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Remote Sensing Monitoring of Tropical Forest Disturbance and Dynamics

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 18993

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Special Issue Editors

Prof. Dr. Guangsheng Chen

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Guest Editor

College of Environmental and Resources, Zhejiang A&F University, Hangzhou 311300, China
Interests: forest disturbance detection; remote sensing monitoring vegetation structure and function changes; modeling of terrestrial ecosystem carbon, nitrogen, and water cycles
Special Issues, Collections and Topics in MDPI journals

Dr. Jia Yang

E-Mail Website
Guest Editor

Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, OK 74078, USA
Interests: remote sensing and GIS applications in land ecosystems; land cover and land use change; terrestrial ecosystem modeling; fire ecology
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Prof. Dr. Zoltan Szantoi

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Guest Editor

Directorate of Earth Observation Programmes, European Space Agency, Paris, France
Interests: artificial Intelligence based image classification; land cover characterization; geospatial big data processing; cloud applications; cash crop mapping for food security; vegetation phenological cycles estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Tropical forests have higher ecological diversity, productivity and biomass compared with other ecosystems on Earth. One of the most significant characteristics is their capacity to act as a major reservoir of carbon within terrestrial ecosystems, helping mitigate climate change, achieve global carbon neutrality target, and simultaneously supply numerous valuable ecosystem services. However, during the past few decades, tropical forests have been extensively affected by the anthropogenic and natural disturbance events (e.g., extreme climate events/flooding/drought, fire, deforestation/afforestation, logging/thinning, insects & diseases, and tropical cyclones). Disturbance agents and severity affect both forest structure (e.g., stand tree composition, fragmentation, spatial structure, and biodiversity) and many ecological functions (e.g., carbon storage and flux, hydrology, and other ecosystem services). An accurate and real-time characterization of forest dynamics under disturbance is of great importance for sustainable management of tropical forests.

With the increasing availability of dense time series of satellite data and field ground truth data, novel methods are being developed to integrate field data with remote sensing data for accurately detecting tropical forest disturbance and its impacts. This special issue will accept manuscripts that focus on both method advancements and their applications in classifying/modeling tropical forest disturbance agents (above mentioned), severity and risks, and detecting their impacts on forest structure and function (above mentioned) using various remote sensing platforms, such as optical sensors (i.e., Landsat, Sentinel, MODIS, and UAV), lidar/radar sensors (i.e., GEDI and SAR), and their fusions.

Submissions shall address any of the following topics:

New method developments for classifying forest disturbance agents (mentioned above), severity and their impacts;
Methods for modeling forest disturbance risks and severity;
Applications of latest remote sensing methods to detect disturbance events, severity and their impacts on forest structure and function (mentioned above);
Remote sensing monitoring of forest management and its effects on forest structure and function;
Remote sensing monitoring of the trajectories of post-disturbance forest growth or recovery.

Prof. Dr. Guangsheng Chen
Dr. Jia Yang
Prof. Dr. Zoltan Szantoi
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

tropical forest
forest disturbance
carbon flux
water storage and flux
productivity and biomass
forest structure
forest degradation
extreme climate events
afforestation and deforestation
landscape management
remote sensing application

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Published Papers (7 papers)

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21 pages, 9460 KiB

Open AccessArticle

Early Prediction of Regional Red Needle Cast Outbreaks Using Climatic Data Trends and Satellite-Derived Observations

by Michael S. Watt, Andrew Holdaway, Pete Watt, Grant D. Pearse, Melanie E. Palmer, Benjamin S. C. Steer, Nicolò Camarretta, Emily McLay and Stuart Fraser

Remote Sens. 2024, 16(8), 1401; https://doi.org/10.3390/rs16081401 - 16 Apr 2024

Cited by 1 | Viewed by 1790

Abstract

Red needle cast (RNC), mainly caused by Phytophthora pluvialis, is a very damaging disease of the widely grown species radiata pine within New Zealand. Using a combination of satellite imagery and weather data, a novel methodology was developed to pre-visually predict the incidence of RNC on radiata pine within the Gisborne region of New Zealand over a five-year period from 2019 to 2023. Sentinel-2 satellite imagery was used to classify areas within the region as being disease-free or showing RNC expression from the difference in the red/green index (R/G_diff) during a disease-free time of the year and the time of maximum disease expression in the upper canopy (early spring–September). Within these two classes, 1976 plots were extracted, and a classification model was used to predict disease incidence from mean monthly weather data for key variables during the 11 months prior to disease expression. The variables in the final random forest model included solar radiation, relative humidity, rainfall, and the maximum air temperature recorded during mid–late summer, which provided a pre-visual prediction of the disease 7–8 months before its peak expression. Using a hold-out test dataset, the final random forest model had an accuracy of 89% and an F1 score of 0.89. This approach can be used to mitigate the impact of RNC by focusing on early surveillance and treatment measures. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Tropical Forest Disturbance and Dynamics)

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17 pages, 11664 KiB

Open AccessArticle

Forest Age Mapping Using Landsat Time-Series Stacks Data Based on Forest Disturbance and Empirical Relationships between Age and Height

by Lei Tian, Longtao Liao, Yu Tao, Xiaocan Wu and Mingyang Li

Remote Sens. 2023, 15(11), 2862; https://doi.org/10.3390/rs15112862 - 31 May 2023

Cited by 4 | Viewed by 2937

Abstract

Forest age is a critical parameter for the status and potential of carbon sequestration in forest ecosystems and reflects major forest disturbance information. However, reliable forest age data with high spatial resolution are lacking to date. In this study, we proposed a forest age mapping method with a 30 m resolution that considers forest disturbance. Here, we used the Landsat time-series stacks (LTSS) data from 1986 to 2021 and implemented the LandTrendr algorithm on the Google Earth Engine (GEE) platform to detect the age of disturbed forests. The age of non-disturbed forests was extracted based on forest canopy height data and the empirical relationship between age and height. High-resolution Google images combined with the forest management archive data of forestry departments and national forest inventory (NFI) data were used for the validation of disturbed and non-disturbed forest age, respectively. The results showed that the LandTrendr algorithm detected disturbance years with producer and user accuracies of approximately 94% and 95%, respectively; and the age of non-disturbed forests obtained using the empirical age–height relationship showed an R² of 0.8875 and a root mean squared error (RMSE) value of 5.776 with NFI-based results. This confirms the reliability of the proposed 30 m resolution forest age mapping method considering forest disturbance. Overall, the method can be used to produce spatially explicit forest age data with high resolution, which can contribute to the sustainable use of forest resources and enhance the understanding of carbon budget studies in forest ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Tropical Forest Disturbance and Dynamics)

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19 pages, 9118 KiB

Open AccessArticle

An Approach Integrating Multi-Source Data with LandTrendr Algorithm for Refining Forest Recovery Detection

by Mei Li, Shudi Zuo, Ying Su, Xiaoman Zheng, Weibing Wang, Kaichao Chen and Yin Ren

Remote Sens. 2023, 15(10), 2667; https://doi.org/10.3390/rs15102667 - 20 May 2023

Cited by 4 | Viewed by 2320

Abstract

Disturbances to forests are getting worse with climate change and urbanization. Assessing the functionality of forest ecosystems is challenging because it requires not only a large amount of input data but also comprehensive estimation indicator methods. The object of the evaluation index of forest ecosystem restoration relies on the ecosystem function instead of the area. To develop the appropriate index with ecological implications, we built the hybrid assessment approach including ecosystem structure-function-habitat representatives. It was based on the Normalized Burn Ratio (NBR) spectral indicator and combined with the local forest management inventory (LFMI), Landsat, Light Detection and Ranging (LiDAR) data. The results of the visual interpretation of Google Earth’s historical imagery showed that the total accuracy of the hybrid approach was 0.94. The output of the hybrid model increased as the biodiversity index value increased. Furthermore, to solve the multi-source data availability problem, the random forest model (R² = 0.78, RMSE = 0.14) with 0.77 total accuracy was built to generate an annual recovery index. A random forest model based on tree age is provided to simplify the hybrid approach while extending the results on time series. The recovery index obtained by the random forest model could facilitate monitoring the forest recovery rate of cold spots. The regional ecological recovery time could be predicted. These two results could provide a scientific basis for forest managers to make more effective forest restoration plans. From the perspective of space, it could ensure that the areas with slow recovery would be allocated enough restoration resources. From the perspective of time, the implementation period of the closed forest policy could also be estimated. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Tropical Forest Disturbance and Dynamics)

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18 pages, 11872 KiB

Open AccessArticle

Trends in Forest Greening and Its Spatial Correlation with Bioclimatic and Environmental Factors in the Greater Mekong Subregion from 2001 to 2020

by Bing He, Xi Wu, Kang Liu, Yuanzhi Yao, Wenjiang Chen and Wei Zhao

Remote Sens. 2022, 14(23), 5982; https://doi.org/10.3390/rs14235982 - 25 Nov 2022

Cited by 6 | Viewed by 1898

Abstract

Understanding trends of vegetation evolution and its spatial characteristics is critical for sustainable social development in the Greater Mekong Subregion (GMS), which is densely populated and still has uneven economic development. Through Theil–Sen/Mann–Kendall tests, polynomial regression and bivariate local autocorrelation analyses, we investigated vegetation greening trends and their spatial correlation with bioclimatic and environmental variables. The study yielded the following results: (1) Land cover in the GMS has changed significantly over the last 20 years. Conversion between forest and grassland was the main type of change. (2) The upward trend in the forest enhanced vegetation index (EVI) significantly exceeded the downward trend in countries over 20 years. In GMS, the spatial variation in forest trend slope values ranged from −0.0297 a⁻¹ to 0.0152 a⁻¹. (3) Anthropogenic activities have played an important role in forest greening; planted, plantation and oil palm forests exhibit the largest contributions to greening. (4) Changes in forest EVI were most spatially correlated with radiation (12.19% for surface net solar radiation and 12.14% for surface solar radiation downwards) and least spatially correlated with seasonality precipitation (8.33%) and mean annual temperature (8.19%). The results of the analysis of EVI trends in vegetation and their spatial correlation with bioclimatic and environmental variables can provide a reference for strategies aimed for protecting the vegetation ecology. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Tropical Forest Disturbance and Dynamics)

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27 pages, 10496 KiB

Open AccessArticle

Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method

by Jianing Shen, Guangsheng Chen, Jianwen Hua, Sha Huang and Jiangming Ma

Remote Sens. 2022, 14(13), 3238; https://doi.org/10.3390/rs14133238 - 5 Jul 2022

Cited by 12 | Viewed by 3370

Abstract

China has implemented a series of forestry law, policies, regulations, and afforestation projects since the 1970s. However, their impacts on the spatial and temporal patterns of forests have not been fully assessed yet. The lack of an accurate, high-resolution, and long-term forest disturbance and recovery dataset has impeded this assessment. Here we improved the forest loss and gain detections by integrating the LandTrendr change detection algorithm with the Random Forest (RF) machine-learning method and applied it to assess forest loss and gain patterns in the Zhejiang, Jiangxi, and Guangxi Provinces of the subtropical vegetation in China. The accuracy evaluation indicated that our approach can adequately detect the spatial and temporal distribution patterns in forest gain and loss, with an overall accuracy of 93% and the Kappa coefficient of 0.89. The forest loss area was 8.30 × 10⁴ km² in the Zhejiang, Jiangxi, and Guangxi Provinces during 1986–2019, accounting for 43.52% of total forest area in 1986, while the forest gain area was 20.25 × 10⁴ km², accounting for 106.19% of total forest area in 1986. Although the interannual variation patterns were similar among three provinces, the forest loss and gain area and the magnitude of change trends were significantly different. Guangxi has the largest forest loss and gain area and increasing trends, followed by Jiangxi, and the least in Zhejiang. The variations in annual forest loss and gain area can be mostly explained by the timelines of major forestry policies and regulations. Our study would provide an applicable method and data for assessing the impacts of forest disturbance events and forestry policies and regulations on the spatial and temporal patterns of forest loss and gain in China, and further contributing to regional and national forest carbon and greenhouse gases budget estimations. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Tropical Forest Disturbance and Dynamics)

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14 pages, 2328 KiB

Open AccessCommunication

Neighbourhood Species Richness Reduces Crown Asymmetry of Subtropical Trees in Sloping Terrain

by Maria D. Perles-Garcia, Matthias Kunz, Andreas Fichtner, Nora Meyer, Werner Härdtle and Goddert von Oheimb

Remote Sens. 2022, 14(6), 1441; https://doi.org/10.3390/rs14061441 - 16 Mar 2022

Cited by 3 | Viewed by 2834

Abstract

Reforestation in sloping terrain is an important measure for soil erosion control and sustainable watershed management. The mechanical stability of such reforested stands, however, can be low due to a strong asymmetric shape of tree crowns. We investigated how neighbourhood tree species richness, neighbourhood pressure, tree height, and slope inclination affect crown asymmetry in a large-scale plantation biodiversity-ecosystem functioning experiment in subtropical China (BEF-China) over eight years. We took the advantage of terrestrial laser scanning (TLS) measurements, which provide non-destructive, high-resolution data of tree structure without altering tree interactions. Neighbourhood species richness significantly reduced crown asymmetry, and this effect became stronger at steeper slopes. Our results suggest that tree diversity promotes the mechanical stability of forest stands in sloping terrain and highlight the importance of TLS-data for a comprehensive understanding of the role of tree diversity in modulating crown interactions in mixed-species forest plantations. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Tropical Forest Disturbance and Dynamics)

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14 pages, 2015 KiB

Open AccessTechnical Note

Crowd-Driven Deep Learning Tracks Amazon Deforestation

by Ian McCallum, Jon Walker, Steffen Fritz, Markus Grau, Cassie Hannan, I-Sah Hsieh, Deanna Lape, Jen Mahone, Caroline McLester, Steve Mellgren, Nolan Piland, Linda See, Gerhard Svolba and Murray de Villiers

Remote Sens. 2023, 15(21), 5204; https://doi.org/10.3390/rs15215204 - 1 Nov 2023

Viewed by 2247

Abstract

The Amazon forests act as a global reserve for carbon, have very high biodiversity, and provide a variety of additional ecosystem services. These forests are, however, under increasing pressure, coming mainly from deforestation, despite the fact that accurate satellite monitoring is in place that produces annual deforestation maps and timely alerts. Here, we present a proof of concept for rapid deforestation monitoring that engages the global community directly in the monitoring process via crowdsourcing while subsequently leveraging the power of deep learning. Offering no tangible incentives, we were able to sustain participation from more than 5500 active contributors from 96 different nations over a 6-month period, resulting in the crowd classification of 43,108 satellite images (representing around 390,000 km²). Training a suite of AI models with results from the crowd, we achieved an accuracy greater than 90% in detecting new and existing deforestation. These findings demonstrate the potential of a crowd–AI approach to rapidly detect and validate deforestation events. Our method directly engages a large, enthusiastic, and increasingly digital global community who wish to participate in the stewardship of the global environment. Coupled with existing monitoring systems, this approach could offer an additional means of verification, increasing confidence in global deforestation monitoring. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Tropical Forest Disturbance and Dynamics)

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