Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
2.4
Journals
Forests
Special Issues
Satellite Time Series Analysis for Forest Mapping and Change Detection
share announcement

Satellite Time Series Analysis for Forest Mapping and Change Detection

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: closed (30 September 2024) | Viewed by 7288

Special Issue Editors

Dr. Chao Ding

E-Mail Website
Guest Editor
Department of Geographic Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
Interests: satellite time series analysis; change detection; vegetation phenology; wildfires; land degradation
Special Issues, Collections and Topics in MDPI journals
Dr. Ling Wu

E-Mail Website
Guest Editor
School of Information Engineering, China University of Geosciences, Beijing 100083, China
Interests: forest disturbances detection; attribution of forest disturbances; classification of tree species; land use/cover change detection; data fusion
Dr. Kai Yan

E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: vegetation parameters production; radiative transfer modeling; leaf area index (LAI); fraction of absorbed photosynthetically active radiation (fPAR); vegetation dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate forest mapping and data on changing information are essential for comprehending forest dynamics under the impacts of climate variability/change and human activities. Earth observation satellite has substantially facilitated studies on forest dynamics across spatio-temporal scales. Nevertheless, challenges remain partially due to the intricate nature of forest ecosystems and issues regarding satellite data quality. We invite researchers to share their insights on novel methods and applications related to forest mapping and change detection with satellite time series data in this Special Issue.

We welcome submissions related to the following topics (but not limited to):

  • Methods for forest mapping and tree species classification;
  • Methods for detecting changes within forests (disturbances, long-term trends, and phenology);
  • Influences of satellite data quality (e.g., data gaps, noise, and terrain shadows) on change detection;
  • Detection of afforestation/deforestation;
  • Forest disturbance and resilience;
  • Forest degradation and mortality;
  • Long-term changes in key forest variables (e.g., leaf area index, gross primary production, and phenology).

Dr. Chao Ding
Dr. Ling Wu
Dr. Kai Yan
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

  • forest mapping
  • change detection
  • remote sensing time series disturbance
  • trend
  • vegetation phenology
  • land cover change

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 14212 KiB  
Article
Spatial Heterogeneity of Mountain Greenness and Greening in the Tibetan Plateau: From a Remote Sensing Perspective
by Zhao Liu, Xingjian Zhang, Shuang Zhao, Panpan Liu and Jinxiu Liu
Forests 2025, 16(4), 576; https://doi.org/10.3390/f16040576 - 26 Mar 2025
Viewed by 269
Abstract
As an important component of terrestrial ecosystems, mountain vegetation serves as an indicator of climate change. Due to the sensitivity of the Tibetan Plateau Mountains (TPM) to climate change and their ecological fragility, their vegetation dynamics (greenness and greening) have become a hot [...] Read more.
As an important component of terrestrial ecosystems, mountain vegetation serves as an indicator of climate change. Due to the sensitivity of the Tibetan Plateau Mountains (TPM) to climate change and their ecological fragility, their vegetation dynamics (greenness and greening) have become a hot spot issue in global environmental change. Topography is a relatively stable environmental factor that shapes vegetation by creating localized microenvironments. However, existing research primarily focuses on the effects of climate change and human activities on vegetation dynamics. Therefore, a more comprehensive understanding of the dependence of vegetation dynamics on topography is needed. To elucidate the relationship between topography and the spatial heterogeneity of vegetation dynamics, we conducted this study using the recently released high-precision Sensor-Independent Leaf Area Index product. Through long-term trend analyses and joint comparisons of multiple topographic variables, this study elucidates key patterns: (1) North-facing slopes exhibit higher vegetation greenness and stronger greening trends than south-facing slopes, whereas east- and west-facing slopes show comparable greenness but stronger greening on west-facing slopes. (2) Vegetation greenness and greening increase with slope steepness. (3) With increasing elevation, greenness decreases progressively, while greening follows a unimodal pattern—initially increasing, then decreasing, and nearing zero at high altitudes. These findings underscore the pivotal role of topography in regulating vegetation responses to climate change. This study provides new insights into the interplay between topography and vegetation dynamics, advancing our understanding of ecological processes on the TPM and informing strategies for ecosystem management under global warming. Full article
(This article belongs to the Special Issue Satellite Time Series Analysis for Forest Mapping and Change Detection)
►▼ Show Figures

Figure 1

26 pages, 9980 KiB  
Article
Detecting Trends in Post-Fire Forest Recovery in Middle Volga from 2000 to 2023
by Eldar Kurbanov, Ludmila Tarasova, Aydin Yakhyayev, Oleg Vorobev, Siyavush Gozalov, Sergei Lezhnin, Jinliang Wang, Jinming Sha, Denis Dergunov and Anna Yastrebova
Forests 2024, 15(11), 1919; https://doi.org/10.3390/f15111919 - 31 Oct 2024
Cited by 1 | Viewed by 1398
Abstract
Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems as it has a strong impact on their natural recovery. This study aimed to investigate how burn severity (BS) levels and climate factors, including land surface temperature (LST) and precipitation variability [...] Read more.
Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems as it has a strong impact on their natural recovery. This study aimed to investigate how burn severity (BS) levels and climate factors, including land surface temperature (LST) and precipitation variability (Pr), affect forest recovery in the Middle Volga region of the Russian Federation. It provides a comprehensive analysis of post-fire forest recovery using Landsat time-series data from 2000 to 2023. The analysis utilized the LandTrendr algorithm in the Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and to quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To evaluate the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator were utilized. The results suggest that post-fire spectral recovery is significantly influenced by the degree of the BS in affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belonged to the BS classes of moderate and high severity. A regression model indicated that land surface temperature (LST) plays a more critical role in post-fire recovery compared to precipitation variability (Pr), accounting for approximately 65% of the variance in recovery outcomes. Full article
(This article belongs to the Special Issue Satellite Time Series Analysis for Forest Mapping and Change Detection)
►▼ Show Figures

Figure 1

17 pages, 8188 KiB  
Article
Identification and Mapping of Eucalyptus Plantations in Remote Sensing Data Using CCDC Algorithm and Random Forest
by Miaohang Zhou, Xujun Han, Jinghan Wang, Xiangyu Ji, Yuefei Zhou and Meng Liu
Forests 2024, 15(11), 1866; https://doi.org/10.3390/f15111866 - 24 Oct 2024
Viewed by 1231
Abstract
Eucalyptus plantations are one of the primary artificial forests in southern China, experiencing rapid expansion in recent years due to their significant socio-economic benefits. This expansion has raised concerns about the ecological environment, necessitating accurate mapping of eucalyptus plantations. In this study, the [...] Read more.
Eucalyptus plantations are one of the primary artificial forests in southern China, experiencing rapid expansion in recent years due to their significant socio-economic benefits. This expansion has raised concerns about the ecological environment, necessitating accurate mapping of eucalyptus plantations. In this study, the phenological characteristics of eucalyptus plantations were utilized as the primary classification basis. Long-term time series Landsat and Sentinel-2 data from 2000 to 2022 were rigorously preprocessed pixel by pixel using the Google Earth Engine (GEE) platform to obtain high-quality observation data. The Continuous Change Detection and Classification (CCDC) algorithm was employed to fit the multi-year observation data with harmonic curves, utilizing parameters such as normalized intercept, slope, phase, and amplitude of the fitted curves to characterize the phenological features of vegetation. A total of 127 phenological indices were generated using the Normalized Burn Ratio (NBR), Normalized Difference Fractional Index (NDFI), and six spectral bands, with the top 20 contributing indices selected as input variables for the random forest algorithm to obtain preliminary classification results. Subsequently, eucalyptus plantation rotation features and the Simple Non-Iterative Clustering (SNIC) superpixel segmentation algorithm were employed to filter the results, enhancing the accuracy of the identification results. The producer’s accuracy, user’s accuracy, and overall accuracy of the eucalyptus plantation map for the year 2020 were found to be 96.67%, 89.23%, and 95.83%, respectively, with a total area accuracy of 94.39%. Accurate mapping of eucalyptus plantations provides essential information and evidence for ecological environment protection and the formulation of carbon-neutral strategies. Full article
(This article belongs to the Special Issue Satellite Time Series Analysis for Forest Mapping and Change Detection)
►▼ Show Figures

Figure 1

14 pages, 5573 KiB  
Article
MART3D: A Multilayer Heterogeneous 3D Radiative Transfer Framework for Characterizing Forest Disturbances
by Lingjing Ouyang, Jianbo Qi, Qiao Wang, Kun Jia, Biao Cao and Wenzhi Zhao
Forests 2024, 15(5), 824; https://doi.org/10.3390/f15050824 - 8 May 2024
Cited by 1 | Viewed by 1536
Abstract
The utilization of radiative transfer models for interpreting remotely sensed data to evaluate forest disturbances is a cost-effective approach. However, the current radiative transfer modeling approaches are either too abstract (e.g., 1D models) or too complex (detailed 3D models). This study introduces a [...] Read more.
The utilization of radiative transfer models for interpreting remotely sensed data to evaluate forest disturbances is a cost-effective approach. However, the current radiative transfer modeling approaches are either too abstract (e.g., 1D models) or too complex (detailed 3D models). This study introduces a novel multilayer heterogeneous 3D radiative transfer framework with medium complexity, termed MART3D, for characterizing forest disturbances. MART3D generates 3D canopy structures accounting for the within-crown clumping by clustering leaves, which is modeled as a turbid medium, around branches, applicable for forests of medium complexity, such as temperate forests. It then automatically generates a multilayer forest with grass, shrub and several layers of trees using statistical parameters, such as the leaf area index and fraction of canopy cover. By employing the ray-tracing module within the well-established LargE-Scale remote sensing data and image Simulation model (LESS) as the computation backend, MART3D achieves a high accuracy (RMSE = 0.0022 and 0.018 for red and Near-Infrared bands) in terms of the bidirectional reflectance factor (BRF) over two RAMI forest scenes, even though the individual structures of MART3D are generated solely from statistical parameters. Furthermore, we demonstrated the versatility and user-friendliness of MART3D by evaluating the band selection strategy for computing the normalized burn ratio (NBR) to assess the composite burn index over a forest fire scene. The proposed MART3D is a flexible and easy-to-use tool for studying the remote sensing response under varying vegetation conditions. Full article
(This article belongs to the Special Issue Satellite Time Series Analysis for Forest Mapping and Change Detection)
►▼ Show Figures

Figure 1

21 pages, 11328 KiB  
Article
Detection of Forest Disturbances with Different Intensities Using Landsat Time Series Based on Adaptive Exponentially Weighted Moving Average Charts
by Tingwei Zhang, Ling Wu, Xiangnan Liu, Meiling Liu, Chen Chen, Baowen Yang, Yuqi Xu and Suchang Zhang
Forests 2024, 15(1), 19; https://doi.org/10.3390/f15010019 - 20 Dec 2023
Cited by 4 | Viewed by 1923
Abstract
Forest disturbance detection is important for revealing ecological changes. Long-time series remote sensing analysis methods have emerged as the primary approach for detecting large-scale forest disturbances. Many of the existing change detection algorithms focus primarily on identifying high-intensity forest disturbances, such as harvesting [...] Read more.
Forest disturbance detection is important for revealing ecological changes. Long-time series remote sensing analysis methods have emerged as the primary approach for detecting large-scale forest disturbances. Many of the existing change detection algorithms focus primarily on identifying high-intensity forest disturbances, such as harvesting and fires, with only a limited capacity to detect both high-intensity and low-intensity forest disturbances. This study proposes an online continuous change detection algorithm for the detection of multi-intensity forest disturbances such as forest harvest, fire, selective harvest, and insects. To initiate the proposed algorithm, the time series of the Normalized Difference Vegetation Index (NDVI) is fitted into a harmonic regression model, which is then followed by the computation of residuals. Next, the residual time series is entered into the adaptive exponentially weighted moving average (AEWMA) chart. This chart adaptively adjusts the smoothing coefficients to identify both high-intensity and low-intensity disturbances. When the chart value consistently deviates from the control limit, the forest pixel is classified as disturbed. With an overall spatial accuracy of 85.2%, including 86.1% producer’s accuracy and 84% user’s accuracy, along with a temporal accuracy of 96.7%, the algorithm enables precise and timely detection of forest disturbances with multiple intensities. This method provides a robust solution for detecting multi-intensity disturbances in forested regions. Full article
(This article belongs to the Special Issue Satellite Time Series Analysis for Forest Mapping and Change Detection)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop