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16 pages, 5785 KiB

Open AccessArticle

Interpreting the Response of Forest Stock Volume with Dual Polarization SAR Images in Boreal Coniferous Planted Forest in the Non-Growing Season

by Huanna Zheng, Jiangping Long, Zhuo Zang, Hui Lin, Zhaohua Liu, Tingchen Zhang and Peisong Yang

Forests 2023, 14(9), 1700; https://doi.org/10.3390/f14091700 - 23 Aug 2023

Cited by 1 | Viewed by 1252

Abstract

Polarimetric Synthetic Aperture Radar (PolSAR) images with dual polarization modes have great potential to map forest stock volume (FSV) by excellent penetration capabilities and distinct microwave scattering processes. However, the response of these SAR data to FSV is still uncertain in the non-growing [...] Read more.

Polarimetric Synthetic Aperture Radar (PolSAR) images with dual polarization modes have great potential to map forest stock volume (FSV) by excellent penetration capabilities and distinct microwave scattering processes. However, the response of these SAR data to FSV is still uncertain in the non-growing season. To further interpret the response of FSV to different dual polarization SAR images, three types of dual polarization SAR images (GF-3, Sentinel-1, and ALOS-2) were initially acquired in coniferous planted forest in the non-growing season. Then, sensitivity between FSV and all alternative features extracted from each type of SAR image was analyzed to express the response of FSV to dual polarization SAR images with bands and polarization modes in the non-growing season in deciduous (Larch) and evergreen (Chinese pine) forests. Finally, mapped FSV using single and combined dual polarization images were derived by optimal feature sets and four machine learning models, respectively. The combined effects were also analyzed to clarify the difference of bands and polarization modes in deciduous and evergreen forests in the non-growing season. The results demonstrated that the backscattering energy from different sensors is significantly different in Chinese pine, and the difference is gradually reduced in Larch forests. It is also implied that the polarization mode is more important than penetration capability in mapping forest FSV in deciduous forest in the non-growing season. By comparing the accuracy of mapped FSV using single and combined images, combined images have more capability to improve the accuracy and reliability of mapped FSV. Meanwhile, it is confirmed that compensation effects with bands and polarization modes not only have great potential to delay the saturation phenomenon, but also have the capability to reduce errors caused by overestimation. Full article

(This article belongs to the Special Issue Forestry Remote Sensing: Biomass, Changes and Ecology)

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17 pages, 4243 KiB

Open AccessArticle

Forest Canopy Cover Inversion Exploration Using Multi-Source Optical Data and Combined Methods

by Yuan Guan, Xin Tian, Wangfei Zhang, Armando Marino, Jimao Huang, Yingwu Mao and Han Zhao

Forests 2023, 14(8), 1527; https://doi.org/10.3390/f14081527 - 26 Jul 2023

Cited by 2 | Viewed by 1393

Abstract

An accurate estimation of canopy cover can provide an important basis for forest ecological management by understanding the forest status and change patterns. The aim of this paper is to investigate the four methods of the random forest (RF), support vector regression (SVR), [...] Read more.

An accurate estimation of canopy cover can provide an important basis for forest ecological management by understanding the forest status and change patterns. The aim of this paper is to investigate the four methods of the random forest (RF), support vector regression (SVR), k-nearest neighbor (KNN), and k-nearest neighbor with fast iterative features selection (KNN-FIFS) for modeling forest canopy cover, and to evaluate three mainstream optical data sources—Landsat8 OLI, Sentinel-2A, Gaofen-1 (GF-1)—and three types of data combined comparatively by selecting the optimal modeling method. The paper uses the Daxinganling Ecological Station of Genhe City, Inner Mongolia, as the research area, and is based on three types of multispectral remote sensing data, extracting spectral characteristics, textural characteristics, terrain characteristics; the Kauth–Thomas transform (K-T transform); and color transformation characteristics (HIS). The optimal combination of features was selected using three feature screening methods, namely stepwise regression, RF, and KNN-FIFS, and the four methods: RF, SVR KNN, and KNN-FIFS, were combined to carry out the evaluation analysis regarding the accuracy of forest canopy cover modeling: (1) In this study, a variety of remote sensing features were introduced, and the feature variables were selected by different parameter preference methods and then employed in modeling. Based on the four modeling inversion methods, the KNN-FIFS model achieves the best accuracy: the Landsat8 OLI with R² = 0.60, RMSE = 0.11, and RMSEr = 14.64% in the KNN-FIFS model; the Sentinel-2A with R² = 0.80, RMSE = 0.08, and RMSEr = 11.63% in the KNN-FIFS model; the GF-1 with R² = 0.55, RMSE = 0.12, and RMSEr = 15.04% in the KNN-FIFS model; and the federated data with R² = 0.82, RMSE = 0.08, and RMSEr = 10.40% in the KNN-FIFS model; (2) the three multispectral datasets have the ability to estimate forest canopy cover, and the modeling accuracy superior under the combination of multi-source data features; (3) under different optical data, KNN- FIFS achieves the best accuracy in the established nonparametric model, and its feature optimization method is better than that of the random forest optimization method. For the same model, the estimation result of the joint data is better than the single optical data; thus, the KNN-FIFS model, with specific parameters, can significantly improve the inversion accuracy and efficiency of forest canopy cover evaluation from different data sources. Full article

(This article belongs to the Special Issue Forestry Remote Sensing: Biomass, Changes and Ecology)

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26 pages, 16612 KiB

Open AccessArticle

Estimation of Species-Scale Canopy Chlorophyll Content in Mangroves from UAV and GF-6 Data

by Liangchao Deng, Bowei Chen, Min Yan, Bolin Fu, Zhenyu Yang, Bo Zhang and Li Zhang

Forests 2023, 14(7), 1417; https://doi.org/10.3390/f14071417 - 11 Jul 2023

Cited by 4 | Viewed by 1843

Abstract

The growth of mangroves is inhibited due to environmental degradation, and changes in the growing health of mangrove forests cause changes in internal physicochemical parameters. The canopy chlorophyll content is an important indicator to monitor the health status of mangroves. We study the [...] Read more.

The growth of mangroves is inhibited due to environmental degradation, and changes in the growing health of mangrove forests cause changes in internal physicochemical parameters. The canopy chlorophyll content is an important indicator to monitor the health status of mangroves. We study the effective inversion data sources and methods of mangrove health indicator parameters to monitor the health of mangrove ecosystems in typical areas of Beibu Gulf, Guangxi. In this study, we evaluated the capability of UAV, GF-6 data, and machine learning regression algorithms in estimating mangrove species-scale canopy chlorophyll content (CCC). Effective measures for mangrove pest and disease pressure, Sporobolus alterniflorus invasion, and anthropogenic risk are also explored, which are important for mangrove conservation and restoration. (1) We obtained several feature variables by constructing a combined vegetation index, and the most sensitive band of mangrove CCC was selected by the characteristic variable evaluation, and the CCC model at the mangrove species-scale was evaluated and validated. Through variable preferences, the feature variables with the highest contribution of Avicennia marina, Aegiceras corniculatum, Kandelia candel, and a collection of three categories of species in the UAV data were indices of RI35, MDATT413, RI35, and NDI35. (2) Random Forest, Gradient Boosting Regression Tree, and Extreme Gradient Boosting were evaluated using the root-mean-square error and coefficient of determination accuracy metrics. Extreme Gradient Boosting regression algorithms were evaluated for accuracy. In both UAV data and GF-6, RF achieved optimal results in inverse mangrove Aegiceras corniculatum species CCC, with higher stability and robustness in machine learning regressors. (3) Due to the sparse distribution of Kandelia candel in the study area and the low spatial resolution of the images, there is an increased possibility that individual image elements contain environmental noise, such as soil. Therefore, the level of CCC can effectively reflect the health status of mangroves and further reflect the increased possibility of the study area being exposed to risks, such as degradation. The establishment of the current protected areas and restoration of degraded ecosystems are effective measures to cope with the risks of mangrove pest and disease stress, invasion of Sporobolus alterniflorus, and anthropogenic activities, which are important for the protection and restoration of mangroves. This study provides an important data reference and risk warning for mangrove restoration and conservation. Full article

(This article belongs to the Special Issue Forestry Remote Sensing: Biomass, Changes and Ecology)

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20 pages, 10204 KiB

Open AccessArticle

Forest Height Inversion via RVoG Model and Its Uncertainties Analysis via Bayesian Framework—Comparisons of Different Wavelengths and Baselines

by Yongxin Zhang, Han Zhao, Yongjie Ji, Tingwei Zhang and Wangfei Zhang

Forests 2023, 14(7), 1408; https://doi.org/10.3390/f14071408 - 10 Jul 2023

Cited by 3 | Viewed by 2104

Abstract

Accurate estimation of forest height over a large area is beneficial to reduce the uncertainty of forest carbon sink estimation, which is of great significance to the terrestrial carbon cycle, global climate change, forest resource management, and forest-related scientific research. Forest height inversion [...] Read more.

Accurate estimation of forest height over a large area is beneficial to reduce the uncertainty of forest carbon sink estimation, which is of great significance to the terrestrial carbon cycle, global climate change, forest resource management, and forest-related scientific research. Forest height inversion using polarimetric interferometry synthetic aperture radar (PolInSAR) data through Random volume over ground (RVoG) models has demonstrated great potential for large-area forest height mapping. However, the wavelength and baseline length used for the PolInSAR data acquisition plays an important role during the forest height estimation procedure. In this paper, X–, C–, L–, and P–band PolInSAR datasets with four different baseline lengths were simulated and applied to explore the effects of wavelength and baseline length on forest height inversion using RVoG models. Hierarchical Bayesian models developed with a likelihood function of RVoG model were developed for estimated results uncertainty quantification and decrease. Then a similar procedure was applied in the L– and P–band airborne PolInSAR datasets with three different baselines for each band. The results showed that (1) Wavelength showed obvious effects on forest height inversion results with the RVoG model. For the simulated PolInSAR datasets, the L– and P–bands performed better than the X– and C–bands. The best performance was obtained at the P–band with a baseline combination of 10 × 4 m with an absolute error of 0.05 m and an accuracy of 97%. For the airborne PolInSAR datasets, an L–band with the longest baseline of 24 m in this study showed the best performance with R² = 0.64, RMSE = 3.32 m, and Acc. = 77.78%. (2) It is crucial to select suitable baseline lengths to obtain accurate forest height estimation results. In the four baseline combinations of simulated PolInSAR datasets, the baseline combination of 10 × 4 m both at the L– and P–bands performed best than other baseline combinations. While for the airborne PolInSAR datasets, the longest baseline in three different baselines obtained the highest accuracy at both L– and P–bands. (3) Bayesian framework is useful for estimation results uncertainty quantification and decrease. The uncertainties related to wavelength and baseline length. The uncertainties were reduced obviously at longer wavelengths and suitable baselines. Full article

(This article belongs to the Special Issue Forestry Remote Sensing: Biomass, Changes and Ecology)

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23 pages, 7898 KiB

Open AccessArticle

Spatial Effects Analysis on Individual-Tree Aboveground Biomass in a Tropical Pinus kesiya var. langbianensis Natural Forest in Yunnan, Southwestern China

by Xilin Zhang, Guoqi Chen, Chunxiao Liu, Qinling Fan, Wenfang Li, Yong Wu, Hui Xu and Guanglong Ou

Forests 2023, 14(6), 1177; https://doi.org/10.3390/f14061177 - 7 Jun 2023

Cited by 4 | Viewed by 1679

Abstract

It is essential to analyze the spatial autocorrelation and heterogeneity of aboveground biomass (AGB). But it is difficult to accurately describe due to the lack of data in clear-cutting plots. Thus, measuring the AGB directly in a clear-cutting plot can provide a reference [...] Read more.

It is essential to analyze the spatial autocorrelation and heterogeneity of aboveground biomass (AGB). But it is difficult to accurately describe due to the lack of data in clear-cutting plots. Thus, measuring the AGB directly in a clear-cutting plot can provide a reference for accurately describing the spatial variation. Therefore, a 0.3-hectare clear-cutting sample plot of Pinus kesiya var. langbianensis natural forest was selected, and the AGB was calculated by each component. The intra-group variance was quantitatively described in terms of spatial heterogeneity, and the spatial autocorrelation was explored by global and local Moran’s I. The results indicated that (1) there was different spatial heterogeneity for the different trees and organs. The intra-group variance tended to be stable after 20 m for P. kesiya var. langbianensis (PK) and other upper trees (UPs) and after 10 m for the other lower trees (LTs). (2) The spatial autocorrelation of AGB and wood biomass was similar, while the bark biomass and foliage biomass were consistent. PK and other UPs also exhibited strong spatial autocorrelation, with maximum Moran’s I values of 0.1537 and 0.1644, respectively. (3) There was spatial heterogeneity in the different components except for the bark of PK. The lowest spatial heterogeneity was found for LT. Full article

(This article belongs to the Special Issue Forestry Remote Sensing: Biomass, Changes and Ecology)

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Figure 1
Location of the clear-cutting plot. (a,b) shows the location of the plot in Mujiang county for Pu’er City, Yunnan Province, and (c) is a satellite image of the study site (Red frame); (d) shows the numbering of trees in the sample plot before logging, and (e–g) the trees that were logged. Full article ">Figure 2
Location of the trees in the clear-cutting plot. PK is Pinus kesiya var. langbianensis, UP is the other trees at the upper layer, and LT is the other trees at the lower layer. The circle diameter is proportional to the tree DBH. Full article ">Figure 3
The intra-group variance of the different biomass components for different tree groups. (a): aboveground biomass, (b): wood biomass, (c): bark biomass, (d): foliage biomass, (e): branches biomass. PK is Pinus kesiya var. langbianensis, UP is the other trees at the upper layer, and LT is the other trees at the lower layer. Full article ">Figure 4
Moran’s I correlogram and Z-score values for the biomass of the different components for all trees. (a): aboveground biomass, (b): wood biomass, (c): bark biomass, (d): foliage biomass, and (e): branches biomass. The dots and rectangles represent the points of Moran’s I and Z-score at the first peak (DFP) with a significant Z-score, and the values were shown in the figure, respectively. Full article ">Figure 5
Moran’s I correlogram and Z-score values for the biomass of the different components for Pinus kesiya var. langbianensis. (a): aboveground biomass, (b): wood biomass, (c): bark biomass, (d): foliage biomass, and (e): branches biomass. The dots and rectangles represent the points of Moran’s I and Z-score at the first peak (DFP) with a significant Z-score, and the values were shown in the figure, respectively. Full article ">Figure 6
Moran’s I correlogram and Z-score values for the biomass of the different components of other upper trees. (a): aboveground biomass, (b): wood biomass, (c): bark biomass, (d): foliage biomass, and (e): branches biomass. The dots and rectangles represent the points of Moran’s I and Z-score at the first peak (DFP) with a significant Z-score, and the values were shown in the figure, respectively. Full article ">Figure 7
Moran’s I correlogram and Z-score values for the biomass of the different components of other lower trees. (a): aboveground biomass, (b): wood biomass, (c): bark biomass, (d): foliage biomass, and (e): branches biomass. The dots and rectangles represent the points of Moran’s I and Z-score at the first peak (DFP) with a significant Z-score, and the values were shown in the figure, respectively. Full article ">Figure 8
Spatial distribution of local Moran’s Ii for Pinus kesiya var. langbianensis (a) for aboveground biomass, (b) for wood biomass, (c) for bark biomass, (d) for foliage biomass, and (e) for branches biomass. NS is the point of no significant spatial autocorrelation, HH is the similar aggregation point with high-high clustering, HL is the dissimilarity aggregation point with a high-low outlier, and LH is the dissimilarity aggregation point with a low-high outlier. Full article ">Figure 9
Spatial distribution of local Moran’s Ii for other upper trees (a) for aboveground biomass, (b) wood biomass, (c) bark biomass, (d) foliage biomass, and (e) for branches biomass. NS is the point of no significant spatial autocorrelation, HH is the similar aggregation point with a high-high clustering, and HL is the dissimilarity aggregation point with a high-low outlier. Full article ">Figure 10
Spatial distribution of local Moran’s Ii for lower trees. The capital letters of the figure codes represent the different components (a) for aboveground biomass, (b) wood biomass, (c) bark biomass, (d) foliage biomass, and for (e) branches biomass. NS is the point of no significant spatial autocorrelation, and LH is the dissimilarity aggregation point with a low-high outlier. Full article ">

16 pages, 7331 KiB

Open AccessArticle

Combination Strategies of Variables with Various Spatial Resolutions Derived from GF-2 Images for Mapping Forest Stock Volume

by Zhaohua Liu, Jiangping Long, Hui Lin, Xiaodong Xu, Hao Liu, Tingchen Zhang, Zilin Ye and Peisong Yang

Forests 2023, 14(6), 1175; https://doi.org/10.3390/f14061175 - 6 Jun 2023

Cited by 1 | Viewed by 1447

Abstract

Spectral features (SFs) and texture features (TFs) extracted from optical remote sensing images can capture the structural composition and growth information of forests, and combining remote sensing variables with a few ground measurement samples is a common method for mapping forest stock volume [...] Read more.

Spectral features (SFs) and texture features (TFs) extracted from optical remote sensing images can capture the structural composition and growth information of forests, and combining remote sensing variables with a few ground measurement samples is a common method for mapping forest stock volume (FSV). However, the accuracy of mapping FSV using optical images with a high spatial resolution (one meter or sub-meters) is often lower than medium resolutions (larger than 10 m) using the same types of features and approaches. To overcome the limitations of high spatial resolution images in mapping FSV, down-scaled images with spatial resolution ranging from 1 to 30 m were obtained by GF-2 image to interpret the relationships between spatial resolutions of features and the accuracy of mapping FSV, and combination strategies of variables with various spatial resolutions were proposed to improve the accuracy of mapping FSV. The results show that the spatial resolution of features significantly affects the performance of employed models in estimating FSV, the sensitivity between SFs and FSV gradually increases with the decreasing of spatial resolution, and the optimal spatial resolutions of two types of features (SFs and TFs) are not synchronized in mapping forest FSV. After using combination strategies of variables with various spatial resolutions, the accuracy of mapping FSV is significantly higher than those derived from variable sets with the same spatial resolutions. It is proved that TFs derived from GF-2 images have great potential to improve the accuracy of mapping FSV, and the contribution of features depends on the approaches of extracting and combination strategies. Full article

(This article belongs to the Special Issue Forestry Remote Sensing: Biomass, Changes and Ecology)

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19 pages, 3544 KiB

Open AccessArticle

Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data

by Li Xu, Qingtai Shu, Huyan Fu, Wenwu Zhou, Shaolong Luo, Yingqun Gao, Jinge Yu, Chaosheng Guo, Zhengdao Yang, Jinnan Xiao and Shuwei Wang

Forests 2023, 14(5), 876; https://doi.org/10.3390/f14050876 - 24 Apr 2023

Cited by 12 | Viewed by 2392

Abstract

Accurately estimating forest biomass based on spaceborne lidar on a county scale is challenging due to the incomplete coverage of spaceborne lidar data. Therefore, this research aims to interpolate GEDI spots and explore the feasibility of approaches to improving Quercus forest biomass estimation [...] Read more.

Accurately estimating forest biomass based on spaceborne lidar on a county scale is challenging due to the incomplete coverage of spaceborne lidar data. Therefore, this research aims to interpolate GEDI spots and explore the feasibility of approaches to improving Quercus forest biomass estimation accuracy in the alpine mountains of Yunnan Province, China. This paper uses GEDI data as the main information source and a typical mountainous area in Shangri-La, northwestern Yunnan Province, China, as the study area. Based on the pre-processing of light spots. A total of 38 parameters were extracted from the canopy and vertical profiles of 1307 light spots in the study area, and the polygon data of the whole study area were obtained from the light spot data through Kriging interpolation. Multiple linear regression, support vector regression, and random forest were used to establish biomass models. The results showed that the optimal model is selected using the semi-variance function for the Kriging interpolation of each parameter of GEDI spot, the optimal model of modis_nonvegetated is a linear model, and the optimal model for rv, sensitivity, and modis_treecover is the exponential model. Analysis of the correlation between 39 parameters extracted from GEDI L2B and three topographic factors with oak biomass showed that sensitivity had a highly significant positive correlation (p < 0.01) with Quercus biomass, followed by a significant negative correlation (p < 0.05) with aspect and modis_nonvegation. After variable selection, the estimation model of Quercus biomass established using random forest had R² = 0.91, RMSE = 19.76 t/hm², and the estimation accuracy was better than that of multiple linear regression and support vector regression. The estimated total biomass of Quercus in the study area was mainly distributed between 26.48 and 257.63 t/hm², with an average value of 114.33 t/hm² and a total biomass of about 1.26 × 10⁷ t/hm². This study obtained spatial consecutive information using Kriging interpolation. It provided a new research direction for estimating other forest structural parameters using GEDI data. Full article

(This article belongs to the Special Issue Forestry Remote Sensing: Biomass, Changes and Ecology)

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17 pages, 5302 KiB

Open AccessArticle

Spatio-Temporal Variation and Climatic Driving Factors of Vegetation Coverage in the Yellow River Basin from 2001 to 2020 Based on kNDVI

by Xuejuan Feng, Jia Tian, Yingxuan Wang, Jingjing Wu, Jie Liu, Qian Ya and Zishuo Li

Forests 2023, 14(3), 620; https://doi.org/10.3390/f14030620 - 20 Mar 2023

Cited by 18 | Viewed by 4890

Abstract

The Yellow River Basin (YRB) is a fundamental ecological barrier in China and is one of the regions where the ecological environment is relatively fragile. Studying the spatio-temporal variations in vegetation coverage in the YRB and their driving factors through a long-time-series vegetation [...] Read more.

The Yellow River Basin (YRB) is a fundamental ecological barrier in China and is one of the regions where the ecological environment is relatively fragile. Studying the spatio-temporal variations in vegetation coverage in the YRB and their driving factors through a long-time-series vegetation dataset is of great significance to eco-environmental construction and sustainable development in the YRB. In this study, we sought to characterize the spatio-temporal variation in vegetation coverage and its climatic driving factors in the YRB from 2001 to 2020 by constructing a new kernel normalized difference vegetation index (kNDVI) dataset based on MOD13 A1 V6 data from the Google Earth Engine (GEE) platform. Using Theil–Sen median trend analysis, the Mann–Kendall test, and the Hurst exponent, we investigated the spatio-temporal variation characteristics and future development trends of the vegetation coverage. The climatic driving factors of vegetation coverage in the YRB were obtained via partial correlation analysis and complex correlation analysis of the associations between kNDVI and both temperature and precipitation. The results reveal the following: The spatial distribution pattern of kNDVI in the YRB showed that vegetation coverage was high in the southeast and low in the northwest. Vegetation coverage fluctuated from 2001 to 2020, with a main significant trend of increasing growth at a rate of 0.0995/5a. The response of vegetation to climatic factors was strong in the YRB, with a stronger response to precipitation than to temperature. Additionally, the main driving factors of vegetation coverage in the YRB were found to be non-climatic factors, which were mainly distributed in Henan, southern Shaanxi, Shanxi, western Inner Mongolia, Ningxia, and eastern Gansu. The areas driven by climatic factors were mainly distributed in northern Shaanxi, Shandong, Qinghai, western Gansu, northeastern Inner Mongolia, and Sichuan. Our findings have implications for ecosystem restoration and sustainable development in the YRB. Full article

(This article belongs to the Special Issue Forestry Remote Sensing: Biomass, Changes and Ecology)

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19 pages, 6956 KiB

Open AccessArticle

Automatic Detection and Classification of Dead Nematode-Infested Pine Wood in Stages Based on YOLO v4 and GoogLeNet

by Xianhao Zhu, Ruirui Wang, Wei Shi, Qiang Yu, Xiuting Li and Xingwang Chen

Forests 2023, 14(3), 601; https://doi.org/10.3390/f14030601 - 17 Mar 2023

Cited by 12 | Viewed by 2286

Abstract

Pine wood nematode disease has harmed forests in several countries, and can be reduced by locating and clearing infested pine trees from forests. The target detection model of deep learning was utilized to monitor a pine nematode-infested wood. The detecting effect was good, [...] Read more.

Pine wood nematode disease has harmed forests in several countries, and can be reduced by locating and clearing infested pine trees from forests. The target detection model of deep learning was utilized to monitor a pine nematode-infested wood. The detecting effect was good, but limited by low-resolution photos with poor accuracy and speed. Our work presents a staged detection and classification approach for a dead nematode-infested pine wood based using You Only Look Once version 4 (YOLO v4) and Google Inception version 1 Net (GoogLeNet), employing high-resolution images acquired by helicopter. Experiments showed that the detection accuracy of the staged detection and classification method and the method using only the YOLO v4 model were comparable for a dead nematode-infested pine wood when the amount of data was sufficient, but when the amount of data was limited the detection accuracy of the former was higher than that of the latter. The staged detection and classification method retained the fast training and detection speed of the one-stage target detection model, further improving the detection accuracy with limited data volume, and was more flexible in achieving accurate classification, meeting the needs of forest areas for pine nematode disease epidemic prevention and control. Full article

(This article belongs to the Special Issue Forestry Remote Sensing: Biomass, Changes and Ecology)

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25 pages, 13146 KiB

Open AccessArticle

Damage Diagnosis of Pinus yunnanensis Canopies Attacked by Tomicus Using UAV Hyperspectral Images

by Yunqiang Ma, Junjia Lu and Xiao Huang

Forests 2023, 14(1), 61; https://doi.org/10.3390/f14010061 - 28 Dec 2022

Cited by 4 | Viewed by 2064

Abstract

It remains challenging to control Tomicus spp., a pest with fast spreading capability, leading to the death of large numbers of Pinus yunnanensis (Franch.) and posing a severe threat to ecological security in southwest China. Therefore, it is crucial to effectively and accurately [...] Read more.

It remains challenging to control Tomicus spp., a pest with fast spreading capability, leading to the death of large numbers of Pinus yunnanensis (Franch.) and posing a severe threat to ecological security in southwest China. Therefore, it is crucial to effectively and accurately monitor the damage degree for Pinus yunnanensis attacked by Tomicus spp. at large geographical scales. Airborne hyperspectral remote sensing is an effective, accurate means to detect forest pests and diseases. In this study, we propose an innovative and precise classification framework to monitor the damage degree of Pinus yunnanensis infected by Tomicus spp. using hyperspectral UAV (unmanned aerial vehicle) imagery with machine learning algorithms. First, we revealed the hyperspectral characteristics of Pinus yunnanensis from a UAV-based hyperspectral platform. We obtained 22 vegetation indices (VIs), 4 principal components, and 16 continuous wavelet transform (CWT) features as the damage degree sensitive features. We classified the damage degree of Pinus yunnanensis canopies infected by Tomicus spp. via three methods, i.e., discriminant analysis (DA), support vector machine (SVM), and backpropagation (BP) neural network. The results showed that the damage degree detected from the BP neural network, combined with 16 CWT features, achieved the best performance (training accuracy: 94.05%; validation accuracy: 94.44%). Full article

(This article belongs to the Special Issue Forestry Remote Sensing: Biomass, Changes and Ecology)

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18 pages, 8356 KiB

Open AccessArticle

Estimate Forest Aboveground Biomass of Mountain by ICESat-2/ATLAS Data Interacting Cokriging

by Hanyue Song, Lei Xi, Qingtai Shu, Zhiyue Wei and Shuang Qiu

Forests 2023, 14(1), 13; https://doi.org/10.3390/f14010013 - 21 Dec 2022

Cited by 8 | Viewed by 2424

Abstract

Compared with the previous full-waveform data, the new generation of ICESat-2/ATLAS (Advanced Terrain Laser Altimeter System) has a larger footprint overlap density and a smaller footprint area. This study used ATLAS data to estimate forest aboveground biomass (AGB) in a high-altitude, ecologically fragile [...] Read more.

Compared with the previous full-waveform data, the new generation of ICESat-2/ATLAS (Advanced Terrain Laser Altimeter System) has a larger footprint overlap density and a smaller footprint area. This study used ATLAS data to estimate forest aboveground biomass (AGB) in a high-altitude, ecologically fragile area. The paper used ATLAS data as the main information source and a typical mountainous area in Shangri-La, northwestern Yunnan Province, China, as the study area. Then, we combined biomass data from 54 ground samples to obtain the estimated AGB of 74,873 footprints using a hyperparametric optimized random forest (RF) model. The total AGB was estimated by combining the best variance function model in geostatistics with the slope that is the covariates. The results showed that among the 50 index parameters and three topographic variables extracted based on ATLAS, six variables showed a significant correlation with AGB. They were, in order, number of canopy photons, Landsat percentage canopy, canopy photon rate, slope, number of photons, and apparent surface reflectance. The optimized random forest model was used to estimate the AGB within the footprints. The model accuracy was the coefficient of determination (R²) = 0.93, the root mean square error (RMSE) = 10.13 t/hm², and the population estimation accuracy was 83.3%. The optimized model has a good estimation effect and can be used for footprint AGB estimation. The spatial structure analysis of the variance function of footprint AGB showed that the spherical model had the largest fitting accuracy (R² = 0.65, the residual sum of squares (RSS) = 2.65 × 10⁻⁴), the nugget (C₀) was 0.21, and the spatial structure ratio was 94.0%. It showed that the AGB of footprints had strong spatial correlation and could be interpolated by kriging. Finally, the slope in the topographic variables was selected as the co-interpolation variable, and cokriging spatial interpolation was performed. Furthermore, a continuous map of AGB spatial distribution was obtained, and the total AGB was 6.07 × 10⁷ t. The spatial distribution of AGB showed the same trend as the distribution of forest stock. The absolute accuracy of the estimation was 82.6%, using the statistical value of the forest resource planning and design survey as a reference. The ATLAS data can improve the accuracy of AGB estimation in mountain forests. Full article

(This article belongs to the Special Issue Forestry Remote Sensing: Biomass, Changes and Ecology)

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16 pages, 3497 KiB

Open AccessArticle

Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA

by Leyre Compains Iso, Alfonso Fernández-Manso and Víctor Fernández-García

Forests 2022, 13(11), 1824; https://doi.org/10.3390/f13111824 - 2 Nov 2022

Cited by 3 | Viewed by 1645

Abstract

Spectral mixture analysis of satellite images, such as MESMA (multiple endmember spectral mixtures analysis), can be used to obtain fraction images in which the abundance of each land occupation class is represented at the pixel level, which is crucial for the analysis of [...] Read more.

Spectral mixture analysis of satellite images, such as MESMA (multiple endmember spectral mixtures analysis), can be used to obtain fraction images in which the abundance of each land occupation class is represented at the pixel level, which is crucial for the analysis of heterogeneous landscapes in which types of habitats vary at fine spatial scales. The objective of this work is to analyze the influence of spectral libraries of various characteristics on the performance of MESMA. To this end, eight spectral libraries from Landsat satellite images were elaborated with different characteristics in terms of size, composition, and temporality. The spectral libraries were optimized using the iterative selection of endmembers (IES) method with the MESMA technique to obtain the fraction images considering five habitat classes (forest, shrubland, grassland, water, and rock and bare soil). The application of MESMA resulted in the classification of more than 95% of pixels in all cases with a root mean square error (RMSE) less than or equal to 0.025. Validation of the fraction images through linear regressions resulted in an RMSE ≥ 0.35 for the shrubland and grassland classes, with a lower RMSE for the remaining classes. A significant influence of library size was observed, as well as a significant effect of temporality, with the best results obtained for the largest monotemporal libraries. Full article

(This article belongs to the Special Issue Forestry Remote Sensing: Biomass, Changes and Ecology)

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Figure 1
Location of the study area in the Cantabrian Mountains, northwest of the Iberian Peninsula (the top-left panel shows the regional boundaries of Spain). Full article ">Figure 2
Habitat types identified in study area: shrubland (S), forest (F), grassland (G), water (W), and rock and bare soil (R). Full article ">Figure 3
Methodology flow chart. WMS: web map service; CNIG: National Geographic Information Centre; ITACyL: Agricultural Technological Institute of Castile and Leon; IES: iterative endmember selection; MESMA: multiple endmember spectral mixture analysis; RMSE: root mean square error. Full article ">Figure 4
Distribution of the 50 plots that represent five habitat types in the study area: forest (F), shrubland (S), grassland (G), rock and bare soil (R), and water (W). Full article ">Figure 5
Procedure for obtaining reference fractions from the sample of the 30 × 30 m plots for validation. By assigning an identifier to each entity, the fractions of each type of existing habitat were visually identified. Full article ">Figure 6
Spectral signatures included in the optimized libraries (L1_IES, L2_IES, L3_IES, L4_IES, 1990_IES, 2000_IES, 2010_IES, and 2020_IES) obtained after IES analysis (water: 3 endmembers; forest: 9 endmembers; shrubland: 48 endmembers; grassland: 31 endmembers; rock and bare soil: 16 endmembers). Full article ">Figure 7
The panel on the left shows the mean predicted RMSE values (±95% confidence intervals) depending on library size: the abscissa axis reflects the number of spectral signatures as the size of the spectral library, and the ordinate axis represents the predicted RMSE value. The panel on the right shows the mean predicted RMSE values (±95% confidence intervals) depending on library type: the abscissa axis reflects the type of spectral library (monotemporal, multitemporal with equitable distribution (Multitemporal_DE), or multitemporal with inequitable distribution (Multitemporal_DNE)). Full article ">Figure A1
Distribution of validation plots in the study area. Full article ">

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