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Advances in Remote Sensing for Forestry: Theory, Methods, Applications, and Validation

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: 25 April 2025 | Viewed by 15278

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Special Issue Editors

Dr. Ying Yu

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Guest Editor

School of Forestry, Northeast Forestry University, Harbin, China
Interests: quantitative remote sensing; carbon cycle modeling; forest parameter estimation; radiation transfer theory; LAI estimation and validation
Special Issues, Collections and Topics in MDPI journals

Dr. Xiguang Yang

E-Mail Website
Guest Editor

School of Forestry, Northeast Forestry University, Harbin 150040, China
Interests: quantitative remote sensing; remote sensing parameterization of carbon cycle model; application of remote sensing technology in resource and environmental investigation and assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As one of the biggest terrestrial ecosystems, forests play an important role in ecosystem services, biodiversity, and climate change. It has been widely acknowledged as one of the largest terrestrial carbon pools. Forests absorb atmospheric CO₂ through photosynthesis and remove a huge amount of carbon every year and play an important role in the global carbon cycle and climate changes. Therefore, forests are are of significant interest to current research.

This Special Issue on “Advances in Remote Sensing for Forestry: Theory, Methods, Applications and Validation” mainly focuses on the new theories and methods for forest survey and monitoring, applications of remote sensing on forest ecosystem services evaluation, biodiversity monitoring, and so on, and technologies for monitoring carbon sinks in forest ecosystems and calls for papers that present original research on the following broad topics:

Potential topics include, but are not limited to:

Quantitative remote sensing for forestry: theory and algorithms of retrieving the forest structure, biochemical or vegetation parameters for forest resource inventory, and ecological function evaluation;
Carbon cycle modeling of forest ecosystem and its impacts of climate change on forests by using remote sensing;
Method of the evaluation of the forest ecosystem services function and biodiversity monitoring by using remote sensing;
Validation of the production of remote sensing for forest research;
New sensor for forest resource inventory.

Dr. Ying Yu
Dr. Xiguang Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

forests
remote sensing
forest resource inventory
carbon cycle
climate change
retrieval algorithm

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Published Papers (8 papers)

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Research

21 pages, 5117 KiB

Open AccessEditor’s ChoiceArticle

Monitoring Forest Dynamics and Conducting Restoration Assessment Using Multi-Source Earth Observation Data in Northern Andes, Colombia

by Carlos Pedraza, Nicola Clerici, Marcelo Villa, Milton Romero, Adriana Sarmiento Dueñas, Dallan Beltrán Rojas, Paola Quintero, Mauricio Martínez and Josef Kellndorfer

Forests 2024, 15(5), 754; https://doi.org/10.3390/f15050754 - 25 Apr 2024

Viewed by 2544

Abstract

Examining the efficacy of current assessment methodologies for forest conservation and restoration initiatives to align with global and national agendas to combat deforestation and facilitate restoration efforts is necessary to identify efficient and robust approaches. The objective of this study is to understand forest dynamics (1996–2021) and assess restoration implementations at the Urra’s supplying basin hydroelectric reservoir in Colombia. The processing approach integrates optical and radar Earth Observation (EO) data from Sentinel-2 and Landsat for forest mapping and multi-temporal forest change assessment (1996–2021), and a Sentinel-1 backscatter time-series analysis is conducted to assess the state of forest restoration implementations. The processing chain was scaled in a cloud-based environment using the Nebari and SEPPO software and the Python language. The results demonstrate an overall substantial decrease in forested areas in the 1996–2000 period (37,763 ha). An accuracy assessment of multi-temporal forest change maps showed a high precision in detecting deforestation events, while improvements are necessary for accurately representing non-forested areas. The forest restoration assessment suggests that the majority of the 270 evaluated plots are in the intermediate growth state (82.96%) compared to the reference data. This study underscores the need for robust and continuous monitoring systems that integrate ground truth data with EO techniques for enhanced accuracy and effectiveness in forest restoration and conservation endeavors. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Forestry: Theory, Methods, Applications, and Validation)

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18 pages, 3912 KiB

Open AccessArticle

Detection and Analysis of Forest Clear-Cutting Activities Using Sentinel-2 and Random Forest Classification: A Case Study on Chungcheongnam-do, Republic of Korea

by Sol-E Choi, Sunjeoung Lee, Jeongmook Park, Suyeon Lee, Jongsu Yim and Jintaek Kang

Forests 2024, 15(3), 450; https://doi.org/10.3390/f15030450 - 27 Feb 2024

Viewed by 1290

Abstract

This study provides the methodology for the development of sustainable forest management activities and systematic strategies using national spatial data, satellite imagery, and a random forest machine learning classifier. This study conducts a regional province-scale approach that can be used to analyze forest clear-cutting in South Korea; we focused on the Chungcheongnam-do region. Based on spatial information from digital forestry data, Sentinel-2 satellite imagery, random forest (RF) classifier, and digital forest-type maps (DFTMs), we detected and analyzed the characteristics of clear-cut areas. We identified forest clear-cut areas (accounting for 2.48% of the total forest area). The methodology integrates various vegetation indices and the RF classifier to ensure the effective detection of clear-cut areas at the provincial level with an accuracy of 92.8%. Specific leaf area vegetation index (SLAVI) was determined as the most important factor for accurately detecting clear-cut areas. Moreover, using a DFTM, we analyzed clear-cutting characteristics in different forest types (including private, national, natural, and planted forests), along with age class and diameter-at-breast-height class. Our method can serve as a basis for forest management and monitoring by analyzing tree-cutting trends in countries with forest areas, such as Republic of Korea. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Forestry: Theory, Methods, Applications, and Validation)

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33 pages, 47746 KiB

Open AccessArticle

Changes in the Vegetation NPP of Mainland China under the Combined Actions of Climatic-Socioeconomic Factors

by Yifeng Liu, Mei Xu, Bing Guo, Guang Yang, Jialin Li and Yang Yu

Forests 2023, 14(12), 2341; https://doi.org/10.3390/f14122341 - 29 Nov 2023

Cited by 6 | Viewed by 1030

Abstract

Under the combined impact of climatic, socioeconomic, and environmental factors, the vegetation NPP change process and its responses to drive factors in the sub-regions of Mainland China are not clear. This study analyzes the changing pattern of vegetation NPP in China from 2000 to 2022 from the perspective of zoning and clarifies its response mechanism to climate-human interaction based on the gravity center model, third-order partial correlation coefficient and geographical detector. The results showed that: (1) There was an overall decreasing trend of vegetation NPP in China from the southeast to the northwest; (2) The vegetation NPP gravity center in Northeast, Northwest, and North China migrated southwards, while that of Southwest, Central South, and East China showed northward migration.;(3) Human activities played a dominant role in zones with increasing vegetation NPP from 2000 to 2010, while climate change greatly contributed to the increase in vegetation NPP during 2011–2022; (4) Human activities, such as deforestation and overgrazing, in Northeast and North China should be reduced to prevent vegetation ecosystem degradation, and the negative impact of human activities should be reduced to maintain the growth of vegetation NPP. This study was conducted to support decision-making for the precise restoration of ecosystems. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Forestry: Theory, Methods, Applications, and Validation)

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17 pages, 8031 KiB

Open AccessArticle

Tree Skeletonization with DBSCAN Clustering Using Terrestrial Laser Scanning Data

by Lei You, Yian Sun, Yong Liu, Xiaosa Chang, Jun Jiang, Yan Feng and Xinyu Song

Forests 2023, 14(8), 1525; https://doi.org/10.3390/f14081525 - 26 Jul 2023

Cited by 2 | Viewed by 1637

Abstract

A tree skeleton reflects the geometric and structural characteristics of a tree. Terrestrial laser scanning (TLS) provides an effective means to construct tree skeletons that can capture the surface and topological features of trees. However, it is difficult to construct a tree skeleton located at the geometric centre of the tree with a detailed hierarchy of branches because of the natural intricate topology of the tree and the defects of the tree point cloud scanned by TLS. In this study, we presented a tree-skeletonization method based on density-based spatial clustering of applications with noise (DBSCAN) using TLS data. First, outliers were removed using DBSCAN, and the point-traversal order of each point was recorded. Next, a tree point cloud was divided into several tree slices using contour planes, and several tree segments were obtained by applying DBSCAN to each tree slice. Tree skeleton points were retrieved from each tree segment after the point-inversion transformation. Then, the adjacent relationship between skeleton points and the flow weight of each skeleton point was calculated based on the point-traversal order. After that, the skeleton points were classified into stems and different levels of branch points using the flow weights of the skeleton points, and the branch hierarchies were identified. Finally, the tree skeleton was optimized using the angle consistency. The positional deviation

d_{p}

and directivity deviation

d_{d}

were presented in this study to verify the validity of the constructed tree skeleton. From the visualization results, the constructed tree skeleton effectively reflected the geometrical structure and branch hierarchies of the tree. The averages of

d_{p}

and

d_{d}

were 0.418 cm and 8.474 degrees, respectively. The results demonstrated the validity of the presented method. It will help improve the visualization and accurate measurement of trees. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Forestry: Theory, Methods, Applications, and Validation)

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17 pages, 6385 KiB

Open AccessArticle

Forest Canopy Water Content Monitoring Using Radiative Transfer Models and Machine Learning

by Liang Liu, Shaoda Li, Wunian Yang, Xiao Wang, Xinrui Luo, Peilian Ran and Helin Zhang

Forests 2023, 14(7), 1418; https://doi.org/10.3390/f14071418 - 11 Jul 2023

Cited by 1 | Viewed by 1777

Abstract

Forests are facing various threats, such as drought, in the context of global climate change. Canopy water content (CWC) is a crucial indicator of forest water stress, mortality, and fire monitoring. However, previous studies on CWC have not adequately simulated forests with heterogeneous and discontinuous canopy structures. At the same time, there is a lack of field validation. This study retrieved the forest CWC across the contiguous U.S. (CONUS) with coupled radiative transfer models (RTMs) and the random forest (RF) algorithm. A Gaussian copula and prior knowledge were used for model parameterization. The results indicated that more accurate simulations of leaf trait dependencies and canopy structure characteristics lead to better CWC inversion. In addition, GeoSail, coupled with PROSPECT-5B, showed good performance (R² = 0.68, RMSE = 0.15 kg m⁻², MAE = 0.12 kg m⁻², rRMSE = 12.78%, Bias = −0.036 kg m⁻²) for forest CWC retrieval. Large variation existed in forest CWC, spatiotemporally, and evergreen needle forest (ENF) showed strong CWC capacity. This study underscores the suitability of 3D RTMs for inversing the parameters of forest canopies. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Forestry: Theory, Methods, Applications, and Validation)

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21 pages, 4348 KiB

Open AccessArticle

Automatic Extraction of the Spatial Distribution of Picea schrenkiana in the Tianshan Mountains Based on Google Earth Engine and the Jeffries–Matusita Distance

by Fujin Xu, Zhonglin Xu, Changchun Xu and Tingting Yu

Forests 2023, 14(7), 1373; https://doi.org/10.3390/f14071373 - 4 Jul 2023

Viewed by 1538

Abstract

As a distinct species in the Tianshan Mountains (TS) of Central Asia (CA), Picea schrenkiana plays a significant role in water purification, soil and water conservation, and climate regulation. In the context of climate change, rapidly and accurately obtaining its spatial distribution has critical decision-making significance for maintaining ecological security in the arid area of CA and the sustainable development of the “Silk Road Economic Belt”. However, conventional methods are extremely challenging to accomplish the high-resolution mapping of Picea schrenkiana in the TS, which is characterized by a wide range (9.97 × 10⁵ km²) and complex terrain. The approach of geo-big data and cloud computing provides new opportunities to address this issue. Therefore, the purpose of this study is to propose an automatic extraction procedure for the spatial distribution of Picea schrenkiana based on Google Earth Engine and the Jeffries–Matusita (JM) distance, which considered three aspects: sample points, remote-sensing images, and classification features. The results showed that (1) after removing abnormal samples and selecting the summer image, the producer accuracy (PA) of Picea schrenkiana was improved by 2.95% and 0.24%–2.10%, respectively. (2) Both the separation obtained by the JM distance and the analysis results of eight schemes showed that spectral features and texture features played a key role in the mapping of Picea schrenkiana. (3) The JM distance can seize the classification features that are most conducive to the mapping of Picea schrenkiana, and effectively improve the classification accuracy. The PA and user accuracy of Picea schrenkiana were 96.74% and 96.96%, respectively. The overall accuracy was 91.93%, while the Kappa coefficient was 0.89. (4) The results show that Picea schrenkiana is concentrated in the middle TS and scattered in the remaining areas. In total, 85.7%, 66.4%, and 85.9% of Picea schrenkiana were distributed in the range of 1500–2700 m, 20–40°, and on shady slope and semi-shady slope, respectively. The automatic procedure adopted in this study provides a basis for the rapid and accurate mapping of the spatial distribution of coniferous forests in the complex terrain. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Forestry: Theory, Methods, Applications, and Validation)

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20 pages, 2079 KiB

Open AccessArticle

Forest Segmentation with Spatial Pyramid Pooling Modules: A Surveillance System Based on Satellite Images

by Fung Xin Ru, Mohd Asyraf Zulkifley, Siti Raihanah Abdani and Martin Spraggon

Forests 2023, 14(2), 405; https://doi.org/10.3390/f14020405 - 16 Feb 2023

Cited by 7 | Viewed by 1851

Abstract

The global deforestation rate continues to worsen each year, and will eventually lead to various negative consequences for humans and the environment. It is essential to develop an effective forest monitoring system to detect any changes in forest areas, in particular, by monitoring the progress of forest conservation efforts. In general, changes in forest status are difficult to annotate manually, whereby the boundaries can be small in size or hard to discern, especially in areas that are bordering residential areas. The previously implemented forest monitoring systems were ineffective due to their use of low-resolution satellite images and the inefficiency of drone-based data that offer a limited field of view. Most government agencies also still rely on manual annotation, which makes the monitoring process time-consuming, tedious, and expensive. Therefore, the goal of this study is to overcome these issues by developing a forest monitoring system that relies on a robust deep semantic segmentation network that is capable of discerning forest boundaries automatically, so that any changes over the years can be tracked. The backbone of this system is based on satellite imaging supplied to a modified U-Net deep architecture to incorporate multi-scale modules to deliver the semantic segmentation output. A dataset of 6048 Landsat-8 satellite sub-images that were taken from eight land parcels of forest areas was collected and annotated, and then further divided into training and testing datasets. The novelty of this system is the optimal integration of the spatial pyramid pooling (SPP) mechanism into the base model, which allows the model to effectively segment forest areas regardless of their varying sizes, patterns, and colors. To investigate the impact of SPP on the forest segmentation system, a set of experiments was conducted by integrating several variants of SPP ranging from two to four parallel paths with different combinations of pooling kernel size, placed at the bottleneck layer of the U-Net model. The results demonstrated the effectiveness of the SPP module in improving the performance of the forest segmentation system by 2.57%, 6.74%, and 7.75% in accuracy (

a c c

), intersection over union (

I o U

), and F1-score (

F 1_{s c o r e}

), respectively. The best SPP variant consists of four parallel paths with a combination of pooling kernel sizes of

2 \times 2

4 \times 4

6 \times 6

, and

8 \times 8

pixels that produced the highest

a c c

I o U

, and

F 1_{s c o r e}

of 86.71%, 75.59%, and 82.88%, respectively. As a result, the multi-scale module improved the proposed forest segmentation system, making it a highly useful system for government and private agencies in tracking any changes in forest areas. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Forestry: Theory, Methods, Applications, and Validation)

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19 pages, 2874 KiB

Open AccessArticle

Using the Error-in-Variable Simultaneous Equations Approach to Construct Compatible Estimation Models of Forest Inventory Attributes Based on Airborne LiDAR

by Chungan Li, Zhu Yu, Xiangbei Zhou, Mei Zhou and Zhen Li

Forests 2023, 14(1), 65; https://doi.org/10.3390/f14010065 - 29 Dec 2022

Cited by 2 | Viewed by 2016

Abstract

Airborne LiDAR has been extensively used for estimating and mapping forest attributes at various scales. However, most models have been developed separately and independently without considering the intrinsic mathematical relationships and correlations among the estimates, which results in the mathematical and biophysical incompatibility of the estimates. In this paper, using the measurement error model approach, the error-in-variable simultaneous equation (SEq) for airborne LiDAR-assisted estimations of four forest attributes (stand volume, V; basal area, G; mean stand height, H; and diameter at breast height, D) for four forest types (Chinese fir, pine, eucalyptus, and broad-leaved forest) is developed and compared to the independence models (IMs). The results indicated that both the SEqs and IMs performed well, and the rRMSEs of the SEqs were slightly larger than those of the IMs, while the increases in rRMSE were less than 2% for the SEqs. There were statistically significant differences (α = 0.05) in the means of the estimates between SEqs and IMs, even though their average differences were less than ±1.0% for most attributes. There were no statistically significant differences in the mean estimates between SEqs, except for the estimates of the D and G of the eucalyptus forest. The SEqs with H and G as the endogenous variables (EVs) to estimate V performed slightly better than other SEqs in the fir, pine, and broad-leaved forests. The SEq that used D, H, and V as the EVs for estimating G was best in the eucalyptus forests. The SEq ensures the definite mathematical relationship among the estimates of forest attributes is maintained, which is consistent with forest measurement principles and therefore facilitates forest resource management applications, which is an issue that needs to be addressed for airborne LIDAR forest parameter estimation. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Forestry: Theory, Methods, Applications, and Validation)

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