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

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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 (1 June 2022) | Viewed by 47721

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Special Issue Editors

Dr. Carlos Portillo-Quintero

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Guest Editor

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

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Guest Editor

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

Gabriela Reyes-Palomeque

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Guest Editor

Research Assistant, Centro de Investigación Científica de Yucatán A.C., Unidad de Recursos Naturales, Calle 43 No.130 x 32 y 34, Colonia Chuburná de Hidalgo, Mérida C.P. 97205, Yucatán, Mexico
Interests: remote sensing; ecological data analysis

MSc. Mukti Subedi

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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

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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.

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Keywords

Tropical forests
Functional diversity
Structural diversity
Vegetation structure and biomass
Data fusion
Integration
Monitoring

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Published Papers (12 papers)

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Editorial

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4 pages, 197 KiB

Open AccessEditorial

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

by Carlos Portillo-Quintero, Jose L. Hernández-Stefanoni, Gabriela Reyes-Palomeque and Mukti R. Subedi

Remote Sens. 2022, 14(20), 5068; https://doi.org/10.3390/rs14205068 - 11 Oct 2022

Cited by 1 | Viewed by 1061

Abstract

For more than three decades, the remote sensing scientific community has successfully generated predictive models of tropical forest attributes and ecological processes at the leaf, canopy, patch and landscape scale by linking field-measured data to remotely sensed spectral values, as well as other variables derived from remotely sensed data. The main interest of these applications is to help describe ecological and functional patterns occurring at larger geographic scales with sufficient accuracy and precision and enable scientists to better understand ecological processes, such as the relationship between atmospheric fluxes, plant structural and ecophysiological traits, soil attributes, anthropogenic use, species occurrence and animal movement. However, as the earth’s environment suffers from ever-increasing human use and abuse, detecting spatiotemporal changes in these variables has become a necessary decision-making tool in conservation action and natural resources’ management. Moving from modeling into the study of soil, plants, wildlife and socioecological processes using remotely sensed data requires the extrapolation of single time-step models to its application on a time series of data with the same expected accuracy. The challenges in this matter are not trivial, since changes in soil moisture conditions, cloud contamination, canopy and leaf-level geometry and physiology can affect the strength of the proposed models. In this context, the term ‘Operationalization’ refers to migration from single time-step models to time series but also refers to the design and implementation of user-friendly tools to increase the efficacy of communicating spatiotemporal trends to the users. [...] Full article

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

Research

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21 pages, 10887 KiB

Open AccessArticle

Forest Clearing Dynamics and Its Relation to Remotely Sensed Carbon Density and Plant Species Diversity in the Puuc Biocultural State Reserve, Mexico

by Carlos Portillo-Quintero, Jose Luis Hernandez-Stefanoni and Juan Manuel Dupuy

Remote Sens. 2023, 15(13), 3445; https://doi.org/10.3390/rs15133445 - 7 Jul 2023

Viewed by 2057

Abstract

The Puuc Biocultural State Reserve (PBSR) is a unique model for tropical dry forest conservation in Mexico. Preserving forest biodiversity and carbon within the PBSR depends on maintaining low-impact productive activities coordinated by multiple communal and private landowners. In this study, we used state-of-the-art remote sensing data to investigate past spatial patterns in forest clearing dynamics and their relation to forest carbon density and forest plant species richness and diversity in the context of the forest conservation goals of the PBSR. We used a Landsat-based continuous change detection product for the 2000–2021 period and compared it to carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data. The estimated error-adjusted area of detected annual forest clearings from the year 2000 until the year 2021 was 230,511 ha in total (±19,979 ha). The analysis of annual forest clearing frequency and area suggests that although forest clearing was significantly more intensive outside of the PBSR than within the PBSR during the entire 2000–2021 period, there is no evidence suggesting that the frequency and magnitude of forest clearing changed over the years after the creation of the PBSR in 2011. However, an emergent hotspot analysis shows that high spatiotemporal clustering of forest clearing events (hotspots) during the 2012–2021 period was less common than prior to 2011, and these more recent hotspots have been confined to areas outside the PBSR. After comparing forest clearing events to carbon density and tree species richness models, the results show that landowners outside the PBSR often clear forests with lower carbon density and species diversity than those inside the PBSR. This suggests that, compared to landowners outside the PBSR, landowners within the PBSR might be practicing longer fallow periods allowing forests to attain higher carbon density and tree species richness and hence better soil nutrient recovery after land abandonment. In conclusion, our results show that the PBSR effectively acted as a stabilizing forest management scheme during the 2012–2021 period, minimizing the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests within the PBSR. We recommend continuing efforts to provide alternative optimal field data collection strategies and modeling techniques to spatially predict key tropical forest attributes. Combining these models with continuous change detection datasets will allow for underlying ecological processes to be revealed and the generation of information better adapted to forest governance scales. Full article

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

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18 pages, 2574 KiB

Open AccessArticle

Estimation of Leaf Area Index in a Typical Northern Tropical Secondary Monsoon Rainforest by Different Indirect Methods

by Xiansheng Xie, Yuanzheng Yang, Wuzheng Li, Nanyan Liao, Weihu Pan and Hongxin Su

Remote Sens. 2023, 15(6), 1621; https://doi.org/10.3390/rs15061621 - 17 Mar 2023

Cited by 6 | Viewed by 2151

Abstract

The leaf area index (LAI) is a crucial indicator for quantifying forest productivity and community ecological processes. Satellite remote sensing can achieve large-scale LAI monitoring, but it needs to be calibrated and validated according to the in situ measurements on the ground. In this study, we attempted to use different indirect methods to measure LAI in a tropical secondary forest. These methods included the LAI-2200 plant canopy analyzer (LAI-2200), Digital Hemispherical Photography (DHP), Tracing Radiation and Architecture of Canopies (TRAC), and Terrestrial Laser Scanning (TLS) (using single-station and multi-station measurements, respectively). Additionally, we tried to correct the measured LAI by obtaining indicators of woody components and clumping effects. The results showed that the LAI of this forest was large, with estimated values of 5.27 ± 1.16, 3.69 ± 0.74, 5.86 ± 1.09, 4.93 ± 1.33, and 3.87 ± 0.89 for LAI-2200, DHP, TRAC, TLS multi-station, and TLS single-station, respectively. There was a significant correlation between the different methods. LAI-2200 was significantly correlated with all other methods (p < 0.01), with the strongest correlation with DHP (r = 0.684). TRAC was significantly correlated with TLS single-station (p < 0.01, r = 0.283). TLS multi-station was significantly correlated with TLS single-station (p < 0.05, r = 0.266). With the multi-station measurement method, TLS could maximize the compensation for measurement bias due to the shadowing effects. In general, the clumping index of this forest was 0.94 ± 0.05, the woody-to-total area ratio was 3.23 ± 2.22%, and the total correction coefficient was 1.03 ± 0.07. After correction, the LAI estimates for all methods were slightly higher than before, but there was no significant difference among them. Based on the performance assessment of existing ground-based methods, we hope to enhance the inter-calibration between methods to improve their estimation accuracy under complex forest conditions and advance the validation of remote sensing inversion of the LAI. Moreover, this study also provided a practical reference to promote the application of LiDAR technology in tropical forests. Full article

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

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15 pages, 13946 KiB

Open AccessArticle

Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Data and Auxiliary Information

by Agustín Escobar-López, Miguel Ángel Castillo-Santiago, José Luis Hernández-Stefanoni, Jean François Mas and Jorge Omar López-Martínez

Remote Sens. 2022, 14(16), 3847; https://doi.org/10.3390/rs14163847 - 9 Aug 2022

Cited by 6 | Viewed by 3036

Abstract

Coffee is one of the most important agricultural commodities of Mexico. Mapping coffee land cover is still a challenge because it is grown mainly on small areas in agroforestry systems (AFS), which are located in hard-to-access mountainous regions. The objective of this research was to map coffee AFS types in a mountainous region using the changing spectral response patterns over the dry season as well as supplementary data. We employed Sentinel-1, Sentinel-2 and ALOS-Palsar images, a digital elevation model, soil moisture layers, and 150 field plots. First, we defined three coffee AFS types based on their structural and spectral characteristics. Then, we performed a recursive feature elimination analysis to identify the most relevant predictor variables for each land use/cover class in the region. Next, we constructed a predictor variable dataset for each AFS type and one for the remaining land use/cover classes. Afterward, four maps were generated using a random forest (RF) classifier. Finally, we combined the four maps into a unique land-cover map through a maximum likelihood algorithm. Using a validation sample of 932 sites derived from Planet images (4.5 m pixel size), we estimated a 95% map overall accuracy. Two AFS types were classified as having low error; the third, with the highest tree density, had the lowest accuracy. The results obtained show that the infrared and near-infrared bands from the Sentinel-2 scenes are particularly useful for coffee AFS discrimination. However, supplementary data are required to improve the performance of the classifier. Our findings also highlight the importance of the multi-temporal and multi-dataset approach for identifying complex production systems in areas of high topographic heterogeneity. Full article

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

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20 pages, 5254 KiB

Open AccessArticle

Mapping the Spatial Distribution of Fern Thickets and Vine-Laden Forests in the Landscape of Bornean Logged-Over Tropical Secondary Rainforests

by Ryuichi Takeshige, Masanori Onishi, Ryota Aoyagi, Yoshimi Sawada, Nobuo Imai, Robert Ong and Kanehiro Kitayama

Remote Sens. 2022, 14(14), 3354; https://doi.org/10.3390/rs14143354 - 12 Jul 2022

Cited by 7 | Viewed by 2300

Abstract

Forest degradation has been most frequently defined as an anthropogenic reduction in biomass compared with reference biomass in extant forests. However, so-defined “degraded forests” may widely vary in terms of recoverability. A prolonged loss of recoverability, commonly described as a loss of resilience, poses a true threat to global environments. In Bornean logged-over forests, dense thickets of ferns and vines have been observed to cause arrested secondary succession, and their area may indicate the extent of slow biomass recovery. Therefore, we aimed to discriminate the fern thickets and vine-laden forests from those logged-over forests without dense ferns and vines, as well as mapping their distributions, with the aid of Landsat-8 satellite imagery and machine learning modeling. During the process, we tested whether the gray-level co-occurrence matrix (GLCM) textures of Landsat data and Sentinel-1 C-band SAR data were helpful for this classification. Our study sites were Deramakot and Tangkulap Forest Reserves—commercial production forests in Sabah, Malaysian Borneo. First, we flew drones and obtained aerial images that were used as ground truth for the supervised classification. Subsequently, a machine-learning model with a gradient-boosting decision tree was iteratively tested in order to derive the best model for the classification of the vegetation. Finally, the best model was extrapolated to the entire forest reserve and used to map three classes of vegetation (fern thickets, vine-laden forests, and logged-over forests without ferns and vines) and two non-vegetation classes (bare soil and open water). The overall classification accuracy of the best model was 86.6%; however, by combining the fern and vine classes into the same category, the accuracy was improved to 91.5%. The GLCM texture variables were especially effective at separating fern/vine vegetation from the non-degraded forest, but the SAR data showed a limited effect. Our final vegetation map showed that 30.7% of the reserves were occupied by ferns or vines, which may lead to arrested succession. Considering that our study site was once certified as a well-managed forest, the area of degraded forests with a high risk of loss of resilience is expected to be much broader in other Bornean production forests. Full article

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

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19 pages, 4002 KiB

Open AccessArticle

Tropical Species Classification with Structural Traits Using Handheld Laser Scanning Data

by Meilian Wang, Man Sing Wong and Sawaid Abbas

Remote Sens. 2022, 14(8), 1948; https://doi.org/10.3390/rs14081948 - 18 Apr 2022

Cited by 8 | Viewed by 2393

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 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

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

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17 pages, 23050 KiB

Open AccessArticle

Fusing Sentinel-1 and -2 to Model GEDI-Derived Vegetation Structure Characteristics in GEE for the Paraguayan Chaco

by Patrick Kacic, Andreas Hirner and Emmanuel Da Ponte

Remote Sens. 2021, 13(24), 5105; https://doi.org/10.3390/rs13245105 - 15 Dec 2021

Cited by 25 | Viewed by 8036

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 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

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

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24 pages, 5219 KiB

Open AccessArticle

Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery

by Jonathan V. Solórzano, Jean François Mas, Yan Gao and José Alberto Gallardo-Cruz

Remote Sens. 2021, 13(18), 3600; https://doi.org/10.3390/rs13183600 - 9 Sep 2021

Cited by 71 | Viewed by 9849

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 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

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

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19 pages, 35160 KiB

Open AccessArticle

Carbon Stocks, Species Diversity and Their Spatial Relationships in the Yucatán Peninsula, Mexico

by José Luis Hernández-Stefanoni, Miguel Ángel Castillo-Santiago, Juan Andres-Mauricio, Carlos A. Portillo-Quintero, Fernando Tun-Dzul and Juan Manuel Dupuy

Remote Sens. 2021, 13(16), 3179; https://doi.org/10.3390/rs13163179 - 11 Aug 2021

Cited by 6 | Viewed by 4187

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 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

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

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17 pages, 3500 KiB

Open AccessArticle

How BFAST Trend and Seasonal Model Components Affect Disturbance Detection in Tropical Dry Forest and Temperate Forest

by Yan Gao, Jonathan V. Solórzano, Alexander Quevedo and Jaime Octavio Loya-Carrillo

Remote Sens. 2021, 13(11), 2033; https://doi.org/10.3390/rs13112033 - 21 May 2021

Cited by 16 | Viewed by 3737

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 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

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

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25 pages, 7330 KiB

Open AccessArticle

A Low-Cost and Robust Landsat-Based Approach to Study Forest Degradation and Carbon Emissions from Selective Logging in the Venezuelan Amazon

by Carlos Pacheco-Angulo, Wenseslao Plata-Rocha, Julio Serrano, Emilio Vilanova, Sergio Monjardin-Armenta, Alvaro González and Cristopher Camargo

Remote Sens. 2021, 13(8), 1435; https://doi.org/10.3390/rs13081435 - 8 Apr 2021

Cited by 6 | Viewed by 3805

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 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

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

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9 pages, 633 KiB

Open AccessEditor’s ChoiceCommunication

The Road to Operationalization of Effective Tropical Forest Monitoring Systems

by Carlos Portillo-Quintero, Jose L. Hernández-Stefanoni, Gabriela Reyes-Palomeque and Mukti R. Subedi

Remote Sens. 2021, 13(7), 1370; https://doi.org/10.3390/rs13071370 - 2 Apr 2021

Cited by 10 | Viewed by 2862

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 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

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

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