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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 August 2025 | Viewed by 4545

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Special Issue Editors

Dr. Yaoliang Chen

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Guest Editor

School of Geographical Sciences, Fujian Normal University, No. 18 Middle Wulongjiang Avenue, Shangjie, Fuzhou 350117, China
Interests: mapping and modeling plantation forests

Dr. Lei Zhao

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Guest Editor

Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, DongXiaoFu No. 1, XiangShan Road, Haidian District, Beijing 100091, China
Interests: forest parameter inversion method of polarimetric SAR; interferometric SAR and polarimetric interferometric SAR; topography radiometric correction algorithm of SAR images

Prof. Dr. Dengsheng Lu

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Guest Editor

School of Geographical Sciences, Fujian Normal University, No. 18 Middle Wulongjiang Avenue, Fuzhou 350117, China
Interests: forest aboveground biomass mapping; classification; change detection
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Special Issue Information

Dear Colleagues,

Forests, which cover nearly a third of the Earth’s terrestrial surface, are among the most complex and vital ecosystems. They provide critical ecosystem services such as carbon sequestration, climate regulation, and biodiversity conservation. However, growing anthropogenic pressures and disturbances threaten these services, making it imperative to improve our ability to monitor and understand changes in forest ecosystems. Geospatial technologies, including remote sensing and GIS, offer powerful tools to map and model forests, providing valuable insights into forest structure, health, services, and dynamics. These technologies also contribute significantly to understanding the roles that forests play in mitigating climate change.

This Special Issue invites contributions that leverage geospatial technologies to map and model forest ecosystems. We encourage studies that employ multi-source (e.g., multispectral, hyperspectral, thermal, microwave, LiDAR), multi-scale, and multi-temporal approaches to address key challenges in forest science. We also welcome research exploring smart, consumer-grade, and low-cost geospatial solutions to make forest monitoring more accessible.

Potential topics include, but are not limited to:

Forest inventories and vegetation parameter mapping;
Detection and monitoring of biotic and abiotic disturbances;
Forest cover change and dynamics analysis;
Phenological trends and vegetation dynamics;
Carbon and ecohydrology modeling in forest ecosystems;
Long-term forest monitoring techniques.
We invite researchers and practitioners to submit innovative studies that advance our understanding of forest ecosystems through the application of cutting-edge geospatial technologies.

Dr. Yaoliang Chen
Dr. Lei Zhao
Prof. Dr. Dengsheng Lu
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

tree species
forests
remote sensing
SAR imagery
optical imagery
lidar
geospatial technology
spatiotemporal pattern
aboveground biomass/carbon storage
forest disturbances
forest activity
modeling

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Published Papers (5 papers)

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Research

20 pages, 6165 KiB

Open AccessArticle

Prediction and Spatiotemporal Dynamics of Vegetation Index Based on Deep Learning and Environmental Factors in the Yangtze River Basin

by Yin Wang, Nan Zhang, Mingjie Chen, Yabing Zhao, Famiao Guo, Jingxian Huang, Daoli Peng and Xiaohui Wang

Forests 2025, 16(3), 460; https://doi.org/10.3390/f16030460 - 5 Mar 2025

Cited by 1 | Viewed by 474

Abstract

Accurately predicting the vegetation index (VI) of the Yangtze River Basin and analyzing its spatiotemporal trends are essential for assessing vegetation dynamics and providing recommendations for environmental resource management in the region. This study selected the key climate factors most strongly correlated with three vegetation indexes (VI): the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and kernel Normalized Difference Vegetation Index (kNDVI). Historical VI and climate data (2001–2020) were used to train, validate, and test a CNN-BiLSTM-AM deep learning model, which integrates the strengths of Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention Mechanism (AM). The performance of this model was compared with CNN-BiLSTM, LSTM, and BiLSTM-AM models to validate its superiority in predicting the VI. Finally, climate simulation data under three Shared Socioeconomic Pathway (SSP) scenarios (SSP1-1.9, SSP2-4.5, and SSP5-8.5) were used as inputs to the CNN-BiLSTM-AM model to predict the VI for the next 20 years (2021–2040), aiming to analyze spatiotemporal trends. The results showed the following: (1) Temperature, precipitation, and evapotranspiration had the highest correlation with VI data and were used as inputs to the time series VI model. (2) The CNN-BiLSTM-AM model combined with the EVI achieved the best performance (R² = 0.981, RMSE = 0.022, MAE = 0.019). (3) Under all three scenarios, the EVI over the next 20 years showed an upward trend compared to the previous 20 years, with the most significant growth observed under SSP5-8.5. Vegetation in the source region and the western part of the upper reaches increased slowly, while significant increases were observed in the eastern part of the upper reaches, middle reaches, lower reaches, and estuary. The analysis of the predicted EVI time series indicates that the vegetation growth conditions in the Yangtze River Basin will continue to improve over the next 20 years. Full article

(This article belongs to the Special Issue Mapping and Modeling Forests Using Geospatial Technologies)

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20 pages, 12491 KiB

Open AccessArticle

Forest Disturbance and Restoration in China's North-South Transition Zone: A Case from the Funiu Mountains

by Qifan Wu, Jiacheng Hou, Shiwen Wu, Fuyuan Su, Shilong Hao, Tailai Yin, Haoyuan Chen, Yunpeng Xu and Hailong He

Forests 2025, 16(2), 269; https://doi.org/10.3390/f16020269 - 4 Feb 2025

Viewed by 701

Abstract

Accurate monitoring and assessment of forest disturbance and recovery dynamics are essential for sustainable forest management, particularly in ecological transition zones. This study analyzed forest disturbance and recovery patterns in China’s Funiu Mountains from 1991 to 2020 by integrating the LandTrendr algorithm with space-time cube analysis. Using Landsat time series data and the Geodetector method, we examined both the spatiotemporal characteristics and driving factors of forest change across three periods. The results showed that (1) between 1991 and 2020, the study area experienced 131.19 km² of forest disturbance and 495.88 km² of recovery, with both processes most active during the 1990s; (2) spatiotemporal analysis revealed that both disturbance and recovery patterns were predominantly characterized by cold spots, suggesting relatively stable forest conditions despite localized changes; (3) human activities were the primary drivers of forest disturbance in the early period, while forest recovery was consistently influenced by the combined effects of topographic conditions and precipitation. Additionally, forest fires emerged as an important factor affecting both disturbance and recovery patterns after 2010. These findings enhance our understanding of forest dynamics in transition zones and provide empirical support for regional forest management strategies. The results also highlight the importance of considering both spatial and temporal dimensions when monitoring long-term forest changes. Full article

(This article belongs to the Special Issue Mapping and Modeling Forests Using Geospatial Technologies)

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21 pages, 3819 KiB

Open AccessArticle

Improving Forest Canopy Height Mapping in Wuyishan National Park Through Calibration of ZiYuan-3 Stereo Imagery Using Limited Unmanned Aerial Vehicle LiDAR Data

by Kai Jian, Dengsheng Lu, Yagang Lu and Guiying Li

Forests 2025, 16(1), 125; https://doi.org/10.3390/f16010125 - 11 Jan 2025

Cited by 1 | Viewed by 823

Abstract

Forest canopy height (FCH) is a critical parameter for forest management and ecosystem modeling, but there is a lack of accurate FCH distribution in large areas. To address this issue, this study selected Wuyishan National Park in China as a case study to explore the calibration method for mapping FCH in a complex subtropical mountainous region based on ZiYuan-3 (ZY3) stereo imagery and limited Unmanned Aerial Vehicle (UAV) LiDAR data. Pearson’s correlation analysis, Categorical Boosting (CatBoost) feature importance analysis, and causal effect analysis were used to examine major factors causing extraction errors of digital surface model (DSM) data from ZY3 stereo imagery. Different machine learning algorithms were compared and used to calibrate the DSM and FCH results. The results indicate that the DSM extraction accuracy based on ZY3 stereo imagery is primarily influenced by slope aspect, elevation, and vegetation characteristics. These influences were particularly notable in areas with a complex topography and dense vegetation coverage. A Bayesian-optimized CatBoost model with directly calibrating the original FCH (the difference between the DSM from ZY3 and high-precision digital elevation model (DEM) data) demonstrated the best prediction performance. This model produced the FCH map at a 4 m spatial resolution, the root mean square error (RMSE) was reduced from 6.47 m based on initial stereo imagery to 3.99 m after calibration, and the relative RMSE (rRMSE) was reduced from 36.52% to 22.53%. The study demonstrates the feasibility of using ZY3 imagery for regional forest canopy height mapping and confirms the superior performance of using the CatBoost algorithm in enhancing FCH calibration accuracy. These findings provide valuable insights into the multidimensional impacts of key environmental factors on FCH extraction, supporting precise forest monitoring and carbon stock assessment in complex terrains in subtropical regions. Full article

(This article belongs to the Special Issue Mapping and Modeling Forests Using Geospatial Technologies)

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25 pages, 9018 KiB

Open AccessArticle

Predicting Forest Evapotranspiration Shifts Under Diverse Climate Change Scenarios by Leveraging the SEBAL Model Across Inner Mongolia

by Penghao Ji, Rong Su and Runhong Gao

Forests 2024, 15(12), 2234; https://doi.org/10.3390/f15122234 - 19 Dec 2024

Viewed by 875

Abstract

This study examines climate change impacts on evapotranspiration in Inner Mongolia, analyzing potential (PET) and actual (AET) evapotranspiration shifts across diverse land-use classes using the SEBAL model and SSP2-4.5 and SSP5-8.5 projections (2030–2050) relative to a 1985–2015 baseline. Our findings reveal substantial PET increases across all LULC types, with Non-Vegetated Lands consistently showing the highest absolute PET values across scenarios (931.19 mm under baseline, increasing to 975.65 mm under SSP5-8.5) due to limited vegetation cover and shading effects, while forests, croplands, and savannas exhibit the most pronounced relative increases under SSP5-8.5, driven by heightened atmospheric demand and vegetation-induced transpiration. Monthly analyses show pronounced PET increases, particularly in the warmer months (June–August), with projected SSP5-8.5 PET levels reaching peaks of over 500 mm, indicating significant future water demand. AET increases are largest in densely vegetated classes, such as forests (+242.41 mm for Evergreen Needleleaf Forests under SSP5-8.5), while croplands and grasslands exhibit more moderate gains (+249.59 mm and +167.75 mm, respectively). The widening PET-AET gap highlights a growing vulnerability to moisture deficits, particularly in croplands and grasslands. Forested areas, while resilient, face rising water demands, necessitating conservation measures, whereas croplands and grasslands in low-precipitation areas risk soil moisture deficits and productivity declines due to limited adaptive capacity. Non-Vegetated Lands and built-up areas exhibit minimal AET responses (+16.37 mm for Non-Vegetated Lands under SSP5-8.5), emphasizing their limited water cycling contributions despite high PET. This research enhances the understanding of climate-induced changes in water demands across semi-arid regions, providing critical insights into effective and region-specific water resource management strategies. Full article

(This article belongs to the Special Issue Mapping and Modeling Forests Using Geospatial Technologies)

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18 pages, 6402 KiB

Open AccessArticle

The Spectral Response Characteristics of Potassium in Camellia oleifera Leaves at Different Growth Stages

by Deqing Liu, Lipeng Yan, Chao Zhang, Yongji Xue, Mengyu Chen, Rui Li and Xuehai Tang

Forests 2024, 15(11), 1930; https://doi.org/10.3390/f15111930 - 1 Nov 2024

Viewed by 1176

Abstract

Camellia oleifera (Camellia oleifera Abel.) is a key woody oilseed tree. In recent years, China’s Camellia oleifera industry has shifted from extensive to refined management, with an action plan launched to boost productivity and efficiency. This study utilized remote sensing technology to diagnose crop nutrient levels. Focusing on 240 Camellia oleifera trees from four varieties at the Dechang Cooperative in Shucheng County, Anhui Province, the study collected full-spectrum canopy reflectance data (350–2500 nm) across five growing stages: spring shoot, summer shoot, fruit expanding, fruit ripening, and full blooming. First-order derivative (FD) and second-order derivative (SD) transformations were used to preprocess the spectral data and analyze the relationships between leaf potassium concentration (LKC) and the raw spectra (R), FD, and SD. The VCPA-IRIV strategy was then applied to identify sensitive wavelengths and artificial neural network algorithms were used to construct LKC estimation models. The main conclusions are as follows. (1) In the spring shoot stage, LKC ranged from 1.93 to 8.06 g/kg, with an average of 3.70 g/kg; in the summer shoot stage, LKC ranged from 2.01 to 8.82 g/kg, with an average of 4.96 g/kg; in the fruit expanding stage, LKC ranged from 1.40 to 18.27 g/kg, with an average of 4.90 g/kg; in the fruit ripening stage, LKC ranged from 1.45 to 8.90 g/kg, with an average of 3.71 g/kg.; and in the full blooming stage, LKC ranged from 2.38 to 9.57 g/kg, with an average of 5.79 g/kg. Across the five growth stages, the LKC content of Camellia oleifera showed a pattern of initially increasing, then decreasing, and subsequently increasing again. (2) The optimal LKC model for the spring shoot stage was FD-[7,6,2], with

R_{c}^{2}

= 0.6559, RMSEC = 0.1906 in the calibration set,

R_{T}^{2}

= 0.4531, RMSET = 0.2009 in the test set. The optimal LKC model for the summer shoot stage was FD-[6,5,4], with

R_{c}^{2}

= 0.7419, RMSEC = 0.2489 in the calibration set, and

R_{T}^{2}

= 0.7536, RMSET = 0.2259 in the test set; the optimal LKC model for the fruit expanding stage was SD-[7,6,2], with

R_{c}^{2}

= 0.3036, RMSEC = 0.2113 in the calibration set, and

R_{T}^{2}

= 0.3314, RMSET = 0.1800 in the test set; the optimal LKC model for the fruit ripening stage was FD-[10,3,2], with

R_{c}^{2}

= 0.4197, RMSEC = 0.2375 in the calibration set, and

R_{T}^{2}

= 0.5649, RMSET = 0.1772 in the test set, and the optimal LKC model for the full blooming stage was SD-[10,3,2], with

R_{c}^{2}

= 0.7013, RMSEC = 0.2322 in the calibration set, and

R_{T}^{2}

= 0.5621, RMSET = 0.2507 in the test set. Full article

(This article belongs to the Special Issue Mapping and Modeling Forests Using Geospatial Technologies)

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