional agricultural practices and livelihoods in relation to rice terraces.

Tropical tree size-frequency distributions from airborne lidar.

In tropical rainforests, tree size and number density are influenced by disturbance history, soil, topography, climate, and biological factors that are difficult to predict without detailed and widespread forest inventory data. Here, we quantify tree size-frequency distributions over an old-growth wet tropical forest at the La Selva Biological Station in Costa Rica by using an individual tree crown (ITC) algorithm on airborne lidar measurements. The ITC provided tree height, crown area, the number of trees >10m height and, predicted tree diameter, and aboveground biomass from field allometry. The number density showed strong agreement with field observations at the plot- (97.4%; 3% bias) and tree-height-classes level (97.4%; 3% bias). The lidar trees size spectra of tree diameter and height closely follow the distributions measured on the ground but showed less agreement with crown area observations. The model to convert lidar-derived tree height and crown area to tree diameter produced unbiased (0.8%) estimates of plot-level basal area and with low uncertainty (6%). Predictions on basal area for tree height classes were also unbiased (1.3%) but with larger uncertainties (22%). The biomass estimates had no significant bias at the plot- and tree-height-classes level (-5.2% and 2.1%). Our ITC method provides a powerful tool for tree- to landscape-level tropical forest inventory and biomass estimation by overcoming the limitations of lidar area-based approaches that require local calibration using a large number of inventory plots.

Tropical PeatLand Forest Biomass Estimation Using Polarimetric Parameters Extracted from RadarSAT-2 Images

This paper was aimed at estimating the forest aboveground biomass (AGB) in the Central Kalimantan tropical peatland forest, Indonesia, using polarimetric parameters extracted from RadarSAT-2 images. Six consecutive acquisitions of RadarSAT-2 full polarimetric data were acquired and polarimetric parameters were extracted. The backscattering coefficient ( σ o ) for HH, HV, VH, and VV channels was computed respectively. Entropy (H) and alpha ( α ) were computed using eign decomposition. In order to understand the scattering behavior, Yamaguchi decomposition was performed to estimate surface scattering ( γ s u r f ) and volume scattering ( γ v o l ) components. Similarly following polarimetric indices were computed; Biomass Index (BMI), Canopy Structure Index (CSI), Volume Scattering Index (VSI), Radar Vegetation Index (RVI) and Pedestal Height ( p h ). The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence, and suitability for forest aboveground biomass estimation across rainy and dry conditions. Regression analysis was performed between referenced biomass measurements and polarimetric parameters; VSI, H, RVI, p h , and γ v o l were found significantly correlated with AGB. Biomass estimation was carried out using significant models. Resultant models were validated using field-based AGB measurements. Validation results show a significant correlation between measured and referenced biomass measurements with temporal consistency over the acquisition time period.

Above-ground biomass estimation of mangrove forest using WorldView-2 imagery in Perancak Estuary, Bali

Global warming is the important issues because is caused by the concentration increase of greenhouse gasses in the atmosphere. Mangrove ecosystem has a function to reduce climate change through carbon sequestration. Estimates of biomass can be done through an approach from the value of vegetation biomass. Allometric equations method is used for calculating biomass values. Remote sensing technology is used for mapping mangrove ecosystems to determine the spatial distribution of mangrove biomass that is WorldView-2 (WV-2) image. Transformation of the vegetation index are Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are used to estimate of mangrove forest biomass. The aim of this study is to determine the most accurate vegetation index in estimating the mangrove above-ground biomass (AGB) value. The field biomass data was obtained from mangrove tree diameter at breast height (DBH) measurement. Using the allometric equation, the field AGB can be calculated. A regression analysis between field biomass and WV-2 image pixel value was performed to build regression function to estimate the mangrove AGB. The results of this study show that the SAVI provided higher accuracy for mangrove AGB estimation with R2 of 0.4125 and resulting the AGB value between 1124.81 to 4499.25 kg/m.

Aboveground Biomass Estimation in Amazonian Tropical Forests: a Comparison of Aircraft- and GatorEye UAV-borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil

Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural parameter estimates for monitoring in native and commercial forests. In this study, we compare products and aboveground biomass (AGB) estimations from LiDAR data acquired using an aircraft-borne system in 2015 and data collected by the unmanned aerial vehicle (UAV)-based GatorEye Unmanned Flying Laboratory in 2017 for ten forest inventory plots located in the Chico Mendes Extractive Reserve in Acre state, southwestern Brazilian Amazon. The LiDAR products were similar and comparable among the two platforms and sensors. Principal differences between derived products resulted from the GatorEye system flying lower and slower and having increased returns per second than the aircraft, resulting in a much higher point density overall (11.3 ± 1.8 vs. 381.2 ± 58 pts/m2). Differences in ground point density, however, were much smaller among the systems, due to the larger pulse area and increased number of returns per pulse of the aircraft system, with the GatorEye showing an approximately 50% higher ground point density (0.27 ± 0.09 vs. 0.42 ± 0.09). The LiDAR models produced by both sensors presented similar results for digital elevation models and estimated AGB. Our results validate the ability for UAV-borne LiDAR sensors to accurately quantify AGB in dense high-leaf-area tropical forests in the Amazon. We also highlight new possibilities using the dense point clouds of UAV-borne systems for analyses of detailed crown structure and leaf area density distribution of the forest interior.

Sampling trees to develop allometric biomass models: How does tree selection affect model prediction accuracy and precision?

Abstract Developing allometric biomass models is an important process because reliability of forest biomass and carbon estimations largely depend on the accuracy and precision of such models. The effects of tree sampling on tree aboveground biomass (AGB) prediction accuracy and precision are complex and can, therefore, be difficult to quantify. In this paper we use a Monte Carlo simulation to investigate how model prediction accuracy and precision are affected by tree sampling approaches. Because diameter at breast height (D, in cm) is the most common predictor of tree AGB (in kg dry weight), we focused our analysis on the AGB-D relationship. The following sample characteristics were investigated: (i) sample size; (ii) extent of the D-range (difference between the largest and the smallest D value); (iii) position of D-range (characterized by the starting point of D-range); and (iv) the size-distribution (distribution of D) of sample trees. We found that, although the natural variability of AGB-D relationship was a key driver for both prediction accuracy and precision, the above sample characteristics were important for improving prediction accuracy. Although having a negligible effect on precision, both sample size and size-distribution of sample trees, greatly influenced prediction accuracy. We demonstrate that selecting a constant number of trees for each D class (i.e. uniform distribution of the sample trees over the D-range) generally produced models that were more accurate predictors of AGB. The extent and position of D-range, although considerably affecting the goodness of fit and the standard errors of allometric model parameters, had only a marginal effect on AGB prediction accuracy and precision. Furthermore, we showed that R2 was a poor indicator of model prediction accuracy and precision, due to its sensitivity to changes in D-range. These findings inform certain practical recommendations we report for improving the accuracy and precision of biomass prediction.

Modeling and mapping aboveground biomass of the restored mangroves using ALOS-2 PALSAR-2 in East Kalimantan, Indonesia

Accurate estimation of forest aboveground biomass (AGB) using remote sensing is a requisite for monitoring, reporting and verification (MRV) system of the United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation. However, attaining high accuracy remains a great challenge in the diverse tropical forests. Among available technologies, l-band Synthetic Aperture Radar (SAR) estimates AGB with reasonably high accuracy in the terrestrial tropical forests. Nevertheless, the accuracy is relatively low in the mangrove forests. In this context, the study was carried out to model and map AGB using backscatter coefficients of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array l-band SAR-2 (PALSAR-2) in part of the restored mangrove forest at Mahakam Delta, Indonesia. PALSAR-2 data was acquired with image scene observation during the peak low tide on 30 July 2018 from Japan Aerospace Exploration Agency. The forest parameters namely tree height and diameter at breast height were measured from 71 field plots in September-October 2018. The parameters were used in mangrove allometry to calculate the field AGB. Finally, HV polarized backscatter coefficients of PALSAR-2 were used to model AGB using linear regression. The model demonstrated a comparatively high performance using three distinct methods viz. independent validation (R2 of 0.89 and RMSE of 23.16 tons ha−1), random k-fold cross validation (R2 of 0.89 and RMSE of 24.59 tons ha−1) and leave location out cross validation (LLO CV) (R2 of 0.88 and RMSE of 24.05 tons ha−1). The high accuracy of the LLO CV indicates no spatial overfitting in the model. Thus, the model based on LLO CV was used to map AGB in the study area. This is the first study that successfully obtains high accuracy in modeling AGB in the mangrove forest. Therefore, it offers a significant contribution to the MRV mechanism for monitoring mangrove forests in the tropics and sub-tropics.

Variability and uncertainty in forest biomass estimates from the tree to landscape scale: the role of allometric equations

BackgroundBiomass maps are valuable tools for estimating forest carbon and forest planning. Individual-tree biomass estimates made using allometric equations are the foundation for these maps, yet the potentially-high uncertainty and bias associated with individual-tree estimates is commonly ignored in biomass map error. We developed allometric equations for lodgepole pine (Pinus contorta), ponderosa pine (P. ponderosa), and Douglas-fir (Pseudotsuga menziesii) in northern Colorado. Plot-level biomass estimates were combined with Landsat imagery and geomorphometric and climate layers to map aboveground tree biomass. We compared biomass estimates for individual trees, plots, and at the landscape-scale using our locally-developed allometric equations, nationwide equations applied across the U.S., and the Forest Inventory and Analysis Component Ratio Method (FIA-CRM). Total biomass map uncertainty was calculated by propagating errors from allometric equations and remote sensing model predictions. Two evaluation methods for the allometric equations were compared in the error propagation—errors calculated from the equation fit (equation-derived) and errors from an independent dataset of destructively-sampled trees (n = 285).ResultsTree-scale error and bias of allometric equations varied dramatically between species, but local equations were generally most accurate. Depending on allometric equation and evaluation method, allometric uncertainty contributed 30–75% of total uncertainty, while remote sensing model prediction uncertainty contributed 25–70%. When using equation-derived allometric error, local equations had the lowest total uncertainty (root mean square error percent of the mean [% RMSE] = 50%). This is likely due to low-sample size (10–20 trees sampled per species) allometric equations and evaluation not representing true variability in tree growth forms. When independently evaluated, allometric uncertainty outsized remote sensing model prediction uncertainty. Biomass across the 1.56 million ha study area and uncertainties were similar for local (2.1 billion Mg; % RMSE = 97%) and nationwide (2.2 billion Mg; % RMSE = 94%) equations, while FIA-CRM estimates were lower and more uncertain (1.5 billion Mg; % RMSE = 165%).ConclusionsAllometric equations should be selected carefully since they drive substantial differences in bias and uncertainty. Biomass quantification efforts should consider contributions of allometric uncertainty to total uncertainty, at a minimum, and independently evaluate allometric equations when suitable data are available.

Ground-Based Remote Sensing of Forests Exploiting GNSS Signals

The estimation of aboveground biomass is commonly recognized for global relevance because of the vegetation role in the carbon cycle. Both active and passive microwave sensors can significantly contribute to this goal because of their high sensitivity to water content and high penetration at lower frequencies (L-/P-bands). In particular, Global Navigation Satellite Systems (GNSSs) are recently receiving increasing interest as source of opportunity to be employed as illuminator for L-band remote sensing, since they could provide low-cost sensors for nondestructive forest biomass estimation over large areas. In this article, we suggest a method to extract forest information using the GNSS direct signals collected in clear sky and below the vegetation canopy at both circular polarizations. An experimental campaign, carried out in the framework of an European Space Agency (ESA) project, was conducted over three poplar forests with different biomass to verify the feasibility of this technique. The relationships between the GNSS measurements and the tree parameters were first assessed and then interpreted and supported by statistical analysis and a theoretical model. The signal collected under the canopy is affected by attenuation and depolarization with respect to the one collected in open air, and this article demonstrated that both direct line-of-sight propagation and volume scattering play a role in the signal magnitude and its fluctuation in time. Although the experimental data set is limited in size and environmental conditions, two inversion algorithms were also tested with the encouraging retrieval results.

Mapping aboveground biomass and carbon in Shanghai's urban forest using Landsat ETM+ and inventory data

Quantifying the spatiotemporal distribution of forest biomass carbon (FBC) is vital for the management of urban forests in accordance with the rapid urbanization. This paper presents a method for quantifying and estimating the urban FBC in Shanghai, China, between 2005 and 2015, using data from 93 sampling forest plots and Landsat ETM + data. Our study included an analysis of the choice of predicted factors to estimate FBC and the method of carbon density estimation employed in the estimation. The results showed that combining regression analysis and spatial analysis to map forest carbon stocks at city level make available FBC estimates for the urban forests of Shanghai, China. Based on the proposal method, there was a decreasing trend of carbon density from downtown areas to outer suburban areas. About 92 % of the overall FBC storage in Shanghai distributed in the suburban areas whereas the urban areas shared a fraction amounting to only 8 % in 2015. From 2011–2015, the total urban forest carbon storage gradually increased by 32.3 %, whereas the average carbon density gradually decreased by 8.21 % in the urban areas. The associated values both increased continuously from 2005–2011, from 2005 to 2011, but the average carbon storage density decreased in the suburban districts. The total carbon stocks in the estimated forest biomass across Shanghai were about 1.5 Tg in 2005 and 2008, and 1.7 Tg in 2011 and 2015, while the associated average FBC density remained at about 17.5 t/ha from 2005 to 2015. This study provides important information required to manage the urban forest stand for optimal carbon sequestration.

Mapping Forest Disturbances between 1987–2016 Using All Available Time Series Landsat TM/ETM+ Imagery: Developing a Reliable Methodology for Georgia, United States

Forest resources have a high economic value in the State of Georgia (USA) and the landscape is frequently disturbed as a part of forest management activities, such as plantation forest management activities. Thus, tracking the stand-clearing disturbance history in a spatially referenced manner might be pivotal in discussions of forest resource sustainability within the State. The two major objectives of this research are (i) to develop and test a reliable methodology for statewide tracking of forest disturbances in Georgia, (ii) to consider and discuss the use and implications of the information derived from the forest disturbance map. Two primary disturbance detection methods, a threshold algorithm and a statistical boundary method, were combined to develop a robust estimation of recent forest disturbance history. The developed model was used to create a forest disturbance record for the years 1987–2016, through the use of all available Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+) data. The final product was a raster database, where each pixel was assigned a value corresponding to the last disturbance year. The overall accuracy of the forest disturbance map was 87%, and it indicated that 4,503,253 ha, equivalent to 29.2% of the total land area in Georgia, experienced disturbances between 1987 and 2016. The estimated disturbed area in each year was highly variable and ranged between 84,651 ha (±36,354 ha) to 211,780 ha (±49,504 ha). By combining the use of the disturbance map along with the 2016 database from the National Land Cover Database (NLCD), we also analyzed the regional variation in the disturbance history. This analysis indicated that disturbed forests in urban areas were more likely to be converted to other land-uses. The forest disturbance record created in this research provides the necessary spatial data and address forest resource sustainability in Georgia. Additionally, the methodology used has application in the analysis of other resources, such as the estimation of the aboveground forest biomass.

An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards

The agricultural and forestry sector is constantly evolving, also through the increased use of precision technologies including Remote Sensing (RS). Remotely biomass estimation (WaSfM) in wood production forests is already debated in the literature, but there is a lack of knowledge in quantifying pruning residues from canopy management. The aim of the present study was to verify the reliability of RS techniques for the estimation of pruning biomass through differences in the volume of canopy trees and to evaluate the performance of an unsupervised segmentation methodology as a feasible tool for the analysis of large areas. Remote sensed data were acquired on four uneven-aged and irregularly spaced chestnut orchards in Central Italy by an Unmanned Aerial Vehicle (UAV) equipped with a multispectral camera. Chestnut geometric features were extracted using both supervised and unsupervised crown segmentation and then applying a double filtering process based on Canopy Height Model (CHM) and vegetation index threshold. The results show that UAV monitoring provides good performance in detecting biomass reduction after pruning, despite some differences between the trees’ geometric features. The proposed unsupervised methodology for tree detection and vegetation cover evaluation purposes showed good performance, with a low undetected tree percentage value (1.7%). Comparing crown projected volume reduction extracted by means of supervised and unsupervised approach, R2 ranged from 0.76 to 0.95 among all the sites. Finally, the validation step was assessed by evaluating correlations between measured and estimated pruning wood biomass (Wpw) for single and grouped sites (0.53 &lt; R2 &lt; 0.83). The method described in this work could provide effective strategic support for chestnut orchard management in line with a precision agriculture approach. In the context of the Circular Economy, a fast and cost-effective tool able to estimate the amounts of wastes available as by-products such as chestnut pruning residues can be included in an alternative and virtuous supply chain.

Potential of texture from SAR tomographic images for forest aboveground biomass estimation

Synthetic Aperture Radar (SAR) texture has been demonstrated to have the potential to improve forest biomass estimation using backscatter. However, forests are 3D objects with a vertical structure. The strong penetration of SAR signals means that each pixel contains the contributions of all the scatterers inside the forest canopy, especially for the P-band. Consequently, the traditional texture derived from SAR images is affected by forest vertical heterogeneity, although the influence on texture-based biomass estimation has not yet been explicitly explored. To separate and explore the influence of forest vertical heterogeneity, we introduced the SAR tomography technique into the traditional texture analysis, aiming to explore whether TomoSAR could improve the performance of texture-based aboveground biomass (AGB) estimation and whether texture plus tomographic backscatter could further improve the TomoSAR-based AGB estimation. Based on the P-band TomoSAR dataset from TropiSAR 2009 at two different sites, the results show that ground backscatter variance dominated the texture features of the original SAR image and reduced the biomass estimation accuracy. The texture from upper vegetation layers presented a stronger correlation with forest biomass. Texture successfully improved tomographic backscatter-based biomass estimation, and the texture from upper vegetation layers made AGB models much more transferable between different sites. In addition, the correlation between texture indices varied greatly among different tomographic heights. The texture from the 10 to 30 m layers was able to provide more independent information than the other layers and the original images, which helped to improve the backscatter-based AGB estimation.