Forests

Research

19 pages, 6072 KiB

Open AccessArticle

Shrub Biomass Estimates in Former Burnt Areas Using Sentinel 2 Images Processing and Classification

by José Aranha, Teresa Enes, Ana Calvão and Hélder Viana

Forests 2020, 11(5), 555; https://doi.org/10.3390/f11050555 - 14 May 2020

Cited by 18 | Viewed by 3253

Shrubs growing in former burnt areas play two diametrically opposed roles. On the one hand, they protect the soil against erosion, promote rainwater infiltration, carbon sequestration and support animal life. On the other hand, after the shrubs’ density reaches a particular size for [...] Read more.

Shrubs growing in former burnt areas play two diametrically opposed roles. On the one hand, they protect the soil against erosion, promote rainwater infiltration, carbon sequestration and support animal life. On the other hand, after the shrubs’ density reaches a particular size for the canopy to touch and the shrubs’ biomass accumulates more than 10 Mg ha⁻¹, they create the necessary conditions for severe wild fires to occur and spread. The creation of a methodology suitable to identify former burnt areas and to track shrubs’ regrowth within these areas in a regular and a multi temporal basis would be beneficial. The combined use of geographical information systems (GIS) and remote sensing (RS) supported by dedicated land survey and field work for data collection has been identified as a suitable method to manage these tasks. The free access to Sentinel images constitutes a valuable tool for updating the GIS project and for the monitoring of regular shrubs’ accumulated biomass. Sentinel 2 VIS-NIR images are suitable to classify rural areas (overall accuracy = 79.6% and Cohen’s K = 0.754) and to create normalized difference vegetation index (NDVI) images to be used in association to allometric equations for the shrubs’ biomass estimation (R² = 0.8984, p-value < 0.05 and RMSE = 4.46 Mg ha⁻¹). Five to six years after a forest fire occurrence, almost all the former burnt area is covered by shrubs. Up to 10 years after a fire, the accumulated shrubs’ biomass surpasses 14 Mg ha⁻¹. The results described in this paper demonstrate that Northwest Portugal presents larger shrubland areas and greater shrub biomass accumulation (average 18.3 Mg ha⁻¹) than the Northeast (average 7.7 Mg ha⁻¹) of the country. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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16 pages, 2703 KiB

Open AccessArticle

Evaluation of Different Algorithms for Estimating the Growing Stock Volume of Pinus massoniana Plantations Using Spectral and Spatial Information from a SPOT6 Image

by Jingjing Zhou, Zhixiang Zhou, Qingxia Zhao, Zemin Han, Pengcheng Wang, Jie Xu and Yuanyong Dian

Forests 2020, 11(5), 540; https://doi.org/10.3390/f11050540 - 12 May 2020

Cited by 22 | Viewed by 2533

Abstract

Precise growing stock volume (GSV) estimation is essential for monitoring forest carbon dynamics, determining forest productivity, assessing ecosystem forest services, and evaluating forest quality. We evaluated four machine learning methods: classification and regression trees (CART), support vector machines (SVM), artificial neural networks (ANN), [...] Read more.

Precise growing stock volume (GSV) estimation is essential for monitoring forest carbon dynamics, determining forest productivity, assessing ecosystem forest services, and evaluating forest quality. We evaluated four machine learning methods: classification and regression trees (CART), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF), for their reliability in the estimation of the GSV of Pinus massoniana plantations in China’s northern subtropical regions, using remote sensing data. For all four methods, models were generated using data derived from a SPOT6 image, namely the spectral vegetation indices (SVIs), texture parameters, or both. In addition, the effects of varying the size of the moving window on estimation precision were investigated. RF almost always yielded the greatest precision independently of the choice of input. ANN had the best performance when SVIs were used alone to estimate GSV. When using texture indices alone with window sizes of 3 × 5 × 5 or 9 × 9, RF achieved the best results. For CART, SVM, and RF, R² decreased as the moving window size increased: the highest R² values were achieved with 3 × 3 or 5 × 5 windows. When using textural parameters together with SVIs as the model input, RF achieved the highest precision, followed by SVM and CART. Models using both SVI and textural parameters as inputs had better estimating precision than those using spectral data alone but did not appreciably outperform those using textural parameters alone. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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15 pages, 3821 KiB

Open AccessArticle

Applying LiDAR to Quantify the Plant Area Index Along a Successional Gradient in a Tropical Forest of Thailand

by Siriruk Pimmasarn, Nitin Kumar Tripathi, Sarawut Ninsawat and Nophea Sasaki

Forests 2020, 11(5), 520; https://doi.org/10.3390/f11050520 - 6 May 2020

Cited by 7 | Viewed by 3258

Abstract

Long-term monitoring of vegetation is critical for understanding the dynamics of forest ecosystems, especially in Southeast Asia’s tropical forests, which play a significant role in the global carbon cycle and have continually been converted into various stages of secondary forests. In Thailand, long-term [...] Read more.

Long-term monitoring of vegetation is critical for understanding the dynamics of forest ecosystems, especially in Southeast Asia’s tropical forests, which play a significant role in the global carbon cycle and have continually been converted into various stages of secondary forests. In Thailand, long-term monitoring of forest dynamics during the successional process is limited to plot scales assuming from the distinct structure of successional stages. Our study highlights the potential of coupling airborne light detection and ranging (LiDAR) technology and stand age data derived from Landsat time-series to track back forest succession, and infer patterns in the plant area index (PAI) recovery. Here, using LIDAR data, we estimated the PAI of the 510 sample plots of a seasonal evergreen forest dispersed over the study area in Khao Yai National Park, Thailand, capturing a successional gradient of tropical secondary forests. The sample plots age was derived from the available Landsat time-series dataset (1972–2017). We developed a PAI recovery model during the first 42 years of the succession process. We investigated the relationship between the model residuals and PAI values with topographic factors, such as elevation, slope, and topographic wetness index. The results show that the PAI increased non-linearly (pseudo-R² of 0.56) during the first 42 years of forest succession, and all three topographic factors have less influence on PAI variability. These results provide valuable information of the spatio-temporal PAI patterns during the successional process and help understand the dynamics of tropical secondary forests in Khao Yai National Park, Thailand. Such information is essential for forest management and local, regional, and global PAI synthesis. Moreover, our results provide significant information for ground-based spatial sampling strategies to enable more accurate PAI measurements. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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19 pages, 6948 KiB

Open AccessArticle

Multi-Sensor Prediction of Stand Volume by a Hybrid Model of Support Vector Machine for Regression Kriging

by Lin Chen, Chunying Ren, Bai Zhang and Zongming Wang

Forests 2020, 11(3), 296; https://doi.org/10.3390/f11030296 - 6 Mar 2020

Cited by 18 | Viewed by 3224

Abstract

Quantifying stand volume through open-access satellite remote sensing data supports proper management of forest stand. Because of limitations on single sensor and support vector machine for regression (SVR) as well as benefits from hybrid models, this study innovatively builds a hybrid model as [...] Read more.

Quantifying stand volume through open-access satellite remote sensing data supports proper management of forest stand. Because of limitations on single sensor and support vector machine for regression (SVR) as well as benefits from hybrid models, this study innovatively builds a hybrid model as support vector machine for regression kriging (SVRK) to map stand volume of the Changbai Mountains mixed forests covering 171,450 ha area based on a small training dataset (n = 928). This SVRK model integrated SVR and its residuals interpolated by ordinary kriging. To determine the importance of multi-sensor predictors from ALOS and Sentinel series, the increase in root mean square error (RMSE) of SVR was calculated by removing the variable after the standardization. The SVRK model achieved accuracy with mean error, RMSE and correlation coefficient in –2.67%, 25.30% and 0.76, respectively, based on an independent dataset (n = 464). The SVRK improved the accuracy of 9% than SVR based on RMSE values. Topographic indices from L band InSAR, backscatters of L band SAR, and texture features of VV channel from C band SAR, as well as vegetation indices of the optical sensor were contributive to explain spatial variations of stand volume. This study concluded that SVRK was a promising approach for mapping stand volume in the heterogeneous temperate forests with limited samples. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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18 pages, 3886 KiB

Open AccessArticle

Influence of Site-Specific Conditions on Estimation of Forest above Ground Biomass from Airborne Laser Scanning

by Jan Novotný, Barbora Navrátilová, Růžena Janoutová, Filip Oulehle and Lucie Homolová

Forests 2020, 11(3), 268; https://doi.org/10.3390/f11030268 - 27 Feb 2020

Cited by 10 | Viewed by 2954

Abstract

Forest aboveground biomass (AGB) is an important variable in assessing carbon stock or ecosystem functioning, as well as for forest management. Among methods of forest AGB estimation laser scanning attracts attention because it allows for detailed measurements of forest structure. Here we evaluated [...] Read more.

Forest aboveground biomass (AGB) is an important variable in assessing carbon stock or ecosystem functioning, as well as for forest management. Among methods of forest AGB estimation laser scanning attracts attention because it allows for detailed measurements of forest structure. Here we evaluated variables that influence forest AGB estimation from airborne laser scanning (ALS), specifically characteristics of ALS inputs and of a derived canopy height model (CHM), and role of allometric equations (local vs. global models) relating tree height, stem diameter (DBH), and crown radius. We used individual tree detection approach and analyzed forest inventory together with ALS data acquired for 11 stream catchments with dominant Norway spruce forest cover in the Czech Republic. Results showed that the ALS input point densities (4–18 pt/m²) did not influence individual tree detection rates. Spatial resolution of the input CHM rasters had a greater impact, resulting in higher detection rates for CHMs with pixel size 0.5 m than 1.0 m for all tree height categories. In total 12 scenarios with different allometric equations for estimating stem DBH from ALS-derived tree height were used in empirical models for AGB estimation. Global DBH models tend to underestimate AGB in young stands and overestimate AGB in mature stands. Using different allometric equations can yield uncertainty in AGB estimates of between 16 and 84 tons per hectare, which in relative values corresponds to between 6% and 37% of the mean AGB per catchment. Therefore, allometric equations (mainly for DBH estimation) should be applied with care and we recommend, if possible, to establish one’s own site-specific models. If that is not feasible, the global allometric models developed here, from a broad variety of spruce forest sites, can be potentially applicable for the Central European region. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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21 pages, 5450 KiB

Open AccessArticle

Spatially Explicit Analysis of Trade-Offs and Synergies among Multiple Ecosystem Services in Shaanxi Valley Basins

by Yijie Sun, Jing Li, Xianfeng Liu, Zhiyuan Ren, Zixiang Zhou and Yifang Duan

Forests 2020, 11(2), 209; https://doi.org/10.3390/f11020209 - 12 Feb 2020

Cited by 17 | Viewed by 3440

Abstract

Understanding the spatiotemporal characteristics of trade-offs and synergies among multiple ecosystem services (ESs) is the basis of sustainable ecosystem management. The ecological environment of valley basins is very fragile, while bearing the enormous pressure of economic development and population growth, which has damaged [...] Read more.

Understanding the spatiotemporal characteristics of trade-offs and synergies among multiple ecosystem services (ESs) is the basis of sustainable ecosystem management. The ecological environment of valley basins is very fragile, while bearing the enormous pressure of economic development and population growth, which has damaged the balance of the ecosystem structure and ecosystem services. In this study, we selected two typical valley basins—Guanzhong Basin and Hanzhong Basin—as study areas. The spatial heterogeneity of trade-offs and synergies among multiple ESs (net primary production (NPP), habitat quality (HQ), soil conservation (SC), water conservation (WC), and food supply (FS)) were quantified using the correlation analysis and spatial overlay based on the gird scale to quantitatively analyze and compare the interaction among ESs in two basins. Our results found that: (1) Trade-offs between FS and other four services NPP, HQ, SC, and WC were discovered in two basins, and there were synergistic relationships between NPP, HQ, SC, and WC. (2) From 2000 to 2018, the conflicted relationships between paired ESs gradually increased, and the synergistic relationship became weaker. Furthermore, the rate of change in Guanzhong Basin was stronger than that in Hanzhong Basin. (3) The spatial synergies and trade-offs between NPP and HQ, WC and NPP, FS and HQ, SC and FS were widespread in two basins. The strong trade-offs between pair ESs were widly distributed in the central and southwest of Guanzhong Basin and the southeast of Hanzhong Basin. (4) Multiple ecosystem service interactions were concentrated in the north of Qinling Mountain, the central of Guanzhong Basins, and the east of Hanzhong Basin. Our research highlights the importance of taking spatial perspective and accounting for multiple ecosystem service interactions, and provide a reliable basis for achieving ecological sustainable development of the valley basin. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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17 pages, 1536 KiB

Open AccessArticle

Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

by Yan Zhu, Zhongke Feng, Jing Lu and Jincheng Liu

Forests 2020, 11(2), 163; https://doi.org/10.3390/f11020163 - 31 Jan 2020

Cited by 46 | Viewed by 4676

Abstract

Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important [...] Read more.

Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha⁻¹ and ranged from 19.1381–195.66 Mg ha⁻¹. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high

R^{2}

and a low root mean square error (RMSE). The

R^{2}

values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha⁻¹, The

R^{2}

values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha⁻¹. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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24 pages, 12190 KiB

Open AccessArticle

Estimating Urban Vegetation Biomass from Sentinel-2A Image Data

by Long Li, Xisheng Zhou, Longqian Chen, Longgao Chen, Yu Zhang and Yunqiang Liu

Forests 2020, 11(2), 125; https://doi.org/10.3390/f11020125 - 21 Jan 2020

Cited by 39 | Viewed by 5127

Abstract

Urban vegetation biomass is a key indicator of the carbon storage and sequestration capacity and ecological effect of an urban ecosystem. Rapid and effective monitoring and measurement of urban vegetation biomass provide not only an understanding of urban carbon circulation and energy flow [...] Read more.

Urban vegetation biomass is a key indicator of the carbon storage and sequestration capacity and ecological effect of an urban ecosystem. Rapid and effective monitoring and measurement of urban vegetation biomass provide not only an understanding of urban carbon circulation and energy flow but also a basis for assessing the ecological function of urban forest and ecology. In this study, field observations and Sentinel-2A image data were used to construct models for estimating urban vegetation biomass in the case study of the east Chinese city of Xuzhou. Results show that (1) Sentinel-2A data can be used for urban vegetation biomass estimation; (2) compared with the Boruta based multiple linear regression models, the stepwise regression models—also multiple linear regression models—achieve better estimations (RMSE = 7.99 t/hm² for low vegetation, 45.66 t/hm² for broadleaved forest, and 6.89 t/hm² for coniferous forest); (3) the models for specific vegetation types are superior to the models for all-type vegetation; and (4) vegetation biomass is generally lowest in September and highest in January and December. Our study demonstrates the potential of the free Sentinel-2A images for urban ecosystem studies and provides useful insights on urban vegetation biomass estimation with such satellite remote sensing data. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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17 pages, 4330 KiB

Open AccessArticle

Comparative Analysis of Seasonal Landsat 8 Images for Forest Aboveground Biomass Estimation in a Subtropical Forest

by Chao Li, Mingyang Li, Jie Liu, Yingchang Li and Qianshi Dai

Forests 2020, 11(1), 45; https://doi.org/10.3390/f11010045 - 31 Dec 2019

Cited by 8 | Viewed by 3067

Abstract

To effectively further research the regional carbon sink, it is important to estimate forest aboveground biomass (AGB). Based on optical images, the AGB can be estimated and mapped on a regional scale. The Landsat 8 Operational Land Imager (OLI) has, therefore, been widely [...] Read more.

To effectively further research the regional carbon sink, it is important to estimate forest aboveground biomass (AGB). Based on optical images, the AGB can be estimated and mapped on a regional scale. The Landsat 8 Operational Land Imager (OLI) has, therefore, been widely used for regional scale AGB estimation; however, most studies have been based solely on peak season images without performance comparison of other seasons; this may ultimately affect the accuracy of AGB estimation. To explore the effects of utilizing various seasonal images for AGB estimation, we analyzed seasonal images collected using Landsat 8 OLI for a subtropical forest in northern Hunan, China. We then performed stepwise regression to estimate AGB of different forest types (coniferous forest, broadleaf forest, mixed forest and total vegetation). The model performances using seasonal images of different forest types were then compared. The results showed that textural information played an important role in AGB estimation of each forest type. Stratification based on forest types resulted in better AGB estimation model performances than those of total vegetation. The most accurate AGB estimations were achieved using the autumn (October) image, and the least accurate AGB estimations were achieved using the peak season (August) image. In addition, the uncertainties associated with the peak season image were largest in terms of AGB values < 25 Mg/ha and >75 Mg/ha, and the quality of the AGB map depicting the peak season was poorer than the maps depicting other seasons. This study suggests that the acquisition time of forest images can affect AGB estimations in subtropical forest. Therefore, future research should consider and incorporate seasonal time-series images to improve AGB estimation. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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24 pages, 4379 KiB

Open AccessArticle

Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms

by Yingchang Li, Chao Li, Mingyang Li and Zhenzhen Liu

Forests 2019, 10(12), 1073; https://doi.org/10.3390/f10121073 - 25 Nov 2019

Cited by 117 | Viewed by 7020

Abstract

Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status of and changes in forests. However, while remote sensing-based forest biomass [...] Read more.

Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status of and changes in forests. However, while remote sensing-based forest biomass estimation in general is well developed and extensively used, improving the accuracy of biomass estimation remains challenging. In this paper, we used China’s National Forest Continuous Inventory data and Landsat 8 Operational Land Imager data in combination with three algorithms, either the linear regression (LR), random forest (RF), or extreme gradient boosting (XGBoost), to establish biomass estimation models based on forest type. In the modeling process, two methods of variable selection, e.g., stepwise regression and variable importance-base method, were used to select optimal variable subsets for LR and machine learning algorithms (e.g., RF and XGBoost), respectively. Comfortingly, the accuracy of models was significantly improved, and thus the following conclusions were drawn: (1) Variable selection is very important for improving the performance of models, especially for machine learning algorithms, and the influence of variable selection on XGBoost is significantly greater than that of RF. (2) Machine learning algorithms have advantages in aboveground biomass (AGB) estimation, and the XGBoost and RF models significantly improved the estimation accuracy compared with the LR models. Despite that the problems of overestimation and underestimation were not fully eliminated, the XGBoost algorithm worked well and reduced these problems to a certain extent. (3) The approach of AGB modeling based on forest type is a very advantageous method for improving the performance at the lower and higher values of AGB. Some conclusions in this paper were probably different as the study area changed. The methods used in this paper provide an optional and useful approach for improving the accuracy of AGB estimation based on remote sensing data, and the estimation of AGB was a reference basis for monitoring the forest ecosystem of the study area. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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19 pages, 9845 KiB

Open AccessArticle

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

by Meng Zhang, Huaqiang Du, Guomo Zhou, Xuejian Li, Fangjie Mao, Luofan Dong, Junlong Zheng, Hua Liu, Zihao Huang and Shaobai He

Forests 2019, 10(11), 1004; https://doi.org/10.3390/f10111004 - 9 Nov 2019

Cited by 24 | Viewed by 3819

Abstract

Dynamic monitoring of carbon storage in forests resources is important for tracking ecosystem functionalities and climate change impacts. In this study, we used multi-year Landsat data combined with a Random Forest (RF) algorithm to estimate the forest aboveground carbon (AGC) in a forest [...] Read more.

Dynamic monitoring of carbon storage in forests resources is important for tracking ecosystem functionalities and climate change impacts. In this study, we used multi-year Landsat data combined with a Random Forest (RF) algorithm to estimate the forest aboveground carbon (AGC) in a forest area in China (Hang-Jia-Hu) and analyzed its spatiotemporal changes during the past two decades. Maximum likelihood classification was applied to make land-use maps. Remote sensing variables, such as the spectral band, vegetation indices, and derived texture features, were extracted from 20 Landsat TM and OLI images over five different years (2000, 2004, 2010, 2015, and 2018). These variables were subsequently selected according to their importance and subsequently used in the RF algorithm to build an estimation model of forest AGC. The results showed the following: (1) Verification of classification results showed maximum likelihood can extract land information effectively. Our land cover classification yielded overall accuracies between 86.86% and 89.47%. (2) Additionally, our RF models showed good performance in predicting forest AGC, with R² from 0.65 to 0.73 in the training and testing phase and a RMSE range between 3.18 and 6.66 Mg/ha. RMSEr in the testing phase ranged from 20.27 to 22.27 with a low model error. (3) The estimation results indicated that forest AGC in the past two decades increased with density at 10.14 Mg/ha, 21.63 Mg/ha, 26.39 Mg/ha, 29.25 Mg/ha, and 44.59 Mg/ha in 2000, 2004, 2010, 2015, and 2018. The total forest AGC storage had a growth rate of 285%. (4) Our study showed that, although forest area decreased in the study area during the time period under study, the total forest AGC increased due to an increment in forest AGC density. However, such an effect is overridden in the vicinity of cities by intense urbanization and the loss of forest covers. Our study demonstrated that the combined use of remote sensing data and machine learning techniques can improve our ability to track the forest changes in support of regional natural resource management practices. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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26 pages, 8660 KiB

Open AccessArticle

Nondestructive Estimation of the Above-Ground Biomass of Multiple Tree Species in Boreal Forests of China Using Terrestrial Laser Scanning

by Shilin Chen, Zhongke Feng, Panpan Chen, Tauheed Ullah Khan and Yining Lian

Forests 2019, 10(11), 936; https://doi.org/10.3390/f10110936 - 23 Oct 2019

Cited by 21 | Viewed by 3638

Abstract

Above-ground biomass (AGB) plays a pivotal role in assessing a forest’s resource dynamics, ecological value, carbon storage, and climate change effects. The traditional methods of AGB measurement are destructive, time consuming and laborious, and an efficient, relatively accurate and non-destructive AGB measurement method [...] Read more.

Above-ground biomass (AGB) plays a pivotal role in assessing a forest’s resource dynamics, ecological value, carbon storage, and climate change effects. The traditional methods of AGB measurement are destructive, time consuming and laborious, and an efficient, relatively accurate and non-destructive AGB measurement method will provide an effective supplement for biomass calculation. Based on the real biophysical and morphological structures of trees, this paper adopted a non-destructive method based on terrestrial laser scanning (TLS) point cloud data to estimate the AGBs of multiple common tree species in boreal forests of China, and the effects of differences in bark roughness and trunk curvature on the estimation of the diameter at breast height (DBH) from TLS data were quantitatively analyzed. We optimized the quantitative structure model (QSM) algorithm based on 100 trees of multiple tree species, and then used it to estimate the volume of trees directly from the tree model reconstructed from point cloud data, and to calculate the AGBs of trees by using specific basic wood density values. Our results showed that the total DBH and tree height from the TLS data showed a good consistency with the measured data, since the bias, root mean square error (RMSE) and determination coefficient (R²) of the total DBH were −0.8 cm, 1.2 cm and 0.97, respectively. At the same time, the bias, RMSE and determination coefficient of the tree height were −0.4 m, 1.3 m and 0.90, respectively. The differences of bark roughness and trunk curvature had a small effect on DBH estimation from point cloud data. The AGB estimates from the TLS data showed strong agreement with the reference values, with the RMSE, coefficient of variation of root mean square error (CV(RMSE)), and concordance correlation coefficient (CCC) values of 17.4 kg, 13.6% and 0.97, respectively, indicating that this non-destructive method can accurately estimate tree AGBs and effectively calibrate new allometric biomass models. We believe that the results of this study will benefit forest managers in formulating management measures and accurately calculating the economic and ecological benefits of forests, and should promote the use of non-destructive methods to measure AGB of trees in China. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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21 pages, 4814 KiB

Open AccessArticle

Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

by Penghua Qiu, Dezhi Wang, Xinqing Zou, Xing Yang, Genzong Xie, Songjun Xu and Zunqian Zhong

Forests 2019, 10(10), 871; https://doi.org/10.3390/f10100871 - 4 Oct 2019

Cited by 47 | Viewed by 4281

Abstract

To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly [...] Read more.

To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly due to the limitation of remote sensing data. Using WorldView-2 imagery, unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data, and field survey datasets, we proposed a novel method for the estimation of mangrove aboveground biomass (AGB) at individual tree level, i.e., individual tree-based inference method. The performance of the individual tree-based inference method was compared with the grid-based random forest model method, which directly links the field samples with the UAV LiDAR metrics. We discussed the feasibility of the individual tree-based inference method and the influence of diameter at breast height (DBH) on individual segmentation accuracy. The results indicated that (1) The overall classification accuracy of six mangrove species at individual tree level was 86.08%. (2) The position and number matching accuracies of individual tree segmentation were 87.43% and 51.11%, respectively. The number matching accuracy of individual tree segmentation was relatively satisfying within 8 cm ≤ DBH ≤ 30 cm. (3) The individual tree-based inference method produced lower accuracy than the grid-based RF model method with R² of 0.49 vs. 0.67 and RMSE of 48.42 Mg ha⁻¹ vs. 38.95 Mg ha⁻¹. However, the individual tree-based inference method can show more detail of spatial distribution of mangrove AGB. The resultant AGB maps of this method are more beneficial to the fine and differentiated management of mangrove forests. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass)

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