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Special Issue "Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization"

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

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 9071

Special Issue Editors

Dr. Carlos Portillo-Quintero
E-Mail Website
Guest Editor
Associate Professor, Department of Natural Resources Management, College of Agricultural Sciences and Natural Resources Management, Texas Tech University, Lubbock, TX, USA
Interests: tropical forest ecology and conservation; remote sensing; ecosystem monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. José Luis Hernández-Stefanoni
E-Mail Website
Guest Editor
Professor, Centro de Investigación Científica de Yucatán A.C., Unidad de Recursos Naturales, Calle 43 # 130, x 32 y 34 Colonia Chuburná de Hidalgo, Mérida CP 97205, Yucatán, Mexico
Interests: tropical forest ecology; landscape ecology; remote sensing; spatial analysis
MSc. Gabriela Reyes-Palomeque
E-Mail Website
Guest Editor
Research Assistant, Centro de Investigación Científica de Yucatán A.C., Unidad de Recursos Naturales, Calle 43 # 130, x 32 y 34 Colonia Chuburná de Hidalgo, Mérida CP 97205, Yucatán, Mexico
Interests: remote sensing; ecological data analysis
MSc. Mukti Subedi
E-Mail Website
Guest Editor
Research Assistant, Department of Natural Resources Management, College of Agricultural Sciences and Natural Resources Management, Texas Tech University, Lubbock, TX, USA
Interests: remote sensing; ecological modeling; ecosystem management

Special Issue Information

Dear Colleagues,

Tropical forests are the most complex ecological systems on Earth. From early successional communities to old growth stands, tropical forests are characterized by vegetation communities with high species richness, high structural and functional diversity, and a myriad of ecological interactions occurring across taxonomic and functional groups. They are also highly heterogeneous across its range with latitudinal, altitudinal, soil, and climatic gradients determining diverse plant and animal community level adaptations. One of the most significant attributes is their capacity to act as a major reservoir of carbon within terrestrial ecosystems, helping to mitigate climate change and additionally providing numerous valuable ecosystem services.

Human–tropical forest interactions have existed since the origin of our species. This relation, however, has only recently climbed to a global scale of forest exploitation that is jeopardizing the mere existence of the ecosystem as we know it. With tropical forests reduced to scattered fragments across its range under threat of ecological collapse due to climate change, there is an urgent need to monitor and preserve these last patches.

Remote sensing can offer tools and techniques to help to characterize their complexity and monitor its patch-level and landscape-level attributes through time. The remote sensing scientific community has come a long way in recent decades by developing ways to link spectral values to tropical forest attributes and generate predictive models of interest in the fields of botany, zoology, forest management, and conservation biology. With the increasing availability of dense time series of optical, radar, and lidar in proximal, airborne, and space-borne sensing systems, novel ways are being developed to integrate field ground truth data to remotely sensed data and to accurately detect changes in tropical forest attributes (e.g., leaf area index, phenology, biomass, canopy gap fraction, taxonomic diversity, biochemical diversity) at multiple geographic and temporal scales. These methods include complex data acquisition and ingestion frameworks, image transformations, data fusion, analysis, and product sharing strategies that currently often rely on cloud-based services.

Efforts to provide ways to operationalize the use of these tools and techniques in landscape-scale observation systems need to be promoted in order to aid tropical forest ecology and conservation action on the ground. This Special Issue will focus on state-of-the-art research that addresses the challenges of upscaling biological, biophysical, and biochemical attributes of tropical forests in complex landscapes and understanding their dynamics at multiple spatial and temporal scales.

We are inviting papers including but not limited to the following research topics:

  • Methods for predicting forest biophysical and biochemical parameters at multiple geographic and temporal scales;
  • Novel data ingestion, optimization, and management techniques for tropical forest monitoring;
  • Integration of field data, ground sensor networks, and remote sensing datasets in near real-time for tropical forest monitoring.

Dr. Carlos Portillo-Quintero
Dr. José Luis Hernández-Stefanoni
MSc. Gabriela Reyes-Palomeque
MSc. Mukti Subedi
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 2500 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 forests
  • Functional diversity
  • Structural diversity
  • Vegetation structure and biomass
  • Data fusion
  • Integration
  • Monitoring

Published Papers (7 papers)

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Research

Article
Tropical Species Classification with Structural Traits Using Handheld Laser Scanning Data
Remote Sens. 2022, 14(8), 1948; https://doi.org/10.3390/rs14081948 - 18 Apr 2022
Viewed by 567
Abstract
Information about tree species plays a pivotal role in sustainable forest management. Light detection and ranging (LiDAR) technology has demonstrated its potential to obtain species information using the structural features of trees. Several studies have explored the structural properties of boreal or temperate [...] Read more.
Information about tree species plays a pivotal role in sustainable forest management. Light detection and ranging (LiDAR) technology has demonstrated its potential to obtain species information using the structural features of trees. Several studies have explored the structural properties of boreal or temperate trees from terrestrial laser scanning (TLS) data and applied them to species classification, but the study of structural properties of tropical trees for species classification is rare. Compared to conventional static TLS, handheld laser scanning (HLS) is able to effectively capture point clouds of an individual tree with flexible movability. Therefore, in this study, we characterized the structural features of tropical species from HLS data as 23 LiDAR structural parameters, involving 6 branch, 11 crown and 6 entire tree parameters, and used these parameters to classify the species via 5 machine-learning (ML) models, respectively. The performance of each parameter was further evaluated and compared. Classification results showed that the employed parameters can achieve a classification accuracy of 84.09% using the support vector machine with a polynomial kernel. The evaluation of parameters indicated that it is insufficient to classify four species with only one and two parameters, but ten parameters were recommended in order to achieve satisfactory accuracy. The combination of different types of parameters, such as branch and crown parameters, can significantly improve classification accuracy. Finally, five sets of optimal parameters were suggested according to their importance and performance. This study also showed that the time- and cost-efficient HLS instrument could be a promising tool for tree-structure-related studies, such as structural parameter estimation, species classification, forest inventory, as well as sustainable tree management. Full article
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Article
Fusing Sentinel-1 and -2 to Model GEDI-Derived Vegetation Structure Characteristics in GEE for the Paraguayan Chaco
Remote Sens. 2021, 13(24), 5105; https://doi.org/10.3390/rs13245105 - 15 Dec 2021
Viewed by 1415
Abstract
Vegetation structure is a key component in assessing habitat quality for wildlife and carbon storage capacity of forests. Studies conducted at global scale demonstrate the increasing pressure of the agricultural frontier on tropical forest, endangering their continuity and biodiversity within. The Paraguayan Chaco [...] Read more.
Vegetation structure is a key component in assessing habitat quality for wildlife and carbon storage capacity of forests. Studies conducted at global scale demonstrate the increasing pressure of the agricultural frontier on tropical forest, endangering their continuity and biodiversity within. The Paraguayan Chaco has been identified as one of the regions with the highest rate of deforestation in South America. Uninterrupted deforestation activities over the last 30 years have resulted in the loss of 27% of its original cover. The present study focuses on the assessment of vegetation structure characteristics for the complete Paraguayan Chaco by fusing Sentinel-1, -2 and novel spaceborne Light Detection and Ranging (LiDAR) samples from the Global Ecosystem Dynamics Investigation (GEDI). The large study area (240,000 km2) calls for a workflow in the cloud computing environment of Google Earth Engine (GEE) which efficiently processes the multi-temporal and multi-sensor data sets for extrapolation in a tile-based random forest (RF) regression model. GEDI-derived attributes of vegetation structure are available since December 2019, opening novel research perspectives to assess vegetation structure composition in remote areas and at large-scale. Therefore, the combination of global mapping missions, such as Landsat and Sentinel, are predestined to be combined with GEDI data, in order to identify priority areas for nature conservation. Nevertheless, a comprehensive assessment of the vegetation structure of the Paraguayan Chaco has not been conducted yet. For that reason, the present methodology was developed to generate the first high-resolution maps (10 m) of canopy height, total canopy cover, Plant-Area-Index and Foliage-Height-Diversity-Index. The complex ecosystems of the Paraguayan Chaco ranging from arid to humid climates can be described by canopy height values from 1.8 to 17.6 m and canopy covers from sparse to dense (total canopy cover: 0 to 78.1%). Model accuracy according to median R2 amounts to 64.0% for canopy height, 61.4% for total canopy cover, 50.6% for Plant-Area-Index and 48.0% for Foliage-Height-Diversity-Index. The generated maps of vegetation structure should promote environmental-sound land use and conservation strategies in the Paraguayan Chaco, to meet the challenges of expanding agricultural fields and increasing demand of cattle ranching products, which are dominant drivers of tropical forest loss. Full article
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Article
Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery
Remote Sens. 2021, 13(18), 3600; https://doi.org/10.3390/rs13183600 - 09 Sep 2021
Cited by 4 | Viewed by 1189
Abstract
The U-net is nowadays among the most popular deep learning algorithms for land use/land cover (LULC) mapping; nevertheless, it has rarely been used with synthetic aperture radar (SAR) and multispectral (MS) imagery. On the other hand, the discrimination between plantations and forests in [...] Read more.
The U-net is nowadays among the most popular deep learning algorithms for land use/land cover (LULC) mapping; nevertheless, it has rarely been used with synthetic aperture radar (SAR) and multispectral (MS) imagery. On the other hand, the discrimination between plantations and forests in LULC maps has been emphasized, especially for tropical areas, due to their differences in biodiversity and ecosystem services provision. In this study, we trained a U-net using different imagery inputs from Sentinel-1 and Sentinel-2 satellites, MS, SAR and a combination of both (MS + SAR); while a random forests algorithm (RF) with the MS + SAR input was also trained to evaluate the difference in algorithm selection. The classification system included ten classes, including old-growth and secondary forests, as well as old-growth and young plantations. The most accurate results were obtained with the MS + SAR U-net, where the highest overall accuracy (0.76) and average F1-score (0.58) were achieved. Although MS + SAR and MS U-nets gave similar results for almost all of the classes, for old-growth plantations and secondary forest, the addition of the SAR band caused an F1-score increment of 0.08–0.11 (0.62 vs. 0.54 and 0.45 vs. 0.34, respectively). Consecutively, in comparison with the MS + SAR RF, the MS + SAR U-net obtained higher F1-scores for almost all the classes. Our results show that using the U-net with a combined input of SAR and MS images enabled a higher F1-score and accuracy for a detailed LULC map, in comparison with other evaluated methods. Full article
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Article
Carbon Stocks, Species Diversity and Their Spatial Relationships in the Yucatán Peninsula, Mexico
Remote Sens. 2021, 13(16), 3179; https://doi.org/10.3390/rs13163179 - 11 Aug 2021
Viewed by 1415
Abstract
Integrating information about the spatial distribution of carbon stocks and species diversity in tropical forests over large areas is fundamental for climate change mitigation and biodiversity conservation. In this study, spatial models showing the distribution of carbon stocks and the number of species [...] Read more.
Integrating information about the spatial distribution of carbon stocks and species diversity in tropical forests over large areas is fundamental for climate change mitigation and biodiversity conservation. In this study, spatial models showing the distribution of carbon stocks and the number of species were produced in order to identify areas that maximize carbon storage and biodiversity in the tropical forests of the Yucatan Peninsula, Mexico. We mapped carbon density and species richness of trees using L-band radar backscatter data as well as radar texture metrics, climatic and field data with the random forest regression algorithm. We reduced sources of errors in plot data of the national forest inventory by using correction factors to account for carbon stocks of small trees (<7.5 cm DBH) and for the temporal difference between field data collection and imagery acquisition. We created bivariate maps to assess the spatial relationship between carbon stocks and diversity. Model validation of the regional maps obtained herein using an independent data set of plots resulted in a coefficient of determination (R2) of 0.28 and 0.31 and a relative mean square error of 38.5% and 33.0% for aboveground biomass and species richness, respectively, at pixel level. Estimates of carbon density were influenced mostly by radar backscatter and climatic data, while those of species richness were influenced mostly by radar texture and climatic variables. Correlation between carbon density and species richness was positive in 79.3% of the peninsula, while bivariate maps showed that 39.6% of the area in the peninsula had high carbon stocks and species richness. Our results highlight the importance of combining carbon and diversity maps to identify areas that are critical—both for maintaining carbon stocks and for conserving biodiversity. Full article
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Article
How BFAST Trend and Seasonal Model Components Affect Disturbance Detection in Tropical Dry Forest and Temperate Forest
Remote Sens. 2021, 13(11), 2033; https://doi.org/10.3390/rs13112033 - 21 May 2021
Cited by 7 | Viewed by 956
Abstract
Time series analysis has gained popularity in forest disturbance monitoring thanks to the availability of satellite and airborne remote sensing images and the development of different time series methods for change detection. Previous research has focused on time series data noise reduction, the [...] Read more.
Time series analysis has gained popularity in forest disturbance monitoring thanks to the availability of satellite and airborne remote sensing images and the development of different time series methods for change detection. Previous research has focused on time series data noise reduction, the magnitude of breakpoints, and accuracy assessment; however, few have looked in detail at how the trend and seasonal model components contribute to disturbance detection in different forest types. Here, we use Landsat time series images spanning 1994–2018 to map forest disturbance in a western Pacific area of Mexico, where both temperate and tropical dry forests have been subject to severe deforestation and forest degradation processes. Since these two forest types have distinct seasonal characteristics, we investigate how trend and seasonal model components, such as the goodness-of-fit (R2), magnitude of change, amplitude, and model length in a stable historical period, affect forest disturbance detection. We applied the Breaks For Additive Season and Trend Monitor (BFAST) algorithm and after accuracy assessment by stratified random sample points, and we obtained 68% and 86% of user accuracy and 75.6% and 86% of producer’s accuracy in disturbance detection, in tropical dry forests and temperate forests, respectively. We extracted the noncorrelated trend and seasonal model components R2, magnitude, amplitude, length of the stable historical period, and percentage of pixels with NA and tested their effects on disturbance detection employing forest-type specific logistic regression. Our results showed that, for all forests combined, the amplitude and stable historical period length contributed to disturbance detection. While for tropical dry forest alone, amplitude was the main predictor, and for the temperate forest alone, the stable historical period length contributed most to the prediction, although it was not statistically significant. These findings provide insights for improving the results of forest disturbance detection in different forest types. Full article
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Article
A Low-Cost and Robust Landsat-Based Approach to Study Forest Degradation and Carbon Emissions from Selective Logging in the Venezuelan Amazon
Remote Sens. 2021, 13(8), 1435; https://doi.org/10.3390/rs13081435 - 08 Apr 2021
Cited by 1 | Viewed by 1331
Abstract
Selective logging in the tropics is a major driver of forest degradation by altering forest structure and function, including significant losses of aboveground carbon. In this study, we used a 30-year Landsat time series (1985–2015) to analyze forest degradation and carbon emissions due [...] Read more.
Selective logging in the tropics is a major driver of forest degradation by altering forest structure and function, including significant losses of aboveground carbon. In this study, we used a 30-year Landsat time series (1985–2015) to analyze forest degradation and carbon emissions due to selective logging in a Forest Reserve of the Venezuelan Amazon. Our work was conducted in two phases: the first, by means of a direct method we detected the infrastructure related to logging at the sub-pixel level, and for the second, we used an indirect approach using buffer areas applied to the results of the selective logging mapping. Pre- and post-logging forest inventory data, combined with the mapping analysis were used to quantify the effects of logging on aboveground carbon emissions for three different sources: hauling, skidding and tree felling. With an overall precision of 0.943, we demonstrate the potential of this method to efficiently map selective logging and forest degradation with commission and omission errors of +7.6 ± 4.5 (Mean ± SD %) and −7.5% ± 9.1 respectively. Forest degradation due to logging directly affected close to 24,480 ha, or about ~1% of the total area of the Imataca Forest Reserve. On average, with a relatively low harvest intensity of 2.8 ± 1.2 trees ha−1 or 10.5 ± 4.6 m3 ha−1, selective logging was responsible for the emission of 61 ± 21.9 Mg C ha−1. Lack of reduced impact logging guidelines contributed to pervasive effects reflected in a mean reduction of ~35% of the aboveground carbon compared to unlogged stands. This research contributes to further improve our understanding of the relationships between selective logging and forest degradation in tropical managed forests and serves as input for the potential implementation of projects for reducing emissions from deforestation and forest degradation (REDD+). Full article
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Communication
The Road to Operationalization of Effective Tropical Forest Monitoring Systems
Remote Sens. 2021, 13(7), 1370; https://doi.org/10.3390/rs13071370 - 02 Apr 2021
Cited by 4 | Viewed by 841
Abstract
The urgency to preserve tropical forest remnants has encouraged the development of remote sensing tools and techniques to monitor diverse forest attributes for management and conservation. State-of-the-art methodologies for mapping and tracking these attributes usually achieve accuracies greater than 0.8 for forest cover [...] Read more.
The urgency to preserve tropical forest remnants has encouraged the development of remote sensing tools and techniques to monitor diverse forest attributes for management and conservation. State-of-the-art methodologies for mapping and tracking these attributes usually achieve accuracies greater than 0.8 for forest cover monitoring; r-square values of ~0.5–0.7 for plant diversity, vegetation structure, and plant functional trait mapping, and overall accuracies of ~0.8 for categorical maps of forest attributes. Nonetheless, existing operational tropical forest monitoring systems only track single attributes at national to global scales. For the design and implementation of effective and integrated tropical forest monitoring systems, we recommend the integration of multiple data sources and techniques for monitoring structural, functional, and compositional attributes. We also recommend its decentralized implementation for adjusting methods to local climatic and ecological characteristics and for proper end-user engagement. The operationalization of the system should be based on all open-source computing platforms, leveraging international support in research and development and ensuring direct and constant user engagement. We recommend continuing the efforts to address these multiple challenges for effective monitoring. Full article
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