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New Methods and Applications in Remote Sensing of Tropical Forests
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New Methods and Applications in Remote Sensing of Tropical Forests

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 March 2025) | Viewed by 8674

Special Issue Editors

Dr. Cibele Hummel Do Amaral

E-Mail Website
Guest Editor
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO, USA
Interests: forest ecology and management; spectroscopy; data fusion; data harmonization; machine learning; cloud-computing; open science
Dr. Polyanna da Conceição Bispo

E-Mail Website
Guest Editor
Department of Geography, School of Environment, Education and Development, University of Manchester, Oxford Rd, Manchester M13 9PL, UK
Interests: tropical forests; forest ecology; forest structure; vegetation dynamics; geomorphometry; aboveground biomass estimations; multisensory approach; SAR; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ricardo Dalagnol

E-Mail Website
Guest Editor
Remote Sensing Department, University of California, Los Angeles (UCLA), Los Angeles, CA, USA
Interests: tropical forests degradation; tree mortality; forest ecology; high resolution optical data; airborne lidar; deep learning
Dr. Ekena Rangel Pinagé

E-Mail Website
Guest Editor
College of Forestry, Oregon State University, Corvallis, OR 97333, USA
Interests: tropical forest disturbances; forest structure; forest function; optical remote sensing; lidar remote sensing

Special Issue Information

Dear Colleagues,

Tropical forests encompass less than one-fifth of the Earth’s surface; however, they are critical to global climate regulation and biodiversity conservation. They provide multiple ecosystem services for local and regional communities and are being increasingly threatened by human and climate pressures. Remote sensing (RS) plays a critical role in helping humanity to understand tropical forest’s structure and functioning, ecosystem services, and how they have been affected by human and climate  drivers. From RS observations, we can prospect the future of these ecosystems and support policies on their use, conservation, restoration, and climate mitigation and adaptation. RS technologies have evolved over the last years exponentially. Nowadays, we have an assemblage of long-term and new-generation sensors including multispectral imagers, spectroradiometers, lidars, and radars being used to collect data from the surface (hand-held), near-surface (tower, UAV), air, and space. Each of these technologies plays a role in the retrieval and up-scaling of useful forest information. In addition to the advances in RS, we have seen a great advancement in algorithms that help us map, model, and scale field and RS data. Among them, machine and deep learning techniques have shown their suitability to deal with multi-source, multi-temporal big data for computer vision and data science. With the advances in cyberinfrastructures, open data, and open-source languages and packages, remote sensing experts are better supported than ever to disentangle the facets of tropical forests and deliver essential information to promote their sustainability.

For this Special Issue, we welcome papers covering all kinds of advancements in remote sensing of tropical forests throughout the world. We expect local to global applications using data from well-established or new-generation sensors from any platform. Papers on data fusion and cross-sensor calibration to enhance and scale forest information are very welcome. New workflows and algorithms, such as those leveraging deep learning models, cloud computing, open development and availability, are preferred. The submission of papers that continue to advance the state-of-the-art remote sensing of tropical forests is appreciated.

Dr. Cibele Hummel Do Amaral
Dr. Polyanna da Conceição Bispo
Dr. Ricardo Dalagnol
Dr. Ekena Rangel Pinagé
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
  • spectroscopy
  • lidar
  • SAR
  • solar-induced chlorophyll fluorescence
  • data fusion
  • time series
  • machine learning
  • deep learning
  • cloud computing
  • open science

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Published Papers (4 papers)

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Research

25 pages, 9300 KiB  
Article
Monitoring Canopy Height in the Hainan Tropical Rainforest Using Machine Learning and Multi-Modal Data Fusion
by Qingping Ling, Yingtan Chen, Zhongke Feng, Huiqing Pei, Cai Wang, Zhaode Yin and Zixuan Qiu
Remote Sens. 2025, 17(6), 966; https://doi.org/10.3390/rs17060966 - 9 Mar 2025
Viewed by 643
Abstract
Biomass carbon sequestration and sink capacities of tropical rainforests are vital for addressing climate change. However, canopy height must be accurately estimated to determine carbon sink potential and implement effective forest management. Four advanced machine-learning algorithms—random forest (RF), gradient boosting decision tree, convolutional [...] Read more.
Biomass carbon sequestration and sink capacities of tropical rainforests are vital for addressing climate change. However, canopy height must be accurately estimated to determine carbon sink potential and implement effective forest management. Four advanced machine-learning algorithms—random forest (RF), gradient boosting decision tree, convolutional neural network, and backpropagation neural network—were compared in terms of forest canopy height in the Hainan Tropical Rainforest National Park. A total of 140 field survey plots and 315 unmanned aerial vehicle photogrammetry plots, along with multi-modal remote sensing datasets (including GEDI and ICESat-2 satellite-carried LiDAR data, Landsat images, and environmental information) were used to validate forest canopy height from 2003 to 2023. The results showed that RH80 was the optimal choice for the prediction model regarding percentile selection, and the RF algorithm exhibited the optimal performance in terms of accuracy and stability, with R2 values of 0.71 and 0.60 for the training and testing sets, respectively, and a relative root mean square error of 21.36%. The RH80 percentile model using the RF algorithm was employed to estimate the forest canopy height distribution in the Hainan Tropical Rainforest National Park from 2003 to 2023, and the canopy heights of five forest types (tropical lowland rainforests, tropical montane cloud forests, tropical seasonal rainforests, tropical montane rainforests, and tropical coniferous forests) were calculated. The study found that from 2003 to 2023, the canopy height in the Hainan Tropical Rainforest National Park showed an overall increasing trend, ranging from 2.95 to 22.02 m. The tropical montane cloud forest had the highest average canopy height, while the tropical seasonal forest exhibited the fastest growth. The findings provide valuable insights for a deeper understanding of the growth dynamics of tropical rainforests. Full article
(This article belongs to the Special Issue New Methods and Applications in Remote Sensing of Tropical Forests)
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19 pages, 6659 KiB  
Article
Post-Logging Canopy Gap Dynamics and Forest Regeneration Assessed Using Airborne LiDAR Time Series in the Brazilian Amazon with Attribution to Gap Types and Origins
by Philip Winstanley, Ricardo Dalagnol, Sneha Mendiratta, Daniel Braga, Lênio Soares Galvão and Polyanna da Conceição Bispo
Remote Sens. 2024, 16(13), 2319; https://doi.org/10.3390/rs16132319 - 25 Jun 2024
Cited by 4 | Viewed by 1978
Abstract
Gaps are openings within tropical forest canopies created by natural or anthropogenic disturbances. Important aspects of gap dynamics that are not well understood include how gaps close over time and their potential for contagiousness, indicating whether the presence of gaps may or may [...] Read more.
Gaps are openings within tropical forest canopies created by natural or anthropogenic disturbances. Important aspects of gap dynamics that are not well understood include how gaps close over time and their potential for contagiousness, indicating whether the presence of gaps may or may not induce the creation of new gaps. This is especially important when we consider disturbances from selective logging activities in rainforests, which take away large trees of high commercial value and leave behind a forest full of gaps. The goal of this study was to quantify and understand how gaps open and close over time within tropical rainforests using a time series of airborne LiDAR data, attributing observed processes to gap types and origins. For this purpose, the Jamari National Forest located in the Brazilian Amazon was chosen as the study area because of the unique availability of multi-temporal small-footprint airborne LiDAR data covering the time period of 2011–2017 with five data acquisitions, alongside the geolocation of trees that were felled by selective logging activities. We found an increased likelihood of natural new gaps opening closer to pre-existing gaps associated with felled tree locations (<20 m distance) rather than farther away from them, suggesting that small-scale disturbances caused by logging, even at a low intensity, may cause a legacy effect of increased mortality over six years after logging due to gap contagiousness. Moreover, gaps were closed at similar annual rates by vertical and lateral ingrowth (16.7% yr−1) and about 90% of the original gap area was closed at six years post-disturbance. Therefore, the relative contribution of lateral and vertical growth for gap closure was similar when consolidated over time. We highlight that aboveground biomass or carbon density of logged forests can be overestimated if considering only top of the canopy height metrics due to fast lateral ingrowth of neighboring trees, especially in the first two years of regeneration where 26% of gaps were closed solely by lateral ingrowth, which would not translate to 26% of regeneration of forest biomass. Trees inside gaps grew 2.2 times faster (1.5 m yr−1) than trees at the surrounding non-gap canopy (0.7 m yr−1). Our study brings new insights into the processes of both the opening and closure of forest gaps within tropical forests and the importance of considering gap types and origins in this analysis. Moreover, it demonstrates the capability of airborne LiDAR multi-temporal data in effectively characterizing the impacts of forest degradation and subsequent recovery. Full article
(This article belongs to the Special Issue New Methods and Applications in Remote Sensing of Tropical Forests)
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30 pages, 4902 KiB  
Article
Examining the Effects of Soil and Water Conservation Measures on Patterns and Magnitudes of Vegetation Cover Change in a Subtropical Region Using Time Series Landsat Imagery
by Xiaoyu Sun, Guiying Li, Qinquan Wu, Dengqiu Li and Dengsheng Lu
Remote Sens. 2024, 16(4), 714; https://doi.org/10.3390/rs16040714 - 18 Feb 2024
Cited by 4 | Viewed by 2274
Abstract
Soil and water erosion has long been regarded as a serious environmental problem in the world. Thus, research on reducing soil erosion has received continuous attention. Different conservation measures such as restoring low-function forests, closing hillsides for afforestation, planting trees and grass, and [...] Read more.
Soil and water erosion has long been regarded as a serious environmental problem in the world. Thus, research on reducing soil erosion has received continuous attention. Different conservation measures such as restoring low-function forests, closing hillsides for afforestation, planting trees and grass, and constructing terraces on slope land have been implemented for controlling soil erosion problems and promoting vegetation cover change. One important task is to understand the effects of different conservation measures on reducing water and soil erosion problems. However, directly conducting the evaluation of soil erosion reduction is difficult. One solution is to evaluate the patterns and magnitudes of vegetation cover change due to implementing these measures. Therefore, this research selected Changting County, Fujian Province as a case study to examine the effects of implementing conservation measures on vegetation cover change based on time series Landsat images and field survey data. Landsat images between 1986 and 2021 were used to produce time series vegetation cover data using the Google Earth Engine. Sentinel-2 images acquired in 2021 and Landsat images in 2010 were separately used to develop land cover maps using the random forest method. The spatial distribution of different conservation measures was linked to annual vegetation cover and land cover change data to examine the effects on the change in vegetation cover. The results showed a significant reduction in bare lands and increase in pine forests. The vegetation coverage increased from 42% in 1986 to 79% in 2021 in the conservation region compared with an increase from 73% to 87% in the non-conservation region during the same period. Of the different conservation measures, the change magnitude was 0.44 for restoring low-function forests and closing hillsides for afforestation and 0.65 for multiple control measures. This research provides new insights in terms of understanding the effects of taking proper measures for reducing soil and water erosion problems and provides scientific results for decisionmaking for soil erosion controls. The strategy and method used in this research are valuable for other regions in understanding the roles of different conservation measures on vegetation cover change and soil erosion reduction through employing remote sensing technologies. Full article
(This article belongs to the Special Issue New Methods and Applications in Remote Sensing of Tropical Forests)
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18 pages, 5112 KiB  
Article
ECOSTRESS Reveals the Importance of Topography and Forest Structure for Evapotranspiration from a Tropical Forest Region of the Andes
by Alejandra Valdés-Uribe, Dirk Hölscher and Alexander Röll
Remote Sens. 2023, 15(12), 2985; https://doi.org/10.3390/rs15122985 - 8 Jun 2023
Cited by 3 | Viewed by 2557
Abstract
Tropical forests are major sources of global terrestrial evapotranspiration (ET), but these heterogeneous landscapes pose a challenge for continuous estimates of ET, so few studies are conducted, and observation gaps persist. New spaceborne products such as ECOsystem Spaceborne Thermal Radiometer Experiment on Space [...] Read more.
Tropical forests are major sources of global terrestrial evapotranspiration (ET), but these heterogeneous landscapes pose a challenge for continuous estimates of ET, so few studies are conducted, and observation gaps persist. New spaceborne products such as ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) are promising tools for closing such observation gaps in understudied tropical areas. Using ECOSTRESS ET data across a large, protected tropical forest region (2250 km2) situated on the western slope of the Andes, we predicted ET for different days. ET was modeled using a random forest approach, following best practice workflows for spatial predictions. We used a set of topographic, meteorological, and forest structure variables from open-source products such as GEDI, PROBA-V, and ERA5, thereby avoiding any variables included in the ECOSTRESS L3 algorithm. The models indicated a high level of accuracy in the spatially explicit prediction of ET across different locations, with an r2 of 0.61 to 0.74. Across all models, no single predictor was dominant, and five variables explained 60% of the models’ results, thus highlighting the complex relationships among predictor variables and their influence on ET spatial predictions in tropical mountain forests. The leaf area index, a forest structure variable, was among the three variables with the highest individual contributions to the prediction of ET on all days studied, along with the topographic variables of elevation and aspect. We conclude that ET can be predicted well with a random forest approach, which could potentially contribute to closing the observation gaps in tropical regions, and that a combination of topography and forest structure variables plays a key role in predicting ET in a forest on the western slope of the Andes. Full article
(This article belongs to the Special Issue New Methods and Applications in Remote Sensing of Tropical Forests)
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