Research

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21 pages, 10161 KiB

Open AccessFeature PaperArticle

Simulation of Spatial and Temporal Distribution of Forest Carbon Stocks in Long Time Series—Based on Remote Sensing and Deep Learning

by Xiaoyong Zhang, Weiwei Jia, Yuman Sun, Fan Wang and Yujie Miu

Forests 2023, 14(3), 483; https://doi.org/10.3390/f14030483 - 27 Feb 2023

Cited by 3 | Viewed by 2002

Abstract

Due to the complexity and difficulty of forest resource ground surveys, remote-sensing-based methods to assess forest resources and effectively plan management measures are particularly important, as they provide effective means to explore changes in forest resources over long time periods. The objective of [...] Read more.

Due to the complexity and difficulty of forest resource ground surveys, remote-sensing-based methods to assess forest resources and effectively plan management measures are particularly important, as they provide effective means to explore changes in forest resources over long time periods. The objective of this study was to monitor the spatiotemporal trends of the wood carbon stocks of the standing forests in the southeastern Xiaoxinganling Mountains by using Landsat remote sensing data collected between 1989 and 2021. Various remote sensing indicators for predicting carbon stocks were constructed based on the Google Earth Engine (GEE) platform. We initially used a multiple linear regression model, a deep neural network model and a convolutional neural network model for exploring the spatiotemporal trends in carbon stocks. Finally, we chose the convolutional neural network model because it provided more robust predictions on the carbon stock on a pixel-by-pixel basis and hence mapping the spatial distribution of this variable. Savitzky–Golay filter smoothing was applied to the predicted annual average carbon stock to observe the overall trend, and a spatial autocorrelation analysis was conducted. Sen’s slope and the Mann–Kendall statistical test were used to monitor the spatial trends of the carbon stocks. It was found that 59.5% of the area showed an increasing trend, while 40.5% of the area showed a decreasing trend over the past 33 years, and the future trend of carbon stock development was plotted by combining the results with the Hurst exponent. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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14 pages, 1784 KiB

Open AccessArticle

Quantitative Assessment of Deforestation and Forest Degradation in Margalla Hills National Park (MHNP): Employing Landsat Data and Socio-Economic Survey

by Hiba Ahmed, Hamayoon Jallat, Ejaz Hussain, Najam u Saqib, Zafeer Saqib, Muhammad Fahim Khokhar and Waseem Razzaq Khan

Forests 2023, 14(2), 201; https://doi.org/10.3390/f14020201 - 20 Jan 2023

Cited by 4 | Viewed by 3419

Abstract

Deforestation and forest degradation is a global concern, especially in developing countries. The Margalla Hills of Pakistan—Himalayan foothills—also face the threat of deforestation and forest degradation. These Margalla Hills, considering the need for forest protection activities in Pakistan, were declared a reserved national [...] Read more.

Deforestation and forest degradation is a global concern, especially in developing countries. The Margalla Hills of Pakistan—Himalayan foothills—also face the threat of deforestation and forest degradation. These Margalla Hills, considering the need for forest protection activities in Pakistan, were declared a reserved national forest and named “the Margalla Hills National Park (MHNP)”. This study quantitively evaluates whether deforestation and forest degradation have occurred at MHNP and identifies their possible drivers. Satellite (Landsat) data 1988–2020 was employed for the land use change analysis, whereas a socio-economic survey of the local population and structured interviews with government officials were conducted to identify the drivers of deforestation. Supervised classification was performed for imagery classification and the Vegetation Condition Index (VCI) was also calculated to measure degradation. Supervised classification showed that the forest cover increased from 65% of the total area in 1988 to 69% in 2020. The VCI results show that the moderate level of degradation has increased from 3.5% of MHNP area in 1988 to 8.8% in 2020. The cumulative measure of degradation from 1988 to 2020 is 1.09% of the total forest (using p < 0.05). Major drivers identified are fuel wood and timber collection. The results reveal a decline in both deforestation and forest degradation. There is a need for further quantitative analysis of the drivers, strict implementation of legislative and control measures, and continuous invigilation of the deforestation trends in MHNP. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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17 pages, 11137 KiB

Open AccessArticle

Use of Mobile Laser Scanning (MLS) to Monitor Vegetation Recovery on Linear Disturbances

by Caren E. Jones, Angeline Van Dongen, Jolan Aubry, Stefan G. Schreiber and Dani Degenhardt

Forests 2022, 13(11), 1743; https://doi.org/10.3390/f13111743 - 22 Oct 2022

Cited by 1 | Viewed by 1862

Abstract

Seismic lines are narrow, linear corridors cleared through forests for oil and gas exploration. Their inconsistent recovery has led to Alberta’s forests being highly fragmented, resulting in the need for seismic line restoration programs and subsequent monitoring. Light detection and ranging (LiDAR) is [...] Read more.

Seismic lines are narrow, linear corridors cleared through forests for oil and gas exploration. Their inconsistent recovery has led to Alberta’s forests being highly fragmented, resulting in the need for seismic line restoration programs and subsequent monitoring. Light detection and ranging (LiDAR) is becoming an increasingly popular technology for the fast and accurate measurement of forests. Mobile LiDAR scanners (MLS) are emerging as an alternative to traditional aerial LiDAR due to their increased point cloud density. To determine whether MLS could be effective for collecting vegetation data on seismic lines, we sampled 17 seismic lines using the Emesent Hovermap™ in leaf-on and leaf-off conditions. Processing the LiDAR data was conducted with GreenValley International’s LiDAR 360 software, and data derived from the point clouds were compared to physically measured field data. Overall, the tree detection algorithm was unsuccessful at accurately segmenting the point clouds. Complex vegetation environments on seismic lines, including small conifers with obscured stems or extremely dense and tall shrubs with overlapping canopies, posed a challenge for the software’s capacity to differentiate trees As a result, tree densities and diameters were overestimated, while tree heights were underestimated. Exploration of alternative algorithms and software is needed if measuring vegetation data on seismic lines with MLS is to be implemented. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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15 pages, 2403 KiB

Open AccessArticle

Second-Entry Burns Reduce Mid-Canopy Fuels and Create Resilient Forest Structure in Yosemite National Park, California

by Lacey E. Hankin and Chad T. Anderson

Forests 2022, 13(9), 1512; https://doi.org/10.3390/f13091512 - 17 Sep 2022

Cited by 2 | Viewed by 1930

Abstract

Understanding the patterns and underlying drivers of forest structure is critical for managing landscape processes and multiple resource management. Merging several landscape-scale datasets, including long-term fire histories, airborne LiDAR, and downscaled topo-climatic data, we assessed complex ecological questions regarding the interactions of forest [...] Read more.

Understanding the patterns and underlying drivers of forest structure is critical for managing landscape processes and multiple resource management. Merging several landscape-scale datasets, including long-term fire histories, airborne LiDAR, and downscaled topo-climatic data, we assessed complex ecological questions regarding the interactions of forest structure, climate, and fire in the Yosemite National Park, a protected area historically dominated by frequent fire and largely free of the impacts of commercial industrial logging. We found that forest structure broadly corresponded with forest types arranged across elevation-driven climatic gradients and that repeated burning shifts forest structure towards conditions that are consistent with increased resilience, biodiversity, and ecosystem health and function. Specifically, across all forest types, tree density and mid-canopy strata cover was significantly reduced compared to overstory canopy and the indices of forest health improved after two fires, but no additional change occurred with subsequent burns. This study provides valuable information for managers who seek to refine prescriptions based on an enhanced understanding of fire-mediated changes in ladder fuels and tree density and those seeking to define the number of treatments needed to mitigate severe fire risk and enhance resiliency to repeated fires. In addition, our study highlights the utility of large-landscape LiDAR acquisitions for supporting fire, forest, and wildlife management prioritization and wildfire risk assessments for numerous valued resources. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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22 pages, 6746 KiB

Open AccessArticle

Annual Change Analysis of Mangrove Forests in China during 1986–2021 Based on Google Earth Engine

by Ziyu Wang, Kai Liu, Jingjing Cao, Liheng Peng and Xin Wen

Forests 2022, 13(9), 1489; https://doi.org/10.3390/f13091489 - 14 Sep 2022

Cited by 7 | Viewed by 2363

Abstract

Mangroves are a key type of protected coastal wetland, with a range of benefits such as protection from wave damage, sand fixation, water purification and ecological tourism. As the academic knowledge of mangroves has gradually increased, the use of remote sensing to monitor [...] Read more.

Mangroves are a key type of protected coastal wetland, with a range of benefits such as protection from wave damage, sand fixation, water purification and ecological tourism. As the academic knowledge of mangroves has gradually increased, the use of remote sensing to monitor their dynamic changes in China has become a hot topic of discussion and has received attention in academic circles. Remote sensing has also provided necessary auxiliary decision-making suggestions and data support for the scientific and rational conservation, restoration and management of mangrove resources. In this paper, we used Landsat satellite series data combined with the normalized difference vegetation index (NDVI) and adaptive threshold partitioning (OTSU method) to monitor mangrove dynamics in coastal China from 1986 to 2021 based on Google Earth Engine (GEE), with three main results. (1) Based on the massive data and efficient computational capability of the GEE platform, we achieved large-scale interannual mangrove distribution extraction. The overall classification accuracy for 2019 exceeded 0.93, and the mangrove distribution extraction effect was good. (2) The total mangrove area and the mean patch size in China showed overall increasing trends, and Guangdong and Guangxi were the top two provinces in China in terms of the largest mangrove area. (3) Except for Dongzhaigang National Nature Reserve, the mangrove areas in other national mangrove reserves mainly showed increasing trends, confirming the effectiveness of the reserves. Data on the spatial structure and area trends of mangroves in China can provide scientific references for mangrove conservation and development, and serve in the further restoration of mangrove ecosystems. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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12 pages, 4809 KiB

Open AccessArticle

An Optimized SIFT-OCT Algorithm for Stitching Aerial Images of a Loblolly Pine Plantation

by Tao Wu, I-Kuai Hung, Hao Xu, Laibang Yang, Yongzhong Wang, Luming Fang and Xiongwei Lou

Forests 2022, 13(9), 1475; https://doi.org/10.3390/f13091475 - 13 Sep 2022

Cited by 2 | Viewed by 1316

Abstract

When producing orthomosaic from aerial images of a forested area, challenges arise when the forest canopy is closed, and tie points are hard to find between images. The recent development in deep leaning has shed some light in tackling this problem with an [...] Read more.

When producing orthomosaic from aerial images of a forested area, challenges arise when the forest canopy is closed, and tie points are hard to find between images. The recent development in deep leaning has shed some light in tackling this problem with an algorithm that examines each image pixel-by-pixel. The scale-invariant feature transform (SIFT) algorithm and its many variants are widely used in feature-based image stitching, which is ideal for orthomosaic production. However, although feature-based image registration can find many feature points in forest image stitching, the similarity between images is too high, resulting in a low correct matching rate and long splicing time. To counter this problem by considering the characteristics of forest images, the inverse cosine function ratio of the unit vector dot product (arccos) is introduced into the SIFT-OCT (SIFT skipping the first scale-space octave) algorithm to overcome the shortfalls of too long a matching time caused by too many feature points for matching. Then, the fast sample consensus (FSC) algorithm was introduced to realize the deletion of mismatched point pairs and improve the matching accuracy. This optimized method was tested on three sets of forest images, representing the forest core, edge, and road areas of a loblolly pine plantation. The same process was repeated by using the regular SIFT and SIFT-OCT algorithms for comparison. The results showed the optimized SIFT-OCT algorithm not only greatly reduced the splicing time, but also increased the correct matching rate. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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23 pages, 5158 KiB

Open AccessArticle

MaxEnt Modelling and Impact of Climate Change on Habitat Suitability Variations of Economically Important Chilgoza Pine (Pinus gerardiana Wall.) in South Asia

by Arshad Mahmood Khan, Qingting Li, Zafeer Saqib, Nasrullah Khan, Tariq Habib, Nadia Khalid, Muhammad Majeed and Aqil Tariq

Forests 2022, 13(5), 715; https://doi.org/10.3390/f13050715 - 02 May 2022

Cited by 63 | Viewed by 6533

Abstract

Chilgoza pine is an economically and ecologically important evergreen coniferous tree species of the dry and rocky temperate zone, and a native of south Asia. This species is rated as near threatened (NT) by the International Union for Conservation of Nature (IUCN). This [...] Read more.

Chilgoza pine is an economically and ecologically important evergreen coniferous tree species of the dry and rocky temperate zone, and a native of south Asia. This species is rated as near threatened (NT) by the International Union for Conservation of Nature (IUCN). This study hypothesized that climatic, soil and topographic variations strongly influence the distribution pattern and potential habitat suitability prediction of Chilgoza pine. Accordingly, this study was aimed to document the potential habitat suitability variations of Chilgoza pine under varying environmental scenarios by using 37 different environmental variables. The maximum entropy (MaxEnt) algorithm in MaxEnt software was used to forecast the potential habitat suitability under current and future (i.e., 2050s and 2070s) climate change scenarios (i.e., Shared Socio-economic Pathways (SSPs): 245 and 585). A total of 238 species occurrence records were collected from Afghanistan, Pakistan and India, and employed to build the predictive distribution model. The results showed that normalized difference vegetation index, mean temperature of coldest quarter, isothermality, precipitation of driest month and volumetric fraction of the coarse soil fragments (>2 mm) were the leading predictors of species presence prediction. High accuracy values (>0.9) of predicted distribution models were recorded, and remarkable shrinkage of potentially suitable habitat of Chilgoza pine was followed by Afghanistan, India and China. The estimated extent of occurrence (EOO) of the species was about 84,938 km², and the area of occupancy (AOO) was about 888 km², with 54 major sub-populations. This study concluded that, as the total predicted suitable habitat under current climate scenario (138,782 km²) is reasonably higher than the existing EOO, this might represent a case of continuous range contraction. Hence, the outcomes of this research can be used to build the future conservation and management plans accordingly for this economically valuable species in the region. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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15 pages, 5986 KiB

Open AccessArticle

Filtering Photon Cloud Data in Forested Areas Based on Elliptical Distance Parameters and Machine Learning Approach

by Yi Li, Jun Zhu, Haiqiang Fu, Shijuan Gao and Changcheng Wang

Forests 2022, 13(5), 663; https://doi.org/10.3390/f13050663 - 25 Apr 2022

Cited by 7 | Viewed by 2113

Abstract

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was successfully launched. Due to its small spot size, multibeam configuration, high sampling rate, and strong immunity to terrain slopes, it has been regarded as a powerful tool for forest resources surveying and managing. However, [...] Read more.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was successfully launched. Due to its small spot size, multibeam configuration, high sampling rate, and strong immunity to terrain slopes, it has been regarded as a powerful tool for forest resources surveying and managing. However, the ICESat-2 photon cloud data contain considerable background photons, which discretely distribute in the background space of signal photons. Therefore, it is necessary to filter these noise photons. In this study, photons are divided into three categories: signal photons, noise photons far away from signal photons, and noise photons adjacent to signal photons. Based on the existing research, forward and backward elliptical distances were used to express the spatial relationship between two photons, and backward local density (BLD) was used to describe the density distribution of the photons. However, the single statistical parameter cannot clearly distinguish three types of photon cloud. Therefore, forward local density (FLD) and neighboring forward local density difference (NFLDD) also were defined to describe the density distribution of the photons. Finally, by combining the support vector machine (SVM), the above three density parameters were used to classify the photons by signal and noise photons. The proposed method was validated with photon cloud data acquired by the Simulated Advanced Terrain Laser Altimeter System (MATLAS), the Multiple Altimeter Beam Experimental Lidar (MABEL), and the ICESat-2 systems over different forested areas. The results demonstrated that the proposed method can well remove the noise photons and retain the signal photons without depending on any statistical assumptions or thresholds. The comprehensive accuracy of the three test sites was 0.99, 0.98, and 0.99, respectively, which was higher than those of the existing method. In addition, the total errors corresponding to the three test sites were about 0.4%, 0.5%, and 1.0% respectively, which were lower than those of the existing method. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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21 pages, 6722 KiB

Open AccessArticle

Place-Based Analysis of Satellite Time Series Shows Opposing Land Change Patterns in the Copperbelt Region of Zambia

by Sana Munawar, Achim Röder, Stephen Syampungani and Thomas Udelhoven

Forests 2022, 13(1), 134; https://doi.org/10.3390/f13010134 - 17 Jan 2022

Cited by 1 | Viewed by 2431

Abstract

The process of land degradation needs to be understood at various spatial and temporal scales in order to protect ecosystem services and communities directly dependent on it. This is especially true for regions in sub-Saharan Africa, where socio economic and political factors exacerbate [...] Read more.

The process of land degradation needs to be understood at various spatial and temporal scales in order to protect ecosystem services and communities directly dependent on it. This is especially true for regions in sub-Saharan Africa, where socio economic and political factors exacerbate ecological degradation. This study identifies spatially explicit land change dynamics in the Copperbelt province of Zambia in a local context using satellite vegetation index time series derived from the MODIS sensor. Three sets of parameters, namely, monthly series, annual peaking magnitude, and annual mean growing season were developed for the period 2000 to 2019. Trend was estimated by applying harmonic regression on monthly series and linear least square regression on annually aggregated series. Estimated spatial trends were further used as a basis to map endemic land change processes. Our observations were as follows: (a) 15% of the study area dominant in the east showed positive trends, (b) 3% of the study area dominant in the west showed negative trends, (c) natural regeneration in mosaic landscapes (post shifting cultivation) and land management in forest reserves were chiefly responsible for positive trends, and (d) degradation over intact miombo woodland and cultivation areas contributed to negative trends. Additionally, lower productivity over areas with semi-permanent agriculture and shift of new encroachment into woodlands from east to west of Copperbelt was observed. Pivot agriculture was not a main driver in land change. Although overall greening trends prevailed across the study site, the risk of intact woodlands being exposed to various disturbances remains high. The outcome of this study can provide insights about natural and assisted landscape restoration specifically addressing the miombo ecoregion. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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24 pages, 10918 KiB

Open AccessArticle

Integrated Segmentation Approach with Machine Learning Classifier in Detecting and Mapping Post Selective Logging Impacts Using UAV Imagery

by Aisyah Marliza Muhmad Kamarulzaman, Wan Shafrina Wan Mohd Jaafar, Khairul Nizam Abdul Maulud, Siti Nor Maizah Saad, Hamdan Omar and Midhun Mohan

Forests 2022, 13(1), 48; https://doi.org/10.3390/f13010048 - 02 Jan 2022

Cited by 13 | Viewed by 2429

Abstract

Selective logging can cause significant impacts on the residual stands, affecting biodiversity and leading to environmental changes. Proper monitoring and mapping of the impacts from logging activities, such as the stumps, felled logs, roads, skid trails, and forest canopy gaps, are crucial for [...] Read more.

Selective logging can cause significant impacts on the residual stands, affecting biodiversity and leading to environmental changes. Proper monitoring and mapping of the impacts from logging activities, such as the stumps, felled logs, roads, skid trails, and forest canopy gaps, are crucial for sustainable forest management operations. The purpose of this study is to assess the indicators of selective logging impacts by detecting the individual stumps as the main indicators, evaluating the performance of classification methods to assess the impacts and identifying forest gaps from selective logging activities. The combination of forest inventory field plots and unmanned aerial vehicle (UAV) RGB and overlapped imaged were used in this study to assess these impacts. The study area is located in Ulu Jelai Forest Reserve in the central part of Peninsular Malaysia, covering an experimental study area of 48 ha. The study involved the integration of template matching (TM), object-based image analysis (OBIA), and machine learning classification—support vector machine (SVM) and artificial neural network (ANN). Forest features and tree stumps were classified, and the canopy height model was used for detecting forest canopy gaps in the post selective logging region. Stump detection using the integration of TM and OBIA produced an accuracy of 75.8% when compared with the ground data. Forest classification using SVM and ANN methods were adopted to extract other impacts from logging activities such as skid trails, felled logs, roads and forest canopy gaps. These methods provided an overall accuracy of 85% and kappa coefficient value of 0.74 when compared with conventional classifier. The logging operation also caused an 18.6% loss of canopy cover. The result derived from this study highlights the potential use of UAVs for efficient post logging impact analysis and can be used to complement conventional forest inventory practices. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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21 pages, 2398 KiB

Open AccessArticle

Multi-Model Estimation of Forest Canopy Closure by Using Red Edge Bands Based on Sentinel-2 Images

by Yiying Hua and Xuesheng Zhao

Forests 2021, 12(12), 1768; https://doi.org/10.3390/f12121768 - 14 Dec 2021

Cited by 9 | Viewed by 1909

Abstract

In remote sensing, red edge bands are important indicators for monitoring vegetation growth. To examine the application potential of red edge bands in forest canopy closure estimation, three types of commonly used models—empirical statistical models (multiple stepwise regression (MSR)), machine learning models (back [...] Read more.

In remote sensing, red edge bands are important indicators for monitoring vegetation growth. To examine the application potential of red edge bands in forest canopy closure estimation, three types of commonly used models—empirical statistical models (multiple stepwise regression (MSR)), machine learning models (back propagation neural network (BPNN)) and physical models (Li–Strahler geometric-optical (Li–Strahler GO) models)—were constructed and verified based on Sentinel-2 data, DEM data and measured data. In addition, we set up a comparative experiment without red edge bands. The relative error (E_R) values of the BPNN model, MSR model, and Li–Strahler GO model with red edge bands were 16.97%, 20.76% and 24.83%, respectively. The validation accuracy measures of these models were higher than those of comparison models. For comparative experiments, the E_R values of the MSR, Li–Strahler GO and BPNN models were increased by 13.07%, 4% and 1.22%, respectively. The experimental results demonstrate that red edge bands can effectively improve the accuracy of forest canopy closure estimation models to varying degrees. These findings provide a reference for modeling and estimating forest canopy closure using red edge bands based on Sentinel-2 images. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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14 pages, 4344 KiB

Open AccessArticle

Tree Species Classification Based on Sentinel-2 Imagery and Random Forest Classifier in the Eastern Regions of the Qilian Mountains

by Minfei Ma, Jianhong Liu, Mingxing Liu, Jingchao Zeng and Yuanhui Li

Forests 2021, 12(12), 1736; https://doi.org/10.3390/f12121736 - 09 Dec 2021

Cited by 20 | Viewed by 4459

Abstract

Obtaining accurate forest coverage of tree species is an important basis for the rational use and protection of existing forest resources. However, most current studies have mainly focused on broad tree classification, such as coniferous vs. broadleaf tree species, and a refined tree [...] Read more.

Obtaining accurate forest coverage of tree species is an important basis for the rational use and protection of existing forest resources. However, most current studies have mainly focused on broad tree classification, such as coniferous vs. broadleaf tree species, and a refined tree classification with tree species information is urgently needed. Although airborne LiDAR data or unmanned aerial vehicle (UAV) images can be used to acquire tree information even at the single tree level, this method will encounter great difficulties when applied to a large area. Therefore, this study takes the eastern regions of the Qilian Mountains as an example to explore the possibility of tree species classification with satellite-derived images. We used Sentinel-2 images to classify the study area’s major vegetation types, particularly four tree species, i.e., Sabina przewalskii (S.P.), Picea crassifolia (P.C.), Betula spp. (Betula), and Populus spp. (Populus). In addition to the spectral features, we also considered terrain and texture features in this classification. The results show that adding texture features can significantly increase the separation between tree species. The final classification result of all categories achieved an accuracy of 86.49% and a Kappa coefficient of 0.83. For trees, the classification accuracy was 90.31%, and their producer’s accuracy (PA) and user’s (UA) were all higher than 84.97%. We found that altitude, slope, and aspect all affected the spatial distribution of these four tree species in our study area. This study confirms the potential of Sentinel-2 images for the fine classification of tree species. Moreover, this can help monitor ecosystem biological diversity and provide references for inventory estimation. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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19 pages, 4236 KiB

Open AccessArticle

Above-Ground Biomass Estimation of Plantation with Complex Forest Stand Structure Using Multiple Features from Airborne Laser Scanning Point Cloud Data

by Linghan Gao and Xiaoli Zhang

Forests 2021, 12(12), 1713; https://doi.org/10.3390/f12121713 - 06 Dec 2021

Cited by 13 | Viewed by 2523

Abstract

Accurate forest above-ground biomass (AGB) estimation is important for dynamic monitoring of forest resources and evaluation of forest carbon sequestration capacity. However, it is difficult to depict the forest’s vertical structure and its heterogeneity using optical remote sensing when estimating forest AGB, for [...] Read more.

Accurate forest above-ground biomass (AGB) estimation is important for dynamic monitoring of forest resources and evaluation of forest carbon sequestration capacity. However, it is difficult to depict the forest’s vertical structure and its heterogeneity using optical remote sensing when estimating forest AGB, for the reason that electromagnetic waves cannot penetrate the canopy’s surface to obtain low vegetation information, especially in subtropical and tropical forests with complex layer structure and tree species composition. As an active remote sensing technology, an airborne laser scanner (ALS) can penetrate the canopy surface to obtain three-dimensional structure information related to AGB. This paper takes the Jiepai sub-forest farm and the Gaofeng state-owned forest farm in southern China as the experimental area and explores the optimal features from the ALS point cloud data and AGB inversion model in the subtropical forest with complex tree species composition and structure. Firstly, considering tree canopy structure, terrain features, point cloud structure and density features, 63 point cloud features were extracted. In view of the biomass distribution differences of different tree species, the random forest (RF) method was used to select the optimal features of each tree species. Secondly, four modeling methods were used to establish the AGB estimation models of each tree species and verify their accuracy. The results showed that the features related to tree height had a great impact on forest AGB. The top features of Cunninghamia Lanceolata (Chinese fir) and Eucalyptus are all related to height, Pinus (pine tree) is also related to terrain features and other broadleaved trees are also related to point cloud density features. The accuracy of the stepwise regression model is best with the AGB estimation accuracy of 0.19, 0.76, 0.71 and 0.40, respectively, for the Chinese fir, pine tree, eucalyptus and other broadleaved trees. In conclusion, the proposed linear regression AGB estimation model of each tree species combining different features derived from ALS point cloud data has high applicability, which can provide effective support for more accurate forest AGB and carbon stock inventory and monitoring. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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Review

Jump to: Research

25 pages, 3911 KiB

Open AccessReview

Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis

by Fejaycris Pillodar, Peter Suson, Maricar Aguilos and Ruben Amparado, Jr.

Forests 2023, 14(6), 1080; https://doi.org/10.3390/f14061080 - 24 May 2023

Viewed by 2208

Abstract

In spite of their importance, mangroves are still threatened by a significant reduction in global forest cover due to conversion to non-forest land uses. To implement robust policies and actions in mangrove conservation, quantitative methods in monitoring mangrove attributes are vital. This study [...] Read more.

In spite of their importance, mangroves are still threatened by a significant reduction in global forest cover due to conversion to non-forest land uses. To implement robust policies and actions in mangrove conservation, quantitative methods in monitoring mangrove attributes are vital. This study intends to study the trend in mangrove resource mapping using remote sensing (RS) to determine the appropriate methods and datasets to be used in monitoring the distribution, aboveground biomass (AGB), and carbon stock (CS) in mangroves. A meta-analysis of several research publications related to mangrove resource mapping using RS in the Philippines has been conducted. A database was constructed containing 59 peer-reviewed articles selected using the protocol search, appraisal, synthesis, analysis, report (PSALSAR) framework and preferred reporting items for systematic reviews and meta-analysis (PRISMA). The study clarified that support vector machine (SVM) has shown to be more effective (99%) in discriminating mangroves from other land cover. Light detection and ranging (LiDAR) data also has proven to give a promising result in overall accuracy in mangrove-extent mapping (99%), AGB, and CS estimates (99%), and even species-level mapping (77%). Medium to low-resolution datasets can still achieve high overall accuracy by using appropriate algorithms or predictive models such as the mangrove vegetation index (MVI). The study has also found out that there are still few reports on the usage of high-spatial-resolution datasets, most probably due to their commercial restrictions. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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17 pages, 4559 KiB

Open AccessReview

Research Opportunity on Fractional Cover of Forest: A Bibliometric Review

by Norzalyta Mohd Ghazali, Mohd Nizam Mohd Said, Wan Shafrina Wan Mohd Jaafar, Aisyah Marliza Muhmad Kamarulzaman and Siti Nor Maizah Saad

Forests 2022, 13(10), 1664; https://doi.org/10.3390/f13101664 - 10 Oct 2022

Cited by 2 | Viewed by 1512

Abstract

Forests are threatened globally by deforestation. Forest restoration at the landscape scale can reduce these threats. Ground-based and remote sensing inventories are needed to assess restoration success. Fractional canopy cover estimated from forest algorithms can be used to monitor forest loss, growth, and [...] Read more.

Forests are threatened globally by deforestation. Forest restoration at the landscape scale can reduce these threats. Ground-based and remote sensing inventories are needed to assess restoration success. Fractional canopy cover estimated from forest algorithms can be used to monitor forest loss, growth, and health via remote sensing. Various studies on the fractional cover of forest have been published. However, none has yet conducted a bibliometric analysis. Bibliometrics provide a detailed examination of a topic, pointing academics to new research possibilities. To the best of the authors’ knowledge, this is the first bibliometric study screening publications to assess the incidence of studies of the fractional cover of forests in Web of Science (WoS) and Scopus databases. This research analyses WoS and Scopus publications on the fractional cover of forest dating from 1984 to 2021. The current study uses the Bibliometrix R-package for citation metrics and analysis. The first paper on the fractional cover of forest was published in 1984 and annual publication numbers have risen since 2002. USA and China were the most active countries in the study of fractional cover of forests. A total of 955 documents from 69 countries with multiple languages were retrieved. Vegetation, forestry, and remote sensing were the most discussed topics. Findings suggest more studies on the fractional cover of forests algorithms should be conducted in tropical forest from developing countries. Full article

(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)

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