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Singh G, Moncrieff G, Venter Z, Cawse-Nicholson K, Slingsby J, Robinson TB. Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction. Sci Rep 2024; 14:16166. [PMID: 39003341 PMCID: PMC11246475 DOI: 10.1038/s41598-024-65954-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 06/25/2024] [Indexed: 07/15/2024] Open
Abstract
Machine learning is increasingly applied to Earth Observation (EO) data to obtain datasets that contribute towards international accords. However, these datasets contain inherent uncertainty that needs to be quantified reliably to avoid negative consequences. In response to the increased need to report uncertainty, we bring attention to the promise of conformal prediction within the domain of EO. Unlike previous uncertainty quantification methods, conformal prediction offers statistically valid prediction regions while concurrently supporting any machine learning model and data distribution. To support the need for conformal prediction, we reviewed EO datasets and found that only 22.5% of the datasets incorporated a degree of uncertainty information, with unreliable methods prevalent. Current open implementations require moving large amounts of EO data to the algorithms. We introduced Google Earth Engine native modules that bring conformal prediction to the data and compute, facilitating the integration of uncertainty quantification into existing traditional and deep learning modelling workflows. To demonstrate the versatility and scalability of these tools we apply them to valued EO applications spanning local to global extents, regression, and classification tasks. Subsequently, we discuss the opportunities arising from the use of conformal prediction in EO. We anticipate that accessible and easy-to-use tools, such as those provided here, will drive wider adoption of rigorous uncertainty quantification in EO, thereby enhancing the reliability of downstream uses such as operational monitoring and decision-making.
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Affiliation(s)
- Geethen Singh
- Department of Botany and Zoology, Centre for Invasion Biology, Stellenbosch University, Stellenbosch, South Africa.
| | - Glenn Moncrieff
- Global Science, The Nature Conservancy, Cape Town, 7945, South Africa
- Department of Statistical Sciences, Centre for Statistics in Ecology, Environment and Conservation, University of Cape Town, Private Bag X3, Rondebosch, Cape Town, 7701, South Africa
| | - Zander Venter
- Norwegian Institute for Nature Research-NINA, Sognsveien 68, 0855, Oslo, Norway
| | - Kerry Cawse-Nicholson
- Carbon Cycles and Ecosystems, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Jasper Slingsby
- Department of Biological Sciences and Centre for Statistics in Ecology, Environment and Conservation, University of Cape Town, Private Bag X3, Rondebosch, Cape Town, 7701, South Africa
- Fynbos Node, South African Environmental Observation Network, Centre for Biodiversity Conservation, Cape Town, South Africa
| | - Tamara B Robinson
- Department of Botany and Zoology, Centre for Invasion Biology, Stellenbosch University, Stellenbosch, South Africa
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2
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Tanase MA, Mihai MC, Miguel S, Cantero A, Tijerin J, Ruiz-Benito P, Domingo D, Garcia-Martin A, Aponte C, Lamelas MT. Long-term annual estimation of forest above ground biomass, canopy cover, and height from airborne and spaceborne sensors synergies in the Iberian Peninsula. ENVIRONMENTAL RESEARCH 2024; 259:119432. [PMID: 38944104 DOI: 10.1016/j.envres.2024.119432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/04/2024] [Accepted: 06/15/2024] [Indexed: 07/01/2024]
Abstract
The Mediterranean Basin has experienced substantial land use changes as traditional agriculture decreased and population migrated from rural to urban areas, which have resulted in a large forest cover increase. The combination of Landsat time series, providing spectral information, with lidar, offering three-dimensional insights, has emerged as a viable option for the large-scale cartography of forest structural attributes across large time spans. Here we develop and test a comprehensive framework to map forest above ground biomass, canopy cover and forest height in two regions spanning the most representative biomes in the peninsular Spain, Mediterranean (Madrid region) and temperate (Basque Country). As reference, we used lidar-based direct estimates of stand height and forest canopy cover. The reference biomass and volume were predicted from lidar metrics. Landsat time series predictors included annual temporal profiles of band reflectance and vegetation indices for the 1985-2023 period. Additional predictor variables including synthetic aperture radar, disturbance history, topography and forest type were also evaluated to optimize forest structural attributes retrieval. The estimates were independently validated at two temporal scales, i) the year of model calibration and ii) the year of the second lidar survey. The final models used as predictor variables only Landsat based metrics and topographic information, as the available SAR time-series were relatively short (1991-2011) and disturbance information did not decrease the estimation error. Model accuracies were higher in the Mediterranean forests when compared to the temperate forests (R2 = 0.6-0.8 vs. 0.4-0.5). Between the first (1985-1989) and the last (2020-2023) decades of the monitoring period the average forest cover increased from 21 ± 2% to 32 ± 1%, mean height increased from 6.6 ± 0.43 m to 7.9 ± 0.18 m and the mean biomass from 31.9 ± 3.6 t ha-1 to 50.4 ± 1 t ha-1 for the Mediterranean forests. In temperate forests, the average canopy cover increased from 55 ± 4% to 59 ± 3%, mean height increased from 15.8 ± 0.77 m to 17.3 ± 0.21m, while the growing stock volume increased from 137.8 ± 8.2 to 151.5 ± 3.8 m3 ha-1. Our results suggest that multispectral data can be successfully linked with lidar to provide continuous information on forest height, cover, and biomass trends.
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Affiliation(s)
- M A Tanase
- Universidad de Alcalá, Environmental Remote Sensing Research Group, Departamento de Geología, Geografía y Medio Ambiente, Colegios 2, 28801, Alcalá de Henares, Spain.
| | - M C Mihai
- Universidad de Alcalá, Environmental Remote Sensing Research Group, Departamento de Geología, Geografía y Medio Ambiente, Colegios 2, 28801, Alcalá de Henares, Spain
| | - S Miguel
- Universidad de Alcalá, Environmental Remote Sensing Research Group, Departamento de Geología, Geografía y Medio Ambiente, Colegios 2, 28801, Alcalá de Henares, Spain
| | - A Cantero
- HAZI Fundazioa, Vitoria-Gasteiz, Spain
| | - J Tijerin
- Universidad de Alcalá, Grupo de Ecología y Restauración Forestal, Departamento de Ciencias de la Vida, Facultad de Ciencias, 28805, Alcalá de Henares, Spain
| | - P Ruiz-Benito
- Universidad de Alcalá, Grupo de Ecología y Restauración Forestal, Departamento de Ciencias de la Vida, Facultad de Ciencias, 28805, Alcalá de Henares, Spain
| | - D Domingo
- iuFOR, EiFAB, Universidad de Valladolid, 42004 Soria, Spain; GEOFOREST-IUCA, Departamento de Geografía, Universidad de Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain
| | - A Garcia-Martin
- Centro Universitario de la Defensa de Zaragoza, Academia General Militar, Ctra. de Huesca s/n, Zaragoza 50090, Spain; GEOFOREST-IUCA, Departamento de Geografía, Universidad de Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain
| | - C Aponte
- Instituto de Ciencias Forestales ICIFOR-INIA, CSIC, Madrid, Spain
| | - M T Lamelas
- Centro Universitario de la Defensa de Zaragoza, Academia General Militar, Ctra. de Huesca s/n, Zaragoza 50090, Spain; GEOFOREST-IUCA, Departamento de Geografía, Universidad de Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain
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3
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Muise ER, Andrew ME, Coops NC, Hermosilla T, Burton AC, Ban SS. Disentangling linkages between satellite-derived indicators of forest structure and productivity for ecosystem monitoring. Sci Rep 2024; 14:13717. [PMID: 38877188 PMCID: PMC11178816 DOI: 10.1038/s41598-024-64615-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 06/10/2024] [Indexed: 06/16/2024] Open
Abstract
The essential biodiversity variables (EBV) framework has been proposed as a monitoring system of standardized, comparable variables that represents a minimum set of biological information to monitor biodiversity change at large spatial extents. Six classes of EBVs (genetic composition, species populations, species traits, community composition, ecosystem structure and ecosystem function) are defined, a number of which are ideally suited to observation and monitoring by remote sensing systems. We used moderate-resolution remotely sensed indicators representing two ecosystem-level EBV classes (ecosystem structure and function) to assess their complementarity and redundancy across a range of ecosystems encompassing significant environmental gradients. Redundancy analyses found that remote sensing indicators of forest structure were not strongly related to indicators of ecosystem productivity (represented by the Dynamic Habitat Indices; DHIs), with the structural information only explaining 15.7% of the variation in the DHIs. Complex metrics of forest structure, such as aboveground biomass, did not contribute additional information over simpler height-based attributes that can be directly estimated with light detection and ranging (LIDAR) observations. With respect to ecosystem conditions, we found that forest types and ecosystems dominated by coniferous trees had less redundancy between the remote sensing indicators when compared to broadleaf or mixed forest types. Likewise, higher productivity environments exhibited the least redundancy between indicators, in contrast to more environmentally stressed regions. We suggest that biodiversity researchers continue to exploit multiple dimensions of remote sensing data given the complementary information they provide on structure and function focused EBVs, which makes them jointly suitable for monitoring forest ecosystems.
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Affiliation(s)
- Evan R Muise
- Department of Forest Resource Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada.
| | - Margaret E Andrew
- Centre for Terrestrial Ecosystem Science and Sustainability, Murdoch University, 90 South St, Murdoch, WA, 6150, Australia
| | - Nicholas C Coops
- Department of Forest Resource Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Txomin Hermosilla
- Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, BC, V8Z 1M5, Canada
| | - A Cole Burton
- Department of Forest Resource Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Stephen S Ban
- BC Parks, Ministry of Environment and Climate Change Strategy, Stn Prov Govt, PO Box 9360, Victoria, BC, V8V 9M2, Canada
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4
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Lausch A, Selsam P, Pause M, Bumberger J. Monitoring vegetation- and geodiversity with remote sensing and traits. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2024; 382:20230058. [PMID: 38342219 PMCID: PMC10859235 DOI: 10.1098/rsta.2023.0058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 11/28/2023] [Indexed: 02/13/2024]
Abstract
Geodiversity has shaped and structured the Earth's surface at all spatio-temporal scales, not only through long-term processes but also through medium- and short-term processes. Geodiversity is, therefore, a key control and regulating variable in the overall development of landscapes and biodiversity. However, climate change and land use intensity are leading to major changes and disturbances in bio- and geodiversity. For sustainable ecosystem management, temporal, economically viable and standardized monitoring is needed to monitor and model the effects and changes in vegetation- and geodiversity. RS approaches have been used for this purpose for decades. However, to understand in detail how RS approaches capture vegetation- and geodiversity, the aim of this paper is to describe how five features of vegetation- and geodiversity are captured using RS technologies, namely: (i) trait diversity, (ii) phylogenetic/genese diversity, (iii) structural diversity, (iv) taxonomic diversity and (v) functional diversity. Trait diversity is essential for establishing the other four. Traits provide a crucial interface between in situ, close-range, aerial and space-based RS monitoring approaches. The trait approach allows complex data of different types and formats to be linked using the latest semantic data integration techniques, which will enable ecosystem integrity monitoring and modelling in the future. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.
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Affiliation(s)
- Angela Lausch
- Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research-UFZ, Permoserstr. 15, 04318 Leipzig, Germany
- Department of Physical Geography and Geoecology, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 4, 06120 Halle, Germany
- Department of Architecture, Facility Management and Geoinformation, Institute for Geoinformation and Surveying, Bauhausstraße 8, 06846 Dessau, Germany
| | - Peter Selsam
- Department of Monitoring and Exploration Technologies, and
| | - Marion Pause
- Department of Architecture, Facility Management and Geoinformation, Institute for Geoinformation and Surveying, Bauhausstraße 8, 06846 Dessau, Germany
| | - Jan Bumberger
- Department of Monitoring and Exploration Technologies, and
- Research Data Management-RDM, Helmholtz Centre for Environmental Research UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
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5
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Hellwig N, Sommerlandt FMJ, Grabener S, Lindermann L, Sickel W, Krüger L, Dieker P. Six Steps towards a Spatial Design for Large-Scale Pollinator Surveillance Monitoring. INSECTS 2024; 15:229. [PMID: 38667359 PMCID: PMC11049859 DOI: 10.3390/insects15040229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 04/28/2024]
Abstract
Despite the importance of pollinators to ecosystem functioning and human food production, comprehensive pollinator monitoring data are still lacking across most regions of the world. Policy-makers have recently prioritised the development of large-scale monitoring programmes for pollinators to better understand how populations respond to land use, environmental change and restoration measures in the long term. Designing such a monitoring programme is challenging, partly because it requires both ecological knowledge and advanced knowledge in sampling design. This study aims to develop a conceptual framework to facilitate the spatial sampling design of large-scale surveillance monitoring. The system is designed to detect changes in pollinator species abundances and richness, focusing on temperate agroecosystems. The sampling design needs to be scientifically robust to address questions of agri-environmental policy at the scales of interest. To this end, we followed a six-step procedure as follows: (1) defining the spatial sampling units, (2) defining and delimiting the monitoring area, (3) deciding on the general sampling strategy, (4) determining the sample size, (5) specifying the sampling units per sampling interval, and (6) specifying the pollinator survey plots within each sampling unit. As a case study, we apply this framework to the "Wild bee monitoring in agricultural landscapes of Germany" programme. We suggest this six-step procedure as a conceptual guideline for the spatial sampling design of future large-scale pollinator monitoring initiatives.
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Affiliation(s)
- Niels Hellwig
- Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany; (F.M.J.S.); (S.G.); (L.L.); (W.S.); (L.K.); (P.D.)
| | - Frank M. J. Sommerlandt
- Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany; (F.M.J.S.); (S.G.); (L.L.); (W.S.); (L.K.); (P.D.)
| | - Swantje Grabener
- Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany; (F.M.J.S.); (S.G.); (L.L.); (W.S.); (L.K.); (P.D.)
| | - Lara Lindermann
- Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany; (F.M.J.S.); (S.G.); (L.L.); (W.S.); (L.K.); (P.D.)
| | - Wiebke Sickel
- Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany; (F.M.J.S.); (S.G.); (L.L.); (W.S.); (L.K.); (P.D.)
| | - Lasse Krüger
- Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany; (F.M.J.S.); (S.G.); (L.L.); (W.S.); (L.K.); (P.D.)
| | - Petra Dieker
- Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany; (F.M.J.S.); (S.G.); (L.L.); (W.S.); (L.K.); (P.D.)
- National Monitoring Centre for Biodiversity, Federal Agency for Nature Conservation, Alte Messe 6, 04103 Leipzig, Germany
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6
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Hackländer J, Parente L, Ho YF, Hengl T, Simoes R, Consoli D, Şahin M, Tian X, Jung M, Herold M, Duveiller G, Weynants M, Wheeler I. Land potential assessment and trend-analysis using 2000-2021 FAPAR monthly time-series at 250 m spatial resolution. PeerJ 2024; 12:e16972. [PMID: 38495753 PMCID: PMC10944167 DOI: 10.7717/peerj.16972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 01/29/2024] [Indexed: 03/19/2024] Open
Abstract
The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000-2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.
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Affiliation(s)
- Julia Hackländer
- OpenGeoHub, Wageningen, Netherlands
- Wageningen University and Research, Wageningen, Netherlands
| | | | | | | | | | | | | | - Xuemeng Tian
- OpenGeoHub, Wageningen, Netherlands
- Wageningen University and Research, Wageningen, Netherlands
| | - Martin Jung
- Biodiversity, Ecology and Conservation Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
| | - Martin Herold
- Wageningen University and Research, Wageningen, Netherlands
- Helmholtz GFZ German Research Centre for Geosciences, Remote Sensing and Geoinformatics, Potsdam, Germany
| | | | - Melanie Weynants
- Max Planck Institute for Biogeochemistry (MPI-BGC), Jena, Germany
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7
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Chaurasia AN, Parmar RM, Dave MG, Krishnayya NSR. Integrating field- and remote sensing data to perceive species heterogeneity across a climate gradient. Sci Rep 2024; 14:42. [PMID: 38167992 PMCID: PMC10761838 DOI: 10.1038/s41598-023-50812-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 12/26/2023] [Indexed: 01/05/2024] Open
Abstract
Tropical forests exhibit significant diversity and heterogeneity in species distribution. Some tree species spread abundantly, impacting the functional aspects of communities. Understanding how these facets respond to climate change is crucial. Field data from four protected areas (PAs) were combined with high-resolution Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) datasets to extract large-scale plot data of abundant species and their functional traits. A supervised component generalized linear regression (SCGLR) model was used to correlate climate components with the distribution of abundant species across PAs. The recorded rainfall gradient influenced the proportion of PA-specific species in the observed species assemblages. Community weighted means (CWMs) of biochemical traits showed better correlation values (0.85-0.87) between observed and predicted values compared to biophysical traits (0.52-0.79). The model-based projection revealed distinct distribution responses of each abundant species to the climate gradient. Functional diversity and functional traits maps highlighted the interplay between species heterogeneity and climate. The appearance dynamics of abundant species in dark diversity across PAs demonstrated their assortment strategy in response to the climate gradient. These observations can significantly aid in the ecological management of PAs exposed to climate dynamics.
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Affiliation(s)
- Amrita N Chaurasia
- Ecology Laboratory, Department of Botany, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, 390002, India
| | - Reshma M Parmar
- Ecology Laboratory, Department of Botany, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, 390002, India
| | - Maulik G Dave
- Ecology Laboratory, Department of Botany, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, 390002, India
| | - N S R Krishnayya
- Ecology Laboratory, Department of Botany, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, 390002, India.
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8
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Lang N, Jetz W, Schindler K, Wegner JD. A high-resolution canopy height model of the Earth. Nat Ecol Evol 2023; 7:1778-1789. [PMID: 37770546 PMCID: PMC10627820 DOI: 10.1038/s41559-023-02206-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/24/2023] [Indexed: 09/30/2023]
Abstract
The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to manage terrestrial ecosystems, mitigate climate change and prevent biodiversity loss. Here we present a comprehensive global canopy height map at 10 m ground sampling distance for the year 2020. We have developed a probabilistic deep learning model that fuses sparse height data from the Global Ecosystem Dynamics Investigation (GEDI) space-borne LiDAR mission with dense optical satellite images from Sentinel-2. This model retrieves canopy-top height from Sentinel-2 images anywhere on Earth and quantifies the uncertainty in these estimates. Our approach improves the retrieval of tall canopies with typically high carbon stocks. According to our map, only 5% of the global landmass is covered by trees taller than 30 m. Further, we find that only 34% of these tall canopies are located within protected areas. Thus, the approach can serve ongoing efforts in forest conservation and has the potential to foster advances in climate, carbon and biodiversity modelling.
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Affiliation(s)
- Nico Lang
- EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland.
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
| | - Walter Jetz
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
| | - Konrad Schindler
- EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland
| | - Jan Dirk Wegner
- EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland.
- Institute for Computational Science, University of Zurich, Zürich, Switzerland.
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9
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Miraglio T, Coops NC, Wallis CIB, Crofts AL, Kalacska M, Vellend M, Serbin SP, Arroyo-Mora JP, Laliberté E. Mapping canopy traits over Québec using airborne and spaceborne imaging spectroscopy. Sci Rep 2023; 13:17179. [PMID: 37821515 PMCID: PMC10567784 DOI: 10.1038/s41598-023-44384-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 10/07/2023] [Indexed: 10/13/2023] Open
Abstract
The advent of new spaceborne imaging spectrometers offers new opportunities for ecologists to map vegetation traits at global scales. However, to date most imaging spectroscopy studies exploiting satellite spectrometers have been constrained to the landscape scale. In this paper we present a new method to map vegetation traits at the landscape scale and upscale trait maps to the continental level, using historical spaceborne imaging spectroscopy (Hyperion) to derive estimates of leaf mass per area, nitrogen, and carbon concentrations of forests in Québec, Canada. We compare estimates for each species with reference field values and obtain good agreement both at the landscape and continental scales, with patterns consistent with the leaf economic spectrum. By exploiting the Hyperion satellite archive to map these traits and successfully upscale the estimates to the continental scale, we demonstrate the great potential of recent and upcoming spaceborne spectrometers to benefit plant biodiversity monitoring and conservation efforts.
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Affiliation(s)
- Thomas Miraglio
- Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada.
| | - Nicholas C Coops
- Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | | | - Anna L Crofts
- Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Margaret Kalacska
- Applied Remote Sensing Lab, Department of Geography, McGill University, Montréal, QC, H3A 0G4, Canada
| | - Mark Vellend
- Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Juan Pablo Arroyo-Mora
- Flight Research Laboratory, National Research Council of Canada, Ottawa, ON, K1A 0R6, Canada
| | - Etienne Laliberté
- Département de Sciences Biologiques, Institut de Recherche en Biologie Végétale, Université de Montréal, Montréal, QC, H3A 0G4, Canada
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Calders K, Brede B, Newnham G, Culvenor D, Armston J, Bartholomeus H, Griebel A, Hayward J, Junttila S, Lau A, Levick S, Morrone R, Origo N, Pfeifer M, Verbesselt J, Herold M. StrucNet: a global network for automated vegetation structure monitoring. REMOTE SENSING IN ECOLOGY AND CONSERVATION 2023; 9:587-598. [PMID: 38505271 PMCID: PMC10946942 DOI: 10.1002/rse2.333] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 03/01/2023] [Accepted: 03/27/2023] [Indexed: 03/21/2024]
Abstract
Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large-scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three-dimensional vegetation structure at local scales. Here we demonstrate the potential of high-frequency ground-based laser scanning to systematically monitor structural changes in vegetation. We present a proof-of-concept high-temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high-temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.
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Affiliation(s)
- Kim Calders
- CAVElab – Computational & Applied Vegetation Ecology, Department of EnvironmentGhent UniversityCoupure links 653Ghent9000Belgium
- School of Forest Sciences, University of Eastern FinlandJoensuu80101Finland
| | - Benjamin Brede
- Helmholtz Center Potsdam GFZ German Research Centre for GeosciencesSection 1.4 Remote Sensing and GeoinformaticsTelegrafenbergPotsdam14473Germany
| | | | - Darius Culvenor
- Environmental Sensing SystemsBentleigh EastVictoria3165Australia
| | - John Armston
- Department of Geographical SciencesUniversity of MarylandCollege ParkMarylandUSA
| | - Harm Bartholomeus
- Laboratory of Geo‐Information Science and Remote SensingWageningen UniversityWageningen6708 PBthe Netherlands
| | - Anne Griebel
- Hawkesbury Institute for the Environment, Western Sydney UniversityLocked Bag 1797PenrithNew South Wales2751Australia
| | - Jodie Hayward
- CSIRO564 Vanderlin DriveBerrimahNorthern Territory0828Australia
| | - Samuli Junttila
- School of Forest Sciences, University of Eastern FinlandJoensuu80101Finland
| | - Alvaro Lau
- Laboratory of Geo‐Information Science and Remote SensingWageningen UniversityWageningen6708 PBthe Netherlands
| | - Shaun Levick
- CSIRO564 Vanderlin DriveBerrimahNorthern Territory0828Australia
| | - Rosalinda Morrone
- Climate and Earth Observation GroupNational Physical LaboratoryHampton Road, TeddingtonLondonUK
| | - Niall Origo
- Climate and Earth Observation GroupNational Physical LaboratoryHampton Road, TeddingtonLondonUK
| | - Marion Pfeifer
- School of Natural and Environmental Sciences, Newcastle UniversityNewcastle Upon TyneNE1 7RUUK
| | - Jan Verbesselt
- Laboratory of Geo‐Information Science and Remote SensingWageningen UniversityWageningen6708 PBthe Netherlands
| | - Martin Herold
- Helmholtz Center Potsdam GFZ German Research Centre for GeosciencesSection 1.4 Remote Sensing and GeoinformaticsTelegrafenbergPotsdam14473Germany
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11
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Torresani M, Rocchini D, Alberti A, Moudrý V, Heym M, Thouverai E, Kacic P, Tomelleri E. LiDAR GEDI derived tree canopy height heterogeneity reveals patterns of biodiversity in forest ecosystems. ECOL INFORM 2023; 76:102082. [PMID: 37662896 PMCID: PMC10316066 DOI: 10.1016/j.ecoinf.2023.102082] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/21/2023] [Accepted: 03/22/2023] [Indexed: 09/05/2023]
Abstract
The "Height Variation Hypothesis" is an indirect approach used to estimate forest biodiversity through remote sensing data, stating that greater tree height heterogeneity (HH) measured by CHM LiDAR data indicates higher forest structure complexity and tree species diversity. This approach has traditionally been analyzed using only airborne LiDAR data, which limits its application to the availability of the dedicated flight campaigns. In this study we analyzed the relationship between tree species diversity and HH, calculated with four different heterogeneity indices using two freely available CHMs derived from the new space-borne GEDI LiDAR data. The first, with a spatial resolution of 30 m, was produced through a regression tree machine learning algorithm integrating GEDI LiDAR data and Landsat optical information. The second, with a spatial resolution of 10 m, was created using Sentinel-2 images and a deep learning convolutional neural network. We tested this approach separately in 30 forest plots situated in the northern Italian Alps, in 100 plots in the forested area of Traunstein (Germany) and successively in all the 130 plots through a cross-validation analysis. Forest density information was also included as influencing factor in a multiple regression analysis. Our results show that the GEDI CHMs can be used to assess biodiversity patterns in forest ecosystems through the estimation of the HH that is correlated to the tree species diversity. However, the results also indicate that this method is influenced by different factors including the GEDI CHMs dataset of choice and their related spatial resolution, the heterogeneity indices used to calculate the HH and the forest density. Our finding suggest that GEDI LIDAR data can be a valuable tool in the estimation of forest tree heterogeneity and related tree species diversity in forest ecosystems, which can aid in global biodiversity estimation.
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Affiliation(s)
- Michele Torresani
- Free University of Bolzano/Bozen, Faculty of Agricultural, Environmental and Food Sciences, Piazza Universitá/Universitätsplatz 1, 39100 Bolzano/Bozen, Italy
| | - Duccio Rocchini
- BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126, Bologna, Italy
- Czech University of Life Sciences Prague, Faculty of Environmental Sciences, Department of Spatial Sciences, Kamýcka 129, Praha - Suchdol 16500, Czech Republic
| | - Alessandro Alberti
- Free University of Bolzano/Bozen, Faculty of Agricultural, Environmental and Food Sciences, Piazza Universitá/Universitätsplatz 1, 39100 Bolzano/Bozen, Italy
| | - Vítězslav Moudrý
- Czech University of Life Sciences Prague, Faculty of Environmental Sciences, Department of Spatial Sciences, Kamýcka 129, Praha - Suchdol 16500, Czech Republic
| | - Michael Heym
- Bavarian State Institute of Forestry (LWF), Hans-Carl-von-Carlowitz-Platz-1, 85354 Freising, Germany
| | - Elisa Thouverai
- BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126, Bologna, Italy
| | - Patrick Kacic
- Department of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Würzburg, Germany
| | - Enrico Tomelleri
- Free University of Bolzano/Bozen, Faculty of Agricultural, Environmental and Food Sciences, Piazza Universitá/Universitätsplatz 1, 39100 Bolzano/Bozen, Italy
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12
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Mori AS, Suzuki KF, Hori M, Kadoya T, Okano K, Uraguchi A, Muraoka H, Sato T, Shibata H, Suzuki-Ohno Y, Koba K, Toda M, Nakano SI, Kondoh M, Kitajima K, Nakamura M. Perspective: sustainability challenges, opportunities and solutions for long-term ecosystem observations. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220192. [PMID: 37246388 DOI: 10.1098/rstb.2022.0192] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 04/11/2023] [Indexed: 05/30/2023] Open
Abstract
As interest in natural capital grows and society increasingly recognizes the value of biodiversity, we must discuss how ecosystem observations to detect changes in biodiversity can be sustained through collaboration across regions and sectors. However, there are many barriers to establishing and sustaining large-scale, fine-resolution ecosystem observations. First, comprehensive monitoring data on both biodiversity and possible anthropogenic factors are lacking. Second, some in situ ecosystem observations cannot be systematically established and maintained across locations. Third, equitable solutions across sectors and countries are needed to build a global network. Here, by examining individual cases and emerging frameworks, mainly from (but not limited to) Japan, we illustrate how ecological science relies on long-term data and how neglecting basic monitoring of our home planet further reduces our chances of overcoming the environmental crisis. We also discuss emerging techniques and opportunities, such as environmental DNA and citizen science as well as using the existing and forgotten sites of monitoring, that can help overcome some of the difficulties in establishing and sustaining ecosystem observations at a large scale with fine resolution. Overall, this paper presents a call to action for joint monitoring of biodiversity and anthropogenic factors, the systematic establishment and maintenance of in situ observations, and equitable solutions across sectors and countries to build a global network, beyond cultures, languages, and economic status. We hope that our proposed framework and the examples from Japan can serve as a starting point for further discussions and collaborations among stakeholders across multiple sectors of society. It is time to take the next step in detecting changes in socio-ecological systems, and if monitoring and observation can be made more equitable and feasible, they will play an even more important role in ensuring global sustainability for future generations. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.
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Affiliation(s)
- Akira S Mori
- Research Center for Advanced Science and Technology, The University of Tokyo, Komaba 4-6-1, Meguro, Tokyo 153-8904, Japan
- Graduate School of Environment and Information Sciences, Yokohama National University, 79-7 Tokiwadai, Hodogaya, Yokohama, Kanagawa 240-8501, Japan
| | - Kureha F Suzuki
- Research Center for Advanced Science and Technology, The University of Tokyo, Komaba 4-6-1, Meguro, Tokyo 153-8904, Japan
- Graduate School of Environment and Information Sciences, Yokohama National University, 79-7 Tokiwadai, Hodogaya, Yokohama, Kanagawa 240-8501, Japan
| | - Masakazu Hori
- Japan Fisheries Research and Education Agency, 6F Technowave100, 1-1-25 Shin-urashima, Kanagawa-ku, Yokohama, Kanagawa 221-8529, Japan
| | - Taku Kadoya
- National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki 305-8506, Japan
| | - Kotaro Okano
- Research Center for Advanced Science and Technology, The University of Tokyo, Komaba 4-6-1, Meguro, Tokyo 153-8904, Japan
| | - Aya Uraguchi
- Conservation International Japan, 1-17 Yotsuya, Shinjuku, Tokyo 160-0014, Japan
| | - Hiroyuki Muraoka
- National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki 305-8506, Japan
- River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu City 501-1193, Japan
| | - Tamotsu Sato
- International Strategy Division, Forestry and Forest Products Research Institute (FFPRI), 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan
| | - Hideaki Shibata
- Field Science Center for Northern Biosphere, Hokkaido University, N9 W9, Kita-ku, Sapporo, Hokkaido 060-0809, Japan
| | - Yukari Suzuki-Ohno
- Graduate School of Life Sciences, Tohoku University, 6-3 Aoba, Aramaki-aza, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Keisuke Koba
- Center for Ecological Research, Kyoto University, Hirano 2-509-3, Otsu, Shiga 520-2113, Japan
| | - Mariko Toda
- Kokusai Kogyo Co., Ltd. Shinjuku Front Tower, 21-1, Kita-Shinjuku 2-chome, Shinjukuku, Tokyo 169-0074, Japan
| | - Shin-Ichi Nakano
- Center for Ecological Research, Kyoto University, Hirano 2-509-3, Otsu, Shiga 520-2113, Japan
| | - Michio Kondoh
- Graduate School of Life Sciences, Tohoku University, 6-3 Aoba, Aramaki-aza, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Kaoru Kitajima
- Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
| | - Masahiro Nakamura
- Tomakomai Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Takaoka, Tomakomai, Hokkaido 053-0035, Japan
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13
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Zhang H, Mächler E, Morsdorf F, Niklaus PA, Schaepman ME, Altermatt F. A spatial fingerprint of land-water linkage of biodiversity uncovered by remote sensing and environmental DNA. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 867:161365. [PMID: 36634788 DOI: 10.1016/j.scitotenv.2022.161365] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/06/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
Aquatic and terrestrial ecosystems are tightly connected via spatial flows of organisms and resources. Such land-water linkages integrate biodiversity across ecosystems and suggest a spatial association of aquatic and terrestrial biodiversity. However, knowledge about the extent of this spatial association is limited. By combining satellite remote sensing (RS) and environmental DNA (eDNA) extraction from river water across a 740-km2 mountainous catchment, we identify a characteristic spatial land-water fingerprint. Specifically, we find a spatial association of riverine eDNA diversity with RS spectral diversity of terrestrial ecosystems upstream, peaking at a 400 m distance yet still detectable up to a 2.0 km radius. Our findings show that biodiversity patterns in rivers can be linked to the functional diversity of surrounding terrestrial ecosystems and provide a dominant scale at which these linkages are strongest. Such spatially explicit information is necessary for a functional understanding of land-water linkages.
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Affiliation(s)
- Heng Zhang
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland; Eawag, Swiss Federal Institute of Aquatic Science and Technology, Department of Aquatic Ecology, Überlandstrasse 133, CH-8600 Dübendorf, Switzerland.
| | - Elvira Mächler
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland; Eawag, Swiss Federal Institute of Aquatic Science and Technology, Department of Aquatic Ecology, Überlandstrasse 133, CH-8600 Dübendorf, Switzerland
| | - Felix Morsdorf
- Remote Sensing Laboratories, Department of Geography, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Pascal A Niklaus
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Michael E Schaepman
- Remote Sensing Laboratories, Department of Geography, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Florian Altermatt
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland; Eawag, Swiss Federal Institute of Aquatic Science and Technology, Department of Aquatic Ecology, Überlandstrasse 133, CH-8600 Dübendorf, Switzerland.
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14
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Thouverai E, Marcantonio M, Lenoir J, Galfré M, Marchetto E, Bacaro G, Cazzolla Gatti R, Da Re D, Di Musciano M, Furrer R, Malavasi M, Moudrý V, Nowosad J, Pedrotti F, Pelorosso R, Pezzi G, Šímová P, Ricotta C, Silvestri S, Tordoni E, Torresani M, Vacchiano G, Zannini P, Rocchini D. Integrals of life: Tracking ecosystem spatial heterogeneity from space through the area under the curve of the parametric Rao’s Q index. ECOLOGICAL COMPLEXITY 2023. [DOI: 10.1016/j.ecocom.2023.101029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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15
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Lin Y, Rong Y, Li L, Li F, Zhang H, Yu J. Spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity in the Mekong subregion in Cambodia. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:4023-4043. [PMID: 35962167 DOI: 10.1007/s11356-022-22469-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 08/06/2022] [Indexed: 06/15/2023]
Abstract
Water resources in the Mekong subregion in Cambodia (MSC) have experienced dramatic changes in past decades, threatening regional ecosystem quality and sustainable development. Thus, it is important to explore the spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity. This study proposed an effective framework including spatiotemporal analysis of land use/cover change (LUCC) and ecological sensitivity assessment by combining remote sensing (RS) and geographic information system/science (GIS). An optimized feature space and a machine learning classification algorithm were constructed to extract four typical land cover types in the MSC from 1990 to 2020. An ecological sensitivity evaluation system, including four sub-sensitivities calculated by twelve indicators, was then constructed. The results suggest that severe shrinkage of water resources occurred before 2006, decreasing by 21.68%. The correlation between water resources and climate conditions displays a high to low level as human activity becomes involved. A significant spatiotemporal evolutionary pattern of ecological sensitivity was observed under the impact of external interference. Generally, the largest proportion of MSC belongs to the lightly sensitive level, which is mainly concentrated in the lower reaches, with an average of 33.93%. The highly sensitive area with a significant value in ecological protection has a slightly downward trend from 23.72 in 1990 to 22.55% in 2020.
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Affiliation(s)
- Yi Lin
- College of Surveying, Mapping and Geo-information, Tongji University, Shanghai, 200092, China
- Research Center of Remote Sensing & Spatial Information Technology, Shanghai, 200092, China
| | - Yu Rong
- College of Surveying, Mapping and Geo-information, Tongji University, Shanghai, 200092, China
| | - Lang Li
- College of Surveying, Mapping and Geo-information, Tongji University, Shanghai, 200092, China
- Institute of Geodesy, University of Stuttgart, Stuttgart, 70174, Germany
| | - Fengting Li
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Hanchao Zhang
- Chinese Academy of Surveying and Mapping, Beijing, 100036, China
| | - Jie Yu
- College of Surveying, Mapping and Geo-information, Tongji University, Shanghai, 200092, China.
- Research Center of Remote Sensing & Spatial Information Technology, Shanghai, 200092, China.
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16
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Blanchard G, Munoz F. Revisiting extinction debt through the lens of multitrophic networks and meta‐ecosystems. OIKOS 2022. [DOI: 10.1111/oik.09435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Grégoire Blanchard
- AMAP, Univ. Montpellier, CIRAD, CNRS, INRAE, IRD Montpellier France
- AMAP, IRD, Herbier de Nouvelle Calédonie Nouméa Nouvelle Calédonie
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17
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Moya-Moraga MR, Pérez-Ruíz C. Application of MaxEnt Modeling and HRM Analysis to Support the Conservation and Domestication of Gevuina avellana Mol. in Central Chile. PLANTS (BASEL, SWITZERLAND) 2022; 11:2803. [PMID: 36297827 PMCID: PMC9607360 DOI: 10.3390/plants11202803] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/11/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
The Chilean hazelnut (Gevuina avellana Mol., Proteaceae) is a native tree of Chile and Argentina of edible fruit-type nut. We applied two approaches to contribute to the development of strategies for mitigation of the effects of climate change and anthropic activities in G. avellana. It corresponds to the first report where both tools are integrated, the MaxEnt model to predict the current and future potential distribution coupled with High-Resolution Melting Analysis (HRM) to assess its genetic diversity and understand how the species would respond to these changes. Two global climate models: CNRM-CM6-1 and MIROC-ES2L for four Shared Socioeconomic Pathways: 126, 245, 370, and 585 (2021−2040; 2061−2080) were evaluated. The annual mean temperature (43.7%) and water steam (23.4%) were the key factors for the distribution current of G. avellana (AUC = 0.953). The future prediction model shows to the year 2040 those habitat range decreases at 50% (AUC = 0.918). The genetic structure was investigated in seven natural populations using eight EST-SSR markers, showing a percentage of polymorphic loci between 18.69 and 55.14% and low genetic differentiation between populations (Fst = 0.052; p < 0.001). According to the discriminant analysis of principal components (DAPC) we identified 10 genetic populations. We conclude that high-priority areas for protection correspond to Los Avellanos and Punta de Águila populations due to their greater genetic diversity and allelic richness.
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Affiliation(s)
- Mario René Moya-Moraga
- Doctoral Program in Biotechnology and Genetic Resources of Plants and Associated Microorganisms (02E4), Polytechnic University of Madrid (UPM), University City, 28040 Madrid, Spain
- Department of Biotechnology, Faculty of Natural Sciences, Mathematics and the Environment (FCNMM), Metropolitan Technological University (UTEM), Ñuñoa 7750000, Chile
| | - César Pérez-Ruíz
- Department of Biotechnology and Plant Biology, School of Agricultural, Food and Biosystems Engineering, Polytechnic University of Madrid (UPM), University City, 28040 Madrid, Spain
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18
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Rocchini D, Santos MJ, Ustin SL, Féret J, Asner GP, Beierkuhnlein C, Dalponte M, Feilhauer H, Foody GM, Geller GN, Gillespie TW, He KS, Kleijn D, Leitão PJ, Malavasi M, Moudrý V, Müllerová J, Nagendra H, Normand S, Ricotta C, Schaepman ME, Schmidtlein S, Skidmore AK, Šímová P, Torresani M, Townsend PA, Turner W, Vihervaara P, Wegmann M, Lenoir J. The Spectral Species Concept in Living Color. JOURNAL OF GEOPHYSICAL RESEARCH. BIOGEOSCIENCES 2022; 127:e2022JG007026. [PMID: 36247363 PMCID: PMC9539608 DOI: 10.1029/2022jg007026] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/27/2022] [Accepted: 08/02/2022] [Indexed: 06/16/2023]
Abstract
Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.
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Affiliation(s)
- Duccio Rocchini
- BIOME Lab, Department of Biological, Geological and Environmental SciencesAlma Mater Studiorum University of BolognaBolognaItaly
- Department of Spatial SciencesCzech University of Life Sciences PragueFaculty of Environmental SciencesPrahaCzech Republic
| | - Maria J. Santos
- Department of GeographyUniversity of ZurichZurichSwitzerland
| | - Susan L. Ustin
- Department of Land, Air, and Water ResourcesUniversity of California DavisDavisCAUSA
| | - Jean‐Baptiste Féret
- UMR‐TETISIRSTEA Montpellier, Maison de la TélédétectionMontpellier Cedex 5France
| | - Gregory P. Asner
- Center for Global Discovery and Conservation ScienceArizona State UniversityTempeAZUSA
| | | | - Michele Dalponte
- Sustainable Ecosystems and Bioresources Department, Research and Innovation CentreFondazione Edmund MachSan Michele all’AdigeItaly
| | - Hannes Feilhauer
- Remote Sensing Center for Earth System ResearchUniversity of LeipzigLeipzigGermany
| | - Giles M. Foody
- School of GeographyUniversity of NottinghamUniversity ParkNottinghamUK
| | - Gary N. Geller
- NASA Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | | | - Kate S. He
- Department of Biological SciencesMurray State UniversityMurrayKYUSA
| | - David Kleijn
- Plant Ecology and Nature Conservation GroupWageningen UniversityWageningenThe Netherlands
| | - Pedro J. Leitão
- Department Landscape Ecology and Environmental System AnalysisTechnische Universität BraunschweigBraunschweigGermany
- Geography DepartmentHumboldt‐Universität zu BerlinBerlinGermany
| | - Marco Malavasi
- Department of Spatial SciencesCzech University of Life Sciences PragueFaculty of Environmental SciencesPrahaCzech Republic
- Department of Chemistry, Physics, Mathematics and Natural SciencesUniversity of SassariSassariItaly
| | - Vítězslav Moudrý
- Department of Spatial SciencesCzech University of Life Sciences PragueFaculty of Environmental SciencesPrahaCzech Republic
| | - Jana Müllerová
- Department of GIS and Remote SensingInstitute of BotanyThe Czech Acad. SciencesPrůhoniceCzech Republic
| | - Harini Nagendra
- Azim Premji UniversityPES Institute of Technology CampusBangaloreIndia
| | - Signe Normand
- Department of Biology, Ecoinformatics and BiodiversityAarhus UniversityAarhus CDenmark
- Center for Biodiversity Dynamics in a Changing World (BIOCHANGE)Department of BiologyAarhus UniversityAarhus CDenmark
| | - Carlo Ricotta
- Department of Environmental BiologyUniversity of Rome “La Sapienza”RomeItaly
| | - Michael E. Schaepman
- Department of Geography, Remote Sensing LaboratoriesUniversity of ZurichZurichSwitzerland
| | - Sebastian Schmidtlein
- Institute of Geography and GeoecologyKarlsruhe Institute of TechnologyKarlsruheGermany
| | - Andrew K. Skidmore
- Faculty of Geo‐Information Science and Earth Observation (ITC)University of TwenteEnschedeThe Netherlands
- Department of Earth and Environmental ScienceMacquarie UniversitySydneyNSWAustralia
| | - Petra Šímová
- Department of Spatial SciencesCzech University of Life Sciences PragueFaculty of Environmental SciencesPrahaCzech Republic
| | - Michele Torresani
- BIOME Lab, Department of Biological, Geological and Environmental SciencesAlma Mater Studiorum University of BolognaBolognaItaly
| | - Philip A. Townsend
- Department of Forest and Wildlife EcologyUniversity of WisconsinMadisonWIUSA
| | - Woody Turner
- Earth Science DivisionNASA HeadquartersWashingtonDCUSA
| | - Petteri Vihervaara
- Natural Environment CentreFinnish Environment Institute (SYKE)HelsinkiFinland
| | - Martin Wegmann
- Department of Remote SensingUniversity of WuerzburgWuerzburgGermany
| | - Jonathan Lenoir
- UMR CNRS 7058 “Ecologie et Dynamique des Systèmes Anthropisés” (EDYSAN)Université de Picardie Jules VerneAmiensFrance
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19
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Carroll KA, Farwell LS, Pidgeon AM, Razenkova E, Gudex-Cross D, Helmers DP, Lewińska KE, Elsen PR, Radeloff VC. Mapping breeding bird species richness at management-relevant resolutions across the United States. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2624. [PMID: 35404493 DOI: 10.1002/eap.2624] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 01/26/2022] [Accepted: 02/01/2022] [Indexed: 06/14/2023]
Abstract
Human activities alter ecosystems everywhere, causing rapid biodiversity loss and biotic homogenization. These losses necessitate coordinated conservation actions guided by biodiversity and species distribution spatial data that cover large areas yet have fine-enough resolution to be management-relevant (i.e., ≤5 km). However, most biodiversity products are too coarse for management or are only available for small areas. Furthermore, many maps generated for biodiversity assessment and conservation do not explicitly quantify the inherent tradeoff between resolution and accuracy when predicting biodiversity patterns. Our goals were to generate predictive models of overall breeding bird species richness and species richness of different guilds based on nine functional or life-history-based traits across the conterminous United States at three resolutions (0.5, 2.5, and 5 km) and quantify the tradeoff between resolution and accuracy and, hence, relevance for management of the resulting biodiversity maps. We summarized 18 years of North American Breeding Bird Survey data (1992-2019) and modeled species richness using random forests, including 66 predictor variables (describing climate, vegetation, geomorphology, and anthropogenic conditions), 20 of which we newly derived. Among the three spatial resolutions, the percentage variance explained ranged from 27% to 60% (median = 54%; mean = 57%) for overall species richness and 12% to 87% (median = 61%; mean = 58%) for our different guilds. Overall species richness and guild-specific species richness were best explained at 5-km resolution using ~24 predictor variables based on percentage variance explained, symmetric mean absolute percentage error, and root mean square error values. However, our 2.5-km-resolution maps were almost as accurate and provided more spatially detailed information, which is why we recommend them for most management applications. Our results represent the first consistent, occurrence-based, and nationwide maps of breeding bird richness with a thorough accuracy assessment that are also spatially detailed enough to inform local management decisions. More broadly, our findings highlight the importance of explicitly considering tradeoffs between resolution and accuracy to create management-relevant biodiversity products for large areas.
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Affiliation(s)
- Kathleen A Carroll
- SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Laura S Farwell
- SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Anna M Pidgeon
- SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Elena Razenkova
- SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - David Gudex-Cross
- SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - David P Helmers
- SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Katarzyna E Lewińska
- SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Paul R Elsen
- SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Volker C Radeloff
- SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA
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20
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Binh NA, Hauser LT, Hoa PV, Thao GTP, An NN, Nhut HS, Phuong TA, Verrelst J. Quantifying mangrove leaf area index from Sentinel-2 imagery using hybrid models and active learning. INTERNATIONAL JOURNAL OF REMOTE SENSING 2022; 43:5636-5657. [PMID: 36386862 PMCID: PMC7613820 DOI: 10.1080/01431161.2021.2024912] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 12/27/2021] [Indexed: 06/16/2023]
Abstract
Mangrove forests provide vital ecosystem services. The increasing threats to mangrove forest extent and fragmentation can be monitored from space. Accurate spatially explicit quantification of key vegetation characteristics of mangroves, such as leaf area index (LAI), would further advance our monitoring efforts to assess ecosystem health and functioning. Here, we investigated the potential of radiative transfer models (RTM), combined with active learning (AL), to estimate LAI from Sentinel-2 spectral reflectance in the mangrove-dominated region of Ngoc Hien, Vietnam. We validated the retrieval of LAI estimates against in-situ measurements based on hemispherical photography and compared against red-edge NDVI and the Sentinel Application Platform (SNAP) biophysical processor. Our results highlight the performance of physics-based machine learning using Gaussian processes regression (GPR) in combination with AL for the estimation of mangrove LAI. Our AL-driven hybrid GPR model substantially outperformed SNAP (R2 = 0.77 and 0.44 respectively) as well as the red-edge NDVI approach. Comparing two canopy RTMs, the highest accuracy was achieved by PROSAIL (RMSE = 0.13 m2.m-2, NRMSE = 9.57%, MAE = 0.1 m2.m-2). The successful retrieval of mangrove LAI from Sentinel-2 can overcome extensive reliance on scarce in-situ measurements for training seen in other approaches and present a more scalable applicability by relying on the universal principles of physics in combination with uncertainty estimates. AL-based GPR models using RTM simulations allow us to adapt the genericity of RTMs to the peculiarities of distinct ecosystems such as mangrove forests with limited ancillary data. These findings bode potential for retrieving a wider range of vegetation variables to quantify large-scale mangrove ecosystem dynamics in space and time.
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Affiliation(s)
- Nguyen An Binh
- Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Ho Chi Minh, Vietnam
| | - Leon T. Hauser
- Department of Environmental Biology, Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands
| | - Pham Viet Hoa
- Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Ho Chi Minh, Vietnam
| | - Giang Thi Phuong Thao
- Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Ho Chi Minh, Vietnam
| | - Nguyen Ngoc An
- Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Ho Chi Minh, Vietnam
| | - Huynh Song Nhut
- Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Ho Chi Minh, Vietnam
| | - Tran Anh Phuong
- Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Ho Chi Minh, Vietnam
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), Parc Científic, Universitat de Valéncia, Paterna, Valéncia, Spain
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21
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Mapping Plant Species in a Former Industrial Site Using Airborne Hyperspectral and Time Series of Sentinel-2 Data Sets. REMOTE SENSING 2022. [DOI: 10.3390/rs14153633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Industrial activities induce various impacts on ecosystems that influence species richness and distribution. An effective way to assess the resulting impacts on biodiversity lies in vegetation mapping. Species classification achieved through supervised machine learning algorithms at the pixel level has shown promising results using hyperspectral images and multispectral, multitemporal images. This study aims to determine whether airborne hyperspectral images with a high spatial resolution or phenological information obtained by spaceborne multispectral time series (Sentinel-2) are suitable to discriminate species and assess biodiversity in a complex impacted context. The industrial heritage of the study site has indeed induced high spatial heterogeneity in terms of stressors and species over a reduced scale. First, vegetation indices, derivative spectra, continuum removed spectra, and components provided by three feature extraction techniques, namely, Principal Component Analysis, Minimal Noise Fraction, and Independent Component Analysis, were calculated from reflectance spectra. These features were then analyzed through Sequential Floating Feature Selection. Supervised classification was finally performed using various machine learning algorithms (Random Forest, Support Vector Machines, and Regularized Logistic Regression) considering a probability-based rejection approach. Biodiversity metrics were derived from resulted maps and analyzed considering the impacts. Average Overall Accuracy (AOA) reached up to 94% using the hyperspectral image and Regularized Logistic Regression algorithm, whereas the time series of multispectral images never exceeded 72% AOA. From all tested spectral transformations, only vegetation indices applied to the time series of multispectral images increased the performance. The results obtained with the hyperspectral image degraded to the specifications of Sentinel-2 emphasize the importance of fine spatial and spectral resolutions to achieve accurate mapping in this complex context. While no significant difference was found between impacted and reference sites through biodiversity metrics, vegetation mapping highlighted some differences in species distribution.
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22
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A Framework for Improving Wall-to-Wall Canopy Height Mapping by Integrating GEDI LiDAR. REMOTE SENSING 2022. [DOI: 10.3390/rs14153618] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Spatially continuous canopy height is a vital input for modeling forest structures and functioning. The global ecosystem dynamics investigation (GEDI) waveform can penetrate a canopy to precisely find the ground and measure canopy height, but it is spatially discontinuous over the earth’s surface. A common method to achieve wall-to-wall canopy height mapping is to integrate a set of field-measured canopy heights and spectral bands from optical and/or microwave remote sensing data as ancillary information. However, due partly to the saturation of spectral reflectance to canopy height, the product of this method may misrepresent canopy height. As a result, neither GEDI footprints nor interpolated maps using the common method can accurately produce spatially continuous canopy height maps alone. To address this issue, this study proposes a framework of point-surface fusion for canopy height mapping (FPSF-CH) that uses GEDI data to calibrate the initial wall-to-wall canopy height map derived from a sub-model of FPSF-CH. The effectiveness of the proposed FPSF-CH was validated by comparison to canopy heights derived from (1) a high-resolution canopy height model derived from airborne discrete point cloud lidar across three test sites, (2) a global canopy height product (GDAL RH95), and (3) the results of the FPSF-CH sub-model without fusing with the GEDI canopy height. The results showed that the RMSE and rRMSE of FPSF-CH were 3.82, 4.05, and 3.48 m, and 18.77, 16.24, and 13.81% across the three test sites, respectively. The FPSF-CH achieved improvement over GDAL RH95, with reductions in RMSE values of 1.28, 2.25, and 2.23 m, and reductions in rRMSE values of 6.29, 9.01, and 8.90% across the three test sites, respectively. Additionally, the better performance of the FPSF-CH compared with its sub-model further confirmed the effectiveness of integrating GEDI data for calibrating wall-to-wall canopy height mapping. The proposed FPSF-CH integrates GEDI LiDAR data to provide a new avenue for accurate wall-to-wall canopy height mapping critical to applications, such as estimations of biomass, biodiversity, and carbon stocks.
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23
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Dronova I, Taddeo S, Harris K. Plant diversity reduces satellite-observed phenological variability in wetlands at a national scale. SCIENCE ADVANCES 2022; 8:eabl8214. [PMID: 35867790 PMCID: PMC9307251 DOI: 10.1126/sciadv.abl8214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 06/08/2022] [Indexed: 06/15/2023]
Abstract
Plant diversity may enhance stability of ecosystem function and its satellite-derived indicators. However, its potential to stabilize phenology, or seasonal changes in plant function, is little understood, especially in understudied systems with high biodiversity potential such as wetlands. Using a large sample of U.S. wetlands and a new satellite-based indicator of phenological stability, we found that plant diversity was negatively associated with interannual phenological variability after controlling for covariates representing climate, site conditions, and spectral fluctuations. Furthermore, plant diversity and covariates better explained phenological variability than stability in annually summarized satellite-based biomass indicators used by earlier studies. Last, a subsequent path analysis indicated that phenological variability could mediate plant diversity relationship with the latter stability. Our results suggest that contributions of plant diversity to seasonality of ecosystems may have a stabilizing role in their functioning and offer a new basis for assessing biodiversity-stability relationships across broad geographic extents.
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Affiliation(s)
- Iryna Dronova
- Department of Environmental Science, Policy, and Management, Rausser College of Natural Resources, University of California Berkeley, Berkeley, CA 94720, USA
- Department of Landscape Architecture and Environmental Planning, College of Environmental Design, University of California Berkeley, Berkeley, CA 94720, USA
| | - Sophie Taddeo
- Negaunee Institute for Plant Conservation Science and Action, Chicago Botanic Garden, Glencoe, IL 60022, USA
| | - Kendall Harris
- Department of Landscape Architecture and Environmental Planning, College of Environmental Design, University of California Berkeley, Berkeley, CA 94720, USA
- San Francisco Estuary Institute, Richmond, CA 94804, USA
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24
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Muise ER, Coops NC, Hermosilla T, Ban SS. Assessing representation of remote sensing derived forest structure and land cover across a network of protected areas. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2603. [PMID: 35366029 PMCID: PMC9286433 DOI: 10.1002/eap.2603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/10/2022] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
Protected areas (PA) are an effective means of conserving biodiversity and protecting suites of valuable ecosystem services. Currently, many nations and international governments use proportional area protected as a critical metric for assessing progress towards biodiversity conservation. However, the areal and other common metrics do not assess the effectiveness of PA networks, nor do they assess how representative PA are of the ecosystems they aim to protect. Topography, stand structure, and land cover are all key drivers of biodiversity within forest environments, and are well-suited as indicators to assess the representation of PA. Here, we examine the PA network in British Columbia, Canada, through drivers derived from freely-available data and remote sensing products across the provincial biogeoclimatic ecosystem classification system. We examine biases in the PA network by elevation, forest disturbances, and forest structural attributes, including height, cover, and biomass by comparing a random sample of protected and unprotected pixels. Results indicate that PA are commonly biased towards high-elevation and alpine land covers, and that forest structural attributes of the park network are often significantly different in protected versus unprotected areas (426 out of 496 forest structural attributes found to be different; p < 0.01). Analysis of forest structural attributes suggests that establishing additional PA could ensure representation of various forest structure regimes across British Columbia's ecosystems. We conclude that these approaches using free and open remote sensing data are highly transferable and can be accomplished using consistent datasets to assess PA representations globally.
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Affiliation(s)
- Evan R. Muise
- Department of Forest Resource ManagementUniversity of British ColumbiaVancouver2424 Main MallBritish ColumbiaCanada
| | - Nicholas C. Coops
- Department of Forest Resource ManagementUniversity of British ColumbiaVancouver2424 Main MallBritish ColumbiaCanada
| | - Txomin Hermosilla
- Canadian Forest Service (Pacific Forestry Centre)Natural Resources CanadaVictoriaBritish ColumbiaCanada
| | - Stephen S. Ban
- BC ParksMinistry of Environment and Climate Change StrategyVictoriaBritish ColumbiaCanada
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25
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Wang J, Ding Y, Wang S, Watson AE, He H, Ye H, Ouyang X, Li Y. Pixel-scale historical-baseline-based ecological quality: Measuring impacts from climate change and human activities from 2000 to 2018 in China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 313:114944. [PMID: 35381526 DOI: 10.1016/j.jenvman.2022.114944] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 03/18/2022] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
Widespread concern about ecological degradation has prompted development of concepts and exploration of methods to quantify ecological quality with the aim of measuring ecosystem changes to contribute to future policy-making. This paper proposes a conceptual framework for ecological quality measurement based on current ecosystem functions and biodiverse habitat, compared with pixel-scale historical baselines. The framework was applied to evaluate the changes and driving factors of ecological quality for Chinese terrestrial ecosystems through remote sensing-based and ecosystem process modeled data at 1 km spatial resolution from 2000 to 2018. The results demonstrated the ecological quality index (EQI) had a very different spatial pattern based upon vegetation distribution. An upward trend in EQI was found over most areas, and variability of 46.95% in EQI can be explained well by change in climate, with an additional 10.64% explained by changing human activities, quantified by population density. This study demonstrated a practical and objective approach for quantifying and assessing ecological quality, which has application potential in ecosystem assessments on scales from local to region and nation, yet would provide a new scientific concept and paradigm for macro ecosystems management and decision-making by governments.
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Affiliation(s)
- Junbang Wang
- National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, 810008, China.
| | - Yuefan Ding
- National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Shaoqiang Wang
- National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Alan E Watson
- USDA Forest Service, Rocky Mountain Research Station, Missoula, MT, 59801, USA.
| | - Honglin He
- National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Hui Ye
- School of Tourism and Geography, Jiujiang University, Jiujiang, 332005, China.
| | - Xihuang Ouyang
- School of Tourism and Geography, Jiujiang University, Jiujiang, 332005, China.
| | - Yingnian Li
- Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, 810008, China.
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26
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Paz A, Silva TS, Carnaval AC. A framework for near-real time monitoring of diversity patterns based on indirect remote sensing, with an application in the Brazilian Atlantic rainforest. PeerJ 2022; 10:e13534. [PMID: 35789655 PMCID: PMC9250313 DOI: 10.7717/peerj.13534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/12/2022] [Indexed: 01/17/2023] Open
Abstract
Monitoring biodiversity change is key to effective conservation policy. While it is difficult to establish in situ biodiversity monitoring programs at broad geographical scales, remote sensing advances allow for near-real time Earth observations that may help with this goal. We combine periodical and freely available remote sensing information describing temperature and precipitation with curated biological information from several groups of animals and plants in the Brazilian Atlantic rainforest to design an indirect remote sensing framework that monitors potential loss and gain of biodiversity in near-real time. Using data from biological collections and information from repeated field inventories, we demonstrate that this framework has the potential to accurately predict trends of biodiversity change for both taxonomic and phylogenetic diversity. The framework identifies areas of potential diversity loss more accurately than areas of species gain, and performs best when applied to broadly distributed groups of animals and plants.
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Affiliation(s)
- Andrea Paz
- Department of Biology, City College of New York, New York, NY, United States of America,Ph.D Program in Biology, City University of New York, Graduate School and University Center, New York, NY, United States of America,Department of Environmental Systems Science, Institute of Integrative Biology, Swiss Federal Institute of Technology, Zurich, Zürich, Switzerland
| | - Thiago S. Silva
- Instituto de Geociências e Ciências Exatas, Departamento de Geografia, Ecosystem Dynamics Observatory, Universidade Estadual Paulista, Rio Claro, São Paulo, Brazil,Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom
| | - Ana C. Carnaval
- Department of Biology, City College of New York, New York, NY, United States of America,Ph.D Program in Biology, City University of New York, Graduate School and University Center, New York, NY, United States of America
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27
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Remote Sensing of Geomorphodiversity Linked to Biodiversity—Part III: Traits, Processes and Remote Sensing Characteristics. REMOTE SENSING 2022. [DOI: 10.3390/rs14092279] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.
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28
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Cavender-Bares J, Schneider FD, Santos MJ, Armstrong A, Carnaval A, Dahlin KM, Fatoyinbo L, Hurtt GC, Schimel D, Townsend PA, Ustin SL, Wang Z, Wilson AM. Integrating remote sensing with ecology and evolution to advance biodiversity conservation. Nat Ecol Evol 2022; 6:506-519. [PMID: 35332280 DOI: 10.1038/s41559-022-01702-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 02/10/2022] [Indexed: 12/31/2022]
Abstract
Remote sensing has transformed the monitoring of life on Earth by revealing spatial and temporal dimensions of biological diversity through structural, compositional and functional measurements of ecosystems. Yet, many aspects of Earth's biodiversity are not directly quantified by reflected or emitted photons. Inclusive integration of remote sensing with field-based ecology and evolution is needed to fully understand and preserve Earth's biodiversity. In this Perspective, we argue that multiple data types are necessary for almost all draft targets set by the Convention on Biological Diversity. We examine five key topics in biodiversity science that can be advanced by integrating remote sensing with in situ data collection from field sampling, experiments and laboratory studies to benefit conservation. Lowering the barriers for bringing these approaches together will require global-scale collaboration.
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Affiliation(s)
| | - Fabian D Schneider
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | | | - Amanda Armstrong
- Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Ana Carnaval
- Department of Biology, Ph.D. Program in Biology, City University of New York and The Graduate Center of CUNY, New York City, NY, USA
| | - Kyla M Dahlin
- Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, USA
| | - Lola Fatoyinbo
- Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - George C Hurtt
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA
| | - David Schimel
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Philip A Townsend
- Department of Forest and Wildlife Ecology, Univ. of Wisconsin-Madison, Madison, WI, USA
| | - Susan L Ustin
- Department of Land, Air and Water Resources and the John Muir Institute of the Environment, University of California, Davis, CA, USA
| | - Zhihui Wang
- Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, China
| | - Adam M Wilson
- Department of Geography, University at Buffalo, Buffalo, NY, USA
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29
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Predicting Species and Structural Diversity of Temperate Forests with Satellite Remote Sensing and Deep Learning. REMOTE SENSING 2022. [DOI: 10.3390/rs14071631] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Anthropogenically-driven climate change, land-use changes, and related biodiversity losses are threatening the capability of forests to provide a variety of valuable ecosystem services. The magnitude and diversity of these services are governed by tree species richness and structural complexity as essential regulators of forest biodiversity. Sound conservation and sustainable management strategies rely on information from biodiversity indicators that is conventionally derived by field-based, periodical inventory campaigns. However, these data are usually site-specific and not spatially explicit, hampering their use for large-scale monitoring applications. Therefore, the main objective of our study was to build a robust method for spatially explicit modeling of biodiversity variables across temperate forest types using open-access satellite data and deep learning models. Field data were obtained from the Biodiversity Exploratories, a research infrastructure platform that supports ecological research in Germany. A total of 150 forest plots were sampled between 2014 and 2018, covering a broad range of environmental and forest management gradients across Germany. From field data, we derived key indicators of tree species diversity (Shannon Wiener Index) and structural heterogeneity (standard deviation of tree diameter) as proxies of forest biodiversity. Deep neural networks were used to predict the selected biodiversity variables based on Sentinel-1 and Sentinel-2 images from 2017. Predictions of tree diameter variation achieved good accuracy (r2 = 0.51) using Sentinel-1 winter-based backscatter data. The best models of species diversity used a set of Sentinel-1 and Sentinel-2 features but achieved lower accuracies (r2 = 0.25). Our results demonstrate the potential of deep learning and satellite remote sensing to predict forest parameters across a broad range of environmental and management gradients at the landscape scale, in contrast to most studies that focus on very homogeneous settings. These highly generalizable and spatially continuous models can be used for monitoring ecosystem status and functions, contributing to sustainable management practices, and answering complex ecological questions.
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30
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Biodiversity and infrastructure interact to drive tourism to and within Costa Rica. Proc Natl Acad Sci U S A 2022; 119:e2107662119. [PMID: 35245152 PMCID: PMC8931240 DOI: 10.1073/pnas.2107662119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Tourism accounts for roughly 10% of global gross domestic product, with nature-based tourism its fastest-growing sector in the past 10 years. Nature-based tourism can theoretically contribute to local and sustainable development by creating attractive livelihoods that support biodiversity conservation, but whether tourists prefer to visit more biodiverse destinations is poorly understood. We examine this question in Costa Rica and find that more biodiverse places tend indeed to attract more tourists, especially where there is infrastructure that makes these places more accessible. Safeguarding terrestrial biodiversity is critical to preserving the substantial economic benefits that countries derive from tourism. Investments in both biodiversity conservation and infrastructure are needed to allow biodiverse countries to rely on tourism for their sustainable development. Nature-based tourism has potential to sustain biodiversity and economic development, yet the degree to which biodiversity drives tourism patterns, especially relative to infrastructure, is poorly understood. Here, we examine relationships between different types of biodiversity and different types of tourism in Costa Rica to address three questions. First, what is the contribution of species richness in explaining patterns of tourism in protected areas and country-wide in Costa Rica? Second, how similar are the patterns for birdwatching tourism compared to those of overall tourism? Third, where in the country is biodiversity contributing more than other factors to birdwatching tourism and to overall tourism? We integrated environmental data and species occurrence records to build species distribution models for 66 species of amphibians, reptiles, and mammals, and for 699 bird species. We used built infrastructure variables (hotel density and distance to roads), protected area size, distance to protected areas, and distance to water as covariates to evaluate the relative importance of biodiversity in predicting birdwatching tourism (via eBird checklists) and overall tourism (via Flickr photographs) within Costa Rica. We found that while the role of infrastructure is larger than any other variable, it alone is not sufficient to explain birdwatching and tourism patterns. Including biodiversity adds predictive power and alters spatial patterns of predicted tourism. Our results suggest that investments in infrastructure must be paired with successful biodiversity conservation for tourism to generate the economic revenue that countries like Costa Rica derive from it, now and into the future.
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31
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Harmonizing Forest Conservation Policies with Essential Biodiversity Variables Incorporating Remote Sensing and Environmental DNA Technologies. FORESTS 2022. [DOI: 10.3390/f13030445] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
It remains difficult to compare the state of conservation of forests of different nations. Essential Biodiversity Variables (EBVs) are a set of variables designed as a framework for harmonizing biodiversity monitoring. Methods to monitor forest biodiversity are traditional monitoring (according to conservation policy requirements), remote sensing, environmental DNA, and the information products that are derived from them (RS/eDNA biodiversity products). However, it is not clear to what extent indicators from conservation policies align with EBVs and RS/eDNA biodiversity products. This research evaluated current gaps in harmonization between EBVs, RS/eDNA biodiversity products and forest conservation indicators. We compared two sets of biodiversity variables: (1) forest conservation indicators and (2) RS/eDNA biodiversity products, within the context of the Essential Biodiversity Variables framework. Indicators derived from policy documents can mostly be categorized within the EBV ‘ecosystem vertical profile’, while ‘ecosystem function’ remains underrepresented. RS/eDNA biodiversity products, however, can provide information about ‘ecosystem function’. Integrating RS/eDNA biodiversity products that monitor ecosystem functioning into monitoring programs will lead to a more comprehensive and balanced reporting on forest biodiversity. In addition, using the same variables and similar RS/eDNA products for forest biodiversity and conservation policies is a requirement for harmonization and international policy reporting.
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Abstract
Conservation approaches in tiger landscapes have focused on single species and their habitat. Further, the limited extent of the existing protected area network in India lacks representativeness, habitat connectivity, and integration in the larger landscape. Our objective was to identify sites important for connected tiger habitat and biodiversity potential in the Greater Panna Landscape, central India. Further, we aimed to set targets at the landscape level for conservation and prioritize these sites within each district in the landscape as specific management/conservation zones. We used earth observation data to derive an index of biodiversity potential. Marxan was used to identify sites that met tiger and biodiversity conservation targets with minimum costs. We found that to protect 50% of the tiger habitat with connectivity, 20% of the landscape area must be conserved. To conserve 100% of high biodiversity potential, 50% moderate biodiversity potential, and 25% low biodiversity potential, 55% of the landscape area must be conserved. To represent both tiger habitat and biodiversity, 62% of the total landscape area requires conservation or restoration intervention. The prioritized zones can prove significant for hierarchical decision making, involving multiple stakeholders in the landscape, including other tiger range areas.
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Priyadarshini P, Bundela AK, Gasparatos A, Stringer LC, Dhyani S, Dasgupta R, Reddy CS, Baral H, Muradian R, Karki M, Abhilash PC, Peñuelas J. Advancing Global Biodiversity Governance: Recommendations for Strengthening the Post-2020 Global Biodiversity Framework. ANTHROPOCENE SCIENCE 2022. [PMCID: PMC8931452 DOI: 10.1007/s44177-022-00013-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Abstract Reversing ecosystem degradation and halting global biodiversity loss due to climate change and other anthropogenic drivers are essential for socioeconomic development and human wellbeing, as well as for advancing global sustainability. The latest initiative in this direction is the ‘Post-2020 Global Biodiversity Framework’, which establishes a blueprint for global coordinated action towards development of national and regional strategies targeting conservation and sustainable utilization of biodiversity. By supporting the notion of ‘ecological civilization’, it emphasises the need for transformative strategies to conserve, monitor and sustainably manage ecosystems by 2030. Arguably the articulation of fit-for-purpose goals and targets is a key precondition for achieving this vision by enhancing cooperation and influencing the development of implementation strategies and regulatory instruments at national and local levels. The present Policy Analysis critically reviews the key features of the draft Post-2020 Global Biodiversity Framework and suggests recommendations to further strengthen it. Graphical Abstract ![]()
Biodiversity conservation is imperative for planetary resilience and human health and wellbeing. The Post-2020 Global Biodiversity framework aims to guide biodiversity governance towards ‘ecological civilization’. Transformative approaches targeting climate adaptation and mitigation, circularity, biodiversity renewal and nature-based solutions require better inclusion. Attainable and widely acceptable indicators for the different targets are necessary to ensure the framework’s effectiveness. The interface of climate change mitigation, adaptation and biodiversity conservation should be further strengthened in the framework.
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Affiliation(s)
- Priya Priyadarshini
- Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, 221005 India
| | - Amit Kumar Bundela
- Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, 221005 India
| | - Alexandros Gasparatos
- Institute for Future Initiatives (IFI), University of Tokyo, Tokyo, 113-0033 Japan
- Institute for the Advanced Study of Sustainability (UNU-IAS), United Nations University, Tokyo, 150-8925 Japan
| | - Lindsay C. Stringer
- Department of Environment and Geography, University of York, Wentworth Way, Heslington, YO10 5NG York UK
| | - Shalini Dhyani
- CSIR-National Environmental Engineering Research Institute, Nagpur, Maharashtra India
| | - Rajarshi Dasgupta
- Nature Resources and Ecosystem Services, Institute for Global Environmental Strategies (IGES), Hayama, Japan
| | - Chintala Sudhakar Reddy
- National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad, 500-037 India
| | - Himlal Baral
- Climate Change, Energy and Low Carbon Development, CIFOR-ICRAF, Bogor Regency, Indonesia
| | - Roldan Muradian
- Universidade Federal Fluminense, Niteroi, Rio di Janeiro, Brazil
| | - Madhav Karki
- Institute of Forestry, Tribhuvan University, Kirtipur, Nepal
| | | | - Josep Peñuelas
- CSIC, Global Ecology, CREAF-CSIC-UAB, 08193 Bellaterra, Catalonia Spain
- CREAF, 08193 Cerdanyola del Valles, Catalonia Spain
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Individual Tree Crown Delineation Method Based on Multi-Criteria Graph Using Geometric and Spectral Information: Application to Several Temperate Forest Sites. REMOTE SENSING 2022. [DOI: 10.3390/rs14051083] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Individual tree crown (ITC) delineation in temperate forests is challenging owing to the presence of broadleaved species with overlapping crowns. Mixed coniferous/deciduous forests with characteristics that differ with the type of tree thus require a flexible method of delineation. The ITC delineation method based on the multi-criteria graph (MCG-Tree) addresses this problem in temperate monospecific or mixed forests by combining geometric and spectral information. The method was used to segment trees in three temperate forest sites with different characteristics (tree types, species distribution, planted or natural forest). Compared with a state-of-the-art watershed segmentation approach, our method increased delineation performance by up to 25%. Our results showed that the main geometric criterion to improve delineation quality is related to the crown radius (performance improvement around 8%). Coniferous/deciduous classification automatically adapts the MCG-Tree criteria to the type of tree. Promising results are then obtained to improve delineation performance for mixed forests.
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Abstract
[Figure: see text].
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Affiliation(s)
- Nico Eisenhauer
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany.,Institute of Biology, Leipzig University, Puschstraße 4, 04103 Leipzig, Germany
| | - Alexandra Weigelt
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany.,Institute of Biology, Leipzig University, Johannisallee 21, 04103 Leipzig, Germany
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Fusing Sentinel-1 and -2 to Model GEDI-Derived Vegetation Structure Characteristics in GEE for the Paraguayan Chaco. REMOTE SENSING 2021. [DOI: 10.3390/rs13245105] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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.
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Zelený J, Mercado-Bettín D, Müller F. Towards the evaluation of regional ecosystem integrity using NDVI, brightness temperature and surface heterogeneity. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 796:148994. [PMID: 34328885 DOI: 10.1016/j.scitotenv.2021.148994] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/10/2021] [Accepted: 07/08/2021] [Indexed: 06/13/2023]
Abstract
Maintaining ecological integrity is globally acknowledged as a strategic goal, yet there is no consensus on a practical and widely usable methodology to assess it. This study proposes a comprehensive approach to quantify regional ecosystem integrity based on FAIR data, obtained using satellite remote sensing and image analysis. Three variables are central to this approach: normalized difference vegetation index (NDVI), at-satellite brightness temperature (BT) and vegetation surface heterogeneity (HG), corresponding to ecosystem integrity indicators exergy capture, biotic water flows and abiotic heterogeneity. The indicators are assessed across the vegetation period and a representative Regional Index of Ecological Integrity (RIEI) is proposed to express the integrity of two case study areas and representative land use types. The proposed approach proved powerful in representing the anthropogenic and autopoietic gradient within study regions in high detail. Arable lands and urban areas ranked lowest, while dense forests and wetlands highest, agriculture being the most significant factor reducing regional integrity. Areas with conservation significance ranked either having the highest integrity, when dense vegetation was present, and mediocre or even low in case of e.g., sand dunes, marches and rock formations. Limitations of the method comprise: insufficient representation of biodiversity, sensitivity to cloud cover and demanding in-situ validation. The approach can be scaled from global to local level, adapted to various remote sensing techniques and complemented by a diversity of data (e.g., ecosystem services, geomorphological, climatic) to provide deeper understanding of landscape ecosystem integrity.
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Affiliation(s)
- Jakub Zelený
- Faculty of Humanities, Charles University in Prague, Pátkova 2137/5, 182 00 Prague 8, Czech Republic; Global Change Research Institute of the Czech Academy of Sciences, Bělidla 986/4a, 603 00 Brno, Czech Republic; Christian-Albrechts-University Kiel, Institute for Natural Resource Conservation, Olshausenstraße 75, 24118 Kiel, Germany.
| | - Daniel Mercado-Bettín
- Universidad de Antioquia, Escuela Ambiental, Medellin, Colombia; Catalan Institute for Water Research, Girona, Spain; Universitat de Girona, Girona, Spain; Christian-Albrechts-University of Kiel, Department of Hydrology and Water Resources Management, Kiel, Germany
| | - Felix Müller
- Christian-Albrechts-University Kiel, Institute for Natural Resource Conservation, Olshausenstraße 75, 24118 Kiel, Germany
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Ghoddousi A, Loos J, Kuemmerle T. An Outcome-Oriented, Social–Ecological Framework for Assessing Protected Area Effectiveness. Bioscience 2021; 72:201-212. [PMID: 35145352 PMCID: PMC8824764 DOI: 10.1093/biosci/biab114] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/21/2021] [Accepted: 10/06/2021] [Indexed: 11/12/2022] Open
Abstract
Abstract
Both the number and the extent of protected areas have grown considerably in recent years, but evaluations of their effectiveness remain partial and are hard to compare across cases. To overcome this situation, first, we suggest reserving the term effectiveness solely for assessing protected area outcomes, to clearly distinguish this from management assessments (e.g., sound planning). Second, we propose a multidimensional conceptual framework, rooted in social–ecological theory, to assess effectiveness along three complementary dimensions: ecological outcomes (e.g., biodiversity), social outcomes (e.g., well-being), and social–ecological interactions (e.g., reduced human pressures). Effectiveness indicators can subsequently be evaluated against contextual and management elements (e.g., design and planning) to shed light on management performance (e.g., cost-effectiveness). We summarize steps to operationalize our framework to foster more holistic effectiveness assessments while improving comparability across protected areas. All of this can ensure that protected areas make real contributions toward conservation and sustainability goals.
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John C, Shilling F, Post E. drp
T
oolkit
: An automated workflow for aligning and analysing vegetation and ground surface time‐series imagery. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Christian John
- Department of Wildlife, Fish, and Conservation Biology University of California, Davis Davis CA USA
| | - Fraser Shilling
- Department of Environmental Science and Policy University of California, Davis Davis CA USA
| | - Eric Post
- Department of Wildlife, Fish, and Conservation Biology University of California, Davis Davis CA USA
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40
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Fahrig L, Watling JI, Arnillas CA, Arroyo-Rodríguez V, Jörger-Hickfang T, Müller J, Pereira HM, Riva F, Rösch V, Seibold S, Tscharntke T, May F. Resolving the SLOSS dilemma for biodiversity conservation: a research agenda. Biol Rev Camb Philos Soc 2021; 97:99-114. [PMID: 34453405 PMCID: PMC9290967 DOI: 10.1111/brv.12792] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 11/29/2022]
Abstract
The legacy of the 'SL > SS principle', that a single or a few large habitat patches (SL) conserve more species than several small patches (SS), is evident in decisions to protect large patches while down-weighting small ones. However, empirical support for this principle is lacking, and most studies find either no difference or the opposite pattern (SS > SL). To resolve this dilemma, we propose a research agenda by asking, 'are there consistent, empirically demonstrated conditions leading to SL > SS?' We first review and summarize 'single large or several small' (SLOSS) theory and predictions. We found that most predictions of SL > SS assume that between-patch variation in extinction rate dominates the outcome of the extinction-colonization dynamic. This is predicted to occur when populations in separate patches are largely independent of each other due to low between-patch movements, and when species differ in minimum patch size requirements, leading to strong nestedness in species composition along the patch size gradient. However, even when between-patch variation in extinction rate dominates the outcome of the extinction-colonization dynamic, theory can predict SS > SL. This occurs if extinctions are caused by antagonistic species interactions or disturbances, leading to spreading-of-risk of landscape-scale extinction across SS. SS > SL is also predicted when variation in colonization dominates the outcome of the extinction-colonization dynamic, due to higher immigration rates for SS than SL, and larger species pools in proximity to SS than SL. Theory that considers change in species composition among patches also predicts SS > SL because of higher beta diversity across SS than SL. This results mainly from greater environmental heterogeneity in SS due to greater variation in micro-habitats within and across SS habitat patches ('across-habitat heterogeneity'), and/or more heterogeneous successional trajectories across SS than SL. Based on our review of the relevant theory, we develop the 'SLOSS cube hypothesis', where the combination of three variables - between-patch movement, the role of spreading-of-risk in landscape-scale population persistence, and across-habitat heterogeneity - predict the SLOSS outcome. We use the SLOSS cube hypothesis and existing SLOSS empirical evidence, to predict SL > SS only when all of the following are true: low between-patch movement, low importance of spreading-of-risk for landscape-scale population persistence, and low across-habitat heterogeneity. Testing this prediction will be challenging, as it will require many studies of species groups and regions where these conditions hold. Each such study would compare gamma diversity across multiple landscapes varying in number and sizes of patches. If the prediction is not generally supported across such tests, then the mechanisms leading to SL > SS are extremely rare in nature and the SL > SS principle should be abandoned.
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Affiliation(s)
- Lenore Fahrig
- Geomatics and Landscape Ecology Laboratory, Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON, Canada
| | - James I Watling
- John Carroll University, 1 John Carroll Blvd., University Heights, OH, U.S.A
| | | | - Víctor Arroyo-Rodríguez
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autonoma de Mexico, Antigua Carretera a Patzcuaro No. 8701, Ex-Hacienda de San Jose de la Huerta, 58190, Morelia, Michoacan, Mexico.,Escuela Nacional de Estudios Superiores, Universidad Nacional Autonoma de Mexico, Tablaje Catastral No. 6998, Carretera Merida-Tetiz km 4.5, Municipio de Ucu, 97357, Merida, Yucatan, Mexico
| | - Theresa Jörger-Hickfang
- German Centre for Integrative Biodiversity Research (Halle-Jena-Leipzig), Deutscher Platz 5e, 04103, Leipzig, Germany.,Institute of Biology, Martin Luther University, Halle-Wittenberg, Am Kirchtor 1, 06108, Halle (Saale), Germany
| | - Jörg Müller
- University of Würzburg, Sanderring 2, 97070, Würzburg, Germany.,Bavarian Forest National Park, Freyunger Str. 2, 94481, Grafenau, Germany
| | - Henrique M Pereira
- German Centre for Integrative Biodiversity Research (Halle-Jena-Leipzig), Deutscher Platz 5e, 04103, Leipzig, Germany
| | - Federico Riva
- Geomatics and Landscape Ecology Laboratory, Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON, Canada
| | - Verena Rösch
- Ecosystem Analysis, Institute for Environmental Science, University of Koblenz-Landau, Fortstraße 7, 76829, Landau, Germany
| | - Sebastian Seibold
- Ecosystem Dynamics and Forest Management Research Group, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, 85354, Freising, Germany.,Berchtesgaden National Park, Doktorberg 6, 83471, Berchtesgaden, Germany
| | - Teja Tscharntke
- Agroecology, University of Göttingen, Wilhelmsplatz 1, 37073, Göttingen, Germany
| | - Felix May
- Freie Universität Berlin, Kaiserswerther Str. 16-18, 14195, Berlin, Germany
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Pinto-Ledezma JN, Cavender-Bares J. Predicting species distributions and community composition using satellite remote sensing predictors. Sci Rep 2021; 11:16448. [PMID: 34385574 PMCID: PMC8361206 DOI: 10.1038/s41598-021-96047-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 08/04/2021] [Indexed: 02/07/2023] Open
Abstract
Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models-including assemblage diversity and composition-obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m2). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km2). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space.
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Affiliation(s)
- Jesús N Pinto-Ledezma
- Department of Ecology, Evolution and Behavior, University of Minnesota, 1479 Gortner Ave, Saint Paul, MN, 55108, USA.
| | - Jeannine Cavender-Bares
- Department of Ecology, Evolution and Behavior, University of Minnesota, 1479 Gortner Ave, Saint Paul, MN, 55108, USA
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