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21 pages, 5357 KiB

Open AccessArticle

Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China

by Lizhi Liu, Qiuliang Zhang, Ying Guo, Yu Li, Bing Wang, Erxue Chen, Zengyuan Li and Shuai Hao

Forests 2024, 15(2), 288; https://doi.org/10.3390/f15020288 - 2 Feb 2024

Viewed by 1213

Abstract

Information about the distribution of coniferous forests holds significance for enhancing forestry efficiency and making informed policy decisions. Accurately identifying and mapping coniferous forests can expedite the achievement of Sustainable Development Goal (SDG) 15, aimed at managing forests sustainably, combating desertification, halting and [...] Read more.

Information about the distribution of coniferous forests holds significance for enhancing forestry efficiency and making informed policy decisions. Accurately identifying and mapping coniferous forests can expedite the achievement of Sustainable Development Goal (SDG) 15, aimed at managing forests sustainably, combating desertification, halting and reversing land degradation, and halting biodiversity loss. However, traditional methods employed to identify and map coniferous forests are costly and labor-intensive, particularly in dealing with large-scale regions. Consequently, a methodological framework is proposed to identify coniferous forests in northwestern Liaoning, China, in which there are semi-arid and barren environment areas. This framework leverages a multi-classifier fusion algorithm that combines deep learning (U²-Net and Resnet-50) and shallow learning (support vector machines and random forests) methods deployed in the Google Earth Engine. Freely available remote sensing images are integrated from multiple sources, including Gaofen-1 and Sentinel-1, to enhance the accuracy and reliability of the results. The overall accuracy of the coniferous forest identification results reached 97.6%, highlighting the effectiveness of the proposed methodology. Further calculations were conducted to determine the area of coniferous forests in each administrative region of northwestern Liaoning. It was found that the total area of coniferous forests in the study area is about 6013.67 km², accounting for 9.59% of northwestern Liaoning. The proposed framework has the potential to offer timely and accurate information on coniferous forests and holds promise for informed decision making and the sustainable development of ecological environment. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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16 pages, 7703 KiB

Open AccessArticle

Individual Tree-Level Monitoring of Pest Infestation Combining Airborne Thermal Imagery and Light Detection and Ranging

by Jingxu Wang, Qinan Lin, Shengwang Meng, Huaguo Huang and Yangyang Liu

Forests 2024, 15(1), 112; https://doi.org/10.3390/f15010112 - 6 Jan 2024

Cited by 2 | Viewed by 1549

Abstract

The infestation of pine shoot beetles (Tomicus spp.) in the forests of Southwestern China has inflicted serious ecological damages to the environment, causing significant economic losses. Therefore, accurate and practical approaches to detect pest infestation have become an urgent necessity to mitigate [...] Read more.

The infestation of pine shoot beetles (Tomicus spp.) in the forests of Southwestern China has inflicted serious ecological damages to the environment, causing significant economic losses. Therefore, accurate and practical approaches to detect pest infestation have become an urgent necessity to mitigate these harmful consequences. In this study, we explored the efficiency of thermal infrared (TIR) technology in capturing changes in canopy surface temperature (CST) and monitoring forest health at the scale of individual tree crowns. We combined data collected from TIR imagery and light detection and ranging (LiDAR) using unmanned airborne vehicles (UAVs) to estimate the shoot damage ratio (SDR), which is a representative parameter of the damage degree caused by forest infestation. We compared multiple machine learning methods for data analysis, including random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), to determine the optimal regression model for assessing SDR at the crown scale. Our findings showed that a combination of LiDAR metrics and CST presents the highest accuracy in estimating SDR using the RF model (R² = 0.7914, RMSE = 15.5685). Our method enables the accurate remote monitoring of forest health and is expected to provide a novel approach for controlling pest infestation, minimizing the associated damages caused. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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17 pages, 9177 KiB

Open AccessArticle

Influence of Topography on UAV LiDAR-Based LAI Estimation in Subtropical Mountainous Secondary Broadleaf Forests

by Yunfei Li, Hongda Zeng, Jingfeng Xiong and Guofang Miao

Forests 2024, 15(1), 17; https://doi.org/10.3390/f15010017 - 20 Dec 2023

Cited by 1 | Viewed by 1552

Abstract

The leaf area index (LAI) serves as a crucial metric in quantifying the structure and density of vegetation canopies, playing an instrumental role in determining vegetation productivity, nutrient and water utilization, and carbon balance dynamics. In subtropical montane forests, the pronounced spatial heterogeneity [...] Read more.

The leaf area index (LAI) serves as a crucial metric in quantifying the structure and density of vegetation canopies, playing an instrumental role in determining vegetation productivity, nutrient and water utilization, and carbon balance dynamics. In subtropical montane forests, the pronounced spatial heterogeneity combined with undulating terrain introduces significant challenges for the optical remote sensing inversion accuracy of LAI, thereby complicating the process of ground validation data collection. The emergence of UAV LiDAR offers an innovative monitoring methodology for canopy LAI inversion in these terrains. This study assesses the implications of altitudinal variations on the attributes of UAV LiDAR point clouds, such as point density, beam footprint, and off-nadir scan angle, and their subsequent ramifications for LAI estimation accuracy. Our findings underscore that with increased altitude, both the average off-nadir scan angle and point density exhibit an ascending trend, while the beam footprint showcases a distinct negative correlation, with a correlation coefficient (R) reaching 0.7. In contrast to parallel flight paths, LAI estimates derived from intersecting flight paths demonstrate superior precision, denoted by R² = 0.70, RMSE = 0.75, and bias = 0.42. Notably, LAI estimation discrepancies intensify from upper slope positions to middle positions and further to lower ones, amplifying with the steepness of the gradient. Alterations in point cloud attributes induced by the terrain, particularly the off-nadir scan angle and beam footprint, emerge as critical influencers on the precision of LAI estimations. Strategies encompassing refined flight path intervals or multi-directional point cloud data acquisition are proposed to bolster the accuracy of canopy structural parameter estimations in montane landscapes. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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13 pages, 4049 KiB

Open AccessArticle

Using a Vegetation Index to Monitor the Death Process of Chinese Fir Based on Hyperspectral Data

by Xuemei Tang, Zhuo Zang, Hui Lin, Xu Wang and Zhang Wen

Forests 2023, 14(12), 2444; https://doi.org/10.3390/f14122444 - 14 Dec 2023

Cited by 2 | Viewed by 1412

Abstract

Chinese fir is one of the most widely distributed and extensively planted timber species in China. Therefore, monitoring pests and diseases in Chinese fir plantations is directly related to national timber forest security and forest ecological security. This study aimed to identify appropriate [...] Read more.

Chinese fir is one of the most widely distributed and extensively planted timber species in China. Therefore, monitoring pests and diseases in Chinese fir plantations is directly related to national timber forest security and forest ecological security. This study aimed to identify appropriate vegetation indices for the early monitoring of pests and diseases in Chinese fir plantations. For this purpose, the researchers used an imaging spectrometer to capture hyperspectral images of both experimental and control groups. The experimental group consisted of Chinese fir trees with two sections of bark stripped off, while the control group consisted of healthy Chinese fir trees. The study then assessed the sensitivity of 11 vegetation indices to the physiological differences between the two groups using the Mann–Whitney U test. The results showed that both the green-to-red region spectral angle index (GRRSGI) and the red edge position index (REP) were able to monitor the difference as early as 16 days after damage. However, GRRSGI performs best in monitoring early death changes in Chinese fir trees because it is less affected by noise and is more stable. The green–red spectral area index (GRSAI) also had high stability, but the monitoring effect was slightly worse than that of GRRSGI and REP. Compared with other indices, GRRSGI and GRSAI can better exploit the advantages of hyperspectral data. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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19 pages, 12433 KiB

Open AccessArticle

Study on Single-Tree Segmentation of Chinese Fir Plantations Using Coupled Local Maximum and Height-Weighted Improved K-Means Algorithm

by Xiangyu Chen, Kunyong Yu, Shuhan Yu, Zhongyang Hu, Hongru Tan, Yichen Chen, Xiang Huang and Jian Liu

Forests 2023, 14(11), 2130; https://doi.org/10.3390/f14112130 - 26 Oct 2023

Cited by 2 | Viewed by 1277

Abstract

Chinese fir (Cunninghamia lanceolata) is a major timber species in China, and obtaining and monitoring the parameters of Chinese fir plantations is of great practical significance. With the help of the K-means algorithm and UAV-LiDAR data, the efficiency of forestry surveys [...] Read more.

Chinese fir (Cunninghamia lanceolata) is a major timber species in China, and obtaining and monitoring the parameters of Chinese fir plantations is of great practical significance. With the help of the K-means algorithm and UAV-LiDAR data, the efficiency of forestry surveys can be greatly improved. Considering that the traditional K-means algorithm is susceptible to the influence of initial cluster centers and outliers during the process of individual tree segmentation, it may result in incorrect segmentation. Therefore, this study proposes an improved K-means algorithm that uses the methods of local maxima and height weighting to optimize and improve the algorithm. The research results are as follows: (1) Compared to the traditional K-means algorithm, the producer accuracy and user accuracy of this research algorithm have imsproved by 10.72% and 11.46%, respectively, with significant differences (p < 0.05). (2) The research algorithm proposed in this study can adapt to Chinese fir plantations of different age groups, with average producer accuracy and user accuracy reaching 78.48% and 83.72%, respectively. In summary, this algorithm can be effectively applied to the forest parameter estimation of Chinese fir plantations and is of great significance for sustainable forest management. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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16 pages, 3125 KiB

Open AccessArticle

Selection of the Optimal Timber Harvest Based on Optimizing Stand Spatial Structure of Broadleaf Mixed Forests

by Qi Sheng, Lingbo Dong, Ying Chen and Zhaogang Liu

Forests 2023, 14(10), 2046; https://doi.org/10.3390/f14102046 - 12 Oct 2023

Cited by 1 | Viewed by 1575

Abstract

There is increasing interest in optimizing stand structure through forest management. The forest structure influences growth and maintains the structure, promoting sustainability. Structure-based forest management (SBFM), which is based on the spatial relationships between a reference tree and its four nearest neighbors, considers [...] Read more.

There is increasing interest in optimizing stand structure through forest management. The forest structure influences growth and maintains the structure, promoting sustainability. Structure-based forest management (SBFM), which is based on the spatial relationships between a reference tree and its four nearest neighbors, considers the best spatial structure for the stand and promotes the development towards a healthy and stable state by selectively thinning specific trees. This management method is a scientific approach for sustainable forest management, and appropriate harvesting is the core principle of uneven-aged forest management. However, the application of this approach in the management of uneven-aged mixed stands is a challenge because their dynamics are more difficult to elucidate than those of planted or pure stands. This study presented a stand spatial structure optimization model with a transition matrix growth model for selecting suitable timber harvest during uneven-aged mixed-forest management optimization. The model was developed using three neighborhood-based structural indices (species mingling, diametric differentiation, and horizontal spatial pattern) and diameter diversity indices. The approach was applied to four broadleaf stands in the Maoershan Forest Farm of the Heilongjiang Province. The results demonstrate that optimizing the stand spatial structure with a transition matrix growth model improved the objective function values (F-index) by 23.8%, 12.8%, 14.6%, and 28.3%, and the optimal removal of trees from the stands ranged from 24.3% to 25.5%. The stand structure in the next cycle (after 5 years) was closer to the uneven-mixed state. The main conclusion of this study is that optimizing the stand spatial structure with a transition matrix growth model can improve the speed and accuracy of tree selection for harvesting in unevenly mixed forests, thus helping regulate stable and diverse forest growth. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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24 pages, 17782 KiB

Open AccessArticle

Estimation of Above-Ground Carbon Storage and Light Saturation Value in Northeastern China’s Natural Forests Using Different Spatial Regression Models

by Simin Wu, Yuman Sun, Weiwei Jia, Fan Wang, Shixin Lu and Haiping Zhao

Forests 2023, 14(10), 1970; https://doi.org/10.3390/f14101970 - 28 Sep 2023

Cited by 1 | Viewed by 1423

Abstract

In recent years, accurate estimation and spatial mapping of above-ground carbon (AGC) storage in forests have been crucial for formulating carbon trading policies and promoting sustainable development strategies. Forest structure complexities mean that during their growth, trees may be affected by the surrounding [...] Read more.

In recent years, accurate estimation and spatial mapping of above-ground carbon (AGC) storage in forests have been crucial for formulating carbon trading policies and promoting sustainable development strategies. Forest structure complexities mean that during their growth, trees may be affected by the surrounding environment, giving rise to spatial autocorrelation and heterogeneity in nearby forest segments. When estimating forest AGC through remote sensing, data saturation can arise in dense forest stands, adding to the uncertainties in AGC estimation. Our study used field-measured stand factors data from 138 forest fire risk plots located in Fenglin County in the Northeastern region, set within a series of temperate forest environments in 2021 and Sentinel-2 remote sensing image data with a spatial resolution of 10 m. Using ordinary least squares (OLS) as a baseline, we constructed and compared it against four spatial regression models, spatial lag model (SLM), spatial error model (SEM), spatial Durbin model (SDM), and geographically weighted regression (GWR), to better understand forest AGC spatial distribution. The results of local spatial analysis reveal significant spatial effects among plot data. The GWR model outperformed others with an R² value of 0.695 and the lowest rRMSE at 0.273, considering spatial heterogeneity and extending the threshold range for AGC estimation. To address the challenge of light saturation during AGC estimation, we deployed traditional linear functions, the generalized additive model (GAM), and the quantile generalized additive model (QGAM). AGC light saturation values derived from QGAM most accurately reflect the actual conditions, with the forests in Fenglin County exhibiting a light saturation range of 108.832 to 129.894 Mg/ha. The GWR effectively alleviated the impact of data saturation, thereby reducing the uncertainty of AGC spatial distribution in Fenglin County. Overall, accurate predictions of large-scale forest carbon storage provide valuable guidance for forest management, forest conservation, and the promotion of sustainable development strategies. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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17 pages, 33700 KiB

Open AccessArticle

Comparing Algorithms for Estimation of Aboveground Biomass in Pinus yunnanensis

by Tianbao Huang, Guanglong Ou, Hui Xu, Xiaoli Zhang, Yong Wu, Zihao Liu, Fuyan Zou, Chen Zhang and Can Xu

Forests 2023, 14(9), 1742; https://doi.org/10.3390/f14091742 - 28 Aug 2023

Cited by 4 | Viewed by 1393

Abstract

Comparing algorithms are crucial for enhancing the accuracy of remote sensing estimations of forest biomass in regions with high heterogeneity. Herein, Sentinel 2A, Sentinel 1A, Landsat 8 OLI, and Digital Elevation Model (DEM) were selected as data sources. A total of 12 algorithms, [...] Read more.

Comparing algorithms are crucial for enhancing the accuracy of remote sensing estimations of forest biomass in regions with high heterogeneity. Herein, Sentinel 2A, Sentinel 1A, Landsat 8 OLI, and Digital Elevation Model (DEM) were selected as data sources. A total of 12 algorithms, including 7 types of learners, were utilized for estimating the aboveground biomass (AGB) of Pinus yunnanensis forest. The results showed that: (1) The optimal algorithm (Extreme Gradient Boosting, XGBoost) was selected as the meta-model (referred to as XGBoost-stacking) of the stacking ensemble algorithm, which integrated 11 other algorithms. The R² value was improved by 0.12 up to 0.61, and RMSE was decreased by 4.53 Mg/ha down to 39.34 Mg/ha compared to the XGBoost. All algorithms consistently showed severe underestimation of AGB in the Pinus yunnanensis forest of Yunnan Province when AGB exceeded 100 Mg/ha. (2) XGBoost-Stacking, XGBoost, BRNN (Bayesian Regularized Neural Network), RF (Random Forest), and QRF (Quantile Random Forest) have good sensitivity to forest AGB. QRNN (Quantile Regression Neural Network), GP (Gaussian Process), and EN (Elastic Network) have more outlier data and their robustness was poor. SVM-RBF (Radial Basis Function Kernel Support Vector Machine), k-NN (K Nearest Neighbors), and SGB (Stochastic Gradient Boosting) algorithms have good robustness, but their sensitivity was poor, and QRF algorithms and BRNN algorithm can estimate low values with higher accuracy. In conclusion, the XGBoost-stacking, XGBoost, and BRNN algorithms have shown promising application prospects in remote sensing estimation of forest biomass. This study could provide a reference for selecting the suitable algorithm for forest AGB estimation. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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18 pages, 13409 KiB

Open AccessArticle

Improving Forest Canopy Height Estimation Using a Semi-Empirical Approach to Overcome TomoSAR Phase Errors

by Hongbin Luo, Cairong Yue, Hua Yuan and Si Chen

Forests 2023, 14(7), 1479; https://doi.org/10.3390/f14071479 - 19 Jul 2023

Cited by 2 | Viewed by 1248

Abstract

Forest canopy height is an important forest indicator parameter. Synthetic aperture radar tomography (TomoSAR) is an effective method to characterize forest canopy height and describe forest 3D structure; however, the residual phase error of TomoSAR affects the focus of the relative reflectance and [...] Read more.

Forest canopy height is an important forest indicator parameter. Synthetic aperture radar tomography (TomoSAR) is an effective method to characterize forest canopy height and describe forest 3D structure; however, the residual phase error of TomoSAR affects the focus of the relative reflectance and can lead to errors in forest canopy height estimation. Therefore, this paper proposes a semi-empirical method to overcome the residual phase effects on forest canopy height estimation. In this study, we used airborne multi-baseline UAVSAR data to estimate forest canopy height via TomoSAR techniques and applied a semi-empirical method to improve forest canopy height estimation without phase calibration to mitigate the effects of phase error. The process is divided into three stages: the first step uses a semi-empirical method to initially determine the optimal relative reflectance loss threshold (K) by excluding the inverse extremes; in the second and third steps, the percentile height was used to gradually reduce the height interval between the upper and lower envelopes to minimize overestimation of extreme values and the lower vegetation. When the root mean square error (RMSE) was minimized, the percentile combinations were determined between the inversion results and a LiDAR dataset of the area. The results show that the canopy height estimation results are not satisfactory when relying solely on the K value to estimate the height difference between the envelope at the top of the forest and the ground; the best result was obtained when K = 0.4, but the corresponding R² value was only 0.13, and the RMSE was 15.23 m. In our proposed method, the K value is determined as 0.3 by excluding the extreme values of the inversion result in the initial step—the corresponding R² and RMSE values were 0.59 and 10.73 m, respectively, representing an RMSE decrease of 29.54% relative to the initial K value. After two steps of correction overestimation, the inversion accuracy was significantly improved with an R² value of 0.65 and an RMSE of 9.69 m, corresponding to an RMSE decrease of 36.38%. Overall, the findings of the study represent an important reference for optimizing future spaceborne TomoSAR forest canopy height estimates. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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13 pages, 4299 KiB

Open AccessArticle

Spatial Changes of Suburban Forest Ecological Functions and Their Impact on Ecological Equity in the Process of Urbanization—A Case Study of Jiangning District, Nanjing, China

by Fang Ren, Liuan Chen, Tao Li and Mingyang Li

Forests 2023, 14(7), 1308; https://doi.org/10.3390/f14071308 - 26 Jun 2023

Cited by 1 | Viewed by 1366

Abstract

After the transformation of counties in urban suburbs into districts, the rapid urbanization and industrialization process in China’s developed regions had a huge impact on the spatial distribution and equity of the suburban forest ecological functions. Accurately describing this impact could provide an [...] Read more.

After the transformation of counties in urban suburbs into districts, the rapid urbanization and industrialization process in China’s developed regions had a huge impact on the spatial distribution and equity of the suburban forest ecological functions. Accurately describing this impact could provide an important reference for the construction of suburban forest engineering and for ecological environmental planning. Jiangning District of Nanjing City, China, was selected as the research area, while the forest resource planning and design survey data in 2007 and 2017, together with the demographic data of the study area, were collected as the main information sources. Following the establishment of the forest ecological function evaluation indicators and the analysis of the spatial change of the forest ecological functions, the Gini coefficient was calculated to analyze the changes of the regional ecological function equality. The results showed that: (1) Compared with 2007, the proportion of areas with low forest ecological functions (abbreviated as FEF) in the study area in 2017 showed a downward trend, and the proportion of areas with medium and high FEF showed an increasing trend; (2) Compared with 2007, the forest landscape in the study area in 2017 was severely fragmented, the spatial aggregation of the FEF showed a significant decline, and the FEF developed towards a direction of spatially balanced distribution; (3) During 2007–2017, the sub-compartments with high-value FEF in the study area (hot spots) shifted to the northwest, where the economy was developed and the population density was higher, and the sub-compartments with low-value (cold spots) shifted to the south, where the economy is underdeveloped and with lower population density; (4) From 2007 to 2017, the Gini coefficient of the FEF in the study area decreased, indicating that the regional ecological equity had initially improved. The urbanization and industrialization process of the urban suburbs is a double-edged sword. On the one hand, the process has caused the fragmentation of forest landscape, the decline of the forest area, and the unbalanced spatial distribution of the population. On the other hand, the huge material wealth and human capital accumulated through industrialization have promoted regional ecological equity and improved the living environment of the local residents. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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20 pages, 4791 KiB

Open AccessArticle

Multispectral Image Determination of Water Content in Aquilaria sinensis Based on Machine Learning

by Peng Wang, Yi Wu, Xuefeng Wang, Mengmeng Shi, Xingjing Chen and Ying Yuan

Forests 2023, 14(6), 1144; https://doi.org/10.3390/f14061144 - 1 Jun 2023

Viewed by 1565

Abstract

The real-time nondestructive monitoring of plant water content can enable operators to understand the water demands of crops in a timely manner and provide a reliable basis for precise irrigation. In this study, a method for rapid estimation of water content in Aquilaria [...] Read more.

The real-time nondestructive monitoring of plant water content can enable operators to understand the water demands of crops in a timely manner and provide a reliable basis for precise irrigation. In this study, a method for rapid estimation of water content in Aquilaria sinensis using multispectral imaging was proposed. First, image registration and segmentation were performed using the Fourier–Mellin transform (FFT) and the fuzzy local information c-means clustering algorithm (FLICM). Second, the spectral features (SFs), texture features (TFs), and comprehensive features (CFs) of the image were extracted. Third, using the eigenvectors of the SFs, TFs, and CFs as input, a random forest regression model for estimating the water content of A. sinensis was constructed, respectively. Finally, the monarch butterfly optimization (MBO), Harris hawks optimization (HHO), and sparrow search algorithm (SSA) were used to optimize all models to determine the best estimation model. The results showed that: (1) 60%–80% soil water content is the most suitable for A. sinensis growth. Compared with waterlogging, drought inhibited A. sinensis growth more significantly. (2) FMT + FLICM could achieve rapid segmentation of discrete A. sinensis multispectral images on the basis of guaranteed accuracy. (3) The prediction effect of TFs was basically the same as that of SFs, and the prediction effect of CFs was higher than that of SFs and TFs, but this difference would decrease with the optimization of the RFR model. (4) Among all models, SSA-RFR_CFs had the highest accuracy, with an R² of 0.8282. These results confirmed the feasibility and accuracy of applying multispectral imaging technology to estimate the water content of A. sinensis and provide a reference for the protection and cultivation of endangered precious tree species. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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18 pages, 3161 KiB

Open AccessArticle

Forest Carbon Density Estimation Using Tree Species Diversity and Stand Spatial Structure Indices

by Tao Li, Xiao-Can Wu, Yi Wu and Ming-Yang Li

Forests 2023, 14(6), 1105; https://doi.org/10.3390/f14061105 - 26 May 2023

Cited by 3 | Viewed by 3309

Abstract

The forest spatial structure and diversity of tree species, as the important evaluation indicators of forest quality, are key factors affecting forest carbon storage. To analyze the impacts of biodiversity indices and stand spatial structure on forest carbon density, five tree diversity indices [...] Read more.

The forest spatial structure and diversity of tree species, as the important evaluation indicators of forest quality, are key factors affecting forest carbon storage. To analyze the impacts of biodiversity indices and stand spatial structure on forest carbon density, five tree diversity indices were calculated from three aspects of richness, diversity and evenness, and three indices (Reineke’s stand density index, Hegyi’s competition index and Simple mingling degree) were calculated from stand spatial structure. The relationships between these eight indices and forest carbon density were explored using the Structural Equation Model (SEM). Then, these eight indices were used as characteristic variables to predict the aboveground carbon density of trees (abbreviated as forest carbon density) in the sample plots of the National Forest Resources Continuous Inventory (NFCI) in Shaoguan City in 2017. Multiple Linear Regression (MLR) and four typical machine learning models of Random Forest (RF), Tree-based Piecewise Linear Model (M5P), Artificial Neural Network (ANN) and Support Vector Regression (SVR) were used to predict the forest carbon density. The results show that: (1) Based on the analysis results of the structural equation model (SED), the species diversity and forest stand spatial structure have greater impacts on carbon density. (2) The R² of all the five prediction models is greater than 0.6, among which the random forest model is the highest. (3) Based on the calculation results of optimal model of RF, the mean forest carbon density of Shaoguan city in 2017 was 43.176 tC/ha. The forest carbon density can be accurately estimated based on the species diversity index and stand spatial structure with machine learning algorithms. Therefore, a new method for the prediction of forest carbon density and carbon storage using species diversity indices and stand spatial structure can be explored. By analyzing the impacts of different biodiversity indices and stand spatial structure on forest carbon density, a scientific reference for the making of management measures for increasing forest carbon sinks and reducing emissions can be provided. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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28 pages, 11346 KiB

Open AccessReview

Application of Geoinformatics in Forest Planning and Management

by Jiani Xing, Shufa Sun, Qiuhua Huang, Zhuchenxi Chen and Zixuan Zhou

Forests 2024, 15(3), 439; https://doi.org/10.3390/f15030439 - 25 Feb 2024

Cited by 1 | Viewed by 2276

Abstract

Rational forest planning and management is the key to a forest’s systematic construction. It is beneficial to many aspects, such as the cultivation and preservation of a forest’s ecological resources, sustainability, forest fire prevention, and others. In recent years, some effective strategies and [...] Read more.

Rational forest planning and management is the key to a forest’s systematic construction. It is beneficial to many aspects, such as the cultivation and preservation of a forest’s ecological resources, sustainability, forest fire prevention, and others. In recent years, some effective strategies and tactics for the planning and management of forests’ systematic construction have been established. Among them, the application of geoinformatics in forest planning and management (AGFPM) is one of the most effective and promising strategies. Therefore, it is necessary to conduct a comprehensive summary and analysis of the current situation. AGFPM has effectively applied in logging operations, forest road development, forest material transport, and forest fire prevention. An analysis of the research results in the past 20 years showed that decision support tools are the most used solutions to problems related to forest planning and management, especially the analytic hierarchy process (AHP). Light detection and ranging (LiDAR) is the second most popular method. With the development of geoinformatics, it will play an increasingly important role in forest planning and management in the future. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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31 pages, 3698 KiB

Open AccessReview

Review of Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation: Progress, Challenges, and Prospects

by Lei Tian, Xiaocan Wu, Yu Tao, Mingyang Li, Chunhua Qian, Longtao Liao and Wenxue Fu

Forests 2023, 14(6), 1086; https://doi.org/10.3390/f14061086 - 24 May 2023

Cited by 35 | Viewed by 14008

Abstract

Quantifying forest aboveground biomass (AGB) is essential for elucidating the global carbon cycle and the response of forest ecosystems to climate change. Over the past five decades, remote-sensing techniques have played a vital role in forest AGB estimation at different scales. Here, we [...] Read more.

Quantifying forest aboveground biomass (AGB) is essential for elucidating the global carbon cycle and the response of forest ecosystems to climate change. Over the past five decades, remote-sensing techniques have played a vital role in forest AGB estimation at different scales. Here, we present an overview of the progress in remote sensing-based forest AGB estimation. More in detail, we first describe the principles of remote sensing techniques in forest AGB estimation: that is, the construction and use of parameters associated with AGB (rather than the direct measurement of AGB values). Second, we review forest AGB remotely sensed data sources (including passive optical, microwave, and LiDAR) and methods (e.g., empirical, physical, mechanistic, and comprehensive models) alongside their limitations and advantages. Third, we discuss possible sources of uncertainty in resultant forest AGB estimates, including those associated with remote sensing imagery, sample plot survey data, stand structure, and statistical models. Finally, we offer forward-looking perspectives and insights on prospective research directions for remote sensing-based forest AGB estimation. Remote sensing is anticipated to play an increasingly important role in future forest AGB estimation and carbon cycle studies. Overall, this comprehensive review may (1) benefit the research communities focused on carbon cycle, remote sensing, and climate change elucidation, (2) provide a theoretical basis for the study of the carbon cycle and global climate change, (3) inform forest ecosystems and carbon management, and (4) aid in the elucidation of forest feedbacks to climate change. Full article

(This article belongs to the Special Issue Advanced Applications in Remote Sensing and GIS to Forest Management and Planning)

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