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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: 31 July 2024 | Viewed by 13835

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Special Issue Editors

Dr. Jianping Wu

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Guest Editor

Guangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, China
Interests: forest ecosystem; soil organic carbon; remote sensing; climate change; urban forest ecosystem

Dr. Zhongbing Chang

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Guest Editor

Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Surveying and Mapping Institute Lands and Resource Department of Guangdong Province, Guangzhou 510663, China
Interests: microwave remote sensing; forest biomass and carbon; vegetation optical depth
Special Issues, Collections and Topics in MDPI journals

Dr. Xin Xiong

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Guest Editor

State Environmental Protection Key Laboratory of Urban Ecological Simulation and Protection, South China Institute of Environmental Sciences, MEE, Guangzhou 510530, China
Interests: ecosystem carbon sink function

Special Issue Information

Dear Colleagues,

Forests cover around one-third of the global land surface, store about half of the terrestrial carbon and are the dominating contributors of terrestrial net primary production. As the largest carbon pool of terrestrial ecosystems, the forest ecosystem plays a critical role in both the global carbon cycle and climate change mitigation. Time-series monitoring is essential for understanding forest ecosystem processes and forest response to anthropogenic activities and climate change. Over the last several decades, satellite records have offered the potential to monitor forest changes by combining diverse remote sensing sources including optical, synthetic aperture radar (SAR), light detection and ranging (LiDAR), and microwave sensors. Remote sensing data from different sources and with various land surface process models could provide better spatial coverage with high resolution, and are available for long-term time series, which can enable the effective global mapping and monitoring of forest trends. In light of these advantages, we organized this Special Issue, “Modeling and Remote Sensing of Forest Ecosystem”. This Special Issue covers potential topics including but not limited to:

Response of forest dynamics to anthropogenic activities and climate change;
Time-series change detection and trend analysis of forest ecosystems;
The impacts of climate extremes (e.g., drought, wetness) on the forest ecosystem;
Monitoring of forest biomass and carbon dynamics;
Mapping of forest structure parameters.

Dr. Jianping Wu
Dr. Zhongbing Chang
Dr. Xin Xiong
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

remote sensing
modeling
forest ecosystem
aboveground biomass
forest mapping
forest management
land use/cover change
climate change

Published Papers (10 papers)

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Research

31 pages, 22271 KiB

Open AccessArticle

A Novel Approach for Simultaneous Localization and Dense Mapping Based on Binocular Vision in Forest Ecological Environment

by Lina Liu, Yaqiu Liu, Yunlei Lv and Xiang Li

Forests 2024, 15(1), 147; https://doi.org/10.3390/f15010147 - 10 Jan 2024

Viewed by 890

Abstract

The three-dimensional reconstruction of forest ecological environment by low-altitude remote sensing photography from Unmanned Aerial Vehicles (UAVs) provides a powerful basis for the fine surveying of forest resources and forest management. A stereo vision system, D-SLAM, is proposed to realize simultaneous localization and dense mapping for UAVs in complex forest ecological environments. The system takes binocular images as input and 3D dense maps as target outputs, while the 3D sparse maps and the camera poses can be obtained. The tracking thread utilizes temporal clue to match sparse map points for zero-drift localization. The relative motion amount and data association between frames are used as constraints for new keyframes selection, and the binocular image spatial clue compensation strategy is proposed to increase the robustness of the algorithm tracking. The dense mapping thread uses Linear Attention Network (LANet) to predict reliable disparity maps in ill-posed regions, which are transformed to depth maps for constructing dense point cloud maps. Evaluations of three datasets, EuRoC, KITTI and Forest, show that the proposed system can run at 30 ordinary frames and 3 keyframes per second with Forest, with a high localization accuracy of several centimeters for Root Mean Squared Absolute Trajectory Error (RMS ATE) on EuRoC and a Relative Root Mean Squared Error (RMSE) with two average values of 0.64 and 0.2 for t_rel and R_rel with KITTI, outperforming most mainstream models in terms of tracking accuracy and robustness. Moreover, the advantage of dense mapping compensates for the shortcomings of sparse mapping in most Smultaneous Localization and Mapping (SLAM) systems and the proposed system meets the requirements of real-time localization and dense mapping in the complex ecological environment of forests. Full article

(This article belongs to the Special Issue Modeling and Remote Sensing of Forests Ecosystem)

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14 pages, 9168 KiB

Open AccessArticle

Spatiotemporal Evolution and Prediction of Ecosystem Carbon Storage in the Yiluo River Basin Based on the PLUS-InVEST Model

by Lei Li, Guangxing Ji, Qingsong Li, Jincai Zhang, Huishan Gao, Mengya Jia, Meng Li and Genming Li

Forests 2023, 14(12), 2442; https://doi.org/10.3390/f14122442 - 14 Dec 2023

Viewed by 975

Abstract

Land-use change has a great impact on regional ecosystem balance and carbon storage, so it is of great significance to study future land-use types and carbon storage in a region to optimize the regional land-use structure. Based on the existing land-use data and the different scenarios of the shared socioeconomic pathway and the representative concentration pathway (SSP-RCP) provided by CMIP6, this study used the PLUS model to predict future land use and the InVEST model to predict the carbon storage in the study area in the historical period and under different scenarios in the future. The results show the following: (1) The change in land use will lead to a change in carbon storage. From 2000 to 2020, the conversion of cultivated land to construction land was the main transfer type, which was also an important reason for the decrease in regional carbon storage. (2) Under the three scenarios, the SSP126 scenario has the smallest share of arable land area, while this scenario has the largest share of woodland and grassland land area, and none of the three scenarios shows a significant decrease in woodland area. (3) From 2020 to 2050, the carbon stocks in the study area under the three scenarios, SSP126, SSP245, and SSP585, all show different degrees of decline, decreasing to 36,405.0204 × 10⁴ t, 36,251.4402 × 10⁴ t, and 36,190.4066 × 10⁴ t, respectively. Restricting the conversion of land with a high carbon storage capacity to land with a low carbon storage capacity is conducive to the benign development of regional carbon storage. This study can provide a reference for the adjustment and management of future land-use structures in the region. Full article

(This article belongs to the Special Issue Modeling and Remote Sensing of Forests Ecosystem)

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12 pages, 1464 KiB

Open AccessArticle

Response Mechanism of Annual Streamflow Decline to Vegetation Growth and Climate Change in the Han River Basin, China

by Mengya Jia, Shixiong Hu, Xuyue Hu and Yuannan Long

Forests 2023, 14(11), 2132; https://doi.org/10.3390/f14112132 - 26 Oct 2023

Viewed by 789

Abstract

Vegetation changes have a significant impact on the underlying surface of a watershed and alter hydrological processes. To clarify the synergistic evolution relationship between climate, vegetation, and hydrology, this study aims to reveal how vegetation restoration influences streamflow decline. This study first applied the trend-free pre-whitening Mann–Kendall (TFPW-MK) method to identify variation trends of various elements at Baihe and Shayang hydrologic stations from 1982 to 2015. Secondly, an extended Budyko equation was improved by fitting the linear relationship between annual NDVI and Budyko parameter (ω). Finally, based on the extended Budyko formula, the elastic coefficient method was applied to identify the influence of vegetation changes on runoff changes of the Baihe and Shayang stations from 1982 to 2015. The results displayed that (1) the annual NDVI of Baihe and Shayang hydrologic stations both presented an increasing trend, and streamflow presented an insignificant decrease trend. The mutation year of the annual runoff depth of Baihe and Shayang stations both occurred in 1990. (2) The annual NDVI had a significant and positive linear relationship with ω. (3) The streamflow decline of Baihe and Shayang stations is mainly influenced by precipitation variation and human activities. (4) Vegetation growth had a positive effect on the streamflow decline of Baihe and Shayang stations, with a contribution rate of 14.06% and 17.87%. This effect of vegetation growth on discharge attenuation should be given high priority. Full article

(This article belongs to the Special Issue Modeling and Remote Sensing of Forests Ecosystem)

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23 pages, 5455 KiB

Open AccessArticle

Vegetation Dynamics of Sub-Mediterranean Low-Mountain Landscapes under Climate Change (on the Example of Southeastern Crimea)

by Vladimir Tabunshchik, Roman Gorbunov, Tatiana Gorbunova and Mariia Safonova

Forests 2023, 14(10), 1969; https://doi.org/10.3390/f14101969 - 28 Sep 2023

Viewed by 803

Abstract

In the context of a changing environment, understanding the interaction between vegetation and climate is crucial for assessing, predicting, and adapting to future changes in different vegetation types. Vegetation exhibits high sensitivity to external environmental factors, making this understanding particularly significant. This study utilizes geospatial analysis techniques, such as geographic information systems, to investigate vegetation dynamics based on remote sensing data and climatic variables, including annual air temperature, annual precipitation, and annual solar radiation. The research methodology encompasses data collection, processing, and analysis, incorporating multispectral imagery and multilayered maps of various parameters. The calculation of the normalized difference vegetation index serves to evaluate changes in vegetation cover, identify areas experiencing variations in green biomass, and establish strategies for the future development of different vegetation types. During the period from 2001 to 2022, the average normalized difference vegetation index value in the Southeastern Crimea region amounted to 0.443. The highest average values were recorded in the year 2006, reaching a magnitude of 0.469. Conversely, the lowest values were observed in the years 2001–2002, constituting 0.397. It has been ascertained that an overarching positive trend in the evolution of NDVI values from 2001 to 2022 is apparent, thus implying a notable augmentation in vegetative biomass. However, adversarial trends manifest in discrete locales adjacent to the cities of Sudak and Feodosia, along with the coastal stretches of the Black Sea. Correlation analysis is employed to establish relationships between vegetation changes and climatic indicators. The findings contribute to our understanding of the vulnerability of various vegetation types and ecosystems in the Southeastern Crimea region. The obtained data provide valuable insights for the development of sustainable vegetation resource management strategies and climate change adaptation in the region. Full article

(This article belongs to the Special Issue Modeling and Remote Sensing of Forests Ecosystem)

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13 pages, 2006 KiB

Open AccessArticle

Assessing the Impact of Vegetation Variation, Climate and Human Factors on the Streamflow Variation of Yarlung Zangbo River with the Corrected Budyko Equation

by Guangxing Ji, Shuaijun Yue, Jincai Zhang, Junchang Huang, Yulong Guo and Weiqiang Chen

Forests 2023, 14(7), 1312; https://doi.org/10.3390/f14071312 - 26 Jun 2023

Cited by 6 | Viewed by 989

Abstract

The Yarlung Zangbo River (YZR) is the largest river on the Qinghai Tibet Plateau, and changes in its meteorology, hydrology and vegetation will have a significant impact on the ecological environment of the basin. In order to deepen our understanding of the relationship of climate–vegetation–hydrological processes in YZR, the purpose of this study is to explore how vegetation growth in the YZR affects its runoff changes. We first identified the abrupt year of discharge in the YZR using a heuristic segmentation algorithm and cumulative anomaly mutation test approach. After that, the functional equation for NDVI and the Budyko parameter (n) was computed. Finally, the NDVI was introduced into the Budyko equation to evaluate the impact of vegetation changes on the streamflow in the YZR. Results showed that: (1) NDVI and discharge in the YZR both presented an increasing trend, and the mutation year of annual runoff in Nuxia station occurred in 1997. (2) n had a significant negative correlation with NDVI in the YZR (p < 0.01). (3) The contributions of Pr, ET₀, NDVI, and n on streamflow change in the S2 period (1998–2015) were 5.26%, 1.14%, 43.04%, and 50.06%. The results of this study can provide scientific guidance and support for the evaluation of the effects of ecological restoration measures, as well as the management and planning of water resources in the YZR. Full article

(This article belongs to the Special Issue Modeling and Remote Sensing of Forests Ecosystem)

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11 pages, 1490 KiB

Open AccessArticle

Aboveground Biomass and Endogenous Hormones in Sub-Tropical Forest Fragments

by Chang Liu, Wenzhi Du, Honglin Cao, Chunyu Shen and Lei Ma

Forests 2023, 14(4), 661; https://doi.org/10.3390/f14040661 - 23 Mar 2023

Cited by 1 | Viewed by 1014

Abstract

Associated endogenous hormones were affected by forest fragmentation and significantly correlated with aboveground biomass storage. Forest fragmentation threatens aboveground biomass (AGB) and affects biodiversity and ecosystem functioning in multiple ways. We ask whether and how forest fragmentation influences AGB in forest fragments. We investigated differences in AGB between forest edges and interiors, and how plant community characteristics and endogenous hormones influenced AGB. In six 40 m × 40 m plots spread across three forest fragments, AGB was significantly higher in plots in the forest interior than in those at the edge of forests. The proportion of individuals with a large diameter at breast height (DBH > 40 cm) in the forest edges is higher than that in the forest interiors. Further, trees within a 15–40 cm DBH range had the highest contribution to AGB in all plots. Trees in interior plots had higher abscisic acid (ABA) and lower indole-3-acetic acid (IAA) concentrations than those in edge plots. In addition, AGB was significantly positively and negatively correlated with ABA and IAA concentrations at the community scale. In this study, we provide an account of endogenous hormones’ role as an integrator of environmental signals and, in particular, we highlight the correlation of these endogenous hormone levels with vegetation patterns. Edge effects strongly influenced AGB. In the future, more endogenous hormones and complex interactions should be better explored and understood to support consistent forest conservation and management actions. Full article

(This article belongs to the Special Issue Modeling and Remote Sensing of Forests Ecosystem)

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18 pages, 7016 KiB

Open AccessArticle

Mapping the Forest Height by Fusion of ICESat-2 and Multi-Source Remote Sensing Imagery and Topographic Information: A Case Study in Jiangxi Province, China

by Yichen Luo, Shuhua Qi, Kaitao Liao, Shaoyu Zhang, Bisong Hu and Ye Tian

Forests 2023, 14(3), 454; https://doi.org/10.3390/f14030454 - 22 Feb 2023

Cited by 8 | Viewed by 2451

Abstract

Forest canopy height is defined as the distance between the highest point of the tree canopy and the ground, which is considered to be a key factor in calculating above-ground biomass, leaf area index, and carbon stock. Large-scale forest canopy height monitoring can provide scientific information on deforestation and forest degradation to policymakers. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in 2018, with the Advanced Topographic Laser Altimeter System (ATLAS) instrument taking on the task of mapping and transmitting data as a photon-counting LiDAR, which offers an opportunity to obtain global forest canopy height. To generate a high-resolution forest canopy height map of Jiangxi Province, we integrated ICESat-2 and multi-source remote sensing imagery, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission, and forest age data of Jiangxi Province. Meanwhile, we develop four canopy height extrapolation models by random forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Gradient Boosting Decision Tree (GBDT) to link canopy height in ICESat-2, and spatial feature information in multi-source remote sensing imagery. The results show that: (1) Forest canopy height is moderately correlated with forest age, making it a potential predictor for forest canopy height mapping. (2) Compared with GBDT, SVM, and KNN, RF showed the best predictive performance with a coefficient of determination (R²) of 0.61 and a root mean square error (RMSE) of 5.29 m. (3) Elevation, slope, and the red-edge band (band 5) derived from Sentinel-2 were significantly dependent variables in the canopy height extrapolation model. Apart from that, Forest age was one of the variables that the RF moderately relied on. In contrast, backscatter coefficients and texture features derived from Sentinel-1 were not sensitive to canopy height. (4) There is a significant correlation between forest canopy height predicted by RF and forest canopy height measured by field measurements (R² = 0.69, RMSE = 4.02 m). In a nutshell, the results indicate that the method utilized in this work can reliably map the spatial distribution of forest canopy height at high resolution. Full article

(This article belongs to the Special Issue Modeling and Remote Sensing of Forests Ecosystem)

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29 pages, 41183 KiB

Open AccessArticle

Response of Vegetation Coverage to Climate Changes in the Qinling-Daba Mountains of China

by Han Ren, Chaonan Chen, Yanhong Li, Wenbo Zhu, Lijuan Zhang, Liyuan Wang and Lianqi Zhu

Forests 2023, 14(2), 425; https://doi.org/10.3390/f14020425 - 19 Feb 2023

Cited by 3 | Viewed by 1281

Abstract

As a major component of the north–south transition zone in China, the vegetation ecosystem of the Qinling-Daba Mountains (QBM) is highly sensitive to climate change. However, the impact of sunshine duration, specifically, on regional vegetation remains unclear. By using linear trend, correlation, and multiple regression analyses, this study systematically analyzed the spatiotemporal characteristics and trend changes of the vegetation coverage in the QBM from 2000–2020. Changes in the main climate elements in different periods and the responses to them are also discussed. Over the past 21 years, the vegetation coverage on the east and west sides of the QBM has been lower than that in the central areas. However, it is showing a continuously improving trend, especially in winters and springs. The findings indicate that change of FVC in the QBM exhibited a positive correlation with temperature, a negative correlation with sunshine hours, and both positive and negative correlation with precipitation. On an annual scale, average temperature was the main controlling climatic factor. On a seasonal scale, the area dominated by precipitation in spring was larger. In summer, the relative importance of the three was weak. In autumn and winter, sunshine duration became the main factor affecting vegetation coverage in most areas. Full article

(This article belongs to the Special Issue Modeling and Remote Sensing of Forests Ecosystem)

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18 pages, 2185 KiB

Open AccessArticle

Estimating Stem Diameter Distributions with Airborne Laser Scanning Metrics and Derived Canopy Surface Texture Metrics

by Xavier Gallagher-Duval, Olivier R. van Lier and Richard A. Fournier

Forests 2023, 14(2), 287; https://doi.org/10.3390/f14020287 - 02 Feb 2023

Viewed by 2060

Abstract

This study aimed to determine the optimal approach for estimating stem diameter distributions (SDD) from airborne laser scanning (ALS) data using point cloud metrics (M_als), a canopy height model (CHM) texture metrics (M_tex), and a combination thereof (M_comb). We developed area-based models (i) to classify SDD modality and (ii) predict SDD function parameters, which we tested for 5 modelling techniques. Our results demonstrated little variability in the performance of SDD modality classification models (mean overall accuracy: 72%; SD: 2%). Our best SDD function parameter models were generally fitted with M_comb, with R² improvements up to 0.25. We found the variable Correlation, originating from M_tex, to be the most important predictor within M_comb. Trends in the performance of the predictor groups were mostly consistent across the modelling techniques within each parameter. Using an Error Index (EI), we determined that differentiating modality prior to estimating SDD improved the accuracy of estimates for bimodal plots (~12% decrease in EI), which was trivially not the case for unimodal plots (<1% increase in EI). We concluded that (i) CHM texture metrics can be used to improve the estimate of SDD parameters and that (ii) differentiating for modality prior to estimating SSD is especially beneficial in stands with bimodal SDD. Full article

(This article belongs to the Special Issue Modeling and Remote Sensing of Forests Ecosystem)

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11 pages, 2544 KiB

Open AccessArticle

Quantitatively Computing the Influence of Vegetation Changes on Surface Discharge in the Middle-Upper Reaches of the Huaihe River, China

by Yuxin Wang, Zhipei Liu, Baowei Qian, Zongyu He and Guangxing Ji

Forests 2022, 13(12), 2000; https://doi.org/10.3390/f13122000 - 25 Nov 2022

Cited by 7 | Viewed by 1299

Abstract

Changes in meteorology, hydrology, and vegetation will have significant impacts on the ecological environment of a basin, and the middle-upper reach of Huaihe River (MUHR) is one of the key regions for vegetation restoration in China. However, less studies have quantitatively accounted for the contribution of vegetation changes to land surface discharge in the MUHR. To quantitatively evaluate the influence of vegetation changes on land surface discharge in the MUHR, the Bernaola–Galavan (B–G) segmentation algorithm was utilized to recognize the mutation year of the Normalized Difference Vegetation Index (NDVI) time sequence data. Next, the functional relationship between the underlying surface parameter and the NDVI was quantitatively analyzed, and an adjusted Budyko formula was constructed. Finally, the effects of vegetation changes, climate factors, and mankind activities on the surface discharge in the MUHR were computed using the adjusted Budyko formula and elastic coefficient method. The results showed the following: (1) the surface runoff and precipitation from 1982 to 2015 in the MUHR presented a falling trend, yet the NDVI and potential evaporation presented an upward trend; (2) 2004 was the mutation year of the NDVI time series data, and the underlying surface parameter showed a significant linear regression relationship with the NDVI (p < 0.05); (3) the vegetation variation played a major role in the runoff variation during the changing period (2005–2015) in the MUHR. Precipitation, potential evaporation, and human activities accounted for −0.32%, −15.11%, and 18.24% of the surface runoff variation, respectively. Full article

(This article belongs to the Special Issue Modeling and Remote Sensing of Forests Ecosystem)

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