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17 pages, 4965 KiB

Open AccessArticle

Aboveground Forest Biomass Estimation by the Integration of TLS and ALOS PALSAR Data Using Machine Learning

by Arunima Singh, Sunni Kanta Prasad Kushwaha, Subrata Nandy, Hitendra Padalia, Surajit Ghosh, Ankur Srivastava and Nikul Kumari

Remote Sens. 2023, 15(4), 1143; https://doi.org/10.3390/rs15041143 - 20 Feb 2023

Cited by 10 | Viewed by 3190

Abstract

Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest [...] Read more.

Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest Range, Uttarakhand, India. The integration was performed to overcome the AGB saturation issue in ALOS PALSAR L-band SAR data for the high biomass density forest of the study area using 13 plots. Various parameters, namely, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, Yamaguchi decomposition components, polarimetric parameters, and backscatter values of HH and HV band intensity, were derived from the ALOS SAR data. However, TLS was used to obtain the diameter at breast height (dbh) and tree height for the sample plots. A total of 23 parameters was retrieved using TLS and SAR data for integration with the LiDAR footprint. The integration was performed using Random Forest (RF) and Artificial Neural Network (ANN). The statistical measures for RF were found to be promising compared with ANN for AGB estimation. The R² value obtained for the RF was 0.94, with an RMSE of 59.72 ton ha⁻¹ for the predicted biomass value. The RMSE% was 15.92, while the RMSE_CV was 0.15. The R² value for ANN was 0.77, with an RMSE of 98.46 ton ha⁻¹. The RMSE% was 26.0, while the RMSE_CV was 0.26. RF performed better in estimating the biomass, which ranged from 122.46 to 581.89 ton ha⁻¹, while uncertainty ranged from 15.75 to 85.14 ton ha⁻¹. The integration of SAR and LiDAR data using machine learning shows great potential in overcoming AGB saturation of SAR data. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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24 pages, 7030 KiB

Open AccessArticle

Integrating Real Tree Skeleton Reconstruction Based on Partial Computational Virtual Measurement (CVM) with Actual Forest Scenario Rendering: A Solid Step Forward for the Realization of the Digital Twins of Trees and Forests

by Zhichao Wang, Xin Lu, Feng An, Lijun Zhou, Xiangjun Wang, Zhihao Wang, Huaiqing Zhang and Ting Yun

Remote Sens. 2022, 14(23), 6041; https://doi.org/10.3390/rs14236041 - 29 Nov 2022

Cited by 6 | Viewed by 2480

Abstract

Digital twins of forests (trees) are computational virtual recreations of forests (trees) in which the entity distributions and physical processes in real-world forests (trees) are duplicated. It is expected that conventional forest science and management can be undertaken in a digital twin of [...] Read more.

Digital twins of forests (trees) are computational virtual recreations of forests (trees) in which the entity distributions and physical processes in real-world forests (trees) are duplicated. It is expected that conventional forest science and management can be undertaken in a digital twin of forests (trees) if the recreation of a real-world forest (tree) has accurate and comprehensive enough information. However, due to the various differences between the current tree model and the real tree, these envisioned digital twins of the forests (trees) stay a theoretical concept. In this study, we developed a processing strategy that partially integrated computational virtual measurement (CVM) process into the tree modeling workflow. Owing to the feature of CVM, partial tree skeleton reconstruction procedures were considered to have higher mechanical objectivity compared to conventional mathematical modeling methods. The reason was that we developed a novel method called virtual diameter tape (VDT), which could provide a certain percentage of modeling elements using CVM. Technically, VDT was able to virtually measure diameters and spatial distribution of cross-sectional area of trees, including the basal area, from point clouds. VDT simulated the physical scenario of diameter tapes, observing point clouds of trees. Diameter and the cross-sectional area of stem and branches were obtained by two consecutive physical measurement processes, one in the forest sample site and another in the virtual space. At the same time, VDT obtained better or a similar accuracy compared to the mathematical methods, i.e., Hough transform-based methods, using the same data sets. The root-mean-square deviation (RMSE) of retrieval of diameter at breast height (DBH) using VDT was 1.02 cm, while DBH obtained from three conventional methods varied from 1.29 cm to 1.73 cm. Based on VDT measurement results, tree skeleton reconstruction and actual forest scenario rendering of our sample plots were further implemented. Beyond the visual consistency, we believe that our work might be a small and solid step in the technological evolution from tree models to the digital twin of forests (trees). Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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18 pages, 4823 KiB

Open AccessArticle

Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images

by Fugen Jiang, Hua Sun, Erxue Chen, Tianhong Wang, Yaling Cao and Qingwang Liu

Remote Sens. 2022, 14(22), 5734; https://doi.org/10.3390/rs14225734 - 13 Nov 2022

Cited by 13 | Viewed by 3023

Abstract

Accurate estimation of forest above-ground biomass (AGB) is critical for assessing forest quality and carbon stocks, which can improve understanding of the vegetation growth processes and the global carbon cycle. Landsat 9, the latest launched Landsat satellite, is the successor and continuation of [...] Read more.

Accurate estimation of forest above-ground biomass (AGB) is critical for assessing forest quality and carbon stocks, which can improve understanding of the vegetation growth processes and the global carbon cycle. Landsat 9, the latest launched Landsat satellite, is the successor and continuation of Landsat 8, providing a highly promising data resource for land cover change, forest surveys, and terrestrial ecosystem monitoring. Regression kriging was developed in the study to improve the AGB estimation and mapping using the Landsat 9 image in Wangyedian forest farm, northern China. Multiple linear regression (MLR), support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF) were used as the original models to predict the AGB trends, and the optimal model was used to overlay the results of kriging interpolation based on the residuals to obtain the new AGB predictions. In addition, Landsat 8 images in Wangyedian were used for comparison and verification with Landsat 9. The results showed that all bands of Landsat 8 and Landsat 9 maintained a high degree of uniformity, with positive correlation coefficients ranging from 0.77 to 0.89 (p < 0.01). RF achieved the highest estimation accuracy among all the original models based on the two data sources. However, kriging regression can significantly reduce the estimation error, with the root mean square error (RMSE) decreasing by 55.4% and 51.1%, for Landsat 8 and Landsat 9, respectively, compared to the original RF. Further, the R² and the lowest RMSE for Landsat 8 were 0.88 and 16.83 t/ha, while, for Landsat 9, they were 0.87 and 17.91 t/ha. The use of regression kriging combined with Landsat 9 imagery has great potential for achieving efficient and highly accurate forest AGB estimates, providing a new reference for long-term monitoring of forest resource dynamics. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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23 pages, 5863 KiB

Open AccessArticle

Forest Fire Occurrence Prediction in China Based on Machine Learning Methods

by Yongqi Pang, Yudong Li, Zhongke Feng, Zemin Feng, Ziyu Zhao, Shilin Chen and Hanyue Zhang

Remote Sens. 2022, 14(21), 5546; https://doi.org/10.3390/rs14215546 - 3 Nov 2022

Cited by 50 | Viewed by 6363

Abstract

Forest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is [...] Read more.

Forest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is due to the difficulty of forecasting forest fires. There are many factors that have an impact on the occurrence of forest fires. The specific contribution of each factor to the occurrence of forest fires is not clear when using conventional analyses. In this study, we leveraged the excellent performance of artificial intelligence algorithms in fusing data from multiple sources (e.g., fire hotspots, meteorological conditions, terrain, vegetation, and socioeconomic data collected from 2003 to 2016). We have tested several algorithms and, finally, four algorithms were selected for formal data processing. There were an artificial neural network, a radial basis function network, a support-vector machine, and a random forest to identify thirteen major drivers of forest fires in China. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We obtained the probability of forest fire occurrence in each province of China using the optimal model. Moreover, the spatial distribution of high-to-low forest fire-prone areas was mapped. The results showed that the prediction accuracies of the four forest fire prediction models were between 75.8% and 89.2%, and the area under the curve (AUC) values were between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and AUC value (0.96). It was determined as the best performance model in this study. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments should improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helped in understanding the main drivers of forest fires in China over the period between 2003 and 2016, and determined the best performance model. The spatial distribution of high-to-low forest fire-prone areas maps were produced in order to depict the comprehensive views of China’s forest fire risks in each province. They were expected to form a scientific basis for helping the decision-making of China’s forest fire prevention authorities. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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26 pages, 13002 KiB

Open AccessArticle

Individual Tree Species Classification Based on a Hierarchical Convolutional Neural Network and Multitemporal Google Earth Images

by Zhonglu Lei, Hui Li, Jie Zhao, Linhai Jing, Yunwei Tang and Hongkun Wang

Remote Sens. 2022, 14(20), 5124; https://doi.org/10.3390/rs14205124 - 13 Oct 2022

Cited by 5 | Viewed by 2269

Abstract

Accurate and efficient individual tree species (ITS) classification is the basis of fine forest resource management. It is a challenge to classify individual tree species in dense forests using remote sensing imagery. In order to solve this problem, a new ITS classification method [...] Read more.

Accurate and efficient individual tree species (ITS) classification is the basis of fine forest resource management. It is a challenge to classify individual tree species in dense forests using remote sensing imagery. In order to solve this problem, a new ITS classification method was proposed in this study, in which a hierarchical convolutional neural network (H-CNN) model and multi-temporal high-resolution Google Earth images were employed. In an experiment conducted in a forest park in Beijing, China, GE images of several significant phenological phases of broad-leaved forests, namely, before and after the mushrooming period, the growth period, and the wilting period, were selected, and ITS classifications based on these images along with several typical CNN models and the H-CNN model were conducted. In the experiment, the classification accuracy of the multitemporal images was higher by 7.08–12.09% than those of the single-temporal images, and the H-CNN model offered an OA accuracy 2.66–3.72% higher than individual CNN models, demonstrating that multitemporal images rich in the phenological features of individual tree species, together with a hierarchical CNN model, can effectively improve ITS classification. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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18 pages, 5908 KiB

Open AccessArticle

DSNUNet: An Improved Forest Change Detection Network by Combining Sentinel-1 and Sentinel-2 Images

by Jiawei Jiang, Yuanjun Xing, Wei Wei, Enping Yan, Jun Xiang and Dengkui Mo

Remote Sens. 2022, 14(19), 5046; https://doi.org/10.3390/rs14195046 - 10 Oct 2022

Cited by 8 | Viewed by 3226

Abstract

The use of remote sensing images to detect forest changes is of great significance for forest resource management. With the development and implementation of deep learning algorithms in change detection, a large number of models have been designed to detect changes in multi-phase [...] Read more.

The use of remote sensing images to detect forest changes is of great significance for forest resource management. With the development and implementation of deep learning algorithms in change detection, a large number of models have been designed to detect changes in multi-phase remote sensing images. Although synthetic aperture radar (SAR) data have strong potential for application in forest change detection tasks, most existing deep learning-based models have been designed for optical imagery. Therefore, to effectively combine optical and SAR data in forest change detection, this paper proposes a double Siamese branch-based change detection network called DSNUNet. DSNUNet uses two sets of feature branches to extract features from dual-phase optical and SAR images and employs shared weights to combine features into groups. In the proposed DSNUNet, different feature extraction branch widths were used to compensate for a difference in the amount of information between optical and SAR images. The proposed DSNUNet was validated by experiments on the manually annotated forest change detection dataset. According to the obtained results, the proposed method outperformed other change detection methods, achieving an F1-score of 76.40%. In addition, different combinations of width between feature extraction branches were analyzed in this study. The results revealed an optimal performance of the model at initial channel numbers of the optical imaging branch and SAR image branch of 32 and 8, respectively. The prediction results demonstrated the effectiveness of the proposed method in accurately predicting forest changes and suppressing cloud interferences to some extent. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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19 pages, 1347 KiB

Open AccessArticle

SERNet: Squeeze and Excitation Residual Network for Semantic Segmentation of High-Resolution Remote Sensing Images

by Xiaoyan Zhang, Linhui Li, Donglin Di, Jian Wang, Guangsheng Chen, Weipeng Jing and Mahmoud Emam

Remote Sens. 2022, 14(19), 4770; https://doi.org/10.3390/rs14194770 - 23 Sep 2022

Cited by 22 | Viewed by 3663

Abstract

The semantic segmentation of high-resolution remote sensing images (HRRSIs) is a basic task for remote sensing image processing and has a wide range of applications. However, the abundant texture information and wide imaging range of HRRSIs lead to the complex distribution of ground [...] Read more.

The semantic segmentation of high-resolution remote sensing images (HRRSIs) is a basic task for remote sensing image processing and has a wide range of applications. However, the abundant texture information and wide imaging range of HRRSIs lead to the complex distribution of ground objects and unclear boundaries, which bring huge challenges to the segmentation of HRRSIs. To solve this problem, in this paper we propose an improved squeeze and excitation residual network (SERNet), which integrates several squeeze and excitation residual modules (SERMs) and a refine attention module (RAM). The SERM can recalibrate feature responses adaptively by modeling the long-range dependencies in the channel and spatial dimensions, which enables effective information to be transmitted between the shallow and deep layers. The RAM pays attention to global features that are beneficial to segmentation results. Furthermore, the ISPRS datasets were processed to focus on the segmentation of vegetation categories and introduce Digital Surface Model (DSM) images to learn and integrate features to improve the segmentation accuracy of surface vegetation, which has certain prospects in the field of forestry applications. We conduct a set of comparative experiments on ISPRS Vaihingen and Potsdam datasets. The results verify the superior performance of the proposed SERNet. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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23 pages, 4069 KiB

Open AccessArticle

Detection of Tree Decline (Pinus pinaster Aiton) in European Forests Using Sentinel-2 Data

by Vasco Mantas, Luís Fonseca, Elsa Baltazar, Jorge Canhoto and Isabel Abrantes

Remote Sens. 2022, 14(9), 2028; https://doi.org/10.3390/rs14092028 - 23 Apr 2022

Cited by 14 | Viewed by 4636

Abstract

Moderate-resolution satellite imagery is essential to detect conifer tree decline on a regional scale and address the threat caused by pinewood nematode (PWN), (Bursaphelenchus xylophilus. This is a quarantine organism responsible for pine wilt disease (PWD), which has caused substantial ecological [...] Read more.

Moderate-resolution satellite imagery is essential to detect conifer tree decline on a regional scale and address the threat caused by pinewood nematode (PWN), (Bursaphelenchus xylophilus. This is a quarantine organism responsible for pine wilt disease (PWD), which has caused substantial ecological and economic losses in the maritime pine (Pinus pinaster) forests of Portugal. This study describes the first instance of a pre-operational algorithm applied to Sentinel-2 imagery to detect PWD-compatible decline in maritime pine. The Random Forest model relied on a pre-wilting and an in-season image, calibrated with data from a 24-month long field campaign enhanced with Worldview-3 data and the analysis of biological samples (hyperspectral reflectance, pigment quantification in needles, and PWN identification). Independent validation results attested to the good performance of the model with an overall accuracy of 95%, particularly when decline affects more than 30% of the 100 m² pixel of Sentinel-2. Spectral angle mapper applied to hyperspectral measurements suggested that PWN infection cannot be separated from other drivers of decline in the visible-near infrared domain. Our algorithm can be employed to detect regional decline trends and inform subsequent aerial and field surveys, to further investigate decline hotspots. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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25 pages, 17255 KiB

Open AccessArticle

An Automatic Procedure for Forest Fire Fuel Mapping Using Hyperspectral (PRISMA) Imagery: A Semi-Supervised Classification Approach

by Riyaaz Uddien Shaik, Giovanni Laneve and Lorenzo Fusilli

Remote Sens. 2022, 14(5), 1264; https://doi.org/10.3390/rs14051264 - 4 Mar 2022

Cited by 28 | Viewed by 5279

Abstract

Natural vegetation provides various benefits to human society, but also acts as fuel for wildfires. Therefore, mapping fuel types is necessary to prevent wildfires, and hyperspectral imagery has applications in multiple fields, including the mapping of wildfire fuel types. This paper presents an [...] Read more.

Natural vegetation provides various benefits to human society, but also acts as fuel for wildfires. Therefore, mapping fuel types is necessary to prevent wildfires, and hyperspectral imagery has applications in multiple fields, including the mapping of wildfire fuel types. This paper presents an automatic semisupervised machine learning approach for discriminating between wildfire fuel types and a procedure for fuel mapping using hyperspectral imagery (HSI) from PRISMA, a recently launched satellite of the Italian Space Agency. The approach includes sample generation and pseudolabelling using a single spectral signature as input data for each class, unmixing mixed pixels by a fully constrained linear mixing model, and differentiating sparse and mountainous vegetation from typical vegetation using biomass and DEM maps, respectively. Then the procedure of conversion from a classified map to a fuel map according to the JRC Anderson Codes is presented. PRISMA images of the southern part of Sardinia, an island off Italy, were considered to implement this procedure. As a result, the classified map obtained an overall accuracy of 87% upon validation. Furthermore, the stability of the proposed approach was tested by repeating the procedure on another HSI acquired for part of Bulgaria and we obtained an overall stability of around 84%. In terms of repeatability and reproducibility analysis, a degree of confidence greater than 95% was obtained. This study suggests that PRISMA imagery has good potential for wildfire fuel mapping, and the proposed semisupervised learning approach can generate samples for training the machine learning model when there is no single go-to dataset available, whereas this procedure can be implemented to develop a wildfire fuel map for any part of Europe using LUCAS land cover points as input. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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18 pages, 7006 KiB

Open AccessArticle

Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

by Jie Tang, Fugen Jiang, Yi Long, Liyong Fu and Hua Sun

Remote Sens. 2022, 14(3), 642; https://doi.org/10.3390/rs14030642 - 28 Jan 2022

Cited by 5 | Viewed by 3030

Abstract

Oil tea (Camellia oleifera) is one of the world’s major woody edible oil plants and is vital in providing food and raw materials and ensuring water conservation. The yield of oil tea can directly reflect the growth condition of oil tea [...] Read more.

Oil tea (Camellia oleifera) is one of the world’s major woody edible oil plants and is vital in providing food and raw materials and ensuring water conservation. The yield of oil tea can directly reflect the growth condition of oil tea forests, and rapid and accurate yield measurement is directly beneficial to efficient oil tea forest management. Light detection and ranging (LiDAR), which can penetrate the canopy to acquire the geometric attributes of targets, has become an effective and popular method of yield identification for agricultural products. However, the common geometric attribute information obtained by LiDAR systems is always limited in terms of the accuracy of yield identification. In this study, to improve yield identification efficiency and accuracy, the red-green-blue (RGB) and luminance-bandwidth-chrominance (i.e., YUV color spaces) were used to identify the point clouds of oil tea fruits. An optimized mean shift clustering algorithm was constructed for oil tea fruit point cloud extraction and product identification. The point cloud data of oil tea trees were obtained using terrestrial laser scanning (TLS), and field measurements were conducted in Changsha County, central China. In addition, the common mean shift, density-based spatial clustering of applications with noise (DBSCAN), and maximum–minimum distance clustering were established for comparison and validation. The results showed that the optimized mean shift clustering algorithm achieved the best identification in both the RGB and YUV color spaces, with detection ratios that were 9.02%, 54.53%, and 3.91% and 7.05%, 62.35%, and 10.78% higher than those of the common mean shift clustering, DBSCAN clustering, and maximum-minimum distance clustering algorithms, respectively. In addition, the improved mean shift clustering algorithm achieved a higher recognition rate in the YUV color space, with an average detection rate of 81.73%, which was 2.4% higher than the average detection rate in the RGB color space. Therefore, this method can perform efficient yield identification of oil tea and provide a new reference for agricultural product management. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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18 pages, 5894 KiB

Open AccessArticle

A Combined Strategy of Improved Variable Selection and Ensemble Algorithm to Map the Growing Stem Volume of Planted Coniferous Forest

by Xiaodong Xu, Hui Lin, Zhaohua Liu, Zilin Ye, Xinyu Li and Jiangping Long

Remote Sens. 2021, 13(22), 4631; https://doi.org/10.3390/rs13224631 - 17 Nov 2021

Cited by 18 | Viewed by 2338

Abstract

Remote sensing technology is becoming mainstream for mapping the growing stem volume (GSV) and overcoming the shortage of traditional labor-consumed approaches. Naturally, the GSV estimation accuracy utilizing remote sensing imagery is highly related to the variable selection methods and algorithms. Thus, to reduce [...] Read more.

Remote sensing technology is becoming mainstream for mapping the growing stem volume (GSV) and overcoming the shortage of traditional labor-consumed approaches. Naturally, the GSV estimation accuracy utilizing remote sensing imagery is highly related to the variable selection methods and algorithms. Thus, to reduce the uncertainty caused by variables and models, this paper proposes a combined strategy involving improved variable selection with the collinearity test and the secondary ensemble algorithm to obtain the optimally combined variables and extract a reliable GSV from several base models. Our study extracted four types of alternative variables from the Sentinel-1A and Sentinel-2A image datasets, including vegetation indices, spectral reflectance variables, backscattering coefficients, and texture features. Then, an improved variable selection criterion with the collinearity test was developed and evaluated based on machine learning algorithms (classification and regression trees (CART), k-nearest neighbors (KNN), support vector regression (SVR), and artificial neural network (ANN)) considering the correlation between variables and GSV (with random forest (RF), distance correlation coefficient (DC), maximal information coefficient (MIC), and Pearson correlation coefficient (PCC) as evaluation metrics), and the collinearity among the variables. Additionally, we proposed a secondary ensemble with an improved weighted average approach (IWA) to estimate the reliable forest GSV using the first ensemble models constructed by Bagging and AdaBoost. The experimental results demonstrated that the proposed variable selection criterion efficiently obtained the optimal combined variable set without affecting the forest GSV mapping accuracy. Specifically, considering the first ensemble, the relative root mean square error (rRMSE) values ranged from 21.91% to 30.28% for Bagging and 23.33% to 31.49% for AdaBoost, respectively. After the secondary ensemble involving the IWA, the rRMSE values ranged from 18.89% to 21.34%. Furthermore, the variance of the GSV mapped by the secondary ensemble with various ranking methods was significantly reduced. The results prove that the proposed combined strategy has great potential to reduce the GSV mapping uncertainty imposed by current variable selection approaches and algorithms. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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21 pages, 6313 KiB

Open AccessArticle

Processing Point Clouds Using Simulated Physical Processes as Replacements of Conventional Mathematically Based Procedures: A Theoretical Virtual Measurement for Stem Volume

by Zhichao Wang, Yan-Jun Shen, Xiaoyuan Zhang, Yao Zhao and Christiane Schmullius

Remote Sens. 2021, 13(22), 4627; https://doi.org/10.3390/rs13224627 - 17 Nov 2021

Cited by 6 | Viewed by 2759

Abstract

Conventional mathematically based procedures in forest data processing have some problems, such as deviations between the natural tree and the tree described using mathematical expressions, and manual selection of equations and parameters. These problems are rooted at the algorithmic level. Our solution for [...] Read more.

Conventional mathematically based procedures in forest data processing have some problems, such as deviations between the natural tree and the tree described using mathematical expressions, and manual selection of equations and parameters. These problems are rooted at the algorithmic level. Our solution for these problems was to process raw data using simulated physical processes as replacements of conventional mathematically based procedures. In this mechanism, we treated the data points as solid objects and formed virtual trees. Afterward, the tree parameters were obtained by the external physical detection, i.e., computational virtual measurement (CVM). CVM simulated the physical behavior of measurement instruments in reality to measure virtual trees. Namely, the CVM process was a pure (simulated) physical process. In order to verify our assumption of CVM, we developed the virtual water displacement (VWD) application. VWD could extract stem volume from an artificial stem (consisted of 2000 points) by simulating the physical scenario of a water displacement method. Compared to conventional mathematically based methods, VWD removed the need to predefine the shape of the stem and minimized human interference. That was because VWD utilized the natural contours of the stem through the interaction between the point cloud and the virtual water molecules. The results showed that the stem volume measured using VWD was 29,636 cm³ (overestimation at 6.0%), where the true volume was 27,946 cm³. The overall feasibility of CVM was proven by the successful development of VWD. Meanwhile, technical experiences, current limitations, and potential solutions were discussed. We considered CVM as a generic method that focuses the objectivity at the algorithmic level, which will become a noteworthy development direction in the field of forest data processing in the future. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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22 pages, 55037 KiB

Open AccessArticle

Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

by Shuhan Lei, Jianbiao Luo, Xiaojun Tao and Zixuan Qiu

Remote Sens. 2021, 13(22), 4562; https://doi.org/10.3390/rs13224562 - 13 Nov 2021

Cited by 29 | Viewed by 5470

Abstract

Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a [...] Read more.

Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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26 pages, 5378 KiB

Open AccessArticle

Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind

by Xingdong Li, Hewei Gao, Mingxian Zhang, Shiyu Zhang, Zhiming Gao, Jiuqing Liu, Shufa Sun, Tongxin Hu and Long Sun

Remote Sens. 2021, 13(21), 4325; https://doi.org/10.3390/rs13214325 - 27 Oct 2021

Cited by 15 | Viewed by 4234

Abstract

Modeling forest fire spread is a very complex problem, and the existing models usually need some input parameters which are hard to get. How to predict the time series of forest fire spread rate based on passed series may be a key problem [...] Read more.

Modeling forest fire spread is a very complex problem, and the existing models usually need some input parameters which are hard to get. How to predict the time series of forest fire spread rate based on passed series may be a key problem to break through the current technical bottleneck. In the process of forest fire spreading, spread rate and wind speed would affect each other. In this paper, three kinds of network models based on Long Short-Term Memory (LSTM) are designed to predict fire spread rate, exploring the interaction between fire and wind. In order to train these LSTM-based models and validate their effectiveness of prediction, several outdoor combustion experiments are designed and carried out. Process data sets of forest fire spreading are collected with an infrared camera mounted on a UAV, and wind data sets are recorded using a anemometer simultaneously. According to the close relationship between wind and fire, three progressive LSTM based models are constructed, which are called CSG-LSTM, MDG-LSTM and FNU-LSTM, respectively. A Cross-Entropy Loss equation is employed to measure the model training quality, and then prediction accuracy is computed and analyzed by comparing with the true fire spread rate and wind speed. According to the performance of training and prediction stage, FNU-LSTM is determined as the best model for the general case. The advantage of FNU-LSTM is further demonstrated by doing comparison experiments with the normal LSTM and other LSTM based models which predict both fire spread rate and wind speed separately. The experiment has also demonstrated the ability of the model to the real fire prediction on the basis of two historical wildland fires. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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Open AccessTechnical Note

A Neural Network-Based Spectral Approach for the Assignment of Individual Trees to Genetically Differentiated Subpopulations

by Carlos Maldonado, Freddy Mora-Poblete, Cristian Echeverria, Ricardo Baettig, Cristian Torres-Díaz, Rodrigo Iván Contreras-Soto, Parviz Heidari, Gustavo Adolfo Lobos and Antônio Teixeira do Amaral Júnior

Remote Sens. 2022, 14(12), 2898; https://doi.org/10.3390/rs14122898 - 17 Jun 2022

Cited by 4 | Viewed by 2686

Abstract

Studying population structure has made an essential contribution to understanding evolutionary processes and demographic history in forest ecology research. This inference process basically involves the identification of common genetic variants among individuals, then grouping the similar individuals into subpopulations. In this study, a [...] Read more.

Studying population structure has made an essential contribution to understanding evolutionary processes and demographic history in forest ecology research. This inference process basically involves the identification of common genetic variants among individuals, then grouping the similar individuals into subpopulations. In this study, a spectral-based classification of genetically differentiated groups was carried out using a provenance–progeny trial of Eucalyptus cladocalyx. First, the genetic structure was inferred through a Bayesian analysis using single-nucleotide polymorphisms (SNPs). Then, different machine learning models were trained with foliar spectral information to assign individual trees to subpopulations. The results revealed that spectral-based classification using the multilayer perceptron method was very successful at classifying individuals into their respective subpopulations (with an average of 87% of correct individual assignments), whereas 85% and 81% of individuals were assigned to their respective classes correctly by convolutional neural network and partial least squares discriminant analysis, respectively. Notably, 93% of individual trees were assigned correctly to the class with the smallest size using the spectral data-based multi-layer perceptron classification method. In conclusion, spectral data, along with neural network models, are able to discriminate and assign individuals to a given subpopulation, which could facilitate the implementation and application of population structure studies on a large scale. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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