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Remote Sensing of Tropical Forest Biodiversity

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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 (31 July 2018) | Viewed by 80255

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Prof. Dr. Gregory P. Asner

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Department of Global Ecology, Carnegie Institution for Science, 260 Panama St, Stanford, CA 94305, USA
Interests: tropical forests; coral reefs; biodiversity; biogeochemistry; functional diversity; plant canopies; remote sensing; imaging spectroscopy; LiDAR; spectranomics; Carnegie airborne observatory
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Dear Colleagues,

Tropical forests contain the richest assemblages of plant species on Earth, which, in turn, harbor a vast array of animal species. The biological diversity of tropical forests is not only defined by the number and composition of plant and animal species present, but also the functional and structural trait variation found among coexisting taxa. Despite this knowledge, spatial and temporal variation in tropical forest biodiversity remains poorly understood, owing to challenges of field-based access over representative areas. In the past decade, remote sensing has expanded its footprint across most tropical forest ecosystems, expanding our understanding of how biodiversity is arranged and changing over time.

This Special Issues calls for the most innovative work on remote sensing of tropical forest biodiversity, as a means to bring the latest research together to communicate the emerging science and technology to a global tropical forest biodiversity community. For this special issue, I seek papers that focus on improving our ability to measure, map and monitor key aspects of tropical forest taxonomic, functional and structural diversity, as well as new insights gained from doing so. The papers are not limited to remote sensing of plants and their functional and structural traits, but also seeks to include papers that incorporate all aspects of tropical forest biodiversity including animals. The Special Issue will be widely promoted and circulated, thereby increasing general readership of the group of papers presented in an open access journal.

Prof. Dr. Gregory P. Asner
Guest Editor

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

biodiversity
biological diversity
functional diversity
structural diversity
tropical forests
imaging spectroscopy
lidar
multispectral remote sensing
synthetic aperture radar
drones
unmanned aircraft systems
UAV
camera traps
forest ecology
species mapping

Published Papers (9 papers)

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Research

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28 pages, 25666 KiB

Open AccessFeature PaperArticle

Tropical Deforestation and Recolonization by Exotic and Native Trees: Spatial Patterns of Tropical Forest Biomass, Functional Groups, and Species Counts and Links to Stand Age, Geoclimate, and Sustainability Goals

by Eileen H. Helmer, Thomas S. Ruzycki, Barry T. Wilson, Kirk R. Sherrill, Michael A. Lefsky, Humfredo Marcano-Vega, Thomas J. Brandeis, Heather E. Erickson and Bonnie Ruefenacht

Remote Sens. 2018, 10(11), 1724; https://doi.org/10.3390/rs10111724 - 01 Nov 2018

Cited by 10 | Viewed by 7054

Abstract

We mapped native, endemic, and introduced (i.e., exotic) tree species counts, relative basal areas of functional groups, species basal areas, and forest biomass from forest inventory data, satellite imagery, and environmental data for Puerto Rico and the Virgin Islands. Imagery included time series of Landsat composites and Moderate Resolution Imaging Spectroradiometer (MODIS)-based phenology. Environmental data included climate, land-cover, geology, topography, and road distances. Large-scale deforestation and subsequent forest regrowth are clear in the resulting maps decades after large-scale transition back to forest. Stand age, climate, geology, topography, road/urban locations, and protection are clearly influential. Unprotected forests on more accessible or arable lands are younger and have more introduced species and deciduous and nitrogen-fixing basal areas, fewer endemic species, and less biomass. Exotic species are widespread—except in the oldest, most remote forests on the least arable lands, where shade-tolerant exotics may persist. Although the maps have large uncertainty, their patterns of biomass, tree species diversity, and functional traits suggest that for a given geoclimate, forest age is a core proxy for forest biomass, species counts, nitrogen-fixing status, and leaf longevity. Geoclimate indicates hard-leaved species commonness. Until global wall-to-wall remote sensing data from specialized sensors are available, maps from multispectral image time series and other predictor data should help with running ecosystem models and as sustainable development indicators. Forest attribute models trained with a tree species ordination and mapped with nearest neighbor substitution (Phenological Gradient Nearest Neighbor method, PGNN) yielded larger correlation coefficients for observed vs. mapped tree species basal areas than Cubist regression tree models trained separately on each species. In contrast, Cubist regression tree models of forest structural and functional attributes yielded larger such correlation coefficients than the ordination-trained PGNN models. Full article

(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)

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25 pages, 12218 KiB

Open AccessArticle

Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery

by Eleanor R. Thomson, Yadvinder Malhi, Harm Bartholomeus, Imma Oliveras, Agne Gvozdevaite, Theresa Peprah, Juha Suomalainen, John Quansah, John Seidu, Christian Adonteng, Andrew J. Abraham, Martin Herold, Stephen Adu-Bredu and Christopher E. Doughty

Remote Sens. 2018, 10(10), 1532; https://doi.org/10.3390/rs10101532 - 24 Sep 2018

Cited by 25 | Viewed by 13055

Abstract

The leaf economic spectrum (LES) describes a set of universal trade-offs between leaf mass per area (LMA), leaf nitrogen (N), leaf phosphorus (P) and leaf photosynthesis that influence patterns of primary productivity and nutrient cycling. Many questions regarding vegetation-climate feedbacks can be addressed with a better understanding of LES traits and their controls. Remote sensing offers enormous potential for generating large-scale LES trait data. Yet so far, canopy studies have been limited to imaging spectrometers onboard aircraft, which are rare, expensive to deploy and lack fine-scale resolution. In this study, we measured VNIR (visible-near infrared (400–1050 nm)) reflectance of individual sun and shade leaves in 7 one-ha tropical forest plots located along a 1200–2000 mm precipitation gradient in West Africa. We collected hyperspectral imaging data from 3 of the 7 plots, using an octocopter-based unmanned aerial vehicle (UAV), mounted with a hyperspectral mapping system (450–950 nm, 9 nm FWHM). Using partial least squares regression (PLSR), we found that the spectra of individual sun leaves demonstrated significant (p < 0.01) correlations with LMA and leaf chemical traits: r² = 0.42 (LMA), r² = 0.43 (N), r² = 0.21 (P), r² = 0.20 (leaf potassium (K)), r² = 0.23 (leaf calcium (Ca)) and r² = 0.14 (leaf magnesium (Mg)). Shade leaf spectra displayed stronger relationships with all leaf traits. At the airborne level, four of the six leaf traits demonstrated weak (p < 0.10) correlations with the UAV-collected spectra of 58 tree crowns: r² = 0.25 (LMA), r² = 0.22 (N), r² = 0.22 (P), and r² = 0.25 (Ca). From the airborne imaging data, we used LMA, N and P values to map the LES across the three plots, revealing precipitation and substrate as co-dominant drivers of trait distributions and relationships. Positive N-P correlations and LMA-P anticorrelations followed typical LES theory, but we found no classic trade-offs between LMA and N. Overall, this study demonstrates the application of UAVs to generating LES information and advancing the study and monitoring tropical forest functional diversity. Full article

(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)

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19 pages, 4014 KiB

Open AccessArticle

Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua

by Lisa C. Kelley, Lincoln Pitcher and Chris Bacon

Remote Sens. 2018, 10(6), 952; https://doi.org/10.3390/rs10060952 - 14 Jun 2018

Cited by 46 | Viewed by 14869

Abstract

Shade-grown coffee (shade coffee) is an important component of the forested tropics, and is essential to the conservation of forest-dependent biodiversity. Despite its importance, shade coffee is challenging to map using remotely sensed data given its spectral similarity to forested land. This paper addresses this challenge in three districts of northern Nicaragua, here leveraging cloud-based computing techniques within Google Earth Engine (GEE) to integrate multi-seasonal Landsat 8 satellite imagery (30 m), and physiographic variables (temperature, topography, and precipitation). Applying a random forest machine learning algorithm using reference data from two field surveys produced a 90.5% accuracy across ten classes of land cover, with an 82.1% and 80.0% user’s and producer’s accuracy respectively for shade-grown coffee. Comparing classification accuracies obtained from five datasets exploring different combinations of non-seasonal and seasonal spectral data as well as physiographic data also revealed a trend of increasing accuracy when seasonal data were included in the model and a significant improvement (7.8–20.1%) when topographical data were integrated with spectral data. These results are significant in piloting an open-access and user-friendly approach to mapping heterogeneous shade coffee landscapes with high overall accuracy, even in locations with persistent cloud cover. Full article

(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)

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15 pages, 3791 KiB

Open AccessArticle

Predicting Tropical Tree Species Richness from Normalized Difference Vegetation Index Time Series: The Devil Is Perhaps Not in the Detail

by Robin Pouteau, Thomas W. Gillespie and Philippe Birnbaum

Remote Sens. 2018, 10(5), 698; https://doi.org/10.3390/rs10050698 - 03 May 2018

Cited by 15 | Viewed by 4736

Abstract

The normalized difference vegetation index (NDVI) derived from remote sensing is a common explanatory variable inputted in correlative biodiversity models in the form of descriptive statistics summarizing complex time series. Here, we hypothesized that a single meaningful remotely-sensed scene can provide better prediction of species richness than any usual multi-scene statistics. We tested this idea using a 15-year time series of six-day composite MODIS NDVI data combined with field measurements of tree species richness in the tropical biodiversity hotspot of New Caledonia. Although some overall, seasonal, annual and monthly statistics appeared to successfully correlate with tree species richness in New Caledonia, a range of individual scenes were found to provide significantly better predictions of both the overall tree species richness (|r| = 0.68) and the richness of large trees (|r| = 0.91). A preliminary screening of the NDVI-species richness relationship within each time step can therefore be an effective and straightforward way to maximize the accuracy of NDVI-based correlative biodiversity models. Full article

(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)

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19 pages, 15447 KiB

Open AccessFeature PaperArticle

Topography and Three-Dimensional Structure Can Estimate Tree Diversity along a Tropical Elevational Gradient in Costa Rica

by Chelsea Robinson, Sassan Saatchi, David Clark, Johanna Hurtado Astaiza, Anna F. Hubel and Thomas W. Gillespie

Remote Sens. 2018, 10(4), 629; https://doi.org/10.3390/rs10040629 - 18 Apr 2018

Cited by 10 | Viewed by 5174

Abstract

This research seeks to understand how tree species richness and diversity relates to field data (1-ha plots) on forest structure (stems, basal area) and lidar derived data on topography and three-dimensional forest structure along an elevational gradient in Braulio Carrillo National Park, Costa Rica. In 2016 we calculated tree species richness and diversity indices for twenty 1-ha plots located along a gradient ranging from 56 to 2814 m in elevation. Field inventory data were combined with large footprint (20 m) airborne lidar data over plots in 2005, in order to quantify variations in topography and three-dimensional structure across plots and landscapes. A distinct pattern revealing an increase in species’ richness and the Shannon diversity index was observed in correlation with increasing elevation, up to about 600 m; beyond that, at higher elevations, a decrease was observed. Stem density and basal area both peaked at the 2800 m site, with a mini-peak at 600 m, and were both negatively associated with species richness and diversity. Species richness and diversity were negatively correlated with elevation, while the two tallest relative height metrics (rh100, rh75) derived from lidar were both significantly positively correlated with species richness and diversity. The best lidar-derived topographical and three-dimensional forest structural models showed a strong relationship with the Shannon diversity index (r² = 0.941, p < 0.01), with ten predictors; conversely, the best species richness model was weaker (r² = 0.599, p < 0.01), with two predictors. We realize that our high r² has to be interpreted with caution due to possible overfitting, since we had so few ground plots in which to develop the relationship with the numerous topographical and structural explanatory variables. However, this is still an interesting analysis, even with the issue of overfitting. To reduce issues with overfitting we used ridge regression, which acted as a regularization method, shrinking coefficients in order to decrease their variability and multicollinearity. This study is unique because it uses paired 1-ha plot and airborne lidar data over a tropical elevation gradient, and suggests potential for mapping species richness and diversity across elevational gradients in tropical montane ecosystems using topography and relative height metrics from spaceborne lidar with greater spatial coverage (e.g., GEDI). Full article

(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)

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17 pages, 7863 KiB

Open AccessFeature PaperArticle

An Approach for High-Resolution Mapping of Hawaiian Metrosideros Forest Mortality Using Laser-Guided Imaging Spectroscopy

by Nicholas R. Vaughn, Gregory P. Asner, Philip G. Brodrick, Roberta E. Martin, Joseph W. Heckler, David E. Knapp and R. Flint Hughes

Remote Sens. 2018, 10(4), 502; https://doi.org/10.3390/rs10040502 - 22 Mar 2018

Cited by 30 | Viewed by 6506

Abstract

Rapid ‘Ōhi‘a Death (ROD) is a disease aggressively killing large numbers of Metrosideros polymorpha (‘ōhi‘a), a native keystone tree species on Hawaii Island. This loss threatens to deeply alter the biological make-up of this unique island ecosystem. Spatially explicit information about the present and past advancement of the disease is essential for its containment; yet, currently such data are severely lacking. To this end, we used the Carnegie Airborne Observatory to collect Laser-Guided Imaging Spectroscopy data and high-resolution digital imagery across >500,000 ha of Hawaii Island in June–July 2017. We then developed a method to map individual tree crowns matching the symptoms of both active (brown; desiccated ‘ōhi‘a crowns) and past (leafless tree crowns) ROD infection using an ensemble of two distinct machine learning approaches. Employing a very conservative classification scheme for minimizing false-positives, model sensitivity rates were 86.9 and 82.5, and precision rates were 97.4 and 95.3 for browning and leafless crowns, respectively. Across the island of Hawaii, we found 43,134 individual crowns suspected of exhibiting the active (browning) stage of ROD infection. Hotspots of potential ROD infection are apparent in the maps. The peninsula on the eastern side of Hawaii known as the Puna district, where the ROD outbreak likely originated, contained a particularly high density of brown crown detections. In comparison, leafless crown detections were much more numerous (547,666 detected leafless crowns in total) and more dispersed across the island. Mapped hotspots of likely ROD incidence across the island will enable scientists, administrators, and land managers to better understand both where and how ROD spreads and how to apply limited resources to limiting this spread. Full article

(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)

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14 pages, 6006 KiB

Open AccessFeature PaperArticle

A Spectral Mapping Signature for the Rapid Ohia Death (ROD) Pathogen in Hawaiian Forests

by Gregory P. Asner, Roberta E. Martin, Lisa M. Keith, Wade P. Heller, Marc A. Hughes, Nicholas R. Vaughn, R. Flint Hughes and Christopher Balzotti

Remote Sens. 2018, 10(3), 404; https://doi.org/10.3390/rs10030404 - 06 Mar 2018

Cited by 36 | Viewed by 9483

Abstract

Pathogenic invasions are a major source of change in both agricultural and natural ecosystems. In forests, fungal pathogens can kill habitat-generating plant species such as canopy trees, but methods for remote detection, mapping and monitoring of such outbreaks are poorly developed. Two novel species of the fungal genus Ceratocystis have spread rapidly across humid and mesic forests of Hawaiʻi Island, causing widespread mortality of the keystone endemic canopy tree species, Metrosideros polymorpha (common name: ʻōhiʻa). The process, known as Rapid Ohia Death (ROD), causes browning of canopy leaves in weeks to months following infection by the pathogen. An operational mapping approach is needed to track the spread of the disease. We combined field studies of leaf spectroscopy with laboratory chemical studies and airborne remote sensing to develop a spectral signature for ROD. We found that close to 80% of ROD-infected plants undergo marked decreases in foliar concentrations of chlorophyll, water and non-structural carbohydrates, which collectively result in strong consistent changes in leaf spectral reflectance in the visible (400–700 nm) and shortwave-infrared (1300–2500 nm) wavelength regions. Leaf-level results were replicated at the canopy level using airborne laser-guided imaging spectroscopy, with quantitative spectral separability of normal green-leaf canopies from suspected ROD-infected brown-leaf canopies in the visible and shortwave-infrared spectrum. Our results provide the spectral–chemical basis for detection, mapping and monitoring of the spread of ROD in native Hawaiian forests. Full article

(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)

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1900 KiB

Open AccessArticle

Species Richness (of Insects) Drives the Use of Acoustic Space in the Tropics

by T. Mitchell Aide, Andres Hernández-Serna, Marconi Campos-Cerqueira, Orlando Acevedo-Charry and Jessica L. Deichmann

Remote Sens. 2017, 9(11), 1096; https://doi.org/10.3390/rs9111096 - 27 Oct 2017

Cited by 64 | Viewed by 10991

Abstract

Acoustic ecology, or ecoacoustics, is a growing field that uses sound as a tool to evaluate animal communities. In this manuscript, we evaluate recordings from eight tropical forest sites that vary in species richness, from a relatively low diversity Caribbean forest to a megadiverse Amazonian forest, with the goal of understanding the relationship between acoustic space use (ASU) and species diversity across different taxonomic groups. For each site, we determined the acoustic morphospecies richness and composition of the biophony, and we used a global biodiversity dataset to estimate the regional richness of birds. Here, we demonstrate how detailed information on activity patterns of the acoustic community (<22 kHz) can easily be visualized and ASU determined by aggregating recordings collected over relatively short periods (4–13 days). We show a strong positive relationship between ASU and regional and acoustic morphospecies richness. Premontane forest sites had the highest ASU and the highest species richness, while dry forest and montane sites had lower ASU and lower species richness. Furthermore, we show that insect richness was the best predictor of variation in total ASU, and that insect richness was proportionally greater at high-diversity sites. In addition, insects used a broad range of frequencies, including high frequencies (>8000 Hz), which contributed to greater ASU. This novel approach for analyzing the presence and acoustic activity of multiple taxonomic groups contributes to our understanding of ecological community dynamics and provides a useful tool for monitoring species in the context of restoration ecology, climate change and conservation biology. Full article

(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)

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Review

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18 pages, 2350 KiB

Open AccessReview

Detecting Human Presence and Influence on Neotropical Forests with Remote Sensing

by Maria J. Santos, Mathias Disney and Jérôme Chave

Remote Sens. 2018, 10(10), 1593; https://doi.org/10.3390/rs10101593 - 05 Oct 2018

Cited by 11 | Viewed by 6354

Abstract

The Amazon, and Neotropical forests, are one of the most important global biomes because of their extent and unique biodiversity, as well as their importance to global climate and as a habitat and resource for humans. Unravelling the influence of human presence on these forests is fundamental to our understanding of the biodiversity, ecosystem function, and service-providing potential. Human presence in these tropical rainforests dates back 13,000 years, and the impacts of this presence are hotly debated. Some authors suggest persistent effects of pre-Columbian plant domestication on current Amazonian forest composition. Other authors suggest that post-Columbian influence on forest composition is orders of magnitude higher than that of pre-Columbian times. Evidence from remote sensing has become increasingly useful as a way to help settle these debates. Here we review past, current, and future uses of remote sensing technology to detect human infrastructure in the Amazon and other Neotropical forests over the several historical periods of human presence, from archaeological to post-modern societies. We define human presence in terms of activities that left behind a footprint, such as settlements, earth-mounds, roads, use of timber and fuelwood, agriculture, soil, etc. Lastly, we discuss opportunities and challenges for the use of remote sensing to provide data and information necessary to expand our understanding of the history of human occupation in the Neotropical forests, and how this human occupation might affect biodiversity. There have been many recent applications of remote sensing to the detection of Pre-Columbian human infrastructure, from visual inspection of aerial photographs over deforested sites to uses of LiDAR on airborne and UAV platforms to detect infrastructure and smaller settlements under the canopy. Similar efforts are yet to be conducted for the Post-Columbian period, especially during the colonization and imperialism periods. Finally, our knowledge of human impacts in the modern era (20th and 21st centuries) is not-surprisingly more extensive. Remote sensing is still under-used and extremely useful for this type of application, and new missions might provide solutions that were unavailable before. Yet systematic ground surveys are irreplaceable, and detection accuracies of human presence from the combination of remote sensing and ground surveys need to be improved. It is vital therefore to understand how Neotropical forest biodiversity has developed in the presence of people in the past, the implications of this for predicting future directions of change in the Amazon and elsewhere. Full article

(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)

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