Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach
Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
862480
EC | EU Framework Programme for Research and Innovation H2020 | H2020 Excellent Science (H2020 Priority Excellent Science)
862480
EC | EU Framework Programme for Research and Innovation H2020 | H2020 Excellent Science (H2020 Priority Excellent Science)
862480
EC | EU Framework Programme for Research and Innovation H2020 | H2020 Excellent Science (H2020 Priority Excellent Science)
862480
EC | EU Framework Programme for Research and Innovation H2020 | H2020 Excellent Science (H2020 Priority Excellent Science)
PubMed
38191639
PubMed Central
PMC10774354
DOI
10.1038/s41598-023-50308-9
PII: 10.1038/s41598-023-50308-9
Knihovny.cz E-zdroje
- MeSH
- bronchiální astma * MeSH
- ekosystém * MeSH
- fotogrammetrie MeSH
- květy MeSH
- pastviny MeSH
- včely MeSH
- zemědělství MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
The ecosystem services offered by pollinators are vital for supporting agriculture and ecosystem functioning, with bees standing out as especially valuable contributors among these insects. Threats such as habitat fragmentation, intensive agriculture, and climate change are contributing to the decline of natural bee populations. Remote sensing could be a useful tool to identify sites of high diversity before investing into more expensive field survey. In this study, the ability of Unoccupied Aerial Vehicles (UAV) images to estimate biodiversity at a local scale has been assessed while testing the concept of the Height Variation Hypothesis (HVH). This hypothesis states that the higher the vegetation height heterogeneity (HH) measured by remote sensing information, the higher the vegetation vertical complexity and the associated species diversity. In this study, the concept has been further developed to understand if vegetation HH can also be considered a proxy for bee diversity and abundance. We tested this approach in 30 grasslands in the South of the Netherlands, where an intensive field data campaign (collection of flower and bee diversity and abundance) was carried out in 2021, along with a UAV campaign (collection of true color-RGB-images at high spatial resolution). Canopy Height Models (CHM) of the grasslands were derived using the photogrammetry technique "Structure from Motion" (SfM) with horizontal resolution (spatial) of 10 cm, 25 cm, and 50 cm. The accuracy of the CHM derived from UAV photogrammetry was assessed by comparing them through linear regression against local CHM LiDAR (Light Detection and Ranging) data derived from an Airborne Laser Scanner campaign completed in 2020/2021, yielding an [Formula: see text] of 0.71. Subsequently, the HH assessed on the CHMs at the three spatial resolutions, using four different heterogeneity indices (Rao's Q, Coefficient of Variation, Berger-Parker index, and Simpson's D index), was correlated with the ground-based flower and bee diversity and bee abundance data. The Rao's Q index was the most effective heterogeneity index, reaching high correlations with the ground-based data (0.44 for flower diversity, 0.47 for bee diversity, and 0.34 for bee abundance). Interestingly, the correlations were not significantly influenced by the spatial resolution of the CHM derived from UAV photogrammetry. Our results suggest that vegetation height heterogeneity can be used as a proxy for large-scale, standardized, and cost-effective inference of flower diversity and habitat quality for bees.
Department of Environmental Science and Policy University of Milan Milan Italy
Eurac Research Inst for Alpine Environment Bolzano Italy
German Centre for Integrative Biodiversity Research Halle Jena Leipzig Leipzig Germany
Remote Sensing Centre for Earth System Research Leipzig University Leipzig Germany
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Akinbiola S, Salami AT, Awotoye OO, Popoola SO, Olusola JA. Application of UAV photogrammetry for the assessment of forest structure and species network in the tropical forests of southern nigeria. Geocarto Int. 2023;38(1):87–107. doi: 10.1080/10106049.2023.2190621. DOI
Anderle M, Brambilla M, Hilpold A, Matabishi JG, Paniccia C, Rocchini D, Rossin J, Tasser E, Torresani M, Tappeiner U, et al. Habitat heterogeneity promotes bird diversity in agricultural landscapes: Insights from remote sensing data. Basic Appl. Ecol. 2023;70:38–49. doi: 10.1016/j.baae.2023.04.006. DOI
Banaszak, J. Effect of habitat heterogeneity on the diversity and density of pollinating insects. Interchanges of insects between agricultural and surrounding landscapes (2000), 123–140.
Bartholomeus H, Calders K, Whiteside T, Terryn L, Krishna Moorthy SM, Levick SR, Bartolo R, Verbeeck H. Evaluating data inter-operability of multiple UAV-lidar systems for measuring the 3d structure of savanna woodland. Remote Sens. 2022;14(23):5992. doi: 10.3390/rs14235992. DOI
Botta-Dukát Z. Rao’s quadratic entropy as a measure of functional diversity based on multiple traits. J. Veg. Sci. 2005;16(5):533–540. doi: 10.1111/j.1654-1103.2005.tb02393.x. DOI
Breeze TD, Bailey AP, Balcombe KG, Potts SG. Pollination services in the UK: How important are honeybees? Agricult. Ecosyst. Environ. 2011;142(3–4):137–143. doi: 10.1016/j.agee.2011.03.020. DOI
Cavender-Bares J, Schneider FD, Santos MJ, Armstrong A, Carnaval A, Dahlin KM, Fatoyinbo L, Hurtt GC, Schimel D, Townsend PA, et al. Integrating remote sensing with ecology and evolution to advance biodiversity conservation. Nat. Ecol. Evolut. 2022;6(5):506–519. doi: 10.1038/s41559-022-01702-5. PubMed DOI
Christin S, Hervet É, Lecomte N. Applications for deep learning in ecology. Methods Ecol. Evol. 2019;10(10):1632–1644. doi: 10.1111/2041-210X.13256. DOI
Curcio AC, Barbero L, Peralta G. UAV-hyperspectral imaging to estimate species distribution in salt marshes: A case study in the Cadiz Bay (SW Spain) Remote Sens. 2023;15(5):1419. doi: 10.3390/rs15051419. DOI
da Silva SDP, Eugenio FC, Fantinel RA, de Paula Amaral L, dos Santos AR, Mallmann CL, dos Santos FD, Pereira RS, Ruoso R. Modeling and detection of invasive trees using UAV image and machine learning in a subtropical forest in Brazil. Eco. Inform. 2023;74:101989. doi: 10.1016/j.ecoinf.2023.101989. DOI
de Castro AI, Shi Y, Maja JM, Peña JM. UAVs for vegetation monitoring: Overview and recent scientific contributions. Remote Sens. 2021;13(11):2139. doi: 10.3390/rs13112139. DOI
De Sa NC, Castro P, Carvalho S, Marchante E, López-Núñez FA, Marchante H. Mapping the flowering of an invasive plant using unmanned aerial vehicles: Is there potential for biocontrol monitoring? Front. Plant Sci. 2018;9:293. doi: 10.3389/fpls.2018.00293. PubMed DOI PMC
DeJong, T. M. A comparison of three diversity indices based on their components of richness and evenness. Oikos (1975), 222–227.
Dubayah R, Armston J, Healey SP, Bruening JM, Patterson PL, Kellner JR, Duncanson L, Saarela S, Ståhl G, Yang Z, et al. Gedi launches a new era of biomass inference from space. Environ. Res. Lett. 2022;17(9):095001. doi: 10.1088/1748-9326/ac8694. DOI
Duelli P. Biodiversity evaluation in agricultural landscapes: An approach at two different scales. Agricult. Ecosyst. Environ. 1997;62(2–3):81–91. doi: 10.1016/S0167-8809(96)01143-7. DOI
Duncanson L, Kellner JR, Armston J, Dubayah R, Minor DM, Hancock S, Healey SP, Patterson PL, Saarela S, Marselis S, et al. Aboveground biomass density models for NASA’s global ecosystem dynamics investigation (GEDI) lidar mission. Remote Sens. Environ. 2022;270:112845. doi: 10.1016/j.rse.2021.112845. DOI
Falk, S., and Lewington, R. Veldgids bijen voor Nederland en Vlaanderen. 2017.
Feilhauer H, Doktor D, Schmidtlein S, Skidmore AK. Mapping pollination types with remote sensing. J. Veg. Sci. 2016;27(5):999–1011. doi: 10.1111/jvs.12421. DOI
Feilhauer H, Zlinszky A, Kania A, Foody GM, Doktor D, Lausch A, Schmidtlein S. Let your maps be fuzzy!-class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation. Remote Sens. Ecol. Conserv. 2021;7(2):292–305. doi: 10.1002/rse2.188. DOI
Gallai N, Salles J-M, Settele J, Vaissière BE. Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecol. Econ. 2009;68(3):810–821. doi: 10.1016/j.ecolecon.2008.06.014. DOI
Gallmann, J., Schüpbach, B., Jacot, K., Albrecht, M., Winizki, J., Kirchgessner, N., and Aasen, H. Flower mapping in grasslands with drones and deep learning. Front. Plant Sci. 12 (2021). PubMed PMC
Gholizadeh H, Gamon JA, Zygielbaum AI, Wang R, Schweiger AK, Cavender-Bares J. Remote sensing of biodiversity: Soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems. Remote Sens. Environ. 2018;206:240–253. doi: 10.1016/j.rse.2017.12.014. DOI
Gonzales, D., Hempel de Ibarra, N., and Anderson, K. Remote sensing of floral resources for pollinators–new horizons from satellites to drones. Front. Ecol. Evolut. 10 (2022).
Hovick TJ, Elmore RD, Fuhlendorf SD. Structural heterogeneity increases diversity of non-breeding grassland birds. Ecosphere. 2014;5(5):1–13. doi: 10.1890/ES14-00062.1. DOI
Howison RA, Piersma T, Kentie R, Hooijmeijer JC, Olff H. Quantifying landscape-level land-use intensity patterns through radar-based remote sensing. J. Appl. Ecol. 2018;55(3):1276–1287. doi: 10.1111/1365-2664.13077. DOI
Hui G, Zhang G, Zhao Z, Yang A. Methods of forest structure research: A review. Curr. For. Rep. 2019;5:142–154. doi: 10.1007/s40725-019-00090-7. DOI
Kleijn D, Kohler F, Báldi A, Batáry P, Concepción E, Clough Y, Díaz M, Gabriel D, Holzschuh A, Knop E, et al. On the relationship between farmland biodiversity and land-use intensity in Europe. Proc. Royal Soc. B Biol. Sci. 2009;276(1658):903–909. doi: 10.1098/rspb.2008.1509. PubMed DOI PMC
Kleijn D, Winfree R, Bartomeus I, Carvalheiro LG, Henry M, Isaacs R, Klein A-M, Kremen C, M’gonigle LK, Rader R, et al. Delivery of crop pollination services is an insufficient argument for wild pollinator conservation. Nat. Commun. 2015;6(1):1–9. doi: 10.1038/ncomms8414. PubMed DOI PMC
Kolarik NE, Gaughan AE, Stevens FR, Pricope NG, Woodward K, Cassidy L, Salerno J, Hartter J. A multi-plot assessment of vegetation structure using a micro-unmanned aerial system (UAS) in a semi-arid savanna environment. ISPRS J. Photogramm. Remote. Sens. 2020;164:84–96. doi: 10.1016/j.isprsjprs.2020.04.011. DOI
Kremen, C., Chaplin-Kramer, R., et al. Insects as providers of ecosystem services: crop pollination and pest control. In Insect conservation biology: proceedings of the royal entomological society’s 23rd symposium (2007), CABI Publishing Wallingford, UK, 349–382.
Kremen C, Williams NM, Thorp RW. Crop pollination from native bees at risk from agricultural intensification. Proc. Natl. Acad. Sci. 2002;99(26):16812–16816. doi: 10.1073/pnas.262413599. PubMed DOI PMC
Kuemmerle T, Erb K, Meyfroidt P, Müller D, Verburg PH, Estel S, Haberl H, Hostert P, Jepsen MR, Kastner T, et al. Challenges and opportunities in mapping land use intensity globally. Curr. Opin. Environ. Sustain. 2013;5(5):484–493. doi: 10.1016/j.cosust.2013.06.002. PubMed DOI PMC
Kumar P, Dobriyal M, Kale A, Pandey A, Tomar R, Thounaojam E. Calculating forest species diversity with information-theory based indices using sentinel-2a sensor’s of Mahavir Swami wildlife sanctuary. PLoS ONE. 2022;17(5):e0268018. doi: 10.1371/journal.pone.0268018. PubMed DOI PMC
Lang, N., Jetz, W., Schindler, K., and Wegner, J. D. A high-resolution canopy height model of the earth. arXiv preprint arXiv:2204.08322 (2022). PubMed PMC
Levin N, Shmida A, Levanoni O, Tamari H, Kark S. Predicting mountain plant richness and rarity from space using satellite-derived vegetation indices. Divers. Distrib. 2007;13(6):692–703. doi: 10.1111/j.1472-4642.2007.00372.x. DOI
Liu M, Yu T, Gu X, Sun Z, Yang J, Zhang Z, Mi X, Cao W, Li J. The impact of spatial resolution on the classification of vegetation types in highly fragmented planting areas based on unmanned aerial vehicle hyperspectral images. Remote Sens. 2020;12(1):146. doi: 10.3390/rs12010146. DOI
Lu B, He Y. Optimal spatial resolution of unmanned aerial vehicle (UAV)-acquired imagery for species classification in a heterogeneous grassland ecosystem. GIScience Remote Sens. 2018;55(2):205–220. doi: 10.1080/15481603.2017.1408930. DOI
Melville B, Lucieer A, Aryal J. Classification of lowland native grassland communities using hyperspectral unmanned aircraft system (UAS) imagery in the tasmanian midlands. Drones. 2019;3(1):5. doi: 10.3390/drones3010005. DOI
Michele, T., Duccio, R., Marc, Z., Ruth, S., and Giustino, T. Testing the spectral variation hypothesis by using the rao-q index to estimate forest biodiversity: Effect of spatial resolution. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (2018), IEEE, 1183–1186.
Moeslund JE, Zlinszky A, Ejrnæs R, Brunbjerg AK, Bøcher PK, Svenning J-C, Normand S. Light detection and ranging explains diversity of plants, fungi, lichens, and bryophytes across multiple habitats and large geographic extent. Ecol. Appl. 2019;29(5):e01907. doi: 10.1002/eap.1907. PubMed DOI PMC
Moudrỳ V, Cord AF, Gábor L, Laurin GV, Barták V, Gdulová K, Malavasi M, Rocchini D, Stereńczak K, Prošek J, et al. Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward. Divers. Distrib. 2023;29(1):39–50. doi: 10.1111/ddi.13644. DOI
Moudrỳ V, Keil P, Gábor L, Lecours V, Zarzo-Arias A, Barták V, Malavasi M, Rocchini D, Torresani M, Gdulová K, et al. Scale mismatches between predictor and response variables in species distribution modelling: A review of practices for appropriate grain selection. Prog. Phys. Geogr. Earth Environ. 2023;47(3):467–482. doi: 10.1177/03091333231156362. DOI
Moudrỳ V, Moudrá L, Barták V, Bejček V, Gdulová K, Hendrychová M, Moravec D, Musil P, Rocchini D, Št’astnỳ K, et al. The role of the vegetation structure, primary productivity and senescence derived from airborne lidar and hyperspectral data for birds diversity and rarity on a restored site. Landsc. Urban Plan. 2021;210:104064. doi: 10.1016/j.landurbplan.2021.104064. DOI
Müllerová J, Brna J, Bartaloš T, Dvořák P, Vítková M, Pyšek P. Timing is important: Unmanned aircraft vs. satellite imagery in plant invasion monitoring. Front. Plant Sci. 2017;8:887. doi: 10.3389/fpls.2017.00887. PubMed DOI PMC
Nagendra H, Rocchini D. High resolution satellite imagery for tropical biodiversity studies: The devil is in the detail. Biodivers. Conserv. 2008;17(14):3431–3442. doi: 10.1007/s10531-008-9479-0. DOI
Nieuwenhuijsen, H., & Peeters, T. Nederlandse bijen op naam brengen. Deel 1. - Stichting Jeugdbondsuitgeverij, ’s Graveland (2015).
Nieuwenhuijsen, H., Peeters, T., & Dijkshoorn, D. Nederlandse bijen op naam brengen. Deel 2. - Stichting Jeugdbondsuitgeverij, ’s Graveland. (2020).
Olden JD, Lawler JJ, Poff NL. Machine learning methods without tears: A primer for ecologists. Q. Rev. Biol. 2008;83(2):171–193. doi: 10.1086/587826. PubMed DOI
Palmeirim AF, Figueiredo MS, Grelle CEV, Carbone C, Vieira MV. When does habitat fragmentation matter? A biome-wide analysis of small mammals in the Atlantic forest. J. Biogeogr. 2019;46(12):2811–2825. doi: 10.1111/jbi.13730. DOI
Peciña MV, Bergamo TF, Ward R, Joyce C, Sepp K. A novel UAV-based approach for biomass prediction and grassland structure assessment in coastal meadows. Ecol. Ind. 2021;122:107227. doi: 10.1016/j.ecolind.2020.107227. DOI
Perrone M, Di Febbraro M, Conti L, Divíšek J, Chytrỳ M, Keil P, Carranza ML, Rocchini D, Torresani M, Moudrỳ V, et al. The relationship between spectral and plant diversity: Disentangling the influence of metrics and habitat types at the landscape scale. Remote Sens. Environ. 2023;293:113591. doi: 10.1016/j.rse.2023.113591. DOI
Petermann JS, Buzhdygan OY. Grassland biodiversity. Curr. Biol. 2021;31(19):R1195–R1201. doi: 10.1016/j.cub.2021.06.060. PubMed DOI
Potapov P, Li X, Hernandez-Serna A, Tyukavina A, Hansen MC, Kommareddy A, Pickens A, Turubanova S, Tang H, Silva CE, et al. Mapping global forest canopy height through integration of gedi and landsat data. Remote Sens. Environ. 2021;253:112165. doi: 10.1016/j.rse.2020.112165. DOI
Potts, S. G., Ngo, H. T., Biesmeijer, J. C., Breeze, T. D., Dicks, L. V., Garibaldi, L. A., Hill, R., Settele, J., & Vanbergen, A. The assessment report of the intergovernmental science-policy platform on biodiversity and ecosystem services on pollinators, pollination and food production.
Rao CR. Diversity and dissimilarity coefficients: A unified approach. Theor. Popul. Biol. 1982;21(1):24–43. doi: 10.1016/0040-5809(82)90004-1. DOI
Redhead JW, Dreier S, Bourke AF, Heard MS, Jordan WC, Sumner S, Wang J, Carvell C. Effects of habitat composition and landscape structure on worker foraging distances of five bumble bee species. Ecol. Appl. 2016;26(3):726–739. doi: 10.1890/15-0546. PubMed DOI
Ricotta C. Additive partitioning of Rao’s quadratic diversity: A hierarchical approach. Ecol. Model. 2005;183(4):365–371. doi: 10.1016/j.ecolmodel.2004.08.020. DOI
Ricotta C, Pavoine S, Bacaro G, Acosta AT. Functional rarefaction for species abundance data. Methods Ecol. Evol. 2012;3(3):519–525. doi: 10.1111/j.2041-210X.2011.00178.x. DOI
Ricotta C, Szeidl L. Towards a unifying approach to diversity measures: Bridging the gap between the Shannon entropy and Rao’s quadratic index. Theor. Popul. Biol. 2006;70(3):237–243. doi: 10.1016/j.tpb.2006.06.003. PubMed DOI
Rocchini D. Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery. Remote Sens. Environ. 2007;111(4):423–434. doi: 10.1016/j.rse.2007.03.018. DOI
Rocchini D, Chiarucci A, Loiselle SA. Testing the spectral variation hypothesis by using satellite multispectral images. Acta Oecologica. 2004;26(2):117–120. doi: 10.1016/j.actao.2004.03.008. DOI
Rocchini D, Marcantonio M, Ricotta C. Measuring Rao’s q diversity index from remote sensing: An open source solution. Ecol. Ind. 2017;72:234–238. doi: 10.1016/j.ecolind.2016.07.039. DOI
Rocchini D, Santos MJ, Ustin SL, Féret J-B, Asner GP, Beierkuhnlein C, Dalponte M, Feilhauer H, Foody GM, Geller GN, et al. The spectral species concept in living color. J. Geophys. Res. Biogeosci. 2022;127(9):e2022JG007026. doi: 10.1029/2022JG007026. PubMed DOI PMC
Rocchini D, Thouverai E, Marcantonio M, Iannacito M, Da Re D, Torresani M, Bacaro G, Bazzichetto M, Bernardi A, Foody GM, et al. rasterdiv-an information theory tailored r package for measuring ecosystem heterogeneity from space: To the origin and back. Methods Ecol. Evol. 2021;12(6):1093–1102. doi: 10.1111/2041-210X.13583. PubMed DOI PMC
Rocchini D, Torresani M, Beierkuhnlein C, Feoli E, Foody GM, Lenoir J, Malavasi M, Moudrỳ V, Šímová P, Ricotta C. Double down on remote sensing for biodiversity estimation: A biological mindset. Commun. Ecol. 2022;23(3):267–276. doi: 10.1007/s42974-022-00113-7. DOI
Rossi C, Kneubühler M, Schütz M, Schaepman ME, Haller RM, Risch AC. Spatial resolution, spectral metrics and biomass are key aspects in estimating plant species richness from spectral diversity in species-rich grasslands. Remote Sens. Ecol. Conserv. 2022;8(3):297–314. doi: 10.1002/rse2.244. DOI
Rossignol N, Chadoeuf J, Carrère P, Dumont B. A hierarchical model for analysing the stability of vegetation patterns created by grazing in temperate pastures. Appl. Veg. Sci. 2011;14(2):189–199. doi: 10.1111/j.1654-109X.2010.01106.x. DOI
Roussel J-R, Auty D, Coops NC, Tompalski P, Goodbody TR, Meador AS, Bourdon J-F, De Boissieu F, Achim A. lidr: An r package for analysis of airborne laser scanning (ALS) data. Remote Sens. Environ. 2020;251:112061. doi: 10.1016/j.rse.2020.112061. DOI
Saunders DA, Hobbs RJ, Margules CR. Biological consequences of ecosystem fragmentation: A review. Conserv. Biol. 1991;5(1):18–32. doi: 10.1111/j.1523-1739.1991.tb00384.x. DOI
Scheper J, Bommarco R, Holzschuh A, Potts SG, Riedinger V, Roberts SP, Rundlöf M, Smith HG, Steffan-Dewenter I, Wickens JB, et al. Local and landscape-level floral resources explain effects of wildflower strips on wild bees across four European countries. J. Appl. Ecol. 2015;52(5):1165–1175. doi: 10.1111/1365-2664.12479. DOI
Scheper J, Reemer M, van Kats R, Ozinga WA, van der Linden GT, Schaminée JH, Siepel H, Kleijn D. Museum specimens reveal loss of pollen host plants as key factor driving wild bee decline in The Netherlands. Proc. Natl. Acad. Sci. 2014;111(49):17552–17557. doi: 10.1073/pnas.1412973111. PubMed DOI PMC
Tamburlin D, Torresani M, Tomelleri E, Tonon G, Rocchini D. Testing the height variation hypothesis with the R Rasterdiv package for tree species diversity estimation. Remote Sensing. 2021;13(18):3569. doi: 10.3390/rs13183569. DOI
ten Harkel J, Bartholomeus H, Kooistra L. Biomass and crop height estimation of different crops using UAV-based lidar. Remote Sens. 2019;12(1):17. doi: 10.3390/rs12010017. DOI
Thessen A. Adoption of machine learning techniques in ecology and earth science. One Ecosyst. 2016;1:e8621. doi: 10.3897/oneeco.1.e8621. DOI
Thouverai E, Marcantonio M, Lenoir J, Galfré M, Marchetto E, Bacaro G, Gatti RC, Da Re D, Di Musciano M, Furrer R, et al. Integrals of life: Tracking ecosystem spatial heterogeneity from space through the area under the curve of the parametric Rao’s q index. Ecol. Complex. 2023;52:101029. doi: 10.1016/j.ecocom.2023.101029. DOI
Titeux N, Brotons L, Settele J. Ipbes promotes integration of multiple threats to biodiversity. Trends Ecol. Evol. 2019;34(11):969–970. doi: 10.1016/j.tree.2019.07.017. PubMed DOI
Torresani M, Kleijn D, de Vries JPR, Bartholomeus H, Chieffallo L, Gatti RC, Moudrỳ V, Da Re D, Tomelleri E, Rocchini D. A novel approach for surveying flowers as a proxy for bee pollinators using drone images. Ecol. Ind. 2023;149:110123. doi: 10.1016/j.ecolind.2023.110123. DOI
Torresani M, Masiello G, Vendrame N, Gerosa G, Falocchi M, Tomelleri E, Serio C, Rocchini D, Zardi D. Correlation analysis of evapotranspiration, emissivity contrast and water deficit indices: A case study in four eddy covariance sites in italy with different environmental habitats. Land. 2022;11(11):1903. doi: 10.3390/land11111903. DOI
Torresani M, Rocchini D, Alberti A, Moudrỳ V, Heym M, Thouverai E, Kacic P, Tomelleri E. Lidar Gedi derived tree canopy height heterogeneity reveals patterns of biodiversity in forest ecosystems. Eco. Inform. 2023;76:102082. doi: 10.1016/j.ecoinf.2023.102082. PubMed DOI PMC
Torresani M, Rocchini D, Sonnenschein R, Zebisch M, Hauffe HC, Heym M, Pretzsch H, Tonon G. Height variation hypothesis: A new approach for estimating forest species diversity with chm lidar data. Ecol. Ind. 2020;117:106520. doi: 10.1016/j.ecolind.2020.106520. DOI
Turner W, Spector S, Gardiner N, Fladeland M, Sterling E, Steininger M. Remote sensing for biodiversity science and conservation. Trends Ecol. Evolut. 2003;18(6):306–314. doi: 10.1016/S0169-5347(03)00070-3. DOI
Wang R, Gamon JA. Remote sensing of terrestrial plant biodiversity. Remote Sens. Environ. 2019;231:111218. doi: 10.1016/j.rse.2019.111218. DOI
Westphal C, Bommarco R, Carré G, Lamborn E, Morison N, Petanidou T, Potts SG, Roberts SP, Szentgyörgyi H, Tscheulin T, et al. Measuring bee diversity in different European habitats and biogeographical regions. Ecol. Monogr. 2008;78(4):653–671. doi: 10.1890/07-1292.1. DOI
Winfree R, Aguilar R, Vázquez DP, LeBuhn G, Aizen MA. A meta-analysis of bees’ responses to anthropogenic disturbance. Ecology. 2009;90(8):2068–2076. doi: 10.1890/08-1245.1. PubMed DOI
Wood DJ, Preston TM, Powell S, Stoy PC. Multiple UAV flights across the growing season can characterize fine scale phenological heterogeneity within and among vegetation functional groups. Remote Sens. 2022;14(5):1290. doi: 10.3390/rs14051290. DOI
Xiang M, Wu J, Wu J, Guo Y, Lha D, Pan Y, Zhang X. Heavy grazing altered the biodiversity-productivity relationship of alpine grasslands in Lhasa River Valley, Tibet. Front. Ecol. Evol. 2021;9:698707. doi: 10.3389/fevo.2021.698707. DOI