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Remote Sensing for Biodiversity Mapping and Monitoring

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 58850

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Special Issue Editors

Dr. David Sheeren

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Guest Editor

DYNAFOR Lab., University of Toulouse, INRA, F-31326 Castanet Tolosan, France
Interests: remote sensing of biodiversity; machine learning for earth observation; time series; hyperspectral imagery; forest ecosystems; landscape ecology
Special Issues, Collections and Topics in MDPI journals

Dr. Jean-Baptiste Féret

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Co-Guest Editor

TETIS Lab., IRSTEA, F-3400 Montpellier, France
Interests: remote sensing of vegetation; biodiversity mapping; vegetation biophysical properties; imaging spectroscopy; tropical ecosystems; physical modeling; leaf traits
Special Issues, Collections and Topics in MDPI journals

Dr. Laurence Hubert-Moy

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Co-Guest Editor

LETG-Rennes, University of Rennes 2, F-35043 Rennes, France
Interests: remote sensing of agricultural landscapes; land use and cover changes; remote sensing of grasslands and wetlands; habitat mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Sophie Fabre

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Co-Guest Editor

ONERA (The French Aerospace Lab.), DOTA, F-31000 Toulouse, France
Interests: hyperspectral imagery; multitemporal change detection; species mapping; vegetation health; anthropogenic impact assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biodiversity is facing dramatic erosion and increasing pressure caused by anthropogenic activities intensifying in magnitude and extent. This leads to strong transformations impacting ecosystems globally, including changes in taxonomic diversity, as well as structure and functions at plant and ecosystem scales. These transformations need to be observed, assessed, and reported with dedicated monitoring programs in order to build efficient actions to mitigate or reverse them. Such monitoring programs need to rely on remote sensing (RS) data, as they potentially provide spatially explicit information from the Earth’s surface at regional to global scale, with regular revisit time, and collect information particularly relevant for the monitoring of vegetated surfaces. Such information is crucial to understanding how biodiversity responds to global environmental changes and directs human activity.

This Special Issue aims to publish original research that specifically addresses various aspects of biodiversity mapping and monitoring over space and time using remote sensing from local to global scales. We invite a wide range of contributions from methodological to applied and multidisciplinary research about the following (non-exclusive) topics:

Taxonomic, structural, and functional diversity mapping from RS data;
Species distribution modeling based on RS data;
Retrieving biophysical and biochemical variables from RS data and radiative transfer models;
Assessing and predicting ecosystem services from RS data;
Ecosystems health monitoring from RS data;
Reconstructing ecosystem trajectories over time from RS data;
Advanced machine learning techniques (deep learning, transfer learning, active learning) for biodiversity mapping based on RS data;
Fusion of multimodal images (optical/thermal/radar/lidar) to improve biodiversity mapping and monitoring.

Reviews covering one or more topics are welcome.
We encourage the authors to make their sample data and computational tools publicly available through online resources to ensure the reproducibility and transparency of all the experiments.

Dr. David Sheeren
Dr. Jean-Baptiste Féret
Dr. Laurence Hubert-Moy
Dr. Sophie Fabre
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

Essential biodiversity variables
Biodiversity mapping
Species traits
Species diversity
Ecosystem functioning
Conservation
Earth observation
Diversity indices from space
Spectroscopy
Spatiotemporal change

Published Papers (11 papers)

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23 pages, 6177 KiB

Open AccessEditor’s ChoiceArticle

Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve

by Bogdan Zagajewski, Marcin Kluczek, Edwin Raczko, Ajda Njegovec, Anca Dabija and Marlena Kycko

Remote Sens. 2021, 13(13), 2581; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132581 - 01 Jul 2021

Cited by 34 | Viewed by 5462

Abstract

Mountain forests are exposed to extreme conditions (e.g., strong winds and intense solar radiation) and various types of damage by insects such as bark beetles, which makes them very sensitive to climatic changes. Therefore, continuous monitoring is crucial, and remote-sensing techniques allow the monitoring of transboundary areas where a common policy is needed to protect and monitor the environment. In this study, we used Sentinel-2 and Landsat 8 open data to assess the forest stands classification of the UNESCO Krkonoše/Karkonosze Transboundary Biosphere Reserve, which is undergoing dynamic changes in recovering woodland vegetation due to an ecological disaster that led to damage and death of a large portion of the forests. Currently, in this protected area, dry big trunks and branches coexist with naturally occurring young forests. This heterogeneity generates mixes, which hinders the automation of classification. Thus, we used three machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—to classify dominant tree species (birch, beech, larch and spruce). The best results were obtained for the SVM RBF classifier, which offered an average median F1-score that oscillated around 67.2–91.5% depending on the species. The obtained maps, which were based on multispectral satellite images, were also compared with classifications made for the same area on the basis of hyperspectral APEX imagery (288 spectral bands with three-meter resolution), indicating high convergence in the recognition of woody species. Full article

(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)

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31 pages, 14106 KiB

Open AccessArticle

Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling

by Dav M. Ebengo, Florian de Boissieu, Grégoire Vincent, Christiane Weber and Jean-Baptiste Féret

Remote Sens. 2021, 13(11), 2120; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112120 - 28 May 2021

Cited by 6 | Viewed by 3302

Abstract

Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART (Discrete Anisotropic Radiative Transfer) to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in a complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Therefore, we recommend future research on forest biodiversity using physical modeling of remote-sensing data to account for LOP variability within crowns and species. Our simulation framework could contribute to better understanding of performances of species discrimination and the relationship between spectral variations and taxonomic and functional dimensions of biodiversity. This work contributes to the improved integration of physical modeling tools for applications, focusing on remotely sensed monitoring of biodiversity in complex ecosystems, for current sensors, and for the preparation of future multispectral and hyperspectral satellite missions. Full article

(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)

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29 pages, 9709 KiB

Open AccessArticle

A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

by Jehyeok Rew, Yongjang Cho and Eenjun Hwang

Remote Sens. 2021, 13(8), 1495; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081495 - 13 Apr 2021

Cited by 15 | Viewed by 3749

Abstract

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models. Full article

(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)

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22 pages, 10237 KiB

Open AccessArticle

The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear

by Pablo Cisneros-Araujo, Teresa Goicolea, María Cruz Mateo-Sánchez, Juan Ignacio García-Viñás, Miguel Marchamalo, Audrey Mercier and Aitor Gastón

Remote Sens. 2021, 13(6), 1138; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061138 - 17 Mar 2021

Cited by 14 | Viewed by 3381

Abstract

Ecological modeling requires sufficient spatial resolution and a careful selection of environmental variables to achieve good predictive performance. Although national and international administrations offer fine-scale environmental data, they usually have limited spatial coverage (country or continent). Alternatively, optical and radar satellite imagery is available with high resolutions, global coverage and frequent revisit intervals. Here, we compared the performance of ecological models trained with free satellite data with models fitted using regionally restricted spatial datasets. We developed brown bear habitat suitability and connectivity models from three datasets with different spatial coverage and accessibility. These datasets comprised (1) a Sentinel-1 and 2 land cover map (global coverage); (2) pan-European vegetation and land cover layers (continental coverage); and (3) LiDAR data and the Forest Map of Spain (national coverage). Results show that Sentinel imagery and pan-European datasets are powerful sources to estimate vegetation variables for habitat and connectivity modeling. However, Sentinel data could be limited for understanding precise habitat–species associations if the derived discrete variables do not distinguish a wide range of vegetation types. Therefore, more effort should be taken to improving the thematic resolution of satellite-derived vegetation variables. Our findings support the application of ecological modeling worldwide and can help select spatial datasets according to their coverage and resolution for habitat suitability and connectivity modeling. Full article

(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)

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24 pages, 16818 KiB

Open AccessArticle

Multi-Sensor Approach to Improve Bathymetric Lidar Mapping of Semi-Arid Groundwater-Dependent Streams: Devils River, Texas

by Kutalmis Saylam, Aaron R. Averett, Lucie Costard, Brad D. Wolaver and Sarah Robertson

Remote Sens. 2020, 12(15), 2491; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152491 - 03 Aug 2020

Cited by 6 | Viewed by 4907

Abstract

Remote sensing technology enables detecting, acquiring, and recording certain information about objects and locations from distances relative to their geographic locations. Airborne Lidar bathymetry (ALB) is an active, non-imaging, remote sensing technology for measuring the depths of shallow and relatively transparent water bodies using light beams from an airborne platform. In this study, we acquired Lidar datasets using near-infrared and visible (green) wavelength with the Leica Airborne Hydrography AB Chiroptera-I system over the Devils River basin of southwestern Texas. Devils River is a highly groundwater-dependent stream that flows 150 km from source springs to Lake Amistad on the lower Rio Grande. To improve spatially distributed stream bathymetry in aquatic habitats of species of state and federal conservation interest, we conducted supplementary water-depth observations using other remote sensing technologies integrated with the airborne Lidar datasets. Ground penetrating radar (GPR) mapped the river bottom where vegetation impeded other active sensors in attaining depth measurements. We confirmed the accuracy of bathymetric Lidar datasets with a differential global positioning system (GPS) and compared the findings to sonar and GPR measurements. The study revealed that seamless bathymetric and geomorphic mapping of karst environments in complex settings (e.g., aquatic vegetation, entrained air bubbles, riparian zone obstructions) require the integration of a variety of terrestrial and remotely operated survey methods. We apply this approach to Devils River of Texas. However, the methods are applicable to similar streams globally. Full article

(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)

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20 pages, 2125 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Mapping Floristic Patterns of Trees in Peruvian Amazonia Using Remote Sensing and Machine Learning

by Pablo Pérez Chaves, Gabriela Zuquim, Kalle Ruokolainen, Jasper Van doninck, Risto Kalliola, Elvira Gómez Rivero and Hanna Tuomisto

Remote Sens. 2020, 12(9), 1523; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091523 - 10 May 2020

Cited by 7 | Viewed by 8290

Abstract

Recognition of the spatial variation in tree species composition is a necessary precondition for wise management and conservation of forests. In the Peruvian Amazonia, this goal is not yet achieved mostly because adequate species inventory data has been lacking. The recently started Peruvian national forest inventory (INFFS) is expected to change the situation. Here, we analyzed genus-level variation, summarized through non-metric multidimensional scaling (NMDS), in a set of 157 INFFS inventory plots in lowland to low mountain rain forests (<2000 m above sea level) using Landsat satellite imagery and climatic, edaphic, and elevation data as predictor variables. Genus-level floristic patterns have earlier been found to be indicative of species-level patterns. In correlation tests, the floristic variation of tree genera was most strongly related to Landsat variables and secondly to climatic variables. We used random forest regression, under varying criteria of feature selection and cross-validation, to predict the floristic composition on the basis of Landsat and environmental data. The best model explained >60% of the variation along NMDS axes 1 and 2 and 40% of the variation along NMDS axis 3. We used this model to predict the three NMDS dimensions at a 450-m resolution over all of the Peruvian Amazonia and classified the pixels into 10 floristic classes using k-means classification. An indicator analysis identified statistically significant indicator genera for 8 out of the 10 classes. The results are congruent with earlier studies, suggesting that the approach is robust and can be applied to other tropical regions, which is useful for reducing research gaps and for identifying suitable areas for conservation. Full article

(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)

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16 pages, 3758 KiB

Open AccessArticle

A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

by Giorgos Mallinis, Irene Chrysafis, Georgios Korakis, Eleanna Pana and Apostolos P. Kyriazopoulos

Remote Sens. 2020, 12(7), 1210; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071210 - 09 Apr 2020

Cited by 21 | Viewed by 4849

Abstract

Earth observation data can provide important information for tree species diversity mapping and monitoring. The relatively recent advances in remote sensing data characteristics and processing systems elevate the potential of satellite imagery for providing accurate, timely, consistent, and robust spatially explicit estimates of tree species diversity over forest ecosystems. This study was conducted in Northern Pindos National Park, the largest terrestrial park in Greece and aimed to assess the potential of four satellite sensors with different instrumental characteristics, for the estimation of tree diversity. Through field measurements, we originally quantified two diversity indices, namely the Shannon diversity index (H’) and Simpson’s diversity (D1). Random forest regression models were developed for associating remotely sensed spectral signal with tree species diversity within the area. The models generated from the use of the WorldView-2 image were the most accurate with a coefficient of determination of up to 0.44 for H’ and 0.37 for D1. The Sentinel-2 -based models of tree species diversity performed slightly worse, but were better than the Landsat-8 and RapidEye models. The coefficient of variation quantifying internal variability of spectral values within each plot provided little or no usage for improving the modelling accuracy. Our results suggest that very-high-spatial-resolution imagery provides the most important information for the assessment of tree species diversity in heterogeneous Mediterranean ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)

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18 pages, 3494 KiB

Open AccessArticle

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

by Francesco Valerio, Eduardo Ferreira, Sérgio Godinho, Ricardo Pita, António Mira, Nelson Fernandes and Sara M. Santos

Remote Sens. 2020, 12(3), 562; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030562 - 08 Feb 2020

Cited by 25 | Viewed by 7151

Abstract

Accurate mapping is a main challenge for endangered small-sized terrestrial species. Freely available spatio-temporal data at high resolution from multispectral satellite offer excellent opportunities for improving predictive distribution models of such species based on fine-scale habitat features, thus making it easier to achieve comprehensive biodiversity conservation goals. However, there are still few examples showing the utility of remote-sensing-based products in mapping microhabitat suitability for small species of conservation concern. Here, we address this issue using Sentinel-2 sensor-derived habitat variables, used in combination with more commonly used explanatory variables (e.g., topography), to predict the distribution of the endangered Cabrera vole (Microtus cabrerae) in agrosilvopastorial systems. Based on vole surveys conducted in two different seasons over a ~176,000 ha landscape in Southern Portugal, we assessed the significance of each predictor in explaining Cabrera vole occurrence using the Boruta algorithm, a novel Random forest variant for dealing with high dimensionality of explanatory variables. Overall, results showed a strong contribution of Sentinel-2-derived variables for predicting microhabitat suitability of Cabrera voles. In particular, we found that photosynthetic activity (NDI45), specific spectral signal (SWIR1), and landscape heterogeneity (Rao’s Q) were good proxies of Cabrera voles’ microhabitat, mostly during temporally greener and wetter conditions. In addition to remote-sensing-based variables, the presence of road verges was also an important driver of voles’ distribution, highlighting their potential role as refuges and/or corridors. Overall, our study supports the use of remote-sensing data to predict microhabitat suitability for endangered small-sized species in marginal areas that potentially hold most of the biodiversity found in human-dominated landscapes. We believe our approach can be widely applied to other species, for which detailed habitat mapping over large spatial extents is difficult to obtain using traditional descriptors. This would certainly contribute to improving conservation planning, thereby contributing to global conservation efforts in landscapes that are managed for multiple purposes. Full article

(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)

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24 pages, 4282 KiB

Open AccessArticle

Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data

by Andreas Dittrich, Stephanie Roilo, Ruth Sonnenschein, Cristiana Cerrato, Michael Ewald, Ramona Viterbi and Anna F. Cord

Remote Sens. 2020, 12(1), 80; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010080 - 24 Dec 2019

Cited by 9 | Viewed by 5159

Abstract

Mountain ecosystems are biodiversity hotspots that are increasingly threatened by climate and land use/land cover changes. Long-term biodiversity monitoring programs provide unique insights into resulting adverse impacts on plant and animal species distribution. Species distribution models (SDMs) in combination with satellite remote sensing (SRS) data offer the opportunity to analyze shifts of species distributions in response to these changes in a spatially explicit way. Here, we predicted the presence probability of three different rove beetles in a mountainous protected area (Gran Paradiso National Park, GPNP) using environmental variables derived from Landsat and Aster Global Digital Elevation Model data and an ensemble modelling approach based on five different model algorithms (maximum entropy, random forest, generalized boosting models, generalized additive models, and generalized linear models). The objectives of the study were (1) to evaluate the potential of SRS data for predicting the presence of species dependent on local-scale environmental parameters at two different time periods, (2) to analyze shifts in species distributions between the years, and (3) to identify the most important species-specific SRS predictor variables. All ensemble models showed area under curve (AUC) of the receiver operating characteristics values above 0.7 and true skills statistics (TSS) values above 0.4, highlighting the great potential of SRS data. While only a small proportion of the total area was predicted as highly suitable for each species, our results suggest an increase of suitable habitat over time for the species Platydracus stercorarius and Ocypus ophthalmicus, and an opposite trend for Dinothenarus fossor. Vegetation cover was the most important predictor variable in the majority of the SDMs across all three study species. To better account for intra- and inter-annual variability of population dynamics as well as environmental conditions, a continuation of the monitoring program in GPNP as well as the employment of SRS with higher spatial and temporal resolution is recommended. Full article

(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)

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Other

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7 pages, 580 KiB

Open AccessLetter

Global Airborne Laser Scanning Data Providers Database (GlobALS)—A New Tool for Monitoring Ecosystems and Biodiversity

by Krzysztof Stereńczak, Gaia Vaglio Laurin, Gherardo Chirici, David A. Coomes, Michele Dalponte, Hooman Latifi and Nicola Puletti

Remote Sens. 2020, 12(11), 1877; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111877 - 09 Jun 2020

Cited by 17 | Viewed by 4517

Abstract

Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS. Full article

(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)

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13 pages, 1455 KiB

Open AccessTechnical Note

Monitoring Plant Functional Diversity Using the Reflectance and Echo from Space

by Xuanlong Ma, Mirco Migliavacca, Christian Wirth, Friedrich J. Bohn, Andreas Huth, Ronny Richter and Miguel D. Mahecha

Remote Sens. 2020, 12(8), 1248; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081248 - 15 Apr 2020

Cited by 20 | Viewed by 5735

Abstract

Plant functional diversity (FD) is an important component of biodiversity. Evidence shows that FD strongly determines ecosystem functioning and stability and also regulates various ecosystem services that underpin human well-being. Given the importance of FD, it is critical to monitor its variations in an explicit manner across space and time, a highly demanding task that cannot be resolved solely by field data. Today, high hopes are placed on satellite-based observations to complement field plot data. The promise is that multiscale monitoring of plant FD, ecosystem functioning, and their services is now possible at global scales in near real-time. However, non-trivial scale challenges remain to be overcome before plant ecology can capitalize on the latest advances in Earth Observation (EO). Here, we articulate the existing scale challenges in linking field and satellite data and further elaborated in detail how to address these challenges via the latest innovations in optical and radar sensor technologies and image analysis algorithms. Addressing these challenges not only requires novel remote sensing theories and algorithms but also urges more effective communication between remote sensing scientists and field ecologists to foster mutual understanding of the existing challenges. Only through a collaborative approach can we achieve the global plant functional diversity monitoring goal. Full article

(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)

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